Evaluating and improving crop-yield forecasting methods during extreme drought
The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical weather predictors (NWP) or a hybrid of ML-NWP models to identify structural and physical relationships between meteorological drivers and crop growth, in order to predict crop yield. Droughts, for example the 2012 Midwestern US (Corn Belt) drought, are extreme events that affect crop production and test the limits of these forecasting models. Using 16 meteorological drivers as predictors, we compare ML (non-deep learning) and deep learning forec
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- FuzzyOverlapping authors or contributors · 62%thedotmack/claude-mem →
“Shared author/contributor keys: ming”
- FuzzyOverlapping authors or contributors · 62%microsoft/ML-For-Beginners →
“Shared author/contributor keys: gupta”
- LinkedLinked via arxiv author · 85%Shrey Gupta →
“Evaluating and improving crop-yield forecasting methods during extreme drought”
- LinkedLinked via arxiv author · 85%Yi Ming →
“Evaluating and improving crop-yield forecasting methods during extreme drought”