A team of agronomists is calling for a new approach to estimate crop yield potential and gaps – information that is critical to plan how to meet growing food demand.
In a paper published in the current edition of the journal Nature Food, the researchers, including Iowa State University Professor of Agronomy Sotirios Archontoulis, call into question current statistical methods essential in making investments in agricultural research and development, both from public and private sources.
“At issue is how best to compile such estimates,” said Archontoulis, a lead modeler on the project.
Estimates that predict both yield potential, as determined by weather and soil properties, and yield gaps (the difference between yield potential and current farm yields) are used to indicate the capacity to increase food production on existing cropland.
However, in the United States, the researchers found that current statistical models tend to rely too heavily on best-case scenarios – the most productive counties with the most fertile soils in a year with the most favorable weather. Current methods also extrapolate a single yield potential across large regions that often have wide variations in climates and soils.
“Therefore, if you use that year as a reference, you are going overestimate your production potential because the best county with the best soils in the best year doesn’t really represent your average climate or your most typical soil across the state,” said Patricio Grassini, the Sunkist Distinguished Professor of Agronomy at the University of Nebraska-Lincoln (UNL), another co-author on the study.
In other parts of the world – Africa, for example – these models might underestimate crop yield, Grassini said. There, farmers may have limited access to inputs compared to producers in other areas, thus attaining yields far below what the climate can support.
The current statistical approach can also lead to conflicting results, with production potential estimates almost doubling from one method to another.
The new study compared estimates of yield potential and yield gaps of major U.S. rainfed crops – corn, soybeans and wheat – derived from four statistical models against those derived from a “bottom-up” spatial scaling approach based on robust crop modeling and local weather and soil data.
These process-based crop models were rigorously validated for their capacity to estimate yield potential based on experimental data from well-managed crops grown across a wide range of environments. This bottom-up approach, which better incorporates long-term data and regional variations, is clearly superior, the team found.
“The approach recommended by our team should better capture yield gaps, which can help identify regions with the largest room to increase crop production. This should provide a more accurate picture to orient agricultural research and development programs,” Archontoulis said.
Collaborators on the study also include Fatima Tenorio, Fernando Aramburu Merlos and Juan Rattalino Edreira, research assistant professors of agronomy, UNL; Romulo Lollato, associate professor of agronomy, Kansas State University; and Antoine Couëdel, former postdoc at UNL and now a researcher at the French Agricultural Research Centre for International Development, France.
Source : iastate.edu