Abstract
Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.
Highlights
The Sustainable Development Goals to eradicate poverty (Goal 1), hunger (Goal 2) and improve human health and well-being (Goal 3) [1] will require a 60% to 110% increase in global agricultural production
The yield gap of maize in eastern India is a complex interplay of climatic variations, soil fertility gradients, differential management intensities and farmer socioeconomics
With an increasing shift to maize-based cropping systems in eastern India replacing the conventional rice-based system, understanding maize yield determinants has become critical for creating effective interventions
Summary
The Sustainable Development Goals to eradicate poverty (Goal 1), hunger (Goal 2) and improve human health and well-being (Goal 3) [1] will require a 60% to 110% increase in global agricultural production. FAO’s State of the World series [2], and IFPRI’s visionary 2050 policy documents have identified food security as the global concern of the 21st Century. Bridging the large yield gaps in smallholder farms of Asia and Africa, with significant regional. Maize yield determination by integrating socio-economic and crop management factors
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