Jeffery biochar cropproduction ma 2011

Agriculture, Ecosystems and Environment 144 (2011) 175–187

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Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

Review

A quantitative review of the effects of biochar application to soils on crop productivity using meta-analysis S. Jeffery a,∗ , F.G.A. Verheijen a,d , M. van der Velde a,b , A.C. Bastos c a

European Commission, Joint Research Centre. Land Management & Natural Hazards Unit. Institute for Environment & Sustainability (IES), Ispra (VA), Italy International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria c Department of Biology & CESAM (Centre for Environmental and Marine Studies), University of Aveiro, 3810-193 Aveiro, Portugal d Department of Environment and Planning & CESAM (Centre for Environmental and Marine Studies), University of Aveiro, 3810-193 Aveiro, Portugal b

a r t i c l e

i n f o

Article history: Received 20 December 2010 Received in revised form 24 August 2011 Accepted 24 August 2011 Keywords: Biochar Soil Crop productivity Meta-analysis Effect size Crop yield

a b s t r a c t Increased crop yield is a commonly reported benefit of adding biochar to soils. However, experimental results are variable and dependent on the experimental set-up, soil properties and conditions, while causative mechanisms are yet to be fully elucidated. A statistical meta-analysis was undertaken with the aim of evaluating the relationship between biochar and crop productivity (either yield or above-ground biomass). Results showed an overall small, but statistically significant, benefit of biochar application to soils on crop productivity, with a grand mean increase of 10%. However, the mean results for each analysis performed within the meta-analysis covered a wide range (from −28% to 39%). The greatest (positive) effects with regard to soil analyses were seen in acidic (14%) and neutral pH soils (13%), and in soils with a coarse (10%) or medium texture (13%). This suggests that two of the main mechanisms for yield increase may be a liming effect and an improved water holding capacity of the soil, along with improved crop nutrient availability. The greatest positive result was seen in biochar applications at a rate of 100 t ha−1 (39%). Of the biochar feedstocks considered and in relation to crop productivity, poultry litter showed the strongest (significant) positive effect (28%), in contrast to biosolids, which were the only feedstock showing a statistically significant negative effect (−28%). However, many auxiliary data sets (i.e. information concerning co-variables) are incomplete and the full range of relevant soil types, as well as environmental and management conditions are yet to be investigated. Furthermore, only shortterm studies limited to periods of 1 to 2 years are currently available. This paper highlights the need for a strategic research effort, to allow elucidation of mechanisms, differentiated by environmental and management factors and to include studies over longer time frames. © 2011 Published by Elsevier B.V.

Contents 1.

2.

3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.1.

Comparisons using meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.2.

Data sources and treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.3.

Data groupings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.4.

Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.5.

Presentation of graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.1.

Application rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.2. pH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.3.

Soil texture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.4.

Biochar and fertilizer interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.5.

Crop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author. Tel.: +39 0332 783682; fax: +39 0332 786394.

E-mail address: simon.jeffery@jrc.ec.europa.eu (S. Jeffery).

0167-8809/$ – see front matter © 2011 Published by Elsevier B.V. doi:10.1016/j.agee.2011.08.015

176 177 177 177 177 177 179 179 179 179 180 180 181

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4.

5.

S. Jeffery et al. / Agriculture, Ecosystems and Environment 144 (2011) 175–187

3.6.

Biochar feedstock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.7.

Plant biomass, crop yield and trial type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.1.

Data and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.2.

Environmental and management representativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.3.

Auxiliary variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.4.

Reporting guidelines for ‘biochar-crop production’ experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.5.

Maintenance of this MA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Biochar is a predominantly stable, recalcitrant organic carbon (C) compound, created when biomass (feedstock) is heated to temperatures usually between 300 and 1000 ◦ C, under low (preferably zero) oxygen concentrations (Verheijen et al., 2010). Biochar application to soils is currently being considered as a means of mitigating climate change by sequestering C, while concurrently improving soil properties and functions. Comparing the incorporation of biochar versus that of ‘fresh’ crop residues into soils may provide an insight into the mechanism of C sequestration through application of biochar to soils. Carbon dioxide from the atmosphere is fixed in vegetation through photosynthesis. Biochar is subsequently created through pyrolysis of the plant material, thereby increasing its inherent recalcitrance with respect to the original biomass. The estimated C-residence time of biochar in soils is in the range of hundreds to thousands of years, while that of crop residue is in the range of decades (Lehmann et al., 2006). Consequently, incorporating biochar from such a feedstock into soils has the potential to reduce the CO2 release back to the atmosphere. It is posited that, if other greenhouse gas emissions from soils are not elevated as a consequence of biochar application, and if those emissions associated with production and transport of biochar and/or its feedstocks do not off-set the sequestered C, then the overall greenhouse effect will be abated (Roberts et al., 2010). The amount of feedstock required for conversion to biochar in order to achieve such a result, is critically dependent on the C retention (i.e. the ratio of the C in the biochar over the C in the initial dry biomass feedstock). Carbon retention of 49% has been reported for slow pyrolysis at atmospheric pressure, while higher C retention (100%) resulted in less stable biochar, with residence times of 4–29 years (Woolf et al., 2010).

Concomitant with carbon sequestration, biochar is intended to improve soil properties and functions relevant to agronomic and environmental performance (Lehmann and Joseph, 2009; Woolf et al., 2010). Hypothesised mechanisms for such a potential improvement are mainly enhanced water and nutrient retention (as well as improved soil structure and drainage). Furthermore, there is experimental evidence that soil microbial communities and their activity, which hold key roles in sustaining soil health and functioning, are directly affected by the addition of biochar to soils (Ogawa, 1994; Rondon et al., 2007; Warnock et al., 2007; Steiner et al., 2008). The full range of mechanisms and consequences behind these effects remain poorly elucidated. However, it is likely that changes in soil microbial activity, community structure and functional diversity could impact on crop productivity.

For example, it has been previously shown that biochar addition to soil increases N2 fixation by both free-living and symbiotic diazotrophs (Ogawa, 1994; Rondon et al., 2007). Whether this can be explained by an increase in diazotrophic biomass or enhanced metabolic activity is yet to be investigated. However, as N is often the limiting factor for crop productivity, particularly in agricultural

181 181 182 182 184 185 185 185 185 186 186

scenarios, improved N fixation by soil microorganisms seems likely to explain to some extent the increase in overall crop productivity.

There is a poor understanding of the general relationship between soil organic matter (SOM) and crop yield (e.g. Loveland and Webb, 2003), the same is currently true regarding the interaction between biochar and crop productivity. In experimental field trials, it is often difficult or impossible to fully account for or control all environmental variables in an experimental design, such as meteorological factors and their annual and inter-annual variability. This can lead to weaknesses in the data obtained from these experiments, and reduce confidence when extrapolating results and formulating predictions for other probable effects under other environmental conditions.

In recent years, various factors have highlighted biochar as a relevant topic for research, as well as for policy and the wider society.

In addition to mitigating climate change and offering the potential for organic waste disposal, helping to achieve food security is an important driver. The global human population is expected to increase to 9.2 billion by 2050 (U.N. Population Division, 2008).

Currently, more than 99% of food supplies (calories) for human consumption comes from the land (FAO, 2003) and this seems unlikely to decrease. Verification of the effects of biochar on crop yields would, therefore, demonstrate the potential for biochar to help or hinder global food security.

