Dataset of production characteristics and performance of small-scale carp aquaculture systems in Bangladesh

Small-scale inland aquaculture has considerable potential to generate socioeconomic benefits in low-and middle-income countries. Hundreds of millions of people globally rely on inland aquaculture, contributing substantial economic and employment benefits to national and regional economies (FAO, 2022). Inland aquaculture also contributes substantially to rural livelihoods and poverty alleviation (Béné et al., 2016), nutrition and food security (Wang et al., 2024) and gender empowerment (Dam Lam et al., 2022), among others. Carps are the most widely produced inland aquaculture species (particularly in Asia), accounting for 53.8% of global inland aquaculture production in 2022 (FAO, 2024). Carp aquaculture (especially in Asia) has contributed substantially to global inland aquaculture expansion (Zhang et al., 2022), especially through carp polyculture systems (Naylor et al., 2022) Small-scale aquaculture systems generally rely on relatively small farms and simple (if not rudimentary) technologies, which often constrain the actual output, productivity and efficiency of the sector, and as an extension its potential to enhance food security and offer sustainable livelihoods (Wang et al., 2023). However, far from being homogenous, small-scale inland aquaculture systems (incl. carp) have radically different characteristics in low-and middle-income countries in terms of production models and commercialization practices (Short et al., 2021). Many efforts have sought to improve the performance of small-scale inland aquaculture systems in low-and middle-income countries. Improvement efforts mainly rely on the promotion of improved production practice/technologies, dissemination of knowledge about production/processing/marketing/nutrition, promotion of improved fish feed and species, or combinations of the above (Henriksson et al., 2021). However, many factors have constrained such efforts (Morgan et al., 2017), including our incomplete understanding of differentiation in the characteristics and performance of aquaculture systems (particularly small-scale systems), as well as of the differentiation in the needs, preferences and capacities of producers (Short et al., 2021). This is compounded by the general lack of comprehensive and consistent national/regional aquaculture statistics, the dynamic and fast-changing nature of the sector, and the lack of high-quality and fine-grained datasets compared to land-based agricultural systems. Collectively such information gaps contribute to the general failure to appreciate the considerable heterogeneity in the sector (both between and within countries), and design fitfor-purpose policies and interventions to improve the performance of small-scale aquaculture systems (Short et al., 2021). Bangladesh experiences many of these circumstances. Aquatic food contributes significantly to the growing demand for animal-based protein due to various interconnected environmental, cultural and socioeconomic reasons. Inland aquaculture caters currently for most of the national fish demand, with the country currently ranking as the 5 th largest aquaculture producer globally (FAO, 2022). The aquaculture sector has expanded substantially in the past decades, with an estimated 4.3 million households (20% of the rural population) operating at least one homestead pond that is usually small in size (0.08-0.10 ha on average). Carps are, by a wide margin, the most widely produced aquaculture species in Bangladesh, accounting for 55.4% of the total aquaculture output (DoF, 2022). Carp aquaculture is practiced across the entire country, and is overwhelmingly geared towards household selfconsumption and/or the national market (Henriksson et al., 2018). However, despite this clear importance of carp aquaculture systems in Bangladesh (and the broader region) we lack consistent and comprehensive datasets about their characteristics and performance across the country (and other parts of South and Southeast Asia). Although many public and private organizations have been actively trying to improve the performance of the sector through diverse interventions, these efforts are constrained by the lack of good understanding of producer differentiation among producers (and especially among small-scale producers that are one of the most dynamic segments in the sector) (Henriksson et al., 2018;Karim et al., 2016). Arguably, the good understanding of the characteristics, performance and needs of (small-scale) producers could help inform the design and targeting of aquaculture interventions in Bangladesh, and other similar contexts. This Data Report outlines such a dataset that contains the production characteristics and socioeconomic performance of small-scale carp aquaculture systems in Bangladesh. The dataset was collected through in-person structured surveys with 4,540 carp producers in 54 areas (upazillas) 1 spanning the entire country. In terms of production characteristics, the dataset contains variables related to farm sizes, fish species, aquaculture production technologies, feed/seed use, aquaculture knowledge, and aquaculture incomes and expenditures, among others. In terms of performance, the dataset contains variables that can generate indicators of rural livelihoods (e.g. on-farm/off-farm income), multidimensional poverty, food security and environmental performance, among others.The dataset was collected through a structured household-level survey that elicited the characteristics and the socioeconomic performance of carp aquaculture systems in Bangladesh. The dataset (and the base household survey) consists 10 modules that collectively contain comprehensive information about the aquaculture production systems, demographic characteristics and socioeconomic profile of households. Module A and B contains variables about the location and demographic characteristics of the respondent and their household (Module A), as well as about land tenure and size (Module B). Considering that the main focus of the survey is carp aquaculture system characteristics and performance, the respondents were the household members mainly responsible about pond operation that also had a good understanding of other livelihood activities within the household (Section 