Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties.

Due to the rapid growth of population in Egypt and the scarcity of land and water resources, the effective management of these resources has become a very urgent necessity to achieve the sustainable agricultural development. Consequently, land suitability assessment is an important tool to manage these resources appropriately. The objective of this study was to use geostatistical approach and geographical information system (GIS) to evaluate the land suitability for some essential crops in Toshka region, Egypt. Soil parameters such as gravel content, soil texture, pH, EC, CaCO3, ESP and CEC were determined, at 1.0 km grid soil sites. Afterward, the geostatistics approach using ordinary kriging interpolation and semivariogram were applied to produce a spatialized and detailed map for each soil parameter. Gaussian, Exponential and Spherical geostatistical models were used to define the spatial variability of soil properties based on RMS, MSE and RMSSE. Also, based on Storie equation the kriged interpolated maps were incorporated in the model builder within GIS environment to achieve the land suitability assessment. The results illustrated that, most of the investigated area are unsuitable (N) for vegetable crops. However, the study area is more promising for field crops where 42.71 % and 11.20 % of the total area are moderately suitable (S2) for barely and alfalfa crops respectively. Furthermore, the results confirmed that, some of orchids crops are very suitable for the study area whereas 6.80 % and 37.81 % of the studied area are highly suitable (S1) and moderately suitable (S2) for olives respectively. On the other hand, the results revealed that the results indicated that, the most limiting factor in the investigated area are EC, pH, calcium carbonate content, CEC and soil texture. Finally, it can be concluded that the geostatistical approach and GIS are powerful and effective tools for land suitability studies and consequently for sustainable planning of land use.

Expanding projects to reclaim marginal land is the most effective way to reduce land use pressures in densely populated areas, such as Egypt’s Nile Valley and Delta; however, this requires careful, sustainable land use planning. This study assessed the agricultural potential of the El-Dabaa area in the northern region of the Western Desert, Egypt. It focused on assessing land capability, evaluating crop suitability, mapping soil variability, and calculating crop water requirements for twenty different crops. In this work, we evaluated land capability using the modified Storie index model and assessed soil suitability using the land use suitability evaluation tool (LUSET). We also calculated crop water requirements (CWRs) utilizing the FAO-CROPWAT 8.0 model. Additionally, we employed ArcGIS 10.8 to create spatial variability maps of soil properties, land capability classes, and suitability classes. Using a systematic sampling grid, 100 soil profiles were excavated to represent the spatial variability of the soil in the study area, and the physicochemical parameters of the soil samples were analyzed. The results indicated that the study area is primarily characterized by flat to gently sloping surfaces with deep soils. Furthermore, there are no restrictions on soil salinity or alkalinity, no sodicity hazards, and low CaCO₃ levels. On the other hand, the soils in the study area are coarse textured and have low levels of CEC and organic matter (OM), which are the major soil limiting factors. As a result, the land with fair capability (Grade 3) accounted for the vast majority of the study area (87.3%), covering 30599.4 ha. Land with poor capability (Grade 4) accounted for 6.5% of the total area, while non-agricultural land (Grade 5) accounted for less than 1%. These findings revealed that S2 and S3 are the dominant soil suitability classes for all the studied crops, indicating moderate and marginal soil suitabilities. Furthermore, there were only a few soil proportions classified as unsuitable (N class) for fruit crops, maize, and groundnuts. Among the crops studied, barley, wheat, sorghum, alfalfa, olives, citrus, potatoes, onions, tomatoes, sunflowers, safflowers, and soybeans are the most suitable for cultivation in the study area. The reference evapotranspiration (ETo) varied between 2.6 and 5.9 mm day⁻¹, with higher rates observed in the summer months and lower rates in the winter months. Therefore, the increase in summer ETo rates and the decrease in winter ones result in higher CWRs during the summer season and lower ones during the winter season. The CWRs for the crops we studied ranged from 183.9 to 1644.8 mm season⁻¹. These research findings suggest that the study area is suitable for cultivating a variety of crops. Crop production in the study area can be improved by adding organic matter to the soil, choosing drought-resistant crop varieties, employing effective irrigation systems, and implementing proper management practices. This study also provides valuable information for land managers to identify physical constraints and management needs for sustainable crop production. Furthermore, it offers valuable insights to aid investors, farmers, and governments in making informed decisions for agricultural development in the study region and similar arid and semiarid regions worldwide.

