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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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141 to 150 of 151 Results
Tab-Delimited - 6.1 KB - MD5: f5eadce8f97c265047a636b3cc9fc82e
Mar 30, 2021
Vågen, Tor-Gunnar; Winowiecki, Leigh Ann; Desta, Luseged; Tondoh, Jerome; Weullow, Elvis; Shepherd, Keith; Sila, Andrew; Dunham, Sarah J.; Hernández-Allica, Javier; Carter, Joanna; McGrath, Steve P, 2021, "Wet chemistry data for a subset of AfSIS: Phase I archived soil samples", https://doi.org/10.34725/DVN/66BFOB, World Agroforestry (ICRAF), V1, UNF:6:15bkgMxXho9IjXVQ0SI0Sw== [fileUNF]
This dataset contains a subset of the samples collected during the AfSIS Phase I project and was a collaborative effort between World Agroforestry (ICRAF) and Rothamsted Research. The soil samples were retrieved from ICRAF Soil Archive: https://worldagroforestry.org/output/icraf-...
Tab-Delimited - 20.1 KB - MD5: 711db1ceb3eb56e803a42ad6dcbc9cdf
Wet chemistry data
Tab-Delimited - 689 B - MD5: ea95c5b576a9c92107de10b74d0c5219
Unknown - 6.8 MB - MD5: 2a26970f2271fee45954c97ad6bd68bd
Forest models for pXRF spectra from the instrument serial number 900F4473 Light Calibration (10 kV, 70 μA and 90 seconds, No filter. Elemental Range: Na, Mg, Al, Si, P, S, K, Ca, Ti, Cr, Mn, and Fe) were run using 1500 trees with 200 resampling events using k-fold cross valida...
Unknown - 7.9 MB - MD5: 4c32bb4d01fe758d0a520fe679090403
Extreme gradient boosting machine learning (XGBoost) models were used with data from portable XRF instrument serial number 900F4473 for the Manure Light Calibration (10 kV, 70 μA and 90 seconds, No filter. Elemental Range: Na, Mg, Al, Si, P, S, K, Ca, Ti, Cr, Mn, and Fe. (ii)...
Unknown - 3.5 MB - MD5: b9f0bcbf4a91de38b325a2644dcdc673
Extreme gradient boosting machine learning (XGBoost) models were used with data from portable XRF instrument serial number 900F4473 for the Manure Trace Calibration (35 kV, 35 μA and 90 seconds, Filter- Cu 75um:Ti 25um:Al 200um. Elemental Range: K, Ca, Ti, Cr, Mn, Fe, Co, Ni,...
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