To be eligible to be nominated for the Best Paper Award in Pedometrics, submissions must meet the following criteria:
- The paper must make a clear and significant contribution to pedometrics, either by advancing methodological or theoretical frameworks in pedometrics or by demonstrating novel and impactful applications of statistical methods in soil science. The work must display originality, innovation, and clear relevance to the pedometrics community.
- The paper must demonstrate methodological rigour, appropriate data analysis, reproducibility, and robustness of findings.
- The paper must be published in an international, peer-reviewed scientific journal. The official publication date must fall within the award year. Articles available online in early access or “in press” form, but not officially published in that year, are not eligible.
- The award is primarily intended for early- to mid-career researchers, defined as individuals with no more than 10 years since their first peer-reviewed publication or PhD graduation. In the case of multi-authored papers, it is preferred that the lead (first) author fulfils this criterion.
- Clear communication of complex methods and findings in a way accessible to a broad soil science audience is highly valued.
Nominations and eligibility:
- Authors are not encouraged to self-nominate their own papers for consideration.
- Members of the award committee are ineligible to have their own papers selected as the winning paper if they are the lead author.
List of previous best papers
1992 – Tang, H., Van Ranst, E., 1992. Testing of fuzzy set theory in land suitability assessment for rainfed maize production. Pedologie 17, 129-147
1993 – Rasiah, V., Kay, B.D., Perfect, E., 1993. New mass-based model for estimating fractal dimensions of aggregates. Soil Science Society of America Journal 57, 891-895.
1994 – Bierkens, M.F.P., Weerts, H.J.T., 1994. Application of indicator simulation to modelling the lithological properties of a complex layer. Geoderma 62, 265-284
1995 – Papritz, A., Webster, R., 1995. Estimating temporal change in soil monitoring : I Statistical theory. European Journal of Soil Science 46, 1-12.
1996 – Brus, D.J., De Gruijter, J.J., Marsman, B.A., Visschers, R., Bregt, A.K., Breeuwsma, A., Bouma, J., 1996. The performance of spatial interpolation methods and chropleth maps to estimate properties at points: A soil survey case study. Environmetrics 7, 1-16.
1997 – De Gruijter, J.J., Walvoort, D.J.J.,d Van Gans, P.F.M.,, 1997. Continuous soil maps – a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models. Geoderma 77, 169-195.
1998 – Boucneau, G., Van Meirvenne M., Thas, O., Hofman, G., 1998. Integrating properties of soil map delineations into ordinary kriging. European Journal of Soil Science 49, 213-229.
1999 – Lark, R.M., Webster, R., 1999. Analysis and elucidation of soil variation using wavelets. European Journal of Soil Science 50, 185-206
2000 – Goovaerts, P., 2000. Estimation or simulation of soil properties? An optimization problem with conflicting criteria. Geoderma 3-4, 165-186.
2001 – Minasny, B. and McBratney, A.B., 2001. A rudimentary mechanistic model for soil production and landscape development II. A two-dimensional model incorporating chemical weathering. Geoderma 103, 161-179.
2002 – Bogaert, P., D’Or, D., 2002. Estimating soil properties from thematic maps: The Bayesian Maximum Entropy approach. Soil Science Society of America Journal 66, 1492-1500
2003 – Walter,C., R.A. Viscarra Rossel and A.B. McBratney, 2003. Spatio-temporal simulation of the field-scale evolution of organic carbon over the landscape. Soil Sci. Soc. Am. J. 67, 1477–1486.
2004 – Finke, P.A., D.J. Brus, M.F.P. Bierkens, T. Hoogland, M. Knotters and F. de Vries, 2004. Mapping groundwater dynamics using multiple sources of exhaustive high resolution data. Geoderma 123, 23–39.
2005 – Savelieva, E., Demyanov, V., Kanevski, M., Serre, M., Christakos, G., 2005. BME-based uncertainty assessment of the Chernobyl fallout. Geoderma, 128(3), pp.312-324.
2006 – Heuvelink G.B.M., Schoorl J.M., Veldkamp A., Pennock D.J. Space-time Kalman filtering of soil redistribution. Geoderma 133, 124-137.
