Key Takeaways

• Decision Support: Forecasts can help producers adjust yield expectations, inform irrigation and fertilizer decisions, guide pest management and support marketing decisions throughout the growing season.

• Regional Coverage: Forecasts will be released for 43 Corn Belt locations, including separate predictions for rainfed and irrigated corn where both production systems are common.

• Collaborative, Research-Based Approach: A multi-university team combines local crop management data, soil information and real-time weather observations to generate regional yield forecasts.

• Powered by Hybrid-Maize: Forecasts are generated using UNL's Hybrid-Maize crop simulation model, which estimates corn yield potential under optimal management and evolving weather conditions.

As the corn season moves from planting into pollination and grain fill, yield expectations can shift quickly with changing weather. The Yield Forecasting Center helps put those conditions into context by showing whether current-season yield potential is trending above, below or near the long-term average.

The Yield Forecasting Center (YFC) will provide real-time information on corn development and yield potential forecasts every three weeks, starting in mid-July, to aid growers and ag industry in making management, logistics and marketing decisions through the 2026 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 43 locations, including separate forecasts for rainfed and irrigated corn in regions where both irrigated and rainfed production are important, such as Nebraska and Kansas (Figure 1). 

This article summarizes the methods used in the YFC to forecast corn development and yield and provides guidelines for interpreting the results. 

The Approach

The YFC relies on four main sources of information: local management data provided and verified by collaborators, dominant soil types in each region, high-quality real-time weather data and the UNL Hybrid-Maize crop simulation model. More information about data sources and modeling can be found at the Yield Potential and Yield Gap Altas website.

Local agronomists and extension educators provide information on management in each state, and help validate and interpret the forecasts. Collaborators provide 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 availability has a large influence on production practices. 

Management data have also been 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 then aggregated by averaging the forecasts across soil types, after weighting them according to prevalence of each soil type in each location (Table 2).

^† Data were retrieved by state collaborators. 

^‡ Approximate date at which 50% of final corn area was planted in 2026 at each location. Soil water balance was initialized around prior crop harvest in the previous year (2025), assuming 50% available soil water. 

TBD: still to be determined.

^† 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 feet) and associated prevalence of each soil type (in %) at each location. (Source: Yield Potential and Yield Gap Altas website)

* Water table coverage was inferred from the most recent data on subsurface tile drainage reported by U.S. Census of Agriculture (USDA-NASS, 2012).

Historical weather data from the past 20-plus 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 (Figure 1), including:

• High Plains Regional Climate Center (HPRCC), 
• National Weather Service station network (NWS), 
• Illinois Water and Atmospheric Resources Monitoring Program (WARM), 
• Ohio Agricultural Research and Development Center Weather Service (OARDC), 
• Indiana Purdue Automated Agricultural Weather Station Network (PAAWS), 
• Minnesota Southern Research and Outreach Center (SROC)
• Minnesota Southwest Research and Outreach Center (SWROC), 
• Missouri Mesonet (AgEBB), 
• North Dakota Agricultural Weather Network (NDAWN), and
• Michigan State University Enviro-Weather.

Weather stations selected from these networks are located in agricultural areas, rather than in urban areas, which helps ensure the weather data are representative of growing conditions for forecasting corn yields and development. Details on the data inputs used as basis for the forecasts are available in 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 achieved when the crop is not limited by nutrient supply, diseases, insect pressure or weed competition — conditions that represent an "optimal management" scenario. 

In rainfed scenarios, soil water balance was initialized following harvest of the previous crop, assuming 50% available soil water. 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 may not accurately represent yield in two situations: (1) a season when a crop suffers very severe heat and drought stress during the silking and pollination (i.e., flowering) window of about seven days, and (2) when an early frost kills the crop well before grain filling is completed.

However, even with these relatively uncommon limitations, the model has been successfully evaluated across a wide range of environments where corn yields ranged from near zero up to 300 bu/ac (Figure 2). 

More details on the 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 U.S. Corn Belt. However, these previous evaluations also indicated that rainfed yields were underestimated at sites with a shallow groundwater 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 groundwater table on corn yield and stability. For each location, the share of fields with shallow groundwater is estimated using tile drainage maps because tile drainage is a reliable indicator of shallow groundwater.

For fields with shallow groundwater table, we will assume that there is no water limitation. The average forecasted yield is then estimated as the average of the forecasted yields for fields with and without groundwater table, weighted by their respective shares of corn harvested area at that location. 

The following table summarizes the environmental factors that are included in the forecasts:

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-plus years) weather data to predict all historical 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), the model estimates the likelihood (probability) for current season yields to be below, near or above the 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 losses due to pests, hail, water logging, poor establishment or other stresses 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 the 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 depending on how similar the current season’s weather is to long-term average weather patterns. 

As the crop approaches maturity (end of August to 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 growers to adjust yield goals for the current growing season in comparison to normal years. 

In some cases, this insight can support management decisions, such as fertilizer timing and application rates, or application of pesticides during the grain filling period. Information from simulations during grain filling provides 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 policymakers). 

Four yield forecasts will be released as CropWatch articles during the 2026 crop season, beginning in late July. We will also provide information on the range of forecasted potential yields, probabilities for above-, near- or below-normal yields, along with information about crop development and the probability of an early killing frost. 

Forecasts will be discussed in relation to current season’s weather compared with normal weather for each location to identify regions where yields are predicted to be well above, below, or near the long-term average.