Stepwise methods for more nuanced adoption analysis: a case study of harvest and post-harvest mechanization in Bangladesh

Abstract The adoption of agricultural harvest and post-harvest mechanization is crucial for addressing drudgery, food losses, climate vulnerability and food security. Despite considerable efforts by government and development partners to prioritize agricultural mechanization, labour-intensive manual (post-)harvest activities continue to dominate in Bangladeshi smallholder systems. Explorations of this has been limited by simplistic binary approaches that ignore the dynamic pathways to usage outcomes. Instead, we apply non-binary analytical methods to district representative data to highlight the value in moving beyond binary adoption analysis. Results highlight that a national (post-)harvest mechanisation rate of 74% is insufficient to capture the true adoption status, given substantial disparity exists across machinery and by district. Deeper exploration of temporal and spatial differences enable the identification of key trends that warrant further in-depth explorations, while only 46% satisfaction with extension systems highlights the need to re-evaluate key information exchange mechanisms. We conclude that there is a clear need for district and machinery specific policy arrangements if Bangladesh is to achieve (post-)harvest mechanisation objectives that aim to reduce food loses and enable greater food security nationwide.