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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
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 - 4.0 MB - MD5: af113c123eafb0e178d1fff26ead388f
Averaged MIR spectra of the manure standards run on the HTS-XT instrument
Tab-Delimited - 1.7 MB - MD5: 04321069d86a1809483d5371167fc029
Averaged MIR spectra of the manure standards run on the MPA instrument
Unknown - 81.6 MB - MD5: a6b8d8504ce61253cae2079c2f3885ed
Using forest regression machine learning models, excellent calibration functions could be established using the MIR spectra. Forest models for there DRIFT-MIR spectra were run using 1500 trees with 200 resampling events using k-fold cross validation over 25 iterations based on th...
Tab-Delimited - 2.9 MB - MD5: 8362ced45ea5d1bdce9773d08bb21e9a
MIR spectra of the manure standards using Alpha KBr instrument
Tab-Delimited - 1.8 MB - MD5: b56570fc695ece2feb1d58627985846c
MIR spectra of the manure standards using Alpha ZnSe instrument
Unknown - 29.3 MB - MD5: ac2c20646882bb5b387f26ed9ed53cae
Extreme gradient boosting machine learning (XGBoost) models were used with data from DRIFT-MIR spectroscopy and these differ from forest in that trees can be weighted differently, have fixed depths, and different resembling. XGBoost (0.82.1) models were run using 400 rounds with...
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...
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