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Italian Journal of Agronomy Editors Michele Perniola, School of Agriculture, Forest, Food and Environmental Science, University of Basilicata, viale dell’Ateneo Lucano 10, 85100 Potenza, Italy. Tel. +39.0971.205381 / +39.320.3606258 E-mail: perniola@unibas.it Michele Rinaldi, Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, S.S. 673 km 25,200, 71122 Foggia, Italy. Tel. +39.0881.742972 int 415 E-mail: michele.rinaldi@crea.gov.it

Editorial Advisory Board Arturo Alvino – Department of Agricultural, Environmental and Food Sciences, University of Molise, Italy Mariana Amato – School of Agriculture, Forest, Food and Environmental Science, University of Basilicata, Potenza, Italy Gaetano Amato – Department of Agricultural and Forest Sciences, University of Palermo, Italy Paolo Annicchiarico – Centre for Fodder Crops and Dairy Productions, Council for Agricultural Research and Economics, Lodi, Italy Luca Bechini – Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, University of Milan, Italy Paolo Benincasa – Department of Agricultural, Food and Environmental Sciences, University of Perugia, Italy Iacopo Bernetti – Department of Agriculture, University of Florence, Italy Giorgio Borreani – Department of Agricultural, Forestry and Food Sciences, University of Turin, Italy Davide Cammarano – James Hutton Institute Invergowrie, Dundee, Scotland, UK Vincenzo Candido – School of Agriculture, Forest, Food and Environmental Science, University of Basilicata, Potenza, Italy Raffaele Casa – Department of Agriculture, Forests, Nature and Energy, University of Tuscia, Viterbo, Italy Cristiano Casucci – Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, Ancona, Italy Enrico Ceotto – Centre for Industrial Crops, Council for Agricultural Research and Economics, Bologna, Italy Giuseppe Corti – Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, Ancona, Italy Nicola Dal Ferro – Department of Agronomy Food Natural Resources Animals Environment, University of Padua, Italy Anna Dalla Marta – Department of Agrifood Production and Environmental Sciences, University of Florence, Italy Antonio Elia – Department of Science of Agriculture, Food and Environment, University of Foggia, Italy

Christof Engels – Department of Animal and Plant Sciences, Plant Nutrition, Humboldt-University Berlin, Germany Massimo Fagnano – Department of Agriculture, Federico II University, Naples, Italy Zina Flagella – Department of Agricultural, Food and Environmental Sciences, University of Foggia, Italy Giuseppe Gatta – Department of Agricultural, Food and Environmental Sciences, University of Foggia, Italy Marcella Giuliani – Department of Agricultural, Food and Environmental Sciences, University of Foggia, Italy Anne Gobin – Flemish Institute for Technological Research, Mol, Belgium Elisa Marraccini – Department of Agronomy and Animal Sciences, Institut Polytechnique UniLaSalle, Beauvais, France Roberta Masin – Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padua, Italy Giovanni Mauromicale – Department of Agricultural and Food Science, University of Catania, Italy Jean Meynard – NRA Institut National de La Recherche Agronomique, Paris, France Eric Ober – National Institute of Agricultural Botany, Cambridge, United Kingdom Alberto Pardossi – Department of Agriculture, Food and Environmental Science, University of Pisa, Italy Cristina Patané – Insitute of Tree and Timber, National Research Council, Catania, Italy Pier Paolo Roggero – Department of Agricultural Sciences and Desertification Research Centre, University of Sassari, Italy Dario Sacco – Department of Agricultural, Forestry and Food Sciences, University of Turin, Italy Mahmoud F. Seleiman – Department of Agricultural Science, University of Finland, Helsinki, Finland Claudio Stockle – Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA Leonardo Sulas – Institute for Animal Production System in Mediterranean Environment, National Research Council, Sassari, Italy Elena Valkama – Bioeconomy and Environment Unit, the Natural Resources Institute Finland, Helsinki, Finland Gaetano Alessandro Vivaldi – Department of Agricultural and Environmental Sciences, University of Bari, Italy Editorial Staff Paola Granata, Managing Editor Cristiana Poggi, Production Editor Tiziano Taccini, Technical Support

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3. multi-authors books: Brouwer I, 1965. Report of the subcommittee on constants and factors. In: KL Blaxter (ed.) Energy metabolism. EAAP Publ. N. 11, Academic Press Ltd., London, UK, pp 441-3. 4. proceedings: Rossi A, Bianchi B, 1998. How writing the references. Proc. 4th World Congr. Appl. Livest. Prod., Armidale, Australia, 26:44-6. or Blanco P, Nigro B, 1970. Not numbered volumes. Page 127 (or pp 12-18) in Proc. 3rd Int. Conf. Cattle Dis., Philadelphia, PA, USA. 5. thesis: Rossi P, 1999. Stima di parametri genetici nella razza Reggiana. Degree Diss., Università di Milano, Italy. 6. material from a www site: Food and Drug Administration, 2001. Available from: http://www.fda.gov 7. in press: Manuscripts that have been accepted for publication but are not yet published can be listed in the literature cited with the designation (In press) following the journal title. 8. other: Citations such as personal communication, unpublished data, etc. should be incorporated in the text and NOT placed into the Reference section.

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Italian Journal of Agronomy Rivista trimestrale registrata al Tribunale di Udine n. 3/97 del 12-2-1997. Direttore Responsabile: Michele Perniola. Proprietà: Società Italiana di Agronomia. Stampa: Press Up s.r.l. via La Spezia, 118/C 00055 - Ladispoli (RM) Tel. e Fax: + 2016 Impact factor: 0.687 ©JCR Clarivate Analytics

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Italian Journal of Agronomy volume 13, issue 3, 2018 Table of Contents Accumulation and concentration of nitrogen, phosphorus and potassium in Jerusalem artichoke in a semi-arid region

Tongcheng Fu, Zuxin Liu, Yang Yang, Guang Hui Xie ...................................................185 Strip-till technology - a method for uniformity in the emergence and plant growth of winter rapeseed (Brassica napus L.) in different environmental conditions of Northern Poland

Iwona Jaskulska, Lech Gałęzewski, Mariusz Piekarczyk, Dariusz Jaskulski..............194 Podolian cattle: reproductive activity, milk and future prospects

Carlo Cosentino, Carmine D’Adamo, Salvatore Naturali, Giovanni Pecora, Rosanna Paolino, Mauro Musto, Francesco Adduci, Pierangelo Freschi...............200 Field bean for forage and grain in short-season rainfed Mediterranean conditions

Marco Mariotti, Victoria Andreuccetti, Iduna Arduini, Sara Minieri, Silvia Pampana....208 Straw uses trade-off only after soil organic carbon steady-state

Agata Novara, Mauro Sarno, Paulo Pereira, Artemi Cerdà, Eric C. Brevik, Luciano Gristina .................................................216 Effect of salinity on Echinochloa crus-galli germination as affected by herbicide resistance

Francesca Serra, Silvia Fogliatto, Francesco Vidotto...............................................221

Compost tea spraying increases yield performance of pepper (Capsicum annuum L.) grown in greenhouse under organic farming system

Massimo Zaccardelli, Catello Pane, Domenica Villecco, Assunta Maria Palese, Giuseppe Celano.................................................229

Contribution of main culm and tillers to grain yield of durum wheat: Influence of sowing date and plant traits

Iduna Arduini, Elisa Pellegrino, Laura Ercoli ........................................................235 Modelling plant yield and quality response of fresh-market spinach (Spinacia oleracea L.) to mineral nitrogen availability in the root zone

Daniele Massa, Luca Incrocci, Luca Botrini, Giulia Carmassi, Cecilia Diara, Pasquale Delli Paoli, Giorgio Incrocci, Rita Maggini, Alberto Pardossi..........................248 Greenhouse gas and ammonia emissions from soil: The effect of organic matter and fertilisation method

Leonardo Verdi, Marco Mancini, Mirjana Ljubojevic, Simone Orlandini, Anna Dalla Marta ...............................................260 All articles are also available at http://www.agroengineering.org

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Italian Journal of Agronomy 2018; volume 13:906

Accumulation and concentration of nitrogen, phosphorus and potassium in Jerusalem artichoke in a semi-arid region Tongcheng Fu,1,2 Zuxin Liu,1-3 Yang Yang,1,2 Guang Hui Xie1,2 1College

of Agronomy and Biotechnology, China Agricultural University, Beijing; 2National Energy R&D Center for Biomass, China Agricultural University, Beijing; 3Chinese Academy of Agricultural Engineering, Beijing, P.R. China


Jerusalem artichoke (Helianthus tuberosus L.) has been recognized as being a biomass crop for energy and livestock forage production. In this study, 26 Jerusalem artichoke clones previously collected from 24 provinces of China were grown under semiarid conditions in 2008 and 2011. At harvest, nitrogen (N), phosphorus (P) and potassium (K) concentrations and accumulations were measured for all clones and levels of both were higher overall for 2008 than 2011, with statistically reasonable results for both years. Notably, N and K concentrations in aboveground parts were higher than in tubers for most clones, yet the tuber P concentration was consistently higher than in aboveground parts. Comparing with other forage and energy plants, it demonstrates that under optimal conditions, diverse Jerusalem artichoke clones could meet the requirements of either energy production or livestock forage feed. Based on N, P and K accumulation and concentration profiles, the 26 Jerusalem artichoke clones clustered into six groups. Three clones of one cluster, CQ-1, GZ-1 and HUN-3, are recommended for use as biomass energy materials due to the lower N concentration level in aboveground parts and higher N concentration level in tubers, while 16 clones are recommended for use as forage due Correspondence: Guang Hui Xie, College of Agronomy and Biotechnology; and National Energy R&D Center for Biomass, China Agricultural University, Beijing 100193, P.R. China. E-mail: xiegh@cau.edu.cn Key words: Nutrient uptake; bio-fuel; biomass crop; forage crop.

Acknowledgements: this research was supported by the Department of Energy Conservation and Technology Equipment of China’s National Energy Administration (Science and Technology Department, No. [2012] 32) and funded by China Datang New Energy Co. LTD and Henan Tianguan Group Co., Ltd.

Received for publication: 23 December 2016. Revision received: 6 October 2017. Accepted for publication: 9 October 2017.

ŠCopyright T. Fu et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:906 doi:10.4081/ija.2018.906

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

to the higher N concentration level in aboveground parts. The phenotypic traits described in this work should facilitate quantitative trait locus mapping and the subsequent use of clone germplasms for development of improved varieties suited to specific growth conditions and applications.


The Jerusalem artichoke (Helianthus tuberosus L.) has garnered much attention for its high potential value as a feedstock source for biomass energy production and livestock forage. High levels of inulin and fructose account for 75% of its total dry tuber weight (Baldini et al., 2004; Kays and Nottingham, 2008); thus, this crop may serve as a promising raw material for the industrial production of biodiesel (Cheng et al., 2009), ethanol (Baldini et al., 2004), methane (Lehtomaki, 2005) and biomass briquettes or pellets (Kowalczyk-Jusko et al., 2012). Importantly, use of phenotypically diverse crops may allow biomass content control, as previous studies of energy biomass materials have suggested that such control should help to avoid negative environmental influences. For example, during combustion of briquettes or pellets (Jenkins et al., 1998), excess nitrogen (N) in raw materials would directly lead to higher emission of nitrides and hydrogen cyanide during combustion. Hence, development of Jerusalem artichoke varieties with lower N content would enhance this crop’s value as a raw material in the biomass briquette industry. However, for other applications, higher nitrogen levels might be a desirable trait because the residues from anaerobic digestion of plant biomass for energy production contain mineralised nitrogen, a readily available N source for growing plants. Thus, higher N levels contribute to higher by-product quality after anaerobic digestion. Because Jerusalem artichoke tubers contain high N levels, tubers would thus be better suited for use in ethanol production than for production of biomass briquettes. The N-rich residues produced by anaerobic digestion could thus be returned to the cultivation soil to serve as a fertiliser and soil-improvement medium (Demuynck, 1984; Hons et al., 1993; Karpenstain-Machan, 2001). Comparing with corn silage, tuber of Jeruslaem artichoke showed to have a higher digestible protein content (Kays and Nottingham, 2008). While tubers show potential value, aboveground parts of the Jerusalem artichoke have already long been used as an ideal raw livestock forage for ruminants in cool, wet areas of Europe (Kosaric et al., 1984; Hay and Offer, 1992; Youngen, 1992; Cosgrove et al., 2000) and in North America (Crawford et al., 1969; Seiler, 1993). For this reason, previous studies have focused on evaluation of in vitro digestible dry matter from aboveground parts of the plant (Tilley and Terry, 1963; Seilier, 1993). Other studies of perennial silage/forage crops have

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Article additionally demonstrated that mineral element composition and quantity should also be assessed (Buxton, 1996) because multiple inorganic elements, including calcium, phosphorus (P), sodium, chlorine, potassium (K), magnesium and sulfur, are essential for normal livestock growth and reproduction (National Academy of Sciences, 2001). Aside from the applications outlined above, the Jerusalem artichoke holds promise for use in desertification control (Ma et al., 2011), due to its high tolerance of drought and saline-alkali conditions (Zhao et al., 2010a). However, the utilisation of the plant’s underground tubers could cause environmental problems, as the harvest of tubers would cause soil erosion. Therefore, harvest of only aboveground parts is recommended in some environmentally stressed areas. For such applications, the assessment of the quantity and concentration of macroelements in the aboveground parts of the Jerusalem artichoke would be a valuable indicator to inform germplasm selection of pasture varieties for use in ecologically sensitive regions. Regardless of application, the uptake and utilisation of mineral nutrients, which are closely related to plant growth and yield, are mainly dependent on environmental water (Pilnik and Vervelde, 1976; Mezencev, 1985; Conde et al., 1988; Ben Chekroun et al., 1996) and soil conditions (Kosaric et al., 1984). Although Jerusalem artichokes could be grown in poor soils without high fertiliser input, the resulting tuber sizes tend to be smaller and are accompanied by low aboveground biomass yields (Huxley, 1992). Therefore, an appropriate fertilisation management strategy is needed for optimising biomass accumulation of both Jerusalem artichoke tubers and aboveground aerial parts depending on their intended applications. In our previous study, we evaluated phenological development, morphological traits, shoot biomass and tuber yields of 26 Jerusalem artichoke clones grown in the semiarid region of the Loess Plateau of China (Liu et al., 2012). The objectives of this study were (i) to investigate changes in concentrations and uptake of N, P and K over a growth period of 180 days for 26 Jerusalem artichoke clones and (ii) to group the clones into different clusters according to the nutrient properties.

Materials and methods Study site

All field experiments were carried out in 2008 and 2011 in a semiarid region of the Loess Plateau Experimental Station of Lanzhou University (35°37´N, 107°48´E, 1298 m above sea level). The multi-year mean annual solar radiation duration, temperature and precipitation from July to September were 2490 h, 556.1 mm and 9.6°C, respectively. The soil at the site was Heilu with a clay texture, pH 7.7, and organic matter of 13.10 g kg–1 in the 0-20 cm layer. Total rainfall were 335 mm and 297 mm during the growth periods in 2008 and 2011, respectively, with 78.3% and 83.8% falling between July and September. Soil water potential at the 020 cm layer fluctuated from –25 to –80 kPa during the 180 d of the Jerusalem artichoke clone growth. More detailed seasonal weather data, soil texture and moisture data at the study site in 2008 and 2011 have been described in our previous report (Liu et al., 2012).

Experimental design

A total of 26 Jerusalem artichoke clones were assessed in this study. The experimental design was a completely randomized block with triplicate plots in 2008 and in quadruplicate plots in [page 186]

2011. Each plot was 1.6×2.8 m in size and allocated into 4 rows with a row spacing of 0.7 m and hill spacing of 0.4 m. 50 g of tubers for propagation collected from different clones were individually planted in each plot on April 1st after the field was well ploughed and harrowed in the early spring. Guard rows were also set up to surround areas containing each single clone to prevent potential crop identification errors. Basal fertiliser consisted of 150 N kg ha–1 as urea, 75 kg P2O5 ha–1 as superphosphate and 120 kg K2O ha-1 as potassium sulphate, which were applied one day before planting. Surface irrigation was applied with an amount of 50 m3 ha–1 water for each plot on March 31st. Weed was removed manually in all plots at the seedling stage. No additional irrigation and weed control was applied prior to the harvest dates on October 1st of each year.

Sampling and measurements

The aboveground biomass in each plot was collected on each harvest date by cutting the plants at ground level and weighing the fresh aboveground biomass. From the leaf, and stem parts were separated, cut into pieces of 2-3 cm in length, then oven-dried to constant weight at 105°C to determine moisture content. Dry biomass weights of aboveground stem and leaf in each plot were calculated by multiplying fresh biomass weight by its dry matter content (100%-moisture content). Fresh tubers in each plot were harvested manually and further washed, counted and weighed to determine tuber size, fresh weight and numbers of tubers per plant. A subset of tubers was cut into 1.5 cm-thick slices and oven-dried to constant weight at 105°C. Thereafter, dried tuber slices were crushed and passed through a 0.5 mm mesh screen for subsequent chemical analyses. The total dry biomass yield of Jerusalem artichokes was calculated by adding the aboveground biomass yield from leaf and stem parts to the tuber biomass yield.

Chemical analysis

Samples of tubers and aboveground parts (leaf and stem) were first digested with H2SO4-H2O2 following a Kjeldahl digestion protocol (Wolf, 1982). N, P and K concentrations were determined using the semimicro-Kjeldahl digestion and distillation method (Nelson and Sommers, 1980), vanadomolybdate yellow method (Jackson, 1958) and flame spectroanalysis, respectively. The determinations of N, P and K concentrations for each plot were derived from the averaged values of 3 replicate plots in 2008 and 4 replicate plots in 2011.

Calculation and statistical analysis

The nutrient accumulations in aboveground parts and tubers were computed as the product of the concentration multiplied by the dry biomass weight for each replicate. Means and standard errors were calculated from replicate data for each treatment. The nutrient data of the 26 Jerusalem artichoke clones were analysed by determining the maximum and minimum values and calculating the standard error. A correlation between concentration and accumulation for each of the three elements was conducted. Two-way ANOVA was conducted for the purpose of examining the effects of year and clone factors on nutrient concentrations using SPSS software package. The significance of the differences between means was determined using the least significant difference at the P<0.05 level. The two-year mean concentration and accumulation values for each clone were used for hierarchical cluster analysis using Ward’s method. Cluster and principal components analysis were performed using the SPSS Statistics 20 software package (SPSS, IBM Corp., 2011).

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Article Results

Cluster analysis

The 26 Jerusalem artichoke clones were sorted into 6 clusters on the basis of N, P and K accumulation and concentration in 2008 and 2012 (Figure 1). Mean values and standard errors of accumulation and concentration for each cluster are presented in Figure 2. Cluster K1 with 3 clones was characterised by relatively high levels concentration and accumulation of N and K in aboveground parts, low levels of N concentration and accumulation in tubers. Cluster K2 including 6 clones exhibited relatively high concentration and accumulation levels of P and K in tubers and low accumulation of P and K in aboveground parts. Cluster K3 including 2 clone shown the lowest P and K concentration levels in tubers, and highest N, and K concentration levels in aboveground part. Cluster K4 with 3 clones characterised the highest concentration and accumulation levels of N and P in tubers. Cluster K5 including 2 clones exhibited relatively low concentration and accumulation levels of both N, P, and K in tubers. Cluster K6 was the largest group with 10 clones and characterised by relatively low N concentration level in tubers and high N accumulation in aboveground parts.

Figure 1. Dendrogram obtained by cluster analysis of concentrations and biomass accumulation of nitrogen (N), phosphorus (P) and potassium (K) in 26 Jerusalem artichoke clones following Wardâ&#x20AC;&#x2122;s method.

Figure 2. Averaged concentrations and accumulations of nitrogen (N), phosphorus (P) and potassium (K) in tubers, and aboveground parts at harvest date for the six clusters of Jerusalem artichoke grown in 2008 and 2011. Vertical error bars represent standard deviations. Different lower letters within the same parameter represent statistical significance between clusters at P<0.05 level.

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Article N, P and K concentrations in aboveground parts

Relative to tubers, sample from all Jerusalem artichoke clone aboveground parts exhibited lower K concentration levels than either N or P concentration levels for both years (Figure 3). The 26 Jerusalem artichoke clones exhibited N concentrations ranging between 9.33-22.66 g N kgâ&#x20AC;&#x201C;1, P concentrations between 0.81-1.65

g P kgâ&#x20AC;&#x201C;1 and K concentrations between 14.53-20.38 g K kgâ&#x20AC;&#x201C;1 for both years. The average N and K concentrations in the aboveground parts of all 26 clones were both lower, by 78.9% and 19.1%, respectively, in 2011 than in 2008, whereas the average P concentration did not vary significantly (P<0.05). Two-way ANOVA demonstrating the effects of factors of planting year,

Figure 3. Concentrations of nitrogen (N), phosphorus (P) and potassium (K) in tubers and aboveground parts at harvest date for six clusters of Jerusalem artichoke grown in 2008 and 2011. Vertical error bars represent standard deviations. Different lower letters within the same parameter represent statistical significance between clusters at P<0.05 level.

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Article clone and their interaction factor on the concentrations of the three elements in aboveground parts demonstrated that most variances in the independent and interaction factors were significant at the P<0.001 level, however the variance for year × clone effects on N concentration was not significant.

N, P and K concentrations in tubers

Cluster average concentrations of N, P and K in tubers in 2008 and 2011 are shown in Figure 3. Generally, the 26 clones tended to have a higher N concentration in aboveground parts than in tubers in both years. In contrast, P and K concentrations were higher in tubers than in aboveground parts. Average N concentrations of the six clusters were 6.85 g N kg–1 and 5.72 g N kg–1, average P concentrations were 2.53 g P kg–1 and 3.65 g P kg–1, and average K concentrations were 4.59 g K kg–1 and 2.45 g K kg–1 in 2008 and 2012, respectively. Average N, P and K concentration in the tubers of the clones planted in 2008 were higher by 21.2% and 89.5%, respectively; whereas average P concentration in tubers in 2008 was lower by 31.1% in comparison with those in 2011. Two-way ANOVA demonstrating effects of factors of year and clone and their interaction factor on tuber N, P and K concentrations generated results that were similar to aboveground part results. However, in tubers the independent and interaction factors were all significantly affected by N, P and K concentrations at the P<0.01 level.

N, P and K accumulations

The N accumulation in aboveground parts was higher than in tubers, due to the higher N concentration and biomass in aboveground parts (Figure 2). Moreover, even though the concentrations of P and K in tubers were both generally higher than in aboveground parts, the accumulation of P and K in aboveground parts of most clones were still higher than for tubers overall, due to the overwhelming higher biomass present in aboveground parts. N accumulation exhibited a significantly much higher level in the aboveground parts (129.98-416.87 kg N ha–1) than that in tubers (27.78-81.67 kg N ha–1) across the six clusters (P<0.01) in both years (Figure 2). K accumulation, ranging between 20.4855.41 kg K ha–1 in tubers and 181.84-522.62 kg K ha–1 in aboveground part, followed the same pattern as N accumulation. However, the difference in P accumulation between aboveground parts (12.63-31.29 kg P ha–1) and tubers (9.17-27.18 kg P ha–1) was not significant for each of all the clusters with the exception of cluster K6 (P<0.05). The variance of the nutrient accumulations between the two years was the same as for nutrient concentrations. The accumulations of the three elements were generally lower in 2011 than in 2008, except for P accumulation in tubers. The mean values of tuber N, P and K accumulations were lower by 22.2%, –34.6% and 50%, respectively. The average N, P and K accumulations in aboveground parts were lower by 60.4%, 28.0% and 41.1%, respectively (Figure 4). Two-way ANOVA showed that the N, P and K accumulations in both aboveground and tuber parts were all significantly affected by the year, clone and the interaction of year × clone factors at the P<0.001 level.

Statistical analysis

Correlations between N, P and K accumulations and concentrations were calculated using the mean values for the 26 clones for both years (Table 1). In agreement with previously established plant nutrition concepts, a significant and positive correlation between N, P and K accumulations and their corresponding concentrations in both aboveground parts and tubers was demonstrated (P<0.01).


Correlation of N, P and K concentrations with biomass yield

According to a previous report (Liu et al., 2011) based on the same field experiment of this study, the 26 Jerusalem artichoke clones exhibited biomass yield ranging from 9.7 ton ha–1 to 31.3 ton ha–1 for aboveground parts and from 3.7 ton ha-1 to 10.6 ton ha-1 for tubers in both year 2008 and 2011. The total biomass of above ground parts and tubers ranged from 16.1 ton ha–1 to 35.0 ton ha–1. The yield data and our finding were used to analyze the correlation of N, P and K concentrations with biomass yield and found that none of the three nutrient concentrations was significantly correlated with biomass yield of aboveground parts and tubers (P<0.05).

N, P and K concentrations in tubers and aboveground parts

All clones in this study exhibited tuber N concentrations ranging between 3.98-9.50 g N kg–1 averaged over both years. This level was about a half of the N concentration values (7.0-21.8 g N kg–1) in tubers reported by Kays and Nottingham (2008), who did a field experiment with 140 Jerusalem artichoke clones. The main reasons accounting for lower N, P and K concentrations observed in this work were likely due to the lower N fertilisation rate and earlier harvest date used here. Although the required N fertiliser rate for this crop was reported to range from 60-120 kg N ha–1 (Barloy, 1988; Fernandez et al., 1988; Honermeier et al., 1996), the practical N requirement varied between clones. The sufficient N fertiliser for numerous clones has been reported to range from 150-225 N kg ha– 1 by many researchers studying Jerusalem artichoke agronomy practices in China (Niu, 2005; Zhao et al., 2010b; Zhu et al., 2014). Therefore, the fertilisation rate of 150 kg N ha–1 applied in our study was a relatively medium or low-level application rate. Considering the proper cropping season for Jerusalem artichoke in China is from March-May to middle October (Niu, 2005), our slightly earlier harvest date might cause our results for the N partition from leaf to tuber to differ from that observed by other researchers who used longer growth season duration. In our study, the N concentration of harvested aboveground parts over both years ranged from 7.87-22.66 kg N ha–1, almost two times higher than the tuber N concentration range of 3.98-9.50 kg N ha–1.

N, P and K uptake and responses to environmental influences

The Jerusalem artichoke has long been reported to be an extremely efficient crop for nutrient uptake of its tubers and above-

Table 1. Coefficients of correlation between concentration and accumulation of nitrogen (N), phosphorus (P), and potassium (K) in tubers and aboveground parts of 26 Jerusalem artichoke clones. Nutrient N P K

Coefficient Tuber

Aboveground part

0.773** 0.622** 0.831**

0.878** 0.457** 0.587**

**Significant effect at P<0.01 level.

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Article ground parts (Kays and Nottingham, 2008). The clones cultivated in this experiment showed a relatively higher N and K uptake levels than sweet sorghum, miscanthus, and switchgrass, which were reported by Han et al. (2011), Beale and Long (1997) and Wilson et al. (2013), respectively. However Jerusalem artichoke exhibited a lower P uptake level than sweet sorghum (Han et al. 2011). The

most possible reason for this could be N and K concentrations in Jerusalem artichoke in this study were higher than those plants in the previous reports, and P concentration with the reverse. A plant is as well responsive to supplemental fertiliser application (Lim and Lee, 1983). Therefore, proper fertilisation management is recommended to ensure sustainable cultivation. Notably,

Figure 4. Accumulations of nitrogen (N), phosphorus (P) and potassium (K) in tubers and aboveground parts at harvest date for six clusters of Jerusalem artichoke grown in 2008 and 2011. Vertical error bars represent standard deviations. Different lower letters within the same parameter represent statistical significance between clusters at P<0.05 level.

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Article on the basis of our experiments, the amount of N, P and K removed from the field were larger than the fertiliser amount applied at the beginning of the experiments. As a consequence of insufficient fertilisation, the tuber yield and N, P and K accumulations in 2011 were generally lower than, or consistent with, those in 2008. Thus, higher fertilisation application rates should be used to ensure continuous Jerusalem artichoke cultivation. Although the Jerusalem artichoke has been considered to be drought tolerant, the level of phloem-mobile elements (e.g. nitrogen, phosphorus, potassium), which would be allocated to the tuber, would also be affected by drought stress late in the season (Nemeth and Izsaki, 2006). Although few studies on the Jerusalem artichoke have reported results of nutrient status analysis, Seiler and Campbell (2006) compared the within-population variation for several minerals. The within-population variations were high for N, Ca and K and low for P and Mg. Monti et al. (2005) reported leaf N concentrations ranging from 30-36 g N kg–1 for the cv. ‘Violet de Rennes’ grown in Bologna, Italy, under irrigated and rain-fed conditions. According to our previous study (Liu et al., 2012), the clones planted in 2011 suffered a longer period of environmental water deficit than clones planted in 2008. The severe soil moisture deficit period, which is defined as a period of soil moisture potential greater than 50 kPa, lasted for 124 days and 147 days in 2008 and 2011, respectively. The N, P and K concentrations and accumulations in 2008 were therefore greater than those in 2011 and were accounted for by the reasons outlined above.

Potential uses of the clones

In comparisons with other main energy crops, N concentration level (3.98-9.50 g N kg–1) in the tuber of Jerusalem artichoke was lower than that in sweet sorghum (9-10 g N kg–1) reported by Han et al. (2011). The tuber also exhibited the similar N concentration level with miscanthus shoot (Beale and Long, 1997) and switch grass shoot (Wilson et al., 2013). Jerusalem artichoke aboveground parts showed relatively higher N (7.87-22.66 g N kg–1) and K concentration levels (14.5320.38 g K kg–1) in this study in comparison with some main forage crops. Sudangrass shoot N concentration was found to be 2.39 g N kg–1 (Li and Lu, 2006). Annual brome (Haferkamp and Heitschmidt, 1996) exhibited K concentration level ranging from 1.7 to 14.8 g K kg–1. However, P concentration in above ground parts, with the reverse, was lower in Jerusalem artichoke (0.831.65 g P kg–1). It was reported in the range between 1.8-2.0 g P kg–1 for red clover (Davies et al., 1966) and between 2.4-2.6 g P kg–1 for white clover (Wang, 1982), and between 1.6-2.9 g P kg–1 for annual brome (Haferkamp and Heitschmidt, 1996). P concentration in alfalfa shoot (4.7 g P kg–1) was found to be even higher (Xiao and Zhao, 2006). Cluster K4, which includes CQ-1, GZ-1 and HUN-3 clones, includes the most appropriate clones for energy production, as these clones contain a high N concentration in tubers (mean 8.42 g N kg–1) and low N concentration in aboveground parts (mean 11.43 g N kg–1). The pellet and briquette industry requires a lower concentration of N in raw materials because the combustion of biomass pellets and briquette would directly release the N in the raw materials into the environment in the form of toxic pollutants (Jenkins et al., 1998). Therefore, aboveground parts derived from these clones would be appropriate for pellet and briquette production. While the high N content of tubers precludes their use for pellet and briquette production, anaerobic digestion of tubers for ethanol production would benefit from their high N concentration; higher N concentrations confer value to post-digestion residues to enhance their use as crop fertiliser (Demuynck, 1984; Hons et al.,

1993; Karpenstain-Machan, 2001). Therefore, these three clones could efficiently furnish ideal raw materials for both pellet/briquette and ethanol energy-production processes, with additional benefits. Clusters K6 and K2, including 16 clones in total, would be most suitable to be used as forage, as these clones contain higher mean N concentrations in aboveground parts. The mean aboveground N concentrations of K6 and K2 were 13.09 and 12.83 g N kg–1, respectively. If the total N had been converted into protein (total N × 6.25), the crude protein in aboveground parts would be calculated to range from 5.0%-14.3% and 2.5%-6.3% for K6 and K2 clusters, respectively. These crude protein content values are higher than those of maize and wheat used for dairy cow feed and thus could be useful as forage (Zhao et al., 2011). Furthermore, Rakhimov et al. (2003) found this plant to have a high nutritive value, as it contains almost all essential amino acids needed for livestock feed. Stauffer et al. (1981) also agreed that the aboveground part is better suited for use as feed than are tubers, as the leaf is rich in lysine and methionine. In contrast to the N results above, however, our results demonstrate that the P content of Jerusalem artichoke shoots is insufficient for dairy feed (0.81-1.65 g P kg–1), in agreement with results reported by Kays and Nottingham (2008). Seiler and Campbell (2006), who have reported the influence of heritable variation on mineral content, recommend elevating the N, P and K content using hybrid-breeding methods. However, the P and Mg content would hardly be elevated using this method, since the P content in this plant is already generally lower than in forage feed. K is an element that is important for its roles in osmotic adjustment and enzyme catalysis. Sufficient supply of K is also important in dairy feed. The 26 clones were demonstrated to possess K concentrations ranging from 11.87 to 20.38 g K kg–1 and would thus provide nutritionally adequate amounts of K (5.1-19.0 g K kg–1) to serve as a valuable ruminate feed supplement (National Academy of Sciences, 2001).


The N, P and K concentrations and biomass accumulations in 26 Jerusalem artichoke clones collected from 18 provinces in China showed significant variation, with levels that tend to be lower under drought conditions. If optimal cultivation conditions can be sustained, this crop is a promising source of raw materials for use in the biomass energy industry, as well as for forage feed from the perspective of macroelement concentration. Three clones, CQ-1, GZ-1 and HUN-3, are recommended for use as biomass energy materials and 16 clones are recommended for use as forage feed. The clones’ phenotypic nutrition traits described in this work should aid quantitative trait locus (QTL) mapping of their germplasms for future development of improved varieties tailored to diverse applications and growth conditions.


The aboveground parts exhibited higher N and K concentration levels and a lower of P concentration level than the tubers at maturity of Jerusalem artichoke. Each of N, P, and K concentration was not significantly correlated with the plant biomass yield for the above ground parts and tubers respectively. Jerusalem artichoke showed a relatively higher N and K uptake levels than sweet sorghum, miscanthus, and switchgrass, and P concentration with the reverse.

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Italian Journal of Agronomy 2018; volume 13:981

Strip-till technology - a method for uniformity in the emergence and plant growth of winter rapeseed (Brassica napus L.) in different environmental conditions of Northern Poland Iwona Jaskulska, Lech Gałęzewski, Mariusz Piekarczyk, Dariusz Jaskulski

Department of Plant Production and Experimentation, University of Science and Technology in Bydgoszcz, Bydgoszcz, Poland


The emergence of plants is especially important for the winter crops that are grown in the challenging environmental conditions of many countries in Central and Eastern Europe. The emergence and initial growth of winter rapeseed were compared in field trials in a randomized block design with three replicates for plants sown in conventional tillage systems (CT) and strip-till (ST), which had different weather conditions and on soil with a non-uniform texture over a period of two years. Sowing in the CT was carried out using Horsch Pronto 4DC (Germany) at a row distance of 0.29 m. The ST operations were performed using a Pro-Til 4T drill manufactured by Mzuri Limited (Great Britain) - row spacing of 0.36 m. In favourable rainfall and thermal conditions, the density of winter rapeseed plants two weeks after sowing was found to be higher if it was sown after the CT than in the ST system. In the year that had a serious shortage of rainfall during the sowing period, a considerably higher density of plants was achieved using the ST system. The uniformity of plant growth using the ST technology in soil with a varied texture, especially in a year with an unfavourable distribution of rainfall, was proven by less variability in the number of leaves in the rosette, in the dry mass of the leaf rosette and in the root neck thickness of the winter Correspondence: Dariusz Jaskulski, Department of Plant Production and Experimentation, University of Science and Technology in Bydgoszcz, 20E Kordeckiego Street, 85-225 Bydgoszcz, Poland. E-mail: darekjas@utp.edu.pl

Key words: Environmental conditions; plant uniformity; tillage systems.

Acknowledgements: we thank Agro-Land Marek Różniak at Śmielin for the opportunity to perform a field experiment.

Received for publication: 5 April 2017. Revision received: 27 July 2017. Accepted for publication: 1 August 2017.

©Copyright I. Jaskulska et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:981 doi:10.4081/ija.2018.981

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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rapeseed than in the CT system. The ST system can create good conditions for the initial development and preparation of rapeseed plants for wintering.


Rapeseed (Brassica napus L.) is one of the most important oilseed crops in the world (Dyer et al., 2008; Hu et al., 2017). Its winter form is mostly cultivated in Europe. The winter rapeseed acreage in Poland is about 0.7-0.8 m ha annually (FAO, 2016). Cultivation success and seed yield considerably depend on the plant development in autumn and overwintering (Balodis and Gaile, 2015). The conditions for the crops to reach the optimal development stage and good overwintering are their evenly distributed emergence and a field that is composed of plants that are even in terms of size and condition (Velicka et al., 2005; Velicka et al., 2012). The emergence rate and speed depend on e.g. soil properties, especially the seedbed (Håkansson et al., 2011). This is of special importance for plants with small seeds, including rapeseed (Håkansson et al., 2013; Alizadeh and Allameh, 2015). The reasons behind uneven emergence and initial growth of seedlings within a plantation are varied soil conditions in the field. Even in small-sized fields, the soil can be strongly diversified in terms of its origin, classification and properties (Brevik et al., 2003; Godwin and Miller, 2003). Soil properties, especially the physical ones, such as moisture, temperature, bulk density, soil penetration, are all shaped by the tillage system (Strudley et al., 2008; Alvarez and Steinbach, 2009). Tillage should facilitate a shallow precise sowing and ensure evenly-distributed germination conditions for all of the seeds. According to many research reports, the best tillage method for plants such as rapeseed is traditional plow tillage (Vanda et al., 2009). Unfortunately, this is very energy-consuming (Kusek et al., 2016). Frequently, it also has a deteriorating effect on the properties of soil and other elements of the environment (Kertesz and Madarasz, 2014). Inversion of soil and deep soil loosening strongly aerate soil and cause water loss (Guan et al., 2015). For that reason, today reduced tillage and direct sowing are frequently applied. In those tillage systems, much post-harvest residues or mulch remains on the soil surface, thus protecting it against erosion. Conservation tillage enhances water management and organic matter, requires low inputs and produces low emissions (Derpsch et al., 2010; Busari et al., 2015; Choudhary, 2015). Plant residues, however, can make sowing difficult and limit germination, emergence and initial plant growth (Wuest et al., 2000). Zero tillage often results in a decreased rapeseed yield and economic effectiveness of its cultivation (Khakbazan and Hamilton, 2012).

