Show simple item record

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

Author: Dutta, S.
Author: Chakraborty, S.
Author: Goswami, R.
Author: Banerjee, H.
Author: Majumdar, K.
Author: Bin Li
Author: Jat, M.L.
Year: 2020
ISSN: 1932-6203 (Print)
URI: https://hdl.handle.net/10883/20784
Format: PDF
Language: English
Publisher: Public Library of Science
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: San Francisco, CA (USA)
Issue: 2
Issue: art. e0229100
Volume: 15
DOI: 10.1371/journal.pone.0229100
Description: 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.
Agrovoc: FARMS
Agrovoc: MAIZE
Agrovoc: CEREAL CROPS
Agrovoc: SOIL CHEMISTRY
Agrovoc: SEED
Agrovoc: FERTILIZERS
Agrovoc: CROP MANAGEMENT
Journal: PLoS ONE


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Sustainable Intensification
    Sustainable intensification agriculture including topics on cropping systems, agronomy, soil, mechanization, precision agriculture, etc.

Show simple item record