Maize yield in smallholder agriculture system—An approach integrating socio-economic and crop management factors

dc.creatorDutta, S.
dc.creatorChakraborty, S.
dc.creatorGoswami, R.
dc.creatorBanerjee, H.
dc.creatorMajumdar, K.
dc.creatorBin Li
dc.creatorJat, M.L.
dc.date2020-03-03T01:25:22Z
dc.date2020-03-03T01:25:22Z
dc.date2020
dc.date.accessioned2023-07-17T20:05:47Z
dc.date.available2023-07-17T20:05:47Z
dc.identifier1932-6203 (Print)
dc.identifierhttps://hdl.handle.net/10883/20784
dc.identifier10.1371/journal.pone.0229100
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512581
dc.descriptionYield 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.
dc.formatPDF
dc.languageEnglish
dc.publisherPublic Library of Science
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dc.rightsOpen Access
dc.source2
dc.sourceart. e0229100
dc.source15
dc.sourcePLoS ONE
dc.subjectFARMS
dc.subjectMAIZE
dc.subjectCEREAL CROPS
dc.subjectSOIL CHEMISTRY
dc.subjectSEED
dc.subjectFERTILIZERS
dc.subjectCROP MANAGEMENT
dc.titleMaize yield in smallholder agriculture system—An approach integrating socio-economic and crop management factors
dc.typeArticle
dc.coverageSan Francisco, CA (USA)


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