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Article In contemporary agriculture, strip-till, which combines the advantages of conventional tillage, zero tillage and direct sowing, is often used. Hybrid machines in strip-till technology are especially valuable because in a single go they till the soil deep, apply mineral fertilisers and sow seeds (Morris et al., 2010). Compared with the traditional tillage, this system has shows a very favourable effect on the agricultural soil properties, economic and organisational plant growing effectiveness and on the environment (Licht and Al-Kaisi, 2005; Jabro et al., 2014). Al-Kaisi et al. (2014), when comparing the effect of five tillage systems, e.g. strip-till and plow tillage, on the soil structure found that the effect of zero tillage and strip-till were most beneficial for the stability of micro- and macroaggregates. A significant positive correlation was also recorded between the content of organic carbon in soil and the waterresistance of micro- and macro-aggregates. As reported by Fernández et al. (2015), after only a few years of applying strip-till, the content of organic matter in soil increased. Strickland et al. (2015) demonstrated that after five years of plant cultivation following the principles of conservation agriculture with a winter soil coverage with rye and pea biomass and strip-till, the content of organic carbon and total nitrogen in soil increased. Most, about 7080%, of the organic carbon got deposited in the surface layer. For the emergence and further plant growth, a favorable effect on soil moisture and temperature, especially in the seedbed, is of similar importance (Al-Kaisi and Yin, 2005). According to Overstreet (2009), strip-till is also favourable due to the lower production costs resulting from decreased fuel consumption as well as a shortening of work time. For that reason, strip-till technology is being more and more frequently applied in plant cultivation, especially those sown in wide-spaced rows (Jackson et al., 2011), mostly corn (Trevini et al., 2013) or sugar beets (Morris et al., 2007). However, there are a few results of research into growing plants with a narrow row spacing, e.g. cereal crops (Hossain et al., 2014) and rapeseed (Schwabe et al., 2016). There are no such results for Poland. A hypothetical assumption has been made that in spite of the beneficial effects of strip-till on soil properties, which is especially important for plant growth and yield, winter rapeseed that is grown in compliance with that technology will have an evenly-distributed emergence and at the end of the autumn plant vegetation period, the plants will be even in terms of size, which determines the final cultivation success. Such an assumption was justified when considering the earlier observations of the performance of a hybrid machine that permits three agrotechnical practices to be performed in one go – soil belt tillage, mineral fertiliser application and rapeseed sowing in 36 cm-spaced rows, which is to the result of the machine design. In traditional winter rapeseed tillage technology in Poland, the row-spacing is smaller and, depending on the seeders that are used, it is 15-30 cm. The scientific literature shows, however, that the effect of row-spacing on rapeseed growth and yield depends on genetic factors, the environmental conditions and agrotechnical practices and that the largest yields can be produced for even 60 cm row-spacing (Waseem et al., 2014; Kuai et al., 2015).

Materials and methods Field trial design

In 2014-2015 and 2015-2016, a one-factor field experiment in a randomised block design was carried out on Cambisols at a farm in the village of Śmielin, North Poland (53°09′04″N; 17°29′11″E), which collaborates with the Department of Agriculture and Biotechnology of the Bydgoszcz University of Science and Technology. The subject of research was the winter rapeseed cv. ‘Apanaci’, which was cultivated in a region with low rainfall - about 500 mm per year. Two tillage and sowing systems were compared - conventional (CT) and strip-till (ST) on large plots (24×300 m) in three replicates. In the CT, after the forecrop harvest (spring barley), the following were used: a stubble field cultivator, a plough, a fertiliser spreader, a pre-sowing cultivation unit and a seed drill. A Horsch Pronto 4DC (Germany) seed drill sowed seeds to a depth of 2.5-3.0 cm at a row spacing of 0.29 m. In the strip-till system, a onepass tillage, fertiliser application and rapeseed sowing operations were performed using a Pro-Til 4T hybrid machine manufactured by Mzuri Limited, Great Britain. The row spacing was 0.36 m and the sowing depth was also 2.5-3.0 cm. Prior to sowing, 18 kg N ha–1 and 20 kg P ha–1 (ammonium phosphate) and 67 kg K ha–1 (potassium chloride) were applied. The winter rapeseed was sown at a density of 45 seeds m–2 on 9 August 2014 and 12 August 2015 respectively.

Soil samples and properties

Prior to establishing the trials, soil samples were collected from all of the replicates from the 0-20 cm layer every 20 m. The soil texture was determined for each sample (Table 1). The soil within the trial field was diversified. Its texture was loam, sandy loam and silt loam. Knowing the impact of the applied tillage and sowing methods on the uniformity of the emergence and initial growth of plants in different environmental conditions of the selection of the research site was justified.

Plant samples

In both study years, plant density was measured three times, 2 and 4 weeks after sowing and at the end of the autumnal growing season. Plant density was assessed for an area of 1 m2 every 20 m at 15 spots for each replicate. At the end of October, random samples of 20 plants were taken from the exact same spots in order to examine the leaf rosette and root neck. The examination included the number of leaves in the rosette of the winter rapeseed, the dry mass of leaves and the thickness of the root neck.

Statistical analyses

Data were subjected to a statistical assessment. A one-factor ANOVA analysis of variance was carried out. The significance of the differences between the means was estimated post-hoc using the

Table 1. The percentages of sand, silt, and clay particles in experimental field soil and their variation. Particle size

2-0.05 mm 0.05-0.002 mm <0.002 mm

Relative proportion (%) in soil

Coefficient of variation (%)




32.2 42.2 5.7

50.2 59.9 7.9

41.1 52.0 7.0

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Article Tukeyâ&#x20AC;&#x2122;s test. The variability of plant densities and biometric properties of winter rapeseed plants within the trial field was evaluated for the respective tillage and sowing systems in both years. The basic statistics that were determined for this purpose were the minimum (min), maximum (max) and mean values; standard deviation (SD) and standard error (SE). Statistica 12.5 (StatSoft Inc., Tulsa, USA) statistical software was used for the data analysis.


The two years differed in terms of weather conditions during the periods of sowing, emergence and initial growth of winter rapeseed. In 2014, in the decade (10-day period) immediately preceding the sowing and following the sowing, the precipitation was uniform and at a level that was close to the long-term mean (LTM). At the same time, the air temperature in the second and third decade of August was lower than the LTM. August 2015 was, on the other hand, very hot and dry. The sum of rainfall in the two decades following the sowing of the winter rapeseed did not exceed 10 mm, while the air temperature in every decade was higher (Figures 1 and 2). In the favorable precipitation and thermal conditions in 2014, the initial density of the winter rapeseed plants was higher than in 2015. Two weeks after sowing, the plant density after the CT was considerably greater than in the ST system, but it did not increase any further in the following weeks of the growing period (Figure 3). In 2015, which had a serious shortage of rainfall in August, a significantly higher plant density was observed in the CT system in the first assessment period. Only after the rains at the beginning of September did the plant densities equalise in both tillage systems four weeks after sowing. At the end of the autumnal growing season, the plant densities were similar and equaled 32.6-35.6 plants mâ&#x20AC;&#x201C;2 in both years and in the CT and ST systems. The beneficial influence of the ST system on the speed and uniformity of emergence in the case of the water shortage in the soil occurred especially in 2015. This was proven by a lower SD and SE and fewer differences between the max and min values than for the CT system (Table 2). The number of leaves in a rosette, the dry mass of the aboveground part of the plants and the thickness of the root neck for the ST technology in both years were higher and more uniform than for the CT. There was a better uniformity of the plants from various places in the differentiated experimental field in the ST system than in the CT, especially in the unfavorable water and thermal conditions of 2015, which caused the lower values of the SD and SE.

Figure 1. 10-day period mean temperature trend during the sowing and initial rapeseed growth in 2014, 2015, and long-term period (LTM) at the experimental site.

Figure 2. 10-day period (decade) and monthly sum of rainfall during the sowing and initial rapeseed growth in 2014, 2015, and long-term period (LTM) at the experimental site.


The uniform emergence of plants is the first and foremost condition for appropriate canopy formation and good yield in crops. The extent and rate of emergence depends on the quality of the sowing material. Ghassemi-Golezani et al. (2010) found that the emergence of rapeseed from seeds with a lower vitality is poorer and lasts longer. No less important are the habitat factors, especially the soil properties and weather conditions, as well as the techniques of soil preparation and sowing. In field production, these factors usually exert a combined influence. Kutcher and Malhi (2010), when conducting research into the impact of forecrop plant residue and tillage with various properties on the yield of plants, [page 196]

Figure 3. Winter rapeseed plant density (plants mâ&#x20AC;&#x201C;2) in two years depending on tillage and sowing system (CT, conventional; ST, strip-till) 2, 4 weeks after sowing and at the end of the autumnal growing season. a, b, significant differences; ns, insignificant differences.

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Article determined, among others, that the habitat and tillage system interacted in their influence on rapeseed emergence. A larger emergence after zero-tillage than in the conventional system occurred in one of two locations, in four out of five years of the research. The authors attributed this to a higher soil moisture content and a lack of destruction of the top soil layer in the zero-tillage system. The results of model tests (Rinaldi et al., 2005) indicate that the germination and emergence of plants is greatly influenced by the soil moisture content and its texture. In our study, when the quantity and distribution of rainfall in the winter rapeseed sowing period of 2014 was favourable for plant emergence, over 90% of the maximum number of plants that were later determined to be growing on individual establishments had emerged after only two weeks, regardless of the tillage system that was used. On the other hand, in 2015, when rainfall was scarce and the soil that was tilled in the CT was severely overdried, the plant density two weeks after sowing failed to reach as little as 50% of the sowing density. This was probably due to the lack tillage after the forecrop harvest and the straw that was used as mulch on surface of the soil and then the rapeseed drilling in the ST system, which allowed the soil to retain enough moisture to permit the seeds to germinate. Thus, the emergence of the winter rapeseed was good and more uniform within the trial field with varied soil conditions than after the CT. Earlier research by other authors indicated that eliminating ploughing may increase the degree of rapeseed emergence (Taghinezhad et al., 2012). Strip-till also has a beneficial influence on the emergence of plants due to the top soil layer being protected against water evaporation and temperature increases in the cultivated strip. Soil conditions, emergence and initial growth of plants in the strip-till system, however, depends on the depth and width of the strip under cultivation and the weather in the respective period (Schillinger, 2005; Celik et al., 2013). In 2015, a rainfall shortage at the time of rapeseed sowing and initial growth caused a considerable, generally greater than in 2014, differentiation of emergence and, subsequently, seedling size within the trial field with a varied soil texture. The uniformity of plant density, the number of leaves in a rosette, the dry mass of leaves and the root neck diameter after the ST was, nevertheless, much higher than after the CT. This was proven by the lower values of the standard deviation of the plant properties, which were investigated in the experiment. Coupled with the results of other

research, this is to be attributed to the beneficial influence of conservation tillage on those soil properties that play a role in plant emergence and growth. The results of numerous research projects (Fuentes et al., 2009; Brunel et al., 2013) indicate that minimumtillage systems retain more water in the soil than conventional systems. This is made possible by the plant residue that is left on the surface, less water runoff, better water infiltration, lower evaporation and a higher organic matter content. Wang et al. (2007) determined that the beneficial influence of conservation tillage on soil moisture and plant yield was especially visible in the dry years. Soil moisture is the key property that impacts plant emergence and growth. In the research conducted by Hosseini et al. (2009), a decrease in the soil moisture content from 75% to 25% field capacity led to the shrinking of chickpea emergence from 86.4% to 56.5% and extended the emergence period from 6.2 to 13.9 days. The plants were 15.3 cm shorter, had a 79.3 cm lower leaf area and a 0.62 g lower mass of the aboveground part. Our and other authorsâ&#x20AC;&#x2122; research indicates that for the emergence to be fast and seedling growth to be uniform, the tillage system used ought to shape other soil properties as well, especially the seedbed. According to Nasr and Selles (1995), the extent and rate of emergence depends on soil density, the size of the aggregates and the interaction between the two. A high soil density and aggregate size are the limiting factors for plant emergence. High soil density is found to have a negative influence on emergence, mainly when the seedbed contains small aggregates. Ă&#x2013;nemli (2004) demonstrated that plant emergence improves together with an increase of the organic matter content in the soil. Its role is particularly important in years with little rainfall and high air temperatures.


In the year in which the period preceding the sowing of the winter rapeseed and immediately afterwards saw a high shortfall of rain, it was the ST system that promoted the fast and uniform emergence of rapeseed. In such weather conditions and in a field with a spatially varied soil texture, the plants grown using the ST system were more uniform prior to wintering than after the CT. The higher uniformity of the plants grown using the ST technology in a field with a varied soil texture than in the CT system, especially in

Table 2. Mean value of characteristics of winter rapeseed plant, their range and variability. Tillage method Plant density (pcs. mâ&#x20AC;&#x201C;2) CT ST Number of leaves in rosette (pcs.) CT ST Dry mass of leaf rosette (g d.m.) CT ST Root neck thickness (mm) CT ST



2014 Max





2015 Max



37.2 33.8

30.0 27.0

44.0 42.0

4.07 3.97

0.74 0.73

21.2 29.8

8.0 21.0

33.0 41.0

7.49 5.67

1.36 1.03

11.2 11.9

9.7 10.7

12.5 13.0

0.70 0.55

0.13 0.10

9.0 10.6

6.3 9.0

11.0 12.0

1.23 0.87

0.22 0.16

5.1 5.6

4.7 5.1

5.5 6.0

0.23 0.19

0.04 0.04

3.8 4.8

2.8 4.0

4.8 5.2

0.60 0.32

0.11 0.06

14.2 15.7

12.1 13.7

15.6 17.3

0.90 0.82

0.17 0.15

10.3 13.9

8.7 12.5

12.4 14.8

1.10 0.66

0.20 0.12

Min, minimum values; Max, maximum values; SD, standard deviation; SE, standard error; CT, conventional; ST, strip-till.

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Article a year with an unfavorable distribution of rainfall, was proven by lower factors of variability in the number of leaves in a rosette, the dry mass of the leaf rosette and root neck thickness. The probability of such plants overwintering and then producing high yields is greater than with strongly diversified plants in terms of their morphology and physiology. The ST may very well be a good system for the tillage and sowing of rapeseed in unfavorable field habitats that have rainfall shortages and different soil textures.


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Italian Journal of Agronomy 2018; volume 13:982

Podolian cattle: reproductive activity, milk and future prospects Carlo Cosentino,1 Carmine D’Adamo,1 Salvatore Naturali,2 Giovanni Pecora,1 Rosanna Paolino,1 Mauro Musto,1 Francesco Adduci,1 Pierangelo Freschi1 1School

of Agricultural, Forest, Food, and Environmental Sciences SAFE, University of Basilicata, Potenza; 2Veterinary surgeon, San Martino d’Agri (PZ), Italy


In the present study, an original article about Podolian cattle and its milk was performed. In detail, the following factors on reproductive career of Podolian cattle in semi-extensive and extensive rearing were analysed: age of calving, gestation lenght, calving interval length, abortions percentage, conception and calving period. Chemical and phisycal parameters (protein, fat and lactose), somatic cell count, lisozyme content and antiradical activity, with ABTS and DPPH assays, of Podolian milk in semiextensive and extensive rearing were evaluated. Finally, suggestions on future prospects of Podolian milk use were proposed: the preparation of hand soap with 5 percentage of podolian milk and the potential role of this breed for prevention and propagation of fire. The data concerning the reproductive career, milk and envinromental role of 677 cows registered in the Herdbook were analysed. The results showed that the 58.56% of the cows manifested a calving interval of 11-14 months. The reproductive activity occurred in spring; over 70% of the cows calved in the period February-April. The chemical-physical aspects of milk are in agreement with literature. It presents an high antioxidant activity values for each group (97.03% and 97.50% for ABTS and 52.09% and 52.60% for DPPH, semiextensive and extensive system Correspondence: Carlo Cosentino, School of Agricultural, Forest, Food and Environmental Sciences, University of Basilicata, via dell’Ateneo Lucano 10, 85100 Potenza, Italy. Tel.: +39.0971.205044 - Fax: +39.0971.205604. E-mail: carlo.cosentino@unibas.it

Key words: Biodiversity; calving interval; cosmetics; milk; Podolian cattle. Acknowledgements: the research was conducted within the project La Zoocosmesi per le imprese e l’innovazione di prodotto financed by the Programma Operativo FSE Basilicata 2007-2013, Asse IV Capitale Umano.

Received for publication: 4 April 2017. Revision received: 11 July 2017. Accepted for publication: 15 July 2017.

©Copyright C. Cosentino et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:982 doi:10.4081/ija.2018.982

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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respectively). The consumer test shows that the subjects appreciated the soap containing 5% of Podolian milk for different aspect. The data about environmental role were obtained from Podolian cows reared in fifteen grazing areas were monitored for five years during the summer pasture, when the Podolian cattle graze on green grazing areas located in Basilicata region (South of Italy). Therefore, an efficient management of grazing by Podolian cattle could be an important tool to prevent the fire propagation.


The Podolian cattle (Bos taurus podolicus) arrived in Italy from the East Asia across the Danube Hungarian plain is well adapted to the harsh environmental conditions of the inland areas of southern Italy (Abruzzo, Basilicata, Calabria, Campania, Molise and Apulia) thanks to its extraordinary ability to survive and reproduce (Felius et al., 2014). This breed presents a lightweight skeletal structure with strong feet and it is very robust and frugal, currently, about 23,000 head are registered in the Herd Book and the animals are reared on more than 600 farms (Maretto et al., 2012; ANABIC, 2014). In past years, this breed was appreciated for its triple attitude (work, meat, milk). Currently, it is instead selected, monitored and evaluated mainly for the production of meat. The Podolian milk is used exclusively for the production of the typical stretched curd cheeses with good quality. This milk is rich in protein (4.06%) and fat (4.87%), contains different bioactive components such as peptides, vitamins (C and E), carotenoids and flavonoids with antioxidant properties (Simos et al., 2011). It is also rich in unsaturated fatty acids (30%), in particular Omega 3 and Omega 6, important in moisturizing functions for the skin (Marsico et al., 1993). The lactation persists about 68 months with a total daily production between 5 and 10 liters (Parisi, 1950). The Podolian cows do not fit the mechanical milking, they must be milked by hand simultaneously with the calf feeding, it involves the higher costs and comprehensible difficulties (Procopio et al., 2005). The calving interval is one of the most important parameters to evaluate the productive and reproductive efficiency in a livestock and/or population, but the pedoclimatic conditions, and a high coefficient of inbreeding in populations of poor consistency as Podolian breed, make the analysis of these reproductive characters particularly complex. Studies conducted in farms with extensive system (Giourga et al., 1998) have shown that certain factors, such as diet and photoperiod, can influence the length gestation of certain Spanish genetic types. Several studies (Zicarelli et al., 1989) also showed that the most innovative reproductive technologies (synchronization and embryo transfer) had poor results, and only the best weather conditions and increased food availability may influence, in part, the reproductive activity. The long duration of calving interval and of seasonal reproductive

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Article activity, limit the desired development and success of Podolian cattle. The enhancement of this model (Musto, 2003), in fact, cannot be tied exclusively to the sale of products that this livestock allows to obtain, but must necessarily be accompanied by the improvement of management. A possible alternative employment and exploitation of the Podolian milk may be to use it as an ingredient for the formulation of natural cosmetic products, being a natural tensor, rich in vitamins, minerals, antioxidants, proteins, enzymes and lipids, the milk well promotes the protection and regeneration of the epidermis (Cosentino et al., 2014). The European market, primarily in Denmark, offers cosmetics based on cow’s milk, with emollient and moisturizing characteristics, without paraben, inorganic polymers or other synthetic arising. In particular, milk of animals raised on mountain natural grazing not treated with herbicides and chemical fertilizers, is a good basic constituent for the preparation of natural cosmetics such as milksoap (Gilbert et al., 2012). Nowadays, the survival of the Podolian cattle and the possibilities for consolidation of their breeding, are closely linked to the protection of marginal lands and to the preservation livestock biodiversity. The Pan-European Strategy on Biological Diversity has encouraged, in Italy, the establishment of new protected areas and parks for the protection of extensive rearing system that is oriented towards the breeding of native species in order to combat the abandonment of internal areas (Cosentino et al., 2010). This type of breeding, if managed with rational criteria, may exercise specific actions in the habitat conservation: with the containment of some invasive species and poor food value, it allows to maintain a high plant diversity, with the appearance of floristic specimens also particularly valuable, such as Anacamptis morio, Orchis italics, etc. (Pihl et al., 2001; Freschi et al., 2015). Moreover, grazing animals remove biomass, one of the most important factors for pasture ecosystem management (Leonard et al., 2010). Podolian cattle is well appreciated also for sustainable and ecological management of the available resources. This article contains the following studies about Podolian cattle conducted in the areas of Basilicata region: i) the seasonality of reproduction and the indices that define the calving interval, and the possible effects of the farming system; ii) chemical-physical characteristics of milk; iii) future employment of milk-cosmetics; iv) environmental role.

Materials and methods

Evaluation of reproduction index

This study was conducted analysing data from 24 farms located in different areas (Val d’Agri, Camastra, Alto and Medio Basento) of Basilicata, South of Italy. The farms were selected for the same number of cows, and were divided for rearing system: extensive and semi-extensive. In the extensive system, the grazing was throughout the year without shelter; in the semiextensive system, the grazing was during the year, with shelter only for the colder season. The data concerning the reproductive career of 677 cows registered in the Herdbook were analysed. For each cow, the dates and the age of the calving have been detected, defining the gestation length, the calving interval and the conception and calving period. In both farming systems, the natural service is with a sex ratio of 1 bull per 30 cows in order to get the calvings in a limited period.

Chemical and physical analysis of Podolian milk

Bulk cow milk was taken on the same day from 4 farms (2 for extensive sistem, 2 for semi-extensive sistem) that used mechanical milking. The farms are situated at about 700 m above sea level in National Park of Appennino Lucano, Basilicata. After collection, milk aliquots were immediately refrigerated at 4°C and transported to the laboratory for analytical determinations. On milk samples we measured pH (HI931410, Hanna Instruments, Padova, Italy), protein, fat, and lactose content according to the International Dairy Federation Standard (ISO, 2013) by Milkoscan FT 6000 FT 6000 (Foss Electric, Hillerod, Denmark). Somatic cells count, expressed as SCS (log10 n × 1000/mL) (ISO 13366-2:2006) we determined using a Fossomatic 5000 (Foss Electric A/S). Moreover, we enumerated bacteria total count. All determinations were carried out in triplicate. Lysozyme quantity of Podolian milk was determined by HPLC fractionation using a reversed-phase column. Sample milk preparation, column equilibration and elution were performed according to Cosentino et al. (2016). The chromatographic separations were run on a Synergi MAX-RP 80 Å column (150×4.6 mm, 4 μm particle size) from Phenomenex (Torrance, CA, USA) with a MAXRP guard column (4×2 mm id). Injection volume was 20 μL and flow rate was 0.8 mL/min. The mobile phase consisted of a gradient of water (A) and acetonitrile (B) both containing 0.1% trifluoroacetic acid (v/v). Eluting conditions are: 0 min 80% A and 20% B; 9 min 60% A and 40% B; 15 min. 60% A and 40% B; 20 min. 80% A and 20% B. Detection was carried out by fluorescence detector (Jasco FP-2020 Plus-Intelligent-fluorescence detector) set at 280 nm excitation and 350 nm emission. Calibration curves were acquired with known amounts of HEW lysozyme in the concentration range of 5 to 100 mg/L. Antiradical activity of Podolian milk was evaluated by using both DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2’-azinobis (3-ethylbenzthiazoline-6-acid)) assays (Cosentino et al., 2015). Both tests were carried out in triplicate. DPPH assay: The stock radical solution of DPPH was prepared by dissolving 20 mg of DPPH in 15 mL of ethanol. After 1 min of agitation with Vortex, 1 mL of stock DPPH solution was diluted in ethanol (1:30). 50 µL of milk were added to 950 µL of DPPH solution and incubated into the darkness for 30 min at room temperature. After centrifuging (5 min, 8000 rpm), absorbance was measured at 515 nm against the reference solvent (ethanol) by using spectrophotometer UV-Vis (LKB Biochrom 4050 Ultrospec II). ABTS assay: The stock solution of the ABTS radical was prepared by dissolving 38 mg of (ABTS) in 10 mL of an aqueous sodium persulphate solution (2.45 mM). The mixture was dark stored for 12-16 h. For the analysis, 1 ml of stock ABTS•+ solution was diluted in ethanol (1:30). 20 µL of milk sample was added to 980 µL of ABTS•+ solution. Milk samples were reacted with ABTS•+ working solution for 2 h in incubation into the darkness at room temperature. After centrifuging (5 min, 8000 rpm), absorbance was measured at 734 nm against the reference solvent (ethanol). The solutions were prepared fresh for the analysis. Antioxidant activity of cow milk was evaluated through the Radical Scavenging Activity (RSA%) utilizing the following formula: Radical Scavenging Activity (RSA%) = (1 − Ai/ A0) × 100%, where Ai is the absorbance of sample and A0 is the absorbance of colorimetric radical substance.

Preparation and efficacy test of hand soap

Hand soap supplemented with 5% percentage of Podolian milk was prepared by specialized and certified laboratory. The surfac-

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Article tants used are derived from cornstarch and coconut oil. The abrasive action is carried out by plant micro-granules obtained by crushing the shell of hazelnut and almond; the fragrance is given by the essential oils of orange and rosemary, able to eliminate bad odours. The Podolian milk and vegetable Glycerine help to restore the hydro-lipid barrier of the skin. The basic formula of liquid soap is the following according to International Nomenclature of Cosmetic Ingredients (INCI): aqua, sodium lauroyl sarcosinate, cocamidopropyl betaine, acrylates copolymer, lauryl glucoside, lac, citrus autantium dulcis oil, prunus amygdalus dulcis shell and corylus avellana shell, glycerin, benzyl alcohol, rosmarinus officinalis oil, sodium benzoate. The soap was tested by 8 women and 7 men regular consumers of liquid soap recruited from our University Campus. Their age was in the range 22 to 54 years. For evaluating the effectiveness in removing dirt, in relation to the target professional profiles identified in our previous study for willingness to buy (Cosentino et al., 2014), soiling products were employed, such as engine oil, grease workshop, plaster paste, universal soil, extra virgin olive oil, margarine, dough for bread, charcoal, minced meat. The soiling of the hands, the movements and the operations of washing, including the amount of soap used was standardized. During efficacy test, the individual washes are controlled for the amount of product used, the times and the movements of cleaning.

Regulation - EC n. 1200/2009), it is defined as follows: 0.0 LU for calves younger than 6 months, 0.6 LU for cattle between 6 months and 2 years, and 1.0 LU for cattle older than 2 years. For each area, the stocking density (SD) was calculate by dividing the number of LU of each area by the extension (ha) of the same area. For pasture utilization, the potential feed intake of Podolian cattle in each area was estimated according to Grenet et al. (1987): dry matter intake for adult cattle is 14 g DM/kg LW (DM dry matter, LW live weight). While the live average weight was 605 kg, mean of data reported on farm registers. The removal of potential biomass by Podolian cattle was estimated by multiplying the feed intake calculated in each area for 120, summer season characterized by highest incidence of fire, as well as the peak of the dry and hot temperatures combined with the lowest rainfall (Pecora et al., 2015).

Results and discussion

Evaluation of reproduction index

In fifteen grazing areas located in 14 different municipalities of Basilicata, the occurrences of fire were monitored for 5 years, 2010-2014 (Pecora et al., 2015). In these areas only Podolian cattle grazed (Figure 1). The study areas were drawn by using open source GIS software, the burned areas were recorded by using GPS (Garmin Montana 60T) and classified in two classes: wooded burned area (WBA) and no-wood burned area (NWBA). The grazing and burned areas were analyzed by GIS technique. The extension (ha) of fire damage was measured within and in the surroundings of each grazing areas (no-grazing area) by overlay. For livestock production system, data of consistency and of live weight on Podolian cattle kept on each grazing area were obtained in personal interviews with 15 farm operators. The number of animals was used to calculate the number of Livestock Unit, which, according to the European official regulation (Commission

The results showed that the average gestation length was 281 days. Similar values were reported by Caballero de la Calle (2003) in a study about the breed de Lidia. The neonatal mortality rate was 3.15%, while spontaneous abortions were 1.95% (Figure 2). There were not marked differences between the two rearing systems, but only slight improvements in reproductive indices in semi-extensive rearing due, probably, to theshelter in cold season and to the dietary supplementation. The cases of neonatal mortality (2.7 vs 3.6%) and of spontaneous abortion (1.8 vs 2.1%) were lower in semi-extensive than extensive rearing. The calving concentration in the period from February to April was higher in semi-extensive than in the extensive rearing (72.51 vs 70.66%; Figure 2). According to Panella et al. (1995) and Montemurro (1996), the facility in calving performing, even in harsh environmental conditions, is due both to the pelvis anatomy that to the functional gymnastics caused by the intensive grazing intrinsic in both rearing systems. The reproductive activity was higher in the late spring and early summer, with the highest concentration of conceptions in May for both considered systems. The calving interval was between 11 and 14 months in 58.56% of the cows, it increases up

Figure 1. Podolian cows on grazing in the national park of Appenino Lucano.

Figure 2. Comparison among the parameters studied in the two rearing systems.

Environmental role of Podolian cattle

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Article to 24 months or more in the 38.24% and is less than 11 months in 3.2% (Figure 3). In our study the percentage of cows with a calving interval within 14 months was lower than that one (70-80%) reported by Montemurro (1996) in a similar production area. In the semi-extensive rearing system, a greater percentage of cows was observed with a calving interval between 11 and 14 months than the extensive rearing (60.20% vs 59.40%). The improvement could be due to the reduction of the inbreeding coefficient, as the significant increase in animals registered in the Herdbook (+26.51%) (ANABIC, 2014) during the last decade and the subsequent reduction of mating among relatives. In the semi-extensive rearing system, in addition, the improvement of reproductive parameters may also be due to a greater quality and availability of forage that, by reducing the energy deficit, reduces the marked seasonality of reproductive characters typical of the Italian beef cattle (Panella et al., 1995). The data show also a cyclical average duration in calving interval of total observed cattle (Figure 3), with two peaks: the one at 12th month (20.43%) and a second one at 24th month (6.00%). The calving period is concentrated from January to June for 80% of total observed cattle, February March and April are the months with the highest concentration of calving (71.05%), only 25.2% of the calves occurs in mid-summer (July-August) (Figure 4). The data reported are difficult to compare with other studies and other Italian rustic breeds (Maremmana, Romagnola, Marchigiana, Chianina), because the analized parameters depend on environmental factors and on observed areas (Sargentini et al., 2009). The average age of the observed cows in the two rearing systems is of 6.58 years, with a minimum of 4 and maximum of 13 years. The longevity is a distinctive feature of farming systems,

especially in farms with extensive system, results in a low culling index.

Chemical and physical analysis of Podolian milk

Several experimental studies have shown that milk from cows fed on pasture has particular characteristics compared to the milk of cows fed indoors. The grazing influences the most important chemical and biological parameters defining the quality of milk (fat, protein, urea, somatic cells). In Table 1 were reported the chemical composition of Podolian

Table 1. Chemical-physical aspects of Podolian milk for each rearing system. Parameters

Semiextensive Mean ± S.D.

pH Protein, g/100 g Fat, g/100 g Lactose, g/100 g Lysozyme, mg/L RSA%, DPPH RSA%, ABTS SCC, cell/mL Bacterial count, CFU/mL

6.65 3.60 3.96 5.11 0.25 52.09 97.03 76,600 340,000

± 0.05 ± 0.06 ± 0.05 ± 0.04 ± 0.04 ± 3.40 ± 1.06 ± 500 ± 1300

Extensive Mean ± S.D. 6.68 3.65 4.00 5.20 0.26 52.60 97.50 78,900 365,000 ±

± 0.06 ± 0.05 ± 0.05 ± 0.06 ± 0.05 ± 3.85 ± 1.20 ± 700 1200

S.D., standard deviation.

Figure 3. Calving interval in each rearing system and in total observed Podolian cows.

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Article milk rearing in semiextensive and extensive system. There were not marked differences between the two rearing systems. The antioxidant activity of Podolan milk is very high for both assay (97.03% and 97.50% for ABTS and 52.09% and 52.60% for DPPH, semiextensive and extensive system respectively). Podolian milk shows a content of lysozyme in trace in agreement of literature. In cow milk lysozyme content was generally <0.6 ppm, according to Claeys et al. (2014) and varies depending on the lactation period, at the beginning it is higher (Dimitrov et al., 2009).

Efficacy test of hand soap

The aim of this preliminary study was to evaluate the effect of an alternative ingredient on some sensory aspects of skin in fifteen volunteers. The results of our study show that the subjects appreciated the soap containing 5% of Podolian milk for different aspect. Results showed maximum preference for following parameters: the exfolianting power, the cleaning power, the skin hydration and the perfume that the soap leaves on the hands. Cow milk preparations (face and body creams, cleansing milk, and tonic) are the most known by consumers. Dairy products are good remedies for imperfect skin, Vitamins B, A and E contained in the milk have a regulating and regenerative effect. Proteins and other components have a strong absorption capacity and water retention, encouraging a high degree of hydration of the skin, and preventing the degradation of the epidermal cells (Temmuujin et al., 2006). Cotte (1991) put in evidence an increased elasticity and a calming effect of the skin of a cream containing cow milk. These results

confirm that podolian milk could be a cosmetic component suitable for all skin types thanks to its balancing skinâ&#x20AC;&#x2122;s moisture.

Environmental role by Podolian cattle

GIS analysis showed that fire affected only 4 areas of 14 municipalities of Basilicata: 1, 8, 12 and 15 (Figure 5). Results showed in the Area 1 (Abriola/Pignola municipalities), the burned area had an extension of 120 ha in 2011, and 3.35 ha in 2012. In the grazing area (2011), 25 ha of WBA and 24 ha of NWBA were recorded, with a percentage incidence on the total grazing area of 4.7% and 4.4%, respectively. In the no-grazing area, there were 35 ha of WBA and 41 ha of NWBA, with a percentage incidence on the total no-grazing area (buffer area) 7.5% and 8.8%, respectively. In the 2012, 3.35 ha of NWBA were recorded in the grazing area (0.6% of grazing area). In 2012 in the Area 8 (Ferrandina municipality), there were only 5 ha of WBA in the grazing area (1.5% of total area); in the Area 12 (Pescopagano municipality), the burned area had an extension of 2 ha of NWBA (0.3% of total of no-grazing area). During 2011, in the Area 15, there were 1 ha of WBA (0.1% of total grazing area), 7 ha of WBA and 3.5 of NWBA (1.8 and 0.9% of total of no-grazing area, respectively). These results show that in the grazing areas, the incidence of fire propagation was very low. The potential effect of removal biomass by Podolian cattle may be useful as a tool to prevent the fire propagation. In fact, these breed showed, during the time, a number of positive environmental effects, such as increased climate stability, improved soil functionality, water quality and footprint and preservation from

Figure 4. Calving frequency in each rearing system and in total observed Podolian cows.

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Article fires (Freschi et al., 2015). In Figure 6 are reported the main characteristics of Podolian cattle reared in the areas. Concerning stocking density (SD), in two grazing areas (Area 6 and 9) the parameter was higher than in the other grazing areas. This was due to both small grazing surface and high consistency of Podolian cattle. SD resulted to be below the threshold values laid down in Nitrates Directive (91/676/CEE). However, it is recommended to keep a low livestock intensity in order to not create overgrazing, which in turn may lead to soil compaction by trampling, reduction of water infiltration, and increased surface run-off and erosion (Strand et al., 2014; Freschi et al.,

2015). Moreover, the value of dry matter intake for grazing time (DM) was the highest in 2013 due to substantial cattle turnover. This estimation allowed understanding how the Podolian cattle browsing may be an important tool to reduce the fuel in order to create a horizontal and/or vertical fuel break. In fact, the Podolian cattle may be an interesting tool for fuel reduction, just like goat (Lovreglio et al., 2014). Obviously, this goal should be achieved through appropriate measures, such as the use of metallic or electrified fence in order to maintain an appropriate stocking density, for browsing both the available foliage and twigs from all woody plants and all herbaceous vegetation.

Figura 5. Grid sampling mapping: areas 1 to 15.

Figure 6. Feed intake estimation of Podolian cattle from 2010 to 2014 - Potential feed intake of Podolian cattle.

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Article Conclusions

This study on the Podolian cattle from the southern Apennines confirms that environmental factors affect the reproductive features. The Podolian cattle is, in fact, characterized by a high concentration of births in spring, when the natural pastures and meadows are in maximum productivity, with direct effects on the physiological state of the cows and on the milk production. This feature allows the rational use of spontaneous forage production. Despite the high containment of the rearing costs, the trade in veal calves depends on the age and live weight during the year, with a maximum between August and December (subjects 15-18 months). Moreover, the calving interval is quite high and shows a periodic trend with maximum at 12th and 24th months. Thus, the preservation of this cattle and the biodiversity conservation in situ native, involves improving the performance and control of inbreeding. Furthermore, we reported for the first time in literature, the chemical-physical aspects of podolian milk about fat, lactose, protein (in particular lysozyme) contents and about pH, bacterial count and antioxidant activity values. Due to the high antioxidant activity of this milk, the Podolian milk should be employed in cosmetics. For the revaluation of Podolian milk, we chose to use it as raw material for a skincare product that was positively evaluated by 15 testers. The placing of a new soap hands in the cosmetic industry would allow to small breeders of Podolian cattle to improve their incomes. The use of Podolian milk to realize a hand soap with exfoliating and cleaning power but delicate and emollient, it aims to capture the large target of consumers of effective products but natural and respectful of the skin. Concerning grazing, the prescribed grazing with Podolian cattle can reduce the fuel load of shrublands, grassland in the short term by partially reducing woody fuels. Moreover, livestock grazing may reduce fire ignition potential and spread by removing live and dead herbaceous vegetation and accelerating litter decay through trampling. However, the stocking density should not excessive in order to manage the pastureland and to reduce the fire propagation. In conclusion, exploiting effectively the local resources, this breed can be regarded as a genetic resource to improve the economy in the marginal areas in a sustainable and environmentally friendly way.


