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AfPCA Proceedings 2024

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Adeboye, M.K
Miller, M
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Authors
Lawal, B
Adeboye, M.K
Tsado, P.A
Crouch, J
Shephard, K
Miller, M
Collinson, J
Singh, P
Pypers, P
van den Bosch, R
van Beek, C
Chernet, M
Aston, S
van Beek, C
Chernet, M
Aston, S
Miller, M
Collinson, J
Shephard, K
Crouch, J
Terhoeven-Urselmans, T
Topics
Precision Agriculture for Small Holders
Plenary
Type
Oral
Year
2020
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Authors

Filter results3 paper(s) found.

1. Spatial variability and mapping of selected soil quality indicators for precision farming at a smallholding level in Minna, Nigeria

Smallholding 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

2. 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

3. 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