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Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study's requirements, with R2 > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a "quick", inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. [ABSTRACT FROM AUTHOR]

Researchers are depending more than ever on remote sensing techniques to monitor and assess the agricultural water status, as well as to estimate crop water usage or crop actual evapotranspiration. In the current work, normal and stressed baselines for irrigated wheat genotypes were developed in an arid part of the Sohag governorate, Egypt, using infrared thermometry in conjunction with weather parameters. The experiment was carried out in a randomized complete block design in the normal and drought stress conditions based on three replicates using ten bread wheat genotypes (G1–G10), including five accessions, under drought stress. A standard Class-A-Pan in the experimental field provided the daily evaporation measurements (mm/day), which was multiplied by a pan factor of 0.8 and 0.4 for normal and stressed conditions, respectively. The relationship between the vapor pressure deficit (VPD) and canopy-air temperature differences (Tc − Ta) was plotted under upper (fully stressed) and lower baseline (normal) equations. Accordingly, the crop water stress indexes (CWSIs) for the stressed and normal baselines for wheat genotypes were developed. Additionally, the intercept (b) and the slope (a) of the lower baseline equation were computed for different genotypes. The results indicate that, before applying irrigation water, the CWSI values were high in both growing seasons and under all irrigation regimes. After that, the CWSI values declined. G10 underwent stress treatment, which produced the greatest CWSI (0.975). Conversely, the G6 condition that received well-watered irrigation yielded the lowest result (−0.007). When compared to a well-watered one, the CWSI values indicated a trend toward rising stress. There existed an inverse link between the CWSI and grain yield (GY); that is, a lower CWSI resulted in better plant water conditions and a higher GY. Under standard conditions, the wheat's highest GY was recorded in G2, 8.36 Ton/ha and a WCSI of 0.481. In contrast, the CWSI result for the stress treatment was 0.883, indicating a minimum GY of 5.25 Ton/ha. The Water Use Efficiency (WUE) results demonstrated that the stress irrigation regime produced a greater WUE value than the usual one. This study makes a significant contribution by investigating the techniques that would allow CWSI to be used to estimate irrigation requirements, in addition to determining the irrigation time. [ABSTRACT FROM AUTHOR]

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is essential to assess the soil organic carbon pool (SOCP) in dry environments to apply appropriate management techniques that address sustainable development. A significant opportunity for sustaining agricultural output and reducing climate change is the storage of soil organic carbon in agricultural soil. The goal of this study was to measure the spatial variability of SOCP content, and determine the effects of soil texture, changes in land use, and land cover on SOCP in surface soil samples. The study additionally investigated the relationships between SOCP and other characteristics, including the normalized vegetation index (NDVI) and land surface temperature (LST), as well as the effects of increasing soil organic carbon on the amount of greenhouse gases. To accomplish this goal, 45 soil surface samples were collected to a depth of 30 cm at the Fayoum depression in Egypt, and analyzed. The soil samples were representative of various soil textures and land uses. The average SOCP concentration in cultivated regions is 32.1 and in bare soils it is 6.5 Mg ha−1, with areas of 157,112.94 and 16,073.27 ha, respectively. According to variances in soil textures, sandy soils have the lowest SOCP (1.8 Mg ha−1) and clay loam soils have the highest concentrations (49 Mg ha−1). Additionally, fruit-growing regions have the greatest SOCP values and may therefore be better suited for carbon sequestration. The overall average SOCP showed 32.12 Mg C ha−1 for cultivated areas. A rise in arable land was accompanied by a 112,870.09 Mg C rise in SOCP. With an increase in soil organic carbon, stored carbon dioxide emissions (greenhouse gases) would be reduced by 414,233.24 Mg CO2. We should consider improving fertilization, irrigation methods, the use of the multiple cropping index, decreasing desertion rates, appropriate crop rotation, and crop variety selection. The research highlights the significance of expanding cultivated areas towards sustainable carbon sequestration and the climate-change-mitigation potential. [ABSTRACT FROM AUTHOR]

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R2) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg−1 for Cr, 20,963.84 ± 4447.83 mg kg−1 for Fe, 1.46 ± 0.42 mg kg−1 for Cd, and 48.71 ± 11.88 mg kg−1 for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination. [ABSTRACT FROM AUTHOR]

Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas. [ABSTRACT FROM AUTHOR]

Soil erosion constitutes one of the main environmental and food security threats, derived from the loss of its productive capacity. With the help of remote sensing (RS), geographic information systems (GIS), and a revised version of the universal soil loss equation (RUSLE), this research has mostly focused on measuring the potential soil erosion hazard and soil water conservation ratio (SWCR) in the El-Minia region of Egypt. Based on the integration of S2A images and the digital elevation model (DEM), geomorphological units of the study area were identified. The RUSLE model includes parameters that allow for mapping soil erosion, such as rain erosivity, soil erodibility, slope length and steepness, soil cover and management, and soil conservation practices. The outcomes revealed that the classes of annual erosion rates of the study area are those of "slight erosion", "low erosion", "moderate erosion" and "moderately high erosion", which represent percentages of 29%, 18%, 33% and 20%, respectively, of the total area. The rate of erosion decreases from east to west. The main erosion factors in the research area are the low vegetation cover and the high slope values. This study highlights the utility of combining the classic RUSLE equation with techniques such as remote sensing (RS) and geographic information systems (GIS) as a basis for assessing current erosion conditions in arid environments and, specifically, for the application of soil management patterns aimed at increasing soil organic matter and any other soil conservation actions. The findings of this study can be used by policymakers to implement soil conservation measures if development projects are to proceed in areas with a high risk of soil erosion. The approach described here is therefore adaptable to similar environments in arid regions. [ABSTRACT FROM AUTHOR]

The study aims to clarify the relationship between soil organic carbon (SOC) and human activity under arid conditions, in the east area of the Nile Delta, Egypt. SOC is one of the critical factors in food production and plays an important role in the climate change because it affects the physio-chemical soil characteristics, plant growth, and contributes to sustainable development on global levels. For the purpose of our investigations, 120 soil samples (0–30 cm) were collected throughout different land uses and soil types of the study area. Multiple linear regressions (MLR) were used to investigate the spatiotemporal relationship of SOC, soil characteristics, and environmental factors. Remote sensing data acquired from Landsat 5 TM in July 1995 and operational land imager (OLI) in July 2018 were used to model SOC pool. The results revealed significant variations of soil organic carbon pool (SOCP) among different soil textures and land-uses. Soil with high clay content revealed an increase in the percentage of soil organic carbon, and had mean SOCP of 6.08 ± 1.91 Mg C ha−1, followed by clay loams and loamy soils. The higher values of SOCP were observed in the northern regions of the study area. The phenomenon is associated with the expansion of the human activity of initiating fish ponds that reflected higher values of SOC that were related to the organic additions used as nutrients for fish. Nevertheless, the SOC values decreased in southeast of the study area with the decrease of soil moisture contents and the increase in the heavy texture profiles. As a whole, our findings pointed out that the human factor has had a significant impact on the variation of soil organic carbon values in the Eastern Nile Delta from 1995 to 2018. As land use changes from agricultural activity to fish ponds, the SOCP significantly increased. The agriculture land-use revealed higher SOCP with 60.77 Mg C ha−1 in clay soils followed by fish ponds with 53.43 Mg C ha−1. The results also showed a decrease in SOCP values due to an increasing in land surface temperature (LST) thus highlighting that influence of temperature and ambient soil conditions linked to land-use changes have a marked impact on surface SOCP and C sequestration. [ABSTRACT FROM AUTHOR]