  The alfalfa crop is globally important as livestock feed, so highly efficient
planting and harvesting could benefit many industries, especially as the global
climate changes and traditional methods become less accurate. Recent work using
machine learning (ML) to predict yields for alfalfa and other crops has shown
promise. Previous efforts used remote sensing, weather, planting, and soil data
to train machine learning models for yield prediction. However, while remote
sensing works well, the models require large amounts of data and cannot make
predictions until the harvesting season begins. Using weather and planting data
from alfalfa variety trials in Kentucky and Georgia, our previous work compared
feature selection techniques to find the best technique and best feature set.
In this work, we trained a variety of machine learning models, using cross
validation for hyperparameter optimization, to predict biomass yields, and we
showed better accuracy than similar work that employed more complex techniques.
Our best individual model was a random forest with a mean absolute error of
0.081 tons/acre and R{$^2$} of 0.941. Next, we expanded this dataset to include
Wisconsin and Mississippi, and we repeated our experiments, obtaining a higher
best R{$^2$} of 0.982 with a regression tree. We then isolated our testing
datasets by state to explore this problem's eligibility for domain adaptation
(DA), as we trained on multiple source states and tested on one target state.
This Trivial DA (TDA) approach leaves plenty of room for improvement through
exploring more complex DA techniques in forthcoming work.