2.4). This was practically always either the household head or the spouse. Module C contains variables about aquaculture production, commercialisation and knowledge. It is by far the most extensive part of the dataset in terms of the number of questions and variables. Specifically, for each pond operated by the household, this module contains by fish species the amounts produced, sold (raw or processed), gifted and selfconsumed, as well as the generated income. The module also contains variables about all the material inputs (e.g. feed, seed), production methods (including Better Management Practices; BMPs), and aquaculture labour, assets, expenditures and knowledge.1 Upazilas are administrative divisions in Bangladesh. The stand at the third tier of administrative levels in Bangladesh, below Divisions and Districts, and above Unions/Municipalities and Villages/Wards. They are analogous to counties.Module D contains variables about other on-farm livelihood activities related to agriculture and livestock production. Variables encompass the production, consumption and sales of all major crops/vegetables and livestock, generated income, as well as relevant input costs (e.g. agrochemicals, labour). Module E contains variables about the participation of households in different producers' groups. Furthermore, it includes variables about their access to credit and aquaculture/agriculture knowledge, training and extension. Module F and G contains the base variables to construct two standardised metrics of food security and poverty respectively. Module F contained the base variables to develop the Food Consumption Score (FCS), a measure of dietary diversity (WFP, 2008). Module G contains the base variables to calculate the multi-dimensional poverty index (MPI), a composite measure of poverty (Alkire & Santos, 2014). Module H contains variables about: (a) off-farm income, such as income streams from paid labour, side-business, or remittances, and (b) expenditure divided by main household budget items. This module does not contain variables about on-farm income and expenditure, as these are included in Modules C-D. Modules I and J contain variables about household vulnerability to climatic shocks and the COVID-19 pandemic. Module I contains variables about: (a) the exposure of the households to different climatic hazards (e.g. floods, droughts, storms), (b) the effect of hazards on aquaculture output, livelihoods and food security, and (c) the coping mechanisms or adaptive strategies implemented by households to mitigate the effects of such hazards. Module J contains similar variables about the disruption of aquaculture activities during the COVID-19 pandemic. Perception variables are measured through Likert scales tailored to reflect the type of disruption and the measured outcome. All questionnaire modules overwhelmingly contained close-ended question. Depending on the question the respondents either provided actual numerical values (numerical variables) or the enumerators had to assign pre-existing options depending on respondent answers (categorical variables). The latter included the perception-based questions where respondents rated their satisfaction with their fish production (in Module C) or vulnerability to climatic hazards and COVID-19 pandemic (in Module I-J). Variables about all productive activities (in Module C-D) and off-farm income and expenditures (in Module E) were for the previous aquaculture production season, and entailed a 12-month recollection period. Finally, there were very few open-ended questions, which were used as an opportunity to ask respondents to justify some of their answers to previous questions if they wished so. These variables are provided as open text in the database.The biggest challenge to collect the dataset was the lack of high-quality, consistent, comprehensive and spatially-disaggregated datasets about the prevalence and characteristics of aquaculture producers. In particular the relevant national statistics in Bangladesh tend to report very basic information about fish output, without delving deeper on production characteristics or reporting information for lower levels of administration (e.g. upazila level). Regarding the latter, the available production data are generally available for higher administration levels such as divisions (n=8) or districts (n=64) (DoF, 2022). This data resolution is too coarse to allow for the good understanding of the distribution and differentiation of inland aquaculture systems because any given division or district may contain several different areas where aquaculture is very intensive or not performed at all (including anything in between). Arguably it is more appropriate to undertake aquaculture characterisation studies at lower levels of administration such as the upazila (n=495), which is roughly equivalent to the level of the county (see Footnote 1). Due to the lack of reliable carp production data (incl. carp output, numbers of producers) at the upazila level, we followed the multi-stage approach outlined below to identify appropriate study upazilas for data collection. First, we conducted eight expert workshops that collectively brought together 215 experts from eight regions of Bangladeshfoot_0 . The experts were collectivelly knoweldgeable on all stages of aquaculture value chains in Bangladesh (and particularly their respective regions), and covered very diverse expertise spanning from farm/agricultural economics to fish biology, breeding, aquatic ecology, and different fields of social sciences. During these expert workshops we elicited information about which areas in each region contain substantial carp production (hotspots), and which areas have strong potential for carp aquaculture in the future (even if there is no substantial carp production currently). The workshops provided a list of possible study areas at the upazila level, which were further corroborated through discussions with regional fisheries offices and triangulated with secondary data from national aquaculture and fisheries statistics (wherever available) (DoF, 2022). Through this process we drafted a list of 54 upazilas for survey implementation, 30 of which were identified as carp aquaculture hotspots and 24 as having this potential (Figure 1a). These upazilas are spread across the entire country, with