  Sellers of crop seeds need to plan for the variety and quantity of seeds to
stock at least a year in advance. There are a large number of seed varieties of
one crop, and each can perform best under different growing conditions. Given
the unpredictability of weather, farmers need to make decisions that balance
high yield and low risk. A seed vendor needs to be able to anticipate the needs
of farmers and have them ready. In this study, we propose an analytical
framework for estimating seed demand with three major steps. First, we will
estimate the yield and risk of each variety as if they were planted at each
location. Since past experiments performed with different seed varieties are
highly unbalanced across varieties, and the combination of growing conditions
is sparse, we employ multi-task learning to borrow information from similar
varieties. Second, we will determine the best mix of seeds for each location by
seeking a tradeoff between yield and risk. Third, we will aggregate such mix
and pick the top five varieties to re-balance the yield and risk for each
growing location. We find that multi-task learning provides a viable solution
for yield prediction, and our overall analytical framework has resulted in a
good performance.
