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Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data Integration
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Advisor(s)
Abstract(s)
The efficient prediction of corn biomass yield is critical for optimizing crop
production and addressing global challenges in sustainable agriculture and renewable
energy. This study employs advanced machine learning techniques, including Gradient
Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and
Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil,
and crop management data from key agricultural regions in the United States. A novel
framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI)
and Growing Degree Days (GDDs), and the incorporation of interaction terms to address
complex non-linear relationships between input variables and biomass yield. We conduct
extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to
enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most
influential features driving yield outcomes. Our findings highlight significant synergies
among these variables, emphasizing their critical role in rural environmental governance
and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and
ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89.
These results underscore the potential of hybrid modeling for real-world applications in
sustainable farming practices. Addressing the concerns of passive farmer participation, we
propose targeted incentives, education, and institutional support mechanisms to enhance
stakeholder collaboration in rural environmental governance. While the models assume
rational decision-making, the inclusion of cultural and political factors warrants further
investigation to improve the robustness of the framework. Additionally, a map of the
study region and improved visualizations of feature importance enhance the clarity and
relevance of our findings. This research contributes to the growing body of knowledge on
predictive modeling in agriculture, combining theoretical rigor with practical insights to
support policymakers and stakeholders in optimizing resource use and addressing environ mental challenges. By improving the interpretability and applicability of machine learning
models, this study provides actionable strategies for enhancing crop yield predictions and
advancing rural environmental governance.
Description
Keywords
biomass yield prediction machine learning gradient boosting machines random forest support vector machines soil fertility index feature engineering sensitivity analysis sustainable agriculture renewable energy crop management
Citation
Abbasi, M., Váz, P., Silva, J., & Martins, P. (2025). Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data Integration. Sustainability, 17(1), 256. https://doi.org/10.3390/su17010256