Understanding biophysical and socio-economic determinants of maize (Zea mays L.) yield variability in eastern India

dc.creatorBanerjee, H.
dc.creatorGoswami, R.
dc.creatorChakraborty, S.
dc.creatorDutta, S.
dc.creatorMajumdar, K.
dc.creatorSatyanarayana, T.
dc.creatorJat, M.L.
dc.creatorZingore, S.
dc.date2019-01-10T17:39:22Z
dc.date2019-01-10T17:39:22Z
dc.date2014
dc.date.accessioned2023-07-17T20:03:18Z
dc.date.available2023-07-17T20:03:18Z
dc.identifier1573-5214
dc.identifierhttps://hdl.handle.net/10883/19738
dc.identifier10.1016/j.njas.2014.08.001
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7511616
dc.descriptionThe aim of this paper was to investigate the key factors limiting maize (Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer's ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be, however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity.A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers.
dc.description79-93
dc.formatPDF
dc.languageEnglish
dc.publisherElsevier
dc.publisherRoyal Netherlands Society for Agricultural Sciences
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dc.rightsOpen Access
dc.source70-71
dc.sourceNJAS - Wageningen Journal of Life Sciences
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectClassification and Regression Tree
dc.subjectProduction Constraints
dc.subjectSmallholder Farms
dc.subjectStochastic Frontier Analysis
dc.subjectCLASSIFICATION SYSTEMS
dc.subjectREGRESSION ANALYSIS
dc.subjectFARM TYPOLOGY
dc.subjectPRODUCTION FACTORS
dc.subjectSMALLHOLDERS
dc.subjectSTOCHASTIC MODELS
dc.subjectYIELD GAP
dc.titleUnderstanding biophysical and socio-economic determinants of maize (Zea mays L.) yield variability in eastern India
dc.typeArticle
dc.coverageWestern India
dc.coverageWestern India
dc.coverageNetherlands


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