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1. Excellence in Agronomy 2030: A new CGIAR-wide initiative to deliver agronomy solutions at scaleRequired increases in crop production and productivity in sub-Saharan Africa (SSA) will not happen without the increased use of appropriate agronomic practices. While several thousand new varieties of nearly all key crops have been produced in the past decade, recent increases in yields in specific countries have only happened when such varieties received the right agro-inputs and management. That said, agronomy is often highlighted as an area that has not delivered impact at scale in SSA, or... B. Vanlauwe, T. Amede, F. Baudron, P. Chivenge, M. Devare, K. Saito, J. Kihara, V. Nangia, P. Pypers, K. Shepherd, E. Vandamme |
2. Biochar and/or Compost for Soil Quality and Maize Yield Improvement in an Acidic Ferralsol Soil in Kenya.The rapidly increasing global population, climate change and dwindling resources have made it very difficult to meet global food demand. To address the issue of food insecurity, sustainable intensification of agriculture (SIA) has been proposed. However, the consequences of poorly managed agricultural intensification can negatively affect the ecosystem. Biochar and compost application has been widely recommended as a highly promising soil fertility replenishment option to promote sustainable agriculture.... |
3. Mapping African soils at 30m resolution - iSDAsoil: leveraging spatial agronomy in farm-level advisory for smallholdersField level soil data has been the foundation of agronomic advisory, but traditional methods involving on-farm sampling are too expensive for a large proportion of African smallholders. Building on the work of the African Soil Information Service (AfSIS), Innovative Solutions for Decision Agriculture (iSDA) and partners have created an agronomic soil database which covers the entire African continent at a spatial resolution of 30 m. “iSDAsoil” combines remote sensing data and other... J. Crouch |
4. Just a moment; the need for streamlining precision agriculture data in AfricaPrecision agriculture (PA) data sources in the era of digital agriculture are diverse in terms of the range of technology options and the types of data they generate. These include proximal sensors, unmanned aerial vehicle (UAV), satellites, farm machinery mounted sensors and robotics to generate static data or real time information (e.g., yield monitoring). Government institutions, scientists and private sectors take the lion’s share in generating PA data at innovation, validation and dissemination... T.B. Gobezie, A. Biswas |
5. The Vision of Future Earth Observation for AgricultureThe main objective of EO4AGRI is to catalyze the evolution of the European capacity for improving operational agriculture monitoring from local to global levels based on information derived from Copernicus satellite observation data and through exploitation of associated geospatial and socio-economic information services. EO4AGRI assists the implementation of the EU Common Agricultural Policy (CAP) with special attention to the CAP2020 reform, to requirements of Paying Agencies, and... K. Charvat, V. Safar, H. Kubickova |
6. SmartAfriHub for SmartAgriculture capacity buidling in AfricaDigital Innovation Hubs (DIH) are multi-actor ecosystems that support farming communities in their digital transformation by providing a broad variety of services from a one-stop shop. DIHs purpose is to provide a social space for community of practices; provide access to digital technologies and competencies; provide access to infrastructure and tests digital innovations (“test before invest”); provide development playground... K. Charvat, C. Miderho , A. Obot, T. Löytty, H. Kubickova |
7. 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 |
8. Spatial variability and mapping of selected soil quality indicators for precision farming at a smallholding level in Minna, NigeriaSmallholding farmers in Nigeria still practice blanket application of fertilizers, without giving consideration to spatial variations in soil properties across their fields. Understanding of spatial variability in soil properties is essential for precision farming, especially in this era of resource scarcity and high cost of fertilizers. This study was carried out to assess and map the spatial variability in selected soil quality indicators in a smallholder farm in Minna, North-central Nigeria,... B. Lawal, M.K. Adeboye, P.A. Tsado |
9. Using Site-Specific Management Zones for Potato Crop Management, East Nile Delta, EgyptThe field management zones (MZ) delineated using soil electrical conductivity (EC) and topographic parameters are the basis for site-specific crop management (SSCM). The objective of this paper was to delineation site-specific management zones of 155 feddans (67.2 ha) of a potato pivot field at East of Nile Delta, Egypt for use in smart farming based on spatial variability of soil and plant properties, yield and topographic attributes. The salinity measurement in the field... A.B. Belal, E.S. Mohamed, M.E. Jalhoum, M. zahran, M.A. Abdellatif, M.S. Emam, E.A. Hendway |
10. Nutrient management tailored to smallholder agriculture enhances productivity and sustainabilityPlant nutrition plays a central role in the global challenge to produce sufficient and nutritious food, lessen rural poverty, and reduce the environmental footprint of crop production. Efficient fertilizer use requires tailored solutions that are scientifically sound, practical and scalable especially for smallholder farmers, such as the crop-led site-specific nutrient management (SSNM) approach developed in the 1990s for cereal production systems in Asia to address variability among farms. Originating... P. Chivenge, K. Saito, M. Bunquin, S. Sharma, A. Dobermann |
11. Irrigation Water Management for Potato crop under Pivot Irrigation System using Remote sensing techniquesWhen water application records low efficiency, the water losses increased. Irrigation systems often ignore soil variability and water applied uniformly on the field; hence, the water losses amplified. Which means more water application, more energy demand, and more money expenses. El-Salhia region contains a big agricultural farm located at the South Eastern of Nile delta. The field NO 34 was chosen to be investigated under the pivot central sprinkler irrigation system which cultivated with... |
12. 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 |
13. Autonomous Hexacopter Spraying drones for plants protectionAbbes KAILIL1, Hassan BENAOUDA2, Abdelhakim MOHCINE3, 1 Eng. Doctor in aerospace engineering, Moroccan Industry Services & Engineering SARL, Morocco. 2 Eng. Doctor in Agriculture, INRA, Morocco. 3 Engineer in agriculture, ONCA, Morocco. Farming technologies have considerably... H. Benaouda, A. Mohsine, A. Kailil |
14. Mapping African soils at 30m resolution - iSDAsoil - Western 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... J. Crouch, K. Shephard, M. Miller, J. Collinson, P. Singh, P. Pypers, R. Van den bosch, C. Van beek, M. Chernet, S. Aston |
15. 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 |
16. 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 |
17. Development of a Decision Support Tool to Derive Site-specific Nutrient Management Recommendations for Maize Production Using Machine LearningAgriculture is the main source of food and income for rural communities in developing countries, especially in Africa. Given current population growth, pressures on agricultural systems will continue to increase. Many countries have agricultural economies that are highly dependent on agricultural productivity. For example, several variables can influence fertilization for optimal grain yields. Quantifying the effects and relative importance of soil properties such as soil type, pH, Olsen-P, climate,... O. Ennaji, L. Vergutz, A. El allali |