Deepor Beel, located in the state of Assam in India, is a Wetland of International Importance with a Wildlife Sanctuary and is the only RAMSAR site in the state. Though of invaluable ecological significance, the wetland is facing anthropogenic stressors, leading to rapid degradation of ecological health. In December 2022, surface water was collected from six stations of Deepor Beel to elucidate biological communities using the eDNA approach. At the time of sampling, in-situ environmental parameters were measured in triplicates. The dissolved nutrients and concentrations of metals and metalloids were estimated using UV–Vis Spectrophotometry and ICP-MS approaches respectively. The study revealed a high concentration of dissolved nitrate in the surface water. High-throughput sequencing using Nanopore sequencing chemistry in a MinION platform indicated the overwhelming abundance of Moraxellaceae (Prokaryotes) and Eumetazoa (Eukaryotes). The abundance of Cyprinidae were also encountered in the studied wetland reflecting the biodiversity of fish populations. High nitrate along with elucidated microbial signals are crucial to designate ecological health status of Deeper Beel. This study is aimed at generating baseline information to aid long-term monitoring and restoration of the Deepor Beel as well as the first comprehensive assessment of a RAMSAR Site located in northeast of India.

Deepor Beel, located in the state of Assam in India, is a Wetland of International Importance with a Wildlife Sanctuary and is the only RAMSAR site in the state. Though of invaluable ecological significance, the wetland is facing anthropogenic stressors, leading to rapid degradation of ecological health. In December 2022, surface water was collected from six stations of Deepor Beel to elucidate biological communities using the eDNA approach. At the time of sampling, in-situ environmental parameters were measured in triplicates. The dissolved nutrients and concentrations of metals and metalloids were estimated using UV–Vis Spectrophotometry and ICP-MS approaches respectively. The study revealed a high concentration of dissolved nitrate in the surface water. High-throughput sequencing using Nanopore sequencing chemistry in a MinION platform indicated the overwhelming abundance of Moraxellaceae (Prokaryotes) and Eumeta- zoa (Eukaryotes). The abundance of Cyprinidae were also encountered in the studied wetland reflecting the biodiversity of fish populations. High nitrate along with elucidated microbial signals are crucial to designate ecological health status of Deeper Beel. This study is aimed at generating baseline information to aid long-term monitoring and restoration of the Deepor Beel as well as the first comprehensive assessment of a RAMSAR Site located in northeast of India.

Deepor Beel, located in the state of Assam in India, is a Wetland of International Importance with a Wildlife Sanctuary and is the only RAMSAR site in the state. Though of invaluable ecological significance, the wetland is facing anthropogenic stressors, leading to rapid degradation of ecological health. In December 2022, surface water was collected from six stations of Deepor Beel to elucidate biological communities using the eDNA approach. At the time of sampling, in-situ environmental parameters were measured in triplicates. The dissolved nutrients and concentrations of metals and metalloids were estimated using UV–Vis Spectrophotometry and ICP-MS approaches respectively. The study revealed a high concentration of dissolved nitrate in the surface water. High-throughput sequencing using Nanopore sequencing chemistry in a MinION platform indicated the overwhelming abundance of Moraxellaceae (Prokaryotes) and Eumetazoa (Eukaryotes). The abundance of Cyprinidae were also encountered in the studied wetland reflecting the biodiversity of fish populations. High nitrate along with elucidated microbial signals are crucial to designate ecological health status of Deeper Beel. This study is aimed at generating baseline information to aid long-term monitoring and restoration of the Deepor Beel as well as the first comprehensive assessment of a RAMSAR Site located in northeast of India. | PR | IFPRI3; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply | Development Strategies and Governance (DSG); Transformation Strategies

