Key message Climate change and Genotype-by-Environment-by-Management interactions
together challenge our strategies for crop improvement. Research to advance prediction …

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 …

Summary Genotype‐by‐environment‐by‐management (G× E× M) interactions for crop
productivity represent both challenges and opportunities for long‐term crop improvement …

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 …

Genotype-by-environment (G× E) interactions can significantly affect crop performance and
stability. Investigating G× E requires extensive data sets with diverse cultivars tested over …

The fast and accurate yield estimates with the increasing availability and variety of global
satellite products and the rapid development of new algorithms remain a goal for precision …

Crop improvement efforts aiming at increasing crop production (quantity, quality) and
adapting to climate change have been subject of active research over the past years. But …

Crop growth models (CGM) can predict the performance of a cultivar in untested
environments by sampling genotype-specific parameters. As they cannot predict the …

Plant physiology can offer invaluable insights to accelerate genetic gain. However,
translating physiological understanding into breeding decisions has been an ongoing and …