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Description of biosecurity practices on shrimp farms in Java, Lampung, and Banyuwangi, Indonesia
Marina K.V.C. Delphino a, * , Emilie Laurin a , Thitiwan Patanasatienkul a , Raynalfie Budhy Rahardjo b , Lukman Hakim b , Wildan Gayuh Zulfikar b , Holly Burnley a , K. Larry Hammell a , Krishna Thakur a
a Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3, Canada b JALA TECH Pte Ltd. Kompleks PT. Indmira, Jl. Kaliurang km 16,3, Kledokan, Ngemplak, Sleman, Daerah Istimewa Yogyakarta, 55584, Indonesia
ARTICLE INFO
Keywords: Shrimp Indonesia
Biosecurity Epidemiology Survey
ABSTRACT
The sustainability of the Indonesian shrimp farming industry is challenged by the presence of bacterial and viral diseases and their associated potential for severe economic losses and environmental impact. In shrimp aquaculture, biosecurity is essential to prevent pathogen introduction, emergence and spread within and between farms. This study was undertaken (1) to design and implement a questionnaire survey addressing key shrimp aquaculture management and biosecurity practices, and (2) to provide an overview and patterns of the current state of biosecurity for grow-out and intensive shrimp farms in Indonesia. A questionnaire was designed to collect information on farm characteristics, farm management, and biosecurity practices. Descriptive statistics, multiple correspondence analyses, and hierarchical clustering were used to summarize and identify patterns of biosecurity practices implemented by farmers and to classify farms into different clusters, based on these practices. Three distinct clusters were identified for 96 grow-out farms in Java and Lampung area, while 7 clusters were identified for 101 intensive farms in Banyuwangi area. For Java and Lampung, 2 clusters were mostly characterized by micro and small-scale farms, with less intense biosecurity practices, including not testing seed quality, not disinfecting people, vehicles, or equipment, not removing dead animals. One cluster was best represented by medium and large-scale farms applying higher intensity biosecurity practices, including greater distance to neighboring farms, disinfection for visitors, vehicles, and staff, testing seed quality, and fallow period greater than 30 days between cycles. In Banyuwangi area, farms could not be distinctly classified into clusters by patterns of biosecurity practices but were defined more by farm management and characteristics of the data structure (e.g., lack of response or missing records). For future sustainable surveillance programs and biosecurity implementation to reduce the risk of disease introduction and spread in these regions, recommendations include: training for improved, transparent, and consistent record-keeping; focusing future data collection on cluster or region-specific priorities and disease-specific biosecurity approaches by farming method (e.g., for intensive vs. grow-out); and increasing evidence-based decisions using epidemiological knowledge developed within industry, government, and shrimp aquaculture farmers.
1. Introduction
Indonesia is one of the largest shrimp producing countries in Southeast Asia (FAO, 2020). The Indonesian shrimp farming industry initially developed in East Java in the late 1980s before spreading throughout the country. The presence of bacterial and viral diseases increases the potential for severe economic losses affecting yield and
* Corresponding author.
survival and challenges the sustainability of shrimp farming in Indonesia, as it does in many other countries (Sunarto et al., 2004; Walker and Mohan, 2009; Ali et al., 2018). As production intensifies with higher stocking densities, feeding rates, and chemical use, water quality and susceptibility to pathogenic agents experience coincident changes (Thakur et al., 2018; Ali et al., 2018). Services supporting the Indonesian shrimp industry are addressing these challenges by
E-mail addresses: mdelphino@upei.ca, mdelphino@upei.ca (M.K.V.C. Delphino), elaurin@upei.ca (E. Laurin), tpatanasatie@upei.ca (T. Patanasatienkul), raynalfie@jala.tech (R.B. Rahardjo), hburnley@upei.ca (H. Burnley), lhammell@upei.ca (K.L. Hammell), kthakur@upei.ca (K. Thakur).
https://doi.org/10.1016/j.aquaculture.2022.738277
Received 11 November 2021; Received in revised form 15 April 2022; Accepted 19 April 2022
Availableonline22April2022
0044-8486/©2022ElsevierB.V.Allrightsreserved.
developing shared records systems aimed at increasing disease predictive capabilities (such as early detection of health and production anomalies at a regional level) using epidemiological principles and advanced statistical models across larger exposure circumstances (Muniesa et al., 2018). This study was initiated as a foundational process to better understand the potential introduction and transmission pathways for pathogens in the Indonesian shrimp industry.
Biosecurity has been defined as those procedures, at the farm and area levels, used to reduce the probability that animals will contract, carry, and spread infectious agents and other non-desirable health conditions (Lotz, 1997; Pruder, 2004). Farms more vulnerable to infectious diseases need to be informed of inadequate biosecurity measures, the advantages of addressing biosecurity deficiencies, and the risk of introduction and spread of pathogens to other farms in the area. Improved biosecurity is an essential component of successful animal production systems. Biosecurity practices represent important tools to prevent infection introduction and spread within and between shrimp farms as shrimp lack adaptive immunity mechanisms making common tools used in other food animal production, such as vaccination, unavailable (Walker and Mohan, 2009; Ali et al., 2018). The presence of wild aquatic animals, sharing of equipment and personnel between farms, connectivity of farms through water channels, shorter fallowing period, not using disease-free or disease-resistant seed are some of the factors and practices that might likely be associated with the introduction and spread of infectious diseases (Corsin et al., 2005; Tang and Bondad-Reantaso, 2019). Comprehensive understanding of biosecurity through improved farmer and worker training and attention to health management will contribute to the sustainability of Indonesian intensive shrimp production by recognizing risky practices.
