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| Filter results5 paper(s) found. |
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1. Orchard Yield Assessment in North-East of Morocco using Satellite ImageryAgricultural sector represents one of the pillars of the Moroccan economy. The Green Morocco Plan (GMP) established in 2008 present the main engine of development of this sector and for local economy. One of the objectives of Pillar I and Pillar II of GMP was to maximize production with less use of water resources. As an effect of climate change, Morocco is experiencing a strong variability in spatial and temporal variability of precipitation with a detrimental effect on yield and quality of crop... K. Aberkani, A. Matese, S. Samri, M. Said, S. Di Gennaro, P. Toscano |
2. QUANTIFICATION OF OPTIMAL FERTILIZERS DEMAND IN WHEAT AND CORN FIELDS IN MOROCCO USING VERY HIGH-RESOLUTION REMOTE SENSED IMAGERY AND HYBRID COMPUTATIONAL APPROACHESAbstract. Demand on agricultural products is increasing as population continues to grow. Data driven management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence to get the most out of soil in terms of crop yield while preserving environment. This study aims to establish a quantitative framework for macronutrient (i.e., nitrogen, phosphorus, and potassium) status (i.e., excess, deficiency) for winter wheat (Triticum aestivum... K. Misbah, A. Laamrani, A. Chehbouni , D. Dhiba , J. Ezzahar, K. Khechba |
3. The Yield Gap In Africa: Closing the gap through soil attributes management using remote sensing & Precision Agriculture Approaches at the field scaleIn Africa, which has the most population growth in the world, the agricultural system is characterized by the predominance of smallhoder farmers. In order to cope with intense population growth and increased production costs through yield gap closing. Yield gap is defined as the difference between potential yield and actual yield and is an inevitable method to improve yields while decreasing the environmental impacts of agricultural systems. The variability of yields is strongly controlled by... K. Khechba, A. Laamrani, A. Chehbouni, D. Dhiba, K. Misbah |
4. 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 |
5. 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 |