The perfect knowledge of yield before harvest has been a wish puzzling human being since
the beginning of agriculture because seasonal forecast of crop yield plays a critical role in …

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 …

Pre-growing season prediction of crop production outcomes such as grain yields and
nitrogen (N) losses can provide insights to farmers and agronomists to make decisions …

Abstract We used the Agricultural Production Systems sIMulator (APSIM) to predict and
explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics …

The bioactive Hemp Seed Oil (HSO) is becoming very popular in the medical and research
fields due to its antimicrobial properties against several diseases caused by bacteria and …

Planting date and cultivar selection are major factors in determining the yield potential of any
crop and in any region. However, there is a knowledge gap in how climate scenarios affect …

Assessing crop production in the field often requires breeders to wait until the end of the
season to collect yield-related measurements, limiting the pace of the breeding cycle. Early …

In the environmental sciences, there are ongoing efforts to combine multiple models to assist
the analysis of complex systems. Combining process-based models, which have encoded …

Abstract Machine learning (ML) along with high volume of satellite images offers an
alternative to agronomists in crop yield predictions for decision support systems. This …