Assessments of biosecurity and management practices in shrimp cultured in Indonesia are largely undocumented or unknown and are mostly described in the pretext of infectious disease outbreaks (Amelia
et al., 2021; Arisa et al., 2021). This is likely the first study specifically designed to properly evaluate the biosecurity practices implemented by Indonesian shrimp farmers. The objectives of this study were (1) to design and implement a questionnaire survey addressing key shrimp aquaculture management and biosecurity practices, and (2) to provide an overview and patterns of the current state of biosecurity for grow-out and intensive shrimp farms in Indonesia. The aim was to use the results to inform the Indonesian shrimp industry of gaps in existing biosecurity practices and contribute to building comprehensive and proactive biosecurity improvements and surveillance plans for shrimp farmers. Furthermore, the long-term goal was to improve evidence-based health management and sustainability of the shrimp aquaculture sector in Indonesia.
2. Methodology
2.1. Data source
We designed a survey to characterize the current state of shrimp farm biosecurity and management practices of JALA’s Indonesian clients. Two separate surveys were conducted in the two major shrimp producing areas of Indonesia serviced by JALA (a data technology company focused on improving shrimp productivity via precision farming by use of data analytics and ioT), namely (1) Java and Lampung area and (2) Banyuwangi area (Fig. 1). Specific questionnaire design methodology is discussed in section 2.2.
2.2. Questionnaire design
To describe the current state of biosecurity practices of shrimp aquaculture in Indonesia, a questionnaire was designed to collect data on farmers’ demographics, farm characteristics, farm management, and
Fig. 1. Surveyed provinces (green) and surveyed farms in Java and Lampung area (pink) and Banyuwangi area (blue), Indonesia, included in the clustering analysis. The number within each circle represents a cluster of farms by proximity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
M.K.V.C.
on-farm biosecurity practices. Biosecurity components of the questionnaire were based on Canadian Food Inspection Agency’s biosecurity guidelines and adapted to shrimp aquaculture practices (Canadian Food Inspection Agency (CFIA), 2012). Questions related to risk of pathogen introduction into the farm was adapted using a previously validated questionnaire from a study of finfish aquaculture in Vietnam (Boerlage et al., 2017). The questionnaire was subsequently revised according to feedback provided by local industry partners. It was initially developed in English and then translated to Bahasa language to facilitate communication when local field workers administered the questionnaire. The questionnaire consisted of 58 questions including close-ended, multiple or limited choice, and was divided into two sections: general information and on-farm biosecurity and management practices (such as discussed in: Lotz, 1997; Walker and Mohan, 2009; Munasinghe et al., 2010; Ali et al., 2018). Questions addressed farmer, employee, and farm characteristics, water connectivity - both between farms as well as to source, distance to other farms in the region; farm-access management; pest and wild species control; record keeping on feed, disease occurrence, and shrimp mortality; health and feed management; pond preparation and management; water management; and sampling methods used to measure shrimp growth and survival.
2.3. Study area
2.3.1. Java and Lampung area
A total of 100 grow-out farms (collection of ponds managed as a distinct group) from Java and Lampung area, including five different provinces of Indonesia (East Java, Central Java, Lampung, North Sumatra, and Sulawesi), were interviewed from November 2019 to June 2020. Four questionnaires from South Sulawesi, West Nusa, and Aceh provinces were excluded from the final analysis since these farms were external to the study area (n = 96 farms). All grow-out farms included in the study cultured Litopenaeus vannamei species of shrimp.
2.3.2. Banyuwangi area
A total of 101 intensive farms from Banyuwangi area, located in 27 different districts, were separately surveyed from January to September 2020. For the purposes of analyses, these 27 districts were categorized into eight groups of similar districts (Table 1). All surveyed farms cultured L. vannamei species, except for one farm (located in Kertosari) that cultured Penaeus monodon species and another farm from Karangrejo district that cultured both species.
2.4. Data management
Data were recorded on paper and then entered into EpiInfo 7.2.3.1 (Centers for Disease Control and Prevention, Atlanta, GA, USA). Data management was carried out using Excel (Microsoft), Stata (Release 15.1; StataCorp, 2019), and R (R Core Team, 2020). Answers to the 58 questions for each survey were formatted into numerical factor responses and/or categories to allow for standardized data analyses between farms, with some questions being split into sub-questions to create new factor variables, when necessary. Indicator variables (e.g., farmer’s name and phone number, farm coordinates, interviewer, and interview date) were dropped from further analyses.
