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ICRAF Soil and Land Health Theme (World Agroforestry Centre)
xrf4473ForestLight.quant
Version 2.0
File Citation
Towett, Erick K.; Drake, Lee B.; Acquah, Gifty E.; Haefele, Stephan M.; McGrath, Steve P.; Shepherd Keith D., 2020, "xrf4473ForestLight.quant", Replication Data for: Comprehensive Nutrient Analysis in Agricultural Organic Amendments Through Non-Destructive Assays Using Machine Learning, https://doi.org/10.34725/DVN/YTJTZQ/PY7AVB, World Agroforestry (ICRAF), V2

This file is part of "Replication Data for: Comprehensive Nutrient Analysis in Agricultural Organic Amendments Through Non-Destructive Assays Using Machine Learning".

Dataset Citation
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 (ICRAF), V2, UNF:6:bmSAWgS3un1yCIZ6kBgkyw== [fileUNF]
File Metrics
3 Downloads
File Metadata  
doi:10.34725/DVN/YTJTZQ/PY7AVB
2a26970f2271fee45954c97ad6bd68bd
2021-06-20
6.8 MB
Unknown
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 validation over 25 iterations based on the R package randomForest (4.6–14). The best forest models were selected using root mean square error (RMSE) in the caret package (6.0–84) using the R language (3.6.0).
2020-11-20
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