Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach

Agricultural mechanization has been pivotal in increasing agricultural production efficiency, but in Nepal, the pace of mechanization is slow with subtle tractor usage in Terai (lowland) regions after 1990s (Paudel et al., 2019), which is driven primarily by outmigration and raising labor wages (Takeshima and Justice, 2020). In recent decades, the availability of smaller tilling machinery, particularly mini tillers suitable for hilly regions has enabled the expansion of machinery usage to hilly regions (Prasad, 2020). The expansion has been supported through subsidies by the Government of Nepal amidst several gaps in utilization of the machinery and its service provision (Brown et al., 2021). Despite these efforts and advancements, there exists substantial gaps remain in the machinery utilization and its services in Nepal, limiting impact of these programs (Devkota et al., 2020). One of the notable lack to promote mechaniuzation is Nepal’s sloped areas. The hilly areas and terrains are ofter considered to complicate mechanization efforts compared to Terai (flat) areas, highlighting the needs to explore the relatively modern and advance tools to guide policy makers in identifying suitable areas for mechanization promotion. In this context, “Mechanization suitability mapping” can help support if accounted together with several factors including land use, ecological fragility, accessibility, socioeconomics have been used to define mechanization suitability (Yang et al., 2023a). Globally, agricultural mechanization began with the introduction of the steam engine in the 1840s, followed by the advent of internal combustion engines in the 1890s. By the 1920s, highly efficient tractors for plowing were developed, leading to the era of mass tractor farming. It is believed that the widespread adoption of tractors was driven by food and labor shortages (both manual and animal) in agriculture during the World the World War (Karasev, 2023).