2.4.1. Java and Lampung area
Using the remaining 52 (after dropping indicator variables) questions from the Java and Lampung surveys, 101 factor variables were created, each with at least 2 levels, and were further grouped into four different categories: (1) general characteristics of the farms, (2) farm management, (3) sanitary status of the farm during the previous production cycle, and (4) implemented biosecurity practices. These variables were analyzed to identify groups of grow-out shrimp farms with different levels of implementation of biosecurity and management practices.
Table 1
Number of farms by cluster, regency, and province in Java and Lampung, Indonesia.
2.4.2. Banyuwangi area
From the remaining questions for Banyuwangi surveys, data for questions about latitude/longitude (16 missing), number of farms (18 missing), and sampling for growth and survival (16 missing) were also excluded from the analyses due to a large proportion of missing answers. Ultimately, 70 factor variables were created, by formatting responses to the survey questions as described previously, with at least 2 levels, and grouped into four different categories: (1) general characteristics of the farms, (2) farm management, (3) sanitary status of the farm during the previous production cycle, and (4) implemented biosecurity practices. To address the fact that Banyuwangi had more missing information in the survey responses, some factor variables were grouped into fewer variables compared to those for the Java and Lampung analysis.
2.5. Data analysis
Descriptive summaries for specific questions (such as farm characteristics, region, species) were calculated, as well as for surveys with incomplete responses to questions for the different question categories. Multiple correspondence analyses (MCA) and hierarchical clustering analyses (HCA), as described in the following sections, were performed using the same methods but separate models for Java/Lampung and Banyuwangi.
2.5.1. Multiple correspondence analysis
After initial data management, we applied MCA as an exploratory method to describe patterns of individual units (i.e., shrimp farms) by categorical variables, to address the high degree of variability within the data set, and for stable clustering (Husson et al., 2010). To address the lack of power for statistical methods applied to high-dimensional data, dimensionality reduction was employed. Low-dimensional data representations that remove high variability but retain the signal of interest was sought to understand hidden structures and patterns (Nguyen and Holmes, 2019). MCA was carried out on an indicator matrix, with farms as rows and the categorical variables as columns, which enables the study of both the relationship between the observations and the
associations between variable categories.
For Java and Lampung dataset, a total of 100 variables, as described earlier, were used as active variables (variables used to perform the MCA) while “province” was used as a supplementary variable, meaning that it does not intervene in the construction of the principal components but is still useful to enrich the interpretation of the MCA. For the intensive farm data from Banyuwangi, a total of 69 active variables were used, keeping regency as the supplementary variable.
2.5.2. Hierarchical clustering and partitioning
Performing a clustering onto the first principal components of MCA is a common approach for questionnaires (Husson et al., 2010) with complex, multivariable data. Hierarchical clustering and partitioning were applied on the principal components of MCA to extract the essential information from the whole data set. The principal components account for as much of the variability in the data as possible. Agglomerative hierarchical clustering and partitioning was performed through the HCPC (Hierarchical Clustering on Principal Components) function (from the FactoMineR package in R Statistical Software), according to the methodology described by Husson et al. (2010). The HCPC function makes use of Euclidean distances to define the distance between individuals and the Ward’s agglomeration method to construct the hierarchical tree. This is based on the Huygens theorem which facilitates decomposition of the total inertia (total variance) in between- and within-group variance, which consists in aggregating two clusters such that the growth of within-inertia (characterizes the homogeneity of a cluster) is minimal at each step of the algorithm (Husson et al., 2010).
A hierarchical tree was constructed which is a sequence of nested partitions, from the individual as a class to a more general partition that includes all individuals in the same cluster. Apart from the similarity among them, farms in a group differ from other grouped farms. The number of clusters was chosen visually by looking at the overall appearance of the tree (Husson et al., 2015). The HCPC function constructs the hierarchy, and the calculation provides information for selecting an optimal division of levels. All variables from the original data set were used for the cluster description.
The MCA and HCPC functions used in the analysis were implemented using the R Statistical Software, specifically the FactoMineR package which is intended for multivariate exploratory data analysis (R Development Core Team, 2008; Husson et al., 2015). We also used, as required, the missMDA package which allows the imputation of missing values in the data sets (Josse et al., 2012; Husson and Josse, 2015).
3. Results
3.1. Grow-out farms from Java and Lampung area
Of the 96 surveys, 100% of the interviewed farms cultivated only L. vannamei. Most interviewees (96%) were male. Although 75% of them had more than 2 years of experience with aquaculture, only 34% received some type of formal training related to shrimp production. Median age of interviewees was 37 years, ranging from 19 to 62 years. Each farm had a median of 2 active ponds; the largest farm had 55 active ponds at the time of the interview. Roughly 70% of farmers produced 2 crops per year (maximum observed was 4 crops per year). Over 90% of the farms had a minimum distances of <1 km to the nearest upstream and downstream farms, and all the surveyed farms were connected to other shrimp farms via water channel(s). Only 13% of farms disinfected staff and visitors at the entrance of the farms, and only 8% disinfected vehicles at the farm’s entrance. Only one farm shared boat(s) between farms, but nearly 30% shared net(s) or water quality parameter testing equipment with other farms. A quarter of the farmers did not have a record keeping system; of the farms with record systems, 58% were paper-based and, and small minority (15%) of farms, used an electronic recording system. Less than half (41%) of farmers recorded mortality information. Feed consumption was the most recorded (67%)
information by farmers. All farms obtained seed (i.e., postlarvae, or PL) from hatcheries, but only 65% assessed the quality of the seed before stocking. The most used test to assess PL quality was a stress test (66%), followed by gut filling screening (10%), vibrio bacterial culture (9%), and hepatopancreas screening (6%). Three-quarters of farmers did not dry ponds before starting a new crop cycle and, when they did, the duration was usually less than a week.