This data paper describes the content of four datasets col- lected by the International Center for Agricultural Research in the Dry Areas (ICARDA) as a partner in the project “Designing InnoVative plant teams for Ecosystem Resilience and agricultural Sustainability (DIVERSify)”with the objec- tive of assessing the feasibility of faba bean-wheat mix- ture in Mediterranean environments under diverse rainfed conditions. Data was collected during the trials conducted in Kfardan-Lebanon during 2017/2018 where 40 faba bean varieties were evaluated as sole and as mixture with 2 wheat cultivars ‘Margherita’ and ‘Miki’ and during 2018/2019 where 40 faba bean varieties and one durum wheat cultivar ‘Margherita’ were evaluated under low rainfall environments. Trials were also conducted in Tal Amara-Lebanon during 2019/2020 where 20 faba bean lines and one durum wheat cultivar ‘Margherita’ were evaluated under high rainfall en- vironments and in Marchouch-Morocco during 2019/2020 where 7 faba bean lines with 3 cultivars and one durum wheat cultivar ‘Margherita’ were evaluated under extremely low rainfall environments. A detailed list of the different bi- ological traits collected for wheat and faba bean is found in the specification table in this article. The Kfardan 2018/ 2019, Tal Amara and Marchouch data is related to the conference paper “Performance of faba bean-wheat mixture under di- verse Mediterranean environments”.

This data paper describes the content of four datasets col- lected by the International Center for Agricultural Research in the Dry Areas (ICARDA) as a partner in the project “Designing InnoVative plant teams for Ecosystem Resilience and agricultural Sustainability (DIVERSify)”with the objec- tive of assessing the feasibility of faba bean-wheat mix- ture in Mediterranean environments under diverse rainfed conditions. Data was collected during the trials conducted in Kfardan-Lebanon during 2017/2018 where 40 faba bean varieties were evaluated as sole and as mixture with 2 wheat cultivars ‘Margherita’ and ‘Miki’ and during 2018/2019 where 40 faba bean varieties and one durum wheat cultivar ‘Margherita’ were evaluated under low rainfall environments. Trials were also conducted in Tal Amara-Lebanon during 2019/2020 where 20 faba bean lines and one durum wheat cultivar ‘Margherita’ were evaluated under high rainfall en- vironments and in Marchouch-Morocco during 2019/2020 where 7 faba bean lines with 3 cultivars and one durum wheat cultivar ‘Margherita’ were evaluated under extremely low rainfall environments. A detailed list of the different bi- ological traits collected for wheat and faba bean is found in the specification table in this article. The Kfardan 2018/ 2019, Tal Amara and Marchouch data is related to the conference paper “Performance of faba bean-wheat mixture under di- verse Mediterranean environments”.

This data paper describes the content of four datasets collected by the International Center for Agricultural Research in the Dry Areas (ICARDA) as a partner in the project “Designing InnoVative plant teams for Ecosystem Resilience and agricultural Sustainability (DIVERSify)” with the objective of assessing the feasibility of faba bean-wheat mixture in Mediterranean environments under diverse rainfed conditions. Data was collected during the trials conducted in Kfardan-Lebanon during 2017/2018 where 40 faba bean varieties were evaluated as sole and as mixture with 2 wheat cultivars ‘Margherita’ and ‘Miki’ and during 2018/2019 where 40 faba bean varieties and one durum wheat cultivar ‘Margherita’ were evaluated under low rainfall environments. Trials were also conducted in Tal Amara-Lebanon during 2019/2020 where 20 faba bean lines and one durum wheat cultivar ‘Margherita’ were evaluated under high rainfall environments and in Marchouch-Morocco during 2019/2020 where 7 faba bean lines with 3 cultivars and one durum wheat cultivar ‘Margherita’ were evaluated under extremely low rainfall environments. A detailed list of the different biological traits collected for wheat and faba bean is found in the specification table in this article. The Kfardan 2018/2019, Tal Amara and Marchouch data is related to the conference paper “Performance of faba bean-wheat mixture under diverse Mediterranean environments” [1].

