How does inclusion of weather forecasting impact in-season crop model predictions?

Kaitlin Togliatti, Sotirios V. Archontoulis, Ranae Dietzel, Laila Puntel, Andy VanLoocke

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Accurately forecasting crop yield in advance of harvest could greatly benefit decision makers when making management decisions. However, few evaluations have been conducted to determine the impact of including weather forecasts, as opposed to using historical weather data (commonly used) in crop models. We tested a combination of short-term weather forecasts from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation, and radiation at four different forecast lengths (14 days, 7 days, 3 days, and 0 days). This forecasted weather data along with the current and historic (previous 35 years) data were combined to drive Agricultural Production Systems sIMulator (APSIM) in-season corn [Zea mays L] and soybean [Glycine max] grain yield and phenology forecasts for 16 field trials in Iowa, USA. The overall goal was to determine how the inclusion of weather forecasting impacts in-season crop model predictions. We had two objectives 1) determine the impact of weather forecast length on WRF accuracy, and 2) quantify the impact of weather forecasts accuracy on APSIM prediction accuracy. We found that the most accurate weather forecast length varied greatly among the 16 treatments (2 years × 2 sites × 2 crops × 2 management practices), but that the 0 day and 3 day forecasts were, on average, the most accurate when compared to the other forecast lengths. Overall, the accuracy of the in-season crop yield forecast was inversely proportional to forecast length (p = 0.026), but there was variation among treatments. The accuracy of the in-season flowering and maturity forecasts were not significantly affected by inclusion of weather forecast length (p = 0.065). The 14 day forecast provided enough lead time to improve flowering prediction in 8 out of the 16 treatments. The fact that maximum temperature was the most accurate predicted variable by WRF was the reason for improvements in flowering predictions. Our results suggest that a weather forecast from WRF was not better than historical weather for yield prediction.

Original languageEnglish (US)
Pages (from-to)261-272
Number of pages12
JournalField Crops Research
StatePublished - Dec 2017
Externally publishedYes


  • Apsim
  • Corn
  • Soybean
  • WRF
  • Yield forecasting

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Soil Science


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