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1. Mapping spatial variability of soil nutrient deficiencies in smallholder villages – a prerequisite for improved crop production in AfricaSmallholder farming is the dominating type of agricultural production in many parts of Africa. If cultivation practices can be adapted to match the specific needs of individual smallholder plots, this can certainly be regarded as a form of precision agriculture (PA), considering their limited size. A fundamental prerequisite for successful application of PA is the availability of basic information on soil properties at a detailed enough level. To fill yield gaps, site specific information must... M. Söderström, K. Piikki, J. Kihara, J. Mutua, J. Wetterlind |
2. Crop water requirements, biomass and grain yields estimation for Sulla (Hedysarum coronarium L.) using cropwat in semi-arid regions of TunisiaDwindling water resources and increasing food requirements require greater efficiency in water use, both in rainfed and in irrigated agriculture. Regulated deficit irrigation provides a means of reducing water consumption while minimizing adverse effects on yield. With the current water shortage in Africa improving crop water use is vital especially in the arid and semi-arid regions. Models can play a useful role in developing practical recommendations for optimizing crop production under... R. Hajri, M. Rezghui, M. Mechri, M. ben younes |
3. 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 |
4. 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 |
5. Evaluation of on –farm oil palm yield parameters in Niger Delta region of NigeriaEvaluation of on –farm oil palm yield parameters in Niger Delta region of Nigeria *Ekhator F1., Osayande P1., Aduramigba-Modupe V.O2., Solomon O1., and Ikuenobe C.E1. 1Nigerian Institute for Oil Palm Research (NIFOR) P.M.B 1030, Benin City, Edo State 2Institute of Agricultural Research and Training, Obafemi Awolowo University, Ibadan, Oyo... E.C. Ikuenobe, K.D. Madukwe, F. Ekhator, V.M. Aduramigba, P.E. Osayande, S. Solomon, N.M. Okoye |
6. Crop monitoring and forecasting system based on cloud data and capabilitiesVarious systems for crop monitoring & forecasting, using satellite images and meteorological data, exist around the world. Their complexity differs from one system to another, according to the temporal and spatial scale and according to the objectives assigned to them. Among the well known are the European Monitoring Agricultural ResourceS system, the USDA system of the Foreign Agricultural Service, the Moroccan CGMS system, the Belgian CGMS system and the Chinese CropWatch system. The... |
7. Tropical Precision Agriculture: the Brazilian ExperienceSoils with limited natural fertility and high dependence on imported fertilizers, in addition to intense natural spatial variability, are factors that made Precision Agriculture (PA) develop in Brazil. More than 20 years of academic and business experience have been accumulated, with intense consulting services with business models adapted to local conditions, involving small, medium and large rural properties. Innovations and challenges are still needed in the field of soil fertility management,... J. Molin |
8. Decision-Making Tools in Soil Management and Plant Nutrition On-Farm Research and Observation Plot: Between Reality and ChallengesIn this presentation, we will address a number of decision-making tools and assess their relevance and limitations for the Mediterranean and Tunisia's agricultural conditions. Decision-making tools can be classified, based on the scale, to different levels, ranging from national and regional scale to farm scale. On a large scale, an agricultural policy can be drawn based on the governmental agricultural plans, and soil maps can be adopted as a decision-making tool and thus guide support in... M. Mechri |
9. The Future of Farming: Bringing Biophotonics and Machine Learning to Revolutionize AgricultureThe future of farming is set to be revolutionized by biophotonics and machine learning. Biophotonics is the study of the interaction of light and living matter, and machine learning is a form of artificial intelligence that allows computers to learn from data. Together, these two technologies will allow for more precise and efficient farming, as well as greater yields and less wastage. Biophotonics will allow for more precise targeting of crops with pesticides and herbicides, as well as more efficient... S. Ndlovu |
10. Design and Development of a LoRa Communication System for Scalable Smart Irrigation SystemsSmart irrigation is a promising tool to optimize irrigation water use but is still faced with challenges related to usability and applicability attributed to several constraints such as high initial costs, complex user interface, poor connectivity, limited farm coverage, etc. This study aimed to solve some of these issues through the development and testing of a LoRa communication system using a Raspberry Pi 4, LoRa HAT concentrator, and Strega smart valve at the Makerere University Agricultural... J. Wanyama , J. Ikabat, I. Kabenge, P. Hess, P. Nakawuka, E. Bwambale , J. Muyonga, S. Felicioni, A. Bühlmann, T. Anken |