Patricio Grassini, UNL Associate Professor of Agronomy and Horticulture, Extension Cropping System Specialist and Water for Food Institute Fellow | Jose Andrade, UNL Affiliate | Juan Ignacio Rattalino Edreira, UNL Research Assistant Professor of Agronomy and Horticulture | Gonzalo Rizzo, UNL PhD student | Haishun Yang, UNL Associate Professor of Agronomy and Horticulture and Water for Food Institute Fellow | Keith Glewen & Jennifer Rees, Nebraska Extension Educators | Jeff Coulter, Associate Professor and Extension Specialist, University of Minnesota | Mark Licht (Extension Cropping System Agronomist) & Sotirios Archontoulis (Assistant Professor), Iowa State University | Ignacio Ciampitti, Crop Production and Cropping System Specialist and Assistant Professor of Agronomy, Kansas State University | Ray Massey, Extension Professor, University of Missouri
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 2020 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 Approach
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 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 2-3 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).
Table 1. Management Data| Location | Water regime | Density (plants/acre) | Hybrid RM (days) | 2020 planting date‡ |
|---|---|---|---|---|
| Alliance, NE | Irrigated | 34,000 | 95 | 6-May |
| North Platte, NE | Irrigated | 34,000 | 110 | 5-May |
| Dryland | 15,000 | 105 | 12-May | |
| McCook, NE | Irrigated | 34,000 | 110 | 6-May |
| Dryland | 15,000 | 105 | 6-May | |
| Holdrege, NE | Irrigated | 34,000 | 113 | 28-Apr |
| Dryland | 17,000 | 105 | 1-May | |
| Clay Center, NE | Irrigated | 34,000 | 113 | 27-Apr |
| Dryland | 26,000 | 113 | 27-Apr | |
| Beatrice, NE | Irrigated | 34,000 | 114 | 29-Apr |
| Dryland | 29,000 | 113 | 29-Apr | |
| Mead, NE | Irrigated | 34,000 | 113 | 29-Apr |
| Dryland | 27,000 | 113 | 4-May | |
| Concord, NE | Irrigated | 34,000 | 111 | 1-May |
| Dryland | 26,000 | 110 | 1-May | |
| Elgin, NE | Irrigated | 34,000 | 113 | 4-May |
| ONeil, NE | Irrigated | 34,000 | 108 | 7-May |
| Manhattan, KS | Dryland | 25,000 | 110 | 1-May |
| Scandia, KS | Irrigated | 34,000 | 116 | 3-May |
| Dryland | 24,000 | 107 | 3-May | |
| Silverlake, KS | Irrigated | 34,000 | 117 | 28-Apr |
| Dryland | 24,000 | 109 | 28-Apr | |
| Hutchinson, KS | Dryland | 20,000 | 105 | 29-Apr |
| Garden City, KS | Irrigated | 26,000 | 113 | 1-May |
| Lamberton, MN | Dryland | 32,000 | 101 | 22-Apr |
| Waseca, MN | Dryland | 34,000 | 103 | 22-Apr |
| Eldred, MN | Dryland | 27,000 | 82 | 23-Apr |
| Dazey, ND | Dryland | 27,000 | 82 | 20-May |
| St Joseph, MO | Dryland | 30,000 | 112 | 28-Apr |
| Brunswick, MO | Dryland | 30,000 | 112 | 7-May |
| Monroe City, MO | Dryland | 29,000 | 111 | 15-May |
| Ames, IA | Dryland | 34,000 | 109 | 27-Apr |
| Crawfordsville, IA | Dryland | 35,000 | 113 | 5-May |
| Kanawha, IA | Dryland | 35,000 | 101 | 26-Apr |
| Lewis, IA | Dryland | 34,000 | 113 | 5-May |
| Nashua, IA | Dryland | 34,000 | 101 | 5-May |
| Sutherland, IA | Dryland | 34,000 | 103 | 26-Apr |
| Bondville, IL | Dryland | 34,000 | 113 | 5-May |
| Freeport, IL | Dryland | 34,000 | 103 | 1-May |
| Olney, IL | Dryland | 29,000 | 113 | 3-May |
| Peoria, IL | Dryland | 33,000 | 113 | 1-May |
| SpringField, IL | Dryland | 35,000 | 113 | 30-Apr |
| Butlerville, IN | Dryland | 32,000 | 113 | 10-May |
| Columbia City, IN | Dryland | 32,000 | 108 | 10-May |
| Davis, IN | Dryland | 33,000 | 108 | 10-May |
| West Lafayette, IN | Dryland | 34,000 | 113 | 10-May |
| Custar, OH | Dryland | 33,000 | 108 | 10-May |
| South Charleston, OH | Dryland | 33,000 | 112 | 26-May |
| Wooster, OH | Dryland | 32,000 | 106 | TBD |
| Ceresco, MI | Dryland | 32,000 | 105 | 15-May |
† Data were retrieved by state collaborators and DuPont Pioneer agronomists. ‡ Approximate date at which 50% of final corn area was planted in 2018 at each location. Soil water balance was initialized around prior crop harvest in the previous year (2019), assuming 50% available soil water. TBD: still to be determined.