Before the large scale implementation of biochar can be contemplated seriously and developed into policy, a robust body of scientific evidence regarding its effects on soil properties, processes and functions is paramount. Some progress has been made in this direction (e.g. Lehmann and Joseph, 2009; Shackley and Sohi, 2010; Verheijen et al., 2010; Woolf et al., 2010), but substantially more work remains to be done. Experimental results, when available, are often inconsistent and largely dependent on experimental conditions and design, while causative mechanisms remain unclear (Atkinson et al., 2010). Increased crop production is the most commonly reported effect of biochar application to soils. Considering the strong and manifold drivers for biochar implementation, and the many sources of heterogeneity between experiments (different crops, feedstocks, soil types and climatic conditions, etc.), a sound quantitative meta-analysis (MA) of current results in the literature is pertinent, if not vital, to allow a clear picture to be drawn, as well as to highlight areas where further research must be targeted. Representativity and potential causative mechanisms are discussed, while recommendations for future research and reporting guidelines are put forward, including those with respect to the maintenance of this MA (as further reports become available).

The use of MA allows for increased objectivity of systematic reviews based on studies involving a range of soil properties, as well as environmental and management conditions. However, as stated by Bobko and Stone-Romero (1998), MA is an “imperfect procedure” owing to the necessary decisions regarding the manner in which data are handled. For a MA to be undertaken, it

S. Jeffery et al. / Agriculture, Ecosystems and Environment 144 (2011) 175–187

is necessary that each study must allow the comparison of an experimental treatment with a control, with the control being consistently defined across all studies used. These criteria were fulfilled using the methodology described below.

2. Methods 2.1. Comparisons using meta-analysis For this MA, the control was defined as being identical to the experimental treatment with regard to all variables apart from the addition of biochar. Therefore, data were extracted from treatments in each study, where a control with zero biochar input could be compared to an equivalent treatment with biochar, at either a single or multiple application rates, with all other factors unchanged.

The “effect size” was then calculated for a wide range of independent variables, such as biochar feedstock, the type of fertilizer, soil and crop used, as well as overall plant biomass versus crop yield.

2.2. Data sources and treatment For the MA, an extensive literature search was performed using Scopus with the search terms “biochar AND crop productivity OR crop production OR crop yield” shows 23 studies published before the cut off date of 1st March 2010. However, many of such studies were not found to include sufficient information regarding environmental parameters or variance in results, or were not relevant in the context of this paper. Searches were also performed using Science Direct and Google Scholar but no additional studies were found suitable for inclusion into the MA. From those 23 studies, 14 (+2 from grey literature) were considered relevant for inclusion in the MA. To maximise the number of studies, both pot and field experiments were recorded, providing the results were quantitative. Studies that did not report quantitative results where excluded from the MA. When no measures of variance were given, efforts were made to obtain these from the corresponding authors, which in most cases were successful. If not, those studies were also excluded from the analysis. Similarly, efforts were made to contact lead researchers on the topic of biochar for the inclusion of unpublished data into the MA, in an attempt to overcome the problems of publication bias. Such efforts resulted in the inclusion of data from one unpublished study (Wisnubroto et al., 2010), as well as from one Master’s thesis (Nehls, 2002), meaning a total of 16 studies and that of 177 “treatments” were used: Chidumayo (1994); Ishii and Kadoya (1994); Nehls (2002); Lehmann et al.

(2003); Yamato et al. (2006); Blackwell et al. (2007); Chan et al.

(2007); Steiner et al. (2007); Chan et al. (2008); Kimetu et al.

(2008); van Zwieten et al. (2009); Asai et al. (2009); Gaskin et al.

(2010); Hossain et al. (2010); Major et al. (2010); Wisnubroto et al.

(2010).

Relevant data were extracted from each study regarding soil type (texture, pH, CEC), biochar feedstock and application rate, fertilizer type and application rate, crop type and the growing season (which in all cases was >2 years highlighting the current paucity of long time scale studies), and on whether above ground biomass was quantified, in addition to crop yield. In instances where such relevant information was omitted, the MA was undertaken using solely those categories for which data was available in all selected studies. As a result, it was not possible to include categories, such as that of the CEC, or to perform a regression MA. When data was only provided in graphic format, DataThief III (Tummers, 2006), was used to extract relevant data points. The selected data were inserted into Excel, with each row representing a ‘treatment’ and then exported to

177

MetaWin Version 2. The cut-off date for publications to be considered for inclusion in this MA was 1st March 2010. The database containing extracted data which was used for the MA will be publically available on the website of the European Soil Data Centre. (http://eusoils.jrc.ec.europa.eu/library/esdac/index.html). This database will be updated periodically as new data becomes available and included in up-coming MA .A concise summary of the studies that contributed to can be found in Table 1.

As with all MA, there is a possibility for publication bias to affect the results. This effect was initially reported by Rosenthal and Rosnow (1991) who suggested that published articles are likely to be drawn from the pool of statistically significant results, while studies showing no significant effects are often not considered for publication. Therefore, it is likely that the primary literature may provide a biased sample of all of the studies undertaken in a given field, reporting a higher proportion of statistically significant findings than actually exist. The inclusion in the MA of data from the grey literature is topic of scientific debate. For example, Cook et al.

(1993) found that 30% of editors surveyed would not publish a MA that included unpublished material. However, McAuley et al. (2000) reported that the use of grey literature is generally regarded as reducing bias and, therefore, preferable. For this MA, two studies from the grey literature were included, where sufficient information was available for confident assessment of the soundness of their experimental design.

2.3. Data groupings In some instances, data required pre-grouping before the MA could be conducted, aiming for maximal in-group homogenisation.

For example, with regard to soil pH, the use of ungrouped data would have lead to many exclusions due to insufficient treatments (i.e. <2) within each group. This is due to the continuous nature of the variable and the relatively high precision of its reporting (1 or 2 decimal places). Therefore, these data were formed into three categories ‘Very acidic’ pH < 5, ‘Acidic’ 5 < pH < 6 and ‘Neutral’ pH > 6, so that the groupings maintain critical information around the Al toxicity threshold Haynes and Mokolobate (2001).

Soil texture was grouped into three basic classes (fine, medium, coarse), because of the inconsistent reporting of soil texture in the literature (e.g. particle size distribution, soil taxonomical unit, qualitative description) using expert judgement, where required.

For example, heavily oxidised tropical soils often have a fine primary texture, while the secondary texture is coarse on account of sesquioxides, causing water-stable micro-aggregation (see e.g.

Pochet et al., 2007 and Barthès et al., 2008). In such instances, secondary texture, i.e. ‘effective texture’, was used and these soils were classified as ‘coarse’.

2.4. Meta-analysis An MA (Rosenberg et al., 2000) was conducted to quantify the effects of biochar addition to soil on crop productivity. For each study, the control mean and experimental means were recorded, or calculated where necessary. Standard deviation was used as a measure of variance, and included, when present, or calculated from the published measure of variance in each study.

Standardisation of the literature results was undertaken through calculation of the effect size. This allows quantitative statistical information to be pooled from, and robust statistical comparisons to be made between effects from a range of studies that reported results based on different experimental variables.

Firstly, the data was normalised using a square-root transformation. The effect size was then calculated in MetaWin Version 2 statistical software (Rosenberg et al., 2000) using the transformed

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S. Jeffery et al. / Agriculture, Ecosystems and Environment 144 (2011) 175–187

Table 1 Matrix showing which studies provided data for each categorical grouping on which the meta-analyses were run.

