Digital Mapping of the Soil Available Water Capacity: Insights for the Resilience of Agricultural Systems to Climate Change

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. These difficulties have led to the use of indirect ways of estimating AWC. Among those, digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models which are not physically interpretable, limiting the possibility to build new knowledge in soil science. In this context, we aimed to identify the spatial patterns estimated by the Random Forest (RF) algorithm to predict AWC and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, a data-driven approach was applied using laboratory-determined soil attributes (clay, sand, and organic matter contents), together with a pedotransfer function (PTF), remote sensing, DSM techniques, Shapley values (model interpretation), and meteorological data. The AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917-1946 and 1991-2020. The selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m-1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. The present case study shows that climate changes at the study site modified the rainfall regime, increasing the amount of water retained in the soil during the dry period (from April to August). The used methodology provides insights to build strategies that allow the adaptation of agricultural systems to the effects of climate change.