Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers’ and farmers’ need. Here we showed the data related to the article entitled “Deploying viscosity and starch polymer properties to predict cooking and eating quality models: a novel breeding tool to predict texture” [1] which provides solution to this problem. The paper compiles all the pasting, starch structure, sensory and routine quality data of the rice sample used in the article into graphical form. It also shows how the data were processed and obtained.

Accurate classification tool for screening varieties with superior eating and cooking quality based on its pasting and starch structure properties is in demand to satisfy both consumers’ and farmers’ need. Here we showed the data related to the article entitled “Deploying viscosity and starch polymer properties to predict cooking and eating quality models: a novel breeding tool to predict texture” [1] which provides solution to this problem. The paper compiles all the pasting, starch structure, sensory and routine quality data of the rice sample used in the article into graphical form. It also shows how the data were processed and obtained.

The data described in this brief were collected in 2018 as part of a national study to elicit preferences and estimate willingness to pay (WTP) for small ruminant market facilities in Ethiopia. We employed multistage sampling method to identify respondents. First, Menz Gishe area was selected from North Shewa administrative zone for its high small ruminant population. Second, three districts from five districts found in Menz Gishe were selected randomly. Then, eight Kebeles1 from fifty one Kebeles were selected randomly. Finally, 360 farmers were randomly selected proportional to the total number of farm households in each Kebele. We used discrete choice experiments to elicit preferences from the 360 respondents across the three districts whereby we presented 12 choice situations to each of them and hence generated 4320 observations. Generalized multinomial logit model (GMNL) and latent class model were used to investigate preferences for the market and heterogeneities around them. We also estimated the GMNL in WTP space to estimate the WTP values for the facilities. The dataset complements an original article entitled “Preference and Willingness to Pay for Small Ruminant Market Facilities in the Central Highlands of Ethiopia”2 and will be useful in replicating results for academic purposes and or employing the data for further development of choice behavior models.

The data described in this brief were collected in 2018 as part of a national study to elicit preferences and estimate willingness to pay (WTP) for small ruminant market facilities in Ethiopia. We employed multistage sampling method to identify respondents. First, Menz Gishe area was selected from North Shewa administrative zone for its high small ruminant population. Second, three districts from five districts found in Menz Gishe were selected randomly. Then, eight Kebeles1 from fifty one Kebeles were selected randomly. Finally, 360 farmers were randomly selected proportional to the total number of farm households in each Kebele. We used discrete choice experiments to elicit preferences from the 360 respondents across the three districts whereby we presented 12 choice situations to each of them and hence generated 4320 observations. Generalized multinomial logit model (GMNL) and latent class model were used to investigate preferences for the market and heterogeneities around them. We also estimated the GMNL in WTP space to estimate the WTP values for the facilities. The dataset complements an original article entitled “Preference and Willingness to Pay for Small Ruminant Market Facilities in the Central Highlands of Ethiopia”2 and will be useful in replicating results for academic purposes and or employing the data for further development of choice behavior models.

The data described in this brief were collected in 2018 as part of a national study to elicit preferences and estimate willingness to pay (WTP) for small ruminant market facilities in Ethiopia. We employed multistage sampling method to identify respondents. First, Menz Gishe area was selected from North Shewa administrative zone for its high small ruminant population. Second, three districts from five districts found in Menz Gishe were selected randomly. Then, eight Kebeles¹ from fifty one Kebeles were selected randomly. Finally, 360 farmers were randomly selected proportional to the total number of farm households in each Kebele. We used discrete choice experiments to elicit preferences from the 360 respondents across the three districts whereby we presented 12 choice situations to each of them and hence generated 4320 observations. Generalized multinomial logit model (GMNL) and latent class model were used to investigate preferences for the market and heterogeneities around them. We also estimated the GMNL in WTP space to estimate the WTP values for the facilities. The dataset complements an original article entitled “Preference and Willingness to Pay for Small Ruminant Market Facilities in the Central Highlands of Ethiopia”² and will be useful in replicating results for academic purposes and or employing the data for further development of choice behavior models.

A consumer survey was conducted in eastern India in 2017 to understand the heterogeneity of consumers’ food choice. Face-to-face interviews were conducted among urban and rural consumers from low- and middle-income households in Odisha and West Bengal, eastern India, using a structured questionnaire. A multi-stage sampling procedure was implemented with stratified random sampling as the first stage and systematic sampling as the second stage. The survey data comprise responses from 501 respondents who have active involvement in grocery purchase decision-making and/or in meal planning or cooking for the household. The survey generated a dataset that was used to unravel five sources of heterogeneity (5Ws) in gastronomic systems that affect consumers' diets: (i) socioeconomic characteristics of the target population (who); (ii) food environments (where); (iii) eating occasions (when); (iv) consumed dishes (what); and (v) ingredient attributes and consumer attitudes towards food (why). The approach and analyses are elaborated in the article “Unraveling heterogeneity of consumers’ food choice: Implications for nutrition interventions in eastern India”. Data from the survey can be further used to design behavioral experiments and interactive food choice tablet applications to elicit behavioral intentions in food choice.

A consumer survey was conducted in eastern India in 2017 to understand the heterogeneity of consumers’ food choice. Face-to-face interviews were conducted among urban and rural consumers from low- and middle-income households in Odisha and West Bengal, eastern India, using a structured questionnaire. A multi-stage sampling procedure was implemented with stratified random sampling as the first stage and systematic sampling as the second stage. The survey data comprise responses from 501 respondents who have active involvement in grocery purchase decision-making and/or in meal planning or cooking for the household. The survey generated a dataset that was used to unravel five sources of heterogeneity (5Ws) in gastronomic systems that affect consumers' diets: (i) socioeconomic characteristics of the target population (who); (ii) food environments (where); (iii) eating occasions (when); (iv) consumed dishes (what); and (v) ingredient attributes and consumer attitudes towards food (why). The approach and analyses are elaborated in the article “Unraveling heterogeneity of consumers’ food choice: Implications for nutrition interventions in eastern India”. Data from the survey can be further used to design behavioral experiments and interactive food choice tablet applications to elicit behavioral intentions in food choice.

