A farming system typology for the adoption of new technology in Bangladesh

Abstract

Over the last three decades, few studies have been conducted to tackle the complexity and heterogeneity of Bangladesh farming systems. We address these research gaps with a new survey. Accordingly, a survey was conducted in North-Western Bangladesh to understand how socio-economic traits influence technology adoption and to identify and characterize key farm types. The survey was based on farm household characteristics, farm structure, farming practices and livestock as well as the economic performance of the farm. Principal component analysis (PCA) and cluster analysis (CA) were used to establish the different farm typologies, and the data set based on 27 variables was carefully analysed. The findings confirmed that the key variables that significantly affect the adoption of new agricultural technologies relate to age, farming experience, level of education of the household head, income, access to markets, land ownership, the proportion of hired labour, savings, food self-sufficiency and income from off-farm activities. Four main farm types were identified in the study area based on resource endowment and livelihood orientation. These are (1) well-resourced farmers entirely dependent on agriculture and less reliant on off-farm activities; (2) moderately resourced households, which are headed by an older male with greater farming experience and which are engaged in both on-farm and off-farm activities; (3) resource-constrained households with cattle as the main livestock and with income generated by the sale of livestock products; and (4) severely resource-constrained households which are headed by young farmers/men and where income is generated by off-farm activities. These four farm categories represent the heterogeneity of farms in North-West Bangladesh, and it is hoped that the development of this farm household typology will help particularly the extension service, to set up appropriate extension advice that will benefit the farming community.