Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome¹

Funções de pedotransferência para estimar parâmetros do modelo de van Genuchten no bioma Cerrado

HIGHLIGHTS:

Machine learning algorithms were superior to stepwise regression in estimating water content saturated and residual parameters.

The high variability of the fit parameters α and n produced a low precision of the PTFs developed for such parameters.

The variables sand, clay, microporosity, and microporosity were the most important variables for the development of PTFs.

ABSTRACT

The Cerrado biome has presented challenges in reconciling its agricultural expansion with water availability. In this sense, water resources planning and management are fundamental for the economic, social, and environmental development of the Cerrado biome, which has been hampered by the lack of data, especially those referring to irrigation strategies, such as, for example, the water retention curve. The water retention curve is essential to understand water dynamics in the soil; however, obtaining it can be laborious, opening an opportunity for Pedotransfer Functions (PTFs). The current study aimed to develop and evaluate PTFs to estimate the fit parameters of the van Genuchten model for the Cerrado biome. Multiple Linear Regression (MLR) and four machine learning (ML) algorithms were used to develop the PTFs. The ML algorithms were the Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Support Vector Regression (SVR), and K Nearest Neighbors (KNN). Two combinations of soil data were evaluated, and the predictor variables used in each set were different. Using the RF and SVR models, the best estimates were obtained concerning the parameter θ_s (saturated water content). As for θ_r (residual water content), the models showed a moderate predictive capacity. For the other parameters, the models did not perform satisfactorily for α and n (fit parameters).

Key words:
machine learning; multiple linear regression; irrigation