This data paper describes the content of four datasets col- lected by the International Center for Agricultural Research in the Dry Areas (ICARDA) as a partner in the project “Designing InnoVative plant teams for Ecosystem Resilience and agricultural Sustainability (DIVERSify)”with the objec- tive of assessing the feasibility of faba bean-wheat mix- ture in Mediterranean environments under diverse rainfed conditions. Data was collected during the trials conducted in Kfardan-Lebanon during 2017/2018 where 40 faba bean varieties were evaluated as sole and as mixture with 2 wheat cultivars ‘Margherita’ and ‘Miki’ and during 2018/2019 where 40 faba bean varieties and one durum wheat cultivar ‘Margherita’ were evaluated under low rainfall environments. Trials were also conducted in Tal Amara-Lebanon during 2019/2020 where 20 faba bean lines and one durum wheat cultivar ‘Margherita’ were evaluated under high rainfall en- vironments and in Marchouch-Morocco during 2019/2020 where 7 faba bean lines with 3 cultivars and one durum wheat cultivar ‘Margherita’ were evaluated under extremely low rainfall environments. A detailed list of the different bi- ological traits collected for wheat and faba bean is found in the specification table in this article. The Kfardan 2018/ 2019, Tal Amara and Marchouch data is related to the conference paper “Performance of faba bean-wheat mixture under di- verse Mediterranean environments”.

Two expert elicitation workshops were conducted in 2017 to capture the diversity and cultural drivers of food choice of low- and middle- income households in the states of West Bengal and Odisha in eastern India. Experts representing the fields of nutrition, home science, food technology, and food service industry were invited to participate. Following the “gastronomic systems research” framework, the food experts determined the eating occasions, dishes and ingredients that would culturally define the target population in their respective states. To zoom in further on the nutritional implications, one of the two states was selected for further in-depth study by expanding the list of dishes and conducting nutritional analysis. The approach is elaborated in the article “Capturing diversity and cultural drivers of food choice in eastern India” [1]. The workshop generated two databases: (i) “List of dishes and ingredients from expert elicitation workshop” and (ii) “Database of eastern Indian dishes”. The former was used to differentiate the eating occasions based on dishes, the proportion of dishes based on dish classification, and the dietary diversity score of each occasion. The dietary diversity score was then used to analyze the nutritional composition of dishes in terms of three macro nutrients such as protein, carbohydrates and fat in each eating occasion. The databases provide a useful baseline for nutritionists, policymakers, and food system actors to design nutrition intervention strategies for the purpose of developing planetary health diets in eastern India.

Two expert elicitation workshops were conducted in 2017 to capture the diversity and cultural drivers of food choice of low- and middle- income households in the states of West Bengal and Odisha in eastern India. Experts representing the fields of nutrition, home science, food technology, and food service industry were invited to participate. Following the “gastronomic systems research” framework, the food experts determined the eating occasions, dishes and ingredients that would culturally define the target population in their respective states. To zoom in further on the nutritional implications, one of the two states was selected for further in-depth study by expanding the list of dishes and conducting nutritional analysis. The approach is elaborated in the article “Capturing diversity and cultural drivers of food choice in eastern India” [1]. The workshop generated two databases: (i) “List of dishes and ingredients from expert elicitation workshop” and (ii) “Database of eastern Indian dishes”. The former was used to differentiate the eating occasions based on dishes, the proportion of dishes based on dish classification, and the dietary diversity score of each occasion. The dietary diversity score was then used to analyze the nutritional composition of dishes in terms of three macro nutrients such as protein, carbohydrates and fat in each eating occasion. The databases provide a useful baseline for nutritionists, policymakers, and food system actors to design nutrition intervention strategies for the purpose of developing planetary health diets in eastern India.

