Khadija Manhou

Salinity stress in wheat ( Triticum aestivum L.) represents a major challenge for agricultural sustainability, grain quality, and global food security, particularly in arid and semi-arid regions increasingly affected by soil salinization and climate variability. This systematic review aimed to synthesize current knowledge on the physiological, biochemical, molecular, microbiological, and technological mechanisms underlying salinity tolerance in wheat and to evaluate how these complementary processes can be integrated to improve grain quality, stress resilience, and the development of climate-resilient wheat production systems under saline environments. Using a PRISMA-guided selection framework, literature published between 2018 and 2025 was systematically screened across major scientific databases, resulting in the identification of 408 records, from which 142 peer-reviewed studies were retained following predefined screening, eligibility, and quality-assessment criteria. Bibliometric mapping with VOSviewer was further applied to identify dominant research themes linking salinity physiology, grain-quality dynamics, microbiome-assisted mitigation, and advanced breeding technologies. The reviewed evidence indicates that salinity-induced reductions in wheat productivity are primarily associated with ionic toxicity, osmotic imbalance, oxidative stress, photosynthetic impairment, and disruption of metabolic and nutritional homeostasis, ultimately affecting grain protein accumulation, starch biosynthesis, mineral balance, and technological quality traits. Current mitigation approaches increasingly emphasize integrated strategies involving organic amendments, beneficial rhizosphere microorganisms, ion-homeostasis regulation, and precision agronomic management. In parallel, advances in genomics-assisted breeding, multi-omics integration, CRISPR/Cas genome editing, high-throughput phenotyping, remote sensing, and AI-assisted predictive breeding are improving candidate-gene prioritization and genotype-to-phenotype prediction under saline environments. This review integrates PRISMA-based screening with bibliometric analysis to provide a structured and mechanistic synthesis linking salinity stress physiology, grain-quality responses, microbiome-assisted approaches, sustainable soil-management practices, and precision breeding technologies. By adopting a cross-scale perspective, this review concludes that integrating physiological, microbiome-assisted, agronomic, and advanced breeding approaches is essential for developing climate-resilient wheat production systems under increasing soil salinity.

Soil quality is central to agricultural sustainability and food security, yet coastal agroecosystems are increasingly threatened by degradation from intensive practices and seawater intrusion. This study aimed to integrate soil quality index (SQI), statistical modeling, machine learning (ML), and decision analysis to assess and manage soil health in the Skhirat coastal plain of Morocco. A total of 30 topsoil samples were collected and analyzed for chemical and nutrient properties. Spatial interpolation revealed strong coast–inland gradients where EC ranged from 0.47 to 6.3 dS/m with the highest salinity in the south-western fringe, while CEC (8.4–39.7 cmol/kg) and OM (0.54–2.81%) peaked inland. Principal component analysis (PCA) explained 65.9% of total variance, with salinity drivers loading negatively against fertility indicators. Redundancy analysis (RDA) biplots highlighted antagonism between salinity and fertility axes. The PCA-minimum data set (MDS)-SQI integrated key indicators and ranged from 0.084 to 0.897 (mean 0.614), classifying 33% of sites as low quality. The ML model linear regression achieved the best performance (R² = 0.907). Multi-criteria decision analysis (MCDA) using TOPSIS and PROMETHEE II prioritized coastal sites with indices up to 0.882, and robust underweight sensitivity (Spearman ρ = 0.992). This integrated framework demonstrates that soil chemical monitoring, AI prediction, and MCDA can jointly deliver robust, site-specific management strategies for vulnerable coastal agroecosystems.

Saline irrigation is increasingly practiced in semi-arid regions to cope with freshwater scarcity; however, it strongly affects crop growth, water use, and soil salinity. This study aims to calibrate and validate the AquaCrop model to simulate key growth parameters of winter wheat (cv. Achtar) under saline irrigation conditions in the Tadla Plain, Morocco, focusing on canopy cover (CC), actual evapotranspiration (ETa), soil water content (SWC), biomass (B), and grain yield (GY). The model was first calibrated using observed data from the 2023 growing season and subsequently validated using data from the 2022 growing season. Overall, AquaCrop effectively reproduced crop growth during both calibration and validation phases. During calibration, canopy cover was accurately simulated, with average RMSE values below 1%, while biomass and grain yield were also well reproduced, with low RMSE values (0.25 t ha⁻¹ for B and 0.10 t ha⁻¹ for GY), confirming the robustness of the calibrated parameters. The model also performed well in simulating ETa and SWC, capturing the seasonal dynamics of crop water use and soil moisture. During validation, ETa was satisfactorily reproduced, with an RMSE of approximately 0.80 mm day⁻¹, while SWC showed good agreement with observations, with NRMSE values ranging from 7.9 to 10.5%. Grain yield and biomass were reliably predicted, with NRMSE values below 4%. These results demonstrate that AquaCrop is a reliable tool for simulating winter wheat under saline irrigation and for assessing crop response under salt-affected conditions, providing an integrated evaluation of crop performance, water use, and soil salinity dynamics to support improved irrigation management and water-use efficiency under semi-arid conditions.