Grouping

Sub-heading

Number of studies

Authors

Soil texture

Coarse

7

Medium

8

Fine

1

Gaskin et al. (2010), Ishii and Kadoya (1994), Lehmann et al. (2003), Major et al. (2010), Nehls (2002), van Zwieten et al. (2009), Wisnubroto et al. (2010) Asai et al. (2009), Blackwell et al. (2007), Chan et al. (2008), Hossain et al. (2010), Kimetu et al.

(2008), van Zwieten et al. (2009), Yamato et al. (2006) Asai et al. (2009)

<5

7

5–6

8

>6

5

Tropical

7

Subtropical

7

Poultry litter Acacia bark Paper pulp and wood chips Wastewater sludge Green waste

1 1 1 1 2

Wood Peanut hull Pine chip Biosolids

8 1 1 1

1–39 t ha−1

12

40–79 t ha−1

5

>80 t ha−1

2

None

10

Inorganic

10

Organic Both

4 2

Asai et al. (2009), Chan et al. (2007), Chan et al. (2008), Chidumayo (1994), Gaskin et al. (2010), Ishii and Kadoya (1994), Nehls (2002), van Zwieten et al. (2009), Wisnubroto et al. (2010) Asai et al. (2009), Blackwell et al. (2007), Chan et al. (2007), Chan et al. (2008), Gaskin et al.

(2010), Kimetu et al. (2008), Steiner et al. (2007), van Zwieten et al. (2009), Wisnubroto et al.

(2010) Asai et al. (2009), Hossain et al. (2010), Lehmann et al. (2003), Nehls (2002) Asai et al. (2009), Major et al. (2010)

Crop type

Rice Wheat Radish Bauhinia trees Maize Tomato Satsuma mandarin trees Cowpea Ryegrass Soybean Sorgum

2 2 3 1 4 1 1 2 1 1 1

Asai et al. (2009), Nehls (2002) Blackwell et al. (2007), van Zwieten et al. (2009) Chan et al. (2007), Chan et al. (2008), van Zwieten et al. (2009) Chidumayo (1994) Gaskin et al. (2010), Kimetu et al. (2008), Major et al. (2010), Yamato et al. (2006) Hossain et al. (2010) Ishii and Kadoya (1994) Lehmann et al. (2003), Yamato et al. (2006) Wisnubroto et al. (2010) van Zwieten et al. (2009) Steiner et al. (2007)

Experiment type

Pot

7

Field

7

Chan et al. (2007), Chan et al. (2008), Hossain et al. (2010), Ishii and Kadoya (1994), Lehmann et al. (2003), van Zwieten et al. (2009), Wisnubroto et al. (2010) Asai et al. (2009), Blackwell et al. (2007), Chidumayo (1994), Gaskin et al. (2010), Kimetu et al.

(2008), Major et al. (2010), Yamato et al. (2006), Steiner et al. (2007), Nehls (2002)

pH class (<5, 5–6, >6)

Latitude

Feedstock

Biochar application rate

Fertilizer coaddition

Blackwell et al. (2007), Chan et al. (2007), Chan et al. (2008), Hossain et al. (2010), Major et al.

(2010), van Zwieten et al. (2009), Yamato et al. (2006) Asai et al. (2009), Blackwell et al. (2007), Chan et al. (2008), Chidumayo (1994), Gaskin et al.

(2010), Kimetu et al. (2008), Lehmann et al. (2003), Wisnubroto et al. (2010) Asai et al. (2009), Gaskin et al. (2010), Ishii and Kadoya (1994), Kimetu et al. (2008), van Zwieten et al. (2009) Asai et al. (2009), Chidumayo (1994), Kimetu et al. (2008), Lehmann et al. (2003), Major et al.

(2010), Steiner et al. (2007), Yamato et al. (2006) Blackwell et al. (2007), Chan et al. (2007), Gaskin et al. (2010), Hossain et al. (2010), Ishii and Kadoya (1994), Nehls (2002), van Zwieten et al. (2009) Chan et al. (2008) Yamato et al. (2006) van Zwieten et al. (2009) Hossain et al. (2010) Chan et al. (2007), Wisnubroto et al. (2010), Lehmann et al. (2003), Steiner et al. (2007), Chidumayo (1994), Nehls (2002), Kimetu et al. (2008), Blackwell et al. (2007) Asai et al. (2009), Major et al. (2010) Gaskin et al. (2010) Gaskin et al. (2010) Wisnubroto et al. (2010) Asai et al. (2009), Blackwell et al. (2007), Chan et al. (2007), Chan et al. (2008), Gaskin et al.

(2010), Hossain et al. (2010), Kimetu et al. (2008), Major et al. (2010), van Zwieten et al.

(2009), Wisnubroto et al. (2010), Yamato et al. (2006) Chan et al. (2007), Chan et al. (2008), Ishii and Kadoya (1994), Lehmann et al. (2003), Yamato et al. (2006) Chan et al. (2007), Lehmann et al. (2003)

data taken as the natural logarithm of the response ratio by using the following equation (Rosenberg et al., 2000):

ln R = ln

x¯ E x¯ C

where x¯ E : mean of experimental group; and x¯ C : mean of control group.

For calculation of grouped effect sizes, a categorical random effects model was used. Groups with fewer than two treatments were excluded from each analysis. Resampling tests were generated from 999 iterations. For each of the analyses, grouped by different categorical predictors, data were analysed using a

random effects model, except in instances where the estimated pooled variance was ≤0, in which case a fixed effects model was used.

To test the effects of publication bias (Rothstein et al., 2005) and the robustness of the MA, the Fail-safe N technique (Orwin, 1983; Rosenthal and Rosnow, 1991) was used. This involved computing the combined P value for all of the studies included, and calculating the number of additional studies showing no effect (i.e. average Z value of 0) that would be needed in order to change the P value from significant to nonsignificant at P = 0.05. Therefore, the robustness of the findings of the MA is directly correlated with the size of the Fail-safe N number.

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Fig. 1. A forest plot showing the mean change in crop productivity as a percentage of the control, for a range of different biochar application rates. Points show means of treatments, bars show 95% confidence intervals. Numbers to the right of bars show biochar application rates (t ha−1 ), while numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold), and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

All results are stated as being statistically significant if P ≤ 0.05.

2.5. Presentation of graphs All graphs show forest plots showing the effect size calculated from each group. Each point represents the mean effect size for each grouping, with the lines representing 95% confidence intervals (CIs). All graphs are formatted with the greatest mean effect shown at the top and the smallest or most negative mean effect size at the bottom. On the x-axis, the ‘effect size’ was exponentially transformed and multiplied by 100 to obtain the percentage change in crop productivity. Numbers to the right of each bar show the grouping upon which the mean and 95% CIs are based. The numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies (bold) and the number of ‘experimental treatments’ (italics) respectively, included in each grouping.

3. Results Rosenthal’s Fail Safe N for the following analyses fell between 1483 and 1612, depending on the number of studies that needed to be excluded from each analysis (due to a lack of data, or fewer than two treatments being available for a given category). This means that a minimum of 1483 studies with an average Z value of 0 would need to be included in the MA, for the P value indicating statistically significant effects of biochar application to soil (as shown by the “Grand Mean”), to be reduced to being not statistically significant at P = 0.05. It suggests that it is unlikely that publication bias exists in the literature to such an extent as to affect the overall statistical significance of these results.

3.1. Application rate Fig. 1 shows the effect of biochar addition to soil on crop productivity, grouped by application rate and reported as t ha−1 .

