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Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services. [ABSTRACT FROM AUTHOR]

Assessment of zooarchaeological data for the principal livestock (i.e., cattle, sheep, goats and pigs) reveals regional and cultural variability in how animals were manipulated, marketed, bred, and utilized over the course of antiquity. This paper focuses upon those developments, as reconstructed through zooarchaeological frequency and morphometric data for the period from c. 1000 BCE to c. 700 CE. Regionally, North Africa encompasses a vast territory, from Egypt, in the East, to Mauretania, in the West. Ecologically, there are variations within each of these zones. Culturally, moreover, these regions witnessed the introduction and influence of different social and economic agents and factors over the timeframe under investigation. This paper examines patterns in these components in a broad synthetic manner, using zooarchaeological data across the entire expanse of Roman North Africa—from Egypt to Mauretania. Focus centers upon the nature and degree of size and shape changes within the principal livestock, modifications that were in part influenced by aspects such as the scale and pattern in trade, dietary appeal for animal products (notably fatty meats), market demands, ecological adaptations, as well as economic and cultural contact among areas. Key similarities and differences in faunal patterns, across time and space within ancient North Africa, and the links these share with changes in animal husbandry schemes are outlined. [ABSTRACT FROM AUTHOR]

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%. [ABSTRACT FROM AUTHOR]

The Middle East and North Africa (MENA) is the most water-scarce region in the world. Recent research suggests that agroecology could be a basis for sustainable agriculture. We assess the spread of agroecology in the region and explore the prospect of self-organization among farming communities as an indicator for self-determination of the farming system. The focus is on Lebanon, Morocco and Tunisia. We present empirical data and propose an analytical framework for capturing the conditions for self-organizing arrangements in a transition to agroecology. [ABSTRACT FROM AUTHOR]

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]

The COVID-19 pandemic affected agri-food systems worldwide. However, while the impacts differed from one country/region to another, the scholarly literature seems to focus on developed countries in the Global North. Consequently, this review scrutinizes the literature on the pandemic's impacts in the Near East and North Africa (NENA) region. A search on the Web of Science in March 2023 generated 334 documents, of which 151 were eligible for inclusion in the systematic review. According to the bibliometric analysis, the most active nations are Saudi Arabia, Egypt, Jordan, the United Arab Emirates, and Lebanon. In general, the coverage of studies is better in the Gulf region than in the less developed countries of North Africa and those suffering from wars (cf. Libya, Syria, and Yemen). Studies generally focus on crop production and the downstream food chain (cf. distribution and consumption). While the pandemic influenced every pillar of food security, this research concentrates on access and utilization. Meanwhile, the lion's share of the literature deals with the pandemic's socio-economic effects, especially those linked to food (in)security and health. The pandemic, which laid bare the agri-food system vulnerabilities, should be seized to foster the transition towards more resilient agri-food systems in the NENA region. [ABSTRACT FROM AUTHOR]

Determining evapotranspiration (ET) is essential for water accounting and for the management of irrigated agriculture from farm to region. We describe here a collaborative initiative aimed at establishing a prototype ET network in six countries of North Africa and the Near East (NENA region). The network utilizes a low-cost and open-source system, termed the CORDOVA-ET, consisting of a base station and sensing nodes to collect the weather data needed to determine the reference and actual ET (ETo and ETa). Here, we describe the network-deployment processes, system architecture, data-collection methodology, quality-control procedures, and some of the ET results obtained so far during a four-year period, starting in 2018. The network has been developed as an iterative and collaborative process, where training and capacity building have been the main drivers. The feedback and experiences gathered from the users have helped improve the different versions of the prototypes and enhance their assembly, deployment, reliability, and ease of operation. At the same time, the involvement in the construction, maintenance, and data analysis has also provided valuable insight into calculating ET from energy-balance methods. The network operated during six cropping seasons and the results were mixed, while data integrity (hourly and daily) varied from 95 to 23% depending on the country and season. Validation of the ET estimates was performed using the ECMWF ERA5 dataset as an independent reference. The energy-balance algorithm implemented in the system to determine the ETa was validated using the OpenCropLib Python library. While the results of the data validation demonstrated the reliability and accuracy of the CORDOVA-ET system, network operations required significant support and special motivation on the part of the users. It is concluded that collaboration among users, together with the support services and participation of different stakeholders interested in agricultural water management, would be essential elements to ensure the sustainability of the ET network. [ABSTRACT FROM AUTHOR]

The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria's 2021 and 2022 winter crop seasons that were affected by drought. [ABSTRACT FROM AUTHOR]