Vote for the Pedometrics Best Paper 2020
The Awards Committee of the Pedometrics Commission has received nominations from an open call for the Best Paper 2020 competition. The committee members each ranked the nominated papers received (with the exception of any on which they were coauthors), and the top five are listed below in alphabetical order for a public vote.
Votes should be received by Murray Lark at murray.lark( at )nottingham.ac.uk before midday, GMT on 30th November 2021.
- Please rank papers in order, with the first paper the one you regard as most deserving. You need not provide a rank for every paper nominated.
- Votes must be received from a traceable email address, and if I cannot verify their origin they will be discarded.
- Authors/co-authors should not vote for their own papers.
- Please use the subject line “Best Paper in Pedometrics, 2020” in your email.
Murray Lark on behalf of the Awards Committee (Sabine Grunwald, Gerard Heuvelink, Lin Yang, Uta Stockman, Alessandro Samuel-Rosa).
Top five papers:
- Chen, S., Mulder, V.L., Heuvelink, G.B.M., Poggio, L., Caubet, M., Dobarco, M.R., Walter, C., Arrouays, 2020. Model averaging for mapping topsoil organic carbon in France, Geoderma, 366, 114237, https://doi.org/10.1016/j.geoderma.2020.114237.
- Liu, F., Rossiter, D. G., Zhang, G.-L., & Li, D.-C. (2020). A soil colour map of China. Geoderma, 379, 114556. https://doi.org/10.1016/j.geoderma.2020.114556
- Padarian, J., McBratney, A.B. and Minasny, B., 2020. Game theory interpretation of digital soil mapping convolutional neural networks. Soil, 6, 389-397. https://soil.copernicus.org/articles/6/389/2020
- Taghizadeh-Mehrjardi, R., Mahdianpari, M., Mohammadimanesh, F., Behrens, T., Toomanian, N., Scholten, T., Schmidt, K. 2020. Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran. Geoderma, 376, 114552 https://doi.org/10.1016/j.geoderma.2020.114552
- Wadoux, A. M. J.-C. Samuel-Rosa, A., Poggio, L., Mulder, V.L. 2020. A note on knowledge discovery and machine learning in digital soil mapping. European Journal of Soil Science, 71, 133–136. https://doi.org/10.1111/ejss.12909