The continuously rising demand for food production has emphasized the importance of efficient land evaluation systems in agriculture. This research study aimed to estimate crop suitability and land capability of some new reclamation areas along El-Dabaa axis in the north-western desert of Egypt, which will participate in the planning of prospective projects aimed at reclaiming desert land in the area. In order to represent different geomorphic units in the area under investigation, 115 soil profiles were dug. Subsequent laboratory analyses were conducted to determine the physicochemical parameters of the examined soils. Using ALESarid-GIS software, land capability evaluation was accomplished and land suitability was performed for 12 crops. The results revealed that the high capability class C3 (Fair) occupied 45.84% of the investigated area, while the moderate capability C4 (Poor) occupied approximately 34.2%. The results of the land suitability analysis illustrated that 16.7% and 8.98% of the studied area were categorized as S1 (highly suitable) for wheat and olive, respectively. It was detected that 63% of the examined area was S2 (moderately suitable) for wheat, more than 50% for olive, bear, and alfalfa, more than 26% for barley, sugar beet, tomato, and onion. Furthermore, the examined area was S3 (marginally suitable) for onion (53.44%), sugar beet (47.23%), soybean and barley (42%), tomato and sorghum (38%), maize, potato, pear, and alfalfa (more than 28%). However, it was observed that sand texture, shortage of available water, high soil permeability, and lack of available nutrients were the key limiting parameters for land capability and crop cultivation. Hence, soil suitability modeling for various crops and mapping of land capability can help decision-makers plan for potential agricultural development and outcome desert land reclamation projects in Egypt.

Rapid population expansion has made food security a global concern for humanity, necessitating a sustainable assessment of natural resources. Well evaluated and managed soil is one of the most significant resources that can assist close the gap between supply and demand for food to attain food security. A precise assessment of land productivity (LP) is essential for sustainable land use management. The primary aim of this research is to predict the land productivity index (LPI) for certain locations on Egypt’s northwest coast under current conditions using soil quality indicators (SQIs). Additionally, the study proposed relevant management actions to boost land productivity in the future. To achieve this aim, a spatial model was created to assess LPI for some areas in the north western coast of Egypt. LP equation was used to estimate current land productivity index (CLPI) and potential land productivity index (PLPI). Results found that the study area mainly located under two classes of CLPI, e.g., average (class ⅡⅠ) represents an area of 13322.63 ha (67.55%) and good grade (class Ⅱ) covers an area of 4339.78 ha (22%). PLPI grades were good (II) and average (III) with an area of 16192.54 ha (82.10%) and 1903.23 ha (9.65%), respectively. The finding showed that more than 60% of the studied area was converted from poor and average class to good class after applying the proposed solutions of soil improvement. The methodology presented here can easily be replicated under similar circumstances in arid zones, allowing local governments and decision-makers to utilize the quantitative results obtained to ensure sustainable development.

Background: Assessing the quality of the soil is a crucial first step in agricultural management. A major obstacle to raising agricultural output, economic growth, and environmental health has been the decline in soil quality. One of the most often used metrics for evaluating soil quality is the soil quality index (SQI), which is frequently calculated using principal component analysis (PCA). Methodology: In this study, a soil quality index in the southwest of the Ismailia Governorate, Egypt, was evaluated and mapped using a geographical information system (GIS) and multivariate analysis. (PCA). Fifty- one soil samples were gathered for this purpose, and they were examined using established procedures. The dataset was broken down into new variables using principal component analysis (PCA) to avoid multi-collinearity. Relative weights (Wi) and soil indicators (Si) were then established and used to calculate SQI. The SQI comprises three quality zones. Results and discussion: The first zone has a very good quality index, accounting for about 65.66 (ha) of the entire area. Soils in this zone were defined by low salinity of the groundwater and adequate values of each soil attribute. The second zone, which makes up about 414.76 ha (67.5%) of the total area, is characterized by its good-quality soil. About 133.91 ha (21.8%) of the total land area is in the third zone, which is fair (bad quality). Low concentrations of soil organic matter (SOM), salinity, accessible nitrogen (N), phosphorus (P), potassium (K), and cation exchange capacity (CEC) had the greatest effects on the SQI of the studied location. Combining PCA and GIS enables a precise and efficient evaluation of the SQI. Conclusion: Decision-makers can identify regions with very good, good, and poor soil quality by examining the generated spatial distribution maps. Additionally, they can learn how each characteristic influences plant growth. In addition, The methodology outlined in this work can be readily replicated in similar situations in arid regions, enabling local authorities and decision-makers to make use of the quantitative results achieved to guarantee long-term development.