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Italian Journal of Agronomy 2018; volume 13:1112

Field bean for forage and grain in short-season rainfed Mediterranean conditions Marco Mariotti,1 Victoria Andreuccetti,1 Iduna Arduini,2 Sara Minieri,1 Silvia Pampana2 1Department

of Veterinary Science, University of Pisa; 2Department of Agricultural, Food and AgroEnvironmental Sciences, University of Pisa, Pisa, Italy


The research was carried out to evaluate the growth rate, the evolution of the nutrient characteristics, and the best stage to obtain the highest yield of nutrients from field bean (Vicia faba var. minor Beck) sown in spring for forage and seed. The best models for quanti-qualitative parameter estimation were curvilinear, such as the one proposed by Hoerl with type y = A xB eCx, and linear, using the sum of the growing degree days (GDD) as the climatic variable. The lengths of both the whole biological cycle and the individual phases of the field bean cycle were related to the amount of GDD of the growing environment and were not affected by the cultivation year. Forage dry matter and nutrient yield of the field bean followed a curvilinear model, while the main quality characteristics followed a linear model over the measured GDD. The highest nutrient and forage yields were not reached at the same time. The highest crude protein, total digestible nutrients and forage dry matter (DM) yields were obtained, at approximately 1230, 1290 and 1360 GDD respectively, when the plants were at stages from the pods being visible in the middle of inflorescence to the end of the pod development. The varieties used in this study presented a similar precocity but a very different productivity. Italian varieties, of which Scuro di Torrelama was the best, produced more than the French variety. With the most productive variety, almost 7 t/ha of forage DM, almost 1.2 t/ha of CP and more than 1.3 t/ha of TDN were obtained. At the GDD of maximum forage production, the CP concentration of the field bean varied from 16 to 18%, EE from 0.6 to 0.7%, NDF from 56 to Correspondence: Marco Mariotti, Department of Veterinary Science, University of Pisa, viale delle Piagge 2, 56124 Pisa, Italy. E-mail: marco.mariotti@unipi.it

Key words: Field bean; forage yield; grain yield; nutrients yield; quality.

Received for publication: 22 September 2017. Revision received: 23 January 2018. Accepted for publication: 27 January 2018.

ŠCopyright M. Mariotti et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:1112 doi:10.4081/ija.2018.1112

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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58%, RFV from 83 to 94%, TDN from 41 to 48%, and NEL from 1.0 to 1.2 Mcal kg-1. The effects of advanced or delayed harvests, compared to those carried out at the maximum yield stage, are discussed. Grain yield, which reached a maximum of 1.9 t/ha DM, 0.56 t CP/ha and 1.5 t TDN/ha, was mainly limited by a reduced seed filling stage.


Field bean (Vicia faba L. var. minor Beck) is grown worldwide as an alternative protein source to soybean for feed, (Jezierny et al., 2010), but also for green forage, hay, silage, or green manure (Onofrii and Tomasoni, 1989; Fraser et al., 2001; Borreani et al., 2009). The role of field bean is becoming increasingly important in low-input cropping systems designed to reduce mineral fertilizer inputs (Sulas et al., 2013) and associated N2O emissions and fossil fuel consumption (Jensen et al., 2012). This is because it has a greater ability to enrich the soil of nitrogen (through biological N2 fixation) compared with other legume crops (Walley et al., 2007). Field bean also facilitate diversification of the agroecosystem, i.e. planned biodiversity over time, via diversified crop rotations (Jensen et al., 2010), and space, via intercropping (Mariotti et al., 2011). This thus indirectly enhances soil fertility, productivity, and system stability, as well as the resilience of the entire agroecosystems (Kopke and Nemecek, 2010). In the Mediterranean climate, the sowing date for the field bean generally falls in the autumn. However, the actual time of the autumn sowing is crucial: if done too early, the plants may die due to the following cold winter, and if late, the plants will start to grow in the following spring, negating the effects of advance sowing. Moreover, the excess autumn rains typical of many areas of the north and central Italy often prevent autumn sowing and the field bean has thus to be sown in the spring. In Italy, research on the forage and seed production by field bean sown in spring is scarce. In Spain, Confalone et al. (2010) reported a reduction in growth cycles from 165 to 93 days and a reduction in grain yield of about 26%, between the autumn-winter and spring sowings. Some authors (Caballero 1989; Fraser et al., 2001) have reported that the optimal harvesting stage to obtain the highest forage yield is when the pods in the lower inflorescences are fully developed in size (stage 78 of StĂźlpnagelâ&#x20AC;&#x2122;s scale - 1984). However it is not clear if the reduction in growth cycles caused by the delayed sowing from autumn to spring also modifies the optimal harvesting stage to obtain the highest forage yield. In addition, the maximum forage yield, the maximum nutrients yield and the maximum forage quality probably not coincide, as usually occur in

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Article other forage crops; however, to the best of our knowledge, no data are available for field bean to establish a precise relationship between these characteristics and the plant growth. The objectives of the present work were: i) to study the growth rate and the evolution of the nutrient characteristics of the field bean sown for forage and seed in spring; ii) to determine the best stage to obtain the highest production of nutrients per unit area; and iii) to evaluate the genotypic differences between varieties widely used in the Mediterranean area.

Materials and methods

The research was carried out in 2009 and 2010 at the experimental station of the Department of Agricultural, Food and Environmental Science of the University of Pisa, Italy, which is located at a distance of approximately 10 km from the sea (43°41’ N, 10°23’ E) and 1 m asl. The climate is hot, humid Mediterranean with mean annual maximum and minimum daily air temperatures of 20.2° and 9.5°C, respectively, and a precipitation of 971 mm, 37% of which fall in autumn (Moonen et al., 2001). During the experiment and the growth cycle of the field bean, the total rainfall was 319 mm in 2009 and 317 mm in 2010, with a mean temperature of 16.1 and 15.5°C, respectively. In both years, treatments were four field bean varieties, three of Italian origin, Chiaro di Torrelama (CH), Scuro di Torrelama (SC), Vesuvio (VE), and the fourth of French origin, Irena (IR). Harvests were carried out at five stages: at the first flower racemes in bloom (stage 61 of the Stülpnagel scale, 1984), at complete flowering (stage 69), when the pods are visible in the middle inflorescences (stage 74), when the first pods lose the green colour (stage 81), and when the seeds in the upper pods are completely hard (stage 92). In both years, the experiment was arranged in a split-plot design with three replicates. Variety was the main plot factor, and harvest stage was the subplot factor. Sub-plot dimensions were 3 by 4 m, each separated by 2 m. Plants were grown in rows spaced 30 cm apart. Sowing took place on 4 March 2009 and 26 February 2010 at densities equivalent to 40 viable seeds m–2. Seeding rates used for field bean reflected rates used normally in the region. Field bean was fertilized with nitrogen, phosphorus, and potassium, applied pre-planting as urea, triple mineral phosphate, and potassium sulphate at a rate of 15 kg ha–1 of N, 50 kg ha–1 of P, and 60 kg ha–1 of K. Nitrogen was applied as a starter dose to prevent the nutritive deficiency that could occur under water and thermal stress condition (Jensen et al., 2010; Di Paolo et al., 2015). The research was carried under rainfed conditions. Weed control was achieved with a post-emergence application of Propaquizafop and Imazamox. At each harvest, forage yield was determined by weighing crop biomass harvested from 1 m2, cutting the plants at 5 cm aboveground level. One half of the biomass harvested was used for chemical analysis and the rest was separated into leaves, stems, inflorescences (or pods), and, in stage 92, seeds. All samples were oven dried at 70°C to constant weight in order to determine the dry matter (DM) yield. Chemical analyses were performed on the entire biomass (leaves, stems and inflorescences) except for the final harvest, in which the chemical analyses were performed separately on the seeds and residues (leaves, stems and pod walls). The parameters analyzed were the concentrations of crude protein (CP), ash, ether extract (EE), neutral-detergent fiber (NDF), acid-detergent fiber

(ADF) and acid-detergent lignin (ADL), according to Martillotti et al. (1987). Forage quality was estimated by the relative feed value (RFV), an index calculated by ADF (related to dry matter digestibility), and NDF (related to intake potential). The following equations were used to estimate the RFV and total digestible nutrients (TDN), as described by Aydin et al. (2010), while net energy for lactation (NEL) was estimated through the equation proposed by Horrocks and Vallentine (1999): RFV = (88.9-(0.77xADF%)) x (120/NDF%) x 0.775, TDN (%) = (1.291xADF%) + 101.35, NEL (Mcal/kg) = (1.044-(0.0119xADF%)) x 2.205.

In the seeds, non-fibrous carbohydrate (NFC) was estimated as NFC=100 – (NDF% + CP% + EE% + Ash%). The CP, NDF and TDN yields per unit area were calculated by multiplying the yield per hectare and the CP, NDF and TDN concentrations. Results were subjected to analysis of variance using CoStat version 6.4 (CoHort Software, Berkeley, CA, USA). The effects of year, variety, harvest stage and their interaction were analyzed using a split-split-plot design with year designed as whole plot, variety as sub-plots, and harvest stage as sub-sub-plots. Significantly different means were separated at the 0.05 probability level by the least significant difference test (Steel et al., 1997). ANOVA revealed no significant differences between years or Year × Variety × Harvest interaction, Year × Variety interaction and Year × Harvest interaction for all the parameters measured. The results were thus averaged over the two years. Changes in field bean and qualitative parameters were evaluated by calculating the relationship between yield and qualitative parameters against time and growing degree days (GDD). GDD were calculated with the NOAA method, assuming 1.7°C as the base temperature (Iannucci et al., 2008). Linear, quadratic and Hoerl equations were tested to describe the relationship between parameters and time/GDD. The Hoerl function of type y = A xB eCx was used, where y is yield or qualitative parameter, x is accumulated GDD, and A, B, C are regression constants. This function, which combines a power and exponential relationship, has already been used in similar experiments and generally in plant science (Singh et al., 1996; Paparozzi et al., 2005). The equation with the highest determination coefficient (R2) and the smallest standard error of estimate was selected as the most appropriate (Hair et al., 1995). All regression analyses were performed using ten pairs of x, y values (five sampling dates for each of the two years, and the mean sampling value over the two years are presented in graphs). For the curvilinear relationships, the first derivative was computed to define the maximum value reached by the curve and the time/GDD corresponding to the maximum value (Bullock and Bullock, 1994).


The growth stages of field bean at harvest are reported in Table 1. About twenty days from sowing and 190 GDD were needed for the plant emergence (Tbase 1.7°C; Iannucci et al., 2008). Field bean completed the growth cycle three months after sowing and after about 1800 GDD accumulated. No appreciable differences among varieties were detected regarding the GDD required to complete the phenological stages. The code of the growth stage of field beans, as reported in the

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Article Stülpnagel scale (1984), was linearly related with the increase in the number of days and the accumulation of GDD from sowing. However, GDD were more appropriate than the number of days to represent the change in growth stage (i.e. the linear regression coefficient was higher for GDD than for days from sowing). Therefore, regardless of the variety, from the stage of first flower racemes in bloom (code 61) onwards, the code increased linearly by about three stages every 100 GDD accumulated (Figure 1).

for each variety, SC presented a 21% leaf proportion and a 38% inflorescence proportion, while IR and VE presented more leaves (26%) and fewer inflorescences (about 31%).

Forage production

The increase by weight of the field bean forage, expressed as a function of the sum of GDDs, showed reduced differences between the two years, and thus can be represented by a single equation. This confirms that GDD provides a sufficiently precise index of all the climatic elements that affect the growth of field bean (Yoldas and Esiyok, 2009). The yield variation of field bean forage, as a function of accumulated GGD, was best represented by the Hoerl equation (Figure 2), and the coefficient of determination was very high for all the varieties (R2≥0.94**). Forage yield increased to about 1300-1400 GDD, and thereafter decreased (Figure 2 and Table 2). The highest forage yield of field bean and the stage in which the maximum yield was reached varied between the varieties: SC presented the highest value (more than 6 t/ha) and IR, the lowest (just under 4 t/ha). IR and CH varieties were the earliest, because they reached the maximum yield when the pods were visible in the upper inflorescences (stage 77), after accumulating about 1320 GDD, while VE was the latest (end of pod development, stage 79, 1391 GDD). The same model was the best to describe the relationship between the GDD and the dry matter forage concentration (Figure 2). The equation parameters did not differ significantly regarding the four varieties, thus a single equation was sufficient to represent them all. At the GDD of the maximum yield, the DM concentration was 20% in CH and IR, 22% in SC and 24% in VE. During the growth cycle, the DM distribution in different plant parts of field bean (leaves, stems and inflorescences) changed appreciably (Figure 3). The leaves decreased from 50 to less than 10%, the inflorescences increased from less than 10 to about 40%, while the stems remained stable from 40% to 50% (data not shown). The SC variety always presented the lowest percentage of leaves and the highest percentage of inflorescences, while the opposite occurred in VE (Figure 3). At the stage of maximum yield

Figure 1. Relationship between the code stage of field bean (Stülpnagel, 1984) and the accumulated growing degree days (GDD). CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio. Values are the means of two years and three replicates. Table 1. Main growth stages of field bean and corresponding number of days after sowing and number of growing degree days. Growth stage

Code stage* DAS

Sowing (Dry seed) Emergence First flower racemes in bloom Flowering complete Pods visible in the middle inflorescences First pod looses green color Ripeness complete

01 10 61 69 74 81 92

0 21 71 84 96 106 121

GDD 0 191 820 1056 1280 1472 1791

*Stülpnagel’s scale (1984). DAS, days after sowing; GDD, growing degree days.

Table 2. Maximum values and corresponding growing degree days (GDD) plus code stage obtained by field bean varieties calculated with the quadratic equations between accumulated GDD and yields of dry matter, crude protein, neutral detergent fiber and total digestible nutrients. Character DM (g m–2) CP (kg ha–1) NDF (kg ha–1) TDN (kg ha–1)

Parameter Max value GDD (Stage°) Max value GDD (Stage) Max value GDD (Stage) Max value GDD (Stage)

Variety CH




462.7b* 1328 (77) 947.0b 1205 (73) 2669.1b 1382 (79) 2240.1b 1262 (75)

370.5a 1311 (77) 781.9a 1186 (73) 2123.2a 1354 (78) 1792.3a 1261 (75)

659.6c 1359 (78) 1168.2c 1245 (75) 4106.5d 1393 (79) 2844.6c 1303 (76)

516.7b 1391 (79) 1005.3b 1298 (76) 3053.8c 1435 (80) 2170.4b 1312 (77)

CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio; DM, dry matter; CP, crude protein; NDF, neutral detergent fiber; TDN, total digestible nutrients. *In a row, values followed by the same letter are not significantly different, for P≤0.05; °Stülpnagel’s scale (1984).

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Article Quality characteristics

The CP concentration of forage decreased linearly with the increase in GDD accumulated by the field bean (Figure 4). The magnitude of the decrease (slope of regression) was almost the same in CH, IR and SC (about –1.5% for each 100 GDD accumulated) and substantially lower in VE (–0.9%). Accordingly, at about 800 GDD (stage 61), the CP forage concentration was the same in all varieties (about 25%), while from about 1400 GDD (stage 79) onwards, VE presented a higher CP concentration than the other varieties (Figure 4). The EE and ash concentration decreased linearly as the GDD increased. The ANOVA indicated that there were no statistical differences among the varieties, thus the EE and ash concentrations of the forage can be represented for all the varieties by the following linear equations: EE = 1.54 – 0.00071x (R2 = 0.96**); Ash = 10.10 – 0.0017x (R2 = 0.88**) (data not shown). The EE and ash concentrations showed a low variation throughout the increase in

GDD: from 800 to 1800 GDD, values changed from 0.9 to 0.3% for the EE and from 10 to 8% for the ash concentration (data not shown). NDF and ADF concentrations showed a linear increase with GDD accumulated by field bean (0.89**≤R2≤0.99**). The SC variety showed the highest concentration in both parameters, while IR showed the lowest. The NDF and ADF rate increase ranged respectively from 1.5 to 1.9% and from 1.1 to 1.4% every 100 GDD accumulated (Figure 4). The ADL concentration in the forage of field bean and between the varieties did not change appreciably with the increase in GDD, showing an average value of 12% (data not shown). The relative feed value decreased linearly during the growth cycle from values higher than 100% at about 800 GDD (stage 61), to 65-75% at 1800 GDD (stage 92). SC always presented the lowest value (from 106 to 64%), while IR and CH presented the highest values (from about 116 to 73%).

Figure 2. Relationship between the forage dry matter (DM) yield, the forage DM concentration and the accumulated growing degree days (GDD). CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio. Values are the means of two years and three replicates.

Figure 3. Relationship between the leaf and inflorescence proportion [as% of the total dry matter (DM)] and the accumulated growing degree days (GDD). CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio. Values are the means of two years and three replicates.

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Article The TDN concentration and the NEL showed a linear decrease with the increase in GDD. The average decrease in TDN ranged from 53% to about 35%, and the decrease in NEL from 1.32 to 0.96 Mcal/kg. Regarding varieties and for both parameters, IR showed the highest values, while SC and VE showed the lowest. The rate of decrease (regression slope) was appreciably lower for

the IR than for the other varieties (Figure 4). The main quality characteristics of the field bean forage were highly positively correlated with the leaf proportion, regardless of the variety or the cultivation year (Figure 5). With the increase in age of the plants, with every 10% decrease in leaf proportion, the CP, RFV and TDN decreased by 2.5, 8.4 and 3.1%, respectively.

Figure 4. Relationship between the concentrations of crude protein (CP), neutral-detergent fiber (NDF), acid-detergent fiber (ADF), relative feed value (RFV), total digestible nutrients (TDN), net energy for lactation (NEL) and the accumulated growing degree days (GDD). CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio. Values are the means of two years and three replicates.

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Article Nutrient yield

The Hoerl model was the best at representing the relationship between GDD and production per unit area of CP, NDF and TDN by field bean (Figure 6). The maximum CP yield was obtained at around 1200-1300 GDD, between the 73 (pods visible in the lower inflorescences) and 76 (pods visible in the upper inflorescences) growth stages (Figure 6). The most productive variety was SC (about 1170 kg CP ha–1) and the lowest productive variety was IR, with a 49% difference between both (Table 2). The NDF yield of field bean increased to about 1400 GDD and subsequently decreased (Figure 5). IR was found to be the earliest variety (maximum NDF yield at stage 78) and VE the latest variety (stage 80) (Table 2). The maximum yield was obtained by SC and the minimum by IR with a 93% difference between both. The TDN yield increased to about 1300 GDD (stages 75-77) with the highest values reached by SC (2.8 t/ha) and lowest by IR (1.8 t/ha) (Table 2).

Discussion and conclusions

From the beginning of the bloom onwards, the phenological stages of the field bean sown in spring, encoded with Stülpnagel (1984) scale digits, followed a linear positive trend with the accumulated GDD, with no differences between the two years and the

Seed yield and quality

Grain yield and the main characteristics of grain production are reported in Table 3. The highest grain yield was obtained by SC (192 g/m2) and the lowest by IR (111 g/m2). The highest yield shown by SC was due to the greater number of pods per plant and the higher 1000 seed weight than the other varieties. The nutrient concentration of the seeds ranged between the varieties from 28 productive variety to 33% CP, from 30 to 36% of NDF, and from 26 to 37% of NFC, while TDN was about 78% for all (Table 3). VE presented the highest crude protein and NDF concentration, but a lower NFC concentration, while the opposite was found for CH. The nutrient yield of grain was always highest in SC and lowest in IR. In terms of SC about 550 kg CP ha–1, 670 kg NDF ha–1 and 1500 kg TDN ha–1 were obtained (Table 3).

Table 3. Grain yield and quality of the four field bean varieties. Values are the means of two years and three replicates. Character CH

Variety IR



DM yield (g m–2)




Pods (n plant–1)




2.4a 190.3b 27.8a 0.71a 3.99b 30.4a 17.2a 2.06a 37.1c 79.2a 377.2b 412.5ab 1074.7b

2.8a 214.3bc 29.0ab 0.72a 4.07b 35.4b 19.4b 2.54ab 30.8ab 76.3a 323.1a 394.5a 850.3aa

2.3a 231.0c 29.0ab 0.69a 3.82a 35.1b 17.7ab 2.83b 31.4ab 78.5a 557.1c 674.4c 1508.4c

127.5b 8.7c 2.3a 146.3a 32.5b 0.86b 4.02b 36.2b 18.0ab 2.68b 26.4a 78.1a 414.4b 461.6b 996.0b

Seeds (n pod–1) Mean seed w. (mg) PG (%) EE (%) Ash (%) NDF (%) ADF (%) ADL (%) NFC (%) TDN (%) CP (kg ha–1) NDF (kg ha–1) TDN (kg ha–1)

CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio. *In a row, values followed by the same letter are not significantly different, for P≤0.05.

Figure 5. Correlation between crude protein, relative feed value (RFV), total digestible nutrients (TDN), and the leaf proportion [as% of the total dry matter (DM)].

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Article four varieties. To complete the flowering stage (code stage 69), the field bean required little more than 1000 GDD, almost the same value recorded by Iannucci et al. (2008), although they sowed field bean in the autumn. Forage dry matter and nutrient yield of the field bean followed a curvilinear model over the measured GDD as there was an increase from about 800 to 1200-1400 GDD, and a decrease thereafter. In contrast, the main bromatological characteristics followed a linear model over the measured GDD. The highest forage yield was reached at the end of the pod development (code stage 78), after the accumulation of about 1360 GDD. The differences in the precocity among the varieties were very low, IR was slightly earlier than the others, and VE, slightly later. On the other hand, the choice of variety was a very important factor in maximizing the yield: from the end of the full blossom phase (about 1000 GDD, stage 67), the SC variety produced a significantly higher forage yield than the others. The yield obtained with the most productive variety exceeded 6 t haâ&#x20AC;&#x201C;1, which was similar to that obtained in the Mediterranean area sowing the field bean in autumn (Caballero, 1989; Colombari et al., 2006; Borreani et al., 2009). In general, from the first flower to the maturity stages, the quality of the field bean forage declined linearly as the accumulated GDD increased. The CP concentration decreased from 25 to 12%, EE from 1 to 0.3%, RFV from 112 to 69%, TDN from 51 to 44% and NEL from 1.3 to 0.9 Mcal kgâ&#x20AC;&#x201C;1. In the same period NDF increased from 49 to 66%, and ADF from 37 to 50%. The modifications in the forage quality during the growth cycle of field bean are in relation to the morphological plant changes, and especially with the fall and senescence of the leaves. At the highest forage production (code stages 77-79), the CP concentration of the field bean varied from 16 to 18% among the four varieties: EE from 0.6 to 0.7%, NDF from 56 to 58%, RFV from 83 to 94%, TDN from 41 to 48%, and NEL from 1.0 to 1.2 Mcal kgâ&#x20AC;&#x201C;1. The highest nutrient yield was achieved earlier, while the maximum NDF yield occurred later than the maximum forage yield. With regard to crude protein, the maximum yield was obtained when the pods were visible in the middle inflorescences (stage 74, 1234 GDD). With the most productive variety (SC), a little less than 1.2 t/ha of CP was obtained, in line with findings in the Mediterranean area by Caballero (1989), and Dordas and Lithourgidis (2011). Considering the TDN, the maximum yield, corresponding to a little less than 3 t/ha for the SC variety, was obtained just before the maximum forage DM yield, i.e. when the pods were visible in the upper inflorescences (stage 76), after accumulating about 1285 GDD. Our models can be used to estimate whether any production losses occur by harvesting the forage in stages other than those of maximum yield. Thus, if the forage was harvested with the highest CP production, the loss of forage DM would reach a maximum of 5% among the different varieties. In addition, if the forage was harvested with the maximum TDN production, the loss of forage DM would be at most 2%. On the other hand, if the forage was harvested at the time of the highest forage DM yield, there would be a lower CP production of 7% and TDN of 2%, compared to the maximum possible. The forage of field bean can be ensiled. However, the high moisture content at cutting makes the crop unsuitable for direct ensiling and thus requires a wilting period, in order to prevent poor fermentation and the production of effluent (Borreani et al., 2009). At the maximum forage yield of CP and DM, the dry matter concentration of the field bean was respectively 17 and 22% in all varieties. In both cases, wilting is necessary, but our equations can be used to estimate when the forage should be harvested to elimi[page 214]

nate this. Considering a target value of 30% DM, such harvesting should be carried out at about 1480 GDD, thus when the first pods lose their green colour (stage 82). If the forage is harvested at this

Figure 6. Relationship between the yields of crude protein, neutraldetergent fiber (NDF), total digestible nutrients (TDN) and the accumulated growing degree days (GDD). CH, Chiaro di Torrelama; IR, Irena; SC, Scuro di Torrelama; VE, Vesuvio. Values are the means of two years and three replicates.

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Article stage, the DM yield loss, compared to that obtained at the maximum forage yield, would be low (up to 6%). However, CP and TDN losses would be high both in terms of concentration (about – 20% for CP and –8% for TDN) and yield (–30% CP and –15% TDN, respectively). Thus, abandoning the wilting by delaying the harvest would lead to low DM losses, but high quality losses. The Italian varieties produced more than the French variety (IR), which was therefore the least suitable for spring sowing. However, IR was found to have a better quality than the others, in relation to the higher leafiness and the lower fibre accumulation. In summary, the spring sowing of the field bean obtained a sufficiently high forage production and the optimal harvesting stage ranged from 74 to 78, depending on whether the highest nutrients or DM yield is preferred. The seed yield of the field bean was in line with other studies carried out on field bean sown in the spring (Battini et al., 2001; Moschini et al., 2014). Among the production characteristics, the average weight of the seeds was rather low, probably because sowing delays may have exposed the plants to high temperatures and water stress (Flores et al., 2013). As a result, the grain nutrient production was also considerably smaller than that obtained with the forage (about half) and smaller than that estimated by Annicchiarico (2017) to match the economic value of a relevant cereal benchmark crop. Consequently, the spring sowing of the field bean seems more suitable for forage than for seed production.


Annicchiarico P, 2017. Feed legumes for truly sustainable cropanimal systems. Ital. J. Agron. 12:151-60. Aydin N, Mut Z, Mut H, Ayan D, 2010. Effect of autumn and spring sowing dates on hay yield and quality of oat (Avena sativa L.) genotypes. J. Animal Vet. Adv. 9:1539-45. Battini F, Ligabue M, Marmo N, 2001. Pisello proteico e favino da granella, alternative per soia e farine proteiche. L’Inf. Agr. 14:61-5. Borreani G, Revello Chion A, Colombini S, Odoardi M, Paoletti R, Tabacco E, 2009. Fermentative profiles of field pea (Pisum sativum), faba bean (Vicia faba) and white lupin (Lupinus albus) silages as affected by wilting and inoculation. Anim. Feed Sci. Technol. 151:316-23. Bullock DG, Bullock DS, 1994. Quadratic and quadratic- plus plateau models for predicting optimal nitrogen rates of corn: A comparison. Agron. J. 86:191-5. Caballero R, 1989. Yields and chemical composition of wholecrop field beans and their components during pod-filling. Grass Forage Sci. 44:347-51. Colombari G, Crovetto GM, Loatelli L, Preus P, 2006. Il favino da foraggio al Nord. L’Inf. Agr. 9:61-6. Confalone A, Lizaso JI, Ruiz-Nogueira B, López-Cedrón FX, Sau F, 2010. Growth, PAR use efficiency, and yield components of field-grown Vicia faba L. under different temperature and photoperiod regimes. Field Crops Res. 115:140-5. Di Paolo E, Garofalo P, Rinaldi M, 2015. Irrigation and nitrogen fertilization treatments on productive and qualitative traits of broad bean (Vicia faba var. minor L.) in a Mediterranean environment. Legume Res. 38:209-18. Dordas CA, Lithourgidis AS, 2011. Growth, yield and nitrogen performance of faba bean intercrops with oat and triticale at varying seeding ratios. Grass Forage Sci. 66:569-77. Flores F, Hybl M, Knudsen JC, Marget P, Muel F, Nadal S, Narits L, Raffiot B, Sass O, Solis I, Winkler J, Stoddard FL, Rubiales

D, 2013. Adaptation of spring faba bean types across European climates. Field Crops Res. 145:1-9. Fraser MD, Fychan R, Jones R, 2001. The effect of harvest date and inoculation on the yield, fermentation characteristics and feeding value of forage pea and field bean silages. Grass Forage Sci. 56:218-30. Iannucci A, Terribile MR, Martiniello P, 2008. Effects of temperature and photoperiod on flowering time of forage legumes in a Mediterranean environment. Field Crops Res. 106:156-62. Jensen ES, Peoples MB, Hauggaard-Nielsen H, 2010. Faba bean in cropping systems. Field Crops Res. 115:203-16. Jensen ES, Peoples MB, Boddey RM, Gresshoff PM, HauggaardNielsen H, Alves BJR, Morrison MJ, 2012. Legumes for mitigation of climate change and the provision of feedstock for biofuels and biorefineries. A review. Agron. Sustain. Dev. 32:329-64. Jezierny D, Mosenthin R, Bauer E, 2010. The use of grain legumes as a protein source in pig nutrition: A review. Anim. Feed Sci. Technol. 157:111-28. Hair J, Anderson R, Tatham R, Black W, 1995. Multivariate data analysis with readings. Prentice-Hall International, Inc., NJ, USA. Horrocks RD, Vallentine JF, 1999. Harvested Forages. Academic Press, London, UK, pp 1-315. Köpke U, Nemecek T, 2010. Ecological services of faba bean. Field Crops Res. 115:217-33. Mariotti M, Masoni A, Ercoli L, Arduini I, 2011. Optimizing forage yield of durum wheat/field bean intercropping through N fertilization and row ratio. Grass Forage Sci. 67:243-54. Martillotti F, Antongiovanni M, Rizzi L, Santi E, Bittante G, 1987. Analysis methods to evaluate animal feeds. CNR, IPRA, Rome, Italy. Moonen C, Masoni A, Ercoli L, Mariotti M, Bonari E, 2001. Longterm changes in rainfall and temperature in Pisa, Italy. Agr. Med. 131:66-76. Moschini V, Casella G, Vivoli R, Vazzana C, Martini A, Lotti C, Migliorini P, 2014. Performance of organic grain legumes in Tuscany. Ital. J. Agron. 9:38-43. Onofrii M, Tomasoni C, 1989. Le foraggere coltivate in Italia. Edagricole, Bologna, Italy. Paparozzi ET, Stroup WW, Conley ME, 2005. How to investigate four-way nutrient interactions in plants: A new look at response surface methods. J. Am. Soc. Hortic. Sci. 130:459-68. Singh RS, Ramakrishna YS, Joshi NL, 1996. Growth response of mustard [Brassica juncea (L.) Czern & Coss] to irrigation levels in relation to temperature and radiation regimes. J. Arid Environ. 33:379-88. Steel RGD, Torrie JH, Dickey DA, 1997. Principles and procedure of statistics. A biometrical approach. McGraw-Hill, New York, USA, pp 1-672. Sulas L, Roggero PP, Canu S, Seddaiu G, 2013. Potential nitrogen source from field bean for rainfed Mediterranean cropping systems. Agron. J. 105:1735-42. Stülpnagel R, 1984. Proposal of growth stages for Vicia faba. In: Hebblethwaite PD, Dawkines TCK, Heath MC, Lockwood G (Eds.), Vicia faba: agronomy, physiology and breeding. In: Martinus Nijhof, The Hague, Netherlands, pp 9-14. Walley FL, Clayton GW, Miller PR, Carr PM, Lafond GP, 2007. Nitrogen economy of pulse crop production in the Northern great plains. Agron. J. 99:1710-18. Yoldas F, Esiyok D, 2009. The influence of temperature on growth and yield of green beans for processing. Int. J. Agric. Res. 4:124-30.

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Italian Journal of Agronomy 2018; volume 13:1101

Straw uses trade-off only after soil organic carbon steady-state Agata Novara,1 Mauro Sarno,1 Paulo Pereira,2 Artemi Cerdà,3 Eric C. Brevik,4 Luciano Gristina1 1Dipartimento

di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy; Management Centre, MykolasRomeris University, Vilnius, Lithuania; 3Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, Valencia, Spain; 4Department of Natural Sciences, Dickinson State University, Dickinson, ND, USA 2Environmental


Soil organic matter (SOM) is the key for a healthy soil and a relevant property to achieve the sustainability on soil management. However, soils are still net exporters of organic matter. One example is the use of wheat straw residue for industrial and energy applications, which has gained attention in the last years. The offfarm use of this abundant and low cost resource should follow sustainability criteria to avoid soil degradation and SOM losses. Straw residue incorporation is recognized as a recommended management practice to control erosion and mitigate CO2 emissions by increasing SOM. The goal of this work was: i) to evaluate the steady-state carbon (C) level in relation to C input and estimate the minimum residue input needed to maintain this SOC level in a durum wheat-based cropping system in long-term experiment; and ii) estimate the potential availability of durum wheat straws for alternative use. Results showed that a C steady-state can be achieved after 3.4 years with an annual organic C input of 4.5 Mgha–1. Only after reaching a steady-state, straws can be used for trade-off, leaving 1.03 Mgha–1y–1 of C input remain in the soil.


The soil system is a key component of the Earth System as it controls the flow of matter and energy within the hydrological, Correspondence: Luciano Gristina, Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, ed. 4, viale delle Scienze, 90128 Palermo, Italy. E-mail: luciano.gristina@unipa.it

Key words: Carbon input maintain; Mediterranean durum wheat-based system; soil carbon sequestration; regional straw assessement.

Received for publication: 29 August 2017. Revision received: 12 February 2018. Accepted for publication: 13 February 2018.

©Copyright A. Novara et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:1101 doi:10.4081/ija.2018.1101

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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erosional, biological, ecological and geochemical cycles (Smith et al., 2015). Soil also contributes key resources, goods, and services to human kind, which makes soil a crucial factor in achieving sustainability of human societies. Human activities change the fate of the carbon cycle and can trigger degradation of the land (Chen et al., 2016; de Moraes Sá et al., 2015). Contemporaneously, a social interest to protect the soil system is increasing. Returning residues into the soil (straw, pruned branches, leaves) is a key strategy to help achieve this challenge (Lafond et al., 2009: Johnson et al., 2014; Cerdà et al., 2016). Straw return into the soil, in fact, has been suggested as a recommended management practice to increase soil organic carbon (SOC) stocks in agricultural lands leading to consequent mitigation of atmospheric CO2 emissions (Liu et al., 2014; Xia et al., 2014). As indicated by Lal (2005), the indiscriminate removal of crop residues can lead to a reduction in soil quality due to the loss of organic matter (Wilhelm et al., 2004), resulting in an overall decline of the soil structure (Samahadthai et al., 2010), water retention and altering the nutrient cycle (Lal, 2005). Straw incorporation as a sustainable management practice for SOC increase has been widely demonstrated by short and long-term experiments and by simulation models for predicting soil carbon (C) stock (Saffih-Hdadi and Mary, 2008; Dikgwatlhe et al., 2014; Bleuler et al., 2017; Farina et al., 2017). Differences in the magnitude of SOC increase are attributable to many factors, including straw C input, C/N ratio and soil condition (e.g. texture, temperature, and water content), nitrogen application, methods of incorporation, and tillage practices.In general, the increase of SOC stock is limited by the capacity of the soil to store additional C (C steady-state), and therefore no linear relationship between C residue input and SOC sequestration rate has been demonstrated in previous works (Garcia-Diaz et al., 2016; Novara et al., 2016). After reaching the C steady-state level, there is equilibrium between C input humification and C losses through mineralization and erosion. The amount of biomass residue that is needed to maintain a given SOC level is defined as C input maintain (Johnson et al., 2014). Assessing C input maintain is a critical need, considering that the removal of crop straw from soil has gained attention for alternative uses in the last few years (energy source or industrial processing) (Wamukonya and Jenkins, 1995; Qureshi et al., 2010; Scarlat et al., 2010). The common management of the straw in the past was to bale and remove it from soils for use in livestock feed rations and as animal bedding, or marginally buried into the soil. Over the last 15-20 years, studies on the potential of lignocellulosic material for the production of biofuels, chemicals, and other by-products and the rising cost of petroleum (prior to 2016) resulted in a growing interest in the use of straw for energy production (Gray et al., 2006; Antoni et al., 2007). If on the one hand, some studies emphasize the important economic potential of the use of wheat straw as an energy source, on the other hand, environmental sus-

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Article tainability and in particular, the depletion of SOM (Powlson et al., 2011) must be taken into account. In fact, the off-farm use of wheat straw has to follow sustainability criteria in order to avoid soil degradation. Hence, the goal of this work is to evaluate the soil C steady-state and C input maintain in a Mediterranean wheat system and to assess the potential availability of straw residue for alternative uses while avoiding negative impacts on SOC stock.

