Karsten Schmidt

TL;DR: The first results from a large biodiversity experiment in a subtropical forest in China suggest strong positive effects of tree diversity on forest productivity and carbon accumulation, and encourage multispecies afforestation strategies to restore biodiversity and mitigate climate change.

TL;DR: It is shown that some soil classes are more prevalent at one scale than at other scales and more related to some terrain attributes than to others, and the most computationally efficient ANOVA-based feature selection approach is competitive in terms of prediction accuracy and the interpretation of the condensed datasets.

TL;DR: In this paper, the authors investigated the main parameters [e.g., mean annual air temperature, mean annual soil temperature, mean annual precipitation, soil moisture (SM), soil chemistry, and physics] influencing soil organic carbon (Corg), soil total nitrogen (Nt) as well as plant available nitrogen (nmin) at 47 sites along a 1200km transect across the high-altitude and low-latitude permafrost region of the central-eastern Tibetan Plateau.

TL;DR: It is shown that memory-based learning (MBL) is a very promising approach to deal with complex soil visible and near infrared (vis–NIR) datasets and that soil vis-NIR distance matrices can be used to further improve the prediction performance of spectral models.

TL;DR: In this paper, the authors analyzed the effects of topography and soil fertility on tree growth in a forest biodiversity and ecosystem functioning (BEF) experiment in subtropical SE China.