The APSIM-Wheat and AQUACROP models were calibrated for the Sakha 95 cultivar using phenological data, grain and biomass yield, and genetic parameters based on field observation. Various treatments of planting dates, irrigation, and fertilization were applied over the two successive winter growing seasons of 2019/2020 and 2020/2021. Both models simulated anthesis, maturity dates, grain yield, and aboveground biomass accurately with high performances (coefficient of determination, index of agreement greater than 0.8, and lower values of root mean square deviation) in most cases. The calibrated models were then employed to explore wheat yield and water productivity (WP) in response to irrigation and nitrogen fertilization applications. Scenario analyses indicated that water productivity and yield of wheat ranged from 1.2–2.0 kg m^–3 and 6.8–8.7 t ha^–1, respectively. Application of 0.8 from actual evapotranspiration and 120% from recommended nitrogen dose was the best-predicted scenario achieving the highest value of crop WP. Investigating the suitable option achieving the current wheat yield by farmers (7.4 t ha^–1), models demonstrated that application of 1.4 from actual evapotranspiration with 80% of the recommended nitrogen dose was the best option to achieve this yield. At this point, predicted WP was low and recorded 1.5 kg m^–3. Quantifying wheat yield in all districts of the studied area was also predicted using both models. APSIM-Wheat and AQUACROP can be used to drive the best management strategies in terms of N fertilizer and water regime for wheat under Egyptian conditions.

Crop yield prediction is critical for investigating the yield gap and potential adaptations to environmental and management factors in arid regions. Crop models (CMs) are powerful tools for predicting yield and water use, but they still have some limitations and uncertainties; therefore, combining them with machine learning algorithms (MLs) could improve predictions and reduce uncertainty. To that end, the DSSAT-CERES-maize model was calibrated in one location and validated in others across Egypt with varying agro-climatic zones. Following that, the dynamic model (CERES-Maize) was used for long-term simulation (1990–2020) of maize grain yield (GY) and evapotranspiration (ET) under a wide range of management and environmental factors. Detailed outputs from three growing seasons of field experiments in Egypt, as well as CERES-maize outputs, were used to train and test six machine learning algorithms (linear regression, ridge regression, lasso regression, K-nearest neighbors, random forest, and XGBoost), resulting in more than 1.5 million simulated yield and evapotranspiration scenarios. Seven warming years (i.e., 1991, 1998, 2002, 2005, 2010, 2013, and 2020) were chosen from a 31-year dataset to test MLs, while the remaining 23 years were used to train the models. The Ensemble model (super learner) and XGBoost outperform other models in predicting GY and ET for maize, as evidenced by R² values greater than 0.82 and RRMSE less than 9%. The broad range of management practices, when averaged across all locations and 31 years of simulation, not only reduced the hazard impact of environmental factors but also increased GY and reduced ET. Moving beyond prediction and interpreting the outputs from Lasso and XGBoost, and using global and local SHAP values, we found that the most important features for predicting GY and ET are maximum temperatures, minimum temperature, available water content, soil organic carbon, irrigation, cultivars, soil texture, solar radiation, and planting date. Determining the most important features is critical for assisting farmers and agronomists in prioritizing such features over other factors in order to increase yield and resource efficiency values. The combination of CMs and ML algorithms is a powerful tool for predicting yield and water use in arid regions, which are particularly vulnerable to climate change and water scarcity.

Soil salinity and climate change have a negative impact on global food production and security, especially in arid regions with limited water resources. Despite the importance of planting methods, irrigation, and soil amendments in improving crop yield, their combined impact on saline soil properties and cereal crop yield is unknown. Therefore, the current study investigated the combined effect of soil amendments (i.e., compost, C and zeolite, Z) and planting methods such as raised bed (M1) and conventional (M2), and different fractions of leaching requirements from irrigation water, such as 5% (L1) and 10% (L2), on the soil physio-chemical properties and wheat and maize productivity in an arid region. The combined application of C + Z, L2, and M1 decreased soil salinity (EC) and sodicity (ESP) after wheat production by 37.4 and 28.0%, respectively, and significantly decreased by these factors by 41.0 and 43.0% after a maize growing season. Accordingly, wheat and maize yield increased by 16.0% and 35.0%, respectively under such a combination of treatments, when compared to crops grown on unamended soil, irrigated with lower leaching fraction and planted using conventional methods. This demonstrates the significance of using a combination of organic and inorganic amendments, appropriate leaching requirements and the raised bed planting method as an environmentally friendly approach to reclaiming saline soils and improving cereal crop production, which is required for global food security.

