Research HighlightsAssessing Causes of Soybean Yield Gaps in the North-Central Region of the U.S.
by Patricio Grassini, Cropping System Extension Specialist, University of Nebraska, and Shawn Conley, Soybean and Wheat Extension Specialist, University of Wisconsin
In the latest issue of the journal Agricultural & Forest Meteorology, we report on a novel approach to assess yield gaps (difference between maximum yield potential and measured producer yields) in soybean in the north-central region of the U.S. The approach combines producer self-reported data, crop modeling, and a spatial biophysical framework to quantify yield gaps and identify causes.
Yield potential is the yield of a specific variety when grown in an environment to which it is adapted, with a non-limiting supply of water and nutrients, and with pests, weeds, and diseases effectively controlled. Under these optimal conditions, crop growth is determined by solar radiation, temperature, atmospheric carbon dioxide concentration, and management practices such as sowing date, cultivar maturity, and plant density.
Yield potential (left red bar) and actual farm yield (right green bar). The arrow indicates the yield gap; that is, the difference between yield potential and actual farm yield.
Currently, the most common approach to identify yield-limiting factors involves conducting on-farm trials, in which inputs or management practices are selectively applied in experimental plots and evaluated for their cost-effectiveness and effect on yield.
An alternative method is the use of producer self-reported yield and crop management data. Having a database containing yield and management data from producer fields across multiple regions and years, properly contextualized relative to the climate and soil, can be considered equivalent to running hundreds of field experiments to capture both major management effects and the interactions of management effects and environmental effects.
With checkoff funds provided by the North Central Soybean Research Program (NCSRP), the Nebraska Soybean Board, and the Wisconsin Soybean Marketing Board, we collected data on soybean yield and management practices over two crop seasons (2014 and 2015) from 3568 producer fields sown with soybean in 10 states in the north-central region of the U.S. : Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Ohio, Nebraska, North Dakota, and Wisconsin.
Most surveyed fields were rainfed (82% of total fields), except for those in Nebraska, where rainfed (34%) and irrigated (66%) production co-exist within the same geographic area. Corn was the predominant prior crop (88% of total fields), except for a few fields where soybean was grown after wheat (5%) or soybean (4%).
The surveyed fields were grouped into 10 climate/soil domains based on four biophysical attributes that govern crop yield:
(1) annual total growing degree-days, which determines the length of crop growing season;
(2) aridity index, which defines the degree of water limitation in rainfed cropping systems,
(3) annual temperature seasonality — the difference between the annual maximum and minimum temperatures, and
(4) plant-available water holding capacity in the rootable soil depth, which determines the ability of the soil to supply water during rain-free periods.
We found that soybean yield gaps in the north-central U.S. over these cropping seasons were relatively small — averaging 22% (rainfed) and 13% (irrigated) of the estimated yield potential. The yield gap tended to be larger in rainfed areas (range: 15–28%) than in irrigated areas (range: 11–16%).
Planting date was the most consistent factor explaining yield variation within the same climate-soil combination, with the magnitude of yield response to planting delay dependent upon the degree of water deficit during the pod-setting phase.
The other management practices that explained yield variation were tillage, in-season foliar fungicide and/or insecticide application, and artificial drainage — but the degree to which each of these practices influenced yield was highly dependent on the climate-soil combination.
The combined use of producer data and a robust spatial framework that captures regional variation in weather and soils represents a cost-effective approach to identify causes of yield gaps across large geographic regions, which, in turn, can help inform and strategize research and extension programs at both local and regional levels.
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