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1. MAPPING AND ASSESSING AFRICAN SOILS FERTILITY USING HIGH-RESOLUTION REMOTE SENSING AND MACHINE LEARNING APPROACHES: STATE-OF-THE-ART AND PERSPECTIVESAfrica is far from exploiting its true agricultural potential. United Nations Food and Agriculture Organization (FAO) indicates that the continent has 60% of non-cultivated lands worldwide. While soil fertility is well highlighted as one of the major limiting factors, only limited information is available on soil nutrient contents and nutrient availability in the African soils. Soil fertility of agricultural fields is related to many physical and chemical properties, such as texture, organic matter... M. Hmimou, A. Laamrani, F. Sehbaoui, A. Chehbouni, S. Khabba, D. Dhiba |
2. A precision irrigation app for smart water management by farmersIn a context of climate change and water scarcity which is globally recognized, Morocco is one of the countries that are facing already insufficient water supply for irrigation in order to sustain productivity and food security. Therefore, there is a strong need for adapting agricultural practices and developing new technologies for efficient and smart irrigation management to make best use of available water and maximize productivity per unit of consumed water. Recent studies have shown that... A. Abouabdillah , R. Bouabid |
3. The status of precision agriculture and its adoption in MoroccoPrecision agriculture (PA), as an integrated crop management system that uses various tools and technologies for assessing and monitoring soil and crop spatial variability and for implementing site-specific (variable rate) applications, is a concept that becomes nowadays in many developed countries a common practice rather than an innovation. On the contrary, in most developing countries, agriculture is still struggling with the basics of farming and is constrained by many factors, such as land... R. Bouabid |
4. Comparison of soil testing and scanning methods for spatial variability assessment of soil fertility: implications for precision agricultureUnderstanding spatial variability of soil fertility is a key to variable rate nutrient applications for precision fertilization. The objective of this study was to assess field spatial variability of soil fertility using two approaches, a gridded soil testing and a scanner-based technique. Measurements were performed on a quarter pivot silage corn field of 13 ha. For the first approach, soil samples were taken on a geopositioned grid and were analyzed for the main physicochemical and nutrient... R. Bouabid |
5. Spatial variability of soil and tree nutrient status in relation to bitter pit incidence in apple orchards in the Sais plateau, MoroccoDiscreet spatial variability of soil fertility affects crop productivity and quality. In general, nutrient deficiencies are the most incriminated. However, excess or unbalanced nutrient can also impact seriously both yield and quality, especially in fruit tree crops. Bitter-pit, a nutrient disorder related to K-Ca-Mg unbalance in apple orchards can cause major loss of apple quality in field, as well as on post-harvest following periods of cold storage. Conventional composite sampling for soil... R. Bouabid |
6. Engaging Stakeholders in Precision Agriculture Toolbox Conception: Case of Cowpea Atlas Platform Establishment in Benin RepublicCowpea [(Vigna Unguiculata (L.) Walpers] is among the most preferred and consumed legumes in West Africa and grown by many smallholder farmers. The crop has huge potential, is easy to grow and constitute a source of income of many actors involved in different value chains. Unfortunately, despite many interventions which aimed at promoting the crop in West Africa mainly Benin, areas under cowpea crop decrease over the years along with the loss of cowpea-based products. Such problem is... N.V. Fassinou hotegni, Y.L. Godonou, L.M. Gnanglè, O.N. Coulibaly, E.G. Achigan-dako |
7. Together, Let's Make Africa the Epicenter of a Sustainable Transformation of Food Systems... P. Bwire |
8. Agricultural Data Market to Empower African FarmersBy transforming the agricultural data into agronomic advices by using AI model, farmer can get a strong tool to help them making the right decision in the right time. Decision about the quantity and the quality of fertilizer to apply, the quantity and the timing of the irrigation,… Also he can get valuable information about yield prediction, phytosanitary risk. All of this information can help famers reducing its operational cost by up to 30%. To develop robust AI model,... F. Sehbaoui |