Unfortunately, biochar incorporation depth in the soil was insufficiently reported. The sample means indicate a small but positive effect on crop productivity, with a grand mean (being the mean of all effect sizes combined) of approximately 10%. However, there was no statistically significant difference (P > 0.05) between any of the application rates. Application rates of 10, 25, 50 and 100 t ha−1 were all found to significantly increase crop productivity when compared to controls, which received no biochar addition. However, other application rates within the range investigated, such as 40 and 65 t ha−1 , showed no statistically significant effect of biochar addition to soil on crop yield. Results demonstrated that while biochar addition to soil may increase crop productivity, there was no correlation between application rate and the effects on crop productivity (r2 = 0.1).

Data from within individual application rate treatments were highly variable. No single biochar application rate was found to have a statistically significant negative effect on the crops from the range of soils, feedstocks and application rates studied.

3.2. pH Fig. 2a shows the effects of biochar addition to soil on crop productivity, grouped by pH. A statistically significant (P < 0.05) increase in crop productivity occurred upon biochar addition to soil in both ‘Acidic’ and ‘Neutral’ soils. There was no statistically significant (P > 0.05) change in crop productivity upon biochar addition to soil in the ‘Very Acidic’ grouping, nor between the three pH groupings.

Fig. 2b shows the effects of biochar addition to soil on crop productivity, categorised by associated changes in soil pH. Studies that found biochar addition to soil to reduce (−0.5 to 0.0 grouping) and to increase (0.6–1.0 pH units) the pH, showed no statistically significant (P > 0.05) effect on crop productivity. However, the remaining groupings, which showed an increase in soil pH upon addition of biochar, showed a positive effect on crop productivity (P < 0.05),

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Fig. 2. (a) A forest plot showing the mean change in crop productivity as a percentage of the control for different pH ranges of soils. Points show means of treatments, bars show 95% confidence intervals. ‘Very acidic’ = pH < 5, ‘Acidic’ 5 ≥ pH ≤ 6 and ‘Neutral’ = pH > 6. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics). (b) A forest plot showing the mean changes in crop productivity as a percentage of the control, as influenced by changes in soil pH, upon addition of biochar. Points show means of treatments, bars show 95% confidence intervals. Numbers beside bars show pH groupings in 0.5 unit intervals. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

with a general positive trend for crop productivity to increase with increasing pH. 3.3. Soil texture Fig. 3 shows the effects of biochar addition to soil on crop productivity, grouped by ‘effective’ soil textural class (see Section 2.3). Significant (P < 0.05) increases in crop productivity occurred in soils of both medium and coarse textures. In contrast, no significant (P > 0.05) effects of biochar application on crop productivity were found in fine-textured soils. The variance in results from the fine textural classes was more than double that of either the medium or coarse textural classes. Grouping by ‘effective’ textural classes, showed no statistically significant (P > 0.05) negative effects of biochar application to soil on crop productivity.

3.4. Biochar and fertilizer interaction There was no statistically significant effect of biochar application to soil between groups as categorised by fertilizer addition (P > 0.05). The control, against which each group was measured to calculate the effect size, was the same as the experimental treatment, but without biochar addition. For example, the group ‘inorganic fertilizer’ shows the effect size between crop productivity for use of inorganic fertilizer alone (control), compared to the experimental treatment of inorganic fertilizer (at various application rates) applied with biochar. No significant difference was found in the effects of biochar on crop productivity whether inorganic, organic, both, or no fertilizer was used (P > 0.05; Fig. 4). However, statistically significant increases in crop productivity were seen when biochar was applied concurrently with inorganic fertilizer,

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Fig. 3. A forest plot showing the mean change in crop productivity as a percentage of the control in response to different rates of biochar application grouped by ‘effective’ soil textural class, as indicated beside bars. Points show means of treatments, bars show 95% confidence intervals. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

compared to applying inorganic fertilizer alone, as well as when biochar was applied to soil without any fertilizer addition (P < 0.05).

There was no statistically significant effect of concurrent application of biochar with organic fertilizer, compared to that of organic fertilizer alone or when biochar was applied concurrently with both inorganic and organic fertilizer (P > 0.05).

3.5. Crop Statistically significant increases in crop productivity were found to occur in both radishes and soybean upon addition of biochar to soil (P < 0.05), while the opposite was observed in ryegrass (P < 0.05). For the remaining crop types investigated, no statistically significant effects were seen on application of biochar to soil (P > 0.05). Levels of variance within treatments ranged considerably with crop type, with peanuts and satsumas showing particularly variable effects regarding yield.

3.6. Biochar feedstock Fig. 6 shows the results of crop productivity experiments using biochar from a range of feedstocks. Significant positive effects (P < 0.05) were found for the following feedstocks: wood, paper pulp, wood chips and poultry litter (both when pyrolysed at 450 ◦ C, and when pyrolysed at 550 ◦ C and activated). A significant negative effect on crop productivity (P < 0.05) was found for biosolids.

All other feedstocks investigated showed no statistically significant effects on crop productivity (P > 0.05).

3.7. Plant biomass, crop yield and trial type Fig. 7a and b show two forest plots with similar means and confidence intervals. Biochar application lead to a statistically significant positive effect on both biomass and yield (fruit or grain; P < 0.05).

The effect on biomass productivity showed a significant increase

Fig. 4. A forest plot showing the mean change in crop productivity as a percentage of the control resulting from the interaction of biochar and type of fertilizer. Note that while three of the groupings show statistically significant increases in the mean between experimental treatment (with biochar) versus control treatment (without biochar), there is no statistically significant difference between the groups. Points show means of treatments, bars show 95% confidence intervals. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

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Fig. 5. A forest plot showing the mean change in crop productivity as a percentage of the control for different crop types. Points show means of treatments, bars show 95% confidence intervals. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

(mean being approximately three times higher) when compared to crop yield (fruit or grain; Fig. 7a).

Biochar application in both pot and field trials also showed a statistically significant positive effect (P < 0.05). Furthermore, the mean increase in crop productivity for pot trials was approximately three times greater than that for field trials (Fig. 7b).

4. Discussion 4.1. Data and analysis A limited number of studies were available for inclusion in this MA, partly due to the relatively recent focus of research on the relationship between biochar and crop productivity. Some categories within the analysis contained as few as two experimental treatments (as defined above), which were combined for calculation of the effect size (and in some instances only one experimental treatment was available leading to their exclusion). Both the number of replicates (n) and the number of experimental treatments are reported for each effect size, to show the confidence associated with a result, as well as to identify where paucity of data exists; this is useful for guiding future research. No studies were found in the literature that had run for more than 2 years. More than 90% of the studies included in this analysis showed results over 1 growing season. Owing to possible changes upon ageing of biochar within the soil, this highlights the urgent need for long term studies on the impacts of biochar applications to soil on crop productivity.

The grand mean in all figures shows a statistically significant positive effect on crop production of approximately 10%, as a response to biochar application to soil. Variability in the grand mean can be seen between figures due to the exclusion of individual studies in some instances where insufficient data were available for a particular analysis.

Variance was heterogeneous between categories in most figures. This is to be expected as the analyses include data from a range of soil types, climatic conditions, experimental designs, biochar

application rates and crop types. However, in some instances, the high levels of variance (as shown by the 95% confidence interval bars), may be accounted for by the low number of studies included in some categories. Further work is necessary to investigate whether large variability is an inherent trait of the category reported, or an artefact of the paucity of data currently available in that given category.