Sandy soils are widespread globally and are increasingly utilized to meet the demands of a growing population and urbanization for food, fiber, energy, and other essential services. However, their poor water and nutrient retention makes crop cultivation challenging. This study evaluated the effects of integrating compost and plant growth-promoting rhizobacteria (PGPR; Azospirillum brasilense SWERI 111 and Azotobacter chroococcum OR512393) on wheat (Triticum aestivum L. var. Misr 1) grown in sandy soil under varying levels of recommended NPK (50%, 75%, and 100%) fertilization. Conducted over two growing seasons, the experiment aimed to assess soil health, nutrient uptake, microbial activity, and plant productivity in response to compost and PGPR treatments. The results demonstrated that combining compost and PGPR significantly improved soil chemical properties, such as reducing soil pH, electrical conductivity (ECe), and sodium adsorption ratio (SAR), while enhancing soil organic matter (SOM). Additionally, compost and PGPR improved soil nutrient content (N, P, K) and boosted the total bacterial and fungal counts. The combined treatment also increased urease and phosphatase enzyme activities, contributing to enhanced nutrient availability. Notably, plant productivity was enhanced with compost and PGPR, reflected by increased chlorophyll and reduced proline content, along with improved grain and straw yields. Overall, the results underscore the potential of compost and PGPR as effective, sustainable soil amendments to support wheat growth under varying NPK levels.

The salinity and alkalinity of soils are two fundamental factors that limit plant growth and productivity. For that reason, a field study conducted at Sakha Agric. Res. Station in Egypt during the 2022–2023 winter season aimed to assess the impact of gypsum (G), compost (C), and zinc foliar application in two images, traditional (Z₁ as ZnSO₄) and nanoform (Z₂ as N-ZnO), on alleviating the saline–sodic conditions of the soil and its impact on wheat productivity. The results showed that the combination of gypsum, compost, and N-ZnO foliar spray (G + C + Z₂) decreased the soil electrical conductivity (EC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP) by 14.81%, 40.60%, and 35.10%, respectively. Additionally, compared to the control, the G + C + Z₂ treatment showed improved nutrient content and uptake as well as superior wheat biomass parameters, such as the highest grain yield (7.07 Mg ha⁻¹), plant height (98.0 cm), 1000-grain weight (57.03 g), and straw yield (9.93 Mg ha⁻¹). Interestingly, foliar application of N-ZnO was more effective than ZnSO₄ in promoting wheat productivity. Principal component analysis highlighted a negative correlation between increased grain yield and the soil EC and SAR, whereas the soil organic matter (OM), infiltration rate (IR), and plant nutrient content were found to be positively correlated. Furthermore, employing the k-nearest neighbors technique, it was predicted that the wheat grain yield would rise to 7.25 t ha⁻¹ under certain soil parameters, such as EC (5.54 dS m⁻¹), ESP (10.02%), OM (1.41%), bulk density (1.30 g cm⁻³), infiltration rate (1.15 cm h⁻¹), and SAR (7.80%). These results demonstrate how adding compost and gypsum to foliar N-ZnO can improve the soil quality, increase the wheat yield, and improve the nutrient uptake, all of which can support sustainable agriculture.

Salinity stress harms crop yield and productivity worldwide. This study aimed to identify genotypes with higher grain yield and/or salinity tolerance from forty bread wheat genotypes evaluated under seawater diluted at 4.0, 8.0, or 12.0 dS/m or control (0.4 dS/m) in the 2019/20 and 2020/21 seasons. Six elite genotypes, namely 6, 16, 31, 33, 34, and 36, were chosen and tested in a lysimeter under diluted seawater stress in 2020/21. The results showed significant differences (p ≤ 0.01) among the genotypes for the traits grain yield (GY), harvest index (HI), chlorophyll content index (CCI), chlorophyll fluorescence parameter Fv/Fm, and their interaction with salinity treatments. Additionally, significant differences (p ≤ 0.01) were detected among ten genotypes for all agronomic traits along with spectral reflectance indices (SRI), e.g., curvature index (CI), normalized difference vegetation index (NDVI), triangular vegetation index (TVI), modified chlorophyll absorption reflectance index (MCARI), and their interaction with salinity treatments. Genotype by traits (GT) and genotype by yield*trait (GYT) biplots are useful for genotypes screening and selection based on grain yield and other associated traits (agronomic, physiological traits, and spectral reflectance indices combinations) as well as genotypes by stress tolerance indices (GSTI). In conclusion, this study identified that genotypes 6, 16, 31, 33, 34, and 36 in the 2019/20 season and genotypes 2 and 1 performed better than Kharchia 65 and Sakha 8 in the 2020/21 season, which detected as superior genotypes and might be recommended for sowing and/or inclusion in the breeding program in salt-affected soils. It was possible to draw the conclusion that spectral reflectance indices were efficient at identifying genotypic variance.