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Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.

Great effort has been recently employed for the development of a modern and competitive agriculture in Morocco, growth in the agricultural sector is determined largely through the realization of thousands of new projects, and the support of the smallholder farmers at a national scale. Modernization of irrigation systems, and enlargement of the extent and spatial distribution of irrigated areas holds the key to increase annual productions. In this context, we established a unique procedure for monitoring the agricultural surfaces not fully exploited in terms of potential and production, in the semi-arid zone of the Haouz plain, central Morocco. We derived Normalized Difference Vegetation Index (NDVI) time series from Sentinel-2 (S2) and Landsat 8 (L8) high spatial resolution satellite images from 2016 to 2018. Seasonal phenological changes and land-cover dynamics, in addition to elevation models and landscape slopes, helped determine periods and thresholds suitable for classes separability, and establish a set of rules to be implemented in a Decision tree classifier model for a detailed land-cover mapping of the last three years. The agricultural zone was successfully separated from mountains and hills, and the derived maps of the three years yielded satisfying result with an OAthat reached above 91% for quite detailed landscape-type information. The outputs of this work hold promise to provide valuable information for planners, decision-makers and regional offices, to help smallholder farmers. Although this approach has been developed at regional-scale, it holds the potential to be adapted to larger scales, with the appropriate selection of land-cover types, and carful adjustment in the threshold values.

Forest resource conservation necessitates a deeper understanding of forest ecosystem processes and how future management decisions and climate change may affect these processes. Argania spinosa (L.) Skeels is one of the most popular species in Morocco. Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offers a powerful resource for mapping, assessing, and monitoring the forest tree species at high spatio-temporal resolution. Multi-spectral Sentinel-2 and Synthetic Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) over the Argan forest in Essaouira province, Morocco, were subjected to pixel-based machine learning classification and analysis. We investigated the influence of different SAR data parameters and DEM layers on the performance of machine learning algorithms. In addition, we evaluated the synergistic effects of integrating remote sensing data, including optical, SAR, and DEM data, for identifying argan trees in the Smimou area. We collected data from Sentinel-2, Sentinel-1, SRTM DEM, and ground truth sources to achieve our goal. Testing different SAR parameters and integrating DEM layers of different resolutions with other remote sensing data showed that the Lee Sigma filter with a size of 11×11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algorithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the combined use of two Sentinel constellation products improved map accuracy and quality. For the best scenario (VV+NDVI), the OA was 88.32% (K = 0.85), while for scenarios NDVI+DEM and VH+NDVI+DEM, the OAs were 93.25% (K = 0.91) and 93.01% (K = 0.91), respectively. Integrating a DEM layer with SAR and optical data has significantly improved the accuracy in the classification of vegetation types, especially in our study area which is characterized by high environmental heterogeneity.

The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.

Frequent toxic cyanoblooms in eutrophic freshwaters produce various cyanotoxins such as the monocyclic heptapeptides microcystins (MCs), known as deleterious compounds to plant growth and human health. Recently, MCs are a recurrent worldwide sanitary problem in irrigation waters and farmland soils due to their transfer and accumulation in the edible tissues of vegetable produce. In such cases, studies about the persistence and removal of MCs in soil are scarce and not fully investigated. In this study, we carried out a greenhouse trial on two crop species: faba bean (Vicia faba var. Alfia 321) and common wheat (Triticum aestivum var. Achtar) that were grown in sterile (microorganism-free soil) and non-sterile (microorganism-rich soil) soils and subjected to MC-induced stress at 100 µg equivalent MC-LR L−1. The experimentation aimed to assess the prominent role of native rhizospheric microbiota in mitigating the phytotoxic impact of MCs on plant growth and reducing their accumulation in both soils and plant tissues. Moreover, we attempted to evaluate the health risk related to the consumption of MC-polluted plants for humans and cattle by determining the estimated daily intake (EDI) and health risk quotient (RQ) of MCs in these plants. Biodegradation was liable to be the main removal pathway of the toxin in the soil; and therefore, bulk soil (unplanted soil), as well as rhizospheric soil (planted soil), were used in this experiment to evaluate the accumulation of MCs in the presence and absence of microorganisms (sterile and non-sterile soils). The data obtained in this study showed that MCs had no significant effects on growth indicators of faba bean and common wheat plants in non-sterile soil as compared to the control group. In contrast, plants grown in sterile soil showed a significant decrease in growth parameters as compared to the control. These results suggest that MCs were highly bioavailable to the plants, resulting in severe growth impairments in the absence of native rhizospheric microbiota. Likewise, MCs were more accumulated in sterile soil and more bioconcentrated in root and shoot tissues of plants grown within when compared to non-sterile soil. Thereby, the EDI of MCs in plants grown in sterile soil was more beyond the tolerable daily intake recommended for both humans and cattle. The risk level was more pronounced in plants from the sterile soil than those from the non-sterile one. These findings suggest that microbial activity, eventually MC-biodegradation, is a crucial bioremediation tool to remove and prevent MCs from entering the agricultural food chain.

