LINCOLN — The Yield Forecasting Center (YFC) will provide real-time information on phenology and corn yield forecasts every three weeks, starting in mid-July, to aid growers and ag industry professionals in making management, logistical, and marketing decisions through the 2017 season. The YFC platform consists of a core team at UNL working in collaboration with agronomists and extension educators from universities throughout the Corn Belt. Forecasts will be provided for 41 locations, which will allow us to have almost full coverage of the Corn Belt (Figure 1).
Our evaluation of the 2016 forecasts indicates that we were able to capture the spatial pattern in rainfed and irrigated corn yields across the US Corn Belt (Hindsight of 2016 Corn Yield Forecasts by the Yield Forecasting Center). However, the evaluation also indicates that forecasted rainfed yields were underestimated at sites with a shallow water table during the crop growing season, especially during the critical silking and pollen-shed period, as was the case in many regions of the central and eastern Corn Belt in 2016. Because reproducing the influence of water table on yield is difficult and there is high uncertainty as to the spatial distribution of the water table and its dynamics during the growing season, our 2017 forecasts will not include areas of the Corn Belt where water table influences rainfed yields (Iowa, Missouri, Illinois, Indiana and Ohio). We will continue to provide in-season yield forecasts for all major irrigated corn-growing regions and for rainfed corn in Nebraska, Kansas, North Dakota, and Minnesota. Real-time information on phenology will be provided for all 41 locations. This article summarizes the methodologies used in the YFC to forecast corn phenology and yield and provides guidelines for interpreting the results.
The YFC relies on
- a network of collaborators providing local management data and verifying forecasted yields,
- precise information on dominant soil types in each region,
- measured high-quality, real time weather data, and
- a well-validated crop simulation model (UNL Hybrid-Maize). More information about data sources and modeling can be found at the Global Yield Gap Atlas website.
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 an observation 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 needed for phenology and yield forecasting. Meteorological variables required for simulating real-time crop growth and development includes solar radiation, maximum and minimum temperature, precipitation, relative humidity, wind speed, and precipitation. The YFC relies on measured data collected through state weather networks, including the High Plains Regional Climate Center (HPRCC), the National Weather Service (NWS) station network, the Illinois Water and Atmospheric Resources Monitoring Program (WARM), the Ohio State University, Ohio Agricultural Research and Development Center (OARDC) Weather Service, the Indiana Purdue Automated Agricultural Weather Station Network (PAAWS), 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 (Figure 1). Meteorological stations selected from these networks are located in agricultural rather than in urban areas, ensuring their appropriateness for forecasting corn phenology and yields. More details on the data inputs used as a basis for the forecasts are available in the ScienceDirect article, Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?, by Francisco Morell, et al (2016).
Hybrid-Maize is a corn simulation model developed by UNL researchers that 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 from flooding or hail. Although the model can account for water stress and high temperatures during vegetative growth and the grain-filling period, it will likely not portray well yield of crops under two situations:
- years in which a crop suffers very severe heat and water stress during the silking and pollen shed (i.e., flowering) window of about seven days, and
- when an early frost kills the crop well before grain filling is completed.
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 the HybridMaize model are available in the ScienceDirect article, Improvements to the Hybrid-Maize model for simulating maize yields in harsh rainfed envrionments (Yang et al, 2017).
|ACCOUNTED FACTORS||NON-ACCOUNTED FACTORS|
Soil water at planting
Plant population density
Hybrid maturity Planting date
Incidence of biotic stresses (weeds, insect pests, pathogens)
Lack of stand uniformity
Severe heat/water stress around silking
Early killing frost
How do we forecast real-time corn yield potential?
Hybrid-Maize uses measured weather data to simulate crop growth until the forecast date (solid black line in Figure 3a). Historical (20+ years) weather data are used 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 end-of-season 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 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 YFC 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 and, instead, represents an estimate of the corn yield potential for an average location in absence of the yield-reducing factors noted in Table 3.
What to expect during the growing season?
Early in the season (May-June), yield forecasts for the current season mainly rely on historical weather and the range of forecasted yields will be wide and almost identical to the range of yields based on the historical weather data alone. As the season progresses (July-early August), and more of the current season’s weather data are used in the simulations, the range of forecast yields will start to narrow and may deviate from the long-term average yield (or not) depending upon similarity of the current season’s weather relative to the long-term average weather patterns. As the crop approaches maturity (end of August-September), the range of forecasted yields will further shrink and final yield can be forecasted with greater confidence.
How can these forecasts be used to inform farm decisions?
When complemented with other sources of information, personal experience, and best judgment, the information provided by the YFC allows adjusting yield goals for the current growing season in comparison to normal years, and in some cases this insight can support management decisions such as timing and amounts of fertilization and irrigation, or application of pesticides during the grain-filling period. Information from simulations during grain filling provides additional information to guide marketing decisions for all participants in the corn supply chain at regional and national levels (producers, livestock sector, ethanol industry, insurance companies, companies involved in commercialization and transportation, and policy makers).
Yield forecasts will be released as CropWatch articles every three weeks during the 2017 crop season, starting in mid-July. We also will provide information on the range of forecasted potential yields, probabilities for above-, near-, or below-normal yields, and information on crop phenology and the probability of an early killing frost. Forecasts will be discussed in relation to the current season’s weather versus the normal weather for each location to identify regions where yields are predicted to be well above, below, or near the long-term average. We will provide information on real-time phenology for all locations while yield forecasts will be limited to irrigated corn and to rainfed corn in Nebraska, Kansas, North Dakota, and Minnesota.
— Patricio Grassini – Cropping Systems Specialist; Gonzalo Rizzo – UNL Visiting Scholar | Juan Ignacio Rattalino Edreira – UNL Assistant Professor of Agronomy and Horticulture | Haishun Yang – Crop Modeler and Associate Professor of Agronomy and Horticulture | Roger Elmore – Extension Cropping Systems Agronomist | Keith Glewen – Extension Educator | Jenny Rees – Extension Educator | Charles Shapiro – Extension Soil Scientist—Crop Nutrition | Jeff Coulter – UMN Extension Specialist | Mark Licht – ISU Extension Cropping System Agronomist | Sotirios Archontoulis – ISU Assistant Professor | Cameron Pittelkow – UI Assistant Professor | Ignacio Ciampitti – KSU Cropping System Specialist | Ray Massey – UM Extension Professor | Peter Thomison – OSU Extension Specialist | Joe Lauer – UWM Professor | Sylvie Brouder – Purdue Professor of Agronomy
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