Datasets

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Ghana. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 500 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Kenya. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 600 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Nepal. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 421 women and 161 male farmers randomly drawn from a pre-listing exercise done for a previous household survey in 2020. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time. This is Round 4 of the five surveys done so far.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Nepal. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 540 women and 219 male farmers randomly drawn from a pre-listing exercise done for a previous household survey in 2020. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time. This is Round 1 of the five surveys done so far.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Nepal. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 490 women and 200 male farmers randomly drawn from a pre-listing exercise done for a previous household survey in 2020. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time. This is Round 2 of the five surveys done so far.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Nepal. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 449 women and 178 male farmers randomly drawn from a pre-listing exercise done for a previous household survey in 2020. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time. This is Round 3 of the five surveys done so far.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Nepal. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 449 women and 178 male farmers randomly drawn from a pre-listing exercise done for a previous household survey in 2020. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time. This is Round 5 of the five surveys done so far.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Nigeria. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 500 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

× This dataset consists of half-degree resolution simulated land use/land cover projections (in hectares) for the entire globe for the years 2005 and 2050. The future climates are derived from five General Circulation Models (GCMs) using Representative Carbon Pathway (RCP) 8.5 (Riahi, K. et al., 2011).
The Land Use and Land Cover (LULC) classes consist of nine natural vegetation categories (barren, savanna, deciduous/mixed, evergreen broadleaf forest, evergreen needleleaf forest, grassland, shrubland, tundra, and woody savanna) and twelve cropland categories (rainfed and irrigated for maize, rice, sorghum, soybeans, wheat, and all other crops). Three different data sources were used to estimate the natural vegetation component and each result simulation is reported separately. One set of cases indicates the potential area for the natural vegetation classes if there were no cropland. The second set includes cropland and thus the natural vegetation areas are reduced. The total cropland areas are derived from the IMPACT model with adjustments made for multi-cropping.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Ghana. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 500 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing for analysis of the impact of COVID-19 through time.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.
The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP).
This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.
The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP).
This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.
The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP).
This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.
The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP).
This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

×
Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.
The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP).
This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.
The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP).
This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

× These data are from phone surveys conducted for 1,767 wheat and tomato farmers spread across four districts in the state of Haryana, India. These surveys were conducted by phone during the harvest of the Rabi (winter) crop of 2020 in the context of an ongoing panel survey on agricultural risk management and coincided with the imposition of COVID-19 related lockdown in India. In the case of wheat farmers, the phone surveys were conducted between April and May 2020 and included a series of questions related to the effects of the lockdown on crop losses, harvesting practices, harvest costs, and commercialization of produce after harvest. In the case of tomato, a multi-picking crop, we administered the same set of questions through several follow-up surveys over the course of the harvesting season from June to July 2020, allowing us to explore the dynamic effects of the lockdown restrictions. In addition, for both wheat and tomato producers, the survey asked about borrowing and included a short module on household’s access to food before and after the lockdown, providing insights on lockdown-related borrowing and disruptions in food security. These datasets provide insight into the effects of lockdown on different phases of production and marketing and the linkages between agricultural income losses and risk coping mechanisms. These datasets consist of:(1) one wheat farmer survey; (2) six tomato farmer surveys, including 4 weekly surveys and a first and last survey; and (3) one dataset presenting select data from earlier rounds of data collection.

× This dataset contains the data and scripts required to reproduce the tables and figures in the study titled "Income, consumer preferences, and the future of livestock-derived food demand." R scripts were run using R version 4.0.5 on Windows 10 x64.
All the data and script should be placed in one folder. Add a R project into the folder (for example, "project_ldfDemand.Rproj"). Open the R project before running the scripts.
The scripts (extension .R) are ordered sequentially, and should be run sequentially for the first time. The script "22masterFile.R" is the master file that runs all scripts sequentially from start to finish.
The study generated simulation results in GAMS. The GAMS code is not part of the scripts in this dataset. Please direct any questions on the GAMS code and input data to Adam Komarek (a.komarek@uq.edu.au)