Materials and methods

Study area biomass and soil analysis

The SOC stock dynamic in relation to Cinput was analysed in a durum wheat-based cropping system under a semiarid environment. The data from a long-term experiment (12 years) carried out in Sparacia (27°37′N, 13°42′E; Sicily, Italy) were used in previousresearch to determine the soil organic carbon behaviour under typical Mediterranean climatic conditions (Novara et al., 2016). In this study, the selected cropping systems were: i) durum wheat monocropping with aboveground residues return into soil (Ws); ii) durum wheat monocropping (W) with aboveground residues moved from the soil; and iii) durum wheat with aboveground residues moved from the soil followed by bare fallow (Wfall) (2years rotation). The mean annual precipitation is 529 mm, and the mean annual temperature 16ºC, with 21.4ºC as maximum and 9ºC as minimum. The annual C input was estimated as the sum of C contained in stubble, straw, root, and rhizodeposition. At maturity, for all systems, straw and stubble were manually harvested and separated. The sampling took place in two 10 m2 in the middle of each plot. After air-drying, portions of the grain, straw, and stubble were randomly selected to be oven-dried at 60°C until the weight stabilised for biomass weight determination. The root plantderived biomass and C were estimated from the straw biomass and the ratio of stubble/root (Kong et al., 2005). In this research, the average proportions of stubble to straw biomass were estimated to be 20% and the rhizodeposition-derived C was assumed to be equal to the root-derived C (Bolinder et al., 1999). The total regional straw availability was calculated by multiply durum wheat harvest area in Sicily and the straw yield per hectar. The C content was assumed to be 40% for all considered inputs (Johnson et al., 2006). The cumulative C input (CCi) was calculated for each cropping system by summing the C input for all years of the experiment. After wheat was harvested soil samples were collected (0-30 cm depth) using a cylinder (10 cm diameter), air-dried, and passed through a 2-mm sieve. To reduce the error tolerance to less th an ±5%, about 2 to 4 kg of soil (Hitz et al., 2002) was collected per sample. SOC was determined according to Walkley and Black (1934). Soil bulk density was measured with core method.

Soil organic carbon steady-state determination by segmented regression

The response between a dependent variable (SOC) and an explanatory variable (cumulative carbon input, CCI) can shows more than one linear relationship at different ranges of CCI, therefore a single linear model was not adequate as well as a nonlinear model. In this case a segmented regression approach can be used to better fit the experimental data. Contemporary the breakpoint can rapresents an estimation of the SOC steady-state in realation to CCI. The unknown value of the breakpoint (Bp) was estimated at CCI=Bp using the following model:

(1) where: SOC1 and SOC2 are soil organic carbon values below and above the break point (Bp) values respectively; a and b are the regression intercept and angular coefficient of regressions and CCI is the cumulative carbon input. In order for the regression function to be continuous at the breakpoint, the two equations for SOC need to be equal at the breakpoint (when CCI = Bp): a1+ b1Bp = a2+ b2Bp


a2= a1+ Bp(b1– b2)


SOC = a1+ b1CCI for CCI≤Bp


Solving for a2:

Then by replacing a2 with the equation above, the result is a piecewise regression model that is continuous at CCI = Bp:

SOC = {a1+ Bp(b1- b2)} + b2CCI for CCI>Bp


Regressions parameters and regressions ANOVA were carried out both on the single regression and on the two segmented regressions using STATA software at 90% confidence interval (STATA StataCorp., College Station, TX, USA).

C input maintain

The C input maintain (Cm), referring to the amount of C input from biomass residue that is needed on a soil to maintain SOC steady-state levels, was calculated using the basic equation of the AMG model (Saffih-Hdadi and Mary, 2008) (Eq. 6). The AMG model estimates SOC stock change with time, considering the annual C input and mineralization coefficient. (6)

where SOCt is the SOC at time t; SOCs is the stable fraction of the initial SOC content, SOCi is the initial content of SOC, k is the mineralization rate constant, m is the amount of Cinput, and h is the humification coefficient. A value of 60% was used for the stable C fraction, as previous studies in the same environment showed that this is the percentage of SOC which is mainly stored in the finest soil aggregate fraction (<25 μm) and therefore the most recalcitrant (Barbera et al., 2012; Novara et al., 2016). The humification coefficient of durum wheat straw was assumed to be 0.15 (Marraccini et al., 2012). The k constant, which is affected by air average temperature, clay and carbonate content, was calculated according to Boiffin et al. (1986) and Bockstaller and Girardin (2003). The C input maintain (Cm) was calculated assuming the difference of SOC between two subsequent years after reaching the steady-state level was equal to 0 and solving the equation for Cm.

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Article Results and discussion

Table 1. ANOVA table for the linear regression without and with breakpoint. Significant regressions were considered at P≤0.05.

Soil carbon steady-state

In durum wheat-based cropping system, the SOC stock is not directly correlated to CCI. The different cropping systems showed the lowest CCI in Wfall (10.2 Mg ha–1), followed by W (17.8 Mg ha–1) and Ws (48.1 Mg ha–1). Therefore, it is needed to assess the lower CCI to maintain SOC at steady-state. Many researches have looked for critical thresholds associated with ecological studies (Andren, 1994; Fahrig, 2001). Critical thresholds occur when the responses of an ecological process are not linear, but changes sharply and suddenly (breakdown) at a determined level that can be considered the threshold level. Soil ecological processes, and in particular SOM sequestration, adheres to these basic principles, with the threshold after breakdown being considered the soil C steady-state level. In fact, changes in management regimes may have different threshold type effects if processes are evaluated through time. Analysis of the segmented regression determined a breakpoint at 16.1 Mg ha–1 of CCI, corresponding to 37.3 Mg ha–1 of SOC. Regressions ANOVA showed the segmented regressions fit data better than a single regression. Less than fifty percent of total variance was explained if the single regression was applied. On the contrary, segmented regression was able to explain more than ninety percent of the total variance. Both analyses were characterized by a high significance level (Table 1). Regression under the breakpoint showed a high SOC sequestration rate (R2=0.96), whereas after the breakpoint the slope regression was not significantly different than 0 (P=0.06) (Figure 1 and Table 2). For this reason the constant in the >Bp regression can be considered the SOC steady-state level. Constant in the <Bp regression is the theoretical SOC stock at zero C input. The slope regression under the breakpoint can be used to estimate the soil C sequestration duration in relation to cropping system C input. The duration represents the ratio between SOC (SOC = SOC at Bp theoretical SOC stock at zero C input) and annual C input (Figure 2). According to annual C input, the C steady-state was achieved after 3.4 years and 9 years with an annual C input of 4.5 Mg ha–1and 1.7 Mg ha–1 (corresponding to 11.3 and 4.3 Mg ha–1 of wheat residue), respectively. In this 12 years long term experiment, the C steadystate level was not reached with the annual C input determined by Wfall cropping system (0.9 Mg ha–1) (Figure 2, dotted line). Knowledge of time over which SOC steady-state occurs for a specific cropping system could be useful for SOC prediction and designing environmental policy based on C accounts.

Without breakpoint Explained Unexplained With breakpoint Explained Unexplained






64.3 83.7

1 16

64.3 5.23

12.29 0.004

139.4 8.54

3 14

46.49 0.61

76.12 0.000

Table 2. Results of regression of SOC (Mg ha–1) against CCI (Mg ha–1) with optimal breakpoint (Bp).

Without Bp x<Bp x>Bp







8.72 16.01 16.01

18 7 11

35.5 32.4 37.5

2.9 2.3 0.8

25.4 10.9 34.6

1.72 2.20 0.16

CCI, independent variable; s.d., standard deviation; d.f., freedom degree.

Figure 1. Regressions with (red and green line) and without (black dotted line) breakpoint. Red and green areas are confidence intervals at 10 and 90% for the breakpoint regression.

Carbon input maintain

The C maintain, calculated after the breakpoint, was 3.15 Mg ha–1 yr–1 of wheat biomass, corresponding to 1.03 Mg ha–1 y–1 of C input. This value represents the critical C input to maintain the C steady-state level and is affected mainly by pedoclimatic characteristics and input quality. Johnson et al. (2014), for instance, estimated that the average minimum residue return of corn stover needed to maintain SOC levels was 5.74±2.4 Mg ha−1yr−1 using a dataset from 19 field in USA. In India Srinivasarao et al. (2012) found that a minimum input of 1.13 Mg C ha–1 yr–1 was required for sustenance of SOC levels in rainfed fingermillet. The value of C maintain estimated in this work was lower than the value of 2 Mg C ha−1 yr−1 estimated by Wang et al. (2016) in another wheat system using the RothC model. However, the Wang et al. study reported high variability depending on soil and climatic [page 218]

Figure 2. Annual carbon input (ACI) in relation to years needed to reach the C steady-state level. The dotted line represents an annual C input of 0.9 Mg ha–1, that is not enough to reach the steady-state after 12 years. Wfall, wheat-fallow cropping system; W, wheat monocropping without straw return; Ws, wheat monocropping with straw return.

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Article characteristics; the values were highest in the United States and Western Europe, indicating that more C input is needed to maintain C steady-state in wetter and warmer regions. Better mapping and modelling of soil properties distribution and incorporating those soil properties into models could help eliminate some of this variability. In this study the low C maintain value is related to the dry climatic condition and the low current soil C stock present in this area. Moreover, a high percentage of the soil C stock can be considered stable because is mineralogically protected by the high clay content of the soil fraction and therefore the potential mineralizable C pool is low (Barbera et al., 2011).

Regional wheat straw assessment and potential availability

Determining an amount of straw that can be removed from soil without altering C stocks must take into account that the straw is important both from an ecological point of view and in economics terms, considering the increasing interest in straw for alternative uses. In Sicily, in the last seven years the durum wheat cultivated area was stable at about 290,000 ha with an average grain yield of 2.8±0.9 Mg ha–1 (ISTAT, 2016). Total durum wheat residue yield (straw, roots, rhizodeposition,and stubble) is estimated as 5.3 Mg ha–1year–1 (Carbon input= 2.12 Mg ha–1 yr–1) with a total regional value of 15,472,217 Mg yr–1. As far as straw availability, it is estimated about 3.2 Mg ha–1yr–1 (Carbon input= 1.28 Mg ha–1 yr–1) and therefore there are 9,283,330 Mg yr–1 of total straws produced in Sicily each year. Considering a durum wheat straw harvest efficiency of 85%, an effective availability of 7,890,830 Mg yr–1 seems to be more realistic. Therefore, the effective C input (1.03 Mg C ha–1 yr–1) is determined by the contribution of roots, rhizodeposition and stubble (0.84 Mg C ha–1 yr–1) and by the 15% of straw left into the soil after harvest (0.19 Mg C ha–1 yr–1). Because the durum wheat higher heating value (HHV) is 17.9 MJ kg–1, around 141 million of MJ represents the regional potential energetic contribution from durum straw (Channiwala and Parikh, 2002). Although the relatively lower HHV of wheat straw in comparison to other material (i.e., the HHV of diesel oil is 45.7 MJ kg–1), the remain straw over C input maintain could be a valid contribution to alternative energy source to reduce petroleum consumption (Channiwala and Parikh, 2002). Upscaling estimates for a whole region based on a long term experiment conducted over a limited geographic extent could be affected by the same uncertainties that affect the models. In fact, stocastical models used to estimate soil C stock show large variability due to uncertainties in climate, soils, cropping systems, etc. and a lack of long term empirical data.


Straw return into the soil is essential to rapidly reaching and then maintaining a SOC steady-state level in wheat systems, and therefore it should be encouraged as a recommended management practice in Mediterranean types ecosystems. Results of this study showed that a C steady-state can be achieved after 3.4 years with an annual C input of 4.5 Mg ha–1. Only after reaching a steadystate is possible to use the straw yield for alternative uses, because 1.03 Mg C ha–1 yr–1 is needed to maintain the SOC level and more than 1.03 Mg ha–1 are provided by roots, stubble, and rhizodeposition. Estimating the duration to reaching SOC steady-state is affected by climatic and pedological uncertains, that should deeply

investigated for a more reliable evaluation of the ecological contribution of the proposed wheat straw management at regional level.


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Italian Journal of Agronomy 2018; volume 13:1046

Effect of salinity on Echinochloa crus-galli germination as affected by herbicide resistance Francesca Serra, Silvia Fogliatto, Francesco Vidotto

Dipartimento di Scienze Agrarie, Forestali e Alimentari, UniversitĂ degli Studi di Torino, Grugliasco (TO), Italy


Salinity is one of the major abiotic stresses that may affect yield and quality of crops. Salinization, in combination with the presence of aggressive weeds, such as barnyard grass (Echinochloa spp.), can be considered one of the factors responsible for reducing yield in rice fields. The aims of the study were to evaluate the salt effect on germination and first seedling growth of six different Italian common barnyard grass (E. crus-galli) populations (three sensitive and three resistant to ALS-inhibitor herbicides) and to verify the presence of differences in salt response between populations sensitive and resistant to the ALS-inhibitor herbicides. Germination tests were conducted under nine different NaCl concentrations (from 0 mM to 400 mM). Significant differences in germination capacity were found between sensitive and resistant populations from 0 mM to 250 mM NaCl; in particular, germination capacity of the sensitive populations was higher (up to 90%) than that of the resistant ones (about 70%). The increase in salinity over 250 mM reduced progressively the germination capacity: from 300 mM onwards, no significant differences were found between sensitive and resistant populations and the germination resulted inhibited for two of them (one sensitive and one resistant). Speed of germination and root and shoot length of seedlings were also inversely related to salt concentration. Time required for achieving 50% of final germination capacity was Correspondence: Francesco Vidotto, Dipartimento di Scienze Agrarie, Forestali e Alimentari, UniversitĂ degli Studi di Torino, Largo Braccini 2, 10095 Grugliasco (TO), Italy. E-mail: francesco.vidotto@unito.it Key words: Salinity; weeds; rice; barnyard grass; herbicide resistance.

Acknowledgements: the authors want to recognize the two anonymous reviewers and the Editor for their valuable contribution to the improvement of this paper. The paper is attributable in equal parts to the authors.

Received for publication: 26 June 2017. Revision received: 4 September 2017. Accepted for publication: 6 September 2017.

ŠCopyright F. Serra et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:1046 doi:10.4081/ija.2018.1046

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

extended from about three days at 0 mM NaCl up to about 10-12 days at 400 mM NaCl. Root length and shoot length ranged from 9.88 cm and 6.16 cm, at 0 mM NaCl, to 0.36 cm and 0.41 cm, at 400 mM NaCl. According to the results, there is no a clear evidence that response to saline conditions was related to resistance towards ALS-inhibitor herbicides, as in some cases significant differences were found between populations showing a similar herbicide sensitivity. Responses of barnyard grass to salinity are may play a role in the importance of this weed in future scenarios of salt intrusion: for example, a lower speed of germination at increasing salt levels could suggest a delayed emergence of this weed during crop establishment and first growth. To evaluate the real consequences in terms of competitions towards the crop, future studies are needed for assessing the response to salinity of the main rice varieties cultivated in the environment in which the E. crus-galli populations tested in this study were collected.


Salinity represents one of the major limitations for yield and quality of a number of different crops (Maggio et al., 2011). According to the estimates of the Food and Agriculture Organization of the United Nations, about 20% of irrigated land worldwide is affected by the increase of the salinity level (Rozema and Flowers, 2008). This phenomenon is accentuated by the competition for fresh water between agricultural and civil uses, which is worsened by climate changes, growing population (Maggio et al., 2011), socio-economic development and water contamination (Balia and Viezzoli, 2015). Salinity conditions are relevant not only to arid and semiarid environments and to the southern regions of the world, but also to the Mediterranean coastal areas. In Europe, 26 countries (Maggio et al., 2011), in particular Spain, Portugal, Italy, Greece (Ghiglieri et al., 2012) and France (Puard et al., 1999) are interested by salinization phenomena (Maggio et al., 2011). In the coastal areas, salinization of aquifers is usually caused by saltwater intrusion (Mongelli et al., 2013) as a result of groundwater overexploitation (Balia and Viezzoli, 2015). In Italy, this phenomenon is found in various regions, such as Sardinia (Capaccioni et al., 2005), the Catania Plain (Capaccioni et al., 2005), Tuscany (Barrocu, 2003), the Tiber Delta (De Luca et al., 2005), Campania, Calabria (Barrocu, 2003) and the Adriatic coast (Ghiglieri et al., 2012), especially the Po Plain (Antonellini et al., 2008). Some of these areas, in particular the Po Plain and the Oristano province (Sardinia), are used for rice cultivation. In these areas, the process of salinization may contribute to reduce crop yield, together with the presence of highly problematic weeds. In fact, salinity represents one of the environmental conditions that affect seed germination and plant growth (Sadeghloo et al., 2013), both in weeds and in crops. The knowledge of the ability of seeds

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Article to germinate in different environmental conditions, including salinity, is considered of fundamental importance not only for crop establishment, but also for estimating weed development in agricultural ecosystems (Koger et al., 2004; Benvenuti, 2011). Among the main weed species that infest rice fields, barnyard grasses (several species of the genus Echinochloa P. Beauv.), weedy rice (Oryza sativa L.), and sedges (mainly the genus Cyperus L.) are some of the most troublesome, able to cause significant yield losses (Panozzo et al., 2013). In particular, Echinochloa spp. have been key weeds in almost all rice systems worldwide, including European and Italian rice fields. These weeds are characterized by C4 photosynthetic pathway and some species are able to grow both in dry and flooded conditions (Vidotto et al., 2007). Echinochloa spp. exhibit great competition effects towards rice, especially during early stages of cultivation. Gibson et al. (2002), for example, found that the competition established by Echinochloa spp. is significantly lower if a rice field is maintained free from these weeds during the first 30 days after seeding. Echinochloa spp. can be distinguished in red or white biotypes on the basis of the different pigmentation at the basal sheaths of the plant. It has been established that this different plant pigmentation could reflect differences in herbicide sensitivity of the weed (Tabacchi et al., 2006). Problems associated with Echinochloa spp. management have worsened in the last decade, due to the selection of populations resistant to different herbicides applied in rice field, in particular ALS and ACC-ase inhibitors (Panozzo et al., 2013). In the search of alternative and effective techniques for controlling these weeds, several methods have been tested, including biocontrol agents (Hershenhorn et al., 2016) and the use of rice varieties tolerant to herbicides (Kraehmer et al., 2017). Even though the area of rice cultivation affected by salinization is increasing at global level, the response of rice weeds to salt conditions and the potential influences on weed ecology and competition effects have been poorly investigated so far. The stress due to the presence of salt causes physiological and biochemical alterations and tolerance to salinity in plants is related to the synthesis (induced by the stress) of several compounds, including abscisic acid (ABA), glycinebetaine, organic acids, proline, polyamine and polyols (Cowan et al., 1992). The functions of these compounds in response to salt stress seem to be those of chemical signals, osmotic adjustment, free radical scavenging, preservation of proteins and membrane integrity. As concerns the grass weeds, it was found that the tolerance to saline conditions is due to their capacity in uptake and translocation of Na+, maintaining K+, and osmotic regulation through the accumulation of proline and glycinebetaine, even though salt tolerance in E. crus-galli seems to be not related to Na+ adsorption changes (Yamamoto et al., 2003). In the case of Echinochloa spp., in particular, it is not yet entirely clear what is the effect of salt stress on their behaviour and physiological pathways. In these terms, a potential delay in germination or a negative influence on seedling establishment and first growth due to salinity could potentially alter the role of these weeds in rice production systems. In addition, the knowledge of the interactions between response to salinity and other agronomical relevant traits, such as herbicide resistance, is essential to estimate potential future impacts of salinity on Echinochloa spp. management in rice. In particular, it is not clear if there is interaction between herbicide resistance and salt stress and what could be the biochemical mechanisms involved. This study aimed to evaluate the effect of salinity on germination of different common barnyard grass (E. crus-galli (L.) P. Beauv.) populations collected in the Italian rice area and to verify [page 222]

the presence of differences in terms of salt sensitivity between populations that are susceptible or resistant to ALS-inhibitor herbicides.

Materials and methods Seed collection

Seeds of six common barnyard grass populations (E. crusgalli) were used during the trials. The seeds were collected between 2010 and 2012 in Italian rice fields, which underwent repeated applications of penoxsulam since many years. Penoxsulam is a broad-spectrum ALS-inhibitor herbicide used in rice to control common barnyard grass, water-plantain (Alisma plantago-aquatica L.), red stem (Ammannia coccinea Rottb.) and other weeds. After collection, the seeds were let dry at room temperature for about one week and then stored at +5°C. At different timings, seedlings from an aliquot of the collected seed lots were tested for resistance to ALS-inhibitor herbicides via a greenhouse bioassay and a molecular study that confirmed the presence of target site resistance in three populations (labelled r1, r2, and r3) due to Trp-574-Leuc point mutation in the ALS gene (unpublished data). Thus, the other three populations (s1, s2, and s3) were considered sensitive to penoxsulam. For the present study, seeds of the six populations were taken for the same collected seed lots.

Effect of water salinity on germination

In order to evaluate the effect of water salinity on the germination, 20 seeds for each population were placed in Petri dishes (9 cm diameter) lined with one filter paper imbibed with 5 mL of deionized water or saline solution. Nine different salt concentrations were applied. Salt solutions were prepared by dissolving NaCl in deionized water at the following concentrations: 0 mM, 50 mM, 100 mM, 150 mM, 200 mM, 250 mM, 300 mM, 350 mM, and 400 mM. These nine salt concentrations were selected among those applied in previous studies (Chauhan and Johnson, 2009; Sadeghloo et al., 2013; Opeña et al., 2014) and according to the water salinity levels found in some European rice cultivation areas (Isla et al., 2003; Gay et al., 2010; Moret-Fernández and Herrero, 2015) in order to be able to compare the results of our trial with those found in other studies carried out in analogous conditions and in order to use saline concentrations that can be found in the area from which the six common barnyard grass populations tested came from. The number of doses was selected following the guidelines provided by Holland-Letz and Kopp-Schneider (2015). Three replicates for each salt treatment were used. Petri dishes were sealed with Parafilm to avoid drying and contamination. Afterward, they were incubated in a growth chamber at a constant temperature of 25°C and arranged in a randomized complete block design. Seed germination was recorded every day for 15 days. Moreover, at the end of the incubation period (15th day), length of roots and shoots of a sample of 10 seeds for each Petri dish was determined. Two runs of the entire experiment were carried out.

Statistical analyses

ANOVA was carried out on data of germination capacity (total of number of seeds germinated by the end of germination test), speed of germination and length of roots and shoots in order to test

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Article the effects of population, NaCl concentration and experiment run. The test was conducted using the open source programme and environment R (R Core Team, 2016) A regression analysis was performed on data of germination capacity. The fitted model was the following 3-parameter loglogistic regression model (Streibig et al., 1993; Knezevic et al., 2007; Vidotto et al., 2013): (1) where Y is the germination capacity, x is the NaCl concentration in mM, d is the upper limit, and b is the relative slope at the point of inflection e. Model fitting was performed using the function drm of the add-on package drc of the programme R (Ritz and Streibig, 2012; Ritz et al., 2015). The same model was also used to describe the effect on speed of germination, as the relationship between duration of germination test and cumulated number of germinated seeds. The analysis was conducted separately for each salt concentration with the time (days) as the parameter x in Eq. (1) and cumulated germination as the dependent variable Y. Furthermore, the same analysis was applied to model the relationship between NaCl concentrations (independent variable) and either the length of roots or length of shoots (dependent variables). The effective concentrations required to reduce by 50% either germination capacity or shoot or root length in comparison to the values obtained at 0 mM salt concentration (EC50) were estimated from the fitted models using the function ED of the package drc. In the case of cumulated germination, the number of days required to obtain 50% of final total cumulated germination was calculated. The values of EC50 were used to perform pair-wise comparisons between populations, or between averages among resistant or susceptible populations, by calculating a Sensitivity Index (SI):

Results and discussion Germination capacity

The results showed that the germination of the tested weed biotypes was affected by the salt treatments (Figure 1). The analysis of variance showed significant differences in terms of germination capacity between resistant and sensitive populations from 0 mM NaCl to 250 mM NaCl (Table 1). At these saline doses, the germination capacity of the sensitive populations ranged from about 84% to 94% and resulted greater than that of the resistant ones, which ranged from about 60% to 71%. Seed germination remained quite stable up to a saline concentration of 250 mM, with values slightly above 60% and 80% for resistant and sensitive populations, respectively. These germination values were similar to those recorded in previous studies on

(2) where A and B refer to two generic populations under comparison. SI was also calculated by considering EC50(A) and EC50(B) in Eq. 2 as estimated by pooling data of resistant or susceptible populations, respectively. The significance of SI of each comparison was calculated by using the function EDcomp of the package drc of the R programme.

Figure 1. Seed germination (%) of the tested barnyard grasses populations as function of NaCl concentration, as fitted by the following log-logistic model Y = d/{1+exp[b(log(x) â&#x20AC;&#x201C; log(e))]}. Blue line: population r1; orange line: r2; green line: r3; cyan line: s1; black line: s2; red line: s3.

Table 1. Seed germination capacity (expressed as percentage of germinated seeds) at 15 days of resistant (R) and sensitive (S) populations from 0 mM to 400 mM NaCl concentrations. R and S are averages from ALS-herbicide resistant (r1, r2, and r3) and sensitive (s1, s2 and s3) populations, respectively. Population R S t-test significance

0 (mM)

50 (mM)

100 (mM)

150 (mM)

200 (mM)

250 (mM)

300 (mM)

350 (mM)

400 (mM)

69.44 91.11 *

71.11 93.89 *

69.44 90.55 *

67.50 92.78 *

60.55 87.78 *

63.33 83.89 *

50.55 69.44 ns

36.11 34.44 ns

13.89 12.78 ns

R, populations resistant to ALS-inhibitor herbicides (r1, r2, and r3); S, populations susceptible to ALS-inhibitor herbicides (s1, s2, and s3); *Pâ&#x2030;¤0.05; ns: P>0.05.

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Article E. glabrescens Munro ex Hook in which at a salt concentration of 200 mM the germination was more than 60% and averaged about 73% at 250 mM NaCl (Opeña et al., 2014); similar germination level (68%) was also found on E. crus-galli at a salt concentration of 225 mM (Sadeghloo et al., 2013). Conversely, in trials carried out by Chauhan and Johnson (2009), germination of E. colona (L.) Link was totally inhibited at the salt concentration of 200 mM. In our study, increasing salt concentration above 250 mM reduced seed germination, until about 13% germination at 400 mM NaCl, with no differences between resistant and sensitive populations. At 400 mM NaCl germination was inhibited for the populations s2 and r2, while it ranged from 5% to 33% for the populations s1 and s3, respectively (Figure 1). At the same NaCl concentration Sadeghloo et al. (2013) found that germination was completely inhibited. Previous studies on E. colona and E. glabrescens also found a linear decrease of germination with the increase of salt concentration (Chauhan and Johnson, 2009; Opeña et al., 2014). The EC50 for germination capacity was the lowest for the resistant population r2 (EC50 274.47 mM), while the highest for the sensitive population s3 (EC50 380.73 mM) (Table 2). The pairwise comparison between populations showed significant differences between population s2 and population r3 and between population r2 and s3 (Table 3). Furthermore, significant differences were also found between the resistant populations r2 and r3. According to these results, there is no a clear evidence that response to saline conditions was influenced by sensitivity towards ALS-inhibitor herbicides, as in some cases significant differences were found between populations showing a similar herbicide sensitivity (such in the case of s2 vs s3 and r2 vs r3).

Speed of germination

The results obtained showed that the appearance of the first germinated seeds delayed with the increase of NaCl concentration (Figure 2). An analogous trend was found by Hakim et al. (2011) during a germination test conducted on different weeds, including E. crus-galli and E. colona. At 0 mM NaCl concentration the seeds began to germinate between the second and the third day from the start of germination test and reached their maximum germination between the fourth and the ninth day. Furthermore, at 0 mM NaCl only populations s1 and r2 showed significant differences in the time required for achieving 50% of their final germination capacity: in particular, population s1 required 2.48 days while population r2 needed 3.84 days (Table 4; Figure 2). For the resistant populations, the time required to reach 50% of germination capacity ranged, on average, from a minimum of 2.85 days at 50 mM NaCl to a maximum of 12.42 days at 400 mM NaCl; averaging among sensitive populations, EC50 ranged from a minimum of 3.42 days to a maximum of 10.26 days, respectively, at 50 mM and 400 mM salt concentration (Figure 2). At the higher saline concentrations, the barnyard grass populations tested in our trials showed a speed of germination greater than that found by Hakim et al. (2011): in fact, in their study, the authors observed that E. crus-galli and E. colona began to germinate after 12-15 days. Pairwise comparisons showed the presence of significant differences between resistant and sensitive populations at 50 mM, 200 mM, 250 mM and 300 mM NaCl concentrations (Table 4). Furthermore, significant differences were found within resistant and within sensitive populations at salt concentrations of 50 mM (s1 vs s2 and r2 vs r3), 200 mM (r2 vs r3), 250 mM (r1 vs r2) and 300 mM (s1 vs s2). As observed for germination capacity, also in the case of speed of germination there is no a clear evidence that sensitive and resistant populations react differently to increasing NaCl concentrations. [page 224]

Table 2. NaCl concentration required to reduce by 50% seed germination capacity (EC50) of the tested populations. Values in brackets are the standard error. Population code


r1 r2 r3 s1 s2 s3

377.46 (58.18) 274.47 (45.63) 379.91 (28.25) 339.61 (19.02) 286.21 (27.66) 380.73 (35.94)

Table 3. SI values of germination capacity (ratio between EC50 of two populations) of the pairwise comparisons between tested populations. SI values are tested against the hypothesis that they are not dissimilar to 1 and the P values of this test are reported. Compared populations


P value

r1/s1 r1/s2 r1/r2 r1/s3 r1/r3 s1/s2 s1/r2 s1/s3 s1/r3 s2/r2 s2/s3 s2/r3 r2/s3 r2/r3 s3/r3

1.11 1.32 1.37 0.99 0.99 1.19 1.24 0.89 0.89 1.04 0.75 0.75 0.72 0.72 1.00

ns ns ns ns ns ns ns ns ns ns * * * * ns


Table 4. SI values of speed of germination (ratio between EC50 of two populations) of the pairwise comparisons between tested populations. SI values are tested against the hypothesis that they are not dissimilar to 1 and the P values of this test are reported. Only comparisons giving P values ≤0.05 are included. NaCl concentrations (mM) 0 50

200 250



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Compared populations


P value

s1/r2 s1/s2 s1/r2 s1/r3 s2/r3 r2/r3 s2/r3 r2/r3 r1/s2 r1/r2 s1/r3 s2/r3 r1/s2 s1/s2 s2/r3

0.64 0.78 0.78 1.47 1.89 1.89 1.73 1.61 0.64 0.60 1.43 1.79 0.55 0.69 1.94

* * * * * * * * * * * * * * *

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Article Length of roots and shoots

The results showed that, in all the tested populations, the length of roots decreased with increasing NaCl concentrations (Figure 3). Root length ranged, on average, from 9.88 cm at 0 mM NaCl to 0.36 cm at 400 mM NaCl. A similar trend was found by

Hakim et al. (2011) on E. crus-galli and E. colona, in which a progressive reduction of root length with the increase of saline concentration was observed. The value of EC50 for root length in resistant populations averaged 162.60 mM, with a minimum of 96.36 mM for the population r2 and a maximum of 230.98 mM for

Figure 2. Cumulated percent of germinated seeds as function of duration of germination test, as fitted by the following log-logistic model Y = d/{1+exp[b(log(x) â&#x20AC;&#x201C; log(e))]} (graphs) and EC50 values (days) of the different populations at the tested NaCl concentrations. Blue line: population r1; orange line: r2; green line: r3; cyan line: s1; black line: s2; red line: s3.

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Article the population r3. In the case of the sensitive populations, EC50 for root length averaged 159.76 mM, with a minimum of 114.81 mM for the population s2 and a maximum of 210.10 mM for the population s3 (Table 5). The pairwise comparisons between populations underlined the presence of significant differences in root length among some of the resistant and sensitive populations (Table 6). Regarding the length of shoots (Figure 4), a similar behaviour was observed, with shoot length decreasing with the increase of salt concentration. Shoot length ranged, on average, from 6.16 cm at 0 mM NaCl to 0.41 cm at 400 mM NaCl. Hakim et al. (2011) obtained analogous results on different species, including E. crusgalli and E. colona. In general, the values of EC50 for shoot length were higher than those recorded for root length, indicating that shoot growth was apparently less affected by salinity than root growth. In resistant populations, EC50 for shoot length was on average 246.10 mM, with a minimum of 226.47 mM for the population r2 and a maximum of 261.67 mM for the population r1 (Table 5). In the case of sensitive populations, EC50 for shoot length was 217.00 mM, with a minimum of 199.23 mM for the population s2 and a maximum of 249.67 mM for the population s3. The pairwise comparisons between populations underlined the presence of significant differences in shoot length among some of the resistant and sensitive populations (Table 6). As observed for germination capacity and speed of germinations, there are no clear trends of different tolerance to salinity conditions in the tested populations that could be attributable to sensitivity to ALS-herbicides. Nevertheless, a similar behaviour was observed in terms of response of shoot length to salinity within resistant populations.


The results obtained in this study suggest that in the tested E. crus-galli populations, seed germination capacity, speed of germination, root and shoot growth were affected by saline conditions. A remarkable barnyard grass tolerance to moderate salinity levels was observed: from 0 mM to 250 mM NaCl the seed germination capacity was up to 90% in the sensitive populations while it was about 70% in the resistant ones. Moreover, one of the populations (s3) showed a good tolerance to salinity, with a percentage of germination equal to 33%, even at 400 mM NaCl. Although the resistant tested populations exhibited an intrinsically lower germination capacity, as shown by the lower values recorder in nonsaline conditions (0 mM NaCl), the response to salinity is similar to that observed in sensitive populations.

These results are interesting both from an ecological and agronomic perspective. For example, the reduction of speed of germination at increasing salt levels could suggest a reduced competitive activity of barnyard grass. Moreover, these results could be poten-

Figure 3. Root length of the different populations at the tested NaCl concentrations, as fitted by the following log-logistic model Y = d/{1+exp[b(log(x) â&#x20AC;&#x201C; log(e))]}. Blue line: population r1; orange line: r2; green line: r3; cyan line: s1; black line: s2; red line: s3.

Table 5. NaCl concentration required to reduce of 50% root and shoot length (EC50) of populations r1, r2, r3, s1, s2 and s3, average EC50 for resistant and sensitive populations and their comparison. Values in brackets are the standard error. Population code r1 r2 r3 s1 s2 s3

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EC50 root length

EC50 shoot length

160.47 (13.47) 96.36 (12.24) 230.98 (7.59) 154.38 (9.29) 114.81 (9.06) 210.10 (10.24)

261.67 (12.57) 226.47 (11.61) 250.15 (8.49) 202.09 (11.56) 199.23 (6.77) 249.67 (6.38)

Figure 4. Shoot length of the different populations at the tested NaCl concentrations, as fitted by the following log-logistic model Y = d/{1+exp[b(log(x) â&#x20AC;&#x201C; log(e))]}. Blue line: population r1; orange line: r2; green line: r3; cyan line: s1; black line: s2; red line: s3.

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Table 6. Comparison between r1, r2, r3, s1, s2 and s3 populations for NaCl concentration required to reduce by 50% (SI) root length and shoot length. Compared populations r1/s1 r1/s2 r1/r2 r1/s3 r1/r3 s1/s2 s1/r2 s1/s3 s1/r3 s2/r2 s2/s3 s2/r3 r2/s3 r2/r3 s3/r3

Root length

Shoot length


P value


P value

1.04 1.40 1.67 0.76 0.69 1.34 1.60 0.73 0.67 1.19 0.55 0.50 0.46 0.42 0.91

ns * * * * * * * * ns * * * * ns

1.29 1.31 1.15 1.05 1.05 1.01 0.89 0.81 0.81 0.88 0.80 0.80 0.91 0.90 1.00

* * ns ns ns ns ns * * * * * ns ns ns

*P≤0.05; ns: P>0.05.

tially exploited for predicting weed emergence dynamics through modelling, also in crops different from rice (Masin et al., 2010, 2012). The real consequences in terms of competitions towards the crop should be evaluated also taking into consideration the negative impact that salinity could have also on germination and first growth of the crop itself. Future studies are then needed for assessing the response to salinity of the main rice varieties cultivated in the environment in which the E. crus-galli populations tested in this study were collected.


Antonellini M, Mollema P, Giambastiani B, Bishop K, Caruso L, Minchio A, Pellegrini L, Sabia M, Ulazzi E, Gabbianelli G, 2008. Salt water intrusion in the coastal aquifer of the southern Po Plain, Italy. Hydrogeol. J. 16:1541-56. Balia R, Viezzoli A, 2015. Integrated interpretation of IP and TEM data for salinity monitoring of aquifers and soil in the coastal area of Muravera (Sardinia, Italy). Boll. Geofis. Teor. Ed Appl. 56:31-42. Barrocu G, 2003. Seawater intrusion in coastal aquifers of Italy. State Seawater Intrusion Coast. Aquifers Mediterr. Coast SWIM-SWICA Alicante Spain Available from: http://aguas.igme.es/igme/publica/tiac-02/ITALIA-I.pdf Benvenuti S, 2011. Potenziale impatto dei cambiamenti climatici nell’evoluzione floristica di fitocenosi spontanee in agroecosistemi mediterranei. Ital. J. Agron. 4:45-68. Capaccioni B, Didero M, Paletta C, Didero L, 2005. Saline intrusion and refreshening in a multilayer coastal aquifer in the Catania Plain (Sicily, Southern Italy): dynamics of degradation processes according to the hydrochemical characteristics of groundwaters. J. Hydrol. 307:1-16. Chauhan BS, Johnson DE, 2009. Seed Germination Ecology of

Junglerice (Echinochloa colona): A Major Weed of Rice. Weed Sci. 57:235-40. Cowan AK, Rose PD, Horne LG, 1992. Dunaliella salina: a model system for studying the response of plant cells to stress. J. Exp. Bot. 43:1535-47. De Luca A, Preziosi E, Giuliano G, Mastroianni D, Falconi F, 2005. First evaluation of the saltwater intrusion in the Tiber delta area (Rome, central Italy). In: 18th Salt Water Intrusion Meeting, Cartagena, Spain. Available from: http://www.swimsite.nl/pdf/swim18_abstracts/DeLuca.pdf Gay F, Maraval I, Roques S, Gunata Z, Boulanger R, Audebert A, Mestres C, 2010. Effect of salinity on yield and 2-acetyl-1pyrroline content in the grains of three fragrant rice cultivars (Oryza sativa L.) in Camargue (France). Field Crops Res. 117:154-60. Ghiglieri G, Carletti A, Pittalis D, 2012. Analysis of salinization processes in the coastal carbonate aquifer of Porto Torres (NW Sardinia, Italy). J. Hydrol. 432-433:43-51. Gibson KD, Fischer AJ, Foin TC, Hill JE, 2002. Implications of delayed Echinochloa spp. germination and duration of competition for integrated weed management in water-seeded rice. Weed Res. 42:351-8. Hakim MA, Juraimi AS, Hanafi MM, Selamat A, Ismail MR, Karim SR, 2011. Studies on seed germination and growth in weed species of rice field under salinity stress. J. Environ. Biol. 32:529. Hershenhorn J, Casella F, Vurro M, 2016. Weed biocontrol with fungi: past, present and future. Biocontrol Sci. Technol. 26:1313-28. Holland-Letz T, Kopp-Schneider A, 2015. Optimal experimental designs for dose–response studies with continuous endpoints. Arch. Toxicol. 89:2059-68. Isla R, Aragüés R, Royo A, 2003. Spatial variability of salt-affected soils in the middle Ebro Valley (Spain) and implications in plant breeding for increased productivity. Euphytica 134:325-34.

