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Nitrogen is an essential element for maize growth, but excessive application can lead to various environmental and ecological issues, including water pollution, air pollution, greenhouse gas emissions, and biodiversity loss. Hence, developing maize hybrids resilient to low-N conditions is vital for sustainable agriculture, particularly in nitrogen-deficient soils. Combining ability and genetic relationships among parental lines is crucial for breeding superior hybrids under diverse nitrogen levels. This study aimed to assess the genetic diversity of maize inbred lines using simple sequence repeat (SSR) markers and evaluate their combining ability to identify superior hybrids under low-N and recommended conditions. Local and exotic inbred lines were genotyped using SSR markers, revealing substantial genetic variation with high gene diversity (He = 0.60), moderate polymorphism information content (PIC = 0.54), and an average of 3.64 alleles per locus. Twenty-one F1 hybrids were generated through a diallel mating design using these diverse lines. These hybrids and a high yielding commercial check (SC-131) were field-tested under low-N and recommended N conditions. Significant variations (p < 0.01) were observed among nitrogen levels, hybrids, and their interaction for all recorded traits. Additive genetic variances predominated over non-additive genetic variances for grain yield and most traits. Inbred IL3 emerged as an effective combiner for developing early maturing genotypes with lower ear placement. Additionally, inbreds IL1, IL2, and IL3 showed promise as superior combiners for enhancing grain yield and related traits under both low-N and recommended conditions. Notably, hybrids IL1×IL4, IL2×IL5, IL2×IL6, and IL5×IL7 exhibited specific combining abilities for increasing grain yield and associated traits under low-N stress conditions. Furthermore, strong positive associations were identified between grain yield and specific traits like plant height, ear length, number of rows per ear, and number of kernels per row. Due to their straightforward measurability, these relationships underscore the potential of using these traits as proxies for indirect selection in early breeding generations, particularly under low-N stress. This research contributes to breeding nitrogen-efficient maize hybrids and advances our understanding of the genetic foundations for tolerance to nitrogen limitations.

Water scarcity is a major environmental stress that adversatively impacts wheat growth, production, and quality. Furthermore, drought is predicted to be more frequent and severe as a result of climate change, particularly in arid regions. Hence, breeding for drought-tolerant and high-yielding wheat genotypes has become more decisive to sustain its production and ensure global food security with continuing population growth. The present study aimed at evaluating different parental bread wheat genotypes (exotic and local) and their hybrids under normal and drought stress conditions. Gene action controlling physiological, agronomic, and quality traits through half-diallel analysis was applied. The results showed that water-deficit stress substantially decreased chlorophyll content, photosynthetic efficiency (FV/Fm), relative water content, grain yield, and yield attributes. On the other hand, proline content, antioxidant enzyme activities (CAT, POD, and SOD), grain protein content, wet gluten content, and dry gluten content were significantly increased compared to well-watered conditions. The 36 evaluated genotypes were classified based on drought tolerance indices into 5 groups varying from highly drought-tolerant (group A) to highly drought-sensitive genotypes (group E). The parental genotypes P3 and P8 were identified as good combiners to increase chlorophyll b, total chlorophyll content, relative water content, grain yield, and yield components under water deficit conditions. Additionally, the cross combinations P2 × P4, P3 × P5, P3 × P8, and P6 × P7 were the most promising combinations to increase yield traits and multiple physiological parameters under water deficit conditions. Furthermore, P1, P2, and P5 were recognized as promising parents to improve grain protein content and wet and dry gluten contents under drought stress. In addition, the crosses P1 × P4, P2 × P3, P2 × P5, P2 × P6, P4 × P7, P5 × P7, P5 × P8, P6 × P8, and P7 × P8 were the best combinations to improve grain protein content under water-stressed and non-stressed conditions. Certain physiological traits displayed highly positive associations with grain yield and its contributing traits under drought stress such as chlorophyll a, chlorophyll b, total chlorophyll content, photosynthetic efficiency (Fv/Fm), proline content, and relative water content, which suggest their importance for indirect selection under water deficit conditions. Otherwise, grain protein content was negatively correlated with grain yield, indicating that selection for higher grain yield could reduce grain protein content under drought stress conditions.