This data paper describes the content of 16 datasets collected under the International Winter Wheat Improvement Program (IWWIP), an alliance between Turkey-CIMMYT-ICARDA (TCI), during the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 seasons. Data was collected from the Facultative and Winter Wheat Observation Nursery (FAWWON) and the International Winter Wheat Yield Trials (IWWYT) conducted under semi-arid and irrigated conditions across different countries. Data on all nurseries was collected during the growing season by IWWIP's team and cooperators in their local environments. It was compiled at the end of the wheat season by IWWIP's team. Multi-locational data can be used to select advanced lines that fit to collaborators’ growing environment. The selected germplasm can either be used as a parent in their breeding programs or be released as a variety in their country.

This data paper describes the content of 16 datasets collected under the International Winter Wheat Improvement Program (IWWIP), an alliance between Turkey-CIMMYT-ICARDA (TCI), during the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 seasons. Data was collected from the Facultative and Winter Wheat Observation Nursery (FAWWON) and the International Winter Wheat Yield Trials (IWWYT) conducted under semi-arid and irrigated conditions across different countries. Data on all nurseries was collected during the growing season by IWWIP's team and cooperators in their local environments. It was compiled at the end of the wheat season by IWWIP's team. Multi-locational data can be used to select advanced lines that fit to collaborators’ growing environment. The selected germplasm can either be used as a parent in their breeding programs or be released as a variety in their country.

This data paper describes the content of 16 datasets collected under the International Winter Wheat Improvement Program (IWWIP), an alliance between Turkey-CIMMYT-ICARDA (TCI), during the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 seasons. Data was collected from the Facultative and Winter Wheat Observation Nursery (FAWWON) and the International Winter Wheat Yield Trials (IWWYT) conducted under semi-arid and irrigated conditions across different countries. Data on all nurseries was collected during the growing season by IWWIP's team and cooperators in their local environments. It was compiled at the end of the wheat season by IWWIP's team. Multi-locational data can be used to select advanced lines that fit to collaborators’ growing environment. The selected germplasm can either be used as a parent in their breeding programs or be released as a variety in their country. Paper accepted for publication on Data in Brief - Volume 41, April 2022, 107902.

This data paper describes the content of 16 datasets collected under the International Winter Wheat Improvement Program (IWWIP), an alliance between Turkey-CIMMYT-ICARDA (TCI), during the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 seasons. Data was collected from the Facultative and Winter Wheat Observation Nursery (FAWWON) and the International Winter Wheat Yield Trials (IWWYT) conducted under semi-arid and irrigated conditions across different countries. Data on all nurseries was collected during the growing season by IWWIP's team and cooperators in their local environments. It was compiled at the end of the wheat season by IWWIP's team. Multi-locational data can be used to select advanced lines that fit to collaborators’ growing environment. The selected germplasm can either be used as a parent in their breeding programs or be released as a variety in their country.

This data paper describes the content of 16 datasets collected under the International Winter Wheat Improvement Program (IWWIP), an alliance between Turkey-CIMMYT-ICARDA (TCI), during the 2015–2016, 2016–2017, 2017–2018 and 2018–2019 seasons. Data was collected from the Facultative and Winter Wheat Observation Nursery (FAWWON) and the International Winter Wheat Yield Trials (IWWYT) conducted under semi-arid and irrigated conditions across different countries. Data on all nurseries was collected during the growing season by IWWIP's team and cooperators in their local environments. It was compiled at the end of the wheat season by IWWIP's team. Multi-locational data can be used to select advanced lines that fit to collaborators’ growing environment. The selected germplasm can either be used as a parent in their breeding programs or be released as a variety in their country.

The data presented in this article are related to the research article entitled “Cover cropping reduces the abundance of the banana weevil Cosmopolites sordidus but does not reduce its damage to the banana plants” (Carval et al., in press) [1]. This article describes how the abundance of the banana weevil, Cosmopolites sordidus, and the abundance of the earwig Euborellia caraibea were affected by the addition of a cover crop. The field data set is made publicly available to enable critical or extended analyzes. (Résumé d'auteur)

The data presented in this article are related to the research article entitled “Cover cropping reduces the abundance of the banana weevil Cosmopolites sordidus but does not reduce its damage to the banana plants” (Carval et al., in press) [1]. This article describes how the abundance of the banana weevil, Cosmopolites sordidus, and the abundance of the earwig Euborellia caraibea were affected by the addition of a cover crop. The field data set is made publicly available to enable critical or extended analyzes.