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Article Knezevic SZ, Streibig JC, Ritz C, 2007. Utilizing R software package for dose-response studies: the concept and data analysis. Weed Technol. 21:840-8. Koger CH, Reddy KN, Poston DH, 2004. Factors affecting seed germination, seedling emergence, and survival of texasweed (Caperonia palustris). Weed Sci. 52:989-95. Kraehmer H, Thomas C, Vidotto F, 2017. Rice production in Europe. In: B.S. Chauhan, K. Jabran, G. Mahajan (eds.). Rice Production Worldwide, pp. 93-116. Maggio A, De Pascale S, Fagnano M, Barbieri G, 2011. Saline agriculture in Mediterranean environments. Ital. J. Agron. 6:7. Masin R, Loddo D, Benvenuti S, Otto S, Zanin G, 2012. Modeling weed emergence in Italian Maize Fields. Weed Sci. 60:254–9. Masin R, Loddo D, Benvenuti S, Zuin MC, Macchia M, Zanin G, 2010. Temperature and Water Potential as Parameters for Modeling Weed Emergence in Central-Northern Italy. Weed Sci. 58:216-22. Mongelli G, Monni S, Oggiano G, Paternoster M, Sinisi R, 2013. Tracing groundwater salinization processes in coastal aquifers: a hydrogeochemical and isotopic approach in the Na-Cl brackish waters of northwestern Sardinia, Italy. Hydrol. Earth Syst. Sci. 17:2917-28. Moret-Fernández D, Herrero J, 2015. Effect of gypsum content on soil water retention. J. Hydrol. 528:122-6. Opeña JL, Chauhan BS, Baltazar AM, 2014. Seed Germination Ecology of Echinochloa glabrescens and Its Implication for Management in Rice (Oryza sativa L.) (J. Ali, Ed.). PLoS One 9:e92261. Panozzo S, Scarabel L, Tranel PJ, Sattin M, 2013. Target-site resistance to ALS inhibitors in the polyploid species Echinochloa crus-galli. Pestic. Biochem. Physiol. 105:93-101. Puard M, Clément G, Mouret JC, Roux-Cuvelier M, 1999. Strategies for rice salinity tolerance in Mediterranean France.

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Cah. Options Méditerr. 40:83-9. R Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available from: https://www.R-project.org/ Ritz C, Baty F, Streibig JC, Gerhard D, 2015. Dose-response analysis using R. PLoS One 10:e0146021. Ritz C, Streibig JC, 2012. Dose response curves and other nonlinear curves in Weed Science and Ecotoxicology with the add-on package drc in R. Available from: www. bioassay.dk Rozema J, Flowers T, 2008. Crops for a Salinized World. Science 322:1478. Sadeghloo A, Asghari J, Ghaderi-Far F, 2013. Seed germination and seedling emergence of velvetleaf (Abutilon theophrasti) and Barnyardgrass (Echinochloa crus-galli). Planta Daninha 31:259-66. Streibig JC, Rudemo M, Jensen JE, 1993. Dose–response curves and statistical models. In: J.C. Streibig, P. Kudsk (Eds.). Herbicide Bioassays. CRC Press, Boca Raton, FL, USA, pp. 29-55. Tabacchi M, Mantegazza R, Spada A, Ferrero A, 2006. Morphological traits and molecular markers for classification of Echinochloa species from Italian rice fields. Weed Sci. 54:1086-93. Vidotto F, De Palo F, Ferrero A, 2013. Effect of short-duration high temperatures on weed seed germination: High temperatures affecting weed seeds. Ann. Appl. Biol. 163:454-65. Vidotto F, Tesio F, Tabacchi M, Ferrero A, 2007. Herbicide sensitivity of Echinochloa spp. accessions in Italian rice fields. Crop Prot. 26:285-93. Yamamoto A, Shim I-S, Fujihara S, Yoneyama T, Usui K, 2003. Physiochemical factors affecting the salt tolerance of Echinochloa crus-galli Beauv. var. formosensis Ohwi. Weed Biol. Manag. 3:98-104.

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Italian Journal of Agronomy 2018; volume 13:991

Compost tea spraying increases yield performance of pepper (Capsicum annuum L.) grown in greenhouse under organic farming system Massimo Zaccardelli,1 Catello Pane,1 Domenica Villecco,1 Assunta Maria Palese,2 Giuseppe Celano3

1Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Centro di ricerca Orticoltura e Florovivaismo, Pontecagnano (SA); 2Università degli Studi della Basilicata, Dipartimento delle Culture Europee e del Mediterraneo: Architettura, Ambiente, Patrimoni Culturali (DICEM), Matera; 3Dipartimento di Farmacia, Università degli Studi di Salerno, Fisciano (SA), Italy


Compost tea (CT) is an organic liquid product derived from quality compost carrying useful microorganism and molecules capable to protect and stimulate growth of the plants. It is gaining a lot of interest for improving productivity of conventional and/or organic vegetable crops. In this research, the effects of an aerated water-extracted CT obtained from vegetable composts, applied as foliar spray on pepper plants, was evaluated for two years. In the first year, total production increased by 21.9% whereas, in the second year, it increased by 16.3%. The increment of the yields was related to an increase of the number of fruits per plant, whereas the weight of the single fruit was not affected by treatment. In both years, physiological and nutritional status of pepper plants were increased, as resulted by leaf-SPAD assessed during crop cycle. Findings indicate the effectiveness of CT application in improving significantly yield performances of vegetable crops under greenhouse organic farming system.

Correspondence: Massimo Zaccardelli, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Centro di ricerca Orticoltura e Florovivaismo, via Cavalleggeri 25, I-84098 Pontecagnano (SA), Italy. Tel.: +39.089.386211 - Fax: +39.089.384170. E-mail: massimo.zaccardelli@crea.gov.it Key words: Plant biostimulation; disease control; organic agriculture; PGPR.

Acknowledgements: this research was supported by the BioCompost Project, funded by the PSR 2007/2013 European funding programme (F.E.A.S.R., Measure 124).

Received for publication: 13 April 2017. Revision received: 2 October 2017. Accepted for publication: 5 October 2017.

©Copyright M. Zaccardelli et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:991 doi:10.4081/ija.2018.991

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.


In the last years, the demand for most nutrient and taste, healthy and eco-friendly foods is increasing. For these reasons, vegetables coming from organic farming systems, in which operators apply natural cultivation methods and sustainable productive tools, are particularly appreciated by consumers. However, a general challenge of the modern agriculture is to increase the yield with the aim of the reduction of chemical pesticides and fertilizers. The use of compost in organic agriculture is very important because it contributes to improve general soil fertility (Pane et al., 2013a; Scotti et al., 2016) and may be crucial for plant disease management by restoring soil suppressive properties (Pane et al., 2013b). Furthermore, another use of this organic amendment regards the production of compost teas (CTs), liquid extracts rich in useful microorganisms and organic and inorganic biomulecules (Ingham, 1999), that can be active in plant protection against phytopathogenic fungi and bacteria and in plant growth and yield promotion. Aerated CTs are produced by a continuous aqueous extraction of compost in presence of oxygen for a time ranging from few hours to few days, with or without other organic additives such as molasses and fish meals (Zaccardelli et al., 2012). The use of CTs is spreading in organic farming worldwide (Litterick et al., 2004; Siddiqui et al., 2008; Hargreaves et al., 2009; Shaheen et al., 2013) because of benefits they provides as fertilizer, biostimulant or foliar spray against pathogens. Literature survey shows many successful experiences with CTs to achieve productive biostimulation of different crops, including okra (Siddiqui et al., 2008, 2009), strawberry (Hargreaves et al., 2009), pak choi (Pant et al., 2012), tomato (Radin and Warman, 2011), Centella asiatica (Siddiqui et al., 2011), orange (Omar et al., 2012), lettuce and kohlrabi (Pane et al., 2014a), cowpea (Hegazi and Algharib, 2014), lettuce, soybean, and sweet corn (Kim et al., 2015). Mechanisms underlying these CT-based biostimulation functions are hypothesized to concern an enhanced plant physiological status due to carried nutrients (fertilization action) and/or dissolved organic moieties, humic substances and hormone-like molecules secreted by microbes (hormonal action) (Zaccardelli et al., 2012). Stimulation may also occur in combination with disease suppressiveness leading to additional yield increases as recently observed on processing tomato, where CT spray has been used in substitution of synthetic fungicides (Pane et al., 2016). The fungicide-like effects of CTs was essentially due to the antagonistic activity of the overall native microbial community

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Article (Pane et al., 2012, 2014b); but also abiotic antimicrobial factors and/or organic molecules can play a role in improving plant defences (Zaccardelli et al., 2012; Praveena Deepthi and Narayan Reddy, 2013). On the base of these perspectives, CTs may contribute to reduce the unsustainable use of chemical-based pesticides and fertilizers in agriculture, but insights regarding their spray application in conventional and organic systems are still necessary. The current research was carried out to investigate the effects of CT-spray treatments on the agronomic performances of pepper under greenhouse organic farming system. Two on-farm composts, obtained from residues of artichoke, fennel and escaroles, that are selected among different others because of their good endowment of macro and micro elements and of high disease suppressive bioactivity (Pane et al., 2013b), were used to produce CT formulation for these trials.

Materials and methods

On-farm compost tea production

Compost and compost tea were produced at CREA experimental farm of Battipaglia (Salerno district) by using the available onfarm composting plant and blowing extractor system. At first, two composting piles were obtained by mixing different vegetable materials as follows: one composting pile contained 78.0% artichoke, 20% woodchips and 2% mature compost used as starter; the second compost pile contained 43.5% artichoke, 23.5% fennel, 11% escaroles, 20% woodchips and 2% mature compost; all percentages are expressed as dry weigh. Initial C/N ratio of the two raw pile was about 30, in order to favour a good trend of composting process. The volume of each on-farm composting pile was about 6 m3 in volume. Under each static pile, a forced aeration system was located to ensure forced aeration during the first 45 days, including thermophylic and mesophilic phases. In particular, mechanical aeration was provided by air injection through a net of tubes connected to a blower (0.75 KW) that was periodically activated (5 min every 3 h) with an electronic timer. Piles wetting was achieved through a PVC irrigation system, manually activated on demand when RH <50%. After forced aeration, a final curing period of about two months without aeration, was made to have mature compost from the piles. Composting temperatures were measured by thermo-sensors placed in the core of the pile at 15 cm from the pile bottom in order to follow the dynamic of the composting process. CTs were produced on farm using a compost extractor constituted by a 50-L polyethylene container connected to a forced air blowing system that, periodically (5 min every 3 h), injected air in 20 L of water in which a plastic bag of holes of 3 mm of diameter, contained 5 L of compost. Duration of fermentation process was one week and, at the end, the two CTs were filtered and mixed together in equal parts, so to have the compost tea mix (CTmix) to use in treatments of pepper plants for the two-year trial. CT mix was stored at 4°C.

Chemical and physico-chemical analyses of compost tea

All analyses were performed twice on CTmix at the end of extraction procedure. Total organic carbon (TOC) was determined as described in Pane et al. (2016), according to the Italian official method for compost analyses (ANPA, 2001). In particular, potassium dichromate (K2Cr2O2) and concentrated H2SO4 are added to 10 mL of CTmix. After 10 min, distilled water was added to the solution to halt digestion. Barium diphenylamine sulfonate was added [page 230]

to the digestate and, then, the excess of Cr2O72− was titrated using Möhr salt (ferrous ammonium sulfate). Heavy metals (Cd, Cr, Cu, Mn, Pb, Zn), alkali metals (Na, K) and alkaline earth metals (Ca, Mg) were analysed as described in Pane et al. (2016). In particular, ten milliliters of such materials were previously subjected to an acid digestion at rising temperature steps using a microwave oven. Metal concentrations were determined in the extracts using an ICP-OES Spectrometer (iCAP 6000 Series - Thermo Scientific, Waltham, MA, USA). Electrical conducivity (EC) and pH were measured at 25°C directly in a sample of CTmix using a Hanna Instruments pHmeter model 211 and a conducimetry-meter Hanna Instruments model 4321, respectively.

Microbiological analyses of compost tea

Microbiological groups determined were total bacteria, pseudomonads, spore-forming bacteria, total fungi, Escherichia coli, Enterobacteria and Clostridia. All these microbiological groups were encountered by three-replicated plating serial ten-fold dilutions on selective substrates. In particular, total bacteria were counted on selective medium (glucose 1 g L−1, proteose peptone 3 g L−1, yeast extract 1 g L−1, K2PO4 1 g L−1, agar 15 g L−1) to which actidione (cycloheximide) 100 mg L−1, was added. Pseudomonads were counted on selective agar medium without iron, to which actidione was added (Scher and Baker, 1982). Spore-forming bacteria were counted on Nutrient Agar (Sadfi et al., 2001) using CTmix preparation previously heated at 90°C for 10 min. Total fungi were counted on PDA (Oxoid) pH 6, to which 150 mg L−1 of nalidixic acid and 150 mg L−1 of streptomycin were added. E. coli and enterococci were counted in sample of the liquid tea (APAT, IRSA-CNR, 2003). The estimation of E. coli was performed using TBX medium (Oxoid); plates were incubated for 24 h at 44°C and blue colonies were counted as E. coli. Enterococci were enumerated on a Slanetz & Bartley medium (Oxoid); after plate incubation for 48 h at 37°C, red colonies were transferred on Bile Esculine Azide Agar (Merck, Germany) and incubated for 2 h at 44°C; when any blacking of the medium occurred, colonies were counted as Enterococci. Sulphite-reducing Clostridium spores were determined according to APAT, IRSA-CNR (2003) and APHA (1998) methods. In detail, compost-tea sample was pre-treated for 10 min at 80°C; spores were enumerated using SPS agar (Merck); plates were incubated for 24-48 h at 37°C in an anaerobic jar with the anaerobic atmosphere generating system Anaerogen (Oxoid); black colonies surrounded by a black zone were considered as sulphite-reducing Clostridium spores. Population densities of all detected microoganisms are reported as log c.f.u. mL−1 of CTmix.

Biostimulation activity of compost tea

Biostimulation activity of CTmix was determined twice on Lepidium sativum seeds. For each Petri dishes containing a disc of blotting paper, 20 seeds of L. sativum were put in, and serial tenfold dilutions of CTmix were added (5 mL) in each plate. Petri dishes containing water were used as control. Plates were incubated in the dark at 25°C for 3 days and, thus, root elongation was measured and registered.

Greenhouse trial, compost tea treatments and relieves

Agronomic trials were carried out in ordinary conditions, in 2012 and 2013 seasons, under a greenhouse system in a loamy soil, at organic farm IdeaNatura located in Eboli (Salerno province, Campania region, Italy); experimental design was a randomized complete block with plot areas of 10.80 m2 each, replicated three

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Article times. Plantlets of pepper cv. Scintilla were transplanted on March 19th 2012 and on April 8th 2013 in double rows, at distances of 0.40 m on each row, 0.90 m among the rows of each double rows and 1.5 m among each double rows, so to have a density of 33,000 plants ha−1. CT mix, water diluted 10% vol., was weekly applied by spraying aerial parts on pepper until run-off. Plant’s vegetative and phytosanitary status were monitored during crop cycles by direct observations, and physiologic-nutritional status of the plants was registered by clorophyll concentration in the first leaf completely developed, using SPAD-meter Minolta. Harvestings, performed on 10 plants (assay area 3.03 m–2) for each replicate, occurred from June 20th to November 13th in 2012 (145 days, 23 harvesting) and from June 19th to October 17th in 2013 (121 days, 17 harvesting). For each assay area, total weight, number of the fruits collected and their longitudinal and equatorial measures, were registered (Figure 1).

manganese and, on the contrary, by the absence or content of heavy metals lower than legal limits established for compost by Italian D.lgs. 75/2010 (Table 1). The microbiological quality of CTmix was high, due to the absence of potentially harmful bacteria such as Escherichia coli, Enterobacteria and Clostridia and larger concentration of beneficial PGPR/antagonistic bacteria such as Pseudomonas spp. and Bacillus-like spore-forming bacteria (Table 2). CTmix specific features make it potential for foliar application as an organic biofertilizer to sustain plant nutrition in all critical phases of the cycle, including growth, flowering and fruiting (Omar et al., 2012). On the other hand, beneficial effects of these organic treatments on growth, development and physiology of the

Statistical analyses

In order to statistically evaluate the effects of compost tea treatments on total yield, number and weight of harvested fruits in and between years were registered; longitudinal and equatorial measures of single fruit for each year, were registered too. Data were submitted to Student’s t test.

Results and discussion

Chemical and microbiological analyses of the CTmix provided precise indications about the quality of the produced tea. CTmix was characterized by the presence of plant nutritive macro and microelements, including potassium, calcium, magnesium and

Figure 1. Comparison of pepper fruits collected from a plot treated with compost tea (on the right) respect to pepper fruits collected from a plot not treated with compost tea (control, on the left).

Table 1. Chemical characterization of the CTmix used. Legal limits are for compost. pH

EC S cm–1

Organic C (g L–1 or g kg–1 dry compost)

7.6±0.16 6.0-8.5

4778±500 -

1.06±0.05 ≥200


CT Legal limits



Mg Na Mn Cd CrVI Cu (mg L–1 for CTmix and mg kg–1 for dry compost)

1417.0±229 21.8±1.9 37.8±3.08 -



92.1±6.59 0.45±0.01 0.00±0.0 0.02±0.01 0.16±0.02 0.03±0.01 0.15±0.01 1.5 0.5 150 140 500

Table 2. Main microbiological populations in the CTmix used in this study. Total bacteria 6.47±0.00

Pseudomonas 5.57±0.23

Spore-forming bacteria Total fungi Log CFU mL–1 5.23±0.34

Escherichia coli


Enterobacteria Clostridia




Table 3. Effects of compost tea-spray treatments on the agronomic performances of pepper over two years of greenhouse trial. Values are the mean ±standard deviation of all data collected in the each harvesting season. Treatments 2012 CTRL CTmix Sign. 2013 CTRL CTmix Sign.

Total yield (T ha–1)

Harvested fruits (N ha–1)

Weight (g)

Single fruit Longitudinal f (cm)

Equatorial f (cm)

122.95±2.83 149.88±6.30 **

481.94±10.69 611.81±52.76 *

229.62±11.22 246.61±4.08 ns

13.94±0.38 14.04±0.06 ns

8.68±0.16 8.56±0.04 ns

94.48±7.21 109.93±5.26 *

357.64±54.02 415.28±18.20 *

291.34±20.89 300.21±14.02 ns

13.83±0.21 13.91±1.04 ns

9.02±0.32 8.96±0.47 ns

* and ** indicate significance levels, P≤0.01 (**) and P≤0.05 (*), of differences among the values according to Student’s t-test. ns, not significant.

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Article plants, has been largely linked to the presence of hormone-like molecules, including gibberellins, indoleacetic acid and cytokinins (Bernal-Vicente et al., 2008; Pant et al., 2012; Ertani et al., 2013; Zhang et al., 2014) that were identified in highly bioactive compost teas and/or extracts. Herein, findings of field trials showed that CTmix treatments enhanced agronomic performances of pepper greenhouse cultivation under organic management. Indeed, it improved significantly pepper production for both years. In detail, total yield of pepper in the treated plots were, on average, higher 21.9% and 16.3% than that of the reference control plots, respectively in 2012 and in 2013 seasons (Table 3). Analysing yields between years, no trends can be highlighted, since a general significant reduction of the production harvested in the 2013 was found (data not showed). Cumulative production graph shows an increasing gap throughout the harvesting season among yields of CTmix treated and untreated plants (Figure 2). Moreover, CTmix foliar-spraying increased the number of the harvested fruits, while it did not affected the weight and dimension of the single berry in comparison with the reference controls in both seasons (Table 3). However, the general trend of the weight of the single fruit throughout the crop cycle showed a decreasing tendency (Figure 3). In the current study, data indicate the occurrence of general beneficial effects of CTmix treatment on the harvested production for the whole cropping cycle and an additional stimulation in its last part, due to source of organic substances used. Indeed, the

dynamic of the yield let to hypothesize that CTmix promoted the longevity of the productive phase of pepper by stimulating plantâ&#x20AC;&#x2122;s fruiting. The enhanced production registered for the pepper greenhouse system, under CTmix spray treatments, is in agreement with Radin and Waeman, (2011) who observed increases in tomato yield by spraying municipal solid waste compost tea very frequently during the growing cycle. On the other hand, Omar et al. (2012) reported similar effects on Washington navel orange, where production enhanced by rice straw compost tea foliar application induced large fruit weight, greater set of fruits and reduction of fruit drop. While, compost tea used in combination with NPK fertilizers, also incited significant increases in seed yield of cowpea (Hegazi and Algharib, 2014). Previous experiments carried-out with the use of CT to enhance sustainability of lettuce, kohlrabi and tomato systems, indicated that the organic-sourced product may act by physiological and/or nutritional biostimulation of the plants (Pane et al., 2014a, 2016). In the current study, since diseases do not occurred in experimental trials, under natural pressure, CTmix disease suppressive mechanisms may be excluded from the hypothetical effectors underlying yield enhancement. In our work, plants under CTmix treatments showed an enhanced global well-being, with improved physiological and nutritional status as indicated by SPAD temporal assessment. SPAD values linked to the chlorophyll foliar content, proved higher on plants treated with CTmix than non-treated ones in a large

Figure 2. Effects of compost tea-spray treatments on cumulate total yield of pepper over cropping cycle in the two years.

Figure 3. Weight of the single fruit, recorded in each harvesting date during the season, from plants cultivated under compost teaspray treatments compared to non-treated control over two years.

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Article part of cultivation cycle (Figure 4). Increases in chlorophyll content due to CT treatments was observed on muskmelon plants exhibiting stimulation of flowering, growth and yield (Naidu et al., 2013) and on okra that showed an enhanced net photosynthesis rate (Siddiqui et al., 2008). Xu et al. (2012) also reported promotion of cucumber growth and increase of chlorophyll content in the leaves, after treatments of the plants with compost extracts. The present research confirm that CTs from agricultural composts may be effective to biostimulate crop productivity, as recently reported by our research group on lettuce and kohlrabi, under greenhouse in organic system (Pane et al., 2014a), and on tomato grown in open field in conventional agrotechnics (Pane et al., 2016). These results encourage the practical use of CTs in organic farming and in conventional farming system too. For these reason, other applicative experiments can be performed to get more knowledge about CTs use on other vegetable crops.


Researches on growth stimulation and productivity of the crops incited by compost teas and extracts, are receiving major attention in the last years. Field trials performed in our study, confirm the efficacy of CTs to induce biostimulant effects on the

Figure 4. Effect of compost tea-spray treatments on physiological-nutritional status of the plants assessed as measure of SPAD units over the crop cycle in the two cropping seasons.

plants, so to improve efficiency use of the inputs and production. For this reason, the use of CTs can play a very crucial role on the development of sustainable agricultural systems focused on the reduction of fertilizers. Therefore, it is desirable to have a greater spread of the application of CTs in agricultural management with efforts of other further studies addressed to the fine individuation of the mechanisms of action, standardization and practical implementation works.


APAT, IRSA-CNR, 2003. Metodi analitici per le acque. Manuali e linee guida 29/2003. APHA, AVWA, WEF, 1998. Standard methods for examination of water and wastewater. 20th edition, Washington DC, USA. Bernal-Vicente A, Ros M, Tittarelli F, Intrigliolo F, Pascual JA, 2008. Citrus compost and its water extract for cultivation of melon plants in greenhouse nurseries. Evaluation of nutriactive and biocontrol effects. Biores. Technol. 99:8722-8. Ertani A, Pizzeghello D, Baglieri A, Cadili V, Tambone F, Gennari M, Nardi S, 2013. Humic-like substances from agro-industrial residues affect growth and nitrose assimilation in maize (Zea mays L.) plantlets. J. Geochem. Explor. 129:103-11. Hargreaves JC, Sina Adl M, Warman PR, 2009. Are compost teas an effective nutrient amendment in the cultivation of strawberries? Soil and plant tissue effects. J. Sci. Food Agric. 89:390-7. Hegazi AZ, Algharib AM, 2014. Utilizing compost tea as a nutrient amendment in open filed cowpea seed production system. J. Bio. Env. Sci. 5:318-28. Ingham ER, 1999. What is compost tea? Part1. BioCycle 40:74-75. Kim MJ, Shim CK, Kim YK, Hong SJ, Park JH, Han EJ, Kim JH, Kim SC, 2015. Effect of aerated compost tea on the growth promotion of lettuce, soybean, and sweet corn in organic cultivation. Plant Pathol. J. 31:259-68. Naidu Y, Meon S, Siddiqui Y, 2013. Foliar application of microbial-enriched compost tea enhances growth, yield and quality of muskmelon (Cucumis melo L.) cultivated under fertigation system. Sci. Hort. 159:33-40. Omar AEl-DK, Belal EB, El-Abd AEl-NA, 2012. Effects of foliar application with compost tea and filtrate biogas slurry liquid on yield and fruit quality of Washington navel orange (Citrus sinenesis Osbeck) trees. J. Air Waste Manage. 62:767-72. Pane C, Celano G, Villecco D, Zaccardelli M, 2012. Control of Botrytis cinerea, Alternaria alternata and Pyrenochaeta lycopersici on tomato with whey compost-tea applications. Crop Prot. 38:80-6. Pane C, Celano G, Zaccardelli M, 2014b. Metabolic patterns of bacterial communities in aerobic compost teas associated with potential biocontrol of soilborne plant diseases. Phytopathol. Mediterr. 53:277-86. Pane C, Palese AM, Celano G, Zaccardelli M, 2014a. Effects of compost tea treatments on productivity of lettuce and kohlrabi systems under organic cropping mamagement. Ital. J. Agron. 9:153-6. Pane C, Palese AM, Spaccini R, Piccolo A, Celano G, Zaccardelli M, 2016. Enhancing sustainability of a processing tomato cultivation system by using bioactive compost teas. Sci. Horticult. 202:117-24. Pane C, Piccolo A, Spaccini R, Celano G, Villecco D, Zaccardelli M, 2013b. Agricultural waste-based composts exhibiting suppressivity to diseases caused by the phytopathogenic soil-

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Article borne fungi Rhizoctonia solani and Sclerotinia minor. Appl. Soil Ecol. 65:43-51. Pane C, Villecco D, Zaccardelli M, 2013a. Short-time response of microbial communities to waste compost amendment of an intensive cultivated soil in Southern Italy. Comm. Soil Sci. Plant Anal. 44:2344-52. Pant AP, Radovich TJK, Hue NV, Paull RE, 2012. Biochemical properties of compost tea associated with compost quality and effects on pak choi growth. Sci. Hort. 148:138-46. Praveena Deepthi K, Narayan Reddy P, 2013. Compost teas - an organic source for crop disease management. Int. J. Innov. Biol. Res. 2:51-60. Radin AM, Warman PR, 2011. Effect of municipal solid waste compost and compost tea as fertility amendments on growth and tissue element concentration in container-grown tomato. Commun. Soil Sci. Plan. 42:1349-62. Sadfi N, Cherif M, Fliss I, Boudabbous A, Antoun H, 2001. Evaluation of bacterial isolates from salty soils and Bacillus thuringiensis strains for the biocontrol of Fusarium dry rot of potato tubers. J. Plant Pathol. 83:101-18. Scher FM, Baker R, 1982. Effect of Pseudomonas putida and a synthetic iron chelator on induction of soil suppressiveness to Fusarium Wilt pathogens. Phytopathology 72:1567-73. Scotti R, Pane C, Spaccini R, Palese AM, Piccolo A, Celano G, Zaccardelli M, 2016. On-farm compost: a useful tool to

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improve soil quality under intensive farming systems. Appl. Soil Ecol. 107:13-23. Shaheen AM, Rizk FA, Sawan OM, Bakry MO, 2013. Sustaining the quality and quantity of onion productivity throughout complementrity treatments between compost tea and amino acids. Middle East J. Agric. Res. 2:108-15. Siddiqui Y, Islam TM, Naidu Y, Meon S, 2011. The conjunctive use of compost tea and inorganic fertiliser on the growth, yield and terpenoid content of Centella asiatica (L.) urban. Sci. Hort. 130:289-95. Siddiqui Y, Meon S, Ismail R, Rahmani M, Ali A, 2008. Bio-efficiency of compost extracts on the wet rot incidence, morphological and physiological growth of okra (Abelmoschus esculentus [(L.) Moench]). Sci. Hort. 117:9-14. Xu DB, Wang QJ, Wu YC, Yu GH, Shen QR, Huang QW, 2012. Humic-like substances from different compost extracts could significantly promote cucumber growth. Pedosphere 22:815-24. Zaccardelli M, Pane C, Scotti R, Palese AM, Celano G, 2012. Use of compost-teas as biopesticides and biostimulants in orticulture. Italus Hort. 19:17-28. Zhang H, Tan SN, Wong WS, Ng CYL, Teo CH, Ge L, Chen X, Yong JWH, 2014. Mass spectrometric evidence for the occurrence of plant growth promoting cytokinins in vermicompos t tea. Biol. Fert. Soils 50:401-3.

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Italian Journal of Agronomy 2018; volume 13:1115

Contribution of main culm and tillers to grain yield of durum wheat: Influence of sowing date and plant traits Iduna Arduini,1 Elisa Pellegrino,2 Laura Ercoli2 1Department


of Agriculture, Food and Environment, University of Pisa; 2Scuola Superiore Santâ&#x20AC;&#x2122;Anna, Pisa,


The question of whether tillers are a burden or a resource in durum wheat is of concern in the variable Mediterranean climates. The contribution of tillers to grain yield was investigated in commercial cultivars differing in time to anthesis, tillering and spike size, in response to three sowing dates:mid-autumn (recommended), winter, and early spring. The thermal time of phenological phases was calculated, and yield-components and floret production were analysed separately in main culm and tillers. Tiller spikes showed higher spikelet abortion coupled to lower spikelet fertility and mean kernel weight, so that grain yield was 40-60% lower than in main culm spikes. Despite this, tillers contributed 35 to 50% to plant yield. The sowing date affected tiller number rather than one tiller yield. In winter sowings (December), lower main culm yield was fully compensated by increased tiller yield, whereas shifts of sowing date to early spring (February) reduced tillering, which caused a yield loss ranging from 12 to 20%. Cultivars differed in one tiller yield rather than in tiller number, and higher grain yield of tillers was primarily due to increased grain recovery. A more equal partitioning of resources within main culm and tillers corresponded to better yield stability across sowing dates. Starting from this, we suggest that early anthesis, a long stem elongation phase, a high primordium initiation-rate and small spikes, could be positive traits for durum wheat yield stability in changing environ-

Correspondence: Iduna Arduini, Department of Agriculture, Food and Environment, University of Pisa, via del Borghetto 80, 56124 Pisa, Italy. Tel.: +39.050.2218902 E-mail: iduna.arduini@unipi.it Key words: Floret; genotype; grain recovery; spikelet; Triticum turgidum L. ssp. durum.

Received for publication: 26 September 2017. Revision received: 2 March 2018. Accepted for publication: 4 March 2018.

ŠCopyright I. Arduini et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:1115 doi:10.4081/ija.2018.1115

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

ments, since they allow plants directing more time and resources to floret production and grain filling both in main culm and tiller spikes. From a methodological point of view, our results show that the number of fertile florets per spike is highly correlated with the average floret number of five given spikelets.


Wheat plants are formed by one main culm and a number of tillers varying from null to several tenths, depending on cultivar and environmental conditions. Grain yield of wheat crops is a quantitative trait determined by the product of three principal components: spike number per plant, or unit surface, mean kernel weight and kernel number per spike. Moreover, the last is a complex component resulting from the product of the number of spikelets per spike and the number of kernels per spikelet, and both these sub-components depend on their initiation and abortion rates, which occur at different stages during the crop cycle. Distinct components may compete or compensate and yield gain can be obtained by selecting for one or the other (Rharrabti et al., 2010; Ferrante et al., 2012; Slafer et al., 2014). The most outstanding success of past wheat breeding was to increase grain number per unit surface, which was primary obtained by increasing kernel number per spike, whereas the number of fertile tillers per plant was even reduced in order to uniform the achievement of maturity and reduce within plant competition (De Vita et al., 2007; Uzik and Zofajova, 2007; Fischer, 2016). In Italy and Spain, however, the genetic improvement of durum wheat in the last century increased both the number of grains per spike and the number of fertile tillers, despite the two yield components directly compete for assimilates (Brdar et al., 2008; Subira et al., 2015). This is because in Mediterranean semiarid environments, characterized by variable and unpredictable climatic conditions, high tillering may compensate for the poor crop establishment caused by late drought or early frost (Acevedo et al., 2002; Elhani et al., 2007). In addition, Dreccer et al. (2013) reported that tillers assist in competing against weeds, and, according to Spink et al. (2000), the production of more fertile tillers per plant can be a strategy to reduce seed costs. Tiller number is the yield component with the highest plasticity (Slafer et al., 2014). Nevertheless, the genetic control of tiller production and the potential of tillers in wheat yield optimization have not been fully elucidated (Xie et al., 2016). According to Dreccer et al. (2013), low tillering wheat lines accumulate more water-soluble carbohydrates in stems, which leads to a higher grain number per spike and heavier kernels, though to only minimal yield increase. Artificial detillered plants and tin mutants have obtained a higher grain yield, which was associated with longer spikes and higher spikelet fertility (Kebrom et al., 2012; Hendriks

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Article et al., 2016). On the opposite, Xie et al. (2016) found that more fertile tillers per plant did not affect the yield of individual shoots and increased yield, and it was also reported that detillered plants yielded more in greenhouse conditions, but not in the field (Guo and Schnurbusch, 2015). In general, free-tillering was found detrimental in unfavourable environmental conditions (Mitchell et al., 2013; Hendriks et al., 2016), but was considered essential to take advantage of favourable conditions (Sadras and Rebetzke, 2013; Slafer et al., 2014). However, Elhani et al. (2007) reported that tillers contributed to grain yield more than the main culm in rainfed durum wheat. Therefore, to find an association between distinct morpho-phenological traits of wheat genotypes and the tiller contribution to yield in response to environmental conditions could be of help in selecting for those traits that are more adapt to changing environments. Durum wheat (Triticum turgidum L. var. durum) is a staple cereal crop in the Mediterranean basin, where it is cultivated under rainfed conditions from autumn to early summer. Shifts from the recommended sowing dates are often necessary because soils are either too dry or too waterlogged, and these events are predicted to become more frequent in the near future (Bassu et al., 2009; Trnka et al., 2014). Wheat yield components are differently sensitive to delays in sowing date, and a Cultivar x Environment interaction exists (Subira et al., 2015). In general, late planting reduces grain yield (Inagaki et al., 2007; Arduini et al., 2009; Kumar et al., 2016), primarily because it shortens the period from emergence to anthesis, thus reducing the time for tiller and spike development (Whitechurch et al., 2007; Fischer, 2016). However, both lower (Akhtar et al., 2012) and higher (Arduini et al., 2009) numbers of spikes per unit surface were found in delayed sowings: the former due to reduced emergence in winter and to a shorter vegetative phase (Mahdi et al., 1999; Dreccer et al., 2013), the latter due to enhanced tiller growth rate at higher temperatures and longer day lengths (Kebrom et al., 2012). Yield losses in delayed sowings are generally associated with a lower kernel number per spike (Bassu et al., 2010; Kumar et al., 2016), but there is no agreement on whether the most affected determinant is the number of spikelets or the spikelet fertility (Li et al., 2001; Fischer, 2008; Arduini et al., 2009; González-Navarro et al., 2015). Elhani et al. (2007) reported that environmental conditions influenced the determination of yield components differently in the two spike types, but, to the best of our knowledge, the effect of sowing date on spike yield components has never been analysed separately in the main culm and tiller spikes. In addition, the response of distinct spikelets to delays in sowing has not been previously investigated, though it is known that floret production and grain recovery differ among spikelets according to their position within the spike (Álvaro et al., 2008; Bancal, 2009). The aim of our research was thus to i) determine the contribution of main culm and tiller spikes to grain yield of durum wheat; ii) analyse the influence of sowing date on the determination of yield components and sub-components in the two spike types; and iii) highlight if spikelet position within the spike may influence spikelet fertility in response to sowing date. Hypothesizing that phenological and morphological traits influence the contribution of tillers to plant yield, investigations were carried out on four durum wheat cultivars differing in time to anthesis, tillering capacity and spike traits (Ercoli et al., 2014), which were sown at three dates between mid-autumn and early spring.

