Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction
methods have been proposed including the standard additive genetic effect model for which …

Technological developments have revolutionized measurements on plant genotypes and
phenotypes, leading to routine production of large, complex data sets. This has led to …

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 …

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 …

Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance
as a response to climate change presents both opportunities and challenges. Applying the …

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

The development of crop varieties with stable performance in future environmental
conditions represents a critical challenge in the context of climate change. Environmental …

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 …

Climate change and exponential population growth are exposing an immediate need for
developing future crops that are highly resilient and adaptable to changing environments to …