Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment...

Area of Science:

• Agricultural Science
• Agronomy
• Crop Science

Background:

• Delayed harvest of maize and soybean crops leads to yield or revenue losses.
• Premature harvest necessitates costly artificial grain drying.
• Current predictive models for ideal harvest dates lack accuracy, impacting US Midwest farm profitability.

Purpose of the Study:

• To analyze factors influencing post-maturity grain drying in maize and soybean.
• To develop scalable algorithms for predicting ideal harvest dates.
• To enhance decision-making for profitable crop harvesting.

Main Methods:

• Collected and analyzed time-series grain moisture data from field experiments in Iowa, Minnesota, and North Dakota.
• Utilized various maize (n=102) and soybean (n=36) genotype-by-environment treatments.
• Developed and evaluated algorithms based on grain equilibrium moisture content, influenced by air temperature and humidity.

Main Results:

• Calibrated algorithms accurately predicted grain dry-down for maize (r²=0.79, RMSE=1.8%) and soybean (r²=0.72, RMSE=6.7%).
• The post-maturity drying coefficient showed minimal influence from genotypes, weather-years, or planting dates.
• Grain moisture at physiological maturity was significantly affected by genotypes, weather-years, and planting dates.

Conclusions:

• Accurate prediction of initial grain moisture content is crucial for algorithm implementation.
• The developed algorithms offer a robust and scalable method for forecasting grain dry-down.
• This research provides valuable insights for optimizing harvest timing and increasing profitability in the US Corn Belt.