Hungary is northernmost temperate rice growing country in Europe. One of the main limiting factors is low temperature, especially at germination and seedling developmental stages. In early developmental stages, low temperature can impair and delay germination, as well as have negative impacts on seedling growth, causing poor stand establishment and non-uniform crop maturation [1]. Temperatures lower than 15 °C are generally detrimental for germination [2] under filed conditions for establishment of the crop. This article describes some key germination parameters of 165 rice accessions including breeding lines and varieties. Each genotype was grown in three replicates in a controlled cabinet under 13 °C for 4 weeks’ duration. Growth was measured every 7th day. Growth traits such as coleoptile and radicle length were measured at the end of the experiment. The average data were calculated for three replicates. This dataset contains germination raw data and five germination parameters such as median germination time (MGT), final germination percentage (FGP), germination index (GI), coleoptile length (CL) and radicle length (RL). These data may provide reliable support for researchers and breeders to select the right rice genotypes for low temperature conditions.

Hungary is northernmost temperate rice growing country in Europe. One of the main limiting factors is low temperature, especially at germination and seedling developmental stages. In early developmental stages, low temperature can impair and delay germination, as well as have negative impacts on seedling growth, causing poor stand establishment and non-uniform crop maturation [1]. Temperatures lower than 15 °C are generally detrimental for germination [2] under filed conditions for establishment of the crop. This article describes some key germination parameters of 165 rice accessions including breeding lines and varieties. Each genotype was grown in three replicates in a controlled cabinet under 13 °C for 4 weeks’ duration. Growth was measured every 7th day. Growth traits such as coleoptile and radicle length were measured at the end of the experiment. The average data were calculated for three replicates. This dataset contains germination raw data and five germination parameters such as median germination time (MGT), final germination percentage (FGP), germination index (GI), coleoptile length (CL) and radicle length (RL). These data may provide reliable support for researchers and breeders to select the right rice genotypes for low temperature conditions.

This article describes the growth of 18 acclimatized and 11 non-acclimatized rice varieties grown in a hydroponic nutrient solution in a glasshouse. Four plants from each variety were grown under control conditions, salinity stress following control conditions (salinity), and salinity stress following acclimation (salinity/acclimation) conditions. Sampling was performed at the end of the salinity treatment (36 days of growth). Growth traits such as shoot and root biomass accumulation and lengths were measured for each variety, and the average was calculated using four replicates. This dataset may aid interested researchers in making comparisons with their data and further advance the research on the salinity acclimation process in rice.

This article describes the growth of 18 acclimatized and 11 non-acclimatized rice varieties grown in a hydroponic nutrient solution in a glasshouse. Four plants from each variety were grown under control conditions, salinity stress following control conditions (salinity), and salinity stress following acclimation (salinity/acclimation) conditions. Sampling was performed at the end of the salinity treatment (36 days of growth). Growth traits such as shoot and root biomass accumulation and lengths were measured for each variety, and the average was calculated using four replicates. This dataset may aid interested researchers in making comparisons with their data and further advance the research on the salinity acclimation process in rice.

The data described in this article are related to drought occurrence in inland valleys and farmers adaptation strategies. The data were collected in 300 inland valleys distributed in 14 regions of West Africa. The data were collected in two phases. In the first phase, 300 inland valleys were identified in 14 regions and their locations were determined with handheld GPS devices. Questionnaires and informal interviews were administered to inland valleys users to collect data on physical and socio-economic characteristics, hydrology, farmers experience with drought affecting rice production in inland valleys and adaptation strategies. In the second phase, the locations of the inland valleys were imported in a GIS environment and were used to extract additional parameters on soil characteristics and water demand from the Shuttle Radar Topography Mission (SRTM), Africa Soil Information Service (africasoils.net) and POWER database (http://power.larc.nasa.gov). In total, the dataset contains 41 variables divided into seven themes: farmers' experience with drought, adaptive management of rice farmers to drought, physical characteristics, hydrology, management practices, socio-economic characteristics and weather data of inland valleys.

The data described in this article are related to drought occurrence in inland valleys and farmers adaptation strategies. The data were collected in 300 inland valleys distributed in 14 regions of West Africa. The data were collected in two phases. In the first phase, 300 inland valleys were identified in 14 regions and their locations were determined with handheld GPS devices. Questionnaires and informal interviews were administered to inland valleys users to collect data on physical and socio-economic characteristics, hydrology, farmers experience with drought affecting rice production in inland valleys and adaptation strategies. In the second phase, the locations of the inland valleys were imported in a GIS environment and were used to extract additional parameters on soil characteristics and water demand from the Shuttle Radar Topography Mission (SRTM), Africa Soil Information Service (africasoils.net) and POWER database (http://power.larc.nasa.gov). In total, the dataset contains 41 variables divided into seven themes: farmers’ experience with drought, adaptive management of rice farmers to drought, physical characteristics, hydrology, management practices, socio-economic characteristics and weather data of inland valleys.

The dataset described in this data article represents four agricultural zones in West-Africa that are located in three countries: Benin, Mali and Sierra Leone. The dataset was created through a research collaboration between the Africa Rice Center (AfricaRice), Sierra Leone Agricultural Research Institute (SLARI) and the Institute for Rural Economy (IER). The dataset was compiled to investigate the potential for rice production in inland valleys of the three countries. The results of the investigation were published in Dossou-Yovo et al. (2017) and Djagba et al. (2018). The dataset describes the biophysical and socioeconomic conditions of 499 inland valleys in the four agricultural zones. In each inland valley data were collected through a focus group interview with a minimum of three farmers. In 499 interviews a total of 7496 farmers participated. The location of each inland valley was determined with handheld GPS devices. The geographic locations were used to extract additional parameters from digital maps on soils, elevation, population density, rainfall, flow accumulation, and distances to roads, market places, rice mills, chemical input stores, and settlements. The dataset contains 65 parameters in four themes (location, biophysical characteristics, socioeconomic characteristics, and inland valley land development and use). The GPS coordinates indicate the location of an inland valley, but they do not lead to the location of individual fields of farmers that were interviewed. The dataset is publicly shared as Supplementary data to this data article.