Two figures (Fig. 2a and b) were included to provide information regarding the interaction of biochar and soil pH on crop productivity. This is because one of the hypothesised mechanisms by which biochar addition to soil affects crop productivity is through a liming effect, resulting in the increase of soil pH (van Zwieten et al., 2009).

This is possibly due to biochar raising the soil pH past the threshold of Al3+ toxicity (i.e. pH 4.8–5.0). However, analysis of the pH change around this threshold was inconclusive (data not shown).

Biochar addition to soil in the ‘Very Acidic’ category did not show a significant effect on crop productivity (Fig. 2a), which might be due to the liming effect of biochar addition to soil not being sufficient to raise the pH of the soil past any metal ion toxicity thresholds.

Varying levels of liming effect were seen from biochar application experiments, for which pre and post amendment soil pH was reported. However, a large amount of variability in the magnitude of any liming effect existed, independently of the feedstock used. For example, wood was used as a feedstock by Blackwell et al. (2007) who reported no change in pH in virtually all instances (starting soil pH of 5.53 and 4.8) and also by Chidumayo (1994) who reported an increase in soil pH of 5.5 to 7 upon application of biochar made from a wood feedstock. This highlights the need for accurate reporting of feedstock as different species of tree wood may lead to different levels of liming effect, a hypothesis that could not be tested using the data available in the literature, as many investigators have reported the umbrella term “wood”.

There was a general trend of biochar addition to soil leading to enhanced soil pH and a concurrent increase in crop productivity (Fig. 2b). This effect was not strictly linear, e.g. the effect size for the pH category 1.1–1.5 units, was lower than that of 0.6–1.0 units.

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Fig. 6. A forest plot showing the mean change in crop productivity as a percentage of the control upon application of biochar from a range of feedstocks to the soil. Points show means of treatments, bars show 95% confidence intervals. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

This may be due to the initial soil pH for different studies varying relative to the Al3+ toxicity threshold, although further research is needed to test this hypothesis. There was no correlation between biochar application rate and change in soil pH post amendment (data not shown). Further to Al3+ toxicity effects, increasing soil pH may also have an effect on nutrient availability by raising the CEC (particularly in the organic matter fraction) of soils. However, pre and post amendment CEC data was not reported consistently or different methodologies were used for its determination. As a result, it was not possible to further investigate through this MA the interactions with CEC. Regarding the concurrent application of fertilizer and biochar (Fig. 4), there was no statistically significant effect of biochar application to soil between groups (as grouped by fertilizer addition), regardless of whether fertiliser was applied concurrently, or whether organic or inorganic fertiliser was used. This is contrary to specific recommendations in the literature, advocating fertilizer addition in order to maximise the positive impacts of biochar application to soil (Yamato et al., 2006; Steiner et al., 2007; Asai et al., 2009). Care must be taken when interpreting Fig. 4. The effect sizes were based on the difference between ‘controls without biochar’ vs. ‘treatments with biochar’ and thus, the “None” treatment (i.e. no fertilizer application) represents the relative effect of biochar addition to soil alone. In the remaining groupings, the controls include the addition of fertiliser in the absence of biochar. In both instances where a significant effect was observed (‘Inorganic’ and ‘None’), biochar addition to soil enhanced crop productivity by approximately 10%. Whereas in the ‘Inorganic’ treatment, this was a 10% increase in addition to the fertilizer effects, in the ‘None’ treatment, it represented a 10% increase in response to the addition of biochar alone, compared to that in the absence of biochar. Chan et al. (2007) reported a lack of response upon addition of biochar alone, i.e. without concurrent N addition. Therefore, it seems likely that available N in soil was not the limiting factor to crop productivity, explained either by the quantity and quality of native SOM, or by previous applications of fertilizer and/or cropping with legumes.

On the other hand, the combined addition of biochar with organic fertiliser was found to have no statistically significant effect when compared to the application of organic fertiliser alone. This may be explained by the high levels of variance associated to the latter treatments.

The only statistically significant negative effects were found when ryegrass (Fig. 5) was grown in the presence of biochar derived from biosolids (Fig. 6). However, there seem to be no practical reasons why rye grass may perform differently to other grasses. It is important to note that because the only studies that used ryegrass also used biosolids as a feedstock (Wisnubroto et al., 2010), it is not currently possible to elucidate mechanisms or distinguish whether the negative effect occurred due to the crop type, feedstock, or a combination of the two. Further research is needed, therefore, to investigate whether ryegrass often, or always, responds negatively to interactions with biochar in the soil. Alternatively, unreported associated factors may also explain the negative effect, either in combination with the previous or on their own (e.g. by introducing heavy metals and/or other contaminants in the biochar; Bridle and Pritchard, 2004; Hospido et al., 2005; Chan and Xu, 2009).

Fig. 7a and b demonstrate, in two graphs, that a strong confounding effect exists between field/pot experiments (upper graph) and biomass/yield parameters (lower graph). In the literature included in this MA, 98% of field trials focused on crop yield (i.e. grain or fruit production), while 70% of pot trials investigated crop biomass.

This indicates that it is not possible, on the basis of this analysis, to determine whether the increased effect on crop productivity is differentiated between crop biomass and grain or fruit yield, or whether biochar causes an increased effect in pot trials compared to field trials, or a combination of the two. This highlights the need for further long-term experiments that quantify both total crop biomass and yield (grain or fruit) following biochar application in field trials. Only once this information is available in the literature will elucidation of the effects of pot vs. field or biomass vs. yield be possible through meta-analysis.

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Fig. 7. (a). A forest plot showing the mean change in crop productivity as a percentage of the control upon addition of biochar to soil. Points show means of treatments, bars show 95% confidence intervals. Biomass refers to the total above ground plant biomass, whereas yield refers to the production of fruit or seeds (depending on crop). Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics). (b). A forest plot showing the mean change in crop productivity as a percentage of the control upon addition of biochar to soil on plants taken from either pot or field trials. Points show means of treatments, bars show 95% confidence intervals. Numbers in the two columns on the right show the total number of ‘replicates’ (n) from the combined studies upon which the statistical analysis is based (bold) and the number of ‘experimental treatments’ that have been grouped for each analysis (italics).

4.2. Environmental and management representativity In order to reduce bias, care has been taken to include all available data, both in the primary, as well as in the grey literature. While the possible influence of publication bias has been accounted for by the calculation of the Fail Safe N Number, the majority of studies were based on trials undertaken in tropical (38%) and subtropical (55%) latitudes, except for one study (reporting 7% of all treatments used) conducted at a temperate latitude >40◦ (Wisnubroto et al., 2010). Whether it is possible to extrapolate these results to temperate regions remains unclear (Atkinson et al., 2010). The inclusion of data from non-tropical environments has helped to reduce bias, by gathering results as representative as possible of biochar effects from a range of biochar feedstocks, under a range of environmental conditions and crop types. Meta-analyses allow for results to be extrapolated to different soil conditions with increased confidence than is possible when looking at each study individually.

Of the total 177 observations, 112 (63%) were field studies and 65 (37%) were mesocosm experiments conducted in pots. The mean reported time period between biochar application and data measurement for the field studies was 1.3 year (range 0.29–4 year), and for the pot experiments <1 year (often only 1 to 2 months). The majority of the field trial data included in this MA were obtained in short-term experiments of 1–2 years. Long-term studies are therefore, urgently required, particularly concerning effects of soil tillage/cultivation on behaviour, mobility and fate of biochar in soil, but also in relation to the influence of biochar application strategies on its functioning. For example, little is still known on the way CEC of biochar changes over time in arable soils, as it weathers

and as influenced by soil management, with potentially relevant implications for soil nutrient retention and crop productivity.

