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.

This dataset belongs to a framed economic field experiment conducted in 2016 in Bhilwara district in Rajasthan state in India. A public good game was framed as dam management challenge. We made incentivized payments based on the game earnings. The data are organized as a panel defined by players and experiment rounds. The dataset contains the experiment decisions in different phases of the experiment as well as socio-economic variables of the anonymized players. The data can be accessed through the Dataverse of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) under the following link: https://doi.org/10.21421/D2/MFT8ZD. The data article is related to the research article Falk et al. [2] on “Experimental games for developing institutional capacity to manage common water infrastructure in India”.

This dataset belongs to a framed economic field experiment conducted in 2016 in Bhilwara district in Rajasthan state in India. A public good game was framed as dam management challenge. We made incentivized payments based on the game earnings. The data are organized as a panel defined by players and experiment rounds. The dataset contains the experiment decisions in different phases of the experiment as well as socio-economic variables of the anonymized players. The data can be accessed through the Dataverse of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) under the following link: https://doi.org/10.21421/D2/MFT8ZD. The data article is related to the research article Falk et al. [2] on “Experimental games for developing institutional capacity to manage common water infrastructure in India”.

The data presented in this article contains information on 189 rice breeders from 51 rice-growing countries around the world. Firstly, this unique dataset permits to lay down a baseline of currently used breeding methods. Secondly, the data allow to make an assessment of the adoption behavior of rice breeders towards alternative breeding methods, and in specific rapid generation advance. A global online survey in Google Forms was conducted to obtain information about different aspect of the adoption process. Both the raw and cleaned data are made available, along with Stata code to promote further research into adoption of breeding methods by public and private breeding institutes.

The data presented in this article contains information on 189 rice breeders from 51 rice-growing countries around the world. Firstly, this unique dataset permits to lay down a baseline of currently used breeding methods. Secondly, the data allow to make an assessment of the adoption behavior of rice breeders towards alternative breeding methods, and in specific rapid generation advance. A global online survey in Google Forms was conducted to obtain information about the different aspects of the adoption process. Both the raw and cleaned data are made available, along with Stata code to promote further research into adoption of breeding methods by public and private breeding institutes.

This data article provides the datasets that are used in the holistic ex-ante impact evaluation of an irrigation dam construction project in Northern Ethiopia [1]. We used an expert knowledge elicitation approach as a means of acquiring the data. The data shared here captures all the parameters considered important in the impact pathway (i.e. the expected benefits, costs, and risks) of the decision to construct an irrigation dam. The dataset is disaggregated for two impact pathway models: one complementing the dam construction with catchment restoration and the other without catchment restoration. Both models are scripted in the R programming language. The data can be used to examine how the construction of an irrigation dam affects the incomes as well as the food and nutritional status of farmers that are affected by the intervention.

This data article provides the datasets that are used in the holistic ex-ante impact evaluation of an irrigation dam construction project in Northern Ethiopia [1]. We used an expert knowledge elicitation approach as a means of acquiring the data. The data shared here captures all the parameters considered important in the impact pathway (i.e. the expected benefits, costs, and risks) of the decision to construct an irrigation dam. The dataset is disaggregated for two impact pathway models: one complementing the dam construction with catchment restoration and the other without catchment restoration. Both models are scripted in the R programming language. The data can be used to examine how the construction of an irrigation dam affects the incomes as well as the food and nutritional status of farmers that are affected by the intervention.

This data article provides the datasets that are used in the holistic ex-ante impact evaluation of an irrigation dam construction project in Northern Ethiopia [1]. We used an expert knowledge elicitation approach as a means of acquiring the data. The data shared here captures all the parameters considered important in the impact pathway (i.e. the expected benefits, costs, and risks) of the decision to construct an irrigation dam. The dataset is disaggregated for two impact pathway models: one complementing the dam construction with catchment restoration and the other without catchment restoration. Both models are scripted in the R programming language. The data can be used to examine how the construction of an irrigation dam affects the incomes as well as the food and nutritional status of farmers that are affected by the intervention.

For several decades, maize (Zea mays L.) management decisions in smallholder farming in tropical regions have been a puzzle. To best balance alternative management practices' environmental and economic outcomes, an extensive dataset was gathered through CIMMYT's knowledge hub in Chiapas, a state in southern Mexico. In a knowledge hub, farmers, with the support of farm advisors, compare conventional and improved agronomic practices side-by-side and install demonstration fields where they implement improved practices. In all these fields data on on-farm operations and results is collected. The dataset was assembled using field variables (yield, cultivars, fertilization and tillage practice), as well as environment variables from soil mapping (slope, elevation, soil texture, pH and organic matter concentration) and gridded weather datasets (precipitation, temperature, radiation and evapotranspiration). The dataset contains observations from 4585 fields and comprises a period of 7 years between 2012 and 2018. This dataset will facilitate analytical approaches to represent spatial and temporal variability of alternative crop management decisions based on observational data and explain model-generated predictions for maize in Chiapas, Mexico. In addition, this data can serve as an example for similar efforts in Big Data in Agriculture.

For several decades, maize (Zea mays L.) management decisions in smallholder farming in tropical regions have been a puzzle. To best balance alternative management practices' environmental and economic outcomes, an extensive dataset was gathered through CIMMYT's knowledge hub in Chiapas, a state in southern Mexico. In a knowledge hub, farmers, with the support of farm advisors, compare conventional and improved agronomic practices side-by-side and install demonstration fields where they implement improved practices. In all these fields data on on-farm operations and results is collected. The dataset was assembled using field variables (yield, cultivars, fertilization and tillage practice), as well as environment variables from soil mapping (slope, elevation, soil texture, pH and organic matter concentration) and gridded weather datasets (precipitation, temperature, radiation and evapotranspiration). The dataset contains observations from 4585 fields and comprises a period of 7 years between 2012 and 2018. This dataset will facilitate analytical approaches to represent spatial and temporal variability of alternative crop management decisions based on observational data and explain model-generated predictions for maize in Chiapas, Mexico. In addition, this data can serve as an example for similar efforts in Big Data in Agriculture.