Table 2. Dominant Soil Types at Each Location| Location | Soil 1 (top- & sub-soil texture) ‡ |
Soil 2 (top- & sub-soil texture) |
Soil 3 (top- & sub-soil texture) |
Water table coverage (%) * |
|---|---|---|---|---|
| Ames IA | silt loam - silt loam (18%) | clay loam - silt loam (12%) | silt clay loam - silt clay loam (7%) | 63 |
| Beatrice NE | silt clay loam - silt clay loam (48%) | silty clay - silt clay loam (14%) | silt loam - silt loam (13%) | 25 |
| Bondville IL | silt clay loam - silt loam (14%) | silt loam - silt clay loam (13%) | silt clay loam - silt clay loam (12%) | 61 |
| Butlerville IN | silt loam - silt loam (64%) | silt loam - silt clay loam (10%) | 26 | |
| Ceresco MI | sandy loam - sandy loam (27%) | silt loam - silt loam (25%) | silt loam - sandy loam (17%) | 31 |
| Clay Center NE | silt loam - silt clay loam (46%) | silt loam - silt loam (33%) | silt clay loam - silt clay loam (21%) | 0 |
| Columbia City IN | silt clay loam - silty clay (13%) | silt clay loam - clay loam (11%) | silt loam - silt loam (11%) | 65 |
| Concord NE | silt loam - silt loam (46%) | silt clay loam - silt clay loam (29%) | - | 25 |
| Crawfordsville IA | silt loam - silt clay loam (23%) | silt clay loam - silt clay loam (20%) | silt loam - silt loam (8%) | 49 |
| Custar OH | clay loam - clay (10%) | clay loam - clay loam (7%) | silt loam - clay loam (6%) | 79 |
| Davis IN | silt loam - silt loam (21%) | silt clay loam - silty clay (8%) | silt loam - silt clay loam (7%) | 64 |
| Dazey ND | silt loam - silt loam (100%) | - | - | 0 |
| Eldred MN | silt clay loam - silt clay loam (36%) | silty clay - silty clay (34%) | silt loam - silt loam (25%) | 5 |
| Freeport IL | silt loam - silt loam (38%) | silt loam - silt clay loam (35%) | - | 27 |
| Holdrege NE | silt loam - silt loam (100%) | - | - | 0 |
| Hutchinson KS | silt clay loam - silt clay loam (44%) | silt loam - silt loam (33%) | silt loam - silt clay loam (23%) | 0 |
| Kanawha IA | clay loam - silt loam (18%) | silt loam - silt loam (9%) | 73 | |
| Lamberton MN | clay loam - silt loam (20%) | silt loam - silt loam (20%) | 60 | |
| Lewis IA | silt clay loam - silt clay loam (48%) | silt loam - silt loam (11%) | - | 41 |
| Manhattan KS | silt clay loam - silt clay loam (41%) | silt clay loam - silty clay (32%) | silt loam - silt loam (27%) | 0 |
| McCook NE | silt loam - silt loam (100%) | - | - | 0 |
| Mead NE | silt clay loam - silt clay loam (35%) | silt loam - silt loam (28%) | silt clay loam - silt loam (12%) | 25 |
| Monroe City MO | silt loam - silty clay (37%) | silt loam - silt clay loam (31%) | silt clay loam - silt clay loam (23%) | 9 |
| Nashua IA | silt loam - silt loam (24%) | silt clay loam - silt loam (12%) | 64 | |
| North Platte NE | silt loam - silt loam (100%) | - | - | 0 |
| Olney IL | silt loam - silt clay loam (47%) | silt loam - silt loam (39%) | 14 | |
| Peoria IL | silt loam - silt clay loam (24%) | silt clay loam - silt clay loam (8%) | silt loam - silt loam (7%) | 61 |
| Scandia KS | silt clay loam - silt clay loam (51%) | silt loam - silt clay loam (26%) | silt loam - silt loam (23%) | 0 |
| Silverlake KS | silt clay loam - silt clay loam (38%) | silt loam - silt loam (31%) | silt clay loam - silty clay (31%) | 0 |
| South Charleston OH | silt loam - silt loam (27%) | silt loam - clay loam (16%) | clay loam - clay loam (10%) | 47 |
| Springfield IL | silt loam - silt clay loam (51%) | silt clay loam - silt clay loam (19%) | 30 | |
| St Joseph MO | silt clay loam - silt clay loam (47%) | silt loam - silt loam (26%) | silt loam - silt clay loam (12%) | 15 |
| Sutherland IA | silt clay loam - silt loam (22%) | silt clay loam - silt clay loam (18%) | silt loam - silt loam (14%) | 46 |
| Waseca MN | silt loam - silt loam (27%) | clay loam - silt loam (17%) | 56 | |
| West Lafayette IN | silt loam - silt loam (31%) | silt clay loam - silt loam (7%) | silt clay loam - silt clay loam (5%) | 57 |
