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1. Estimating soil organic carbon from cell phone imagesSoil organic matter (SOM) is considered as the backbone of soil health and soil quality. Thus, its’ estimation is critical to support the development of management decision including precision agriculture. To overcome challenges of laborious, rather expensive and time-consuming laboratory measurements, recent advances in image acquisition systems provided a new dimension of image-based SOM prediction. However, challenges remain in using soil images taken directly in the field due to variable... A. Biswas, Y. Fu, P. Taneja, S. Lin, P. Daggupati, H. Vasava |
2. Implementation of Proximal Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Management at a Field ScaleIn the context of a rapid increase in phosphorus (P) fertilizers prices, new techniques are needed for geospatial predictions of soil P for improved P fertilizer management, while increasing farmer profitability and reducing environmental concerns. One of the biggest issues in site-specific phosphorus management is the substantial spatial variability in plant available P across fields. This leads to an expensive and laborious process for accurate mapping soil P using a traditional soil sampling... A. Lachgar |
3. Spectral assessment of chickpea morpho-physiological traits from space, air and groundChickpea (Cicer arietinum) is an important grain legume in semi-arid regions and water-stress is a major constraint to its productivity. Area under chickpea cultivation is growing but climate change toward greater aridity results in higher precipitation instability and risks yields. The ability to assess water potential can support irrigation decisions. Thus, improved ability to spatially assess plants water status can promote more efficient irrigation. The current... I. Herrmann, R. Sadeh, A. Avneri, Y. Tubul, R. Lati, S. Abbo, D.J. Bonfil, Z. Peleg |
4. Tomato yield and economic performance under organic and mineral fertilizer applications in coastal TogoResearch efforts towards enhancing vegetables production are still needed in Togo. We assessed tomato (Solanum lycopersicum) yield and associated economic returns under three soil fertility management strategies in a 3-yr period study. Three tomato varieties were used including: MONGAL-F1 (V1), SUMO-F1 (V2) and COBRA 26-F1 (V3). The fertilization regimes were: no fertilizer application as the control (F1), application of 200 kg of N15P15K15 + 100... J.M. Sogbedji, M. Lare |
5. Using Remote Sensing to Develop Site-Specific Nitrogen Management in Citrus OrchardsIntegrating multivariate spatial analysis with the delineation of site-specific management zones (MZ) provides a basis for practical and cost-effective management of water and nitrogen (N) fertilization in precision agricultural (PA). In many crops, measurements of leaf N content are used to assess the plant’s nutritional status and to develop fertilizer application plans for optimal yields. Accordingly, the aim of this study was to develop leaf N content prediction for citrus based on multispectral... E. Rave, N. Ohana, R. linker, D. Termin, A. Beryozkin, T. Paz-kagan, S. Baram |
6. Keynote 3 - Setting the Record Straight on Precision Agriculture AdoptionAdoption of precision agriculture (PA) around the world has been uneven. Some PA technologies have been the most rapidly adopted agricultural technologies in history and others have lagged. Among the disappointments is variable rate fertilizer, which was among the first PA technologies, but has become standard practice only for some farming niches. PA is a tool box – Farmers take the tools that they need and leave the rest. For example, in countries with mechanized... J. Lowenberg-deboer |
7. From Drone to Satellite – Does It Work?Multispectral drone-sensors are useful for detailed studies of crop characteristics in field trials, e.g. to create prediction models on nitrogen (N) uptake, or even estimates of optimal N rate to apply. To enable wide application of such models, they may be applied in satellite image-based decision support systems for farmers. However, successful transfer of models based on spectral data from one platform to another, requires strong and stable correlation between data from the different sensors.... M. Söderström, K. Persson |
8. Maximisation De L’efficience D’utilisation Des Nutriments : Recommandation De Fertilisation à La Carte Pour Le Maïs Sur Les Ferralsols Du Sud-togoL'amélioration de la nutrition des plantes à travers l'agriculture de précision devient incontournable pour l'optimisation de l'entreprise agricole et la protection de l'environnement. Nous avons conduit pendant la grande saison culturelle de 2019 et 2020, sous culture de maïs (Zea mays L.), des essais soustractifs à base de l'azote (N), du phosphore (P) et du potassium (K) à la station d'expérimentations agronomiques (SEAL)... J. Sogbedji, L. William, M. Lare, A. Sekaya, K. Sika , E. Tagba |
9. A Multi-scale Evaluation of Precision Weed Control Strategies in Corn Fields with Drone TechnologyCorn (Zea mays L.) is a worldwide priority crop, whose potential yields are closely affected by weed competence, especially in the early stages of crop development. A major concern in corn-growing areas is the occurrence of Sorghum halepense L., as this weed shows reduced sensitivity to pre-emergence herbicides and, therefore, it is necessary to use post-emergence treatments that entail an increase in cost. This research studied the impact of applying a precision weed (S.... J. Peña, A. De castro |
10. Capacity Building of African Young Scientists in Precision Agriculture Through Cross Regional Academic MobilityClimate change is one of the main problems affecting food and nutrition globally, particularly in sub-Saharan Africa. Adapting to and/or mitigating climate change in the agri-food sector requires merging information technologies, genetic innovations, and sustainable farming practices to empower the agricultural youth sector to create effective and locally adapted solutions. Precision Agriculture applied to crops (PAAC), has been advocated as a strategic solution to mitigate/adapt agriculture at... K. Frimpong, V. Fassinou hotegni, A. Karangwa, A. Manyatsi, M. Amri, D. Cammarano, C. Lesueur, J. Taylor, S. Phillips, E.G. Achigan-dako, G. Houessou, O. Linkpon, D. Tchokponhoué, C. Houdegbe, C. Adje |