Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach

Abstract

Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on “suitability” and “potential improvement in farmland productivity”, we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support.

A GIS-based land suitability model for agricultural tractors in CALABARZON Region, Philippines

“…A study conducted by Akinci et al 46 revealed that slope has the highest contributed weight on the selection of land for agricultural use when AHP was used as weighing method. Yang et al 47 studied the land suitability of agricultural machines on consolidated lands in which 15 different assessment indicators were considered and machine learning approach was utilized to allocate weights of the indicators. Their study showed that among the 15 indicators, slope has the significant influence on land suitability for agricultural machines.…”