× This new database of emissions in agricultural production provides data on emissions by type, commodity, source, and country. Wherever possible, this matrix was derived by reverse-engineering the Food and Agriculture Organization (FAO) emission data to ensure that the total matched the FAOSTAT. Where this was not possible, as in the case of emissions from pesticides, a similar Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodology was used to generate comparable estimates. Emission sources are identified used eleven FAOSTAT-based categories plus emissions from agricultural pesticides. The first step was to define the activity levels associated with commodity outputs, such as the area used for rice cultivation. The second was to calculate the emission coefficients (EC) for CH4, CO2, and N2O by activity level using, wherever possible, the FAOSTAT database. Finally, emissions of N2O and CH4 were converted to CO2 equivalents using 310 and 21 for N2O and CH4 respectively.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Kenya. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 600 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Kenya. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 600 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

× This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Kenya. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 500 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

× This is a processed dataset of approximately 20,000 near-surface remote sensing images acquired using inexpensive smartphones within the context of a picture-based insurance (PBI) initiative of 1,685 smallholder farmers fields in northwest India. Monitoring crop growth and disturbances is critical in strengthening farmers’ ability to manage production risks. The data presented monitors winter wheat growth and includes meta-data, either manually or automatically derived, to quantify 5 crop greenness, phenology and damage events as well as management practices. Our dataset offers granular visual field data, with processed images and detailed meta-data that provide information on the timing of key developmental phases of winter wheat and crop growth disturbances which are not registered by common satellite remote sensing vegetation indices or national crop cut surveys. The purpose of this high-resolution dataset is to provide a rich source of inputs in supporting of crop modeling and production risk assessment in support of food security in smallholder agricultural systems. We, therefore, foresee that these data will find applications in crop modeling, remote sensing validation and machine learning-based crop assessment.

× This dataset contains observations from 604 farmers interviewed in 2018 to study the impact of the use of Laser Land Leveller (LLL) in a severe drought struck region of India. LLL offers various advantages like precise land levelling, reduced time and water for irrigation, uniform distribution of water in fields, good germination, and growth of crops, etc. Thus, various studies have tried to examine the effectiveness of LLL using an ex-ante assessment but in this study, we try to evaluate the impact of LLL on farmers’ livelihood using an ex-post analysis in Raichur district of India.

× In the Sudanian region of Mali where sorghum is an important crop, inorganic fertilizer use is limited due to the high cost and limited availability, and limited soil moisture availability. However, fertilizers from livestock and poultry manure are largely available. This study evaluated different fertilizer response scenarios which will combine both organic and inorganic sources. Our target is to increase productivity (grain and biomass). Biomass so produced could further be used as a source of feed for the livestock and vice-versa. Furthermore, data collected from different agro-ecologies will be used to set up crop simulation models with climate model output of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) to assess climate change impacts on sorghum yields and to evaluate the marginal cost-benefit of different fertility scenarios and varieties as adaptation options to climate change.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.

At IMPACT's core is a partial equilibrium, the multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research center through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and for the population at risk of hunger, by region and for selected countries. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.

At IMPACT's core is a partial equilibrium, the multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research center through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and trade of major food commodity groups, by regions and country. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× This study contains data originated from on-farm trials that were conducted to provide viable options to smallholder farmers to improve soil fertility and human nutrition, to increase biomass for fodder and groundcover and to enhance the resilience of current farming systems against the negative effects of climate variability and change. Green manures or cover crop were used as the option. The trials were conducted at farms in Chipata and Lundazi districts of the eastern province of Zambia. The following treatments were used for the trial: - Sole cropping maize; - Maize ? Pigeonpea intercropping; - Maize ? Pigeonpea intercropping with 60-day cowpea; - Maize ? Dolichos Lablab intercropping; - Maize/Gliricidia sepium/pigeonpea intercrop (year 1: groundnuts/Gliricidia; Year 2 soybean/pigeonpea/Gliricidia; year 3 - Maize/Pigeonpea/Gliricidia

× The IFPRI-CCAFS Gender and Climate Change Survey data were collected in four sites in Africa south of the Sahara in early 2013 as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) under the project "Increasing Women’s Resilience to Confront Climate Change." It aimed to enhance understanding of gendered climate change perceptions, impacts, adaptation and coping strategies, as well as the constraints to adaptation within selected CCAFS baseline sites in Kenya, Senegal, and Uganda. Specifically, the survey was designed to answer questions such as:
• How do men and women perceive climate change and, particularly, the livelihood risks associated with climate change?

