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Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.
Dutta, Sudarshan; Chakraborty, Somsubhra; Goswami, Rupak; Banerjee, Hirak; Majumdar, Kaushik; Li, Bin; Jat, M L.
Afiliación
  • Dutta S; African Plant Nutrition Institute, Benguérir, Morocco.
  • Chakraborty S; Agricultural and Food Engineering Department, IIT Kharagpur, Kolkata, India.
  • Goswami R; IRDM Faculty Centre, RKMVERI, Kolkata, India.
  • Banerjee H; IRDM Faculty Centre, RKMVERI, Kolkata, India.
  • Majumdar K; Regional Research Station (CSZ), BCKV, Kakdwip, India.
  • Li B; African Plant Nutrition Institute, Benguérir, Morocco.
  • Jat ML; Department of Experimental Statistics, Louisiana State University, Baton Rouge, Louisiana, United States of America.
PLoS One ; 15(2): e0229100, 2020.
Article en En | MEDLINE | ID: mdl-32092077
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Productos Agrícolas / Zea mays / Producción de Cultivos Tipo de estudio: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Aspecto: Determinantes_sociais_saude / Equity_inequality País/Región como asunto: Asia Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Marruecos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Productos Agrícolas / Zea mays / Producción de Cultivos Tipo de estudio: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Aspecto: Determinantes_sociais_saude / Equity_inequality País/Región como asunto: Asia Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2020 Tipo del documento: Article País de afiliación: Marruecos Pais de publicación: Estados Unidos