Digital soil mapping outputs on soil classification and sugarcane production in Brazil

Soil maps at regional and farm levels are vital for the best management agricultural practices (BMAP). The soil is the substrate for plant growth and essential to ensure food security. In this context, soil maps require a detailed cartographic scale for BMAP. This study (i) investigated the use of digital soil mapping (DSM) products, such as soil chemical and physical attributes, indices, mineralogy, and properties to extrapolate late soil survey maps at 1:20,000 scale; (ii) created the digital yield environment for sugarcane based on the DSM products; and (iii) evaluated qualitatively the predict soil maps and relationship with previous studies and the predicted yield environment. The region of interest covers eight municipalities and almost 2598 km(2) in Sao Paulo State, Brazil. The soil survey at farm level conducted covered almost 86.52 km(2), ~3.33% of the total area (96.67% of the unmapped area). We created a point grid (centroid) with the same spatial resolution (30 m) of the rasters used as covariates for soil mapping unit (SMU) predictions. This grid intended to retrieve the representative soil mapping unit of each geometric polygon. It was retrieved 117,413 points representing 27 SMU of seven soil orders at a first categorical level, according to the Brazilian Classification System, and seven yield environment for sugarcane production. SMU predictions and their respective soil orders were performed using the random forest machine learning regression method. The level of association between SMU and yield environments was 0.34 (alpha = 0.01) by the Cramer's V coefficient with a very strong relationship. Our approach could provide the first digital yield environment for sugarcane based on the DSM products. Furthermore, a qualitative evaluation of our framework was substantiated with previous research in the same study site. This framework could be replicated and fulfil the need for DSM at regional and farm levels for policy-makers and farmers.