Olivia Mendivil Ramos (Cold Spring Harbor Laboratory); Linda Petrini (Mila)
Agriculture is facing the disastrous effects of frequent drastic climate changes. Efforts have increased towards the implementation of inexpensive solutions for crop-yield prediction using publicly available data to prevent severe long-term problems like food scarcity and security, amongst others. Agricultural productiv- ity is intrinsic to the choice of plant species (i.e. cultivar) and represents oppor- tunity cost for farm managers. The currently used cultivars have been artificially selected for productivity at the expense of not being flexible to survive drastic cli- mate changes. Current state-of-the-art machine learning models have modelled holistically all agricultural counterparts (i.e. soil, management, weather, crop cul- tivars etc), albeit, oversimplifying some of the biological features of their culti- vars without taking advantage of their data properties. Specifically, these models oversimplify some biological features like the genotype making them irrelevant or depicting incomplete conclusions since not all of the information from the cul- tivar is incorporated. With the goal of creating new models that perform well on the yield prediction task in unstable weather conditions (e.g. under the effect of climate change), here the authors argue for the importance of incorporating additional biological features inferred from the genotype, like stability, and hy- pothesise that current state-of-the-art models for grain-yield prediction are blind to such features, and hence not applicable in such scenario.