Under the conditions of Moroccan rainfed agricultural areas, wheat cropping systems—the population’s basic staple food—are subject to a set of limitations that seasonally impact crop production and farmers’ incomes, thus national food security. In the last decades, the major constraints were often related to the country’s Mediterranean-type climate, through the intense recurrence of drought events and high inter- and intra-annual rainfall fluctuations. Similarly, various forms of soil degradation inhibit the potential of this slowly renewable resource to support wheat crop intensification and ensure livelihoods. However, the limitations sometimes surpass the environmental factors to implicate the inappropriate crop management strategies applied by farmers. In Moroccan rainfed areas, production problems linked to crop management practices result principally from a shortage in the provision of knowledge to Moroccan small farmers, or their indigent economic situation that limits farmers’ capacity to adopt, qualitatively and quantitatively, efficient strategies. Advanced technologies (remote sensing or crop modeling) play key roles in assessing wheat cropping systems in Moroccan rainfed areas. Due to the difficulties of using conventional experience-based agronomic research to understand Genotype × Environment × Management (G × E × M) interactions, the substantial benefits of crop modeling approaches present a better alternative to provide insights. They allow the provision of simpler, rapid, less expensive, deep, and potentially more accurate predictive knowledge and understanding of the status of cropping systems. In the present study, we highlight the constraints that surround wheat cropping systems in Moroccan rainfed conditions. We emphasize the efficiency of applying crop modelling to analyze and improve wheat cropping systems through three main themes: (i) preserving food security, (ii) supporting general adaptation strategies to face climate change effects and extreme events, and (iii) recommending within-season and on-farm crop management advice. Under Moroccan context, crop modeling works have mainly contributed to increase understanding and address the climate change effects on wheat productivity. Likewise, these modeling efforts have played a crucial role in assessing crop management strategies and providing recommendations for general agricultural adaptations specific to Moroccan rainfed wheat.

Crop models have evolved over the past decade to incorporate more soil-related processes. While this may open avenues to support farmers regarding fertilization practices, it also widens the pitfalls related to model parametrization. Open-access georeferenced soil databases are often a solution for modelers to derive soil parameters. However, they can potentially add to model uncertainty depending on database resolution and the variability of the characteristics it contains. Fertimap is an online spatial database recently released in Morocco. In this study, we aim at assessing how Fertimap could support the use of crop model in the rainfed wheat production areas of Morocco. Data including local soil analysis, farmers’ practices, wheat biomass, and yield were collected on 126 farmers’ fields distributed across the rainfed wheat production area in Morocco from 2018 to 2020. Data were first used to parameterize, calibrate, and assess the model, using site-specific data to infer soil parameters. Then, the impact of soil data source on model uncertainty was assessed by rerunning the simulations while using alternatively locally measured soil inputs or inputs extracted from Fertimap. To disentangle the effect of data source from model sensitivity on model outputs, the model’s sensitivity to labile phosphorus, pH, and organic carbon parameters was also tested. The APSIM-wheat model was found to reasonably simulate wheat phenological stages, biomass, and yield. The comparison of model outputs using one or another source of soil data indicated that using Fertimap had no significant effect on the model’s outputs. This study provides the first assessment of the APSIM-wheat model for simulation of widely used wheat cultivars in Moroccan rainfed areas. It is also the first proof of the practical utility of Fertimap database for modeling purposes in Morocco. This preliminary study delivers a robust basis for model-assisted agricultural advising to take off in Morocco.

In the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider from this perspective. In this study, multiple regression (MR) and random forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally, MR and RF models were calibrated both with or without remotely sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (normalized root mean square errors (NRMSEs) below 35 %), but were always outperformed by the APSIM model. Both RF and MR selected remotely sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely sensed LAI in the calibration process reduced NRMSE by 4.5 % and 1.8 % on average for MR and RF models, respectively. Calibration of region-specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short-term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.

In regions facing water scarcity and soil salinity, mitigating these abiotic stresses is paramount for sustaining crop production. This study aimed to unravel the synergistic effects of organic matter and phosphorus management in reducing the adverse effect of saline water for irrigation on red pepper (Capsicum annuum L.) production, fruit quality, plant physiology, and stress tolerance indicators. The study was carried out in the arid Tadla region of Morocco and involved two key experiments: (i) a field experiment during the 2019 growing season, where red pepper plants were subjected to varying phosphorus fertilizer rates (120, 140, and 170 kg of P₂O₅.ha⁻¹) and saline water irrigation levels (0.7; 1.5; 3; and 5 dS.m⁻¹); and (ii) a controlled pot experiment in 2021 for examining the interaction of saline water irrigation levels (EC values of 0.7, 2, 5, and 9 dS.m⁻¹), phosphorus rates (30, 36, and 42 kg of P₂O₅.ha⁻¹), and the amount of organic matter (4, 8, 12, and 16 t.ha⁻¹). The field study highlighted that saline irrigation significantly affected red pepper yields and fruit size, although phosphorus fertilization helped enhance productivity. Additionally, biochemical markers of stress tolerance, such as proline and glycine betaine, along with stomatal conductance, were impacted by increasing salinity levels. The pot experiment showed that combining organic amendments and phosphorus improved soil properties and stimulated red pepper growth and root weight across all salinity levels. The integration of phosphorus fertilization and organic amendments proved instrumental for counteracting salinity-induced constraints on red pepper growth and yield. Nonetheless, caution is necessary as high salinity can still negatively impact red pepper productivity, necessitating the establishment of an irrigation water salinity threshold, set at 5 dS.m⁻¹.