Accurate yield forecasting is essential for making informed policies and long-termdecisions for food security. Earth Observation (EO) data and machine learning al-algorithms play a key role in providing a comprehensive and timely view of cropconditions from field to national scales. However, machine learning algorithms'prediction accuracy is often harmed by spatial heterogeneity caused by exogenous factors not reflected in remote sensing data, such as differences in crop management strategies. In this paper, we propose and investigate a simple technique called state-wise additive bias to explicitly address the cross-region yield heterogeneity in Kazakhstan. Compared to baseline machine learning models (Random Forest, CatBoost, XGBoost), our method reduces the overall RMSE by 8.9% and the highest state-wise RMSE by 28.37%. The effectiveness of state-wise additive bias indicates machine learning’s performance can be significantly improved by explicitly addressing the spatial heterogeneity, motivating future work on spatial-aware machine learning algorithms for yield forecasts as well as for general geospatial forecasting problems.