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Predicting soil loss in small watersheds under different emission scenarios from CMIP6 using random forests
CONFLICT OF INTEREST STATEMENT The authors declare that there are no conflicts of interest. REFERENCES , , , & (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221(8), 1119–1130. Available from: https://doi.org/10.1016/j.ecolmodel.2010.01.007 , , & (1998) Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resources Association, 34(1), 73–89.
Evaluation and Promotion of Alluvial Fan Land Suitability for Agriculture in the Lhasa River Basin, Qinghai-Tibet Plateau
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Field experiments show no consistent reductions in soil microbial carbon in response to warming - Nature Communications
arising from G. Patoine et al. Nature Communications https://doi.org/10.1038/s41467-022-31833-z (2022) Soil microbes play an essential role in maintaining soil functions and services, but the dynamics of soil microbial biomass carbon (MBC) under global climate change remain unclear1. Recently, Patoine et al.2 combined a global MBC data set with Random Forest modeling and reported that global MBC decreased over 1992–2013, mainly driven by increasing temperatures.
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