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Materials and methods

Site and experimental design

A two-year experiment was carried out from 2011 to 2013 at the Research Station of the Department of Agriculture, Food and Environment (University of Pisa, Italy), which is located approximately 4 km away from the sea (43°40′N, 10°19′E) and is 1 m above sea level. The climate of the area is hot-summer Mediterranean (Csa): the mean annual maximum and minimum daily air temperatures are 20.2°C and 9.5°C, respectively, and the mean rainfall is 971 mm per year. For the period of the research, the minimum and maximum daily temperatures and daily rainfall were obtained from a weather station located at the experimental site. In both years, experimental treatments consisted of three sowing dates and four cultivars of durum wheat (Triticum turgidum L. ssp. durum). Sowing dates were: November (mid-autumn, recommended sowing for central Italy), December (winter sowing) and February (early spring sowing). The tested cultivars were Claudio (Cl), Normanno (Nm), Saragolla (Sg) and Svevo (Sv), all semidwarf modern genotypes selected in Italy for good-quality pasta production (Ercoli et al., 2011). The cultivars Normanno and Svevo are promoted as good tillering types, whereas for Saragolla and Claudio no information on tillering is given. Other cultivar features are reported in Table 1. Each year, treatments were arranged in a split-plot factorial design, with four replicates. Sowing dates were the main plots and cultivars the subplots.

Experimental equipment and crop management

Plants were grown outdoors in 29-L pots. The pots were made from polyvinyl chloride (PVC) tubes (60 cm long by 25 cm diameter) fitted with a PVC base serving as a bottom. A 30-mm-diameter hole was drilled in the bottom of each pot and a 5-cm layer of drainage gravel was added before soil. Pots were filled with 30 kg of soil, the main characteristics of which were similar over the two years and were approximately 54.9% sand (2-0.05 mm), 33.5% silt (0.05-0.002 mm), 11.6% clay (<0.002 mm), 7.7 pH, 0.7 g kg–1 total N (Kjeldahl method), 4.4 mg kg–1 available phosphorus (Olsen method) and 69.3 mgkg–1 available potassium (BaCl2-TEA method). Pots were placed side-by-side into growth boxes built with a reticular net in order to allow plants to form a relatively normal and uniformly distributed aerial canopy structure. Edge effects were avoided by supplement pots placed around those used for harvests. All pots were embedded in expanded clay and growth boxes

Table 1. Genetic background and year of release of wheat cultivars used in the research. Cultivar

Genetic origin

Claudio Normanno Saragolla Svevo

Cimmyt35/Durango//IS193B/Grazia Simeto/F22//L35 Iride/LineaPSB 0114 Linea Cimmyt/Zenit

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Year of release 1998 2002 2004 1996

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Article surrounded by polystyrene panels to reduce daily fluctuations in soil temperature. Sowing dates were 9 and 19 November, 28 and 20 December, and 9 and 19 February, in the growing seasons 2011-2012 and 2012-2013, respectively. Pots were sown with 20 seeds, placed in two rows spaced 12.5 cm. After emergence, the seedlings were thinned to ten plants per pot, corresponding to approximately 200 plants m–2. This density is low compared to that of 300-400 plant m–2 conventionally applied in central Italy, but it was chosen to reduce inter-plant competition for tiller development. Due to pot arrangement, canopy density was slightly lower, approximately 160 plants m–2. As a whole, 96 pots per year were harvested: 48 were harvested at anthesis for the determination of floret number, and 48 at maturity for the determination of grain yield and its components. Phosphorus and potassium were applied pre-planting as triple mineral phosphate and potassium sulphate, at rates of 100 kg ha–1 of P2O5 and K2O. Following the management techniques typically used (Ercoli et al., 2013), nitrogen was applied at a rate of 150 kg ha–1 and was split into three applications: at sowing as ammonium sulphate, at pseudo-stem erection and at first node detectable as urea, in the following proportions: 30-60-60 kg N ha–1. Weed control was performed by hand hoeing and pots were watered near to field capacity throughout the growing season. As the pots were well drained, both limiting and excess water conditions were avoided and therefore the effects of the very different rainfall in the two years were minimized.

Plant measurements

Over the entire growth cycle, phenological stages were recorded following the Zadoks’ scale (Zadoks et al., 1974), as were the time of reaching emergence, anthesis and maturity, as reported in Table 2. In each pot, stages were considered achieved when they were observed on more than 50% of the plants. To distinguish main culm from tillers, at the stage 3-leaves unfolded (stage 13) the 3rd leaf blade of all plants was marked with a spot of black nail polish, andat the stage the 5th leaf tip was visible (stage 14) a red wool-thread was tied around the sheath of the 4th leaf. As low order leaves are hard to detect at maturity, at later stages the 5th and the 7th leaf blades were also marked with different coloured nail polish. At anthesis, five main culm (MC) and five tiller (T) spikes per pot were randomly chosen and immediately analysed, or stored in a refrigerator for maximum 48 h. The number of spikelets per spike was counted and classified as complete (with well-developed glumes and awns) and aborted (with very small glumes and awns missing or very short). The number of fertile florets was counted under a stereomicroscope (Leica Z16 APO) with a magnification up to 92x on five sample spikelets (two basal, two central, and the terminal spikelet), as shown in Figure 1. Florets were considered fertile when they had green anthers and a bifidum stigma (Álvaro et al., 2008). At maturity, plants were harvested and separated into main culm, fertile tillers (bearing a spike) and sterile tillers. On each plant we determined the number of fertile and sterile tillers, the number of leaves formed on the main culm, and the number of complete and aborted spikelets of MC and T spikes. In addition, on five MC and five T spikes per pot, we counted the number of kernels on five spikelets where the position was the same as chosen for floret counts (Figure 1). Main culm and tiller spikes of each pot were separately threshed with a laboratory thresher and the grain yield and kernel numbers were determined for each spike type.


The duration of phenological stages was expressed in thermal time, calculated as the sum of heat units measured in growing degree-days (GDD), as GDD = ((Tmax + Tmin)/2) – Tb. In the formula, Tmax and Tmin are the daily maximum and minimum air temperatures, and Tb is the base temperature below which no significant crop development occurs. If Tmin < Tb then Tmin = Tb was also incorporated into the equation. An upper threshold temperature (Tut), above which crop development is negatively affected, was also incorporated, i.e., if Tmax > Tut then Tmax = Tut (McMaster and Wilhelm, 1997). The utilized cardinal intervals were 0-25°C up to the stage the first node was detectable and 730°C for the following phases (Porter and Gawith, 1999; Eshaghi et al., 2011). The number of total primordia initiated on main culms was obtained as the sum of final leaves and total spikelets. Spikelet abortion rate was calculated as the percentage ratio of aborted and total (complete+aborted) spikelets. Mean kernel weight was calculated separately for MC and T spikes by dividing MC (or T) grain yield per pot by the number of MC (or T) kernels per pot. The number of florets, or kernels, in basal and central spikelets was averaged for the two spikelets analysed for each position. Grain recovery was calculated for each position, as the percentage of the number of kernels per spikelet on the number of florets per spikelet. Floret counts are laborious and time-consuming and must be carried out over a short time span, so it is more convenient to estimate the total number of florets within a spike from that of a few sample spikelets. Thus, in the first year of the research, we counted

Figure 1. Position of spikelets utilised for floret counts: basal (C, D); central (A, B); terminal spikelet (TS).

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Article all fertile florets of one MC and one T spike per pot (96 in total) and plotted the obtained values against the average floret numbers of two basal, two central and the terminal spikelet of the same spikes, multiplied by the number of complete spikelets. We found that counted and estimated values were closely related when the average of two basal, two central and the terminal spikelet were used (Figure 2). In addition, we also found a significant relationship between the counted kernels per spike and the average kernel number of two basal, two central and the terminal spikelet multiplied by the number of complete spikelets (Figure 2). These findings allowed usto estimate the number of fertile florets per spike by multiplying the average number of florets of the five chosen spikelets per the number of complete spikelets per spike, and then calculating grain recovery on a spike basis.

Statistical analysis

Results were subjected to analysis of variance (Tables 3 and 4). The experimental design was a split-split-plot with four replicates: years were located as main plots, sowing dates as subplots and cultivars as sub-subplots. To assess if the number of florets and kernels per spikelet and grain recovery were affected by spikelet posi-

tion within the spike, we arranged these data in a split-split-splitplot experimental design. Years were located as main plots, sowing dates as subplots, cultivars as sub-subplots, and spikelet positions as sub-sub-subplots. Percentage data were arcsin transformed before analysis. Significantly different means were separated at a 0.05 probability level by Tukey’s test (Steel et al., 1997).

Results Climate

The mean temperature of the entire wheat growing season (November-July) was quite similar in 2011-12 and 2012-13, at 11.6 and 12°C, respectively. However, in the first season the average minimum temperature was lower (4.9 vs 6.9°C) and the maximum temperature higher (18.3 vs 17.1°C) than in the second (Figure 3). Rainfall differed more than twofold (499 vs 1155 mm) in the two growing seasons. Differences were greatest in winter, with a cumulative rainfall of 70 mm in 2011-2012, and 596 mm in

Table 2. Time (days after sowing) to emergence, anthesis and maturity, as affected by sowing date and cultivar, in the growing seasons 2011-2012 and 2012-2013. Stage Emergence Anthesis


09-Nov 2011

19-Nov 2012

28-Dec 2011

20-Dec 2012

09-Feb 2012


Claudio Normanno Saragolla Svevo

13 172 180 169 169 224

14 165 170 162 162 218

32 134 134 131 131 186

36 138 139 134 134 190

20 97 97 93 95 146

19 90 90 86 87 140


Table 3. Results of ANOVA for the experimental design split-split-plot Year × Sowing date × Cultivar. Treatments Grain yield (g plant–1)


Total Grain primordia yield (n) (n) (g spike–1)

Year (Y) * Sowing date (S) * Y×S n.s. Cultivar (C) * Y×C n.s. S×C * Y×S×C n.s. Tiller

n.s. * n.s. * n.s. n.s. n.s. Spikes Cumulative grain yield (n plant–1) (n plant–1) (g plant–1)

Year (Y) Sowing date (S) Y×S Cultivar (C) Y×C S×C Y×S×C [page 238]

n.s. * n.s. * n.s. n.s. n.s.

n.s. * n.s. n.s. n.s. n.s. n.s.

* * n.s. * n.s. n.s. n.s.

n.s. * n.s. * n.s. n.s. n.s. CTY

Main culm Kernels


(n spike–1)


n.s. * n.s. n.s. n.s. n.s. n.s. Kernels

n.s. * n.s. * n.s. n.s. n.s. MKW


n.s. * n.s. * n.s. n.s. n.s. Grain yield (g spike–1)

(n spike–1)


* * n.s. * n.s. n.s. n.s.

n.s. n.s. n.s. * n.s. n.s. n.s.

n.s. n.s. n.s. * n.s. n.s. n.s.

n.s. * n.s. * n.s. n.s. n.s.

Complete Initiated Spikelet Fertile spikelets spikelets abortion florets (n spike–1) (n spike–1) (%) (n spike–1) n.s. n.s. n.s. n.s. * * * n.s. n.s. n.s. n.s. n.s. * * * * n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Complete Initiated Spikelet Fertile spikelets spikelets abortion florets (n spike–1) (n spike–1)(n spike–1)(n spike–1)

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n.s. * n.s. * n.s. n.s. n.s.

n.s. * n.s. * n.s. n.s. n.s.

n.s. * n.s. * n.s. n.s. n.s.

n.s. * n.s. * n.s. n.s. n.s.

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Article approximately one week earlier in November and February sowings, but four days later in December, probably because of slower emergence (Table 2). Time to anthesis was reduced with the delay of sowing, by approximately 40 days in December and by 77 days in February, so that the length of the entire growth cycle decreased from approximately 221 days in November to 188 in December and

2012-2013 (Figure 3). Around anthesis and during grain-filling, conversely, rainfall was 83 mm higher in the dryer growing season.


In the warmer growing season of 2012-2013, growth cycles were 4-6 days shorter than in 2011-2012, whereas anthesis occurred

Table 4. Results of ANOVA for the experimental design split-split-split-plot Year × Sowing date × Cultivar × Spikelet position. Treatments Fertile florets (n spikelet–1) Year (Y) Sowing date (S) Y×S Cultivar (C) Y×C S×C Y×S×C Spikelet position (P) Y×P S×P C×P Y×S×P Y×C×P S×C×P Y×S×C×P

n.s. * n.s. * n.s. n.s. n.s. * n.s. n.s. * n.s. n.s. n.s. n.s.

Main culm Kernels Grain recovery per (n spikelet–1) spikelet (%) n.s. n.s. n.s. * n.s. n.s. n.s. * n.s. n.s. n.s. n.s. n.s. n.s. n.s.

Figure 2. Relationships between the numbers of florets, or kernels, per spike counted and estimated as the mean value of five spikelets per spike (two basal, two central and the terminal spikelet) multiplied by the number of complete spikeletsper spike (n=96).

n.s. * n.s. * n.s. n.s. n.s. * n.s. n.s. n.s. n.s. n.s. n.s. n.s.

Fertile florets (n spikelet–1)

Tillers Kernels (n spikelet–1)

Grain recovery per spikelet (%)

n.s. n.s. n.s. n.s. n.s. n.s. n.s. * n.s. n.s. * n.s. n.s. n.s. n.s.

n.s. n.s. n.s. * n.s. n.s. n.s. * n.s. n.s. n.s. n.s. n.s. n.s. n.s.

n.s. n.s. n.s. * n.s. n.s. n.s. * n.s. n.s. n.s. n.s. n.s. n.s. n.s.

Figure 3. Decadic maximum and minimum temperatures and rainfall over the durum wheat growing seasons 2011-12 and 2012-13.

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Article 143 in February. In both years and all sowings, Saragolla and Svevo reached anthesis earlier than Claudio and Normanno, whereas maturity was achieved by all cultivars on the same date. The thermal time of plant phases was differently affected by the delay of sowing. In November and February sowings plants needed approximately 130°Cd to complete emergence, whereas they required 215°Cd in December (data not shown). The length of the vegetative phase (emergence-1st node detectable) decreased up to 48% from November to February, with the highest reduction between November and December (Figure 4A). The duration of stem elongation (1st node detectable-anthesis) was less affected by sowing date and patterns differed slightly among cultivars (Figure 4B). Finally, the grain-filling phase (anthesis-maturity) increased between November and December and then did not change further (Figure 4C). Cultivars did not differ in the time to emergence and differences in the length of following phases were more appreciable in November than in later sowings. The thermal time of the vegetative phase decreased in the order Normanno>Claudio> Saragolla>Svevo, with an overall 13% decrease; the stem elongation phase was 18% longer in Normanno and Svevo compared to Claudio and Saragolla, and the grain filling phase was 14% shorter in Normanno (Figure 4). As a result the thermal time to anthesis equalled in the cultivars Saragolla and Svevo and was from 5 to 12% longer in the cultivars Claudio and Normanno, respectively.

lower in the growing season 2012-2013 (2.6 vs 3.1 g plant–1), which was a consequence of the lower contribution of tillers to plant yield (Table 5). Tiller number, fertility and yield slightly and not significantly increased with the delay of sowing from November to December, whereas a further delay to February sig-

Grain yield of plants

Analysis of variance highlighted a significant year mean effect for the grain yield per plant, the cumulative grain yield of tillers, and the contribution of tillers to yield (Table 3). The interactions with year were never significant, and those between sowing date and cultivar were only found to be significant for grain yield per plant. Accordingly, all data were presented as means of the two growing seasons and, except for grain yield per plant, they were presented as sowing date and cultivar mean effects. Averaged over years, in all cultivars grain yield per plant did not change significantly between November and December sowings and decreased in February, but the decrease was by 12% in Svevo and by approximately 20% in the other cultivars (Figure 5). The cultivar Claudio yielded 8% less than the others in November. Averaged over seeding dates and cultivars, grain yield was 16%

Table 5. Number of tillers and tiller spikes per plant, tiller yield, and contribution of tillers to grain yield per plant (CTY), as affected by the growing season, sowing date, and cultivar mean effects. Treatment Growing season 2011-2012 2012-2013 Sowing date November December February Cultivar Claudio Normanno Saragolla Svevo

Tillers (n plant–1)

T spikes (n plant–1)

T yield (g plant–1)

CTY (%)

4.2a 4.1a

1.8a 1.4a

1.4a 1.0b

46.1a 39.0b

4.3a 4.7a 3.6b

1.8a 2.0a 1.3b

1.3a 1.5a 0.9b

44.4a 49.9a 35.5b





3.8b 4.9a 3.6b

1.7a 1.5a 1.7a

1.2b 1.2b 1.5a

41.4b 40.7b 49.2a

Within each block, values followed by different letters are significantly different (P<0.05).

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Figure 4. Thermal times of the vegetative (emergence-1st node detectable, A), stem elongation (1st node detectable-anthesis, B) and grain filling (anthesis-maturity, C) periods, as affected by sowing date and cultivar. Cl, Claudio; Nm, Normanno, Sg, Saragolla; Sv, Svevo. Data are means of two years and four replicates (n=8). Vertical bars represent honest significant difference at P<0.05.

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Article nificantly decreased these parameters. Accordingly, the contribution of tillers to plant yield ranked in the order December≥November>February (Table 5). Despite differences in tiller production, cultivars did not significantly differ in the number of tiller spikes. Conversely, the cumulative grain yield of tillers per plant was 25% higher in Svevo, which increased the contribution of tillers to plant yield by approximately eight percentage points.

Despite the above differences among cultivars and independently of sowing date, in MC and T spikes, grain yield per spike was positively and significantly correlated with the number of kernels per spike, which in turn was correlated with the number of kernels per spikelet (Figure 6). Otherwise, no relationships were found for grain yield per spike, or kernels per spike, with other yield components and sub-components (data not shown).

Grain yield and yield components of main culm and tiller spikes

Sowing date only slightly affected the grain yield of MC spikes and did not affect that of T spikes (Table 6). Yield components responded differently to sowing date, with similar trends in MC and T spikes. The mean kernel weight was lowest in November, whereas the number of kernels per spike was highest. The number of complete spikelets per spike progressively decreased with the delay of sowing, whereas the number of kernels per spikelet was unaffected. Grain yield per spike significantly differed among cultivars, with different rankings for MC and T (Table 6). The former yielded more in Normanno and Saragolla, whereas the latter in Svevo and Saragolla. Accordingly, the grain yield of MC spikes was always markedly higher than that of T spikes, but the increase was by 67%, 111%, 139% and 159%, in Svevo, Saragolla, Normanno and Claudio, respectively. The mean kernel weight was significantly lower in the cultivar Normanno in both spike types, while the highest values were recorded in Claudio. The number of kernels per spike did not differ among cultivars in MC and was approximately five kernels lower in Claudio in T. In both spike types, the number of complete spikelets ranked in the order Saragolla>Normanno>Claudio>Svevo, with significant higher values in Saragolla compared to Claudio and Svevo. Finally, the number of kernels per spikelet was lower in Saragolla in MC spikes and higher in Svevo in T spikes.

Figure 5. Grain yield per plant, as affected by sowing date and cultivar. Cl, Claudio; Nm, Normanno, Sg, Saragolla; Sv, Svevo. Data are means of two years and four replicates (n=8). Vertical bars represent honest significant difference at P<0.05.

Table 6. Grain yield and yield components of main culm and tiller spikes, as affected by the sowing date and cultivar mean effects. Treatment

Sowing date November December February Cultivar Claudio Normanno Saragolla Svevo Tiller spike Sowing date November December February Cultivar Claudio Normanno Saragolla Svevo

Grain yield (g spike–1)

Mean kernel weight (mg)

Kernels (n spike–1) Main culm spike

Complete spikelets (n spike–1)

Kernels (n spikelet–1)

1.7a 1.5b 1.6ab

46.1b 51.1a 51.3a

36.4a 30.0b 30.6b

16.5a 14.7b 13.5c

2.2a 2.1a 2.3a

1.6ab 1.7a 1.7a 1.5b

53.7a 45.7b 50.4a 48.2ab

29.6a 36.0a 33.2a 30.5a

13.6b 15.8ab 17.2a 13.1b

2.2a 2.3a 1.9b 2.3a

0.76a 0.76a 0.70a

43.6b 46.3a 46.2a

17.6a 16.4a 14.9a

13.1a 11.6ab 10.3b

1.4a 1.4a 1.4a

0.61b 0.69b 0.79a 0.87a

47.4a 41.4b 45.4a 47.2a

12.8b 16.5a 17.4a 18.5a

11.0bc 12.4ab 13.0a 10.2c

1.2b 1.4b 1.3b 1.8a

Within each block, values followed by different letters are significantly different (P<0.05).

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Article Primordia initiation and abortion

The number of complete spikelets in a spike is a function of the number of spikelet primordia initiated by the culm apex and their abortion rate. Our results highlighted that the number of initiated spikelets was less than two units lower in T compared to MC, whereas the abortion rate was markedly higher in the former, at 13 vs 4% (Table 7). The delay in sowing reduced both the initiation and the abortion of spikelets, so that the decrease in complete spikelets was lower than that in total spikelets, particularly in tillers (Tables 6 and 7). Averaged over sowing dates, the number of initiated spikelets was significantly higher in the cultivars Normanno and Saragolla, and the latter cultivar also showed the lowest abortion rate (Table 7). The number of leaves formed on the main culm decreased progressively from November to February, averaged over cultivars (Table 8). Thus, the total leaf and spikelet primordial initiated on the main culm apex decreased by approximately five units, from 28.3 to 23 with the delay of sowing. Averaged over sowings, the number of MC leaves was significantly higher in Claudio and lower in Saragolla, while intermediate values were recorded in Normanno and Svevo. Accordingly, the total number of initiated MC primordia was significantly lower in Svevo compared with the other cultivars.

approximately 0.5 florets lower in Claudio in the other positions (Figure 7). In T spikes, differences were found only in the terminal spikelet, with Svevo and Normanno showing approximately 2.1 florets and Claudio and Saragolla 1.6. The number of fertile florets per spikelet was always higher in MC than in T spikes and depended on spikelet position, decreasing significantly in the order central>basal≥terminal in both spike types. On average, the number of

Floret formation and grain recovery

Analysis of variance highlighted that sowing date, cultivar and spikelet position affected floret number and grain recovery per spikelet, but no year effect was detected (Table 4). The only significant interaction was found between cultivar and spikelet position for the floret number. The number of fertile florets per spikelet increased progressively with the delay of sowing, but the increment was significant only in MC between November and February (Table 7). In MC spikes, the number of fertile florets per spikelet did not differ significantly among cultivars in the basal spikelets, whereas it was

Figure 6. Relationship between grain yield per spike and kernels per spike, and between kernels per spike and kernels per spikelet, in main culms (MC) and tillers (T) (n=96).

Table 7. Determinants of the numbers of spikelets and kernels in main culm and tiller spikes, as affected by the sowing date and cultivar mean effects. Treatment

Sowing date November December February Cultivar Claudio Normanno Saragolla Svevo Sowing date November December February Cultivar Claudio Normanno Saragolla Svevo

Initiated spikelets (n spike–1)

Spikelet abortion (%)

Fertile florets (n spikelet–1) Main culm spike

Grain recovery per spikelet (%)

Fertile florets (n spike–1)

17.4a 15.1b 13.9c

5.3a 2.5b 2.7b

2.5b 2.7ab 3.0a

88.0a 77.8b 76.7b

42.0a 41.8a 38.7a

14.2b 16.5a 17.3a 13.9b

4.4a 4.8a 0.3b 5.6a

2.5b 2.9a 2.9a 2.8a Tiller spike

88.0a 79.3b 65.5c 82.1a

34.2b 44.7a 49.2a 35.4b

15.8a 12.8b 11.9b

16.9a 9.5b 13.4ab

2.1a 2.2a 2.3a

66.7a 63.6a 60.9a

27.3a 28.3a 23.8b

12.7b 15.0a 14.2a 12.1b

13.2b 17.3a 7.9c 15.7ab

2.1a 2.3a 2.2a 2.3a

57.1b 60.9b 59.1b 78.3a

22.6b 28.2a 28.1a 27.0a

Within each block, values followed by different letters are significantly different (P<0.05).

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Article florets per MC spikelet was 2.5 in Claudio compared to approximately 2.9 in the other cultivars, whereas in T spikelets it was not affected by sowing date and cultivar and was approximately 2.2. Grain recovery per spikelet, i.e., the proportion of fertile florets that actually produced kernels within each spikelet, was markedly higher in MC than in T spikes, at 80 vs 63% on average (Table 7). In MC, grain recovery was significantly higher in the November sowing, whereas in T it was not affected by the sowing date. Grain recovery differed among varieties: in MC it ranked in the order Claudio>Svevo>Normanno>Saragolla, whereas in T it was significantly higher in Svevo. Finally, grain recovery was significantly affected by spikelet position. In MC spikes it was 87% in the central spikelets and approximately 76% in the other positions, while in T spikes it was approximately 71% in the basal and central spikelets but only 47% in the terminal one (Figure 8).

Figure 7. Number of florets per spikelet of main culm (MC) and tiller (T) spikes, as affected by cultivar and spikelet position within the spike. Cl, Claudio; Nm, Normanno, Sg, Saragolla; Sv, Svevo. Data are means of two years, three sowing dates and four replicates (n=24). Vertical bars represent honest significant difference at P<0.05.

Table 8. Number of leaves and total primordia formed on the main culm apex, as affected by the sowing date and cultivar mean effects. Treatment Sowing date November December February Cultivar Claudio Normanno Saragolla Svevo

Leaves (n culmâ&#x20AC;&#x201C;1)

Total primordia (n apexâ&#x20AC;&#x201C;1)

10.9a 9.8b 9.1c

28.3a 24.9b 23.0b

10.8a 9.8b 9.3c 9.9b

25.0ab 26.3a 26.6a 23.8b

Within each block, values followed by different letters are significantly different (P<0.05).

Figure 8. Grain recovery in spikelets of main culm (MC) and tiller (T) spikes, as affected by spikelet position within the spike. Data are means of two years, three sowing dates, four cultivars and four replicates (n=96). Vertical bars represent honest significant difference at P<0.05.

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Article The number of fertile florets per spike, estimated from the product of the average floret number of five sample spikelets and the number of complete spikelets per spike, was lower in February than in earlier sowings, but differences were significant only in T (Table 7). In MC spikes, the number of florets was approximately 30% higher in Saragolla and Normanno compared to Claudio and Svevo, whereas in T spikes it was approximately 20% lower in Claudio than in the other cultivars. Grain recovery calculated on spike basis showed patterns similar to those calculated for spikelets (data not reported).


The durum wheat cultivars utilized in this research produced between 2.3 and 3.1 g of grain per plant. Between-years differences in grain yield were low compared to the marked differences in rainfall, probably because plants were grown in well-drained pots, which avoided waterlogging in 2012-2013, and also received artificial irrigation, which avoided drought stress up to anthesis in 2011-2012, and during grain filling in both years. Variations in grain yield per plant were not significant between mid-autumn and winter sowings (November and December), while the delay to early spring (February) markedly decreased grain yield, primarily because of the lower tiller production, whereas the yield of single spikes did not, or only slightly, decreased. Yield decrease was less pronounced in Svevo, which was associated with the higher yield of tiller spikes compared to the other cultivars. The number of fertile tillers did not vary significantly among cultivars and, on average, the promoted good-tillering cv. Normanno and Svevo showed only 5% higher yield compared to Claudio and Saragolla. These findings confirm that the plasticity in the number and yield of tiller spikes is crucial to responding positively to high yielding conditions (Sadras and Rebetzke, 2013; Slafer et al., 2014; Subira et al., 2015), and to compensate for the lower yield of main culm spikes (Elhani et al., 2007). They also highlight that the four tested cultivars perform similarly at stand densities ideal for good-tillering genotypes when grown in optimal water conditions (ValĂŠrio et al., 2009). Although tillers contributed substantially to grain yield, the average yield of one tiller spike was approximately half that of one main culm spike, which resulted from the sum of slightly lower spikelet initiation and higher spikelet abortion, on one hand, and a lower number of florets per spikelet, grain recovery and mean kernel weight, on the other.

Effect of sowing date on spike yield components

The delay of sowing markedly shortened the vegetative phase, which reduced the initiation of both leaf and spikelet primordia. Spikelet abortion also decreased, possibly because of lower intraspike competition, so that the reduction in the number of complete spikelets was less pronounced. According to Serrago et al. (2008), later sowings expose plants to a longer photoperiod during the phase of rapid stem elongation, thus reducing its duration and the number of fertile florets per spike. In contrast, we found that the thermal time of the period was only slightly affected and the number of florets per spikelet tended to increase, with a higher degree in main culm than in tiller spikes. Li et al. (2001) also reported that spikelet number was more sensitive to delays in sowing than floret production, which can be interpreted as an adjustment to the lower number of spikelets (Sinclair and Jamieson, 2006). We found, however, that excess florets were not fertilized, as shown by the [page 244]

lower grain recovery in later sowings, and, therefore, the number of kernels per spikelet was generally unaffected, and the number of kernels per spike decreased. It is worth noting that though floret production and grain recovery differed according to spikelet position, all spikelets responded similarly to sowing date, suggesting that they equally suffered from assimilates shortening in later sowings. Mean kernel weight is the last-determined yield component, and thus it may compensate for the lower number of kernels produced in later sowings, provided that environmental conditions are favourable to ovary growth around anthesis and to grain filling later on (Moragues et al., 2006; Ferrise et al., 2010; Slafer et al., 2014). In many wheat growing areas, however, this does not occur because heat stress and terminal drought reduce grain growth duration and kernel weight (Brdar et al., 2008; Modarresi et al., 2010; Mitchell et al., 2013). In our research, grain-filling duration was not greatly affected and the thermal time was even longer in later sowings. The kernels were accordingly heavier in December and in February compared to November, but increments were not enough to compensate for the lower kernel number. Though our results could be influenced by the fact that the pots were irrigated, Khaledian et al. (2013) estimated that in the northern Mediterranean area, terminal growth is generally also maintained in rainfed field conditions. In summary, delays from the conventional sowing date slightly decreased the yield of MC spikes, reducing primarily spikelet number, which was not compensated by higher production of florets per spikelet and only in part by increased kernel weight. As grain yield per plant did not differ significantly between November and December sowings, our results highlight that in the winter sowing, tillers fully compensated for the smaller MC spike by slightly increasing the number of fertile spikes per plant and by maintaining their size and grain set. Tillers thus play a key role in maintaining yield stability across autumn-winter sowings.

Effect of genotype on spike yield components

The four durum-wheat cultivars differed in the strategies of yield determination (Fischer, 2008), primarily in spike size, spikelet fertility, mean kernel weight, and in the contribution of tillers to yield, and also differed in the duration of phenological phases. Nevertheless, for all yield components, the response to sowing date showed similar patterns among cultivars, but differences in amplitude, so that a Cultivar Ă&#x2014; Sowing date interaction existed only for grain yield per plant. Genotypic variation in response to the environment is often associated with differences in phenology (Miralles and Slafer, 2007; Modarresi et al., 2010; BorrĂĄs-Gelonch et al., 2012) and in our research it could depend on the wide variation in daylength sensitivity of durum wheat genotypes released in Italy (Motzo and Giunta, 2007; Subira et al., 2015). We found that cultivars differed in the relative length of vegetative, stem elongation and grain-filling phases, but, similar to the findings of Whitechurch et al. (2007), these differences tended to disappear in the winter and early spring sowings. Accordingly, differences in yield stability in late sowings cannot be ascribed to differences in the duration of phenological phases. Tillering capacity is another trait associated with the responsiveness of genotypes to the environment (Dreccer et al., 2013; Sadras and Rebetzke, 2013; Hendriks et al., 2016). In our research, the cultivar Svevo exhibited the highest contribution of tillers to plant yield and the smallest yield loss in the February sowing, which relied on the elevated grain yield of single tiller spikes rather than on their number. Consistently, Slafer et al. (2014) reported

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Article that, while environment mainly drives variations in spike number, genotype drives those in kernels per spike. The good tiller yield was associated with higher grain recovery and mean kernel weight compared to those of other cultivars. Analysing the performances of Svevo against phenological and yield-component traits, we hypothesize that the higher yield stability depended on its more pronounced spring habit and very low sensitivity to daylength (Motzo and Giunta, 2007), which supported a faster initiation of both leaf and spikelet primordia and a higher growth rate of tillers (Arduini et al., 2010; Dreccer et al., 2013). This allowed Svevo to shorten the vegetative phase maintaining high leaf number and tiller yield (Borrás-Gelonch et al., 2012), and lengthen the stem elongation phase without delaying anthesis (Miralles and Slafer, 2007). Indeed, we found that both leaf number and tiller yield were higher in Svevo compared to the other early flowering cultivar Saragolla. In addition, because of the earlier start of stem elongation, Svevo formed overall less primordia (leaves + initiated spikelets) on the main culm compared to Saragolla, which probably reduced within and between spike competitions. These are both crucial for adequate floret development and fertilization (Bancal, 2009; González-Navarro et al., 2015). Our hypothesis is supported by the higher grain recovery and tiller contribution to yield in Svevo than in Saragolla, despite coincident anthesis, and according to Li et al. (2001), this can be consistent with the spring habit. Conversely, the worse yield performance of the cultivar Claudio was primarily because of low spikelet fertility, which was due to lower floret production in both spike types, particularly in the central and upper spikelets, and to the lower grain recovery in tillers. We associated these traits with the longer vegetative phase, evidenced by the higher main-culm leaf number compared to other cultivars, and the short stem elongation phase, which reduces the time for floret differentiation and makes this cultivar similar to older genotypes (Motzo and Giunta, 2007; Álvaro et al., 2008). Ferrante et al. (2012) associated the lower yield of Claudio with lower grain set and fruiting efficiency. In our research, however, reduced spikelet fertility was in part compensated during grain filling, so that this cultivar showed the highest mean kernel weight. The length of the entire growth cycle was equal in the four cultivars, so that earlier anthesis corresponded to longer grain filling. Accordingly, the latest flowering Normanno had the lowest mean kernel weight both in main culm and tillers, suggesting that late anthesis is detrimental to grain filling in Mediterranean photothermal conditions, independently of sowing time and despite an optimal water supply. In line with the variability in spike size within Italian durum wheat germoplasm (De Vita et al., 2007; Álvaro et al., 2008), the four cultivars differed in spike size, Normanno and Saragolla showing larger spikes than Claudio and Svevo. In our research, larger main culm spikes produced more florets per spike, but suffered from intra-spike competition, as suggested by lower grain recovery and mean kernel weight. The higher yield of tiller spikes observed in Svevo compared to Normanno and Saragolla suggests that large main culm spikes are also more competitive for resources against tillers. In summary, we suggest that the higher yield stability of Svevo relies on the lowest difference in grain yield between main culm and tiller spikes, the former being the lowest yielding and the latter the highest. Independent of sowing date and cultivar, the grain yield of both main culm and tiller spikes was closely related to the number of kernels per spike, but not to the mean kernel weight, which confirms compensation and less plasticity for the latter yield component (Slafer et al., 2014). In addition, we found that the number of kernels per spike was significantly and positively related to the

number of kernels per spikelet, but not to the number of spikelets per spike, suggesting that lower spikelet numbers accommodate higher floret production and recovery.


We suggest that the question of whether tillers are a burden or a resource in durum wheat should be reconsidered. In fact, our results highlight that, at low plant density, tillers contributed at least 40% to plant yield and a better yield stability in later sowings was associated with a higher contribution of tillers to yield. We found that variations in tiller yield in response to sowing date depended primary on tiller number, whereas differences among cultivars relied on the yield of single tiller spikes. A Cultivar × Sowing date interaction was revealed for grain yield per plant, but not for the yield of main culm and tiller spikes and their components. The analysis of spike types and their components showed that large main culm spikes are more competitive against tiller spikes, but also suffer from intra-spike competition and are more sensitive to delays in the sowing date. Therefore, we hypothesize that higher yield stability can be achieved with a more equal partitioning of resources within main culm and tillers, which can be obtained by reducing the number of main culm primordia and increasing their initiation rate. This allows for the stem elongation and grain filling phases to be longer without delaying anthesis, but it also reduces the sink strength of the main culm spike. Accordingly, more time and resources can be allocated to floret production and to grain fillingboth in main culm and tillers. From a methodological point of view, our results show that the number of florets per spike can be effectively estimated by counting florets on only five spikelets from given positions: two basal, two central and the terminal spikelet.


