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AfPCA Proceedings 2024

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Pirotti, F
Pypers, P
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Authors
Vanlauwe, B
Amede, T
Baudron, F
Chivenge, P
Devare, M
Saito, K
Kihara, J
Nangia, V
Pypers, P
Shepherd, K
Vandamme, E
Kayad, A
Sozzi, M
Pirotti, F
Marinello, F
Sartori, L
Gatto, S
Crouch, J
Shephard, K
Miller, M
Collinson, J
Singh, P
Pypers, P
van den Bosch, R
van Beek, C
Chernet, M
Aston, S
Topics
Decision Support Systems
Satellite Imagery
Plenary
Type
Oral
Year
2020
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Authors

Filter results3 paper(s) found.

1. Excellence in Agronomy 2030: A new CGIAR-wide initiative to deliver agronomy solutions at scale

Required 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. Monitoring Corn (Zea mays) Yield using Sentinel-2 and Machine Learning for Precision Agriculture Applications

Currently, 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

3. 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