| Wooster OH | silt loam - silt clay loam (25%) | silt loam - silt loam (22%) | 53 |
† Data were retrieved from GSSURGO soil database (NRCS, Soil Division). Only soils under dryland crop cultivation were considered. Soil data were used only for rainfed corn simulations (soil data were not needed for simulation of irrigated crops). ‡ Soil texture in the topsoil (0-1 ft) and subsoil (2-4 ft) and associated prevalence of each soil type (in %) at each location. (source: Global Yield Gap Atlas; www.yieldgap.org). * Water table coverage was inferred from the most recent data on subsurface tile drainage reported by US Census of Agriculture (USDA-NASS, 2012).
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 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). Weather stations selected from these networks are located in agricultural areas, rather than in 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 basis for the forecasts are available in "Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?" from 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 of flooding or hail. Although the model can account for drought stress and high temperatures during vegetative growth and the grain filling, it will unlikely portray well crop yield in two situations:
- a season when a crop suffers very severe heat and drought stress during the silking and pollination (i.e., flowering) window of about 7 days, and
- when an early frost kills the crop well before grain filling is completed.
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 "Improvements to the Hybrid-Maize model for simulating maize yields in harsh rainfed environments" from 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 it was the case of many regions in the central and eastern Corn Belt. Since the 2019 crop season, we 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 the latter is a robust indicator of the presence of ground water table. For fields with presence of 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 corn harvested area at that location.
The following table summarizes the environmental factors that are accounted for by our forecasts:
| Accounted factors | Non-accounted factors |
|---|---|
| Solar radiation | Nutrient supply |
| Temperature | Incidence of biotic stresses (weeds, insect pests, pathogens) |
| Relative humidity | Flooding |
| Wind speed | Hail |
| Reference evapotranspiration | Non-uniform stand |
| Precipitation | Soil crusting |
| Soil type | Severe heat/drought stress around silking |
| Soil water at planting | Early killing frost |
| Irrigation | Green snap/lodging |
| Plant population density | |
| Hybrid maturity | |
| Planting date | |
| Shallow ground water table |
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). 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 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. Instead, it represents an estimate of the average corn yield potential for a given location in absence of the yield reducing factors.
What to Expect During the Growing Season?
Early in the season (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 (early-July to August), and more of the current season’s weather data are used in the simulations, the range of forecast yields will start to converge 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 converge and the final yield can be forecasted with greater confidence.
How to Use These Forecasts 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 2020 crop season, starting from mid-July. We will also provide information on the range of forecasted potential yields, probabilities for above-, near-, or below-normal yields, and also information about crop phenology and the probability of an early killing frost. Forecasts will be discussed in relation to 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.