A cautionary note should also be made regarding the classic difference between carefully managed scientific field trials and ‘real-life’ farming. In this context, long-term effects of agricultural management practices on biochar properties that are beneficial to crop production may not be well represented by scientific trials.

Accelerated ageing of biochar to study long-term processes currently lacks standardised methods (Sohi et al., 2009; Cross et al., 2010) and are unlikely to incorporate the complex interactions that can develop over time with soil organic and inorganic matter (Brodowskia et al., 2005), as well as with soil organisms (Saito, 1990; Pietikäinen et al., 2000), which may lead to changes in crop productivity. This demonstrates that the experiments are not representative of what would occur in soil upon biochar addition, where any interactions between the biochar, soil and biota can develop over a period of years or decades.

While only limited statistically significant negative effects (that of the influence of biochar application to soil on ryegrass productivity or that of the influence of use of biosolids as a feedstock), was identified by this MA, it must be stressed that the range of studies included here does not cover a wide range of latitudes, with data being heavily skewed towards (sub) tropical conditions.

While this analysis provides evidence of the generally positive or not statistically significant effects of biochar addition to soil on crop productivity, care needs to be taken when extrapolating these results to higher latitudes, crops, soil types and agricultural systems that are not covered in the current analysis. Importantly, agricultural management practices may not always be beneficial to both components of the dual objective of biochar (i.e. increasing

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crop production and carbon sequestration). Certain agricultural practices such as tillage, ploughing, etc. may increase crop production when performed in combination with biochar addition, but may actually be disadvantageous to carbon sequestration through biochar.

In most of the studies in the literature, soil texture was described, although particle size distributions were mostly not reported. Soil taxonomical classification was sometimes provided, in different taxonomical systems and to different degrees of detail.

Many combinations of soil-climate-hydrology-topography factors have not been included in reported experiments so far, including those combinations that are largely found in agricultural areas, e.g. chernozems, vertisols, histosols. A lack of data also exists for studies in semi-arid and arid regions.

All studies in this analysis were conducted under arable or horticultural land use. Additional land uses where biochar application is considered possible, such as pasture and forest, need to be included.

Data on soil management (e.g. tillage and cultivation type and intensity) are also lacking, while a variety of mainly arable (combinable) and some horticultural crops has been reported. However, arable root and permanent crops, pasture, forest and shrubland were not represented in the data set analysed due to a lack of studies in the literature involving these crops.

4.3. Auxiliary variables Only 62% of studies reported the value of soil pH both before and after biochar application, reducing the number of studies that could be included in this analysis. Similarly, only 38% of studies reported soil CEC before and after biochar addition to soil, while methodologies for CEC determination varied between studies. Furthermore, data on soil texture was reported inconsistently, while that on soil structure was mostly lacking. These data are vital for clear elucidation of possible mechanisms and, as such, it is strongly recommended that such data are reported in upcoming studies to aid future meta-analysis and reviews of mechanisms.

It is also worth emphasising that the dependent variable of this study was crop productivity and that any results described here do not hold any relevance to other soil (sub)functions, like for example a possible priming effect (soil organic carbon regulation function), or potential toxicity to parts of the soil biota (habitat function).

4.4. Reporting guidelines for ‘biochar-crop production’ experiments Soils are highly heterogeneous systems at a multitude of spatiotemporal scales, while biochar itself can also be very heterogeneous when physical–chemical characteristics are concerned. As new studies emerge, this MA on the effect of biochar application to soil on crop production can be updated (and refined) periodically, as necessary. In addition, the effects of biochar at other levels of soil processes and functions can be analysed by MA once a large enough body of research has been established.

It is paramount that upcoming studies on the effects of biochar provide as much information as possible in regard to the study conditions, as well as a consistent and complete description of the data. Such a description should also include the Z or F statistic and clear measures of variance for comparative data analysis, ideally as standard deviations or standard errors for each treatment and the control, rather than an LSD (least significant difference), which has been pooled for several treatments. In all cases, it should be absolutely clear what the sample number is for every treatment (including the control). To enable MA on effects of a factor that is not the dependent variable of a study, it is also recommended to include all sample numbers, standard deviations or standard errors of other parameters measured as auxiliary variables (e.g. CEC, pH,

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bulk density, microbial activity, etc.). Finally, it is recommended that all data be reported in tabular format, possibly as an annex.

This will help to ensure that data can be effectively included in future MAs and those results are obtained with the highest possible confidence.

4.5. Maintenance of this MA The Cochrane Collaboration handbook (Higgins and Green, 2009) stated that systematic reviews should be updated every two years. This time period was selected for studies in medical intervention, while those in environmental or agricultural sciences may generally be expected to be less ‘time pressing’. Nevertheless, in the case of biochar, and considering strong drivers from climate change, waste management, food security, commercial pyrolysis facility producers, and soil quality requirements, it seems reasonable that a regular and frequent update of this MA is warranted.

Substantial amounts of data derived from biochar field and pot (greenhouse) trials (including crop production data) are expected to become available during the next few years, as many projects have started during the preparation of this manuscript. Moreover, a proportion of the field experiments used in this MA (mostly of 1–2 year duration) will be maintained during the next years, providing more and longer-term data, assuming the results are published in the primary literature or made available via other means.

All existing data should be made available as much as possible in a transparent way, with full disclosure of data, statistics and funding. This can imply translation of research papers into English or the posting of experimental results in an online public database.

In all cases, it is advised that studies are reported according to the guidelines described above. The database used for this MA is publicly available to download at ESDAC, the European Soil Data Centre (ESDAC, 2010).

5. Conclusions Evaluating the evidence regarding the relationship between biochar and crop productivity, this MA shows an overall relatively small (approximately 10%) but statistically significant, positive effect of biochar application to soils on crop production. Such a result is robust and useful, as it provides a sound basis for the potential benefits of biochar use on crop productivity. However, it must be stressed that this should not be taken to imply that the random addition of biochar to a field anywhere in the world, will always lead to a small yield increase, nor does it provide any information about additional potential effects and consequences of such a practice (e.g. regarding the environmental regulation function of soils). It is worth stressing that this MA is not capable of predicting the longevity of effects of biochar addition to soil. In fact, this MA highlights the need for long-term experiments in order to include and quantify the influence of ageing of biochar in soil on the expected effects of its application on productivity.

The greatest positive effects were seen in biochar application rates of 100 t ha−1 (39%). Other positive effects were seen in acidic (14%) and neutral pH soils (13%), and in soils with a coarse (10%) or medium texture (13%). This suggests that two of the main mechanisms for yield improvement may be a liming effect and the influence on the water holding capacity of the soil.

The variation in effect sizes ranged considerably, both within and between treatments. To understand the variation in effect sizes, the MA data were partitioned, thereby providing useful preliminary insights into the relationship between biochar and crop production, as well as experimental methodologies However, what

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Table 2 Main weaknesses in the current scientific evidence on the relationship between biochar addition to soils and crop production (see Sections 4.1 to 4.5 for more detailed discussion).

Categories

Short description

Biochar properties

Some properties are known to vary widely, while other properties are unknown and/or not measured/reported. Feedstocks are not always representative of likely feedstocks under consideration.

Reported studies are limited to (sub) tropical regions mostly. Other regions across a range of soil types and environmental conditions, with large global agricultural areas have been scarcely studied.