The dataset described in this data article represents four agricultural zones in West-Africa that are located in three countries: Benin, Mali and Sierra Leone. The dataset was created through a research collaboration between the Africa Rice Center (AfricaRice), Sierra Leone Agricultural Research Institute (SLARI) and the Institute for Rural Economy (IER). The dataset was compiled to investigate the potential for rice production in inland valleys of the three countries. The results of the investigation were published in Dossou-Yovo et al. (2017) and Djagba et al. (2018). The dataset describes the biophysical and socioeconomic conditions of 499 inland valleys in the four agricultural zones. In each inland valley data were collected through a focus group interview with a minimum of three farmers. In 499 interviews a total of 7496 farmers participated. The location of each inland valley was determined with handheld GPS devices. The geographic locations were used to extract additional parameters from digital maps on soils, elevation, population density, rainfall, flow accumulation, and distances to roads, market places, rice mills, chemical input stores, and settlements. The dataset contains 65 parameters in four themes (location, biophysical characteristics, socioeconomic characteristics, and inland valley land development and use). The GPS coordinates indicate the location of an inland valley, but they do not lead to the location of individual fields of farmers that were interviewed. The dataset is publicly shared as Supplementary data to this data article.

This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers’ fields, 91 plots of 100 m2 and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain.

This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers’ fields, 91 plots of 100 m² and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain.

The paper presents a dataset of the e-registration of rice value chain actors in Benin and Côte d'Ivoire for assessing the adoption of innovations and the diffusion of new rice technologies. Data were collected from actors after a census conducted in three steps. In the first step, main rice production regions and rice value chain actors were identified. In the second step, we updated the list of actors based on membership of actors’ associations. In third step, we did the census of all individual actors and geo-localized all farmers’ fields and villages using GPS device. Data were collected for the 2018 growing seasons. The dataset contains 17,639 observations (9,000 in Benin and 8,639 in Côte d'Ivoire) with 159 variables divided into six sections: (i) preliminary information on the respondents; (ii) socio-economic characteristics; (iii) information on the rice plots; (iv) knowledge, use and access to rice varieties; (v) knowledge, use and access to agricultural equipment and methods; and (vi) information on post-harvest activities. Six categories of actors were identified: foundation seed producers (420), certified seed producers (1,212), paddy rice producers (14,230), parboilers (1,735), millers (188) and traders (1,429). The dataset is available online at Mendeley data repository. The dataset is valuable for the diffusion at large scale of improved technologies and an effective monitoring of the dissemination. Data can be used by scientists to have better understanding of the rice value chains, rice production systems, the level of knowledge, accessibility and adoption of improved rice varieties and agricultural technologies, for further research regarding rice value chain development, technologies testing and socioeconomics study of rice value chain actors. Because of the large number of observations (17,639), data can be used as sampling frame for further experiment or surveys based on random samples. Moreover, the dataset has the potential of generating descriptive statistics at the most disaggregated level of administrative units or villages for different equipment, methods and varieties adopted by gender and country.

The paper presents a dataset of the e-registration of rice value chain actors in Benin and Côte d'Ivoire for assessing the adoption of innovations and the diffusion of new rice technologies. Data were collected from actors after a census conducted in three steps. In the first step, main rice production regions and rice value chain actors were identified. In the second step, we updated the list of actors based on membership of actors’ associations. In third step, we did the census of all individual actors and geo-localized all farmers’ fields and villages using GPS device. Data were collected for the 2018 growing seasons. The dataset contains 17,639 observations (9,000 in Benin and 8,639 in Côte d'Ivoire) with 159 variables divided into six sections: (i) preliminary information on the respondents; (ii) socio-economic characteristics; (iii) information on the rice plots; (iv) knowledge, use and access to rice varieties; (v) knowledge, use and access to agricultural equipment and methods; and (vi) information on post-harvest activities. Six categories of actors were identified: foundation seed producers (420), certified seed producers (1,212), paddy rice producers (14,230), parboilers (1,735), millers (188) and traders (1,429). The dataset is available online at Mendeley data repository. The dataset is valuable for the diffusion at large scale of improved technologies and an effective monitoring of the dissemination. Data can be used by scientists to have better understanding of the rice value chains, rice production systems, the level of knowledge, accessibility and adoption of improved rice varieties and agricultural technologies, for further research regarding rice value chain development, technologies testing and socioeconomics study of rice value chain actors. Because of the large number of observations (17,639), data can be used as sampling frame for further experiment or surveys based on random samples. Moreover, the dataset has the potential of generating descriptive statistics at the most disaggregated level of administrative units or villages for different equipment, methods and varieties adopted by gender and country.

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.

We present raw sequence reads and genome assemblies derived from 17 accessions of the Ethiopian orphan crop plant enset (Ensete ventricosum (Welw.) Cheesman) using the Illumina HiSeq and MiSeq platforms. Also presented is a catalogue of single-nucleotide polymorphisms inferred from the sequence data at an average density of approximately one per kilobase of genomic DNA.

We present raw sequence reads and genome assemblies derived from 17 accessions of the Ethiopian orphan crop plant enset (Ensete ventricosum (Welw.) Cheesman) using the Illumina HiSeq and MiSeq platforms. Also presented is a catalogue of single-nucleotide polymorphisms inferred from the sequence data at an average density of approximately one per kilobase of genomic DNA.

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.