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Modelling Farmers Investment in Agrochemicals: The Experience of Smallholder Cocoa Farmers in Ghana

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TLDR
In this article, the Tobit regression model was used to identify and quantify the effects of the determinants of farmer's investment in agrochemicals, and the results indicated that the sex of the farmer, age of the farmers, household size, educational attainment, mean age of cocoa farms, farm size and farmers previous output are all important variables in explaining farmers decision to invest in agricultural products.
Abstract
The study analyzes the effects of the determinants of farmer’s adoption of and investment in agrochemicals by collecting primary data from 156 sampled cocoa farmers in the Sefwi-Wiawso Municipality of Ghana. Descriptive statistical technique was employed to analyze demographic and farm-specific characteristics as well as the adoption rate and expenditure on agrochemicals. The findings highlighted the fact that, though a large proportion of farmers in the study area use agrochemicals, the intensity and amount spent to purchase these inputs are quite low. The Tobit regression model was used to identify and quantify the effects of the determinants of farmer’s investment in agrochemicals. The results generally indicate that the sex of the farmer, age of the farmer, household size, educational attainment, mean age of cocoa farms, farm size and farmers previous output are all important variables in explaining farmers decision to invest in agrochemicals. The paper concludes by recommending that strategic policies orienting towards improving the adoption rate as well as targeting farmers who are less likely to invest in agrochemicals is a pressing issue.

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Investing in agrochemicals in the cocoa sector of Côte d’Ivoire: hypotheses, evidence and policy implications

TL;DR: In this paper, the authors present empirical evidence to show how socioeconomic factors affect the adoption of and investment in agrochemicals in the cocoa sector of Cote d'Ivoire.
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On measuring indebtedness of African countries

TL;DR: In this article, the authors used a stochastic frontier production function approach and the technical efficiency computation procedure to develop an indebtedness index for 46 African countries, which ranges from a minimum of 3.6% (South Africa) to a maximum of 92% (Zambia).
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