• What are the gender disparities in access to and control over assets and how and to what degree does the disparity in assets affect how men and women experience climate shocks and change?

• How and to what degree does asset disparity determine how men and women respond to climate shocks and change?

• Which coping strategies and adaptation options are favored by women and men, respectively, and why?

The survey collected detailed gender-disaggregated data on these issues to inform strategies to increase climate change resilience among both women and men.The gender survey contained 13 modules, posing questions at the household and individual levels. These modules are: 1) a household roster; 2) sketch of the farm (pre-populated from a previous survey—IMPACT Lite); 3) land ownership, management, and decision making; 4) decision-making authority on agricultural, livestock, and household decisions; 5) adoption and knowledge of climate-smart agriculture practices; 6) access to and use of climate and agricultural information services; 7) access to and use of credit; 8) membership in groups; 9) fuel and water use; 10) experience with climate shocks and coping strategies; 11) perception of climate change and its potential impacts; 12) identification of adaptation strategies; and 13) cognitive decision-making and personal values questions.

Four sets of data are generated from the survey conducted in four sites-Nyando and Wote in Kenya, Rakai in Uganda, and Kaffrine in Senegal. This study contains data from Kaffrine.

× The IFPRI-CCAFS Gender and Climate Change Survey data were collected in four sites in Africa south of the Sahara in early 2013 as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) under the project "Increasing Women’s Resilience to Confront Climate Change." It aimed to enhance understanding of gendered climate change perceptions, impacts, adaptation and coping strategies, as well as the constraints to adaptation within selected CCAFS baseline sites in Kenya, Senegal, and Uganda. Specifically, the survey was designed to answer questions such as:
• How do men and women perceive climate change and, particularly, the livelihood risks associated with climate change?

• What are the gender disparities in access to and control over assets and how and to what degree does the disparity in assets affect how men and women experience climate shocks and change?

• How and to what degree does asset disparity determine how men and women respond to climate shocks and change?

• Which coping strategies and adaptation options are favored by women and men, respectively, and why?

The survey collected detailed gender-disaggregated data on these issues to inform strategies to increase climate change resilience among both women and men. The gender survey contained 13 modules, posing questions at the household and individual levels. These modules are: 1) a household roster; 2) sketch of the farm (pre-populated from a previous survey—IMPACT Lite); 3) land ownership, management, and decision making; 4) decision-making authority on agricultural, livestock, and household decisions; 5) adoption and knowledge of climate-smart agriculture practices; 6) access to and use of climate and agricultural information services; 7) access to and use of credit; 8) membership in groups; 9) fuel and water use; 10) experience with climate shocks and coping strategies; 11) perception of climate change and its potential impacts; 12) identification of adaptation strategies; and 13) cognitive decision-making and personal values questions.

Four sets of data are generated from the survey conducted in four sites-Nyando and Wote in Kenya, Rakai in Uganda, and Kaffrine in Senegal. This study contains data from Nyando.

× This is a compiled unique long-term panel dataset that consists of data on (i) pest severity and insecticide applications per annum per county by pest species, and (ii) land cover/use data. The counties in the database are the 51 most important cotton-growing counties, by production, in the Yangtze River valley and Yellow River valley cotton production regions, while the data covered the years 1991–2015, with complete coverage of counties in all years when cotton was cultivated. Between 2011 and 2013, eight counties in our sample stopped cultivating cotton. The number increased to 11 and 12 counties in 2014 and 2015, respectively, resulting in 47 missing records.