The data presented in this article are related to the research article entitled "Cover cropping reduces the abundance of the banana weevil <em>Cosmopolites sordidus</em> but does not reduce its damage to the banana plants" (Carval et al.,in press). This article describes how the abundance of the banana weevil, <em>Cosmopolites sordidus</em>, and the abundance of the earwig <em>Euborellia caraibea</em> were affected by the addition of a cover crop.The field dataset is made publicly available to enable critical or extended analyzes.

Conservation agriculture (CA) is an agronomic management system based on zero tillage and residue retention. Due to its potential for climate change adaptation through the reduction of soil erosion and improved water availability, CA is becoming more important in many regions of the world. However, increased bulk density and large amounts of crop residues may be a constraint for early plant establishment. This holds especially true under irrigated production areas with high yield potential. Genotype × tillage effects on yield are not well understood and it is unclear whether tillage should be an evaluation factor in breeding programs. Fourteen CIMMYT bread (Triticum aestivum) and thirteen durum (Triticum turgidum) wheat genotypes, created between 1964 and 2011, were tested for yield and agronomic performance at CIMMYT's experimental station near Ciudad Obregon, Mexico, during nine seasons. The genotypes were subjected to different tillage and irrigation treatments which consisted of conventional and permanent raised beds with full and reduced irrigation. The dataset includes traits collected during the growing period (days to emergence, days to flowering, maturity, plant height, NDVI, days from flowering to maturity, grain production rate) and at harvest (yield, harvest index, thousand grain weight, spikes/m², grains/m², test weight) and weather data (daily minimum and maximum temperature, rainfall). Six years of data of 26 genotypes were published along with the Honsdorf et al. (2018) paper in Field Crops Research (DOI: s10.1016/j.fcr.2017.11.011). This updated dataset includes three additional seasons of data (harvest years 2016 to 2018) and an additional bread wheat genotype (Borlaug100).

Conservation agriculture (CA) is an agronomic management system based on zero tillage and residue retention. Due to its potential for climate change adaptation through the reduction of soil erosion and improved water availability, CA is becoming more important in many regions of the world. However, increased bulk density and large amounts of crop residues may be a constraint for early plant establishment. This holds especially true under irrigated production areas with high yield potential. Genotype × tillage effects on yield are not well understood and it is unclear whether tillage should be an evaluation factor in breeding programs. Fourteen CIMMYT bread (Triticum aestivum) and thirteen durum (Triticum turgidum) wheat genotypes, created between 1964 and 2011, were tested for yield and agronomic performance at CIMMYT's experimental station near Ciudad Obregon, Mexico, during nine seasons. The genotypes were subjected to different tillage and irrigation treatments which consisted of conventional and permanent raised beds with full and reduced irrigation. The dataset includes traits collected during the growing period (days to emergence, days to flowering, maturity, plant height, NDVI, days from flowering to maturity, grain production rate) and at harvest (yield, harvest index, thousand grain weight, spikes/m², grains/m², test weight) and weather data (daily minimum and maximum temperature, rainfall). Six years of data of 26 genotypes were published along with the Honsdorf et al. (2018) paper in Field Crops Research (DOI: s10.1016/j.fcr.2017.11.011). This updated dataset includes three additional seasons of data (harvest years 2016 to 2018) and an additional bread wheat genotype (Borlaug100).

