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ICRAF Soil and Land Health Theme (World Agroforestry Centre)
Land Health Decisions has a vision to support the growing political momentum for large-scale commitments to prevent land degradation, and to restore or regenerate degraded natural resources and ecosystem services, contributing to an unprecedented change in national and global agendas, and a unique opportunity for research to influence policy and action. The theme is contributing to the ICRAF strategy by supporting wise stakeholder decision making on climate-smart land management options that work towards a more equitable world where all people have viable livelihoods supported by healthy and productive landscapes. These options include harnessing the multiple benefits trees provide for agriculture, livelihoods, resilience and the future of our planet, from farmers’ fields through to continental scales.
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41 to 50 of 151 Results
Dec 6, 2021
Teteh, Francis; Weullow, Elvis; Dickens, Ateku; Karari, Valentine; Walsh G. Markus; McGrath, Steve P.; Acquah, Gifty E.; Haefele, Stephan M.; Gregory, Wendy; Durenkamp, Mark; Skilton, Ruth; Brookman, Melanie; Garwood, Chloe; Dobermann, Achim; Sila, Andrew; Shepherd, Keith, 2021, "Soil Spectra and Wet Chemistry Measurements from ICRAF Soil-Plant Spectral Diagnostics Laboratory and Rothamsted Research: Africa Soil Information Service (AfSIS) Phase 2 Project Data 2014-2018 - GhaSIS", https://doi.org/10.34725/DVN/SPRSFN, World Agroforestry - Research Data Repository, V1, UNF:6:5sSipqmujkHvXiy3E8YN0w== [fileUNF]
AfSIS Phase 2 was a collaborative project funded by the Bill and Melinda Gates Foundation (BMGF). Partners included, Columbia Global Center Africa (CGCA), Earth Institute (EI), International Soil Reference and Information Centre (ISRIC), The Council for Scientific and Industrial...
Tab-Delimited - 764.5 KB - MD5: 1177f30e690fabb6859c861c44b0d6f9
Tab-Delimited - 765.5 KB - MD5: 3a2ed86b77f5215590eda26731b33aec
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Tab-Delimited - 12.9 KB - MD5: bca3ab39e1f23ef559a97d280ed9dc4a
Jun 20, 2021
Towett, Erick K.; Drake, Lee B.; Acquah, Gifty E.; Haefele, Stephan M.; McGrath, Steve P.; Shepherd Keith D., 2020, "Replication Data for: Comprehensive Nutrient Analysis in Agricultural Organic Amendments Through Non-Destructive Assays Using Machine Learning", https://doi.org/10.34725/DVN/YTJTZQ, World Agroforestry - Research Data Repository, V2, UNF:6:bmSAWgS3un1yCIZ6kBgkyw== [fileUNF]
Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. We developed machine learning methods to rapidly quantify the concentrations of macro-...
Tab-Delimited - 340 B - MD5: c412830b7ae55c9020cb017bd056d911
Tab-Delimited - 24.1 KB - MD5: 1135e78879d1e70b700e9f1c65412083
Tab-Delimited - 1.5 KB - MD5: 55d359a61b794353505b676f30b498c0
Predicted soil variables description
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