AUTHOR=Beniaich Adnane , Otten Wilfred , Shin Ho-Chul , Cooper Hannah V , Rickson Jane , Soulaimani Aziz , El Gharous Mohamed 

TITLE=Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco

JOURNAL=Frontiers in Environmental Science

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1090688

DOI=10.3389/fenvs.2023.1090688

ISSN=2296-665X

ABSTRACT=<p>Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using 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 (<italic>w</italic><sub><italic>0</italic></sub>), field capacity (<italic>w</italic><sub><italic>330</italic></sub>) and permanent wilting point (<italic>w</italic><sub><italic>15000</italic></sub>). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, models based on clay, silt and soil organic matter as input variables showed the best performance after calibration (R<sup>2</sup> of 0.590, 0.785, 0.786 for <italic>w</italic><sub><italic>0</italic></sub>, <italic>w</italic><sub><italic>330</italic></sub>, and <italic>w</italic><sub><italic>15000</italic></sub>, respectively). Regarding the techniques based on machine learning, random forest showed the best performance after calibration compared with other algorithms (R<sup>2</sup> of 0.930, 0.955, 0.954 for <italic>w</italic><sub><italic>0</italic></sub>, <italic>w</italic><sub><italic>330</italic></sub>, and <italic>w</italic><sub><italic>15000</italic></sub>, respectively). PTFs represent a low cost and easy technique to estimate soil hydraulic properties, to improve water management efficiency for the farmers.</p>