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Conventional tillage coupled with monocropping and limited recycling of crop residues along with increased pressure from human and livestock population has led to the degradation of the soil, water resources, and the environment in the Middle East and North Africa (MENA). Conservation Agriculture (CA) considered as "climate-smart" agriculture, strives to achieve acceptable profits along with high and sustained production levels while concurrently conserving and regenerating soils and protecting the environment. The objectives of this paper are to review and synthesize available findings on CA, its past trends, current opportunities and challenges, evidence on potential benefits from its adaptation, to discuss its future outlook, and to make relevant recommendations for interventions and novel research needs for its wider diffusion in the region. In MENA, major research on CA has been mainly conducted in Morocco, Tunisia, Iran, and Syria, while Jordan, Lebanon, Iraq, Egypt embarked CA research fairly recently. About 25-40% out of the 53 million hectares total arable land is estimated to be suitable for CA in the region. In recent years a growing number of studies carried under a range of climatic, soil, management and cropping conditions in many countries of MENA reported several benefits including higher and more stable yields and profits, reduced risks of crop loss, labor requirements, soil erosion, and improved soil moisture and quality under CA system. Despite its benefits, adoption of CA in MENA is still very low for various reasons including: lack of affordable and well-adapted seeders, the complexity of the CA system which posed a major challenge for mostly uneducated farmers to comprehend, ill-conceived policies that promote cereal intensification hence inadvertently promote monocropping, tradeoffs between residue retention and livestock feed, lack of adequate policy and institutional framework and incentives to enhance farmers' adoption, and the private sector's active involvement in the dissemination of CA. The wider acceptance of CA requires the development of affordable and versatile no-till seeder alongside the implementation of integrated crop management practices. CA-based bundled agronomic management practices must be tailored to the biophysical and socio-economic environment. Finally, effective strategies for upscaling CA in MENA has to be developed by taking into consideration the unique features of the region. Despite the daunting past adoption trends and current challenges, we predict a bright future for CA in the region due to several factors including: rising energy prices and wage rates, the emergence of younger and more educated farmers, the advent of climate change, the increasing awareness on the degradation of land and water resources among farmers and policy makers, changes in dietary preferences subsequently increasing trends in demand and prices of legumes are expected to increase the desirability of CA and enhance its wider diffusion in MENA. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for mapping polygenic depressions colonised by vegetation is presented. These spatially periodic vegetation patterns situated in the arid areas of North Africa are known locally as "Dayas". The mapping of these structures is an important component in monitoring their evolution which can be regarded as an indicator of socio-environmental conditions. For this purpose, a method based on satellite image analysis is proposed. First, Landsat Thematic Mapper image inspection is performed to highlight the vegetation spots using the Normalized Difference Vegetation Index (NDVI). Then, a Differential Morphological Profile (DMP) which includes both spectral and morphological information, is built for each NDVI pixel. We adapt the F-Score technique combined with the SVM classifier, to select and classify the most effective DMP images in order to extract the Daya's signature. Using feature selection, overall classification accuracy reached 97.8% with a kappa value of 95.61 but also reduced the dimension of the DMP vector. Dayas' morphological and vegetation characteristics are related to their degree of evolution. Furthermore, we categorise categorise the 170 extracted Dayas into three classes according to their size. On the resulting map, each class corresponds to a specific morphological and vegetation evolution stage. • The established methodology allows Dayas identifying in semi-arid areas. • Differential Morphological Profile is a powerful tool for spots vegetation mapping. • Size-based classification of Dayas enables a chronological distribution. • F-Score combined with SVM for feature selection improves classification accuracies. [ABSTRACT FROM AUTHOR]