Saline water irrigation increasingly threatens agricultural productivity and food security, particularly in arid regions with limited freshwater availability. Improving soil management through green waste compost represents a sustainable approach to enhance soil health, reduce reliance on chemical fertilizers, and stimulate crop growth. This study evaluated the effects of three compost doses (15, 20, and 30 t ha⁻¹) on soil physicochemical properties, plant growth, and yield components of durum wheat (Triticum durum Desf., cv. Faraj) under four levels of saline irrigation (0.2, 8, 12, and 16 dS m⁻¹) using a completely randomized design (CRD) in a greenhouse during the 2023–2024 growing season. Saline irrigation significantly increased soil electrical conductivity and sodium accumulation, while soil pH and organic matter content decreased by 13.45% and 28.57%, respectively. Compost application improved soil fertility, particularly by increasing potassium and calcium availability; at 8 dS m⁻¹, potassium and calcium contents increased by 23.25% and 16.7%, respectively. Compost significantly enhanced plant growth under saline conditions, with increases of 10–48% in plant height, 18–40% in leaf number, and 9–19% in chlorophyll fluorescence (Fv/Fm) compared with the untreated control. Grain yield increased by 11.25% at 8 dS m⁻¹ and by 31.58% at 12 dS m⁻¹ under the highest compost dose, while straw yield, spike length, and awn length were also improved. Overall, the findings suggest that green waste compost application represents an effective strategy to improve soil quality, sustain durum wheat growth, and partially mitigate salinity stress, particularly under moderate saline irrigation conditions.

This study assesses groundwater quality and nitrate-related health risks in the Skhirat coastal aquifer (Morocco) using a multidisciplinary approach. A total of thirty groundwater wells were sampled and analyzed for physico-chemical properties, including major ions and nutrients. Multivariate statistical analyses were employed to explore contamination sources. Pollution indices such as the Groundwater Pollution Index (GPI) and Nitrate Pollution Index (NPI) were computed, and Monte Carlo simulations (MCSs) were conducted to assess nitrate-related health risks through ingestion and dermal exposure. Furthermore, Random Forest (RF), Gradient Boosting Regression (GBR), Support Vector Regression (SVR) with radial basis function kernel, and Artificial Neural Networks (ANN) models were tested for predicting groundwater pollution indices. Results of hydrochemical facies revealed Na⁺-Cl⁻ dominance in 47% of the samples, suggesting strong marine influence, while nitrate concentrations reached up to 89.3 mg/L, exceeding World Health Organization (WHO) limits in 26.7% of the sites. Pollution indices indicated that 33.3% of samples exhibited moderate to high GPI values, with 36.7% of the samples exceeding the threshold for NPI. The MCS for nitrate health risk revealed that 43% of the samples posed non-carcinogenic health risks to children (Hazard Index (HI) > 1). RF outperformed other models in predicting GPI (R² = 0.76) and NPI (R² = 0.95). Spatial prediction maps visualized contamination hotspots aligned with intensive horticultural activity. This integrated methodology offers a robust framework to diagnose groundwater pollution sources and predict future risks.

Composting represents a sustainable and effective strategy for converting organic waste into nutrient-rich soil amendments, providing a safer alternative to raw manure, which poses significant risks of soil, crop, and water contamination through pathogenic microorganisms. This study, conducted under semi-arid Moroccan conditions, investigated the efficiency of co-composting green garden waste with sheep manure in an open window system, with the objective of assessing pathogen inactivation and evaluating compost quality. The process, conducted over 120 days, maintained thermophilic temperatures exceeding 55 °C, effectively reducing key pathogens including Escherichia coli, total coliforms, Staphylococcus aureus, and sulfite-reducing Clostridia (SRC), while Salmonella was not detected throughout the composting period. Pathogen reductions exceeded 3.52-log despite moderate temperature fluctuations, indicating that additional sanitization mechanisms beyond heat contributed to inactivation. Compost quality, assessed using the CQI, classified Heap 2 (fallen leaves + sheep manure) as good quality (4.06) and Heap 1 (green waste + sheep manure) as moderate quality (2.47), corresponding to differences in microbial dynamics and compost stability. These findings demonstrate that open windrow co-composting is a practical, low-cost, and effective method for safe organic waste management. It supports sustainable agriculture by improving soil health, minimizing environmental and public health risks, and providing guidance for optimizing composting protocols to meet regulatory safety standards.