The national cotton pests monitoring network, maintained by the Ministry of Agriculture mandates the main cotton-producing counties to collect yearly data on pest infestation levels and insecticide applications for key cotton pests following national standardized monitoring and categorization methods. Tailored scouting methods were used for different pests. In each county, 10–20 fields were selected for pest monitoring in each year. Insect populations were recorded every 3–10 days from early June to late August each year, and the seasonal average abundance across the surveyed fields were used for scoring using a five-point scale of levels I–V. Data on the number of insecticide applications targeted at specific pests were collected by interviewing farmers at each scouting to estimate yearly pest-specific total number of sprays for each county. While the detailed data collection methods and protocols should inspire confidence in the data, the reliability of the pest level data depends on the accuracy, knowledge, and honesty of the respondents, as is the case with any non–first-hand data.

County-level land use data were drawn from a national land cover/use database developed by the Chinese Academy of Sciences, using satellite remote-sensing data from the Landsat Thematic Mapper/Enhanced Thematic Mapper images. The database offers the most comprehensive coverage of China’s land use/cover and has been used in a number of published studies. The land use data for 6 years (1990-2015) at 5 years interval were extracted. The proportional area for each six main land use classes as well as the Shannon index for land use diversity for each county for six years was computed. Land use proportions in the intermediate years (e.g., 1991, 1992, 1993, 1994, 1996, etc.) were calculated by linear interpolation between the data.

× The IFPRI-CCAFS Gender and Climate Change Survey data were collected in four sites in Africa south of the Sahara in early 2013 as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) under the project "Increasing Women’s Resilience to Confront Climate Change." It aimed to enhance understanding of gendered climate change perceptions, impacts, adaptation and coping strategies, as well as the constraints to adaptation within selected CCAFS baseline sites in Kenya, Senegal, and Uganda. Specifically, the survey was designed to answer questions such as:
• How do men and women perceive climate change and, particularly, the livelihood risks associated with climate change?

• What are the gender disparities in access to and control over assets and how and to what degree does the disparity in assets affect how men and women experience climate shocks and change?

• How and to what degree does asset disparity determine how men and women respond to climate shocks and change?

• Which coping strategies and adaptation options are favored by women and men, respectively, and why?

The survey collected detailed gender-disaggregated data on these issues to inform strategies to increase climate change resilience among both women and men. The gender survey contained 13 modules, posing questions at the household and individual levels. These modules are: 1) a household roster; 2) sketch of the farm (pre-populated from a previous survey—IMPACT Lite); 3) land ownership, management, and decision making; 4) decision-making authority on agricultural, livestock, and household decisions; 5) adoption and knowledge of climate-smart agriculture practices; 6) access to and use of climate and agricultural information services; 7) access to and use of credit; 8) membership in groups; 9) fuel and water use; 10) experience with climate shocks and coping strategies; 11) perception of climate change and its potential impacts; 12) identification of adaptation strategies; and 13) cognitive decision-making and personal values questions.

Four sets of data are generated from the survey conducted in four sites-Nyando and Wote in Kenya, Rakai in Uganda, and Kaffrine in Senegal. This study contains data from Rakai.

× As part of an IFPRI-funded project, raw and partially processed secondary data have been harmonized and analyzed to examine the effects of weather variability on household welfare, the latter measured using per capita total household expenditure. This cross-country project covers rural Ghana, Uganda, and Tanzania. Household survey data, a time series of monthly precipitation and temperature, as well as other biophysical data have been harmonized and analyzed using Stata software. Several Stata do files have been created for data processing and analysis as noted in the attached “READ ME.txt” file.

The sources of household survey data are the following: National Household Budget Surveys and National Panel Surveys (for Tanzania); National Household Surveys and National Panel Surveys (for Uganda); and Ghana Living Standards Surveys (for Ghana). Precipitation data are obtained from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Temperature data are obtained from the Center for Climatic Research at the University of Delaware. Other landscape-level biophysical data analyzed include night lights, population density, and agroecological zones (AEZ).