All reported studies involving effects of biochar on crop productivity are currently under arable and horticultural land use. Other land uses where biochar implementation is considered possible, such as pasture and forest, need to be included.

Data lacking on soil management (e.g. tillage and cultivation type and intensity). A variety of mainly arable (combinable) and some horticultural crops has been reported. However, arable root crops, permanent crops, pasture, forest and shrubland are not represented.

Pot experiments often only measure plant biomass, and commonly maintain the soil at field capacity. Most experiments have a duration of <6 months. Methodologies that integrate field with pot experiments in long-term trials could prove useful to evaluate mechanisms.

Most field data are available for only 1–2 years. Much longer term data is required, particularly concerning effects of soil tillage/cultivation on biochar. Studies using methodologies for accelerated biochar ageing by tillage/cultivation, freezing/thawing and wetting/drying simulations have not been reported.

Even basic auxiliary data, e.g. pH and CEC of soil, are often not measured both before and after biochar application, thereby disabling statistical analysis of potential causal mechanisms. Other, potentially important auxiliary variables, e.g. soil texture, structure, changes in plant-available soil water retention, are measured sporadically. Much wider and consistent measurement of auxiliary variables needs to be considered in study design.

Future publication of longer term field data should be accompanied by site-specific meteorological data. Data on all variables needs to be reported with a full and coherent statistical description.

The number of observations (experimental treatments) is low considering the variation in dependent variables (environmental and management), as well as in different parameters used as the independent variable.

Studies measured/reported different crop production parameters, e.g. plant biomass; grain yield, plant height, etc. Although this may be useful for mechanistic understanding, if measured consistently, the necessary inclusion of different parameters may have ‘masked’ effects in this MA.

Environmental Land use Land/soil management

Pot

Field

Study design

Study reporting Numbers Crop production parameters

is evident from Figs. 1–7, are the very large 95% confidence intervals around the mean effect size for each partitioning. This is due to the amount of variation within the data concerning both independent and dependent variables, relative to the number of observations.

For a responsible implementation of biochar policy at any scale (in any area), there is an urgent need for both qualitative and quantitative understanding of the causative mechanisms behind the range of effects of biochar on soil functions, as well as of the environmental and management factors relevant to that scale/area and of the half-life of biochar in soil, as influenced by the site-specific characteristics. Currently, mechanistic understanding remains very limited, particularly for longer time series (i.e. more than a few years). Besides time series, three additional main weaknesses in the current scientific evidence need to be addressed: representativity, auxiliary data, and observations, as discussed in detail above and summarized in Table 2. It is strongly recommended that these issues are taken into account in future studies.

Acknowledgements We would like to acknowledge the work carried out by the researchers whose published data was used for this meta-analysis.

Particular thanks go to Wisnubroto et al., for giving us permission to use their pre-published data. Thanks also to the European Soil Data Centre for hosting the MA raw data on their servers to allow for the updating of this MA as new results become available. Finally, this work was partly funded by iSOIL-Interactions between soil related sciences–Linking geophysics, soil science and digital soil mapping, a Collaborative Project (Grant Agreement number 211386) cofunded by the Research DG of the European Commission within the RTD activities of the FP7 Thematic Priority Environment; iSOIL is one member of the SOIL TECHNOLOGY CLUSTER of Research Projects funded by the EC. This publication reflects the authors’ views. The European Commission is not liable for any use that may be made of the information contained therein.

References Asai, H., Samson, B.K., Stephan, H.M., Songyikhangsuthor, K., Homma, K., Kiyono, Y., Inoue, Y., Shiraiwa, T., Horie, T., 2009. Biochar amendment techniques for upland rice production in Northern Laos: 1. Soil physical properties, leaf SPAD and grain yield. Field Crops Research 111, 81–84.

Atkinson, C.J., Fitzgerald, J.D., Hipps, N.A., 2010. Potential mechanisms for achieving agricultural benefits from biochar application to temperate soils: a review. Plant and Soil, 1–18.

Barthès, B.G., Kouakoua, E., Larré-Larrouy, M.C., Razafimbelo, T.M., de Luca, E.F., Azontonde, A., Neves, C.S.V.J., de Freitas, P.L., Feller, C.L., 2008. Texture and sesquioxide effects on water-stable aggregates and organic matter in some tropical soils? Geoderma 143 (1–2), 14–25.

Blackwell, P., Shea, S., Storer, P., Solaiman, Z., Kerkmans, M., Stanley, I., 2007.

Improving wheat production with deep banded Oil Mallee charcoal in Western Australia. In: International Agrichar Initiative Conference, Terrigal, New South Wales, Australia.

Bobko, P., Stone-Romero, E., 1998. Meta analysis may be another useful research tool but it is not a panacea. In: Ferris, G. (Ed.), Research in Personnel and Human Resources Management. AI Press, Greenwich, CT, pp. 359–397.

Brodowskia, S., Amelungb, W., Haumaiera, L., Abetzc, C., Zech, W., 2005. Morphological and chemical properties of black carbon in physical soil fractions as revealed by scanning electron microscopy and energy-dispersive X-ray spectroscopy.

Geoderma 128, 126–129.

Bridle, T.R., Pritchard, D., 2004. Energy and nutrient recovery from sewage sludge via pyrolysis. Water Science Technology 50, 169–175.

Chan, K.Y., van Zwieten, L., Meszaros, I., Downie, A., Joseph, S., 2007. Agronomic values of greenwaste biochar as a soil amendment. Australian Journal of Soil Research 45, 629–634.

Chan, K.Y., van Zwieten, L., Meszaros, I., Downie, A., Joseph, S., 2008. Using poultry litter biochars as soil amendments. Australian Journal of Soil Research 46, 437–444.

Chan, K.Y., Xu, Z., 2009. Biochar: nutrient properties and their enhancement. In: Joseph, J., Earthscan, S. (Eds.), Biochar for Environmental Management: Science and Technology. Lehmann.

Chidumayo, E.N., 1994. Effects of wood carbonization on soil and initial development of seedlings in miombo woodland, Zambia. Forest Ecology and Management 70, 353–357.

Cook, D.J., Guyatt, G.H., Ryan, G., Clifton, J., Buckingham, L., Willan, A., McIlroy, W., Oxman, A.D., 1993. Should unpublished data be included in meta-analyses? Current convictions and controversies. Journal of the American Medical Association 269, 2749–2753.

Cross, A., Sohi, S., Borlinghaus, M., 2010. Influence of feedstock and production conditions on the stability of biochar in soils. Geophysical Research Abstracts 12, EGU2010-13895-1, 2010, EGU General Assembly 2010.

ESDAC, 2010. European Soil Data Centre: http://esdac.jrc.ec.europa.eu/; accessed 04-10-2010.

S. Jeffery et al. / Agriculture, Ecosystems and Environment 144 (2011) 175–187 FAO, 2003. Food Balance Sheet. Last modified 08/06/2008: http://faostat.fao. org/site/502/default.aspx.

Gaskin, J.W., Speir, R.A., Harris, K., Das, K.C., Lee, R.D., Morris, L.A., Fisher, D.S., 2010.

Effect of peanut hull and pine chip biochar on soil nutrients, corn nutrient status, and yield. Agronomy Journal 102, 623–633.

Haynes, R.J., Mokolobate, M.S., 2001. Amelioration of Al toxicity and P deficiency in acid soils by additions of organic residues: a critical review of the phenomenon and the mechanisms involved. Nutrient Cycling in Agroecosystems 59 (1), 47–63.