Conversion of tropical peat swamp forests to increase and agricultural production has generated substantial peat carbon loss in the Asia-Pacific region. Different land-uses and management practices oxidize the tropical peat at diverse rates due mainly to different water table levels. In recent years, several studies have measured soil carbon dioxide emissions in-situ; however, only few studies have evaluated the effect of moisture on carbon dioxide fluxes in incubation experiments. Here, we present the dataset of an incubation performed with 360 intact peat cores from three different land-uses (i.e. 120 from intact peat swamp forest; 120 from drained logged peat forest; and 120 from oil palm plantation) collected on the peat dome of Jambi Sumatra Indonesia. Different moisture levels in the intact cores were set by either drying the intact peat cores for short period of time or by adding extra water before the incubation. Dynamic dark aerobic incubation in airtight containers coupled with carbon dioxide measurement with an infrared gas analyser and the gas fluxes was used to measure to gas fluxes. The average carbon dioxide fluxes were 5.38 ± 0.91, 4.15 ± 0.35 and 1.55 ± 0.13 µg CO2-C g−1 h−1 for the intact peat swamp forest, drained logged peat forest and oil palm plantation, respectively.

Conversion of tropical peat swamp forests to increase and agricultural production has generated substantial peat carbon loss in the Asia-Pacific region. Different land-uses and management practices oxidize the tropical peat at diverse rates due mainly to different water table levels. In recent years, several studies have measured soil carbon dioxide emissions in-situ; however, only few studies have evaluated the effect of moisture on carbon dioxide fluxes in incubation experiments. Here, we present the dataset of an incubation performed with 360 intact peat cores from three different land-uses (i.e. 120 from intact peat swamp forest; 120 from drained logged peat forest; and 120 from oil palm plantation) collected on the peat dome of Jambi Sumatra Indonesia. Different moisture levels in the intact cores were set by either drying the intact peat cores for short period of time or by adding extra water before the incubation. Dynamic dark aerobic incubation in airtight containers coupled with carbon dioxide measurement with an infrared gas analyser and the gas fluxes was used to measure to gas fluxes. The average carbon dioxide fluxes were 5.38 ± 0.91, 4.15 ± 0.35 and 1.55 ± 0.13 µg CO₂-C g⁻¹ h⁻¹ for the intact peat swamp forest, drained logged peat forest and oil palm plantation, respectively.

This dataset describes the performance of cattle in smallholder livestock systems of Bomet county in western Kenya. Information on live weight, milk production and quality, herd dynamics, and other production parameters were collected from field visits. Animals were weighed on scales; milk yield was recorded using a Mazzican® milk collection and transport vessel provided to each farm and milk was analyzed for butterfat content (%). Pasture biomass yield was determined, and feed samples collected for each agro-ecological zone and nutrient composition was determined for nitrogen (N) using the Kjeldahl method and gross energy (GE) using a bomb calorimeter. Distance covered while grazing was determined using GPS collars fitted to several animals for three consecutive days per area. Enteric methane (CH4) emissions factors (EF) were estimated for five animal classes to develop site-specific EFs as per the Intergovernmental panel on climate change (IPCC) protocol. This dataset has the potential to be used, amongst other purposes, for animal-scale life cycle assessment (LCA) to evaluate the efficacy of various greenhouse gas (GHG) mitigation options.

This dataset describes the performance of cattle in smallholder livestock systems of Bomet county in western Kenya. Information on live weight, milk production and quality, herd dynamics, and other production parameters were collected from field visits. Animals were weighed on scales; milk yield was recorded using a Mazzican® milk collection and transport vessel provided to each farm and milk was analyzed for butterfat content (%). Pasture biomass yield was determined, and feed samples collected for each agro-ecological zone and nutrient composition was determined for nitrogen (N) using the Kjeldahl method and gross energy (GE) using a bomb calorimeter. Distance covered while grazing was determined using GPS collars fitted to several animals for three consecutive days per area. Enteric methane (CH₄) emissions factors (EF) were estimated for five animal classes to develop site-specific EFs as per the Intergovernmental panel on climate change (IPCC) protocol. This dataset has the potential to be used, amongst other purposes, for animal-scale life cycle assessment (LCA) to evaluate the efficacy of various greenhouse gas (GHG) mitigation options.

This dataset describes the performance of cattle in small- holder livestock systems of Bomet county in western Kenya. Information on live weight, milk production and quality, herd dynamics, and other production parameters were collected from field visits. Animals were weighed on scales; milk yield was recorded using a Mazzican ®milk collection and trans- port vessel provided to each farm and milk was analyzed for butterfat content (%). Pasture biomass yield was deter- mined, and feed samples collected for each agro-ecological zone and nutrient composition was determined for nitrogen (N) using the Kjeldahl method and gross energy (GE) using a bomb calorimeter. Distance covered while grazing was de- termined using GPS collars fitted to several animals for three consecutive days per area. Enteric methane (CH 4 ) emissions factors (EF) were estimated for five animal classes to develop site-specific EFs as per the Intergovernmental panel on climate change (IPCC) protocol. This dataset has the potential to be used, amongst other purposes, for animal-scale life cycle assessment (LCA) to evaluate the efficacy of various green- house gas (GHG) mitigation options.

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.

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.