The data was collected in the Karagwe and Kyerwa districts of the Kagera region in north-west Tanzania. It encompasses 150 smallholder farming households, which were interviewed on the composition of their household, agricultural production and use of organic farm waste. The data covers the two previous rainy seasons and the associated vegetation periods between September 2016 and August 2017. The knowledge of experts from the following institutions was included in the discussion on the selection criteria: two local non-profit organisations, i.e., WOMEDA and the MAVUNO Project; the International Institute of Tropical Agriculture (IITA); and the National Land Use Planning Commission (NLUPC). Households were selected for inclusion if all of the following applied to them: 1) less than 10 acres of land (4.7 ha) registered in the village offices, 2) no agricultural training, and 3) decline in the fertility of their land since they started farming (self-reported). We selected 150 smallholder households out of a pool of 5,000 households known to WOMEDA in six divisions of the Kyerwa and Karagwe districts. The questionnaire contained 54 questions. The original language of the survey was Kiswahili. All interviews were audio recorded. The answers were digitalised and translated into English. The data set contains the raw data with 130 quantitative and qualitative variables. For quantitative variables, the only analysis that was made was the conversion of units, e.g., land area was converted from acres to hectares, harvest from buckets to kilograms and then to tons, and heads of livestock to Tropical Livestock Units (TLU). Qualitative variables were summarised into categories. All data has been anonymised. The data set includes geographical variables, household information, agricultural information, gender-specific responsibilities, economic data, farm waste management, and water, energy and food availability (Water-Energy-Food (WEF) Nexus). Variables are written in italics. The following geographical variables are part of the data set: district, division, ward, village, hamlet, longitude, latitude, and altitude. Household information includes start of farming, household size, gender and age of household members. Agricultural information includes land size, size of homegarden, crops, livestock and livestock keeping, trees, and access to forest. Gender-specific responsibilities includes producing and exchanging seeds, weed control, terracing, distributing organic material to the fields, care of annual and perennial crops, harvesting of crops, decisions about the harvest and animal products, selling and buying products, working on their own farm and off-farm, cooking, storing food, collecting and caring for drinking water, washing, and toilet cleaning. Economic data includes distance to the market, journey time to market, transport methods, labourers employed by the household, working off-farm, and assets such as type of house. Variables relevant to the WEF Nexus are drinking water source and treatment, meals per day, months without food, cooking fuel, and type of toilet. Variables on farm waste management are the use of crop residues, food and kitchen waste, livestock manure, cooking ash, animal bones, and human urine and faeces. The data can be potentially reused and further developed for the purpose of agricultural production analysis, socio-economic analysis, comparison to other regions, conceptualisation of waste and nutrient management, establishment of land use concepts, and further analysis on food security and healthy diets.

The data was collected in the Karagwe and Kyerwa districts of the Kagera region in north-west Tanzania. It encompasses 150 smallholder farming households, which were interviewed on the composition of their household, agricultural production and use of organic farm waste. The data covers the two previous rainy seasons and the associated vegetation periods between September 2016 and August 2017. The knowledge of experts from the following institutions was included in the discussion on the selection criteria: two local non-profit organisations, i.e., WOMEDA and the MAVUNO Project; the International Institute of Tropical Agriculture (IITA); and the National Land Use Planning Commission (NLUPC). Households were selected for inclusion if all of the following applied to them: 1) less than 10 acres of land (4.7 ha) registered in the village offices, 2) no agricultural training, and 3) decline in the fertility of their land since they started farming (self-reported). We selected 150 smallholder households out of a pool of 5,000 households known to WOMEDA in six divisions of the Kyerwa and Karagwe districts. The questionnaire contained 54 questions. The original language of the survey was Kiswahili. All interviews were audio recorded. The answers were digitalised and translated into English. The data set contains the raw data with 130 quantitative and qualitative variables. For quantitative variables, the only analysis that was made was the conversion of units, e.g., land area was converted from acres to hectares, harvest from buckets to kilograms and then to tons, and heads of livestock to Tropical Livestock Units (TLU). Qualitative variables were summarised into categories. All data has been anonymised. The data set includes geographical variables, household information, agricultural information, gender-specific responsibilities, economic data, farm waste management, and water, energy and food availability (Water-Energy-Food (WEF) Nexus). Variables are written in italics. The following geographical variables are part of the data set: district, division, ward, village, hamlet, longitude, latitude, and altitude. Household information includes start of farming, household size, gender and age of household members. Agricultural information includes land size, size of homegarden, crops, livestock and livestock keeping, trees, and access to forest. Gender-specific responsibilities includes producing and exchanging seeds, weed control, terracing, distributing organic material to the fields, care of annual and perennial crops, harvesting of crops, decisions about the harvest and animal products, selling and buying products, working on their own farm and off-farm, cooking, storing food, collecting and caring for drinking water, washing, and toilet cleaning. Economic data includes distance to the market, journey time to market, transport methods, labourers employed by the household, working off-farm, and assets such as type of house. Variables relevant to the WEF Nexus are drinking water source and treatment, meals per day, months without food, cooking fuel, and type of toilet. Variables on farm waste management are the use of crop residues, food and kitchen waste, livestock manure, cooking ash, animal bones, and human urine and faeces. The data can be potentially reused and further developed for the purpose of agricultural production analysis, socio-economic analysis, comparison to other regions, conceptualisation of waste and nutrient management, establishment of land use concepts, and further analysis on food security and healthy diets.