For the MCA analysis, we used data from 96 farms, described by 101 categorical variables. The missing values in the raw data set of 101 categorical variables made up a total of approximately 12% of the entries. The variables with the highest proportion of missing values were related to questions regarding the use of chemicals to treat water before stocking animals or pond preparation, test frequency to check seed quality, and type of information recorded by farmers. Visually, the shape of the dendrogram (Fig. 2A) resulting from the clustering performed on the outputs of the MCA, suggested partitioning the farms into three distinct clusters. The optimal level of division suggested by the HCPC function and represented with a solid black line also indicated three clusters. Fig. 2B shows 3-dimensional representation of the hierarchical tree on the map produced by the first two principal components, and the farms were colored according to their cluster affiliation.
Table 1 shows the number of farms by cluster, regency, and province. Table 2 summarizes practices that best represent each of these clusters. Cluster 1 was mostly characterized by small-scale farms with poor biosecurity practices in place (e.g., do not test seed quality; do not disinfect people, vehicles, and equipment; do not collect/remove dead animals); and eat fresh fish from other farms daily. Cluster 2 was mainly represented by micro-scale farms that although had a few biosecurity practices in place to prevent pathogen introduction and spread, these farms would benefit from improvement (e.g., by not dumping dead animals in sea/river; having longer fallowing period between cycles). In contrast, Cluster 3 was best represented by medium and large-scale farms applying good biosecurity practices (e.g., to treat fish before stocking; greater distance to neighboring farms; applying disinfection on visitors, vehicles, and staff; testing to check seed quality; fallowing period between cycles above 30 days). All variables from the original data set were used for this analysis. However, only variable categories significantly linked to the clusters (see Supplemental Table 1) are included in the description.
3.2. Intensive farms from Banyuwangi area
The analysis included data from 101 farms described by 70 categorical variables. Approximately half of the interviewees (46.5%) did not have any aquaculture-related training. Of surveyed farms, 29% of the farmers had each 6 farms or more, with 2 to 4 production cycles per pond per year (98%). The number of ponds at each farm varied, ranging from <6 (24.7%) to >30 (11.9%). (24.7% had fewer than 6 ponds, and 11.9% had 30 or more ponds). The number of active ponds per farm was also highly variable with most farms (59.4%) varying between 2 and 5 active ponds (30.7%) or 10–19 active ponds (28.7%). More than 85% of surveyed farms reported pond size between 0.1 and 0.5 ha. Approximately half of surveyed farms used freshwater (50.5%), while the other half (49.5%) used partly saltwater for cultivation. The reported water source for farms was either the sea (67.3%), a canal (<1%), or underground or well-water (55.4%); and 76.2% reported keeping water in a reservoir before releasing it to the ponds.
Most farms (97%) were < 1 km away from the nearest neighboring farm of the same species, and only 22.8% of farms had any water connection to any other farm of the same species. Although most farms (84.2%) used a fence surrounding the farm, approximately half of all farms (43.6%) did not use a gate or checkpoint at the farm entrance. Staff, visitors, and vehicles were not disinfected at the farm entrance for 95%, 94%, and 84.2% of farms, respectively. Also, most farms reported that they did not share equipment (94.1%) nor staff (89.1%) with other farms but did share equipment (91.1%) between ponds within their
M.K.V.C. Delphino
farm. Just under half of all the farms surveyed (43.6%) reported disease occurring in a neighboring farm when disease occurred on their farm. Approximately half of farms (54.5%) used either chemical water treatment(s) or a physical barrier (net, rope, screen, or fence) (43.6%) to prevent or control pests. However, 63.4% of all farms reported pests in or around the ponds. Most farms also reported preparing or eating (i.e., human consumption) fresh aquatic food animals sourced from outside of
the farm either weekly (49.5%) or daily (32.7%).
In the survey responses concerning shrimp health management, 60.4% on average always pretested seed quality prior to stocking, 97% dried ponds before starting a new crop cycle, 99% used either chemical or non-chemical methods for pond preparation. Most of the farms sampled shrimp monthly to assess growth and survival rates (84.2%) and 97% had some form of record-keeping system in place (60% of these
Fig. 2. Hierarchical clustering for Java and Lampung, Indonesia, farms, sorted according to (A) coordinates on the first principal components (black line represents the optimal level of division) and (B) represented on the map induced by the first two principal components.
