  Yield forecasting, the science of predicting agricultural productivity before
the crop harvest occurs, helps a wide range of stakeholders make better
decisions around agricultural planning. This study aims to investigate whether
machine learning-based yield prediction models can capably predict Kharif
season rice yields at the district level in India several months before the
rice harvest takes place. The methodology involved training 19 machine learning
models such as CatBoost, LightGBM, Orthogonal Matching Pursuit, and Extremely
Randomized Trees on 20 years of climate, satellite, and rice yield data across
247 of Indian rice-producing districts. In addition to model-building, a
dynamic dashboard was built understand how the reliability of rice yield
predictions varies across districts. The results of the proof-of-concept
machine learning pipeline demonstrated that rice yields can be predicted with a
reasonable degree of accuracy, with out-of-sample R2, MAE, and MAPE performance
of up to 0.82, 0.29, and 0.16 respectively. These results outperformed test set
performance reported in related literature on rice yield modeling in other
contexts and countries. In addition, SHAP value analysis was conducted to infer
both the importance and directional impact of the climate and remote sensing
variables included in the model. Important features driving rice yields
included temperature, soil water volume, and leaf area index. In particular,
higher temperatures in August correlate with increased rice yields,
particularly when the leaf area index in August is also high. Building on the
results, a proof-of-concept dashboard was developed to allow users to easily
explore which districts may experience a rise or fall in yield relative to the
previous year.