2007 – Viscarra Rossel, R.A., Taylor, H.J. & McBratney, A.B., 2007. Multivariate calibration of hyperspectral gamma-ray energy spectra for proximal soil sensing. European Journal of Soil Science 58, 343–353.
2008 – Grinand, C., Arrouays, D., Laroche, B. and Martin, M.P., 2008. Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context. Geoderma 143(1-2), 180–190.
2009 – Marchant, B.P. , S. Newman, R. Corstanje, K.R. Reddy, T.Z. Osborne & R.M. Lark. 2009. Spatial monitoring of a non-stationary soil property: Phosphorus in a Florida water conservation area. European Journal of Soil Science 60, 757– 769.
2010 – Marchant, B.P., Saby, N.P.A., Lark, R.M., Bellamy, P.H., Jolivet, C.C., Arrouays, D., 2010. Robust analysis of soil properties at the national scale: cadmium content of French soils. European Journal of Soil Science, 61(1), pp.144-152.
2011 – D.J. Brus and J.J. de Gruijter: Design-based Generalized Least Squares estimation of status and trend of soil properties from monitoring data. Geoderma164,172–180.
2012 – R.M. Lark: Towards soil geostatistics. Spatial Statistics 1,92–98
2013 – Mulder, V.L., Plötze, M., de Bruin, S., Schaepman, M.E., Mavris, C., Kokaly, R.F., Egli, M., 2013. Quantifying mineral abundances of complex mixtures by coupling spectral deconvolution of SWIR spectra (2.1–2.4 μm) and regression tree analysis. Geoderma 207–208, 279–290.
2014 – Odgers, N. P., Sun, W., McBratney, A. B., Minasny, B., Clifford, D., 2014. Disaggregating and harmonising soil map units through resampled classification trees. Geoderma, 214, 91-100.
2015 – Orton, T.G., Pringle, M.J., Bishop, T.F.A., 2016. A one-step approach for modelling and mapping soil properties based on profile data sampled over varying depth intervals. Geoderma, 262, 174–186.
2016 – Viscarra Rossel, R.A., T. Behrens et al., 2016. A global spectral library to characterize the world’s soil. Earth-Science Reviews 155, 198–230.
2016 – Poggio, L., Gimona, A., Spezia, L., & Brewer, M.J,. 2016. Bayesian spatial modelling of soil properties and their uncertainty: The example of soil organic matter in Scotland using R-INLA. Geoderma, 277, 69–82.
2017 – Angelini, M. E., Heuvelink, G. B. M. and Kempen, B. (2017). Multivariate mapping of soil with structural equation modelling. European Journal of Soil Science, 68(5), 575–591
2018 –
2019 – Chen, S, Mulder VL, Martin MP, Walter C, Lacoste M, Richer-de Forges AC, Saby NPA, Loiseau T, Hu B, Arrouays D. 2019. Probability mapping of soil thickness by random survival forest at a national scale. Geoderma, 344, 184 – 194. https://doi.org/10.1016/j.geoderma.2019.03.016
2020 – Liu, F., Rossiter, D.G., Zhang, G-L., Li, D.C. 2019. A soil colour map of China. Geoderma, 379, 114556. https://doi.org/10.1016/j.geoderma.2020.114556
2021 – Bennett, J.M., Roberton, S.D., Ghahramani, A. and McKenzie, D.C., 2021. Operationalising soil security by making soil data useful: Digital soil mapping, assessment and return-on-investment. Soil Security, 4, p.100010. https://doi.org/10.1016/j.soisec.2021.100010
2022 – Nenkam, A.M., Wadoux, A.M.C., Minasny, B., McBratney, A.B., Traore, P.C., Falconnier, G.N. and Whitbread, A.M., 2022. Using homosoils for quantitative extrapolation of soil mapping models. European Journal of Soil Science, 73(5), p.e13285. https://doi.org/10.1111/ejss.13285
2023 – 2023: Padarian, J. e McBratney, A.B., 2023. QuadMap: Variable resolution maps to better represent spatial uncertainty. Computers & Geosciences, 181, p.105480. https://doi.org/10.1016/j.cageo.2023.105480