Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Determinants of Pesticide Application in Nepalese Vegetable Farming: An Empirical Analysis using Multivariate Probit Model

Version 1 : Received: 28 November 2017 / Approved: 29 November 2017 / Online: 29 November 2017 (13:27:57 CET)

How to cite: GC, A.; Ghimire, K. Determinants of Pesticide Application in Nepalese Vegetable Farming: An Empirical Analysis using Multivariate Probit Model. Preprints 2017, 2017110191. https://doi.org/10.20944/preprints201711.0191.v1 GC, A.; Ghimire, K. Determinants of Pesticide Application in Nepalese Vegetable Farming: An Empirical Analysis using Multivariate Probit Model. Preprints 2017, 2017110191. https://doi.org/10.20944/preprints201711.0191.v1

Abstract

Currently, the pesticides are the global core concern because it is a boon to farmers against increasing disease-pest and simultaneously, pesticide residue is the major anxiety regarding human health. For that reason, identification and determination of factors affecting the application of pesticides are essential. To identify and evaluate determinants of pesticides application in Nepal, a household survey of 300 households was carried-out and an empirical analysis was done using multivariate probit model. Moreover, powder and liquid forms of pesticides were considered for summer and winter season in vegetable farming, which was assigned as outcome variables. Likewise, socio-economic, demographic, farm-level and perception data were considered as explanatory variables. Use of chemical fertilizers, age and gender of head of household, household size and access to weather information were found the most influencing factors. Moreover, forms of pesticides and growing seasons were found complementary to each other. Therefore, devising the policy options accordingly should balance needs of farmers and health of consumers.

Keywords

Pesticides, Vegetable, Nepal, Determinant, Multivariate Probit

Subject

Business, Economics and Management, Econometrics and Statistics

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