Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco - Beniaich, Adnane; Otten, Wilfred; Shin, Ho-Chul; Cooper, Hannah V; Rickson, Jane; Soulaimani, Aziz; El Gharous, Mohamed (Frontiers in Environmental Science, 2023)
Soil hydraulic properties are an important factor to optimize and adapt the water management system for a given crop. Pedotransfer functions (PTFs) presented a solution to predict soil variables such as hydraulic properties, using to fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), at field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya, and Tadla. The data was divided in calibration and validation data. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest. PTFs developed by Dijkerman (1988) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, model based on clay, silt, and soil organic matter as input variables showed the best performance after calibration (R2 of 0.590, 0.785, 0.786 for w0, w330, and w15000, respectively). Regarding the techniques base on machine learning, random forest showed the best performance after calibration compared with other algorithms (R2 of 0.930, 0.955, 0.954 for w0, w330, and w15000, respectively). PTFs represent a low-cost and easy technique to estimate soil hydraulic properties, for to improve water management efficiency for the farmers.