Higgins, J.P.T., Green, S. (Eds.) 2009. Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.2]. The Cochrane Collaboration, www.cochranehandbook.org accessed June 2010.

Hospido, A., Moreira, M.T., Martin, M., Rigola, M., Feijoo, G., 2005. Environmental evaluation of different treatment processes for sludge from urban wastewater treatments: anaerobic digestion versus thermal processes. International Journal of Life Cycle Analysis 5, 336–345.

Hossain, M.K., Strezov, V., Yin Chan, K., Nelson, P.F., 2010. Agronomic properties of wastewater sludge biochar and bioavailability of metals in production of cherry tomato (Lycopersicon esculentum). Chemosphere 78, 1167–1171.

Ishii, T., Kadoya, K., 1994. Effects of charcoal as a soil conditioner on citrus growth and vesicular-arbuscular mycorrhizal development. Journal of the Japanese Society for Horticultural Science 63, 529–535.

Kimetu, J.M., Lehmann, J., Ngoze, S.O., Mugendi, D.N., Kinyangi, J.M., Riha, S., Verchot, L., Recha, J.W., Pell, A.N., 2008. Reversibility of soil productivity decline with organic matter of differing quality along a degradation gradient. Ecosystems 11, 726–739.

Lehmann, J., Pereira da Silva Jr., J., Steiner, C., Nehls, T., Zech, W., Glaser, B., 2003.

Nutrient availability and leaching in an archaeological Anthrosol and a Ferralsol of the Central Amazon basin: fertilizer, manure and charcoal amendments. Plant and Soil 249, 343–357.

Lehmann, J., Gaunt, J., Rondon, M., 2006. Bio-char sequestration in terrestrial ecosystems—a review. Mitigation and Adaptation Strategies for Global Change 11 (2), 403–427.

Lehmann, J., Joseph, S. (Eds.), 2009. Biochar for Environmental Management: Science and Technology. Earthscan, London.

Loveland, P., Webb, J., 2003. Is there a critical level of organic matter in the agricultural soils of temperate regions: a review. Soil & Tillage Research 70, 1–18.

McAuley, L., Ba’Pham, Tugwell, P., Moher, D., 2000. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in metaanalyses? Lancet 356, 1228–1231.

Major, J., Rondon, M., Molina, D., Riha, S.J., Lehmann, J., 2010. Maize yield and nutrition during 4 years after biochar application to a Colombian savanna oxisol. Plant and Soil 333, 117–128.

Nehls, T., 2002. Fertility improvement of a terra firme oxisol in central Amazonia by charcoal application. Final thesis in Geoecology, University of Bayreuth, Institute of Soil Science and Soil Geography, 81.

Ogawa, M., 1994. Symbiosis of people and nature in the tropics. Farming Japan 28, 10–34.

Orwin, R.G., 1983. A Fail-Safe N for Effect Size in MA. Journal of Educational and Behavioral Statistics 8, 157–159.

Pietikäinen, J., Kiikkilä, O., Fritze, H., 2000. Charcoal as a habitat for microbes and its effect on the microbial community of the underlying humus. Oikos 89, 231–242.

Pochet, G., van der Velde, M., Vanclooster, M., Delvaux, B., 2007. Hydric properties of high charge, halloysitic clay soils from the tropical South Pacific region.

Geoderma 138, 96–109.

187

Roberts, K.G., Gloy, B.A., Joseph, S., Scott, N.R., Lehmann, J., 2010. Life cycle assessment of Biochar systems: Estimating the energetic, economic and climate change potential. Evironmental Science and Technology 44, 827–833.

Rondon, M., Lehmann, J., Ramirez, J., Hurtado, M., 2007. Biological nitrogen fixation by common beans (Phaseolus vulgaris L.) increases with bio-char additions.

Biology and Fertility of Soils 43, 699–708.

Rosenberg, M.S., Adams, D.C., Gurevitch, J., 2000. MetaWin Statistical Software for Meta-Analysis, Version 2. Department of Ecology and Evolution, State University of New York at Stony Brook. Sinauer Associates, Inc., Sunderland, Massachusetts, U.S.A. Rosenthal, R., Rosnow, R., 1991. Essentials of Behavioral Research: Methods and Data Analysis. McGraw Hill, New York.

Rothstein, H., Sutton, A., Borenstein, M., 2005. Publication Bias in MA: Prevention, Assessment and Adjustments. Wiley, Hoboken, NJ. Saito, M., 1990. Charcoal as micro habitat for VA mycorrhizal fungi, and its practical application. Agriculture Ecosystems and Environment 29, 341–344.

Shackley, S., Sohi, S., (Eds.), 2010. An assessment of the benefits and issues associated with the application of biochar to soil. A report commissioned by the United Kingdom Department for Environment, Food and Rural Affairs, and Department of Energy and Climate Change, pp. 132.

Sohi, S., Lopez-Capel, E., Krull, E., Bol, E., 2009. Biochar, climate change and soil: A guide to future research. CSIRO. Steiner, C., Teixeira, W.G., Lehmann, J., Nehls, T., Vasconcelos de. Macêdo, J.L.V., Blum, W.E.H., Zech W., 2007. Long term effects of manure, charcoal and mineral fertilization on crop production and fertility on a highly weathered Central Amazonian upland soil. Springer, Dordrecht, PAYS-BAS. Steiner, C., Glaser, B., Teixeira, W.G., Lehmann, J., Blum, W.E.H., Zech, W., 2008.

Nitrogen retention and plant uptake on a highly weathered central Amazonian Ferralsol amended with compost and charcoal. Journal of Plant Nutrition and Soil Science 171 (6), 893–899.

Tummers, B., 2006. DataThief III. 2006 http://datathief.org/.

U.N. Population Division, 2008. World Population Prospects: The 2008 Revision Population Database, at esa.un.org/unpp, updated 11 March 2009. van Zwieten, L., Kimber, S., Morris, S., Chan, K., Downie, A., Rust, J., Joseph, S., Cowie, A., 2009. Effects of biochar from slow pyrolysis of papermill waste on agronomic performance and soil fertility. Plant and Soil 327, 235–246.

Verheijen, F.G.A., Jeffery, S., Bastos, A.C., van der Velde, M., Diafas, I., 2010. Biochar Application to Soils - A Critical Scientific Review of Effects on Soil Properties, Processes and Functions. EUR 24099 EN, Office for the Official Publications of the European Communities, Luxembourg.

Warnock, D., Lehmann, J., Kuyper, T., Rillig, M., 2007. Mycorrhizal responses to biochar in soil–concepts and mechanisms. Plant and Soil 300, 9–20.

Wisnubroto, E. I., Hedley, M., Hina, K., Camps-Arbestain M.,2010. Personal communication. The use of biochar from biosolids on waitarere sandy soils: effect on the growth of rye grass. NZBRC workshop, 11-12 February New Zealand Biochar Research Centre, Massey University, Palmerston North, New Zealand.

Woolf, D., Amonette, J.E., Street-Perrott, F.A., Lehmann, J., Joseph, S., 2010. Sustainable biochar to mitigate global climate change. Nature Communications 1 (56), doi:10.1038/ncomms1053.

Yamato, M., Okimori, Y., Wibowo, I.F., Anshori, S., Ogawa, M., 2006. Effects of the application of charred bark of Acacia mangium on the yield of maize, cowpea and peanut, and soil chemical properties in South Sumatra, Indonesia. Soil Science and Plant Nutrition 52, 489–495.