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Article tivar on spike weight and kernel number in durum wheat. Crop Pasture Sci. 61:287-95. Borrás-Gelonch G, Rebetzke GJ, Richards RA, Romagosa I, 2012. Genetic control of duration of pre-anthesis phases in wheat (Triticum aestivum L.) and relationships to leaf appearance, tillering, and dry matter accumulation. J. Exp. Bot. 63:69-89. Brdar MD, Kraljević-Balalić MM, Kobiljski BD, 2008. The parameters of grain filling and yield components in common wheat (Triticum aestivum L.) and durum wheat (Triticum turgidum L. var. durum). Cent. Eur. J. Biol. 3:75-82. De Vita P, Li Destri Nicosia O, Nigro F, Platani C, Riefolo C, Di Fonzo N, Cattivelli L, 2007. Breeding progress in morphophysiological, agronomica land qualitative traits of durum wheat cultivars released in Italy during the 20th century. Eur. J. Agron. 26:39-53. Dreccer MF, Chapman SC, Rattey AR, Neal J, Song Y, Christopher J(J)T, Reynolds M, 2013. Developmental and growth controls of tillering and water-soluble carbohydrate accumulation in contrasting wheat (Triticum aestivum L.) genotypes: can we dissect them? J. Exp. Bot. 64:143-60. Elhani S, Martos V, Rharrabti Y, Royo C, García del Moral LF, 2007. Contribution of main stem and tillers to durum wheat (Triticum turgidum L. var. durum) grain yield and its components grown in Mediterranean environments. Field Crop. Res. 103:25-35. Ercoli L, Lulli L, Arduini I, Mariotti M, Masoni A, 2011. Durum wheat grain yield and quality as affected by S rate under Mediterranean conditions. Eur. J. Agron. 35:63-70. Ercoli L, Masoni A, Pampana S, Mariotti M, Arduini I, 2013. As durum wheat productivity is affected by nitrogen fertilisation management in Central Italy. Eur. J. Agron. 44:38-45. Ercoli L, Masoni A, Pampana S, Mariotti M, Arduini I, 2014. The response of durum wheat to the preceding crop in a Mediterranean environment. Sci. World J. 2014:717562. Eshaghi L, Pouryousef M, Kamkar B, 2011. Quantification of stem elongation rate in response to temperature and photoperiod by 24 multiplicative models. Int. J. Plant Dev. Biol. 5:67-72. Ferrante A, Savin R, Slafer GA, 2012. Differences in yield physiology between modern, well adapted durum wheat cultivars grown under contrasting conditions. Field Crop. Res. 136:52-64. Ferrise R, Triossi A, Stratonovich P, Bindi M, Martre P, 2010. Sowing date and nitrogen fertilisation effects on dry matter and nitrogen dynamics for durum wheat: An experimental and simulation study. Field Crop. Res.117:245-57. Fischer RA, 2008. The importance of grain or kernel number in wheat: a reply to Sinclair and Jamieson. Field Crop. Res. 105:15-21. Fischer RA, 2016. The effect of duration of the vegetative phase in irrigated semi-dwarf spring wheat on phenology, growth and potential yield across sowing dates at low latitude. Field Crop. Res. 198:188-99. González-Navarro OE, Griffiths S, Molero G, Reynolds MP, Slafer GA, 2015. Dynamics of floret development determining differences in spike fertility in an elite population of wheat. Field Crop. Res. 172:21-31. Guo Z, Schnurbusch T, 2015. Variation of floret fertility in hexaploid wheat revealed by tiller removal. J. Exp. Bot. 66:5945-58. Hendriks PW, Kirkegaard JA, Lilley JM, Gregory PJ, Rebetzke GJ, 2016. A tillering inhibition gene influences root-shoot carbon partitioning and pattern of water use to improve wheat productivity in rainfed environments. J. Exp. Bot. 67:327-40. [page 246]

Inagaki MN, Valkoun J, Nachit MM, 2007. Effect of soil water deficit on grain yield in synthetic bread wheat derivatives. Cereal Res. Commun. 35:1603-8. Kebrom TH, Chandler PM, Swain SM, King RW, Richards RA, Spielmeyer W, 2012. Inhibition of tiller bud outgrowth in the tin mutant of wheat is associated with precocious internode development. Plant Physiol. 160:308-18. Khaledian MR, Mailhol JC, Ruelle P, Dejean C, 2013. Effect of cropping strategies on the irrigation water productivity of winter wheat. Plant Soil Environ. 59:29-36. Kumar PV, Rao VUM, Bhavani O, Dubey AP, Singh CB, 2016. Effect of temperature and photothermal quotient on the yield components of wheat (Triticum aestivum L.) in Indo-Gangetic plains of India. Exp. Agr. 52:14-35. Li C, Cao W, Dai T, 2001. Dynamic characteristics of floret primordium development in wheat. Field Crop. Res. 71:71-6. Mahdi L, Bell CJ, Ryan J, 1999. Establishment and yield of wheat (Triticum turgidum L.) after early sowing at various depths in a semi-arid Mediterranean environment. Field Crop. Res. 58:187-96. McMaster GS, Wilhelm WW, 1997. Growing degree-days: one equation, two interpretations. Agr. Forest Meteorol. 87:291-300. Miralles DJ, Slafer GA, 2007. Sink limitations to yield in wheat: how could it be reduced? J. Agr. Sci. 145:139-49. Mitchell JH, Rebetzke GJ, Chapman SC, Fukai S, 2013. Evaluation of reduced-tillering (tin) wheat lines in managed, terminal water deficit environments. J. Exp. Bot. 64:3439-51. Modarresi M, Mohammadi V, Zali A, Mardi M, 2010. Response of wheat yield related traits to high temperature. Cereal Res. Commun. 38:23-31. Moragues M, García del Moral LF, Moralejo M, Royo C, 2006. Yield formation strategies of durum wheat landraces with distinct pattern of dispersal within the Mediterranean basin I: Yield components. Field Crop. Res. 95:194-205. Motzo R, Giunta F, 2007. The effect of breeding on the phenologyof Italian durum wheats: from landraces to modern cultivars. Eur. J. Agron. 26:462-70. Porter JR, Gawith M, 1999. Temperatures and the growth and development of wheat: a review. Eur. J. Agron. 10:23-36. Rharrabti Y, Miralles DJ, Martos V, García del Moral LF, 2010. Grain weight of durum wheat cultivars released in Italy and Spain during the 20th century as affected by source-sink manipulations. Cereal Res. Commun. 38:134-45. Sadras VO, Rebetzke GJ, 2013. Plasticity of wheat grain yield is associated in plasticity of ear number. Crop Pasture Sci. 64:234-43. Serrago RA, Miralles DJ, Slafer GA, 2008. Floret fertility in wheat as affected by photoperiod during stem elongation and removal of spikelets at booting. Eur. J. Agron. 28:301-8. Sinclair TR, Jamieson PD, 2006. Grain number, wheat yield, and bottling beer: An analysis. Field Crop. Res. 98:60-7. Slafer GA, Savin R, Sadras VO, 2014. Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crop. Res. 157:71-83. Spink JH, Semere T, Sparkes DL, Wahley JM, Foulkes MJ, Clare RW, Scott RK, 2000. Effect of sowing date on the optimum plant density of winter wheat. Ann. Appl. Biol. 137:179-88. Steel RGD, Torrie JH, Dickey DA, 1997. Principles and procedure of statistics: a biometrical approach. McGraw Hill, New York, USA. Subira J, Álvaro F, García del Moral LF, Royo C, 2015. Breeding effects on the cultivar x environment interaction of durum wheat yield. Eur. J. Agron. 68:78-88.

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Seeding density in wheat genotypes as a function of tillering potential. Sci. Agr. 66:28-39. Whitechurch EM, Slafer GA, Miralles DJ, 2007. Variability in the duration of stem elongation in wheat and barley genotypes. J. Agr. Crop Sci. 193:138-45. Xie Q, Mayes S, Sparkes DL, 2016. Optimizing tiller production and survival for grain yield improvement in a bread wheat x spelt mapping population. Ann. Bot. 117:51-66. Zadoks JC, Chang TT, Konzak CF, 1974. A decimal code for the growth stages of cereals. Weed Res. 14:415-21.

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Italian Journal of Agronomy 2018; volume 13:1120

Modelling plant yield and quality response of fresh-market spinach (Spinacia oleracea L.) to mineral nitrogen availability in the root zone Daniele Massa,1 Luca Incrocci,2 Luca Botrini,2 Giulia Carmassi,2 Cecilia Diara,2 Pasquale Delli Paoli,3 Giorgio Incrocci,2 Rita Maggini,2 Alberto Pardossi2 1CREA

Research Centre for Vegetable and Ornamental Crops, Council for Agricultural Research and Economics, Pescia (PT); 2Department of Agriculture, Food and Environment, University of Pisa, Pisa; 3Coop Agricoltura 2000, Venturina (LI), Italy Abstract

Spinach is one of the most important green-leafy vegetables, consumed worldwide, and its intake is beneficial for human beings. In this crop, produce yield and quality are closely related to plant nitrogen (N) nutrition. A precise supply of N is also essential for high environmental and economic sustainability. Main aims of the work were: i) to establish relationships between produce yield or quality and mineral N availability in the root zone; and ii) to define an optimal mineral N level to be maintained in the root zone for spinach. Eight experiments were carried out during a four-year-long period under typical Mediterranean climate conditions. Different amounts of N fertilisers were supplied leading to twenty different levels of mineral N in the root zone. Experimental measurements included climate parameters, plant growth, tissue and soil analyses, produce yield and quality indicators. A segmented linear model significantly represented the relationship between crop yield (1.7 to 21.7 t ha–1) and soil mineral N concentration (7.6 to 41.0 mg kg–1). Basing on this model, an optimal mineral N Correspondence: Daniele Massa, CREA Research Centre for Vegetable and Ornamental Crops, Council for Agricultural Research and Economics, via dei Fiori 8, 51012 Pescia (PT), Italy. Tel.: +39.0572.451033 - Fax: +39.0572.453309. E-mail: daniele.massa@crea.gov.it Key words: Fertilisation; leaf nitrates; leafy vegetable; nitrogen nutrition index; photothermal units; SPAD index.

Acknowledgements: this work was carried out with funds from the Ministry of Agricultural, Food and Forestry Policies (Decree N. 25279 of 23 December 2003) as part of the AZORT project. The authors are also grateful to Prof. A. Ferrante for his support in the revision of the paper.

Received for publication: 30 September 2017. Revision received: 17 February 2018. Accepted for publication: 19 February 2018.

©Copyright D. Massa et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:1120 doi:10.4081/ija.2018.1120

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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threshold was fixed at 23.4 mg kg–1. Above this threshold, crop yield did not show any significant variations as well as tissue characteristics and produce quality. Plants grown under suboptimal N levels showed reduction in growth, tissue mineral (nutrients) content, and SPAD index. The proposed models could be implemented in fertilisation protocols for the optimization of N supply and the estimation of spinach growth and yield.


In the Mediterranean basin, savoy spinach (Spinacia oleracea L.) represents an important typical produce exported in many countries of northern Europe. For fresh market, savoy spinach is harvested at early growth stage. The most important quality attributes of this leafy vegetable are related to the leaf greenness and morphology (i.e., wrinkledness), to the content of beneficial mineral elements, and to the low content of toxic compounds such as oxalic acid and nitrates (Santamaria et al., 1999; Cavaiuolo and Ferrante, 2014). Well-balanced nitrogen (N) supply is crucial for high yield and market quality of spinach. Many authors reported a positive relationship between crop yield and increasing N fertiliser rate in spinach cultivated in open field or under greenhouse (Biemond et al., 1996; Wang and Li, 2004; Gülser, 2005; Lefsrud et al., 2007; Stagnari et al., 2007; Rodriguez-Hidalgo et al., 2010). The spinach content in mineral elements and antioxidants, such as lutein and β-carotene, are both positively related to N availability (Lefsrud et al., 2007; Stagnari et al., 2007). On the other hand, N excess may lead to large leaf accumulation of oxalic acid and nitrates in leaf tissues, especially when N is supplied in the nitric form (Chen et al., 2004; Wang and Li, 2004; Zhang et al., 2005; Stagnari et al., 2007). Similarly to nitrates, oxalic acid and related compounds are harmful molecules for human beings and their continuous intake can induce blood diseases, especially in infants, nutritional disorders, and other health disturbs in human body (Noonan, 1999; Bryan and Loscalzo, 2011; Agnoli et al., 2017). In the European Union (EU) specific limits have been laid down for the nitrate content of some leafy vegetables (The Council of the European Community, 2006; EFSA, 2008). In spinach, these limits are 2000 and 3500 mg NO3– kg–1 (fresh weight basis) for frozen and fresh products, respectively. Excessive N supply also results in increased water content of leaf tissues (Lefsrud et al., 2007), which may negatively influence plant resistance to pathogens (Dordas, 2008) and its shelf life as well (Lombardo et al., 2016). A correct N fertiliser management is essential for reducing the crop environmental impact associated with nitrate leaching (Robertson and Vitousek, 2009; Zhou and

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Article Butterbach-Bahl, 2014), which easily occurs in sandy soils where spinach is typically cultivated. In the EU, the Nitrates Directive (The Council of the European Communities, 1991) was issued to preserve the quality of ground and surface water bodies from the pollution of nitrates produced by agricultural activity, and to promote the adoption of good agricultural practices. According to the Nitrates Directive, growers must follow mandatory rules to tackle nitrate loss from their crops; for example, in the area of Val di Cornia (Tuscany, Italy), where the experiment took place, a maximum N dose of 120 kg ha–1 and well defined (limited) periods of distribution have been ruled for spinach by the local authorities. Moreover, N waste has negative economic impact on the production costs of field crops (Robertson and Vitousek, 2009). Some authors have related crop yield and N supply to economic parameters, to calculate the N fertiliser rate that maximizes grower’s incomes (Wang and Li, 2004; Milne et al., 2012). Literature on leafy vegetables mostly focuses on the effects of N on yield and quality (Chen et al., 2004; Wang and Li, 2004; Zhang et al., 2005; Lefsrud et al., 2007; Stagnari et al., 2007) while less attention is paid to effective N management (Canali et al., 2014). However, most works attempt to describe growth and yield response curves as a function of fertiliser doses instead of the actual N availability in the root zone. For optimal N management different authors have therefore introduced the concept of minimum optimal concentration (Heckman et al., 2002; Cui et al., 2008; Bottoms et al., 2012); this would represent the reference value to be maintained in the root zone to minimize crop environmental impact and to support high yield and quality (Incrocci et al., 2017). To the best of our knowledge, no previous study addresses the above issue for spinach, thus an N optimal concentration for this crop has not been yet determined. The paper reports experimental data collected in eight different experiments carried out during a four-year-long period. Main aims of the work are: i) to assess the effects of soil N concentration on spinach yield and quality throughout a medium-long observation

period under different climate conditions; ii) to define an optimal soil mineral N concentration for effective N management; and iii) to test an optical sensor for the quick monitoring of N nutritional status in fresh-market spinach grown under open-field Mediterranean climate conditions.

Materials and methods

Growing conditions and treatments

Experimental data were collected in eight different experiments (E), on spinach (Spinacia oleracea L.) crops, during a fouryear-long period (from September 2007 to April 2011). Experimental fields were located in Val di Cornia (Tuscany, Italy), a coastal area with sandy-loam soils. The area is intensively cultivated with vegetables under typical Mediterranean climate conditions (Figure 1) with mild winters and 650 mm annual rainfall (tenyear average). The physico-chemical characteristics of the soil (Table 1) were determined in the root zone of spinach (5-40 cm) prior to sowing. The quantity of fertilisers containing P, K, Ca, Mg and micronutrients was then calculated through a soil nutrient balance aimed to: i) replace the nutrients taken up by a crop grown under optimal conditions; and ii) restore the initial soil fertility if necessary. Only N was supplied in a variety of different doses (Table 2) based on the following criteria. During the first two experiments (E1 and E2), N was supplied at the fixed rate of 0, 80, 120 or 160 kg ha–1. In the other experiments (E3-E8), N was supplied following growers’ fertilisation practice. This is based on the standard fertilisation rate of 120 kg N ha–1, according to the blueprint laid down for spinach production in the Val di Cornia area. The above quantity is usually increased by growers up to 175 kg ha–1 in relation to the rainfalls occurred in the growing period, which may increase the risk for N shortage due to nitrate leaching. Each experiment always

Table 1. Chemical and physical characteristics in the 0-40 cm depth layer of the different experimental fields (n=8) used for spinach cultivation. Parameter Sand (%) Silt (%) Clay (%) Bulk density (t m–3) Field capacity (%, v/v) Wilting point (%, v/v) Organic matter (%) Total N (mg kg–1) N-NO3– (mg kg–1) N-NH4+ (mg kg–1) P2O5 (mg kg–1) K2O (mg kg–1) CaO (mg kg–1) MgO (mg kg–1) CEC (meq 100g–1) pH (H2O) EC (dS m–1)

Maximum value

Minimum value




76.1 19.8 26.0 1.48 26.7 16.7 2.1 800.0 19.7 11.3 109.0 235.0 3190.0 230.0 18.1 7.8 0.7

58.6 10.8 7.8 1.40 13.2 5.7 1.0 700.0 3.6 3.6 37.0 110.0 1451.0 113.0 9.2 6.8 0.4

69.1 15.4 15.5 1.45 18.4 10.3 1.4 757.1 13.2 7.3 73.4 187.2 1946.0 174.0 12.4 7.4 0.5

6.4 3.1 5.6 0.3 4.5 3.5 0.4 53.5 5.0 2.9 27.1 48.2 711.3 49.6 4.0 0.5 0.1

0.1 0.2 0.4 0.3 0.2 0.3 0.1 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.1 0.3 0.1

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Article included a dose of N equal to 0 as untreated control (Table 2). In all treatments, the total amount of N was split over three periods: i) 40% blended with the soil, before sowing (4-6 days), using a mixed organic-mineral fertiliser (1% organic, 5% ammonia and 30% ureic); ii) 30% distributed as top-dressing fertilisation, at 1/3 of the cultivation cycle (roughly 4-5 true leaves), using ammonium nitrate; iii) 30% distributed as top-dressing fertilisation, at 2/3 of the cultivation cycle (roughly 10-11 true leaves), using calcium nitrate. In the case of rainy periods (i.e., when total rainfall in the 8-14 true-leaf phase was 50% higher than the average of the previous ten years), with high N leaching, a third top-dressing fertilisation (40-50 kg N ha–1) was applied when spinach had 14-15 true leaves (E3, E4 and E8). Soil preparation of seedbed included ploughing, harrowing and levelling for bringing the soil into the better tilt for water drainage. Spinach (cv. Spitfire, Seminis®, Monsanto Company, USA) was sown in order to have a plant density of 30 plants m–2 taking into account the percentage of emergency. Each treatment was applied in a completely randomized experimental design on an area of 800-1000 m2. Irrigation was applied rarely (only once at germination in E1 and E3), using traveling sprinklers, to restore the field capacity when rainfall events were not sufficient to preserve the quantity of easily available water (50-60% of the available water) in the root zone (Table 1). Soil moisture was monitored by using a tensiometer (Delta-T SWT 4, Delta-T Device Ltd, Cambridge UK) with the ceramic cup positioned within 20-30 cm depth in the driest area of the field. Crop protection was accomplished following the standard protocol used by local growers that includes treatment against insects, fungi and weeds. Climate parameters were monitored hourly using a meteorological station (Pessl Instruments GmbH, Weiz, Austria) located in the experimental area. Air and soil temperature, radiation, wind speed, rainfall and air humidity data were collected and summarized in Figure 1 and Table 2.

sisted of an area of 3.5 m2 that corresponded roughly to 100 plants. Sample units were collected randomly in each treatment. Plants were harvested by hand, stored in plastic bags to limit water loss, and moved rapidly to the laboratory for growth and tissue analyses. The growth analysis consisted in the measurement of fresh (FW) and dry weight (DW, obtained in a forced-air oven at 80°C for 96 h), number of true leaves, and leaf area, determined by a planimeter (Delta-T Device, Cambridge, UK) for the calculation of leaf area index. Leaf chlorophyll was assessed through SPAD index (SPAD-502, Konica Minolta Optics, 2970 Ishikawa-machi, Hachioji, Tokyo, Japan). Plant dry matter was analysed for its mineral nutrient content. In more detail, total N was determined as the sum of reduced N (by the Kjeldhal method) and N-NO3; the latter was determined in the aqueous extract of dry matter (1:300, w/w) using a colorimetric method (Cataldo et al., 1975). After nitric-perchloric acid digestion of dried samples (90 min at 150°C), K, Ca, and Mg were determined by atomic absorption spectroscopy (Spectra-AA240 FS, Varian, Australia), while P was measured through a colorimetric method (Olsen and Sommers, 1982). In occasion of plant destructive analyses, soil samples were also collected in each replication unit and analysed for mineral N content (Nmin). Nitrate was measured in the aqueous extract of dry soil (soil-water ratio 1:2 w/w) using the Cataldo’s method (1975). Ammonium was extracted from soil using 1 M KCl (soil-KCl ratio 1:2 w/w) and quantified spectrophotometrically through the indophenol method (Kempers and Kok, 1989).

Data analysis and modelling

Most of the variables analysed in the work refer to the averaged concentration of total (N-NH4+ plus N-NO3–) mineral N (Nmin) in the root zone. Nitrogen concentration in this work is mostly expressed as mg of element per kg of dry soil. The conversion from mg kg–1 N to N expressed as kg per hectare can be computed using the following equation: (1)

Plant and soil analysis

Plant samples were collected at different crop stages, i.e., at: i) 4-5 true leaves; ii) 10-11 true leaves; and iii) harvest time; for each treatment, three replicates were collected in the first and second sampling, and four replicates at harvest. Each replication unit con-

where BD is the soil bulk density (1.45 t m–3, on average; Table 1), RD is the root depth (0.40 m), and 10 is a multiplicative factor for unit conversion (from mg kg–1 to kg ha–1); then in our growing

Table 2. Period of cultivation (dates), experiment duration (days after sowing, DAS), and nominal doses of N supplied in each experiment (E) are reported in the table as single values. Mean air temperature (Ta), growing degree days (GDD), mean and cumulative daily global radiation (Rad and Cum. Rad, respectively), photothermal units (PTU) and cumulative rainfall (Rain) recorded in each experiment (E). Experiment Sowing



E1 E2 E3 E4 E5 E6 E7 E8 Average SD CV

07/01/2008 25/02/2008 29/12/2008 19/02/2009 29/12/2009 30/03/2010 10/01/2011 04/04/2011 -

98 124 91 122 84 132 94 109 -

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01/10/2007 24/10/2007 29/09/2008 20/10/2008 06/10/2009 18/11/2009 08/10/2010 16/12/2010 -

Dose of N applied Ta (kg ha–1) (°C) 0-80-120-160 0-80-120-160 0-160 0-155 0-130 0-120 0-145 0-175 -

11.5 9.2 12.9 9.9 12.3 9.2 11.2 9.0 10.6 1.5 0.1

GDD Rad (°C) (MJ m–2 day–1) 831.3 769.0 907.3 862.2 783.7 815.3 772.0 652.6 799.2 75.9 0.1

6.3 5.2 6.4 4.9 7.4 8.5 5.4 8.4 6.6 1.4 0.2

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Cum. Rad PTU (MJ m–2) (°C MJ m–2) 622.1 643.2 583.5 596.3 624.3 1117.0 511.4 911.7 701.2 204.8 0.3

3383.9 2575.8 3553.7 2666.0 2620.1 5550.3 2297.7 4588.3 3404.5 1140.2 0.3

Rain (mm) 256.0 369.4 649.0 829.8 271.4 485.6 344.1 219.4 428.1 214.4 0.5

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Article conditions the product of BD, RD and 10 was 5.8. To compare the yield (Y) of different experiments carried out under different temperature and radiation levels (Figure 1 and Table 1) the photothermal use efficiency of each single treatment (YPTU; mg MJ–1 °C–1) was calculated according to Thornley and Johnson (1990). For each experiment, Y was divided by the photothermal units (PTU; MJ m–2 °C) accumulated during in the growing period from the appearance of the first true leaf to harvest, (Eq. 2): (2) where Radi and Tai are the radiation and the mean air temperature measured at the ith day, respectively, and Tb represents the base temperature below which plant development does not occur. The base temperature was estimated by empirical methods, as suggested in other works (Wolfe et al., 1989; Jenni et al., 1996). Specifically, Tb was calculated as the value (ranging between –4°C and 8°C, with steps of 1°C) that maximized the determination coefficient of the linear regression (Eq. 3) between the number of n true leaves (nLeaves) and growing degree (∑ i =1 (Tai-Tb); GDD): (3) In our case, the best fit was obtained with the following equation, using Tb=3°C: nLeaves = –6.47 + 0.030 • GDD, (n=260; R2=0.81, P<0.001). For each experiment, normalized values of YPTU (Y*PTU) were max obtained as the ratio between YPTU and its maximum value (YPTU ): (4)

Following the approach proposed by Magán et al. (2008), max (YPTU ) was represented by the average yield of those treatments that did not differ statistically (i.e., following ANOVA results) from the maximum yield obtained among all treatments. A segmented linear-plateau model was adopted to fit normalized data of dry and fresh YPTU (Y*DW and Y*FW, respectively). With this model, biomass production is assumed to be zero if Nmin is less than or equal to a minimum threshold value (N0); afterward, Y starts to increase linearly with Nmin up to the optimal N concentration (Nopt) that represents the level of Nmin above which Y reaches its maximum value (Eq. 5): (5) where Y*PTU is the ratio YPTU/(YPTU ). Following the approach proposed by Magán et al., (2008), the coefficients a and b were determined by consecutive linear regression analyses run fitting the complete group of treatments and step by step subtracting the treatments at the right of a possible Nopt threshold till achieving the highest coefficient of determination (R2). Finally, Nopt and N0 were obtained solving the equation Y*PTU=a+b•Nmin when Y*PTU was equal to 1 and 0, respectively. max

A linear model was used to describe the relationship between the N nutrition index (NNI) and the SPAD index of spinach. Both parameters were calculated by the average of data collected during the entire cultivation cycle in each treatment. The NNI was calculated as the ratio between the tissue total N concentration for each treatment and the tissue average N concentration of those treatments with optimal Nmin levels in the root zone (i.e., Nmin ≥ Nopt). Other data (i.e., crop Y and N uptake at harvest, and tissue nutrient concentrations) were analysed through one-way ANOVA and Tukey test (HSD) for the separation of the means. The programs Statgraphics Centurion XV (Statpoint Technologies, Inc., Warrenton, Virginia, USA) and Prism 5 (GraphPad Software, Inc., La Jolla, California, USA) were used for data analysis.


Growing conditions

Figure 1 and Table 2 report the main climate variables monitored during the whole experimental period. The average values of Ta did not differ much among the different trials, showing the lowest CV as compared with other climate variables (Table 2). Mean daily Ta was used to calculate GDD (see Eq. 2 for details), which averaged 799.2°C among all trials, with the minimum and maximum values recorded for E8 (652.6°C) and E3 (907.3°C), respectively (Table 2). Mean daily Rad varied more than Ta during the experiment (Table 2) with the minimum daily-averaged value recorded for E5 and E6 (0.44 MJ m2 day–1) in December 2009 (Figure 1), and the maximum value recorded for E6 in March 2010 (25.0 MJ m2 day–1). The combination of Ta and Rad in Eq. 2 resulted in different PTU values that ranged between 2297.7°C MJ m–2 for E7 and 5550.3°C MJ m–2 for E6 with an average of 3404.5°C MJ m–2 (Table 2). The accumulated rainfall, among the other climate variables (Table 2), showed the highest variability with the minimum and maximum value recorded for E8 (219.4 mm) and E4 (829.8 mm), respectively. In general, rainfall events were regularly distributed during the growing seasons, excluding some exceptional precipitations above 60 mm day–1 recorded for E3, E4 and E5 (Figure 1). Due to diverse climate conditions, the duration of different crop cycles varied over the entire experimental period (Figure 1 and Table 2). The shortest culture was recorded in 2009, when E5 lasted 84 days, while the longest one (E6) was registered in 20092010 and lasted 132 days (Table 2). The different N treatments (Table 2) and growing conditions produced a variety of Nmin levels in the root zone as summarized in Figure 2. Data reported in Figure 2A represent the averages of soil samples collected during the experimentation in each treatment (5 samplings). To evaluate the time-dependent variability of collected data, CV and SD were also calculated and averaged at each sampling time. The coefficient of variation averaged 30% and the maximum values were generally recorded for those treatments with no supply of N fertiliser (N0). High CV values in these treatments were therefore due to high variability in Nmin that generally tended to decrease with time. This effect could mainly be related to N depletion in the root zone, which occurred because of the absence of N fertilisers supply. On the contrary, the lowest variability for Nmin concentration was observed in those treatments that underwent N fertilisations and scarce rainfall with limited N-NO3– leaching. However, the different experiments differed significantly

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Article (ANOVA, P<0.001) for the actual mean values of N-NO3–, N-NH4+ and as a consequence Nmin concentration in the root zone (Figure 2A); the latter ranged from 7.6 to 41.0 mg kg–1 that corresponded to 44.3 and 237.6 kg ha–1 Nmin available in the soil profile explored by the root system (see Eq. 1 for conversion coefficients). Mineral

N was strongly correlated to the concentration of N-NO3– (R=1.00, n=300, P<0.001), which accounted for roughly 81% Nmin on the average of all treatments. A significant albeit weak correlation was also found between N-NH4+ and Nmin (R=0.49, n=300, P=0.02). On the other hand, it appears crucial to highlight that a very

Figure 1. Daily global radiation (Rad, MJ m–2 day–1), mean air temperature (Ta, °C) and rainfall (Rain, mm) plotted versus days after sowing (DAS) in the different experiments (E, see Table 2 for details). The date reported below 0 represents the sowing date.

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Article poor relationship was instead found between the actual Nmin and the nominal N dose supplied by fertilisers (Figure 2B).

Biomass accumulation and biometric parameters

At harvest, plant DW and FW significantly varied among treatments (Table 3). The dry weight accumulated in the aboveground biomass ranged between 0.25 (E6-0) and 2.43 (E1-160) t ha–1 while FW varied from 1.72 (E6-0) to 21.69 (E1-120) t ha–1. Leaf area index ranged between 0.3 and 2.3 and was significantly correlated to both the accumulated DW (R=0.75, n=80, P<0.001) and FW (R=0.71, n=80, P<0.001). Similar results were also found for the total N uptake calculated on a DW basis, which was significantly correlated to plant biomass accumulation at harvest (R=0.95, n=80, P<0.001); N uptake increased from 6.28 (E60) to 87.37 kg ha–1 (E1-160) depending on treatments (Table 3). Significant differences among N treatments were also observed for the specific leaf area that increased with Nmin availability. With respect to the thermal time (GDD), the specific leaf area significantly changed during the crop cycle, decreasing linearly from emergence to harvest (157.8 to 88.1 cm2 g–1 DW, calculated as the average of all treatments).

Modelling crop response to Nmin

Figure 3A clearly shows that N nominal doses, supplied by fertilisers, and plant biomass accumulation were poorly correlated. This was consistent with: i) the poor correlation observed between N nominal dose and actual Nmin in the root zone (Figure 2B); and ii) the high variability of Y in relation to the different growing conditions (i.e., Ta and Rad, Table 2 and Figure 1). Yield values were therefore divided by the PTU accumulated in each experiment, thus obtaining YPTU, and then plotted versus Nmin (Eq. 2 for details).

Figure 2. A) Ammonia (N-NH4), nitric (N-NO3) and total mineral N concentration (Nmin) in the root zone (0-40 cm depth) averaged over the cultivation in the different experiments; each column represents the average of replicates ±SD. B) Relationship between actual Nmin and the nominal doses of N supplied with fertilisers during the cultivation period; each point represents the average of replicates ±SE.

Table 3. Effect of different N supplies on dry weight and fresh weight accumulated at harvest. Each value represents the average of replicates ±SD. Treatment* E1-0 E1-80 E1-120 E1-160 E2-0 E2-80 E2-120 E2-160 E3-0 E3-160 E4-0 E4-155 E5-0 E5-130 E6-0 E6-120 E7-0 E7-145 E8-0 E8-175 Significance

Dry weight (t ha–1) 1.56±0.12 cde 1.88±0.30 abc 2.31±0.15 ab 2.43±0.19 a 1.86±0.10 bc 1.97±0.07 abc 1.94±0.12 abc 1.97±0.26 abc 0.97±0.27 efg 1.18±0.30 def 1.50±0.21 c-f 1.99±0.11 abc 1.46±0.02 c-f 1.88±0.17 abc 0.25±0.04 h 1.13±0.58 def 0.44±0.19 fgh 0.95±0.11 e-h 0.31±0.10 gh 1.62±0.25 cd P<0.001

Fresh weight (t ha–1) 17.69±1.32 ab 18.70±2.65 ab 21.69±1.95 a 21.60±1.30 a 17.46±1.31 abc 18.16±0.62 ab 18.43±1.01 ab 17.66±2.44 ab 8.15±2.31 efg 10.66±2.78 def 12.65±1.93 cde 17.71±1.34 ab 14.75±0.60 bcd 17.83±1.72 ab 1.72±0.26 h 6.87±3.51 fgh 4.20±1.77 gh 10.40±0.81 def 2.17±0.92 h 15.81±1.47 bcd P<0.001

Total N uptake (kg ha–1) 57.12±4.18 64.91±10.39 78.41±5.18 87.37±6.66 68.13±3.57 78.16±4.07 76.14±4.35 73.72±10.35 33.01±4.92 45.65±9.34 52.38±4.75 81.62±4.38 57.82±6.51 82.40±12.12 6.28±1.27 27.29±5.15 14.48±3.22 38.55±7.81 6.55±2.42 53.93±8.24 P<0.001

c-f a-e abc a a-e abc a-d a-d gh efg d-g ab b-f a i ghi hi fgh i c-g

*The abbreviations represent Experiment number-Dose of N supplied with fertilisers (kg ha–1, see Table 2); P-value for one-way ANOVA is reported. Different letters in each column represent significant differences according to Tukey’s (HSD) test (P≤0.05).

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Article This expedient allowed the standardization of Y in terms of different growing seasons with significant improvement in data analysis (Figure 3B). YPTU response to increasing Nmin was zero below a minimum threshold; then, it began to increase linearly with Nmin up max to a maximum YPTU (YPTU ), after which no significant variation was

observed. Data analysis produced (YPTU ) values of 73.0 and 675.5 mg MJ–1 °C–1, respectively for DW (Figure 3B) and FW (data not shown). These quantities were finally used for the normalization of the biomass datasets. The normalized YPTU (Y*PTU), which repremax sents YPTU as a proportion of (YPTU ), was fitted using a segmented

Figure 3. Relationship between: A) the nominal doses of N supplied with fertilisers during the cultivation period and the dry weight accumulated at harvest (YDW); B) the actual mineral nitrogen concentration in the root zone (Nmin) and crop photothermal use efficiency (YPTU); or C) its normalized values (Y*PTU), both calculated on the basis of dry weight accumulated at harvest. Each point represents the mean of replicates (±SE). Continuous lines in panel C represent the model fitting the experimental data by Eq. 5; dotted lines represent the optimal value of Nmin (Nopt= 23.44 NNO3 mg kg–1) for fresh-market spinach.

Figure 4. Relationship between the mineral nitrogen concentration in the root zone (Nmin) and A) dry matter percentage; B) total nitrogen concentration; and C) leaf nitrate content of spinach tissues (shoot) as determined on samples collected at harvest. Each point represents the mean of replicates (±SE). Continuous lines represent the non-linear equation proposed for data fitting.

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Article linear model (Figure 3C). Equation 5 significantly fitted Y*PTU, calculated for both YDW (Figure 3C) and YFW (data not shown), explaining 89 and 91% of the experimental variability, respectively (P<0.001). Model parameterization produced the following coefficients: i) an intercept (a; in Eq. 5) equal to –0.40 for Y*DW and –0.47 for Y*FW; ii) a slope (b; in Eq. 5), which represents the relative yield increase per Nmin unit, equal to 0.06 for both Y*DW and Y*DW; iii) N0 equal to 6.85 mg kg–1 for Y*DW and 7.36 mg kg–1 for Y*FW; iv) Nopt equal to 23.80 mg kg–1 for Y*DW and 23.08 mg kg–1 Y*FW. Since no significant difference was found for the parameterisation of Eq. 5 using the two datasets, it could be concluded that the averaged values of N0 (i.e., 7.11 mg kg–1) and Nopt (i.e., 23.44 mg kg–1) were representative for spinach crops. Basing on Eq. 1, the quantity of Nmin per surface unit corresponded to Nopt=140.0 kg ha–1 and N0=41.2 kg ha–1.

cultivation cycle for each treatment. The latter data for spinach grown under optimal N conditions (i.e., excluding the treatments with Nmin below Nopt in Figure 3C) are reported in Figure 5 as a function of the GDD accumulated from sowing. The number of true leaves, which is also reported in Figure 5, is a further parameter that could be used to estimate the N uptake rate for spinach. Data on leaf mineral content (Figure 6) and SPAD index (Figure 7) were pooled into two groups corresponding to Nmin values above Nopt (N+opt) or below Nopt (N–opt). The concentrations of N, P and Mg in plant tissues analysed at harvest were significantly

Plant tissue analyses and N uptake

Nmin in the root zone significantly affected dry matter percentage in plant tissues. A one-phase exponential decay equation was fitted to experimental data, explaining 62% and 66% of the measurement variability for data averaged over the whole crop cycle (data not shown) or only at harvest (Figure 4A), respectively. At harvest, DW percentages were 9.1 and 14.6%, for treatments with Nmin above and below Nopt, respectively. Data averaged during the whole crop cycle showed a more restricted range, from 9.6 to 12.9%. An opposite pattern was observed for tissue total N concentration. In this case, a one-phase exponential growth equation was fitted to experimental data explaining 63% and 69% of the measurement variability for data averaged over the whole crop cycle (data not shown) or only at harvest (Figure 4B). At harvest, a positive relationship was found between N-NO3– accumulation in plant tissues and Nmin in the root zone. However, in all treatments, N-NO3– concentration did not exceed the limits suggested by the European Food Safety Authority (EFSA) for this crop (Figure 4C). A one-phase exponential equation fitted the experimental data, showing a plateau value at 1030 g kg–1 FW (Figure 4C). Plant DW and tissue N content (Figure 4C) were combined to calculate the crop total N uptake at harvest (Table 3) and over the

Figure 5. Relationship between crop nitrogen uptake and growing degree days (GDD) or number of true leaves. Dotted lines correspond to the growing phases at which 50% of the total N uptake occurs. Each point represents the mean of replicates (±SE) of only the treatments (seven) grown under optimal N levels (see Figure 3C).

Figure 6. Concentration of total nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg), determined at harvest, in plant tissues (shoot) of spinach grown under suboptimal (N–opt) or optimal (N+opt) concentration of Nmin in the root zone. Columns represents the average (±SD) of treatments below (N–opt) or above (N+opt) the optimal threshold (Nopt) established for fresh-market spinach. Not significant (n.s.) or significant differences are also reported for P≤0.05 (*), 0.01 (**) and 0.001 (***) according to one-way ANOVA.

Figure 7. Linear regression between normalized nitrogen index (NNI) and SPAD index estimated by the averaged values of samples collected during the whole cultivation cycle in each treatment. Full and empty symbols represent values corresponding to plants grown under optimal (N+opt) and suboptimal concentrations (N–opt) of Nmin in the root zone, respectively. The horizontal dotted line represents NNI = 1 for spinach; vertical dotted lines represent the values of SPAD index within which NNI is optimal as calculated by the 95% confidence interval of the linear regression (dashed lines). Each point represents the mean of three replicates (±SE).

