Socio-economic assessment on maize production and adoption of open pollinated improved varieties in Dang, Nepal

Sanjiv Subedi, Yuga Nath Ghimire, Deepa Devkota

Abstract

Research was conducted from February to May, 2017 for socioeconomic assessment on maize production and adoption of open pollinated improved maize varieties in Dang district of Nepal. Altogether, 100 samples were taken by simple random sampling from the major maize growing areas and relevant publications were reviewed. Focal Group Discussion and Key Informant Survey were also done. Descriptive statistics, unpaired t-test, probit regression and indexing were used for data analysis using statistical tools- SPSS, STATA and MS-Excel. Probit econometric model revealed that ethnicity (1% level), gender (5% level), area under open pollinated improved maize (1% level), seed source dummy (1 % level) and number of visits by farmers to agrovet (5% level) significantly determined the adoption of open pollinated improved maize varieties. In addition, unpaired t-test revealed that the productivity of open pollinated improved maize varieties was significantly higher (at 1% level) than local; also, the multinational companies' hybrids showed significantly higher productivity (at 1% level) when compared to open pollinated improved varieties. Furthermore, indexing identified- lack of availability of quality seeds and fertilizers (I= 0.86) as the major problem associated with the maize production. Giving aggressive subsidy on open pollinated improved seeds and dealership to registered agrovets for selling the subsidy seeds could enhance the adoption. Moreover, government organizations working in the areas of agricultural extension and research must focus on adoption of open pollinated improved maize varieties among the farmers, substituting the local and developing the high yielding hybrid varieties in Nepal to increase the maize productivity. 

Keywords

Adoption, maize productivity, probit regression, socioeconomic assessment

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DOI: http://dx.doi.org/10.3126/jmrd.v3i1.18916

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Copyright (c) 2017 Sanjiv Subedi, Yuga Nath Ghimire, Deepa Devkota

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.