Soil amendments may increase the slate tolerance of plants consequently; it may increase the opportunity of using saline water in agricultural production. In the present pot trial, the effects of biochar (BIC) and compost (COM) on roselle ( Hibiscus sabdariffa L.) irrigated with saline water (EC = 7.50 dS m −1 ) was studied. Roselle plants were amended with biochar (BIC 1 and BIC 2 ) or compost (COM 1 and COM 2 ) at rates of 1 and 2% (w/w), as well as by a mixture of the two amendments (BIC 1 +). The experiment included a control soil without any amendments. Biochar and compost significantly enhanced the soil quality and nutrients availability under saline irrigation. Compost and biochar improved the degree of soil aggregation, total soil porosity and soil microbial biomass. BIC 1  + COM 1 increased the soil microbial biomass carbon and nitrogen over the individual application of each amendments and control soil. BIC 1  + COM 1 increased the activity of dehydrogenase and phosphatase enzymes. Growth of roselle plants including: plant height, shoot fresh and dry weight, and chlorophyll were significantly responded to the added amendments. The maximum sepal’s yield was achieved from the combined application of compost and biochar. All the investigated treatments caused remarkable increases in the total flavonol and anthocyanin. BIC 1  + COM 1 increased the total anthocyanin and flavonol by 29 and 17% above the control. Despite the notable improvement in soil and roselle quality as a result of the single addition of compost or biochar, there is a clear superiority due to mixing the two amendments. It can be concluded that mixing of biochar and compost is recommended for roselle plants irrigated with saline water.

Two of the most significant issues confronting arid and semi-arid countries are soil degradation and the need to reclaim sandy soils and improve their properties to enhance the agricultural area and ensure food security. Many attempts to improve sandy soil properties have been attempted using soil amendments, but further research is needed to explore the combined impact of cost-effective amendments. To that purpose, we investigated the impact of various soil amendments, including single and combination applications of synthetic Super Absorbent Polymer (SAP), compost, and biochar, on sandy soil physiochemical characteristics and bean (Vicia faba L.) production and quality throughout three growing seasons. In a randomized complete block design with three replicates per treatment, different treatments such as control (without application), lower dose of SAP (SAP1), higher dose of SAP (SAP2), biochar, compost, SAP1 plus biochar, SAP1 plus compost, SAP2 plus biochar, SAP2 plus compost, and biochar plus compost were used. The combined treatments, such as SAP2 plus biochar (T8), SAP2 plus compost (T9), and biochar plus compost (T10), improved soil physiochemical characteristics and crop production significantly. Application of T10 decreased soil bulk density by 15%, 17%, and 13% while increasing soil available water by 10%, 6%, and 3% over the first, second, and third growing seasons, respectively, compared to untreated soil (T1). The application of treatment (T9) surpassed other treatments in terms of yield, quality, and economic return, significantly increasing the seed yield by 24%, 26%, and 27% for the first, second, and third season compared with untreated soil. The higher rate of polymer combined with compost could be considered a cost-effective soil amendment to improve sandy soil productivity in arid and semi-arid regions.

Agriculture is the largest consumer of water, particularly in arid and semi-arid regions, so identifying and managing surface water quality in these areas is critical to preserving water resources and ensuring sustainable agriculture. Irrigation water quality (IWQ) assessment integrated with geographic information system (GIS) of West Nile Delta, Egypt, was carried out using suitability indicators such as hazards of salinity, permeability hazard, specific ion toxicity, and miscellaneous impacts on sensitive crops. In ArcGIS 10.7, inverse distance-weighted algorithms and the Model Builder function were used to categorize irrigation water quality into different classes. According to the findings, 87% and 13% of the water samples from the study area were categorized as medium and high suitability for irrigation, respectively. The heavy metal pollution index (HPI), Nemerow index (NeI), ecological risks of heavy metal index (ERI), heavy metal evaluation index (HEI), pollution load index (PLI), and modified degree of contamination (mCd) for five selected metals, namely As, Co, Cu, Ni, and Zn, were calculated to assess heavy metal contamination levels in the study area. The results showed that HPI had 3.7% medium contamination and 96.3% high contamination; NeI was 7.4% moderately contaminated and 92.6% heavily contaminated; ERI has almost 7% low risk, 30% moderate risk, 41% considerable risk, and 22% very high risk; HEI had 100% low contamination; PLI was 100% polluted; and mCd has 18.5% moderately-heavily polluted, 63% heavily polluted, and 18.5% severely polluted samples. This research can help decision-makers manage water resources more effectively for sustainable agriculture.

Egypt produces half of the 20 million tons of wheat that it consumes with irrigation and imports the other half. Egypt is also the world's largest importer of wheat. The population of Egypt is currently growing at 2.2% annually, and projections indicate that the demand for wheat will triple by the end of the century. Combining multi-crop and -climate models for different climate change scenarios with recent trends in technology, we estimated that future wheat yield will decline mostly from climate change, despite some yield improvements from new technologies. The growth stimulus from elevated atmospheric CO2 will be overtaken by the negative impact of rising temperatures on crop growth and yield. An ongoing program to double the irrigated land area by 2035 in parallel with crop intensification could increase wheat production and make Egypt self-sufficient in the near future, but would be insufficient after 2040s, even with modest population growth. Additionally, the demand for irrigation will increase from 6 to 20 billion m3 for the expanded wheat production, but even more water is needed to account for irrigation efficiency and salt leaching (to a total of up to 29 billion m3). Supplying water for future irrigation and producing sufficient grain will remain challenges for Egypt.

