[AACS] Nominaciones - Best Paper in Pedometrics 2016

Jue Jun 1 13:46:57 ART 2017

Felicitaciones!!
Muchas gracias por la información.
saludos cordiales
Silvia
  ----- Original Message ----- 
  From: Héctor J. M. Morrás 
  To: Lisaacs en listas.agro.uba.ar 
  Sent: Friday, May 26, 2017 8:20 PM
  Subject: [AACS] Nominaciones - Best Paper in Pedometrics 2016

  Estimados colegas,

  tengo el placer de hacerles saber que dos trabajos en los que algunos miembros del Instituto de Suelos-INTA hemos participado, han sido seleccionados como los mejores artículos para el premio: 
  Pedometrics Award: Best Paper in Pedometrics 2016.

  Sobre los ocho trabajos nominados para dicho premio, los dos trabajos referidos son los de:

  -Viscarra Rossel, R.A., T. Behrens et al. (2016). 

  -Angelini, M. E., Heuvelink, G. B. M., Kempen, B., & Morrás, H.J.M. 

  (2016)

  Aquí a continuación agrego la comunicación sobre los ocho trabajos seleccionados, en lo que se incluye la dirección para acceder a los textos completos. Asímismo, incluyo los Abstracts de nuestros dos trabajos nominados.

  Como verán, hasta el 15 de junio también es posible votar por el mejor artículo.

  Cordiales saludos,

  Héctor Morrás

  ---------- Forwarded message ----------

  Vote for the Best Paper in Pedometrics 2016

  D G Rossiter, Chairman Pedometrics Awards Committee

  Pedometrics commission of the International Union of Soil Sciences

  e-mail: dgr2 en cornell.edu                  04-May-2017

  Dear fellow Pedometricians,

  The Pedometrics Awards committee for the best paper award (Grunwald, McBratney, Oliver, Rossiter, Yang) received a strong response to our call for nominations: 26 papers spread over sixteen journals. These were scored by the committee. Because of the large number of excellent submissions we’ve decided to present the top eight papers for your reading pleasure and evaluation. These are a good mix of pedometrics: novel and low-cost proximal sensing, a global spectral library, landscape complexity using spatial adjacency graphs, Bayesian spatial modelling, boundary-line analysis, structural equation modelling for digital soil mapping, and sampling optimization. Reading these papers will bring you up-to-date on some of the most exciting developments in pedometrics published in 2016.

  Both the 2015 and this 2016 awards will be presented at Pedometrics 2017 (25thanniversary of the first Pedometrics conference) in Wageningen (NL) 26 June through 1 July 2017 (see information at http://www.pedometrics2017.org).Please send in your votes for the best paper 2016 by 15-June-2017.

  Please rank the papers in the “instant runoff” system: first choice, second choice, etc. up till the last paper you are willing to vote for, i.e., the last paper that you think would deserve the award. Votes should then be sent to me from a traceable e-mail address (to prevent over-voting). I will apply the instant runoff system[1] to determine the winner. A co-author may not vote for her/his own paper(s). What defines “best”? It’s up to you to decide, but I think the best paper should be the one that most advances pedometrics.

  The papers are listed here in order of DOI.

  Lark, R. M., & Milne, A. E. (2016). Boundary line analysis of the effect of water-filled pore space on nitrous oxide emission from cores of arable soil. European Journal of Soil Science, 67(2), 148–159. https://doi.org/10.1111/ejss.12318

  Lobsey, C. R., & Viscarra Rossel, R. A. (2016). Sensing of soil bulk density for more accurate carbon accounting. European Journal of Soil Science, 67(4), 504–513.https://doi.org/10.1111/ejss.12355

  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. https://doi.org/10.1016/j.earscirev.2016.01.012

  Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible–near infrared (vis–NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of.

  Phillips, J. D. (2016). Identifying sources of soil landscape complexity with spatial adjacency graphs. Geoderma, 267, 58–64. https://doi.org/10.1016/j.geoderma.2015.12.019

  Musafer, G. N., & Thompson, M. H. (2016). Optimal adaptive sequential spatial sampling of soil using pair-copulas. Geoderma, 271, 124–133.https://doi.org/10.1016/j.geoderma.2016.02.018

  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. https://doi.org/10.1016/j.geoderma.2016.04.026

  Angelini, M. E., Heuvelink, G. B. M., Kempen, B., & Morrás, H.J.M. 

  (2016). Mapping the soils of an Argentine Pampas region using structural equation modelling.Geoderma, 281, 102–118. https://doi.org/10.1016/j.geoderma.2016.06.031

  Current digital soil mapping (DSM) methods have limitations. For instance, it is difficult to predict a large number of soil properties simultaneously, while preserving the relationships between them. Another problem is that prevalent prediction models use pedological knowledge in a very crude way only. To tackle these problems, we investigated the use of structural equation modelling (SEM). SEM has its roots in the social sciences and is recently also being used in other scientific disciplines, such as ecology. SEM integrates empirical information with mechanistic knowledge by deriving the model equations from known causal relationships, while estimating the model parameters using the available data. It distinguishes between endogenous and exogenous variables, where, in our application, the first are soil properties and the latter are external soil forming factors (i.e. climate, relief, organisms). We introduce SEM theory and present a case study in which we applied SEM to a 22,900 km2 region in the Argentinian Pampas to map seven key soil properties. In this case study, we started with identifying the main soil forming processes in the study area and assigned for each process the main soil properties affected. Based on this analysis we defined a conceptual soil-landscape model, which was subsequently converted to a SEM graphical model. Finally, we derived the SEM equations and implemented these in the statistical software R using the latent variable analysis (lavaan) package. The model was calibrated using a soil dataset of 320 soil profile data and 12 environmental covariate layers. The outcomes of the model were maps of seven soil properties and a SEM graph that shows the strength of the relationships. Although the accuracy of the maps, based on cross-validation and independent validation, was poor, this paper demonstrates that SEM can be used to explicitly include pedological knowledge in prediction of soil properties and modelling of their interrelationships. It bridges the gap between empirical and mechanistic methods for soil-landscape modelling, and is a tool that can help produce pedologically sound soil maps.

  Lark, R. M. (2016). Multi-objective optimization of spatial sampling. Spatial Statistics,18, Part B, 412–430. https://doi.org/10.1016/j.spasta.2016.09.001

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