Factors affecting the productivity of coffee in Gulmi and Arghakhanchi districts of Nepal

Abstract

Coffee is one of the major potential cash crops with lucrative export value grown in mid-hills of Nepal. Nepalese coffee production has suffered long by low productivity. Research was conducted from February to May, 2019 to analyze the factors affecting the productivity of coffee in Arghakhanchi and Gulmi districts of Nepal. These two districts were, purposively selected for this study taking account of comparative advantage and past studies recommendations for coffee sector. Altogether, 100 coffee growing households 50 from each, Arghakhanchi and Gulmi, were sampled by using multistage sampling technique. A pre-tested semi-structured interview schedule was used to collect the primary information while secondary information was collected reviewing the relevant publications. Ordinary Least Square (OLS) regression model was used to determine the factors affecting the productivity of coffee. The study revealed that the number of active family members involved in coffee production (0.000), adoption of income diversification through intercropping (0.005), training (0.072) and technical assistance (0.021) had positive and significant effect on coffee productivity. Encouraging the household to have coffee production as their primary occupation, providing technical assistance on rational land utilization and intercropping and strengthening the skill and knowledge of farmers through trainings could significantly support in increasing the productivity of coffee.

Determinants of gross income from carp production in Bara district, Nepal

“…The study revealed that the gross income among carp producers who had received training was 0.209 unit more than carp producers who were devoid of training, all other factors held constant. The finding aligned with Bhattarai et al, (2020), who reported training had positive and significant association with productivity of coffee production in Arghakhanchi and Gulmi District of Nepal. The finding was consistent with Dahal and Rijal, (2019), who reported 47.89% higher profitability of potato cultivation in large scale farmer who received training.…”

“…where, I = Index (0 < I <1) S i = Scale value at i th severity f i = frequency of the i th severity n = total number of respondents = This scaling technique was used by Bhattarai et al (2020) to identify the problems in coffee production, Subedi et al (2019) in potato production and Shrestha and Shrestha (2017) in maize seed production.…”

Rice production in Chitwan district of Nepal: An analysis from economic and environmental perspectives

“…The rice productivity (yield) was used as dependent variable whereas different socio-economic and demographic characteristics of the respondents were used as explanatory variables. Multiple regression models have been used in several studies to assess the factors affecting the crop yield (Adhikari et al, 2018;Subedi et al, 2020;Bhattarai et al, 2020). The multiple regression model specified in this study is, Y (rice yield) = f (age of the household head, gender of the household head, primary occupation of the household head, number of family members involved in agriculture, livestock standard unit, membership of any organization, subsidy from the government in inputs, area of rice cultivated land) Also, LnY = α0 + βiXi + ei Where; LnY = Rice yield (in natural log form) α0 = Constant βi = Coefficient Xi = Explanatory variables ei = Error term…”

Coffee income and its determinants: A case of Deusa village, Nepal

“…Our study also showed that household size and access to irrigation facilities are crucial for coffee earning. Bhattarai et al (2020) stated that family members, when provided with technical support, boost their productivity. Lack of irrigation induces water stress that often affects the node formation, lowering flower formation and opening, and thus reducing coffee yield (Alvim, 1960;Cannell, 1971).…”

“…The maintenance of intermediate shade with regular pruning of branches, pest control, and soil conservation practices increases productivity(Sarmiento-Soler et al, 2020). in the households during the survey period Bhattarai et al (2020). found the number of active members and technical help improved the coffee productivity; hence, increased earning of the households.+ TRAININGWhether respondent received training on the coffee plantations or not, measured in dummy (if the respondent received trainings 1; otherwise 0) Khanal et al (2019).…”