Microcystins (MCs) produced in eutrophic waters may decrease crop yield, enter food chains and threaten human and animal health. The main objective of this research was to highlight the role of rhizospheric soil microbiota to protect faba bean plants from MCs toxicity after chronic exposure. Faba bean seedlings were grown in pots containing agricultural soil, during 1 month under natural environmental conditions of Marrakech city in Morocco (March–April 2018) and exposed to cyanobacterial extracts containing up to 2.5 mg·L−1 of total MCs. Three independent exposure experiments were performed (a) agricultural soil was maintained intact “exposure experiment 1”; (b) agricultural soil was sterilized “exposure experiment 2”; (c) agricultural soil was sterilized and inoculated with the rhizobia strain Rhizobium leguminosarum RhOF34 “exposure experiment 3”. Overall, data showed evidence of an increased sensitivity of faba bean plants, grown in sterilized soil, to MCs in comparison to those grown in intact and inoculated soils. The study revealed the growth inhibition of plant shoots in both exposure experiments 2 and 3 when treated with 2.5 mg·L−1 of MCs. The results also showed that the estimated daily intake (EDI) of MCs, in sterilized soil, exceeded 2.18 and 1.16 times the reference concentrations (0.04 and 0.45 µg of microcysin-leucine arginine (MC-LR). Kg−1 DW) established for humans and cattle respectively, which raises concerns about human food chain contamination.

Sunlight and water are factors that affect seedling development. However, the effects of acclimatization of seedlings to sunlight and water stress remain poorly understood. This study aimed to examine the interactive effects of acclimatization to light and water stress on argan tree (Argania spinosa) seedlings in nurseries. An experiment was conducted with 504 seedlings using two sunlight treatments (L1 and L2, i.e. 100% and 60% of full sunlight, respectively), and three watering treatments (well-watered, moderate stress and severe stress, i.e. 100%, 50% and 25% of field capacity, respectively). According to our results, water stress treatments caused a reduction in total biomass accumulation, nitrogen and phosphorus uptake. Water stress significantly increased other macroelements, H2O2 and MDA levels and antioxidant enzyme activities compared to well-watered seedlings. Seedlings grown under moderate shade (L2) showed higher macroelement uptake, which probably contributed to the increase in total biomass in all water treatments. The highest membrane stability index (MSI) values, H2O2 and MDA levels and lowest antioxidant enzyme activities were recorded in acclimatized argan seedlings under moderate shading (L2). These results suggest that moderate shade can effectively prevent stress caused by light excess and can also mitigate the harmful effects of water stress on A. spinosa seedlings.

Plant species characteristic of arid and semi-arid zones, such as Argania spinosa (L.) Skeels, have a taproot that allows them to reach the soil horizons more quickly. Unfortunately, in the nursery, the containers of culture used for the production of seedlings do not support an excellent development of the root architecture that can be able to resist the shock of transplantation, in particular of the hydric stress. This study aimed to evaluate the physiological and biochemical behavior of Argania spinosa seedlings grown in containers of different depths under water stress. An experiment was conducted with 90 seedlings from the different containers (P1 for depth of 16 cm, P2 for depth of 30 cm, and P3 for depth of 60 cm), and three watering treatments (well-watered 100% of field capacity, moderate stress with 50% of field capacity and severe stress with 25% of the field capacity). Our results showed that seedlings from the 16 cm container had lower values of water status. Malondialdehyde content, electrolyte leakage, hydrogen peroxide, and superoxide radical content gave higher values on seedlings from the shallow container. The benefits of increasing the container depth of nursery seedlings contribute to the improvement of physiological and biochemical responses of seedlings under water stress. To fully validate our findings, a long-term field study must be conducted.

An irrigation plan for citrus fruit in the climatic context of the area studied seems possible on the basis of evaporation from a class A pan. In principle, a coefficient value of 0.6 could be retained. Indeed, the use of this coefficient together with a high frequency of irrigation has made it possible not only to satisfy the water requirements of the citrus fruit, but also to obtain a good yield, namely 40 tons per hectare. In addition, it has been possible to achieve a great economy in water in comparison to the considerably larger quantities otherwise needed by many farmers. Hence, a great saving in water compared to quantities commonly bought by other farmers. This was reflected not only in the yield but also in the quality of the fruit, which turned out to be even better, as indicated by its size, since the quantity of water was closer to that needed. A poor yield was obtained when the water deficit was severe.