Assessment and Mapping of Soil Salinization Risk in an Egyptian Field Using a Probabilistic Approach

Shaddad, Sameh M.; Buttafuoco, Gabriele; Castrignanò, Annamaria

doi:10.3390/agronomy10010085

Cited by 19 publications

(7 citation statements)

References 33 publications

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“…Moreover, the northwest coast is one of the desert areas in Egypt suffering from land degradation which leads to decreased agricultural production. The assessment of soil capability, crop suitability, remote sensing analysis and GIS helps the decision making for sustainable agriculture development [85][86][87][88][89][90].…”

Section: Crop Suitabilitymentioning

confidence: 99%

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

et al. 2020

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The development of the agricultural sector is considered the backbone of sustainable development in Egypt. While the developing countries of the world face many challenges regarding food security due to rapid population growth and limited agricultural resources, this study aimed to assess the soils of Sidi Barrani and Salloum using multivariate analysis to determine the land capability and crop suitability for potential alternative crop uses, based on using principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHC) and the Almagra model of MicroLEIS. In total, 24 soil profiles were dug, to represent the geomorphic units of the study area, and the soil physicochemical parameters were analyzed in laboratory. The land capability assessment was classified into five significant classes (C1 to C5) based on AHC and PCA analyses. The class C1 represents the highest capable class while C5 is assigned to lowest class. The results indicated that about 7% of the total area was classified as highly capable land (C1), which is area characterized by high concentrations of macronutrients (N, P, K) and low soil salinity value. However, about 52% of the total area was assigned to moderately high class (C2), and 29% was allocated in moderate class (C3), whilst the remaining area (12%) was classified as the low (C4) and not capable (C5) classes, due to soil limitations such as shallow soil depth, high salinity, and increased erosion susceptibility. Moreover, the results of the Almagra soil suitability model for ten crops were described into four suitability classes, while about 37% of the study area was allocated in the highly suitable class (S2) for wheat, olive, alfalfa, sugar beet and fig. Furthermore, 13% of the area was categorized as highly suitable soil (S2) for citrus and peach. On the other hand, about 50% of the total area was assigned to the marginal class (S4) for most of the selected crops. Hence, the use of multivariate analysis, mapping land capability and modeling the soil suitability for diverse crops help the decision makers with regard to potential agricultural development.

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Section: Crop Suitabilitymentioning

confidence: 99%

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

et al. 2020

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“…Several studies have shown that irrigated agricultural activities can result in soil salinization of farmland [29], and the risk of salinization is particularly high in the rainy season due to the change in the lake water volume [30]. However, in the study area, to restore the water volume of the lake, the local government prohibits irrigation activities around the lake area.…”

Section: Geofluidsmentioning

confidence: 98%

Effects of Groundwater Depth and Salt Content on Vegetation in Dry Lake Basins: A Case Study of Chahan Lake, Northern China

Chen

Shi

et al. 2022

Geofluids

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Under the dual influences of global climate change and human activities, inland lakes in arid areas are shrinking and drying up, and a large area of bare lake bed has become the source of the release of chemical dust. The aim of this study is to study the control of groundwater on the distribution and development of natural vegetation and the effects of the groundwater conditions on soil salinization. In this study, a typical modern dry lake in northern China, Chahan Lake, was taken as the study area. Through field investigations, field sampling and analysis, and statistical analysis, the influence of groundwater on the ecosystem of this dry lake was studied. The results revealed that the vegetation communities in the lakeside zone were Kalidium foliatum, Nitraria tangutorum, Suaeda glauca, Leymus chinensis, Chloris virgata, and Carex duriuscula communities from the dry lake bed outwards. The groundwater table suitable for vegetation growth in Chahan Lake is 2.0–3.0 m deep. The groundwater table suitable for the growth of Kalidium foliatum vegetation is 1.5–2.5 m deep. The groundwater table suitable for the growth of Leymus chinensis vegetation is 3.0–4.0 m deep. In Chahan Lake, the critical groundwater depth and total dissolved solids (TDS) for moderate salinization, severe salinization, and saline soil occurrence are 4.0 m and 2.0 g/L, 3.0 m and 3.0 g/L, and 1.5 m and 4.0 g/L, respectively. Regarding the prevention and control of salt-dust storms, the ecological threshold of the groundwater, which can effectively increase the vegetation coverage and prevent soil salinization, is groundwater depths of 2.0–4.0 m and TDS values of <2 g/L.

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“…The interpolation accuracy was generally lower in study areas at the micro-level, which are The recent studies which utilized conventional interpolation methods for soil prediction in fertilization, sorted by the area of sampling sites, are presented in Table 1. The interpolation accuracy was generally lower in study areas at the micro-level, which are highly common cases for precision fertilization [33,34]. Conventional interpolation methods were particularly unable to accurately predict and detect in-field variabilities in cases of lower spatial autocorrelation, which are a foundation of variable rate technology in precision agriculture [7].…”

Section: Conventional Approach To Fertilization In Precision Agriculturementioning

confidence: 99%

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

2022

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The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.

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Assessment and Mapping of Soil Salinization Risk in an Egyptian Field Using a Probabilistic Approach

Cited by 19 publications

References 33 publications

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

Effects of Groundwater Depth and Salt Content on Vegetation in Dry Lake Basins: A Case Study of Chahan Lake, Northern China

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

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