  Crop yield forecasting depends on many interactive factors, including crop
genotype, weather, soil, and management practices. This study analyzes the
performance of machine learning and deep learning methods for winter wheat
yield prediction using an extensive dataset of weather, soil, and crop
phenology variables in 271 counties across Germany from 1999 to 2019. We
proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional
convolution operation to capture the time dependencies of environmental
variables. We used eight supervised machine learning models as baselines and
evaluated their predictive performance using RMSE, MAE, and correlation
coefficient metrics to benchmark the yield prediction results. Our findings
suggested that nonlinear models such as the proposed CNN, Deep Neural Network
(DNN), and XGBoost were more effective in understanding the relationship
between the crop yield and input data compared to the linear models. Our
proposed CNN model outperformed all other baseline models used for winter wheat
yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher
correlation coefficient than the best performing baseline across test data). We
aggregated soil moisture and meteorological features at the weekly resolution
to address the seasonality of the data. We also moved beyond prediction and
interpreted the outputs of our proposed CNN model using SHAP and force plots
which provided key insights in explaining the yield prediction results
(importance of variables by time). We found DUL, wind speed at week ten, and
radiation amount at week seven as the most critical features in winter wheat
yield prediction.
