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Modelling and Mapping of Soil Organic Matter in Doukkala Plain, Moroccan Semi-Arid Region

Bel-Lahbib Sanae, Namr Khalid Ibno, El Hafyani Mohammed, Al Masmoudi Yassine, Essahlaoui Ali

Ecological Engineering & Environmental Technology

2022

Vol. 23, iss. 5

188--197

The Doukkala plains one of the largest irrigated areas in Morocco with a very important agricultural potential. With the integration of new technologies in agriculture, the plain has been subjected to intensive agriculture which has negative impacts on soil quality especially the soil organic matter loss. Therefore, the objective of this study is to combine remote sensing and modelling for monitoring of organic matter content. The obtained results showed that all the examined models showed satisfactory results in the prediction of organic matter with a coefficient of determination R2 ranging from 0.58 to 0.71 and the Root Mean Square Error (RMSE) varied 0.25 and 0.26%. Based on the findings, we can infer that this approach is both efficient and valid for modelling and mapping soil organic matter and may moreover be applied for other areas with same characteristics.

Application of the Random Forest Model to Predict the Plasticity State of Vertisols

Al Masmoudi Yassine, Bouslihim Yassine, Doumali Kaoutar, El Aissaoui Abdellah, Namr Khalid Ibno

Journal of Ecological Engineering

2021

Vol. 22, nr 2

36--46

Vertisol plasticity is related to moisture content, and it requires an in-depth physicochemical characterization. This information allows us to use the land under the most adequate conditions and avoid soil physical degradation, especially its compaction. The objective of this study was to characterize the Vertisol in the Moroccan region of Doukkala-Abda and to predict soil plasticity based on the physicochemical parameters of soil, such as texture, electrical conductivity, Soil Organic Matter (SOM) and other chemical parameters for 120 samples. Determination of soil plasticity using Atterberg limits is a challenging and time-consuming method. Thus, this study aimed to develop a new model that can predict soil plasticity using the Random Forest algorithm. The soils presented homogeneity in the majority of physicochemical parameters, except a significant difference observed in the SOM and the electrical conductivity, which in turn influenced the soil plasticity state. The results showed significant and positive correlations between SOM, Soil Clay Content (SCC), Electrical Conductivity (EC), and plasticity in the Vertisol fields of the region. For the training phase, the model gave excellent results with a coefficient of determination of 0.995 and an RMSE of 0.164. Almost the same results were observed in the validation phase with a coefficient of determination of 0.974 and an RMSE of 0.361, which shows that the model succeeded in predicting plasticity in both phases. On the basis of these results, this model can be used for the plasticity prediction using other physicochemical parameters and the Random Forest Model. The prediction of soil plasticity is an important parameter to respect the timing of introducing machines/tools in the fields and avoid Vertisol degradation.