Given limited land and water resources available for crop production and population soon to exceed 9 billion, ensuring food security while protecting carbon-rich and biodiverse rainforests, wetlands, and grasslands depends on our ability to increase current crop yields on existing farmland through sustainable intensification. The Global Yield Gap and Water Productivity Atlas (GYGA) provides robust estimates of untapped crop production potential on existing farmland based on current climate and available soil and water resources.
Results from the atlas can serve for identifying regions with greatest potential for investment in agricultural development and technology transfer and to monitor impact over time. Likewise, the atlas provides essential information to assess the feasibility of a country to achieve food self-sufficiency through crop intensification and, if this cannot be achieved, for assessing how much extra land clearing or food import will be needed to meet future demand for food. The atlas is a foundation for studies aiming to explain and mitigate yield gaps and investigate impact of climate change, land use, and environmental footprint of agriculture.
GYGA is an international project requiring collaboration among agronomists with knowledge of production systems, soils, and climate governing crop performance in their countries. A standard protocol for assessing yield potential (Yp), water-limited yield potential (Yw), yield gaps (Yg) and water productivity (WP) is applied for all crops and countries based on best available data, robust crop simulation models, and a bottom-up approach to upscale results from location to region and country. GYGA aspires for global coverage of yield gaps for all major food crops and countries that produce them. The first phase of the project (2012-2015) focussed on cereal crops. Recently, the crop list has been extended with soybean, sugarcane and potato. Detailed maps and associated databases are displayed and available to download.
Currently the country by crop combinations included in the atlas account for 91%, 86%, 58* and and 82%, respectively, of the global rice, maize, wheat and soybean production.
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NEW: A publication on future food self-sufficiency in Iran and one on impacts of intensifying or expanding cereal cropping in sub‐Saharan Africa on greenhouse gas emissions and food security.
Please look at our references pages or look inside 'read more' below.
Soltani, A., S.M. Alimagham, A. Nehbandani, B. Torabi, E. Zeinali, E. Zand, V. Vadez,,M. P. van Loon, M. K. van Ittersum. 2020. Future food self-sufficiency in Iran: A model-based analysis. Global Food Security. 24 (2020):100351. https://doi.org/10.1016/j.gfs.2020.100351
Loon, Marloes P, Renske Hijbeek, Hein F. M. ten Berge, Veronique De Sy, Guus A. ten Broeke, Dawit Solomon, Martin K. van Ittersum. 2019. Impacts of intensifying or expanding cereal cropping in sub‐Saharan Africa on greenhouse gas emissions and food security. Global Change Biology. 25:3720–3730. https://doi.org/10.1111/gcb.14783
Data from the Atlas are available for use under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
For commercial use a license agreement is needed. We have different commercial license types available, either for a single use of the data within a project context or multiple use and commercialization, suiting your needs. Please email Patricio Grassini (UNL) or Martin van Ittersum (WUR) to discuss options.
Yield gap assessments in the Global Yield Gap Atlas require detailed information about cropping systems, soil properties, long-term weather data, and output from crop simulation models. The Atlas strives to utilize the best available data and most appropriate models following the protocols outlined in the GYGA Methods page. The quality of the best available data vary, however, depending on country and location within country. Therefore, neither the University of Nebraska-Lincoln, Wageningen University, or any of the collaborators on the GYGA project accept any liability for errors or incorrect results in the datasets, the estimates of yield gap or any of the parameters used to estimate yield gaps. Users are referred to the detailed descriptions of methods used and sources of underpinning data as provided as background information underpinning the yield gap assessments for each location included in the Atlas.
- South East Asia:
- Rice (addition of rainfed rice)
- Update on Maize
- Mali, Tanzania:
- Update on sorghum
- Grain legumes