× The IFPRI-CCAFS Gender and Climate Change Survey data were collected in four sites in Africa south of the Sahara in early 2013 as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) under the project "Increasing Women’s Resilience to Confront Climate Change." It aimed to enhance understanding of gendered climate change perceptions, impacts, adaptation and coping strategies, as well as the constraints to adaptation within selected CCAFS baseline sites in Kenya, Senegal, and Uganda. Specifically, the survey was designed to answer questions such as:
• How do men and women perceive climate change and, particularly, the livelihood risks associated with climate change?

• What are the gender disparities in access to and control over assets and how and to what degree does the disparity in assets affect how men and women experience climate shocks and change?

• How and to what degree does asset disparity determine how men and women respond to climate shocks and change?

• Which coping strategies and adaptation options are favored by women and men, respectively, and why?

The survey collected detailed gender-disaggregated data on these issues to inform strategies to increase climate change resilience among both women and men. The gender survey contained 13 modules, posing questions at the household and individual levels. These modules are: 1) a household roster; 2) sketch of the farm (pre-populated from a previous survey—IMPACT Lite); 3) land ownership, management, and decision making; 4) decision-making authority on agricultural, livestock, and household decisions; 5) adoption and knowledge of climate-smart agriculture practices; 6) access to and use of climate and agricultural information services; 7) access to and use of credit; 8) membership in groups; 9) fuel and water use; 10) experience with climate shocks and coping strategies; 11) perception of climate change and its potential impacts; 12) identification of adaptation strategies; and 13) cognitive decision-making and personal values questions.

Four sets of data are generated from the survey conducted in four sites-Nyando and Wote in Kenya, Rakai in Uganda, and Kaffrine in Senegal. This study contains data from Wote.

× The IFPRI-CCAFS Gender and Climate Change Survey data were collected between April and May 2013 as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) under the project "Increasing Women’s Resilience to Confront Climate Change." It aimed to enhance understanding of gendered climate change perceptions, impacts, adaptation, and coping strategies, as well as the constraints to adaptation within two CCAFS research sites in Bangladesh, Bagerhat and Satkhira.
Data were collected on the three previous agricultural seasons, that is, from August 2011 to August 2012. The survey covered the same households that were sampled for the IMPACT Lite surveys, designed by researchers at the International Livestock Research Institute (ILRI) in order to supplement the detailed productivity-related information collected through that survey.

Gender-disaggregated data were gathered on climate change perceptions, agricultural activities, decision-making, weather information, risk perceptions, and adaptation. Information was also collected on assets, farming decisions, agricultural practices, respondents’ access to information, extension services, and credit; and their participation in community groups. Data were gathered through the use of questionnaires administered through face-to-face interviews. Two adult decision makers (one male and one female) were interviewed separately per household in order to capture independent perceptions.

× IMPACT lite is a tool, developed by the International Livestock Research Institute (ILRI) that provides a unifying framework for collecting detailed information on farm resources, farm management strategies, farm productivity and household economics at the household-level.
The objective of this survey is to capture within-site variability on key livelihood indicators that could be used for a range of analysis including the modelling of impact of adaptation and mitigation strategies on livelihoods, food security and the environment. The survey was carried out in 13 other benchmark sites across East Africa, West Africa, and South Asia. IMPACT Lite data from Satkhira, Bangladesh were collected by IFPRI as part of the IFPRI-CCAFS Gender and Climate Change Survey Data.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for total demand and demand index of various agricultural commodities, by region and for selected countries. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and for the population at risk of hunger, by region and for selected countries. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.

Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.

The 2017 GHI has been calculated for 119 countries for which data on the four component indicators are a

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and trade of major food commodity groups, by regions and country. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and trade of major food commodity groups, by regions and country. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other l

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset is an extended set of results from IMPACT version 3.2.1 generated for the analysis originally presented in Sulser et al (2015) and covers “baseline scenarios” of different socioeconomic assumptions, climate change, and no climate change from 2010 to 2050.

× This is replication data used in the analysis for the correspondence article published at Nature Climate Change: "Subnational socio-economic dataset availability."

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.

Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.

The 2016 GHI has been calculated for 118 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators.

