Abstract Models are key tools in our quest to better understand the impacts of soil
waterlogging on plant growth and crop production. Here, we reviewed the state of the art of …

The art of predicting crop production is done before the crop is harvested. Crop output
forecasts will help people make timely judgments concerning food policy, prices in markets …

This study investigates whether coupling crop modeling and machine learning (ML)
improves corn yield predictions in the US Corn Belt. The main objectives are to explore …

As the world's leading corn producer, the United States supplies more than 30% of the
global corn production. Accurate and timely estimation of corn yield is therefore essential for …

The emergence of new technologies to synthesize and analyze big data with high-
performance computing has increased our capacity to more accurately predict crop yields …

Crop yield prediction is crucial for global food security yet notoriously challenging due to
multitudinous factors that jointly determine the yield, including genotype, environment …

Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The
current predictive models of yield and soil–crop dynamics during the crop growing season …

Yield prediction is essential in food security, food trade, and field management. However,
due to the associated complex formation mechanisms of yield, accurate and timely yield …

Abstract A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for
predicting grain yield outcomes for Genotype by Environment by Management (G× E× M) …

This study investigated the differences in microbial community abundance, composition, and
diversity throughout the depth profiles in soils collected from corn and soybean fields in Iowa …