[AACS] Nominaciones - Best Paper in Pedometrics 2016

Vie Mayo 26 20:20:39 ART 2017

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* <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 (25
thanniversary 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]
<https://mail.google.com/mail/u/0/#m_8095133275538495468_m_-7997396037533001242__ftn1>
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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