The continuous addition of phosphorus (P) fertilizers above plant requirements increases P loss risks, especially if such fertilization practices continue long-term. The current study aims to determine the threshold value of P in plants and soil, which achieves the maximum mango fruit yield without P loss risk. P fertilizer doses (0–240 g tree−1) were added to 12-year-old mango (Mangifera indica L.) cv Hindy planted in sandy soil for four consecutive years. Soil and plant samples were collected each year to estimate the critical p values by linear–linear, quadratic, and exponential models. The relationships between fruit yield and available soil P were positive and significant in all the mathematical models. Mango fruit yield is expected to reach its maximum value if the sandy calcareous soil contains an available P amount ranging between 10–12 mg kg−1 and increasing the soil available P above this level leads to negligible increases in the fruit yield. Increasing the available soil P above 20.3 mg kg−1 increases P-loss risk. P concentrations in blades and petioles of mango leaves can be arranged as follows: beginning of the flowering stage > the full blooming stage > beginning of the fruiting stage. The analysis of petioles of mango leaves in the beginning of the flowering stage significantly corelated with mango fruit yield and can be used in predicting the response of mango to P fertilization. The findings of the present investigation revealed that the critical P in mango petioles ranged between 2.34 and 3.53 g kg−1. The threshold of available soil P for maximum fruit yield is half of P loss risks. The combined analysis of soil and plants is a powerful diagnostic tool for P management in sandy degraded soil. The findings of the current study are a good tool in achieving the optimum utilization of P fertilizer resources in maximizing mango fruit yield and reducing the risks of environmental pollution that result from excessive fertilization doses.

Agroforestry systems (AFSs) enhance biodiversity, productivity, and climate resilience, yet their inherent complexity challenges traditional modeling approaches. This study presents an integrated framework combining process-based modeling (PBM), machine learning, and life cycle assessment (LCA) into a user-friendly decision support system (DSS) for AFS analysis and optimization. Multi-scenario simulations using Hi-sAFe (16 configurations varying in latitude and orientation) for maize–poplar systems produced detailed outputs, including land equivalent ratios (LER up to 1.29), crop yield (ranging from 4.3 to 8.1 t ha−1), and N2O emissions (1.6–9.4 kg N ha−1). We trained Random Forest models on PBM diagnostics, microclimate, and design covariates; post-hoc SHapley Additive exPlanations (SHAP) identified maximum air temperature (Tmax), distance to tree rows, and photosynthetically active radiation (PAR) as the dominant predictors of yield and LER. To address data gaps, long short-term memory (LSTM) and TrAdaBoost-LSTM models were used for microclimate forecasting, achieving high fidelity (R2 = 0.88) in capturing hourly temperature and humidity trends. Outputs have been integrated into an LCA workflow, producing CO2-equivalent and nitrogen footprint metrics that feed into a functional web-based DSS. This platform enables robust, transparent, and scalable AFS evaluation, supporting stakeholders with real-time scenario exploration and scientifically grounded decision-making tools. Furthermore, the framework enables greenhouse gas (GHG) accounting in AFSs by integrating modeled soil organic carbon dynamics and N2O emissions. A Germany-wide assessment illustrates the significant GHG reduction potential achievable through large-scale implementation of AFSs.

Wheat’s nutritional value is critical for human nutrition and food security. However, more attention is needed, particularly regarding the content and concentration of iron (Fe) and zinc (Zn), especially in the context of climate change (CC) impacts. To address this, various controlled field experiments were conducted, involving the cultivation of three wheat cultivars over three growing seasons at multiple locations with different soil and climate conditions under varying Fe and Zn treatments. The yield and yield attributes, including nutritional values such as nitrogen (N), Fe and Zn, from these experiments were integrated with national yield statistics from other locations to train and test different machine learning (ML) algorithms. Automated ML leveraging a large number of models, outperformed traditional ML models, enabling the training and testing of numerous models, and achieving robust predictions of grain yield (GY) ( R 2 > 0.78), N ( R 2 > 0.75), Fe ( R 2 > 0.71) and Zn ( R 2 > 0.71) through a stacked ensemble of all models. The ensemble model predicted GY, N, Fe, and Zn at spatial explicit in the mid-century (2020–2050) using three Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, and MRI-ESM2-0 under two shared socioeconomic pathways (SSPs) specifically SSP2-45 and SSP5-85, from the downscaled NEX-GDDP-CMIP6. Averaged across different GCMs and SSPs, CC is projected to increase wheat yield by 4.5%, and protein concentration by 0.8% with high variability. However, it is expected to decrease Fe concentration by 5.5%, and Zn concentration by 4.5% in the mid-century (2020–2050) relative to the historical period (1980–2010). Positive impacts of CC on wheat yield encountered by negative impacts on nutritional concentrations, further exacerbating challenges related to food security and nutrition.

Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R 2 = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R 2 = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and 5% under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5-8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.