Heavy metals content in soils of Western Siberia in relation to international soil quality standards

Semenkov, Ivan; Королева, Т. В.

doi:10.1016/j.geodrs.2020.e00283

Cited by 8 publications

(2 citation statements)

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“…The highest values of CaCO 3 are due to shell fragments, which can lead to the formation of solid layers impermeable to crops and water, as well as the fixation of P fertilizer [3,100]. CaCO 3 has a positive effect on soil quality by enhancing WHC and reducing HC [6]. Previous studies demonstrated that adding gypsum can lower the high soil sodium saturation content [73,101], because of its ability to absorb calcium instead of sodium in soil particles, which causes aggregation improvement and pH decline [101,102].…”

Section: Soil Physicochemical Featuresmentioning

confidence: 99%

“…Soil is one of the critical resources that can help fill the gap in food demand and ensure food security [3]. However, in recent decades, rapid growth in industry and agriculture, as well as degradation and degrading the earth's environment in various ways, have posed a significant challenge in promoting environmental sustainability and improving agricultural productivity [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

El Behairy,

El Arwash,

El Baroudy

et al. 2024

Agriculture

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Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.

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