Agro-Fruit-Forest Systems Based on Argan Tree in Morocco: A Review of Recent Results

The argan tree, Argania spinosa (L.) Skeels, is a horticultural forestry species characterized by its endemicity and adaptation to arid and semi-arid zones in the southwest of Morocco. Despite its limited geographical distribution, argan tree presents large genetic diversity, suggesting that improvement of argan is possible. This species plays important ecological, and socioeconomic roles in the sustainable development of the country. The integration of arganiculture into Moroccan agricultural policy has been implemented through a sector strategy, which is fully aligned with the conservation and regeneration of argan forest. A. spinosa is suitable for incorporation into different agroforestry productive systems under agro-fruit-forest model and its domestication will provide a powerful means of socio-economic and environmental management. Here, we provide an overview of the argan tree literature and highlight the specific aspects of argan stands, as agro-forest systems, with the aim of developing an adequate strategy of conservation and domestication of this species. We introduce promising programs and projects for argan plantations and arganiculture, which have been adopted to relieve anthropogenic pressure on the natural argan forest.

Impact of Thermal Stress on the Moroccan Argan Agroecosystem

The argan tree is exclusively endemic in the drylands of Southwest Morocco, an agroecosystem of great ecological, cultural, and economic importance. The argan agroecosystem is already damaged. It is particularly vulnerable to climate change as well as the harsh natural conditions aggravated by the current population growth and the exploitation in excess of the production capacities. Unfortunately, during the 20th century, its area has been reduced by half. Current projections indicate an increase in temperature under climate scenarios. Anticipated climate change could accelerate this trend resulting in the argan tree degradation. To assess the climate change impact, the authors used the SDSM model at the argan agroecosystem scale and the thermal stress model to assess its vulnerability and estimate its tolerance response in relation to temperature stress for a projected climate in the near term (2010-2025 years). In this chapter, the authors explored the impact of climate change on the argan tree regeneration.

CARTOGRAPHY OF MOROCCAN ARGAN TREE USING COMBINED OPTICAL AND SAR IMAGERY INTEGRATED WITH DIGITAL ELEVATION MODEL

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.

Study of the Swelling of a Composite Based on Argan Nut, Urea-Formaldehyde and Water as a Non-Polluting Solvent

In the center and southwest of Morocco, there is an endemic tree «Argania Spinosa» known as the ironwood. The miraculous product of this millenary tree is argan oil. Known for its therapeutic and cosmetic properties. Only 20% of the fruit of the argan tree is intended for the manufacture of argan oil while the shell, which represents 80%, remains an unexploited resource. This hull, which is sold by farmers at low prices, is used as fuel for baths and Moorish bakeries. In order to value the shells; first, we sort, grind and sieve them. Second, we bind the particles into adhesive. Three biomaterials are based on three particle sizes of shell grains. The designed particles are bound with an adhesive powder that is produced from a pre-catalyzed urea-formaldehyde resin. Moreover, the water used is a non-polluting solvent. The biomaterials and two samples of Red and Beech Wood were immersed in water for 15 days, with mass measurements that were done on a daily basis. It was concluded that the swelling coefficient of the large distribution of biomaterial is smaller than the small distribution of biomaterial. However, Red and Beech Wood have the highest coefficient.

The significant symbiotic interactions between a native mycorrhizal fungi complex newly identified and the endemic tree Argania spinosa Skeels mediate growth, photosynthesis and enzymatic responses under drought stress conditions.

Argan tree (Argania spinosa skeels) is one of the most affected species by desertification and global warming. To advance knowledge on how this tree can withstand drought stress, Arbuscular mycorrhizal fungi (AMF) inoculation with a native complex, mainly formed of Glomus genus, was studied on a set of growth and physiological parameters. Under controlled conditions, inoculated and non- inoculated Argan seedlings were grown for three months under three water regimes (25%, 50%, 75% relatively to the field capacity of used soil substrate). Results showed that the Argan tree had different growth abilities to develop and withstand the various applied water limitations. The AMF complex stimulates growth and mineral nutrition of Argan seedlings under the different imposed levels of water deficiency). The Relative water content (RWC) in leaves, the hydric potential and the stomatal conductance in Argan leaves had shown a general improvement in inoculated seedlings compared to non-inoculated ones. Soluble sugar and proline contents significantly increased in non-inoculated compared with inoculated seedlings under water-limiting conditions (25%). This was similar to oxidative enzyme (Catalase, peoxydase, superoxide dismutase) whose activity increased significantly in drought stressed seedlings.

Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco

Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single-source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification.

Spatial distribution of argan tree influence on soil properties in southern Morocco

The endemic argan tree (Argania spinosa) populations in southern Morocco are highly degraded due to overbrowsing, illegal firewood extraction and the expansion of intensive agriculture. Bare areas between the isolated trees increase due to limited regrowth; however, it is unknown if the trees influence the soil of the intertree areas. Hypothetically, spatial differences in soil parameters of the intertree area should result from the translocation of litter or soil particles (by runoff and erosion or wind drift) from canopy-covered areas to the intertree areas. In total, 385 soil samples were taken around the tree from the trunk along the tree drip line (within and outside the tree area) and the intertree area between two trees in four directions (upslope, downslope and in both directions parallel to the slope) up to 50 m distance from the tree. They were analysed for gravimetric soil water content, pH, electrical conductivity, percolation stability, total nitrogen content (TN), content of soil organic carbon (SOC) and C/N ratio. A total of 74 tension disc infiltrometer experiments were performed near the tree drip line, within and outside the tree area, to measure the unsaturated hydraulic conductivity. We found that the tree influence on its surrounding intertree area is limited, with, e.g., SOC and TN content decreasing significantly from tree trunk (4.4 % SOC and 0.3 % TN) to tree drip line (2.0 % SOC and 0.2 % TN). However, intertree areas near the tree drip line (1.3 % SOC and 0.2 % TN) differed significantly from intertree areas between two trees (1.0 % SOC and 0.1 % TN) yet only with a small effect. Trends for spatial patterns could be found in eastern and downslope directions due to wind drift and slope wash. Soil water content was highest in the north due to shade from the midday sun; the influence extended to the intertree areas. The unsaturated hydraulic conductivity also showed significant differences between areas within and outside the tree area near the tree drip line. This was the case on sites under different land usages (silvopastoral and agricultural), slope gradients or tree densities. Although only limited influence of the tree on its intertree area was found, the spatial pattern around the tree suggests that reforestation measures should be aimed around tree shelters in northern or eastern directions with higher soil water content or TN or SOC content to ensure seedling survival, along with measures to prevent overgrazing.