We use and develop spatial data for livestock, nutrient production, and agricultural systems at multiple resolutions and spatial extent (regional, national, and global). Spatial data are important to us because they help us better understand the drivers of agriculture at the local and global scales. Spatial data analysis is a principal component of many of our projects that address decision-making at multiple locations.
Biologically consistent, spatially disaggregated global livestock datasets for eight livestock production systems for four animal species (cattle, small ruminants, pigs, and poultry). These datasets contain information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions. Herrero et al. (2013)
Global gridded maps of nutrient production and nutrient diversity for different farm sizes. The collection includes data for production levels of 41 major crops, seven livestock, and 14 aquaculture and fish products and vitamin A, vitamin B₁₂, folate, iron, zinc, calcium, calories, and protein as well as food production diversity indices; the Shannon diversity index [H], the Modified Functional Attribute Diversity (MFAD), and species richness [S]. Herrero et al. (2017)
Our analyses draw on a number of different household datasets collected by different organizations from around the world. We often combine household datasets for meta-analyses. This allows us to gain a broader perspective of the trends and patterns of farming systems in households of different sizes, locations, farming practices and incomes. Understanding the drivers of these trends is critical to be able to make an impact in these areas.
Afrint collects household level data for approximately 4000 smallholder farms to establish a baseline through survey data. From this baseline, assessments are made on the patterns of change among these households. These include linkages between farm and non-farm sources of livelihood as well as gendered patterns of access to income both within and outside agriculture.
The initial phase (Afrint I) started in 2002 and aimed to look at the possibilities for an Asian style Green Revolution in nine countries in Sub-Saharan Africa. The second phase (Afrint II) was launched in 2008 and aimed to trace patterns of change among these households.
IMPACTLite is a household survey which has been implemented in 15 benchmark sites in 12 countries across East Africa, West Africa and South Asia, between 2011 and 2014, managed by the Consultative Group on International Agricultural Research (CGIAR) Research Program on Climate Change, Agriculture and Food Security (CCAFS). The IMPACTLite data helps to capture the diversity of farming activities and characterises the main agricultural production systems in these sites.
The dataset includes information on:
- household composition and agriculture production systems and activities
- land and labour allocation within households
- farmers’ income from on-farm and off-farm activities, and
- household consumption on food and assets.
N2Africa is a nitrogen fixation project assisting smallholder farmers in Africa to grow legume crops. By 2019 the project aims to have tailored and adapted legume technologies to close yield gaps and reduce yield variability in target countries, build local expertise, provide opportunities for low socio-economic households, address gender disparities and reach more than 550,000 farmers. The baseline survey was undertaken for 3403 households across Ghana, Nigeria, Rwanda, DRC, Kenya, Malawi, Mozambique and Zimbabwe in 2011.
The Centre for Environmental Economics and Policy in Africa (CEEPA) survey aims to gather information about farming systems across different agro-climatic zones in Africa. The survey was undertaken on 9500 households across Burkina Faso, Cameroon, Ghana, Niger, Senegal, Egypt, Ethiopia, Kenya, South Africa, Zambia and Zimbabwe. The project aims to characterise the farming systems in these households to assess their ability to cope with short and long-term climate change and extreme weather events.
The Living Standards Measurement Study (LSMS) is a household survey program focussed on generating high-quality data, improving survey methods, and building capacity for eight partner countries in Sub-Saharan Africa. The LSMS aims to facilitate the use of household survey data for evidence-based policy making.
Rural Socioeconomic Survey (ERSS) strengthens the production of household-level data on agriculture. The project aims to improve agriculture statistics and the link between agriculture and other household activities. The ERSS is a nationally representative survey of 3,969 households living in rural and village areas in Ethiopia. Data collection was focussed on rural areas and covered all regional states except the capital city, Addis Ababa. The survey consists of three rounds of visits to the households.
Through its partners, the Consultative Group on International Agricultural Research (CGIAR) Research Program on Climate Change, Agriculture and Food Security (CCAFS) conducted baselines at household, village and organisational levels across the five target regions of West Africa, East Africa, South Asia, Latin America and Southeast Asia over the period 2011-2014. These surveys will be repeated in 2017-2018 and again in 2022-2023 so that the impacts of the CCAFS program can be quantified at target sites. The household baseline data are used by several organisations with a particular interest in cross-site comparisons. All data, publications and questionnaires are publicly available on CCAFS Dataverse website.
The Rural Household Multi-Indicator Survey (RHoMIS) is a rapid, affordable, digital farm household-level survey and analytical engine for characterising, targeting and monitoring agricultural performance. RHoMIS captures information describing farm productivity and practices, nutrition, food security, gender equity, climate and poverty. Since it was developed in 2015, RHoMIS has been used in Central America; West, East and Central Africa; and South and Southeast Asia to characterise more than 7,000 farm households. RHoMIS is developed by a team of researchers from the International Livestock Research Institute (ILRI), the World Agroforestry Center (ICRAF), and Bioversity International. For further information please refer to Fraval et al. 2019 or Hammond et al. 2017.
