Technical Efficiency in Agriculture and Its Implication on Forest Conservation in Tanzania: The Case Study of Kilosa District (Morogoro)

Peer Reviewed
1 December 2018

Tanzania Economic Review

Lokina, Razack, Lwiza, Samwel S.

This paper examines technical efficiency in farming activities and its implication on forest conservation in Kilosa District. The empirical analysis is based on data collected from 301 households selected randomly from five villages in Kilosa district, of which three villages were under the REDD+ project. Two empirical models were estimated: stochastic frontier Translog production function, and forest resources extraction model. The stochastic frontier Translog production function was estimated using the FRONTIER 4.1 program, whereas Ordinary Least Square (OLS) method was used to estimate the forest extraction model. The empirical findings indicated that the mean technical efficiency of small-scale farmers in Kilosa district was 64 percent, implying that farmers in Kilosa District still have a room to improve their farming efficiency by 36 percent. In addition, farming technical efficiency among the households indicated to be influenced by the level of farming inputs usage, gender and educational level of the household head, extension services, farm experience and access to formal credits. Furthermore, the study indicated that technical efficiency, sex and distance of a village from the forest are significantly negatively related to extraction of forest resources; whereas household size and primary education of the household head showed to be strongly positively related to forest extraction. The results suggest that efficiency can be improved with appropriate policy intervention, and which will hence reduce deforestation and forest degradations.

Country
Sustainable Development Goals
Publication reference
Lokina, Razack, Lwiza, Samwel S. (2019). echnical Efficiency in Agriculture and Its Implication on Forest Conservation in Tanzania: The Case Study of Kilosa District (Morogoro)
Publication | 21 November 2019