the exception of the eastern part of Chittagong Division that has low inland aquaculture output and potential in general (also corroborated from national statistics (DoF, 2022)). For the final triangulation, we cross-checked each of the 54 upazilas with the outcomes of an aquaculture suitability assessment that identified aquaculture suitability accross the country. The suitability analysis was developed by the research team using secondary spatiallydisaggregated datasets for 10 indicators of aquaculture suitability. We created in a GIS environment layers for each of these indicators for the entire country, and then combined them in a composite metric that identified areas of high, moderate and low suitability for aquaculture. The final aquaculture suitability map had a resolution of 30x30m. We then overlayed the suitability map with the upazilas identified through the expert consultation process (Figure 1a) to ensure that the selected upazilas mainly consisted of areas characterised with good suitability for aquaculture (Figure 1b). This cross-referencing demonstrated that all of the 54 selected upazilas fell overwhelmingly in areas of high and moderate aquaculture suitability. The Supplementary Material contains more information about the analytical approach and underlying datasets of the suitability analysis (see Section S1, Table S1, and Figure S1 in Supplementary Material).<> 2.3 and The lack of an consistent and comprehensive of carp producers in Bangladesh (see Section also and collection. we followed a multi-stage approach to identify respondents and their In respondent on the of carp producer in each study upazila and the of respondents from that First, we with input from the in each selected (a) the main of carp production (e.g. and (b) possible of comprehensive carp producer In most study upazilas such producers' were either or This the of carp producers' list in each study the of producers' with the of and/or in each upazila that contained carp production, as by the respective (see through in-person in these we all available carp that could all such producers were we to selected we drafted of carp producers in each These collectively covered carp producers across all the 54 study we selected carp producers in each We at producers in each we producers in of the 54 in the eight upazilas we or carp producers. The final total contained 4,540 Table outlines distribution across survey (see Section was in and in for in-person data using enumerators (see Section The and data were using the by in with the research The enumerators and were with the of the research team 2021). the enumerators conducted surveys for with aquaculture producers in some of the selected study areas to further with the The was also for questions with (e.g. in terms of or and that the in the questions for values were questions and options were to reflect the from the through discussions between the and the research The household survey was conducted between th and th survey depending on the answers of the to for households operating respondents were that they were selected and were that participation in the survey was and as well as that they could from any questions at any They were also that all information and used for research were to further information as to the nature of these research (e.g. of data for research of survey answers to research to that Furthermore, were that they not or or in any other the respondents to mechanisms were implemented during the of the the training of and survey to ensure high and consistent data Section a comprehensive of these data was performed within the (Section and in The dataset was then and using with and conducted in to ensure and Furthermore, we conducted analysis to the of some of the main variables that can used in different (Section First, we the in production variables across households using We this analysis for including household income aquaculture income aquaculture aquaculture of food and aquaculture production All variables were using the of to for The distribution analysis was performed using we the prevalence of major aquaculture species across the households. We the prevalence of major species small species, and as well as carp Furthermore, we the diversity of fish species by households. We these through using we two major demographic of the household head and household We the demographic through generated using of of the including the of survey questions, and data was by the of the of which is by the of and and can data in surveys such as the one outlined in this Data Report et al., et al., 2016). In the of the dataset these can the of and performance at the farm level, and the design of interventions to improve farm performance and The research team was of these the of the study we implemented various to these to the generally from the of the During the stages of research design we identified that the main of in our study with the (a) study areas with and/or very carp production systems, and (b) respondents aquaculture producers) with carp production systems compared to the systems in their respective study with study could by (a) study areas with carp production, (b) study areas in few within Bangladesh (e.g. upazilas in some divisions or districts for and/or (c) study areas that collectively to the diversity of carp production systems in Bangladesh. study could or collectively to a understanding of the national of carp production systems. the from study we followed the multi-stage process outlined in Section that consisted of expert aquaculture suitability analysis and the with secondary data by the of of the Bangladesh of and (DoF, 2022). the upazilas identified through the expert consultation (Figure 1a) with the aquaculture suitability map generated by the research team we that of the selected upazilas within areas of low aquaculture suitability (Figure 1b). This multi-stage approach to study areas with carp production, and particularly areas that lack carp production or areas that collectively to the diversity of carp production systems in Bangladesh. on data and a number of experts we to the (a) that could from on the of a few and (b) due to the of some with respondent could through in producer and/or lack of during respondent in each study We that such of are to due to the lack of consistent and datasets