This year's GHI reflects the most recent available country-level data and projections available between 2011 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2016. The 1992, 2000, 2008, and 2016 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available.

The four component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1992, 2000, 2008, and 2016 GHI scores. Undernourishment data and projections for the 2016 GHI are for 2014-2016.

2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; and statistical tables from UNICEF. For the 2016 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2011-2015.

3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1992, 2000, 2008, and 2016 GHI scores. For the 2016 GHI, data on child mortality are from 2015.

× This data study includes highly disaggregated Social Accounting Matrix (SAM) for the Mexican economy for year 2008. The Mexico SAM was built as the main data base for the calibration of a Computable General Equilibrium (CGE) Model, used to investigate the quantitative effects of climate change on the Mexican economy, with emphasis on analyzing its distributional impacts. The Mexico SAM thus presents a significant disaggregation of agricultural activities and to income distribution across households and subnational regions.

× Pakistan Rural Household Panel Survey (PRHPS) 2012, Round 1.5 is a sub-sample consisting of agricultural households captured in Round 1 of the panel survey. It covers information of households, who either managed or cultivated agricultural plots, during the production year 2011-12, and two crop seasons, namely: (i) Kharif 2011; (ii) Rabi 2011-12. The survey gathers information on agricultural production (including inputs and outputs of crop and livestock production), agricultural water use, farm and household assets, access to extension services, climate change, credit, employment and income, household consumption and expenditures, and the linkage between pesticides and health. This survey covers 942 agricultural households in 76 primary sampling units in the rural areas of three provinces namely: (i) Punjab; (ii) Sindh; and (iii) Khyber Pakhtunkhwa (KPK). The sample is nationally representative of the rural areas of the three provinces. Data was collected using a questionnaire based on discussion among IFPRI, PSSP and IDS staff and feedback from enumerators.

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.

This year, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.

The 2015 GHI has been calculated for 117 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators.

This year's GHI reflects the most recent available country-level data and projections available between 2010 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2015. The 1990, 1995, 2000, 2005, and 2015 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available.

The four component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2015 GHI scores. Undernourishment data and projections for the 2015 GHI are for 2014-2016.

2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; statistical tables from UNICEF; and the latest national survey data for India from UNICEF India. For the 2015 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2010-2014.

3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2015 GHI scores. For the 2015 GHI, data on child mortality are for 2013.

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low.

The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013.

2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014.

3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012.

× The Bangladesh Climate Change Adaptation Survey (BCCAS), Round II is a follow up survey to BCCAS, Round I. About 97.34 percent of households from BCCAS I were reinterviewed in 2012. The survey captures information on demographic characteristics, social capital, land tenure, crop and livestock management, input use, extension, incidence of climatic shocks in the last five years and adaptation options and if there are any changes from 2010 survey. . The survey was conducted from September to October 2012, covering data from the previous production year. It covers households in the 7 broad agroecological zones as grouped by the Bangladesh Center for Advanced Studies. (2014-11-21)

× The Bangladesh Climate Change Adaptation Survey (BCCAS), Round I contains data on 800 farming households. It provides information on demographic characteristics, social capital, land tenure, crop and livestock management, input use, extension, incidence of climatic shocks in the last five years and adaptation options. The survey was conducted from December 2010 to February 2011, covering data from the previous production year. It covers households in the 7 broad agroecological zones a s grouped by the Bangladesh Center for Advanced Studies. (2014)

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2013 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low.

The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2008 to 2012. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2013 GHI scores. Undernourishment data for the 2013 GHI are for 2010-2012.
2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint database by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2013 GHI, data on child underweight are for the latest year for which data are available in the period 2008-2012.
3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2013 GHI scores. For the 2013 GHI, data on child mortality are for 2011.

