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.

Understanding and quantitative delineation of Portable X-Ray Fluorescence (PXRF) -quantified elements and soil properties spatial variability are important for healthy turf development for golf courses. In this study, PXRF-quantified elements and soil properties (except soil acidity and alkalinity (pH), electric conductivity (EC), and textures) of 200 soil samples were measured by PXRF analyzer at different golf courses in Lubbock, Amarillo, and Midland in Texas, and Hobbs in New Mexico. Furthermore, principal component analysis (PCA), empirical bayesian kriging (EBK) and the ordinary least square model (OLSM) were used in the study. Two kinds of components were extracted and interpreted by PCA, the results showed Zn, Ti, Fe, Rb, V, Mn and Zr were associated with the component 1, while Sr was associated with the component 2, the preliminary classification of PXRF-quantified elements was formed by PCA. The EBK approach was used to evaluate the spatial patterns of PXRF-quantified elements and soil properties. The OLSM model quantitatively related pH to EC, silt texture and the PXRF-quantified K, Ca and Sr. The integration of PCA, EBK and OLSM revealed quantitative links between soil pedogenesis and causes, spatial variability and couple relationships of PXRF-quantified elements and soil properties over golf courses.

To advance site-specific management of soil volumetric moisture content (VMC), this study analyzed the spatio-temporal evolution characteristics of soil VMC using the method of sequential Gaussian simulation (SGS) during the different growth stages of winter wheat. This was compared with data measured by time domain reflectometry (TDR) which is a well-established electromagnetic technique to measure soil VMC. The spatial autocorrelation coefficient of VMC indicated the strongest clustering of VMC in the tillering stage, and the least clustering of VMC in the harvest stage. A threshold of VMC in topsoil in the jointing stage of winter wheat was put forward. This threshold is 26, signifying that at a lower value, irrigation should be performed and irrigation efficiencies can be improved. Stable and sub-stable areas in the spatial variability maps of VMC were identified in the winter wheat jointing stage. Furthermore, the optimal irrigation stage was the early jointing stage, and irrigation was performed once as a guide. A loose-couple spatial model was constructed using the VMC in topsoil and the volume of water for irrigation. The VMC in the jointing stage of winter wheat was linked with efficient and water-saving irrigation.

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits.

Open-source R language can implement quantitative research using the flexibility, adaptability and simplicity of bayesian inference models. Counts of drought which are regarded as the realizations of drought “event” in a Poisson process follows an Gamma distribution (prior distribution) in the case study of Yucheng city with the support of R language. That is, the annual drought counts in Yucheng City during the 10 years from 1974 to 1983 is regarded as the 10 realizations of the Poisson process, and the drought counts in Yucheng city during the 10 years is described by the Poisson distribution. The Gamma (1,1) of theta is used as the prior distribution, and its posterior distribution is calculated to be Gamma (60,11). The sample value of theta is obtained by random sampling from the posterior distribution of theta, and a Poisson sample is randomly generated for each value as the predicted value. The posterior mean value of theta sampling results, the mean and quantiles of the predicted value, and the probability estimation of theta and the predicted value are calculated to obtain the highest density region of theta. Studies have shown that Poisson model of drought prediction in Yucheng city has more flexibility, adaptability and simplicity and can provide posterior mean, highest-posterior density of the data, and the drought prediction of Yucheng city is a feasible method on basis of the stochastic Poisson process, thereby providing better reference value for drought reduction.