The micronutrient application in agriculture takes place through soil application, foliar spraying or added as seed treatments. The latter method, the nutri-priming, is an appealing option due to the easiness in handling it, environment-friendly, cost effectiveness and efficient against multiple environmental stressors. To assess the feasibility of Zn-priming technique on seeds germination, two experiments were conducted and assessed the efficiency on the growth rate, yield and biofortification on the forage maize (Zea mays L.). The first laboratory experiment assessed the effect of Zn-priming for three-time exposures (i.e., 8, 16 and 24 h) on germination parameters. The second experiment was done in a greenhouse, by using the 10 seeds obtained from 24 h priming. Five seed pretreatments were studied (0, 0.1, 0.5, 1 and 11 2 % of zinc sulfate heptahydrate (ZnSO₄·7H₂O)) compared to the recommended dose (5 ppm of Zn at 5–9 leaf stage) provided by soil application. The obtained results revealed that all seed priming, including hydro-priming, improve seed germination performance. Zn-priming increased the grain yield and helped to enrich the seeds in this element, especially seedlings treated with 0.5 % Zn sulphate for 24 h leading to an increase in yield by 47 % and in Zn content by 15 %. The comparison of the results from both techniques showed that Zn-priming could be was very effective than the traditional direct application in soil.

Quinoa is a highly nutritious and gluten-free crop. It is a good alternative crop to cereals in the context of climate change. In the process of introducing quinoa to an arid region of Morocco (Rehamna), late sowing results in stunted plants and low yields due to insufficient precipitations and high temperatures around the flowering stage. Early sowing of short-cycle cultivars constitutes a good strategy to enhance growth and yields. A field experiment was conducted in the Rehamna region in 2020–2021 to investigate the effect of the sowing date on quinoa growth, development, and yield. Two cultivars, ICBA-Q5 and Titicaca, and five sowing dates from 15 November to 15 March were evaluated. Results showed that December sowing enhanced plant height, total leaf area, the number and dry weight of branches, leaves, and panicles, and enhanced quinoa productivity, due to high precipitations, optimal temperatures, and a short photoperiod. The highest grain yield (0.84 t ha⁻¹) was obtained with ICBA-Q5. Late sowing decreased the yield and growth and reduced the number of days to panicle emergence, flowering, and maturity for both cultivars. Early sowing of ICBA-Q5 is recommended to increase quinoa yield in arid regions of Morocco.

Throughout the last decade, and particularly in 2022, water scarcity has become a critical concern in Morocco and other Mediterranean countries. The lack of rainfall during spring was worsened by a succession of heat waves during the summer. To address this drought, innovative solutions, including the use of new technologies such as hydrogels, will be essential to transform agriculture. This paper presents the findings of a study that evaluated the impact of hydrogel application on onion (Allium cepa) cultivation in Meknes, Morocco. The treatments investigated in this study comprised two different types of hydrogel-based soil additives (Arbovit^® polyacrylate and Huminsorb^® polyacrylate), applied at two rates (30 and 20 kg/ha), and irrigated at two levels of water supply (100% and 50% of daily crop evapotranspiration; ETc). Two control treatments were included, without hydrogel application and with both water amounts. The experiment was conducted in an open field using a completely randomized design. The results indicated a significant impact of both hydrogel-type dose and water dose on onion plant growth, as evidenced by various vegetation parameters. Among the hydrogels tested, Huminsorb^® Polyacrylate produced the most favorable outcomes, with treatment T9 (100%, HP, 30 kg/ha) yielding 70.55 t/ha; this represented an increase of 11 t/ha as compared to the 100% ETc treatment without hydrogel application. Moreover, the combination of hydrogel application with 50% ETc water stress showed promising results, with treatment T4 (HP, 30 kg, 50%) producing almost the same yield as the 100% ETc treatment without hydrogel while saving 208 mm of water.

Quinoa is a potential alternative crop for an adaptation strategy for salinization and climate change effects in dryland. A sowing date of February practiced in the Rehamna region, Morocco, resulted in stunted plants and low yields due to insufficient precipitations and high temperatures around the flowering stage. For this reason, a field experiment was conducted to investigate the effect of sowing date on quinoa water use efficiency, growth, and yield. The experiment was conducted at the UM6P experimental farm to evaluate five sowing dates for two short cycle quinoa cultivars. The results showed that the most early suitable sowing date of quinoa in the Rehamna region was December. Late sowing dates resulted in a significant decrease in WUE, growth and yield. The highest grain yield (0.84 t ha⁻¹) was obtained by ICBA-Q5 sown in December.

Crop residue cover (CRC) plays a crucial role in sustainable farming systems by improving soil structure, regulating water retention, and reducing soil erosion. Accurate CRC monitoring is therefore important for evaluating field management practices at scale. Existing field methods (e.g., line-transect and visual estimation) are labor-intensive and difficult to scale, while optical retrievals are often confounded by soil moisture. Moisture introduces nonlinear spectral distortions that can bias residue estimates, particularly in the shortwave infrared range. We propose a Deep Moisture-Invariant Autoencoder (DMIA) framework that performs conditional spectral normalization—referred to as moisture normalization (dry-equivalent spectral transformation)—before linear spectral unmixing. The workflow has two stages: (1) a conditional autoencoder that transforms moisture-affected spectra to dry-equivalent spectra, and (2) fully constrained linear unmixing on dry-equivalent spectra. The experiment included 63 controlled wheat-residue scenes at a semi-arid site in Morocco, spanning three moisture levels and seven residue proportions (0–100%) measured with ASD spectroscopy. Within this controlled experimental dataset, DMIA achieved a global coefficient of determination of R² = 0.93, outperforming ordinary least squares (R² = 0.65), fully constrained least squares (R² = 0.68), and ELMM (R² = 0.71), and matching the performance of MESMA (R² = 0.93) while requiring only a single forward pass at inference rather than iterative library matching. Although both methods showed similar overall accuracy, a closer analysis reveals that DMIA’s advantage over MESMA widens under wetter, coarser-resolution conditions, which are highly representative of operational monitoring. This finding is further validated by a Monte Carlo uncertainty propagation, proving the results are unaffected by reference noise. Using spectrally resampled ground data to simulate satellite responses, performance remained robust for PRISMA (R² = 0.93) and Sentinel-2 simulation (R² = 0.87). Reconstruction diagnostics (mean SAM below 5°) support the physical plausibility of the learned transformation. These results suggest that conditional spectral normalization can reduce moisture-related distortions while preserving compositional signals under controlled experimental conditions; however, the use of three discrete moisture levels represents an experimental simplification; in open operational fields, soil moisture varies continuously and pixel-level states are unknown. This framework provides a proof-of-concept basis for further investigation across diverse soils, residue types, and operational sensor configurations.