× The 2012 GHI report focuses particularly on the issue of how to ensure sustainable food security under conditions of water, land, and energy stress. Demographic changes, rising incomes and associated consumption patterns, and climate change, alongside persistent poverty and inadequate policies and institutions, are all placing serious pressure on natural resources. In this report, IFPRI describes the evidence on land, water, and energy scarcity in developing countries and offers two visions of a future global food system—an unsustainable scenario in which current trends in resource use continue, and a sustainable scenario in which access to food, modern energy, and clean water improves significantly and ecosystem degradation is halted or reversed. Concern Worldwide and Welthungerhilfe provide on-the-ground perspectives on the issues of land tenure and title as well as the impacts of scarce land, water, and energy on poor people in Sierra Leone and Tanzania and describe the work of their organizations in helping to alleviate these impacts.

× The household survey was carried out in the Nile River Basin in Ethiopia. The household sampling frame in Ethiopia was developed to ensure representation for the Nile River Basin at the woreda (district) level regarding level of rainfall patterns in terms of both annual total and variation; the four classes of traditionally defined agro-ecological zones (AEZs) found in the basin; vulnerability of food production systems (through the proxy of frequency of food aid in the past ten years); and irrigation prevalence. All data used for the sample frame is from the Atlas of the Ethiopian Rural Economy (Benson et al., 2006).; Each woreda was classified based on : agroecological zone (Kolla, Weynadega, Dega, and Bereha), the percent of cultivated land under irrigation (no data, 0-2%, 2-4%, 4-8%, and 8% or greater), average annual rainfall (0-854mm, 854-1133mm, 1133-1413mm, 1413-1692mm, 1692mm or greater), rainfall variability (coefficient of variation for annual rainfall), and vulnerability (number of years of food aid received in the past 10 years). ;Twenty woredas were selected such that across each of the above dimensions the proportion falling into each class for the sample matched as closely as possible the proportions for the entire Ethiopian Nile basin. Peasant associations (administrative units lower than districts) were also purposely selected to include households that irrigate their farms. One peasant association was selected from every woreda for a total of 20 peasant associations. Random sampling was used in selecting 50 households from each peasant administration within the 20 woredas. Thus, the final dataset contains 1,000 observations from 20 woredas in 5 regional states in Ethiopia (Tigray, Amhara, Oromiya, Benishangul Gumuz, and Southern Nations Nationalities and Peoples (SNNP)).

× This household survey was conducted as part of a project aimed to provide policymakers and stakeholders in South Africa with tools to better understand, analyze, and form policy decisions to adapt to global change. The survey was conducted in the Limpopo River Basin in South Africa during 2005. A total of 794 households from 19 districts, in 5 Water Management Areas (WMAs), across 4 provinces of South Africa were sampled. The South Africa sample was designed to capture the diverse agricultural patterns in the basin area: farming strata, type of cultivation (dry land and irrigation), major and minor crops and livestock, and all the sub-catchment areas in each of the 5 WMAs. Topics covered include household roaster including background information, farm and non-farm activities, assets, basic services, disease, shocks; land ownership and land holdings; farm machinery, farm buildings, wells and pumps, and wage rates; crop production including production cost and income for seasonal crops and for perennial crops; crop water use; livestock production; access to extension, markets and credit; expenditures on food and income; and climate change and adaptation options.

× The Agro-MAPS database has been created primarily to fill some critical gaps in the range of data needed for undertaking analyses related to land degradation, food security, farming systems studies, nutrient balances and climate change effects. The database currently contains data on crop production, area harvested and crop yields, for one or more years, for each country. The current web version of this database contains data for a subset of 20 crops that are considered to be of regional and global importance based on relative harvested areas. A tabular summary of the data contained in the Agro-MAPS database is available at http://www.fao.org/landandwater/agll/agromaps/agromapsdatasummary.pdf.; It is envisaged that other sub-national land-use information that are useful for characterizing agricultural production systems, will eventually be incorporated in Agro-MAPS. Detailed information on the Agro-MAPS initiative, including database preparation and future plans, are presented in a background paper at http://www.fao.org/landandwater/agll/agromaps/agromapsbackground.doc.; The Agro-MAPS database, which would eventually cover all regions of the world, is being prepared separately by FAO (for Africa and the Middle East), IFPRI (Latin America, Asia, Australia and New Zealand) and SAGE (the rest of the world).