Skip to main content

Dataverse

Looking for the numbers behind our science? Browse through our datasets, or search by key terms.

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.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Find Advanced Search

51 to 60 of 131 Results
Tab-Delimited - 741.3 KB - MD5: 246d11c640313ab7fc6a2e65b26ddcab
Tab-Delimited - 932.4 KB - MD5: cf5a614f29869239d2eb635ce1b92ff0
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 - 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 - 27.2 MB - MD5: 07a83a619303878809426e2e7a3bb1c3
MIR Spectral data
Tab-Delimited - 1.8 MB - MD5: b56570fc695ece2feb1d58627985846c
MIR spectra of the manure standards using Alpha ZnSe instrument
Tab-Delimited - 2.9 MB - MD5: 8362ced45ea5d1bdce9773d08bb21e9a
MIR spectra of the manure standards using Alpha KBr instrument
Unknown - 14.7 MB - MD5: 98d025104365df48f79a66dca730d89e
Spectra are saved in this files in RDS format and they work with open-source code repositories (CloudCal and cloudFTIR, both open github projects). The CloudFTIR can be found at: https://github.com/leedrake5/cloudFTIR."
Add Data

Sign up or log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.

Contact World Agroforestry (ICRAF) Support

World Agroforestry (ICRAF) Support

Please fill this out to prove you are not a robot.

+ =