Soil contamination by heavy metals (HMs) threatens crop productivity, food safety, and ecosystem health, especially in intensively cultivated Mediterranean regions. This study investigated the influence of soil HM contamination on nutrient uptake, photosynthetic traits, and metal bioaccumulation in avocado (Persea americana Mill.) orchards. Twenty orchard sites were sampled, collecting paired soil and mature leaf samples. Soil physicochemical properties and HM concentrations were determined, while leaves were analyzed for macro- and micronutrients, photosynthetic pigments, and metal contents. Bioaccumulation Factors (BAFs) were computed, and multivariate analyses (Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR)) were applied to assess soil–plant relationships, complemented by Monte Carlo simulations to quantify probabilistic contamination risks. Results revealed substantial inter-site variability, with leaf Cd and Pb concentrations reaching 0.92 and 3.54 mg/kg, and BAF values exceeding 1 in several orchards. PLSR models effectively predicted leaf Cd (R² = 0.789) and Pb (R² = 0.772) from soil parameters. Monte Carlo simulations indicated 15–25% exceedance of FAO/WHO safety limits for Cd and Pb. These findings demonstrate that soil metal accumulation substantially alters avocado nutrient balance and photosynthetic efficiency, highlighting the urgent need for site-specific soil monitoring and sustainable remediation strategies in contaminated orchards.

Freshwater scarcity in arid regions forces farmers to use saline water, reducing durum wheat (Triticum turgidum L. subsp. durum) productivity, particularly during early growth stages. This study evaluated two Moroccan varieties, Faraj and Nachit, on silty clay soil under five salinity levels (0.2, 4, 8, 12, and 16 dS m⁻¹) in a randomized complete block design with three replications, aiming to identify tolerance thresholds and characterize physiological and agronomic responses. Key traits measured included germination percentage, germination stress index, mean germination time, root and coleoptile length, plant height, leaf number, chlorophyll fluorescence, grain yield, weight of 200 grains, and straw yield. Germination percentage declined from 8 dS m⁻¹, with delayed germination and inhibited vegetative growth at higher salinity. Both varieties maintained grain yield up to 8 dS m⁻¹ and weight of 200 grains and straw yield up to 12 dS m⁻¹, with Nachit showing higher tolerance. Multivariate analyses, including principal component analysis and heatmaps, linked soil sodium, chloride, and electrical conductivity negatively to growth and yield, whereas potassium, calcium, and magnesium supported plant growth and physiological activity. These findings provide insights for breeding and irrigation strategies to sustain durum wheat under salinity stress.

The productivity and resilience of durum wheat have been enhanced through the selection of accessions, optimizing agronomic and quality traits to address environmental challenges. This study evaluates the performance of 219 durum wheat accessions, including 120 elite lines from a national breeding program (G1 to G120), 63 international lines (G121 to G183), 27 Moroccan varieties (including Faraj, Karim, Tomouh, Marzak, Amria, Chaoui, IRDEN, and others), and nine landraces (G211 to G219, from Imilchil, Rich, and Taounate regions). Trials were conducted at the Jemâa Shaïm experimental station (INRA-Morocco) with an “Alpha lattice” design and two replications. Significant correlations were observed between spike length (SL) and number of spikelets per spike (SPS) (r = 0.950; p < 0.001), and between grain yield (GY) and thousand-kernel weight (TKW) (r = 0.530; p < 0.01), while no correlation was found between quality parameters and GY (r = 0.010; p > 0.05). Principal component analysis (PCA) revealed that agronomic traits explained 77.12% of variability, while quality traits accounted for 95.54%. Elite lines exhibited a high yellow pigment index (14.90), important for technological quality. Traditional landraces performed well in spike length (8.78 cm), thousand-kernel weight (50.23 g), protein content (17.07%), and gluten content (36.90%). Moroccan varieties such as Faraj achieved a grain yield of 6.12 t/ha, while international lines showed the highest SDS value (9.39 mL). These findings highlight the potential of diverse accessions for developing high-yielding, high-quality durum wheat.

This study investigates the effectiveness of organic compost as a sustainable alternative to chemical fertilizers for improving soil health and enhancing crop productivity under greenhouse conditions. The experiment focused on sweet basil (Ocimum basilicum L.), an aromatic herb highly sensitive to soil fertility and structure, cultivated in sandy loam soil—a prevalent substrate in arid and semi-arid regions, often limited by poor water and nutrient retention. Using a randomized complete block design with six compost application rates, this study evaluated the physicochemical, biochemical, and agronomic responses of both soil and plants. The results demonstrated significant improvements across all parameters (p < 0.05), with the 30 t/ha compost treatment yielding the most notable enhancements in soil structure, nutrient content, and plant performance while maintaining acceptable levels of heavy metals. Soil organic matter (SOM) increased to 13.71%, while shoot length (SL), essential oil content (EOC), and the 100-seed weight improved to 42 cm, 0.83%, and 0.32 g, respectively, compared to the control. These finding underscore the benefits of high compost application rates in boosting greenhouse horticultural productivity while promoting sustainable agriculture. Moreover, this study supports the reduction in chemical fertilizer dependency and encourages the adoption of circular economy principles (CEPs) through organic waste recycling.