M.K.V.C. Delphino
Table 2
Clusters description for grow-out farms from Java and Lampung, Indonesia. Cluster 1 Cluster 2 Cluster 3
• Small-scale farms with poor biosecurity practices in place (e.g. do not test to check seed quality; do not disinfect people, vehicles, and equipment; do not collect/remove dead animals); and eat fresh fish from other farms daily
• Best represented by farms in the province of Lampung
• Past crop cycle presented external and internal signs suggestive of the presence of infectious disease
• Mostly small-scale farms (up to 4 ponds or up to 0.6 ha of pond area) that have 3 to 4 production cycles per year, and make use of liner on the pond side
• Owner is the person that looks after the animals, education level is high/secondary school. Despite not having training in aquaculture, has experience in aquaculture industry (>16 years)
• Do NOT have farm record keeping system and do not record any data such as the number of dead animals, feed consumption, disease signs (internal and/or external)
• Aware of reportable diseases (e.g., WSD and MYO) but report to non-vet-related people/organization
• Do NOT send samples for disease testing, nor record such results; consult non-vet-related technical assistance when there is a possible disease outbreak
• Do NOT remove dead animals from pond
• Observe the presence of other animals in or around ponds (e.g. bird, wild fish, dogs, cats, lizards) and make use of chemicals to
• Micro-scale farms few biosecurity practices are in place to prevent disease introduction and spread, key practices need to be improved (e.g., throwing dead animals in sea/river; short fallowing period between cycles)
• Best represented by farms in the province of Central and East Java
• Lack of information regarding health status
• Mostly micro-scale farms (1 pond or up to 0.4 ha of pond area) that have 2 to 3 production cycles per year, and make use of liner for the whole pond
• Education level of the person looking after the animals is high/ secondary school with no aquaculturerelated training
• Have a paper-based record keeping system but was not possible to identify which information is recorded (NA)
• Have limited awareness about reportable disease
• Do NOT consult/ request technical assistance when disease event occurs
• Medium and large-scale farms applying good biosecurity practices (e. g. to treat fish before stocking; greater distance to neighboring farms; applying disinfection on visitors, vehicles and staff; testing to check seed quality; fallowing period between cycles above 30 days)
• Best represented by farms in the province of West Java
• Lack of information regarding health status
• Mostly medium and large-scale farms (≧ 6 ponds or up to >1.8 ha of pond area) that have 1 to 2 production cycles per year, and make use of concrete for pond bottom
• Use of hired labor (education level is mostly characterized by College/University, and aquaculture-related training) to look after the animals,
• Have a record keeping system that is a mix of paper-based and electronic in which daily (e. g. number of dead animals; feed consumption), weekly, or monthly (e.g. externa signs) records are maintained
• Aware about the reportable disease and to whom must report it (vet-related people/ organization)
• Send samples for disease testing and record results but not systematic, and consult vet-related assistance when disease event occurs
Table 2 (continued )
Cluster 1
Cluster 2
Cluster 3
prevent or controls pests pests, but do make use of fence to control it net/rope) or full cover to prevent or controls pests
• Do NOT make use of chemicals to treat water before stocking with animals
• Connected to other shrimp farm via water channel
• Remove dead animals from the pond (e.g. siphoning) but also throw it in sea/river
• Do NOT observe the presence of other animals in or around ponds and do NOT make use of chemicals to prevent or controls
• Observe the presence of animals in or around ponds (e.g. bird, wild fish); however, make use of chemicals, physical barriers (e.g.
• Make use of calcium hypochlorite to treat water before stocking with animals
• Fallowing period is up to 7 days
• Make use of a combination of chemicals to treat water before stocking
• Distance to neighboring farm is >1 km
• Long duration fallow periods (>30 days) and keep water in a reservoir before releasing it
used entirely paper records). Frequency of recording was reported for the following: feed consumption (99%), external disease signs (51.5%), abnormal behavior including reduced appetite (89%), necropsies for internal disease signs (18.8%), sending samples to a lab for disease testing (69.3%), disease test results (68.3%), and mortalities (22.8%). Most farms used a siphon to collect or remove dead animals from the pond (70.3%) and buried the dead animals in a specific area on the farm (71.3%). Farms also reported disease in the previous crop cycle (67.3%) and that disease was the reason for treating ponds (e.g., with feed, water/bath, or other methods) in the previous crop cycle (78.2%). A high proportion of farms reported either not having high mortality or morbidity (87.1%), not observing any internal signs during necropsies (80.2%), or observing the presence of external signs (95%); but 90% did report consulting external expertise when disease outbreaks were suspected during the previous crop cycle. See Supplemental Table 1 and Table 2 for the complete list of descriptive information for each of the completed surveys, as well as the number of missing responses for each variable.
After considering and addressing the missing values, the cluster analysis, the shape of the dendrogram, and the HCPC function (Fig. 3A) visually suggested partitioning the farms into 7 clusters. Fig. 3B shows 3dimensional representation of the hierarchical tree on the map produced by the first two principal components, and the individual farms are colored according to their cluster affiliation. However, the inertia in dimension 1 was only approximately 8%, indicating that patterns should be interpreted cautiously, and that the variability explained, between farms, was low even in this primary dimension.
