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 …

Rice–wheat (RW) systems are critical to food security and livelihoods of rural and urban
poor in south Asia and China, and to regional economies in southeast Australia. The …

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 …

The application of machine learning in crop yield prediction has gained considerable
traction, yet uncertainties persist regarding the impact of the yield trends on these …

The recurrent flooding during monsoon and subsequent waterlogging in the northern Bihar
plains and the magnitude of losses due to these hazards indicate the continuing …

Conventionally wheat is irrigated at 4–5 growth stages with an irrigation water depth of 7.5
cm (conventional irrigation practice, CP) in North-Western India, resulting in wastage of …

Data mining applications in agriculture is a relatively new approach for
forecasting/predicting of agricultural crop/animal management. In the present study an …

A methodology was developed for large area yield forecast using a crop simulation model
and a discrete technology trend, and was applied to the coherent wheat yield variability …

Crop simulation models (CSM) were built to simulate the crop response to environment and
soil on plot levels and were gradually applied for simulating regional behaviours of crop …