2018 Corn Yield Forecasts: Approach and Interpretation of Results

 

The Yield Forecasting Center (YFC) will provide real-time information on corn phenology and forecasts of corn yield potential every three weeks, starting in mid-July, to aid growers and ag industry in making management, logistics, and marketing decisions through the 2018 season. The YFC platform consists of a core team at UNL in collaboration with agronomists and extension educators from universities throughout the Corn Belt.

 

Forecasts will be provided for 41 locations, including separate forecasts for rainfed and irrigated corn in regions where both irrigated and rainfed production are important, as it is the case of Nebraska and Kansas (Figure 1). This article summarizes the methodologies used in the YFC to forecast corn phenology and yield and provides guidelines for interpreting the results.

 

 

 

The Yield Forecasting Center relies on

Local agronomists and extension educators provide information on management in each state, and help validate and interpret the forecasts. Information provided by collaborators includes site-specific average planting date (i.e., average calendar date at which 50% of the corn area was planted in the current season), plant density, and hybrid maturity (Table 1). When both rainfed and irrigated production occurs at a location, separate sets of management practices are used for simulating rainfed and irrigated crops at those sites because water regime has a large influence on production practices. Management data have been further validated using information provided by DuPont Pioneer agronomists, especially with regard to hybrid maturity and plant density. Yield forecasts are based on the two to three dominant soil types at each location. Yield is forecasted separately for each soil type. Yield predictions are subsequently aggregated by averaging the forecasts across soil types, after weighting them according to prevalence of each soil type in each location (Table 2).

 

Historical (last 20+ years) and real-time daily weather data are used for phenology and yield forecasting. Daily weather variables required for simulating real-time crop growth and development include solar radiation, maximum and minimum temperature, precipitation, relative humidity, and wind speed. The YFC relies on measured data collected through state weather networks, including the High Plains Regional Climate Center (HPRCC), the National Weather Service station network (NWS), the Illinois Water and Atmospheric Resources Monitoring Program (WARM), the Ohio State University, Ohio Agricultural Research and Development Center Weather Service (OARDC),  the Indiana State Climate Office Purdue Automated Agricultural Weather Station Network (PAAWS), the Southern Research and Outreach Center (SROC) and the Southwest Research and Outreach Center (SWROC) form the University of Minnesota, the Missouri Mesonet (AgEBB), the North Dakota Agricultural Weather Network (NDAWN), and the Michigan State University Enviro-Weather. Weather stations selected from these networks are located in agricultural rather than urban areas, which helps to ensure the representativeness of the weather data for forecasting corn yields and phenology. Details on the data inputs used as a basis for the forecasts are available in Morell et al (2016).

 

 

Hybrid-Maize is a corn simulation model developed by UNL researchers. It simulates daily corn growth and development and final grain yield under irrigated and rainfed conditions. The model estimates "yield potential," which is the yield obtained when the crop is not limited by nutrient supply, diseases, insect pressure, or weed competition — conditions that represent an "optimal management" scenario. It also assumes uniform plant stand at the specified plant population and no problems with flooding or hail. Although the model can account for drought stress and high temperatures during vegetative growth and the grain filling, it is unlikely to well portray crop yield in two situations:

However, even with these relatively uncommon provisos, the model has been successfully evaluated across a wide range of environments where corn produced yields varying from near zero up to 300 bu/ac (Figure 2). More details on Hybrid-Maize model are available in Yang et al (2017).

 

Previous evaluations indicated that our forecasts captured the spatial pattern in rainfed and irrigated corn yields across the US Corn Belt. However, these previous evaluations also indicated that rainfed yields were underestimated at sites with a shallow ground water table during the cropping season, and especially during the critical silking and pollination period, as was the case in many regions in the central and eastern Corn Belt.

 

 

During the 2018 crop season, we will use a new approach to account for the positive impact of ground water table on corn yield and stability. For a given location, the fraction of fields with shallow ground water table will be derived from maps on tile drainage because these are a robust indicator of the presence of ground water table. For fields where there is a shallow ground water table, we will assume that there is no water limitation, and subsequently, the average forecasted yield for the given location will be estimated as the average of the forecasted yields for fields with and without ground water table, weighted by their respective shares of harvested corn area at that location.

 

 

 

 

 

Hybrid-Maize uses measured weather data to simulate crop growth until the forecast date (solid black line in Figure 3a. It will then use historical (20+ years) weather data to predict all possible weather scenarios for the rest of the season (blue dotted lines in Figure 3a). This results in a range of possible final yields (blue distribution in Figure 3b). By comparing the distribution of forecasted yields (blue-shaded area in Figure 3c) against the average yield simulated for the same location using only the historical weather data (vertical arrow in Figure 3c), it is possible to determine the likelihood (probability) for current season yields to be below, near, or above long-term average yield (dashed area in Figure 3c). Yields reported by the Yield Forecasting Center more closely track yields achieved in fields that did not suffer severe yield losses due to pests, hail, waterlogging, poor establishment or substantially affected by a stress not accounted for in the Hybrid-Maize model. Finally, it is important to keep in mind that the yield forecast is not field-specific. Instead, it represents an estimate of the average corn yield potential for a given location in absence of the yield reducing factors.