about carp aquaculture in Bangladesh (e.g. Section 1 and with respondent and we followed a The respondent process on a from carp producer developed by the research team (Section This at the and by methods (e.g. or by using producers' ensure a relatively and of we selected respondents in each study from more extensive of producers that were developed by the research team for this (see more in Section We that we not identify carp producer in each study such a of all carp producers in the study was the of the study and have been However, we also that despite our efforts there is some that was not conducted in all some this the lack of comprehensive and carp producers' as this However, we that the final a good of the carp aquaculture sector in Bangladesh the analysis of the of major variables (Section the from the expert workshops et al., and the of other aquaculture studies in the country (Henriksson et al., et al., et al., to all other that can during data which can the collected data to from their values et al., if (a) data from the study and (b) are or to the information (e.g. recollection et al., During the stages of research we identified that the main of with and et al., 2016). We that such of could for two reasons. First, was the team for data the extensive and size that were to the possible was the of information in the survey and the recollection for some of the we developed and to a and comprehensive for the design and of the household were research and data design and the questionnaire to ensure the of the characteristics and performance of carp and respondent enumerators to to the study the questionnaire to survey using the generated from training and all collected surveys for and and and to Finally, we that it is not possible to the of the selected or the dataset due to the lack of national statistics about the main areas of carp production in Bangladesh, as well as the output, and characteristics of carp aquaculture producers. However, we in the of the analysis of the dataset analysis and the expert elicited during the expert In a characterisation study using the dataset to identify differentiation in the production models within the et al., the production models identified by the experts during the et al., for some of the main production and variables in the which can used for different of (Section The substantial for all variables. by in each households an income of with aquaculture output contributing and aquaculture inputs costs of These income and variables with most households lower to levels and the distribution a number of or producers. production was on with a distribution that a few producers have substantially higher fish output the of producers. The production was with the distribution a the substantially higher prevalence of small-scale producers in the carp aquaculture Finally, food were relatively across at a these the heterogeneity among carp producers in the the of our dataset that can used to the different carp production models in the country (Section Figure S1 the prevalence of different fish species in the the many different carp species (see many producers and different species Supplementary The most carp species were carp and Supplementary practically all producers polyculture models Supplementary of producers between and fish species, with aquaculture systems with and species being the most Supplementary Figure some of the main demographic characteristics of the households. household are is and most households contain members household size is outlined in Section 1 small-scale carp aquaculture is a particularly aquatic food system in the low-and middle-income countries (especially of Asia). The dataset can have different in Bangladesh and First, the dataset can used to the heterogeneity and differentiation of carp (and small-scale more aquaculture production systems in Bangladesh, in terms of their characteristics and The main variables to the aquaculture systems are included in Module C and D that contain comprehensive information in terms of species and output, aquaculture and market among others. Variables in Modules G and H can used to develop different performance indicators related to income income in Module off-farm income in Module multi-dimensional poverty (Module food security (Module and environmental performance (Module Regarding the latter, Module C contains comprehensive information about aquaculture inputs and that can used to develop for to efficiency or environmental such as and and related to the previous the dataset can used to in more which factors the performance of these aquatic food systems. factors can very spanning (e.g. of BMPs), demographic (e.g. household (e.g. access to extension (e.g. access to economic (e.g. access to or (e.g. participation in producer variables can in different parts of the Module demographic Module A), Module A and Modules A and economic Modules Module to the previous such studies can focus on Bangladesh or used in if combined with similar datasets from other countries. studies are used to food system and food system vulnerability to and livelihood However, such models not usually have good data for their aquatic food system compared to land-based agricultural systems and dataset contains diverse variables that can help such depending on the focus of the particular could variables related to the production characteristics (Module (Module G and and vulnerability to and livelihood shocks (Module Data a dataset that contains variables that can used to the characteristics and the performance of carp aquaculture systems in Bangladesh. Considering the number of and it wide upazilas across the entire the dataset a of the of the carp aquaculture sector in Bangladesh. Considering that the sector is very dynamic and experiences this information can used to develop a national of this aquatic food This can offer a base to design future interventions and/or the performance of future interventions in the study Although the actual data are in studies on Bangladesh, the of studies using this dataset can have the global importance of small-scale carp aquaculture, especially in countries The between the dataset and the household questionnaire are the to the data of each to the actual question. of household members and other are to ensure All other data are provided in without any