Maize, Rice, Soybean, and Wheat Yield Trends

Annual geospatial crop data from 1961 to 2008 tracking maize, rice, wheat, and soybean performance

  • Source: EarthStat/UMN IonE/LUGE Lab at UBC
  • Last update: -

This geospatial crop database was created by EarthStat, the University of Minnesota's Institute on the Environment and the Land Use (UMN IonE), and the Global Environment Lab at the University of British Columbia (LUGE Lab at UBC). It covers the period between 1961 and 2008 annually and tracks maize, rice, wheat, and soybean performance across about 13,500 political units. These data were further quality controlled, standardized, and converted into yield information at 3 variable spatial levels based on data availability: national, state, and county/district/municípios/departments, and geographic units. Data availability varied among regions. Missing data values were more common in the early years of the data set. Average values from a 5-year window were then used to interpolate missing subnational data, constrained by values from the political unit within which the data were nested. EarthStat linearly regressed 20 years of crop yields in each political unit to determine the average linear rates of yield improvement over the observed period. Yield trends were analyzed by parsimoniously choosing among regression models of increasing order at each political unit for each crop: an intercept-only model, a linear model, a quadratic model, and a cubic model. EarthStat used the Akaike Information Criterion to decide which model best fit the observed data. Next, F-tests were conducted at each political unit to determine the quality of the model fit against the null hypothesis of a constant model. Population data and their projections per country were from the UN medium variant projections. Crop production was determined using the projected crop yields at current observed rates of yield change and harvested areas fixed at ~2007. Per capita harvested production is the ratio of production to population, and a greater than ±10% change from ~2007 is considered significant either in the short (2025) or long term (2050). Resource Watch shows only a subset of the data set. For access to the full data set and additional information, see the Learn More link.

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Formal name

Yield Trends for Maize, Soybean, Rice and Wheat


These data do not contain unique data for each grid cell as they are aggregated based on administrative unit reporting. These data are compiled using information gathered from individual countries' agricultural censuses. These data may be accurate to country level, admin1 level, or admin2 level. Data quality may be poor in some countries and years where complete or true information is lacking because of political strife, weak institutions, incentives to misreport data, lack of access to proprietary data, and so on.

Suggested citation

(1) Ray DK, N Ramankutty, ND Mueller, PC West, JA Foley. 2012. Recent patterns of crop yield growth, stagnation, and collapse. Nature Communications. 3:1293 doi: 10.1038/ncomms2296. Accessed through Resource Watch, (date). (2) Ray DK, ND Mueller, PC West, JA Foley. 2013. Yield trends are insufficient to double global crop production by 2050. Public Library of Science - ONE. doi: 10.1371/journal.pone.006642. Accessed through Resource Watch, (date).


University of Minnesota's Institute on the Environment (UMN IonE)
Land Use and Global Environment Lab at the University of British Columbia (LUGE Lab at UBC)

Geographic coverage


Spatial resolution

5 arc minute

Date of content


Published language



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