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. 2017 Oct 24;17(10):2428. doi: 10.3390/s17102428

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© 2017 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Comparison of root mean square error (RMSE) values for (A) total nitrogen (TN) and (B) total carbon (TC) predictions obtained with (a) gradient boosted machines (GBM); (b) artificial neural networks (ANNs) and (c) random forests (RF) analyses in cross-validation (Cal), laboratory prediction (Lab) and on-line prediction (Online). Results were generated with local field datasets of 122 and 149 samples for the Hessleskew and Hagg fields, respectively, and a spiked European dataset (528 samples).