Yang Heng, Ma Wenqiu, Liu Tongxin, Li Wenqing
College of Engineering, China Agricultural University, Beijing, China.
Key Laboratory of Land Consolidation and Rehabilitation, Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing, China.
Front Plant Sci. 2023 Mar 6;14:1084886. doi: 10.3389/fpls.2023.1084886. eCollection 2023.
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.
识别适合农业机械作业的可用农田是优化农业生产和提高农业机械化水平最具前景的途径。实施适合农业机械作业的农田整治（FCAM）是提高可持续生产和机械化农业的有效手段。本研究采用机器学习方法，基于自然资源禀赋、农业机械可达性、社会经济水平和生态限制这四个参数，评估土地整治方案中农田对农业机械作业的适宜性。并根据“适宜性”和“农田生产力的潜在提升”，将土地划分为四个区域：优先整治区、适度整治区、综合整治区和后备整治区。结果表明，大部分农田（76.41%）基本或适度适合FCAM。尽管坡度通常是土地适合农业机械作业的一个指标，但其他因素，如机耕道可达性差、人口持续减少和生态脆弱性，对降低土地对FCAM的整体适宜性有很大影响。此外，据估计，如果实施FCAM，农田的潜在生产力将提高720.8公斤/公顷。四个区域为确定FCAM方案的实施顺序和差异化策略提供了有用的依据。因此，这种分区是实施FCAM方案的有效解决方案。然而，要成功实施FCAM方案并实现现代化和可持续农业系统，还需要一些额外的策略，如加强农田生态系统保护、促进适合丘陵地形的农业机械研发，以及更多的财政支持。