Table 3 shows the number of farms by cluster and regency group for Banyuwangi, Indonesia. Table 4 summarizes practices that best represented each of these clusters. When considering the hierarchical clustering map (Fig. 3B) and the outcomes from the HCPC model, Clusters 1 and 2 were more similar and closer to the origin, reflecting that these groups of farms had less impact on the overall variability of the model. The closer the farms were to each other, the greater their profile similarity, while those farther apart were less similar. Clusters 3 and 4 were separate from each other and from all other clusters, while the remaining clusters (5–7) were closer to each other, but separate, from the other groups of clusters. In summary, Clusters 1 and 2 both had larger number of farms related to each cluster and have a broad range of available information (across variable categories) for most of the variables (farm scale, education, districts, biosecurity practices, water source, farm and health management, and disease and treatment information) with little or no missing categories. In both of these clusters, farmers were more experienced in the aquaculture industry; but, in Cluster 2, farms were small to medium-sized and farmers were more middle-aged. Farms in both Clusters 1 and 2 sourced influent water from a canal, while Cluster 1 farms reported mixing animals in different ponds, doing more pond preparation between production cycles, and reported one or two known diseases in the past crop cycle. The low
M.K.V.C. Delphino
Fig. 3. Hierarchical clustering for Banyuwangi, Indonesia, farms, sorted according to (A) coordinates on the first principal components (black line represents the optimal level of division) and (B) represented on the map induced by the first two principal components.
number of farms per cluster for Clusters 3, 4, and 7 necessitate careful inferences of patterns, but to highlight their differences, briefly, Cluster 3 (1 farm) was from the Sukabumi_Mayangan district and had smallsized ponds. Although this cluster also had a broad range of information on biosecurity, health management, diseases and treatment, the
interviewee also reported some better biosecurity practices.
Likewise, Cluster 4 (2 farms) recorded some better biosecurity practices, but farmers had graduate level education, and treatments in the past crop cycles were done only because of disease and not for poor water quality. The remaining group of clusters (5,6,7) had more missing
M.K.V.C. Delphino
M.K.V.C.
Table 3
Number of farms by cluster, regency group, and individual regency in Banyuwangi district in Indonesia.
a Wongsorejo (Sumberkencono, Alasbulu, Alasrejo, Bengkak, Bangsring); Banyuwangi (Pakis, Sobo, Kertosari, Karangrejo, Cungkingan, Sobo Gareng); Muncar (Wringinputih, Tembokrejo, Sumbersewu, Kedungringin, Tegalpare, Palukuning); Blimbingsari (Badean, Blimbingsari, Sukojati, Bomo, Patoman, Watukebo, Jatisari); Kabat (Pondoknongko); Kalipuro (Bulusan); Sukabumi_Mayangan; Kemendung.
b All farms have only Litopenaeus vannamei species, except for 1 farm in cluster 5 with both species and 1 farm in cluster 7 with just P. monodon species.
categories. The one farm representing Cluster 7 included larger-sized ponds, farmed both species of shrimp, used canal water, had no record keeping, and implemented poorer biosecurity practices. Both Clusters 5 and 6 were best represented by farms from the Banyuwangi district and reported a broad range of information on general characteristics, biosecurity practices, farm and health management. Specifically, Cluster 5 included small-scale farms with small-medium sized ponds, farmers were older-aged with high school to university level education. These farmers always recorded external disease signs observed and used a scoop or siphon to remove dead animals from their ponds and treated for disease and water quality issues using either feed or both feed and water/bath methods. Farms in Cluster 6 had poorer recording practices and buried dead animals anywhere on the farm, and some reported disease in the past crop cycle and treated because of disease and not water quality.
Overall, there were large variations between responses between farms and their practices, and this occurred over different dimensions. Clusters of farms were not clearly distinguished from other clusters by particular practices, but rather the missing responses to certain questions, and individual category answers seemed to be more influential on cluster distinctions. Also, some variables with categories that had low responses also influenced some of the clustering: e.g., regency, where some categories only had a one or a few farm surveys applicable (such as Clusters 3, 4, and 7), or species farmed (Cluster 7).
4. Discussion and conclusions
This study documents basic knowledge of common biosecurity practices in grow-out and intensive shrimp farms in Indonesia. We identified gaps (discussed below) in farming methods and management where biosecurity practices could be improved, and industry-wide sharing of data could contribute to the capacity for predictive risk analyses based on enhanced data precision and accuracy. Such data could form the basis for longitudinal surveillance of biosecurity practices for improved shrimp production and decreased probability of pathogen introduction and spread.