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Article affected by suboptimal Nmin availability in the root zone (Figure 6). Nitrogen was reduced by 15% from 3.9 to 3.3 g 100 g–1 DW while P and Mg were more severely affected with a reduction of 21 and 23%, respectively. Conversely, no significant difference was observed at harvest for K and Ca tissue concentrations (Figure 6). Suboptimal Nmin levels also affected the SPAD index that provides an indirect estimation of chlorophyll content. For this parameter, no significant difference was found between data averaged at harvest or during the whole crop cycle (ANOVA, P>0.05) while a significant reduction was observed in N–opt treatments compared with N+opt treatments. Figure 7 shows the relationship between NNI and SPAD index. The linear regression model significantly fitted the measured data, explaining 78% of the experimental variability. The collected data suggest that values of SPAD index between 64.0 and 68.5 would be optimal for savoy spinach to avoid N deficiency or excess. This range was calculated on the basis of the confidence interval resulting from the linear regression analysis (Figure 7).


Yield response curve to Nmin concentration in the root zone

Very poor correlations between the nominal dose of N, applied through mineral fertilisers, and the actual Nmin concentration in the root zone or the harvested biomass were found in this work. In some cases (i.e., treatments E1-0 and E2-0; Table 3) high Y occurred without any N supply, since a sufficient (optimal) level of Nmin was already present in the root zone before sowing, which supported adequately plant N nutrition throughout the whole cultivation cycle. Similar results have been obtained in previous studies with spinach (Gülser, 2005; Stagnari et al., 2007). Defining the relationship between crop Y and Nmin appears therefore of fundamental importance for a balanced N supply whereby both economic and environmental sustainability of the crop can be maximized (Schroder et al., 2000; Cui et al., 2008; Bai et al., 2013). The harvest time varied widely among the different experiments. Spinach was harvested when the plants achieved 16 to 22 leaves, according to the market requirement and the weather (for example, rainfalls can delay the harvest). Furthermore, the quite different climate conditions (mainly Ta and Rad) during the years of observation caused a large variability in terms of plant growth. Crop Y was therefore standardized by PTU thus obtaining comparable data (i.e., YPTU) collected over different growing seasons. The use of YPTU was successfully applied by other authors to assess differences in the theoretical Y of several crops grown in different regions (Hou et al., 2012). For modelling crop response to soil nutrient concentration, several authors fitted crop Y using linear-plateau models (Reid, 2002; Cui et al., 2008; Bai et al., 2013; Magán et al., 2008) as was done in the present work by Eq. 5. On the other hand, many other authors described the response of different crops to Nmin or N fertiliser dose using non-linear equations (Thornley and Johnson, 1990; Van Noordwijk and Wadman, 1992; Reid, 2002; Milne et al., 2012). Therefore, the preliminary data analysis in the present work included non-linear models consisting in quadratic and hyperbolic functions as suggested by Thornley and Johnson (1990). However, the resulting determination coefficients (in the range of 0.83-0.86), or other error performance indices, were close to the ones achieved with the proposed linear model or even worse. The same was [page 256]

obtained for the other observed statistical parameters. With respect to non-linear models, Eq. 5 has the advantage of producing an unequivocal value for Nopt; in contrast, the use of non-linear equations may lead to higher uncertainty in the identification of Nopt. The Nopt value found in the present study (23.44 mg kg–1) for spinach was quite similar to others reported in literature for different crops. In fact, optimal Nmin in the root zone have been found in the range of 16-30 mg kg–1 dry soil for corn (Cui et al., 2008; Peng et al., 2013), 20-21 mg kg–1 for wheat (Bundy and Andraski, 2004), 20-30 mg kg–1 for potato (Doll et al., 1971), 24 mg kg–1 for cabbage (Heckman et al., 2002), and 20-24 mg kg–1 for celery and lettuce (Hartz et al., 2000; Bottoms et al., 2012).

Crop quality response to Nmin concentration in the root zone

Nitrate accumulation in spinach leaves is due to many factors depending on both environmental growth conditions (e.g., temperature, radiation, N fertilisation management) and plant-specific characteristics (e.g., nitrate reductase activity, leaf age) (Lasa et al., 2001; Chen et al., 2004; Gülser, 2005; Stagnari et al., 2007). High availability of Nmin in the root zone indeed promotes nitrates and total N accumulation in leaves. Similar results have been observed in other leafy vegetables, such as romaine and red-oak leaf lettuce (Di Gioia et al., 2017). However, the tissue nitrate content observed in this work was within the limits suggested by EFSA (2008) for spinach. It was likely due to the quite low plant density and to the N level in the soil that was not too much exceeding Nopt (maximum N-NO3 value was 36 mg kg–1, recorded in the treatment E2-160). Tissue content of other plant mineral nutrients responded differently to Nmin treatments depending on the nutrient element. Literature on spinach is quite heterogeneous with regard to the relationship between tissue content of plant mineral nutrients and Nmin in the root zone. In several studies, positive correlations between N, Ca, or Mg and Nmin have been reported (Lefsrud et al., 2007; Staganari et al., 2007). However, contrasting findings have been reported for P or K that were found to increase (Stagnari et al., 2007), to be constant (Lefsrud et al., 2007, only K) or even to decrease (Gülser, 2005, only P) by increasing Nmin in the root zone. A significant positive correlation between P or Mg and Nmin in the root zone (P<0.001, R=0.70 or 0.71, respectively) was observed in this work, as previously reported by other authors (Lefsrud et al., 2007; Stagnari et al., 2007). On the other hand, no significant correlation was found between K or Ca tissue content and N treatments, in agreement with Gülser (2005). Increasing Nmin in the root zone led to an enhanced blade colour. This is a relevant reference extrinsic characteristic, much appreciated by consumers of fresh-market spinach. The SPAD index was significantly lower in N–opt treatments, in which plants were grown under suboptimal nutritional conditions. SPAD index is significantly correlated to leaf N and leaf chlorophyll concentration (Schepers et al., 1992), on which spinach colour depends mostly. Leaf chlorophyll concentration has been found positively correlated to Nmin in the root zone in spinach (Lefsrud et al., 2007), and to leaf N and Mg concentration in many species (Shaahan et al., 1999) as also observed in this work. The use of chlorophyll meters, such as the SPAD, could be not reliable to estimate on-time crop N requirement because of possible delays between the nutrient status of the root zone and its effects on plants (Westerveld et al., 2003; Wu et al., 2007). Nevertheless, the information provided by these tools and other optical sensors may be valuable if coupled with data on soil com-

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Article position obtained through either laboratory analyses or easy-to-use and rapid methods available at farm level (Hartz et al., 2000; Maggini et al., 2010; Thompson et al., 2017).

Implementation of the results for improving spinach N fertilisation management

Growers usually adopt very simple N fertilisation plans for fresh-market spinach. In Tuscany (Italy), where the experiment took place, they usually apply from 120 to 175 kg ha–1 N for each growing cycle, often as fixed reference dosages without pre-sowing soil analyses. This fertilisation approach is quite robust and effective from the growers’ point-of-view. In common practice, the application of 120 kg ha–1 N to a crop like savoy spinach, which takes up from roughly 60 to 75% of the above quantity, consists in a sort of insurance against possible low soil N concentration and rainy periods that could hinder N top dressing applications. Obviously, this approach contributes to N loss phenomena related to excess N in the root zone and may reduce produce quality. In the above scenario, the risk for high crop environmental impact and production costs increases drastically (Massa et al., 2013). According to our results, the presence of roughly 23 mg kg–1 of Nmin in the soil is enough for a regular spinach growth and development without any yield and/or quality reduction. An ideal management of N fertilisation would consist in the supply of small fertiliser amounts at high frequency to keep Nmin concentration close to Nopt, as in fertirrigated crops. Nevertheless, a similar fertiliser application plan would not be sustainable for winter spinach under open-field operative conditions from both the agronomic and the economic point of view. Based on the crop N uptake curve reported in Figure 5, 50% of the total N absorbed by spinach is concentrated in the last part of the growing cycle (the last three to five weeks between the 13-15 true-leaf phase and harvest time). Similar results have been reported for other leafy vegetables such as lettuce (Bottoms et al., 2012). Therefore, when N fertilisers have to be applied in advance, data reported in Figure 5 must be carefully taken into account in terms of doses and distribution frequency to avoid excess N supply when plants are not ready to take this element up. The results obtained in this work could be implemented for advanced N fertilisation strategies, which are based on a pre-sowing Nmin soil analysis followed by Nmin monitoring in the root zone (Thompson et al., 2017). At the pre-sowing stage, if the soil Nmin is lower than Nopt, a base fertilisation is necessary. After sowing, topdressing fertilisations will then be necessary only if the level of Nmin in the root zone drops below Nopt. Nowadays, soil Nmin can be easily monitored at farm level by using quick tests that are faster, simpler, and cheaper than the conventional laboratory analyses (Hartz et al., 2000; Maggini et al., 2010; Incrocci et al., 2017). For example, this approach has been validated for the fertilisation plan of lettuce and celery in California (Hartz et al., 2000), and for cabbage in North America (Heckman et al., 2002) or in The Netherlands (Everaarts and de Moel, 1998). Furthermore, the model calibrated in the present work (Eq. 5) can be implemented in decision support systems for the precise nutrient management of spinach. In addition to the estimation of Nopt, the implementation of Eq. 5 in decision support systems can be useful to simulate spinach growth, as a function of Nmin in the root zone, and eventually calculate the dose and distribution fre-

quency of N fertilisers based on real plant needs. The relationship between NNI and SPAD index provides additional information for the precise management of N nutrition in spinach using optical sensors, as previously reported for other vegetable crops (Padilla et al., 2015; Thompson et al., 2017; Incrocci et al., 2017). Spinach for fresh market is harvested at early growth stage. We observed that, within this growing period, N tissue concentration did not vary significantly; it was therefore possible to find a significant correlation between SPAD index and NNI using data averaged over the whole cultivation cycle. On the other hand, much higher variability was observed for other vegetable crops in different growing periods (Padilla et al., 2014, 2015). As observed in the present work, SPAD values lower than 64.0 would imply N deficiency with reduced Y for fresh-market spinach. Lower values were observed for open-field processing (Canali et al., 2014) and greenhouse-grown spinach (Liu et al., 2006; Muchecheti et al., 2016). However, the threshold values reported in Figure 7 (in the range of 64.0-68.5) were assessed in this work for the first time for NNI-based quick N monitoring in fresh-market spinach grown under open-field Mediterranean climate conditions. The combination of soil analyses and optical leaf sensors, like the SPAD used in this study, indeed appears of great interest for the optimized management of N fertilisation in vegetable crops (Incrocci et al., 2017).

Conclusions and remarks

Meaningful relationships between crop yield or quality and mineral nitrogen availability in the root zone are reported in this work for fresh-market spinach. A linear-plateau model significantly represented the yield response to N concentration in the root zone. The adopted model was successful in determining an optimal threshold value (i.e., Nopt) of 23.44 mg kg–1 DW (140.0 kg ha–1 in our experimental conditions) to be maintained in the root zone for efficient N fertilisation plans of this crop. Plant tissue analyses supported the hypothesis that there is no reason to exceed Nopt in the root zone since above this threshold most of plant tissue characteristics tend to be unaffected. In contrast, plants grown under suboptimal levels of N in the root zone (i.e., below Nopt) show a reduced yield and tissue content of mineral nutrients and SPAD index, which imply that Nopt must be maintained in the root zone to obtain high yield and quality. Maintaining the suggested conditions in the root zone may therefore significantly improve the environmental and economic sustainability of the crop. The use of optical sensors (e.g., the SPAD by Minolta used in the present work) can be helpful for growers to check quickly the N nutritional status of the crop; SPAD index higher than 64.0 would ensure optimal N nutrition in fresh-market spinach. The combined monitoring of the root zone and crop canopy therefore appears a strategy worth exploring for the correct management of N fertilisers in this crop. The results reported in this paper can be implemented in protocols, algorithms and decision support systems for the optimized N fertilisation of fresh-market spinach.

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Article Abbreviations Symbol CV DW E FW GDD LAI N0

nLeaves N–opt N+opt Nmin Nopt






Coefficient of variation Dry weight Experiment Fresh weight Growing degree days or thermal time Leaf area index Soil mineral nitrogen concentration below which yield is zero mg kg–1, kg ha–1 Number of true leaves Pool of treatments below Nopt Pool of treatments above Nopt Total mineral nitrogen in the root zone

Optimal nitrogen concentration to be maintained in the root zone for spinach Nitrogen nutrition index Photothermal units Global radiation Standard deviation Standard error Air temperature Base air temperature Crop yield Photothermal use efficiency or potential yield Maximum photothermal use efficiency or potential yield max YPTU/YPTU


Units t ha–1

t ha–1 °C n mg kg–1 DW or kg ha–1

mg kg–1, kg ha–1

MJ m–2 °C–1 MJ m2 day–1

°C °C t ha–1 t MJ–1 m–2 °C–1 t MJ–1 m–2 °C–1

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Article Massa D, Incrocci L, Pardossi A, Delli Paoli P, Battilani A, 2013. Application of a decision support system for increasing economic and environmental sustainability of processing tomato cultivated in Mediterranean climate. Acta Hortic. 971:51-8. Milne AE, Webster R, Ginsburg D, Kindred D, 2012. Spatial multivariate classification of an arable field into compact management zones based on past crop yields. Comput. Electron. Agric. 80:17-30. Muchecheti F, Madakadze C, Soundy P, 2016. Leaf chlorophyll readings as an indicator of nitrogen status and yield of spinach (Spinacia oleracea L.) grown in soils amended with Luecaena leucocephala prunings. J. Plant Nutr. 39: 539-61. Noonan SC, 1999. Oxalate content of foods and its effect on humans. Asia Pac. J. Clin. Nutr. 8:64-74. Olsen SR, Sommers EL, 1982. Phosphorus. In: A.L. Page (ed.) Methods of Soil Analysis. Madison, Wisconsin, USA, pp 403-30. Padilla FM, Peña-Fleitas MT, Gallardo M, Thompson RB, 2015. Threshold values of canopy reflectance indices and chlorophyll meter readings for optimal nitrogen nutrition of tomato. Ann. Appl. Biol. 166: 271-85. Padilla FM, Teresa Peña-Fleitas M, Gallardo M, Thompson RB, 2014. Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon. Eur. J. Agr. 58:39-52. Peng Y, Yu P, Li X, Li C, 2013. Determination of the critical soil mineral nitrogen concentration for maximizing maize grain yield. Plant Soil 372:41-51. Reid JB, 2002. Yield response to nutrient supply across a wide range of conditions. 1. Model derivation. Field Crops Res. 77:161-71. Robertson GP, Vitousek PM, 2009. Nitrogen in agriculture: Balancing the cost of an essential resource. Ann. Rev. Environ. Res. 34:97-125. Rodriguez-Hidalgo S, Artes-Hernandez F, Gomez PA, Fernandez JA, Artes F, 2010. Quality of fresh-cut baby spinach grown under a floating trays system as affected by nitrogen fertilisation and innovative packaging treatments. J. Sci. Food Agric. 90:1089-97. Santamaria P, Elia A, Serio F, Todaro E, 1999. A survey of nitrate and oxalate content in fresh vegetables. J. Sci. Food Agric. 79:1882-8. Schepers JS, Francis DD, Vigil M, Below FE, 1992. Comparison of corn leaf nitrogen concentration and chlorophyll meter readings. Commun. Soil Sci. Plant Anal. 23:2173-87. Schroder JJ, Neeteson JJ, Oenema O, Struik PC, 2000. Does the crop

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Italian Journal of Agronomy 2018; volume 13:1124

Greenhouse gas and ammonia emissions from soil: The effect of organic matter and fertilisation method Leonardo Verdi,1 Marco Mancini,1 Mirjana Ljubojevic,2 Simone Orlandini,1 Anna Dalla Marta1 1Department

of Agrifood Production and Environmental Sciences, University of Florence, Italy; for Fruit Growing, Viticulture, Horticulture and Landscape Architecture, Faculty of Agriculture, Novi Sad, Serbia



Greenhouse gas (GHGs) emissions into the atmosphere derived from the use of fertilisers is a serious issue for the sustainability of agricultural systems, also considering that the growing global demand for food requires an increasingly productive agriculture. Emissions dynamics are very variable and are determined by many factors and their reciprocal interactions. Among driving factors, soil type (mineral, organic and microbiological composition), fertilisation method, climate, and the cropping system. In the present experiment, the combined effect of soil organic matter (SOM) and fertilisation method on the emissions of GHGs and Correspondence: Leonardo Verdi, Department of Agrifood Production and Environmental Sciences (DISPAA), University of Florence, Piazzale delle Cascine 18, 50144 Firenze, Italy. Tel.: + E-mail: leonardo.verdi@unifi.it Key words: Agriculture; carbon; nitrogen; compost; digestate; static chamber.

Acknowledgements: the author thank Department of Agrifood Production and Environmental Sciences (DISPAA) of University of Florence for the financial support; the Department for Fruit Growing, Viticulture, Horticulture and Landscape Architecture, Faculty of Agriculture of Novi Sad for technical support on the production of this manuscript. Moreover, the authors thank Azienda Agricola Marchese Deâ&#x20AC;&#x2122; Frescobaldi, Fattoria di Corte and Alia Servizi Ambientali Spa for kindly providing digestate and compost; and Roberto Vivoli from DISPAA for his assistance in the field. This article is supported by the H2020-TWINN-2015 SERBIA FOR EXCELL project. This project has received founding from the European Unionâ&#x20AC;&#x2122;s Horizon 2020 research and innovation program under grant agreement No. 691998.

Received for publication: 9 October 2017. Revision received: 17 January 2018. Accepted for publication: 22 January 2018.

ŠCopyright L. Verdi et al., 2018 Licensee PAGEPress, Italy Italian Journal of Agronomy 2018; 13:1124 doi:10.4081/ija.2018.1124

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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ammonia (NH3) was investigated. Liquid fraction of digestate from pig slurries, compost from organic fraction of municipal solid wastes, and urea were applied on bare soil with two levels of organic matter (OM1: 1.3% and OM2: 4.3%). Emissions were directly monitored by a static chamber system and a portable gas analyser. Results show that soil organic matter as well as the composition of the fertilisers affect greenhouse gasses emissions. Emissions of methane (CH4) produced by digestate and compost during experimental period were higher in correspondence of lower organic matter content (0.58-0.49 kg CH4 C/ha/day and 0.37-0.32 kg CH4 C/ha/day for digestate and compost respectively), contrary to what was observed for urea. For all fertilisers, carbon dioxide (CO2) and nitrous oxide (N2O) emissions were higher in correspondence of higher organic matter level. In particular, CO2 emissions were 11.05%, 67.48% and 82.84% higher in OM2 than OM1 for digestate, urea and compost respectively. Likewise, N2O emissions were 87.45%, 68.97% and 92.11% higher in OM2 than OM1 for digestate, urea and compost respectively. The obtained results show that the content of organic matter in soils plays a key role on the emissions of GHGs, generally enhancing the levels of gas emissions.


Several strategies were developed and proposed in the last decades to reduce the environmental impacts from agriculture. In particular, fertilisation is one of the most studied practices due to its detrimental effects on the environment, such as groundwater pollution, eutrophication and greenhouse gasses (GHGs) emissions. An alternative to chemical fertilisers is the use of recycled organic waste materials, as slurries and manure, characterised by low environmental impact and satisfactory crop yields (Alburquerque et al., 2012; Walsh et al., 2012). In addition to slurry, organic wastes from household and food processing industries are increasingly used as fertilisers in agricultural systems (Alluvione et al., 2010). Of increasing relevance in this context is the combined anaerobic fermentation of organic wastes with slurry in biogas (Wulf et al., 2002) and compost plants. On the other hand, the inputs of organic matter (OM) into the soil play a key role in the productivity of arable land by providing nutrients, through decomposition, and by maintaining soil fertility through OM turnover (Palm et al., 2001). Researchers (Miller and Wali, 1995) have increasingly emphasised the benefits of a balanced fertilisation, by using organic amendment (e.g., crop residues, manure, compost) for enhancing or maintaining soil OM level in soils. However, the efficient and appropriate use of organic fertilisers coming from organic wastes requires more in-depth

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Article knowledge both in terms of quality and fertiliser value (Rowell et al., 2001) aiming to support crop production and protect the environment while saving the soil resource (Mamo et al., 1999). Moreover, a deep knowledge is also required for managing organic fertilisers. Digestate management plays an important role on the real GHGs impact reduction. Due to its composition, rich in easily available nitrogen (N) for plants and organic carbon (C) (Alburquerque et al., 2012), digestate can increase emissions of ammonia (NH3) and GHGs such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O). An excessive application of digestate to agricultural soil without taking into account strategy to minimise losses through emissions can represent a point of weakness of the system. Within this context, the definition of appropriate management techniques represents one of the best opportunities for GHGs mitigation (Pezzolla et al., 2012). Bouwman et al. (2010) suggested that the recycling of N in animal manure, human excreta and compost to reduce inorganic fertiliser decreased N2O emissions from agricultural ecosystems. In a Spanish typic xerofluent with a sandy loam texture, LopezFernandez et al. (2007) demonstrated that organic fertilisers reduced N2O emissions by 74% (compost) to 27% (pig slurry) in comparison with urea. This reduction was due to the consumption of N2O by denitrifying bacteria during the irrigation period, which was driven by the addition of labile organic C (Vallejo et al., 2006). In contrast, Hayakawa et al. (2009) observed that adding poultry manure and especially pelleted poultry manure to an andisol increased N2O emissions by approximately 2 and 7 times, respectively, but reduced NO emissions by 49% and 56%, respectively, compared with inorganic fertiliser. These inconsistent results and reports in the literature may reflect differences in manure composition, C:N ratios, incorporation method and depth into the soil, and the effect of their interaction with soil properties, such as soil organic carbon (SOC) and texture, on N2O production under different environmental, soil moisture and temperature conditions (Huang et al., 2004; van Groenigen et al., 2004; Stehfest and Bouwman, 2006). For a better understanding of the emission dynamics from agricultural lands, particular attention has to be addressed to the system in absence of crops, which of course affect N and C cycles through uptake and assimilation processes. As affirmed by several authors, nowadays available data are scarce and referred only to specific areas and crops (Le Mer and Roger, 2001; Oertel et al., 2016). Authors affirm there is an inadequate data availability in Mediterranean area, and bare soil in general, with a strong bias towards temperate climate regions. Le Mer and Roger (2001) observed that available data on CH4 emissions are mainly focused on wetlands that represent the main source of CH4 from soils. In this way, upland CH4 emission dynamics are less explored. Despite the fact that N2O emissions are widely explored, contradictory results are observed regarding the effect of soil organic matter on N2O emissions. Velthof et al. (2003) observed that an addiction of organic c on arable soils encourage N2O emissions through denitrification. However, authors recommend further investigation on the interaction between manure/fertiliser composition and soil characteristic and utilisation. Instead, Oertel et al. (2016) reported a different behavior and described how the addiction of OM into the arable soils decrease N2O emissions. Considering the often discordant results, but also the great variability of soil and fertiliser compositions, and the influence of local climate factors (temperature, rainfall, wind, etc.), GHGs emissions dynamics need more indepth specific investigations. Moreover, as affirmed by Minoli et al. (2015), also NH3 emission dynamics need a deep-in-knowledge

assessment mainly due, especially for Italy, to inconsistencies in the measurement methods. The aim of this research is to study the emissions (GHGs and NH3) of liquid fraction of digestate and compost after incorporation into bare soil, and to investigate the effect of organic matter in emission dynamics.

Materials and methods

Experimental field was located at the ITAGR (Istituto Tecnico Agrario Statale) via delle Cascine, Firenze (43°47’02.3”N 11°13’13.4”E), Italy. The experiment was conducted on 24 pots (9.5 L volume) 24 cm high and diameter of 24 cm, placed in the open field and exposed to the environmental conditions. Each pot was filled with 8 kg of a silty-clay soil (24% clay, 31% silt and 45% sand) from experimental fields of Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria - Centro di ricerca per l’agrobiologia e la pedologia (CREA-ABP) located in Scarperia, Firenze (43°58’56” N, 11°20’53” E). The experiment was set on bare soil in order to investigate the effect of the different fertilisers excluding any possible interference of the crop. A layer of 30 cm of soil was taken from the experimental site including top and sub soil layer, and mixed before filling the pots in order to homogenise it. Soil sample was analysed in laboratory for elemental characterisation (Table 1). Experimental design consisted of two contrasting levels of soil organic matter - OM1 1.3% (that was the original OM content in the soil) and OM2 4.3% - with four treatments. Enrichment of OM into the soil was performed by adding 320 g pot–1 of commercial manure, GoldenAgro Ecolife, (from poultry, piggery and horse manure) with 25% of organic C content and 2% of total N, which did not significantly affected soil mineral composition. Treatments included two types of organic fertilisers (liquid fraction of digestate from pig slurries and compost from organic fraction of municipal solid waste) as well as one organo-mineral fertiliser (urea), with the non-fertilised pots as control treatment. Replicates were carried out in a randomised block design. The digestate was produced by Fattoria di Corte Marchesi De’ Frescobaldi farm (Florence, Italy, 43°58’29” N, 11°23’21” E), while the compost derived from composting plant of Alia Servizi Ambientali Spa (Florence, Italy, 43°55’580.95” N, 11°21’00.09” E). The amount of each fertiliser varied according to its N content (Table 2) and was calculated on the base of a pre-defined quantity of 150 kg N/ha. Fertilisers were incorporated into the soil by manually replacing injection, for digestate and mechanical harrowing incorporation for compost and urea. Immediately after fertilisation, the anchors were placed into the soil (10 cm depth) and the cham-

Table 1. Soil characterisation.

Texture Silt Clay Sand N total P total K total pH

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% % % % % %

31 24 45 0.14 0.07 0.23 8.06

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Article bers were connected. Emission measurements were conducted three times in the first week after fertilisation (0 h, 48 h and 96 h) and once a week in the following three weeks to investigate the emission trend (26 days of measuring period). In accordance to Parkin and Venterea (2010), measurements were performed in mid-morning - early evening (times more closely corresponding to the daily average temperatures) to account diurnal variability. Experimental pots remained opened between successive measurements to enable volatilisation, as these conditions would be the closest to the ones occurring naturally. CO2, CH4, N2O and NH3 emission rates were measured by means of a static chambers system (Parkin and Ventera, 2010), equipped by two thermocouples per chamber, and a portable gas analyser XCGM 400 (Madur) that use nondispersive infrared sensors (NDIR) technology for CO2, CH4 and N2O analysis and electrochemical technology for NH3. Chambers are composed of two parts: the lid of the chamber (cylinder of 20 cm of diameter and 25 cm high) and the anchor system (cylinder of 20 cm diameter and 15 cm high). Samplings were performed by holding a needle, connected to XCGM 400, inside the chamber for 1-min recording gas accumulation at time 0 (immediately after chamber closing) and at time 1 (after 1 h). Gas fluxes were calculated starting from the gas concentration into the chamber, chamber dimensions (area and volume), closing time and molecular weight of each gas. As temperature had a similar trend inside each chamber (data not shown), the whole experiment was assumed to be at standard temperature and pressure (STP) conditions and the molar volume of the air is assumed as 22.4 L. An automatic meteorological station placed 20 meters far from the experimental field continuously monitored air temperature, atmospheric pressure and precipitations (Figure 1). However, during the experiment any precipitation were observed. In the second and the third day after fertilisation, two hours prior to the gas measurements 10 mm of water were added to each pot for accelerating the beginning of the emissions process. The observed data were statistically processed using STATISTICA 13.0 (StatSoft, DELL, USA). In order to test the differences of measured (calculated) parameters between the samples Duncan’s multiple range tests with the confidence of P≤0.05 was performed.


Carbon dioxide

Observed data from 26-measurement days showed that CO2 was the most emitted gas from all fertilisers, although a high variability in the amount of emissions was observed (Table 3). The highest rate of CO2 emissions was produced by digestate. In particular, emissions were more than ten times higher than the other treatments in OM1 (23.24 Kg CO2-C ha–1 day–1), and more than three-to-four times than other treatments in OM2 (26.14 Kg CO2-C ha–1 day–1). As for digestate, emissions from other fertilisers were positively affected by the increase of OM into the soil. In all treatments, emissions were higher compared to control with the exception of compost in OM1 that produced less CO2 than control. Emissions trend show a specific behavior for each treatments (Figure 2). Urea and compost (and control) emit 16-30 Kg CO2C/ha/day and, except for urea in OM2, emissions increased until the third-fourth day and then decreased following a similar trend. In OM2, urea produced the highest amount of emissions in the first days and then emissions decrease regularly. Digestate showed the highest daily emission of 327 (OM2) and 259 (OM1) of Kg CO2C ha–1 day–1. At the end of the measurement period, CO2 emissions were still observed.

Table 2. Elemental characterisation of tested fertilisers. Urea



46 -

0.319 0.284 0.035 1.84 6.94

2.27 0.15 0.0013 0.34 0.97

N content total% N-NH4+% N-NO3–% P content total% K content total%

Figure 1. Temperature (°C) and atmospheric pressure trend (hPa).

Table 3. Cumulative emission fluxes on 26-days measuring period for each fertiliser and OM rate.

No-fertiliser Digestate Urea Compost

kg CO2-C ha–1 OM1 OM2

kg CH4-C ha–1 OM1 OM2

kg N2O-N ha–1 OM1


kg NH3-N ha–1 OM1 OM2











67.04f 29.22h

206.67c 169.35d

8.95d 9.62cd

11.17bc 8.38d

0.09c 0.03c

0.31bc 7.65a 0.29bc 0.38bc

0.00e 0.61b 0.09de 0.26cde

Values marked with the same letter for each gas do not differ significantly according to Duncan’s multiple range tests.

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0.06de 0.59b 1.15a 0.54bc

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Article Methane

In contrast with previous results, digestate and compost produced more CH4 emissions in correspondence of the lower OM content of soil. If for digestate differences are significant in compost they are negligible (Table 3). CH4 emissions from urea were still higher in OM2 than in OM1. For all fertilisers, emissions decreased immediately after spreading; at day 5, an increase in the emissions from urea in OM2 and from digestate, and compost, in OM1 were observed (Figure 3). As for CO2, at the end of measurement period CH4 emissions were still occurring.

Nitrous oxide

In accordance to CO2 fluxes, N2O emissions were positively correlated to OM content of soil. However, significant differences were observed only for digestate that produced roughly seven times more N2O in OM2 than in OM1 (Table 3). For all treatments N2O was produced a few days after fertilisers spreading in correspondence of irrigation. A peak of emissions in the third day was observed; then emissions decreased regularly until complete depletion in the first week, for urea, and in the second week for digestate and compost (Figure 4).

Figure 2. Daily CO2 emission trend (parts per million) on 26-day measuring period for control (A), digestate (B), urea (C) and compost (D) at OM1 (▲) and OM2 (●).


Any influence of OM on NH3 emissions from bare soil was observed (Table 3). All fertilisers produced a similar amount of NH3 emissions. The only exception was represented by urea in OM2 that showed a higher production of NH3. However, the main part of the emissions were produced by urea in OM2 during the first day after fertilisation. For all fertilisers emissions occurred only during the first week, with complete emission depletion on the fifth day. Urea in OM2 and compost in both OM levels had the highest emission rate on the first day and a regular decrease in the following days. Urea on OM2 and digestate in both OM levels had a peak of emissions on the third day with a consequentially complete depletion on the fifth day, as other treatments (Figure 5).


In this experiment, GHGs and NH3 emissions were measured in absence of crop, so that no C and N removal from plant uptake occurred and soil nutrients content was assumed constant during the measurement period. This may have caused higher emission

Figure 3. Daily CH4 emission trend (parts per million) on 26-day measuring period for control (A), digestate (B), urea (C) and compost (D) at OM1 (▲) and OM2 (●).

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CO2 is produced in soil as result of decomposition of organic material by microorganisms and root respiration (Schlesinger and Andrews, 2000). In accordance to that, results show that an enrichment in soil OM content positively affects CO2 emissions. As affirmed by several authors, CO2 emissions dynamics from agricultural soil are affected by a wide range of factors (Six et al., 1999; La Scala et al., 2000; Paustian et al., 2000). In this respect, OM represents one of the main ones due to its influence on soil res-

piration. A higher soil OM is able to increased soil respiration and consequently CO2 emissions, as observed in the experiment. Digestate produced higher emissions compared to urea. In particular, this is due to digestate composition, rich in water, which allows the infiltration into the soil. An enrichment of water content of soil combined to the mild air temperatures occurred probably encouraged the proliferation of soil microorganisms and consequentially soil respiration. However, as observed by Maucieri et al. (2016), CO2 emissions immediately decreased after fertiliser spreading in both OM levels and differences between OM1 and OM2 were not statistically significant. Urea produced a higher level of CO2 compared to compost, and the role of OM was evident. In fact, cumulative CO2 emissions in OM2 were more than 3 times higher than in OM1. This effect was also enhanced by irrigation that ensured hydrolysis of urea with a consequent production of CO2. As observed by Schlesinger and Andrews (2000) CO2 emissions are related to soil respiration following organic residues degradation. In agricultural soils the presence of crops and crop residues continuously provide organic materials for degradation. These observations are in accordance to ours were CO2 emissions were still occurring, in all treatments, at the end of measuring period.

Figure 4. Daily N2O emission trend (parts per million) on 26-day measuring period for control (A), digestate (B), urea (C) and compost (D) at OM1 (▲) and OM2 (●).

Figure 5. Daily NH3 emission trend (parts per million) on 26-day measuring period for control (A), digestate (B), urea (C) and compost (D) at OM1 (▲) and OM2 (●).

compared to open field conditions. However, especially for compost, fertilisers are often applied several weeks before crop sowing. In this period, between soil fertilisation and the presence of the crop in the field, C and N mineralisation and nitrification, with consequent emissions, may occur. In this context, a careful evaluation of most appropriate agronomic strategies to mitigate the risk of emissions is needed. In addition, soil temperature into the plot might be higher than in open field conditions, thus causing higher emissions. Nevertheless, the relative difference among treatments is not affected. Further studies on emission dynamics from open field bare soil are needed for a more in depth understanding of the process.

Carbon dioxide emissions

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Article Methane emissions

Results obtained from manures (digestate and compost) showed that CH4 had an opposite trend compared the other gasses monitored. In particular, digestate and compost produced more emissions in OM1 than in OM2. As described by Le Mer and Roger (2001) CH4 emissions from soil are again affected by many factors and a negative correlation between CH4 emissions and C/N ratio was reported. An enrichment of available C stimulates the population soil microorganisms that use a great part of C for their metabolism with a reduction of available C for CH4 production (Bernet et al., 2000; Norberg et al., 2016). In this respect, the composition of manure used to obtain the two levels of OM, which represent the 25% of total organic C, partially explain the behavior of CH4 emissions from organic fertilisers. In addition, the composition of organic fertilisers, rich in total organic C (34.5% and 25.6% for digestate and compost, respectively), may have reduced CH4 emissions. Moreover, an addiction of liquid (digestate) and fine milled (compost) fertilisers to the soil may had created compaction and so anaerobic conditions that modified the balance between denitrifying and methanogenic bacteria, in favor of the first ones (Saggar et al., 2004; Bunemann et al., 2006). In the case of urea, that does not contain organic C, the positive correlation between OM level and CH4 emissions was confirmed.

Nitrous oxide emissions

Results obtained demonstrated that N2O emissions are positively affected by the OM content of soil. For all tested fertilisers N2O emissions in OM2 were higher than in OM1. In particular, digestate produced the highest emissions and this was due to its high water content and irrigation that determines anaerobic conditions with consequent higher N2O losses compared to the other fertilisers (Wulf et al., 2002). Moreover, the higher amount of organic C available into the soil in OM2 probably encouraged denitrification activity and N degradation (Velthof et al., 2003). The high rate of readily available N compounds of digestate and the mild temperature occurred during the experiment (average of 28.4°) enhanced N losses in the first two weeks after fertilisation. On the other hand, compost emitted a N2O rate comparable with the control, probably due to its low water content and its stable chemical composition. This result, in fact, is in accordance with the findings of Dalal et al. (2010), confirming that the application of compost can be considered an efficient strategy to reduce N2O emissions. Moreover, differences on emissions between the two fertilisers are in accordance to Aguilera et al. (2013) that found more N2O emissions from liquid than solid organic fertilisers. Finally, concerning urea, its low water content reduces the risk of anaerobic conditions at soil level and the consequent N2O emissions that are comparable with those of compost. Further, during hydrolysis the majority of N contained in urea is transformed into NH3 with a reduction of N available for denitrification.

Ammonia emissions

NH3 emissions were nearly five times higher in OM2 than OM1 treated with urea. Again, this confirms that higher organic C content into the soil modifies the C/N ratio and encourages bacteria activity with greater degradation of N and NH3 losses. Moreover, as observed by Rochette et al. (2013), the increase of soil pH caused by urea hydrolysis encouraged NH3 volatilisation losses. Further, NH3 emissions were also favored by irrigation. On the other hand, contribution of manure, used for soil OM enrichment to NH3 emissions, can be consider negligible due to its neutral (7) pH.

Digestate and compost are an exception: digestate showed the highest rate of NH3 emissions of them. However, no differences between emissions in the two OM levels were observed. As on digestate, also on compost no significant differences were observed between the OM levels. This suggests that OM content of soil does not affect NH3 volatilisation dynamics. Moreover, the neutral pH of digestate (7.7) and compost (6.8) had reduced NH3 volatilisation losses compared to urea.


This experiment was performed to evaluate the effect of soil organic matter on GHGs emissions that occur from soil after fertilisation with different fertilisers. A wide range of factors affects emission dynamics into the soil, however, organic matter is one of them and plays a key role, generally enhancing the levels of GHGs emissions. In addition, fertiliser spreading emphasises emissions dynamics from soil. In this regard, compost represents an alternative to mineral fertilisers for GHGs and NH3 mitigation. In particular, compared to urea, reduced CH4 and N2O, and comparable CO2 and NH3 make compost an interesting strategy for sustainable fertilisation management. However, further investigations in open field are needed to exclude the influence of pot. Likewise, CH4 emission dynamics from digestate and compost require additional studies that consider soil microorganisms population.


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Italian Journal of Agronomy - volume 13, issue 3, 2018  

Italian Journal of Agronomy - volume 13, issue 3, 2018  


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