Greschuk Lucas T, Demattê José A M, Silvero Nélida E Q, Rosin Nícolas Augusto
Department of Soil Science, University of São Paulo (ESALQ/USP), Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Sci Rep. 2023 Aug 29;13(1):14103. doi: 10.1038/s41598-023-39981-y.
Food production is extremely dependent on the soil. Brazil plays an important role in the global food production chain. Although only 30% of the total Brazilian agricultural areas are used for crop and livestock, the full soil production potential needs to be evaluated due to the environmental and legal impossibility to expand agriculture to new areas. A novel approach to assess the productive potential of soils, called "SoilPP" and based on soil analysis (0-100 cm) - which express its pedological information - and machine learning is presented. Historical yields of sugarcane and soybeans were analyzed, allowing to identify where it is still possible to improve crop yields. The soybean yields were below the estimated SoilPP in 46% of Brazilian counties and could be improved by proper management practices. For sugarcane, 38% of areas can be improved. This technique allowed us to understand and map the food yield situation over large areas, which can support farmers, consultants, industries, policymakers, and world food security planning.
粮食生产极度依赖土壤。巴西在全球粮食生产链中发挥着重要作用。尽管巴西农业总面积中仅有30%用于作物种植和畜牧养殖，但由于环境和法律方面的限制，无法将农业扩展到新的区域，因此需要对土壤的全部生产潜力进行评估。本文提出了一种名为“SoilPP”的新颖方法，用于评估土壤的生产潜力，该方法基于土壤分析（0 - 100厘米）——以表达其土壤学信息——以及机器学习。对甘蔗和大豆的历史产量进行了分析，从而确定哪些地方仍有可能提高作物产量。在巴西46%的县，大豆产量低于估计的SoilPP，通过适当的管理措施产量有望提高。对于甘蔗，38%的种植区域产量可得到提升。这项技术使我们能够了解并绘制大面积区域的粮食产量情况，可为农民、顾问、企业、政策制定者以及世界粮食安全规划提供支持。