For Lampung and Java areas, we characterized the surveyed growout farms into three clusters based on their biosecurity practices. It was possible to infer that Cluster 1 (Lampung) and Cluster 2 (Central and East Java), were mostly represented by smaller farms, had more biosecurity deficiencies than Cluster 3 (consisting of medium and largescale farms from West Java) and, therefore, were in higher risk categories for pathogen introduction and spread that could be mitigated through improvement of targeted biosecurity practices. Both Lampung and Central and East Java clusters of farms, for example, did not test the quality of the seed before stocking, although this is considered one of the primary pathways for the introduction of pathogens (Corsin et al., 2001). One explanation for the practices observed by these two clusters of farms may be due to lack of financial resources, as small-scale farmers
and family-owned farms are less likely to be able to afford better diagnostics, consulting, biosecurity, and therapeutic supplies to fully implement recommended biosecurity practices (Munasinghe et al., 2010; Ali et al., 2018). Another explanation may be personnel availability to implement consistent biosecurity practices, such as sharing of utensils and equipment, disinfection (people, vehicles, equipment), fallowing for longer duration, and consistent record keeping. Paperbased record-keeping is more likely to lead to inaccuracies in data collection, errors in translation of paper information to electronic format, repeated entry of the same information, and timeliness of reporting and response. Data generated and accessible in real-time (e.g., using an electronic information system) would assist the capture of relevant disease signals or management events and timely sharing of such data would allow proactive detection of emerging health threats and trends.
For farms in Banyuwangi, clusters were more difficult to distinguish by specific practices as most farms in the clusters (other than Cluster 7) reported a broad range of practices. Cluster 7 was also distinguished as farming both shrimp species. However, three clusters (Clusters 3, 4, and 7) were only represented by one or two farms each, necessitating careful interpretation of any highlighted differences or similarities within and between clusters. In addition, some clusters tended to lack responses to certain questions in the survey, which may not necessarily reflect a reluctance to provide data by the farmer but possibly a difference in interviewer approach. Record-keeping inadequacies were also present for many intensive farms from Banyuwangi, despite farm size and farmer characteristics.
The clusters characterized for Banyuwangi did not have a specific group of practices distinguishable across the whole survey, but combinations of responses were observed. This finding may be partly explained by: (1) incomplete or inconsistent responses to survey questions across the farms surveyed; (2) intensive farming methods, despite farmer or farm characteristics such as farm size, may require a different survey instrument with questions tailored towards intensive farming practices than grow-out farms (Boston Consulting Group (BCG), 2019).
Future surveys may need to be repeated as separate and tailored surveys for the different clusters of farms based on study area, with specific questions targeting variables (or combinations of variables) that are most influential for those clusters. Future surveys should also be developed to focus on improving farmer response rates, by adding possible responses to indicate that the question was not relevant, inconsistent, or unknown to the respondent. Follow-up surveys will assist assessment of differences between management and biosecurity protocols while accounting for missing data. Future studies should also incorporate aspects of social science to identify challenges that preclude farmers from implementing improved biosecurity and disease management practices.
A gap common to all clusters was the lack of use of diagnostic testing for shrimp pathogens and clinical disease data. Stocking
Table 4
Description of larger clusters for intensive farms from Banyuwangi, Indonesia. Italic text refers to a missing response category. See supplementary material for description of smaller clusters (3, 4, 6, 7).
CLUSTER 1 CLUSTER 2 CLUSTER 5
• Small to large scale farms
• Few small ponds, few to many medium ponds, few to several active ponds
• 2–4 average production cycles/year
• High school to university education, with or without aquaculture training, 16–30 years aquaculture industry work
• Farmer may or may not be owner and/or worker
• Well represented in Blimbingsari, Wongsorejo, Banyuwangi districts
• May or may not use: water channel connection to another farm, gate or fence to farm, vehicle disinfection, sharing equipment and staff with other farms
• Mixed animals in or around ponds, use 1 pest-control method, may or may not use physical barriers
• Eat fresh fish from other farms never, weekly, or monthly
• Water source: canal and may or may not be from sea or underground/wellwater
• May or may not keep water in reservoir before releasing
• Line pond sides or whole with HDPE
• Use freshwater or part saltwater
• Record keeping all paper or some paper/ some electronic
• 1 or ≥ 3 pond preparation methods
• Treat water prestocking with >3 chemicals (or missing to report number and no non-chemicals)
• No seed quality testing pre-stocking
• Hatchery or farmer transport seed
• Farmer or source transport feed
• 1 or 2 external signs and none or 1 internal sign (necropsies) observed in past crop cycle
• Small to medium scale farms
• Few small ponds, several active ponds
• About 2 average production cycles/year
• Middle-aged farmers
• High school to university education, with or without aquaculture training, 16–30 years aquaculture industry work
• Well represented in Muncar, Blimbingsari districts
• May or may not use: water channel connection to another farm, gate or fence to farm, vehicle disinfection, sharing equipment between ponds in the farm
• May or may not use water chemical treatment to control pests, may or may not leave pests in or around ponds
• Water source: canal and may or may not be from sea or underground/wellwater
• May or may not keep water in reservoir before releasing
• Do not line ponds or line sides with HDPE
• Use freshwater or part saltwater
• Record keeping all paper or some paper/ some electronic
• May never use or always none or 1 test for seed quality prestocking
• Small scale farms
• Small and medium ponds, 1–5 active ponds
• 2–4 average production cycles/year
