"We are in a race to feed the world and to try to feed the population with the available agricultural land that we have," said Patricio Grassini, Sunkist Distinguished Professor of Agronomy and one of the paper's authors.
To do so requires estimates that predict both yield potential, as determined by weather and soil properties, and yield gaps, which is the difference between yield potential and current farm yields, which indicates the room that exists to increase food production on existing cropland. Those estimates are essential in making investments in agricultural research and development, both from public and private sources.
At issue is how best to compile those estimates.
In the Nature Food paper, a team that includes scientists from Nebraska and three other institutions calls into question the statistical methods now widely used. In addition to Grassini, Husker authors of the study included Fatima Tenorio, Fernando Aramburu Merlos and Juan Rattalino Edreira, research assistant professors of agronomy.
In the United States, for example, 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, Grassini said. The methods also extrapolate a single yield potential across large regions with a wide diversity of climates and soils that likely would produce a similarly wide range in yield potential.
"Therefore, if you use that year as a reference, you are going to be overestimating 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," Grassini said.
But in other parts of the world Africa, for example these models might underestimate crop yield. There, farmers may have limited access to inputs compared to producers in other areas, thus attaining yields far below what the climate can support.
This statistical approach also leads to conflicting results, with production potential estimates almost doubling from one method to another. Grassini said this approach driven mostly by geographers and statisticians, not agronomists has been largely accepted, and more rigorous analysis is needed.
The research team's conclusions are explained in the paper, titled "Statistical approaches are inadequate for accurate estimation of yield potential and gaps at regional level."
The 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, such as the Global Yield Gap and Water Productivity Atlas developed at Nebraska.
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