Nutrition & Health Data
The World Cancer Research Fund maintains a global database on implemented policies that promote healthy diets and reduce obesity called the NOURISHING database. Policies are grouped according to ten different policy areas across three domains which influence dietary patterns including the food environment, the food system and behaviour change communication.
In collaboration with institutions throughout the world, the International Food Policy Research Institute (IFPRI) is involved in the collection of primary data and the compilation and processing of secondary data. These datasets provide a wealth of information at the local (household and community), national, and global levels. IFPRI freely distributes many of these datasets and encourages their use in research and policy analysis. Please contact IFPRI-Data [at] cgiar.org (IFPRI-Data[at]cgiar[dot]org) for questions about IFPRI datasets.
The Food and Agriculture Organization of the United Nations (FAO) food balance sheets provide a comprehensive picture of the pattern of a country’s food supply during a specified reference period. Data on per capita food supplies are expressed in terms of quantity and, by applying appropriate food composition factors for all primary and processed products, also in terms of dietary energy value, protein and fat content. provide a comprehensive picture of the pattern of a country’s food supply during a specified reference period. Data on per capita food supplies are expressed in terms of quantity and, by applying appropriate food composition factors for all primary and processed products, also in terms of dietary energy value, protein and fat content.
The USDA National Nutrient Database for Standard Reference is a comprehensive online database of food composition for the United States of America. It contains nutrient information on nearly 9,000 foods.
INFOODS is the International Network of Food Data Systems. The INFOODS website serves as a repository for country and region specific food composition databases, as well as a number of useful guidelines and tools for improving the quality, availability, reliability and use of food composition data.
Models are a key component of our toolbox. We use them to find solutions to a range of challenges facing agricultural systems. See below for descriptions of some of the models we commonly use.
RUMINANT is an animal-level model that simulates the effects of nutrition (feed quality and availability) on the growth and production of cattle, sheep and goats. The RUMINANT model consists of a dynamic section that estimates intake and the supply of nutrients to the animal from the knowledge of the fermentation kinetics and passage of feed constituents (carbohydrate and protein) through their gastrointestinal tract and their subsequent excretion.
The IMPACT model was developed to explore the challenges facing policymakers in reducing hunger, and poverty. IMPACT is a network of linked economic, water, and crop models. The central module is a partial equilibrium multi-market economic model, which simulates agricultural markets. The links to water and crop models support the integrated analysis of changing environmental, biophysical, and socioeconomic trends. IMPACT is developed by the International Food Policy Research Institute (IFPRI). Read more about IMPACT in Model Description for Version 3 by Robinson et al. 2015.
G-Range is a global rangeland model that simulates generalized changes in rangelands through time. Spatial data and a set of parameters that describe plant growth in landscape units combine with computer code representing ecological processes to represent soil nutrient and water dynamics, vegetation growth, fire, and wild and domestic animal offtake. The model is spatial, with areas of the world divided into square cells. Those cells that are rangelands have ecosystem dynamics simulated. A graphical user interface allows users to explore model output. G-Range was developed from a joint collaboration between CSIRO, Colorado State University, International Livestock Research Institute (ILRI) and CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Refer to Boone et al. (2018) for more information about the G-Range model.
Global Biosphere Management Model (GLOBIOM) is used to investigate the various trade-offs and synergies around land use and ecosystem services. It is a global, recursively dynamic, and partial equilibrium model. It is capable of capturing the multiple interrelationships between different systems involved in provision of agricultural and forestry products and draws on comprehensive socioeconomic and geospatial data. It accounts for the 18 most globally important crops, a range of livestock production activities, forestry commodities, first- and second-generation bioenergy, and water. Production is spatially explicit and takes into account land, management, and weather characteristics.
GLOBIOM is developed by the International Institute for Applied Systems Analysis (IIASA). We use the GLOBIOM model for a range of applications such as investigating climate change mitigation through livestock systems transition: read more about this work in Havlík et al. (2014).
The Modular Applied GeNeral Equilibrium Tool (MAGNET) it is an economy-wide model that simulates production, demand, and trade globally across all economic sectors. We use MAGNET to assess agricultural policies and their impacts not only in the agriculture sector but across the whole economy. MAGNET has a modular structure, which allows the model to be tailored to a wide range of applications. The MAGNET consortium, led by LEI Wageningen UR, includes the Institute for Prospective Technological Studies (IPTS), which is an institute of the European Commission’s Joint Research Centre (JRC) and the Thünen-Institute (TI).
The Model of Agricultural Production and its Impact on the Environment (MAgPIE) is a global land use allocation model. It is coupled to the grid-based dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJmL), with a spatial resolution of 0.5°x0.5°. The MAgPIE model accounts for regional economic conditions such as demand for agricultural commodities as well as spatially explicit data on potential crop yields, land and water constraints. Based on these, the model derives specific land use patterns, yields and total costs of agricultural production.
The MAgPIE model is developed by the Potsdam Institute for Climate Impact Research (PIK). We use the MAgPIE model for a range of applications such as investigating livestock production systems in a changing climate: read more about this work in Weindl et al. (2015).