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1. SIMULATION OF CASSAVA YIELD UNDER DIFFERENT CLIMATIC SCENARIOS IN KILEMBWE, SOUTH-KIVU PROVINCE EASTERN DR CONGOClimate variability and change are projected to significantly impact agricultural production across Africa. This study assessed the effects of climate variability and change on cassava yield in Kilembwe, South-Kivu province Eastern DR Congo. The assessment relies on the DSSAT crop model simulation of cassava under current and future climate. The period 1980–2010 was used to represent the baseline, while future projection covers three periods including the near future (2010–2039), mid-century... A.B. Yamungu, A. Egeru, M.J. Majaliwa, B.M. Dossa |
2. 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 |
3. Performance agronomique et économique de différentes stratégies de gestion de la fertilité du sol sous culture de soja (Glycine max L. Merril) dans la zone littorale du Togo.Ce travail a pour objectif de valoriser les émondes de deux légumineuses arbustives et quelques fertilisants organiques pour améliorer la production du soja. Afin de parvenir à cet objectif, les paramètres comme la masse de mille graines, les rendements en gousses, en graines, en fanes du soja et autres ont été déterminés. L’étude a eu lieu à la Station d’Expérimentation Agronomique de Lomé (SEAL)... K.M. Amouzouvi, K.E. Ozou, L. Kolani, K.A. Amouzouvi, J.M. Sogbedji |
4. Monitoring Corn (Zea mays) Yield using Sentinel-2 and Machine Learning for Precision Agriculture ApplicationsCurrently, there is a growing demand to apply precision agriculture (PA) management practices at agricultural fields expecting more efficient and more profitable management. One of PA principal components for site-specific management is crop yield monitoring which varies temporally between seasons and spatially within-field. In this study, we investigated the possibility of monitoring within-field variability of corn grain yield in a 22ha field located in Ferarra, North Italy. Archived yield data... A. Kayad, M. Sozzi, F. Pirotti, F. Marinello, L. Sartori, S. Gatto |
5. Monitoring irrigation water use at large scale irrigated areas using remote sensing in water scarce environmentIncreasing pressure on available water resources in semi-arid region will affect the availability of water for irrigated agriculture. In this context, adoption of innovative and cost-effective tools for water management and analysis of water use patterns in irrigated areas is required for an efficient and sustainable use of water resources. This study aims to evaluate a remote sensing-based approach which allows estimation of the temporal and spatial distribution of crop evapotranspiration... M. Kharrou, V. Simonneaux, M. Le Page, S. Er-raki, G. Boulet, J. Ezzahar, S. Khabba, A. Chehbouni |
6. Deep Learning is bringing pan-African small holder advisory services based on mid-infrared spectroscopic soil analysis to the next levelThe majority of African smallholder farmers do not have access to soil analytical services. The main reasons are relatively high costs of wet chemical services and difficult logistics. As a result they have to rely on blanket fertilizer recommendations. This often causes poor soil management due to very heterogeneous soil conditions. As a result, the return on investment from blanket fertilizer recommendations is low and fertilizer acceptance is not growing among smallholder farmers. Soil spectral... T. Terhoeven-urselmans, D. Fletcher, M.M. Karanja, J.W. Kamau |
7. Mapping African soils at 30m resolution - iSDAsoil - Eastern Time Zones“iSDAsoil” combines remote sensing data and other geospatial information with carefully stratified point samples subjected to spectral analysis and traditional wet chemistry reference analysis. State of the art machine learning techniques were used to create digital maps of 17 agronomically important soil properties at 3 depths, including estimates of uncertainty. iSDAsoil is designed to encourage sharing and we hope that the owners of other soil and agronomic data, in industry... C. Van Beek, M. Chernet, S. Aston, M. Miller, J. Collinson, K. Shephard, J. Crouch, T. Terhoeven-urselmans |
8. 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 |
9. Improving Lime and Fertiliser Recommendations for Smallholders Using Co-variate Zoning and Low Cost Mir Soil Testing TechnologySmall-holder farmers lack for them affordable access to crop and field specific lime and fertilisation advice. Another challenge is that while crop and region specific fertiliser blends could be produced, high resolution, unbiased and up to date soil information is lacking and thus crop and region specific blends are not produced. As a result, the farmers are left with a small number of available compound and fertiliser blends that often do not match the crop needs. This is not a convincing situation... T. Terhoeven-urselmans |
10. 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 |