• Older farmers
• High school to university education
Table 4 (continued )
CLUSTER 1 CLUSTER 2 CLUSTER 5
• May or may not record external or internal disease signs, abnormal behavior or feeding, test results, mortalities
• May (every day or weekly) or may not consult about possible disease outbreak in past crop cycle
• May or may not remove dead animals from ponds; bury dead anywhere on farm
• 1 or 2 known diseases in past crop cycle
• May or may not report disease in past crop cycle
• Farmer may or may not be owner and/or worker
• Well represented in Banyuwangi district
• May or may not share equipment and staff with other farms, share equipment between ponds in the farm
• May or may not use physical barriers to control pests, may or may not leave pests in or around ponds
• Eat fresh fish from farms never or monthly
• Water source: may or may not be from sea or underground/ wellwater
• May or may not keep water in reservoir before releasing
• Treat using water/bath or feed methods in past crop cycle because of disease and water quality
disease signs, but record test results monthly
• May or may not consult about possible disease outbreak in past crop cycle
• Treat in past crop cycle because of disease or water quality issues
• Record external disease signs every day or ever time observed; may or may not record abnormal behavior or feeding, samples sent to lab for testing, test results, mortalities
• Missing info about whether farmer consults about possible disease outbreak in past crop cycle, but if they do, it is daily or weekly or monthly
• Use scoop or siphon to remove dead animals from ponds
• May or may not report disease in past crop cycle
• Treat using feed or both feed and water/bath methods in past crop cycle because of disease or water quality issues
pathogen-free shrimp post-larvae should be encouraged as well as incorporating targeted field testing for selected pathogens would provide useful measures for improved and timely detection of disease emergence and implementation of control decisions (Lotz, 1997). Integrated management practices to reduce pathogen introduction or spread would benefit from further farmer training and awareness of clinical signs and availability of cost-efficient veterinary investigative services and epidemiological consulting (Munasinghe et al., 2010; Ali et al., 2018). Training for workers, managers, and owners should include improving consistent recording systems and enhancing the capacity of farms to implement improved biosecurity measures that can lead to increased productivity.
Consequently, the primary recommendation of this study is to improve availability of consistent disease and mortality records, including within-cycle health determinants and outcomes. Improved surveillance knowledge requires reliable, consistent, and accessible data recording with increased frequency for prospective longitudinal surveillance programs (Boerlage et al., 2017). When training is sufficient, financial assessments should be implemented to address the economic deficiencies that can contribute to poor or incomplete adherence to diagnostic and biosecurity protocols (Munasinghe et al., 2010).
• 1 internal sign (necropsies) observed in past crop cycle
• May or may not record external or internal
• Record keeping some paper/some electronic
• May or may not dry pond before new crop
• Missing info on nonchemical water treatment pre-stocking
• Farmer or source transport feed
• Disease may or may not have occurred in both this and neighboring farm at same time
A collective approach to sharing existing epidemiological and biological scientific knowledge across regions within the shrimp farming industry, including input from government and service providers, will strengthen the effectiveness of surveillance programs addressing both mandatory biosecurity protocols and voluntary management practices. Additionally, improved biosecurity and pathogen detection contributes to sustainable production and decreased risk of pathogen introduction, clinical disease occurrence, and spread (Walker and Mohan, 2009). These programs also need to consider the impacts of specific pathogens on risk assessment and cost/benefit analyses. Varying biosecurity practices, early detection of infection and disease patterns, mitigation of outbreaks and biosecurity gaps, and decreasing the risk of pathogen spread between ponds/farms in close proximity or due to shared staff/ equipment are important considerations to reducing disease impacts (Lotz, 1997; Munasinghe et al., 2010; Boerlage et al., 2017). Unlike larger farms, smaller farms with limited resources may have difficulty adopting some biosecurity recommendations despite that would enhance their sustainable productivity and disease risk management (Walker and Mohan, 2009; Munasinghe et al., 2010). Therefore, revised
biosecurity programs should be tailored to specific clusters identified in primary analyses as presented in this study. Biosecurity compliance applied throughout the shrimp aquaculture sector in Indonesia will contribute to prevention of emergence and spread of economically and environmentally devastating diseases.
Author statement
1. Marina K V.C. Delphino - Data analysis and writing
2. Emilie Laurin - Data analysis and writing
3. Thitiwan Patanasatienkul - Questionnaire design and writing
4. Raynalfie Budhy - Rahardjo Data collection and writing
5. Lukman Hakim - Data collection and wirting
6. Wildan G. Zulfikar - Data collection and writing
7. Holly Burnley - Supervision of the project and writing
8. Krishna Thakur - Design of the study, supervision of the project and critical revision of the article
9. K. Larry Hammell - Design of the study, supervision of the project and critical revision of the article
Declaration of Competing Interest
MD, EL, TP, RR, LH, WZ, HO, HB, LH and KT declare no competing interests.
RR, LH, and WZ are employed by JALA, that is a data technology company focused on improving shrimp productivity via precision farming by use of data analytics and ioT, which has been declared in the institutional affiliation.
Acknowldegements
We thank Liris Maduningtyas, and Jala Tech Pte. Ltd. (Indonesia) team, as well as all farmers from Jala’s Tambak Pintar program for their participation and assistance with data collection and interpretation. We also thank Lisa van Wageningen and Trini Pratiwi for their assistance with project management. This research was co-funded by IDH FIT Fund Projects & Jala Tech Pte. Ltd. (Indonesia).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi. org/10.1016/j.aquaculture.2022.738277.
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