The data was collected in the Karagwe and Kyerwa districts of the Kagera region in north-west Tanzania. It encompasses 150 smallholder farming households, which were interviewed on the composition of their household, agricultural production and use of organic farm waste. The data covers the two previous rainy seasons and the associated vegetation periods between September 2016 and August 2017. The knowledge of experts from the following institutions was included in the discussion on the selection criteria: two local non-profit organisations, i.e., WOMEDA and the MAVUNO Project; the International Institute of Tropical Agriculture (IITA); and the National Land Use Planning Commission (NLUPC). Households were selected for inclusion if all of the following applied to them: 1) less than 10 acres of land (4.7 ha) registered in the village offices, 2) no agricultural training, and 3) decline in the fertility of their land since they started farming (self-reported). We selected 150 small- holder households out of a pool of 5,000 households known to WOMEDA in six divisions of the Kyerwa and Karagwe districts. The questionnaire contained 54 questions. The original language of the survey was Kiswahili. All interviews were audio recorded. The answers were digitalised and translated into English. The data set contains the raw data with 130 quantitative and qualitative variables. For quantitative variables, the only analysis that was made was the conversion of units, e.g., land area was converted from acres to hectares, harvest from buckets to kilograms and then to tons, and heads of livestock to Tropical Livestock Units (TLU). Qualitative variables were summarised into categories. All data has been anonymised. The data set includes geographical variables, household information, agricultural information, gender-specific responsibilities, economic data, farm waste management, and water, energy and food availability (Water-Energy-Food (WEF) Nexus). Variables are written in italics. The following geographical variables are part of the data set: district, division, ward, village, hamlet, longitude, latitude, and altitude. Household information includes start of farming, household size, gender and age of household members. Agricultural information includes land size, size of homegarden, crops, livestock and livestock keeping, trees, and access to forest. Gender-specific responsibilities includes producing and exchanging seeds, weed control, terracing, distributing organic material to the fields, care of annual and perennial crops, harvesting of crops, decisions about the harvest and animal products, selling and buying products, working on their own farm and off-farm, cooking, storing food, collecting and caring for drinking water, washing, and toilet cleaning. Economic data includes distance to the market, journey time to market, transport methods, labourers employed by the household, working off-farm, and assets such as type of house. Variables relevant to the WEF Nexus are drinking water source and treatment, meals per day, months without food, cooking fuel, and type of toilet. Variables on farm waste management are the use of crop residues, food and kitchen waste, livestock manure, cooking ash, animal bones, and human urine and faeces. The data can be potentially reused and further developed for the purpose of agricultural production analysis, socio-economic analysis, comparison to other regions, conceptualisation of waste and nutrient management, establishment of land use concepts, and further analysis on food security and healthy diets.