Vote for Best Paper in Pedometrics 2007

Nominations have now been made for the title, “Best Paper in Pedometrics 2007”. All members of IUSS were invited to submit nominations (in Pedometron 23, page 3 and on the Pedometrics website). The response was less than overwhelming, and so we asked a senior pedometrician to nominate five papers, as has been done in previous years. We are grateful that Richard Webster agreed to nominate papers this time. His nominations are listed below, in alphabetical order by the first author’s name.

By kind agreement of the Journals involved, these papers are made available on the web free of charge until the end of 2008, and URLs are given by each. The Journals have also agreed that we may reproduce the abstracts of these papers. We give links to PDF full-text versions, but if you visit the Journal websites you will also find that the HTML versions are available (apart from paper 1).

We urge all members of IUSS to examine these papers, and to participate in the vote. To vote send an email to b dot minasny at usyd dot edu dot au with “Best Paper 2007” in the subject line. You should indicate your order of preference for all five papers. Any unambiguous notation is acceptable, it would be easiest if you simply list the papers by number (as below) in order of preference, with the paper you regard as the most worthy winner listed first.

The vote will end at midnight (Sydney time) on 30th November 2008, and the result will be announced in Pedometron. Certificates will be presented at Pedometrics 2009 in Beijing.

As an incentive to participate, the Chair of Pedometrics has been rummaging in the skip of the Rothamsted Library again. The names of voters will be randomly sampled without replacement to identify two winners. The first name to be drawn will be awarded a (second hand but good quality) copy of a classic text: Journel and Huijbregts’s Mining Geostatistics. Second prize (but equally classic) is Clark’s Practical Geostatistics. So ensure that your postal address is on your vote.

Murray Lark
The Chair of Pedometrics

Nominations for “Best Paper in Pedometrics 2007”.

1. Kerry, R. & Oliver, M.A. 2007. The analysis of ranked observations of soil structure using indicator geostatistics. Geoderma, 140, 397–416. PDF File

Structure is an important physical feature of the soil that is associated with water movement, the soil atmosphere, microorganism activity and nutrient uptake. A soil without any obvious organisation of its components is known as apedal and this state can have marked effects on several soil processes. Accurate maps of topsoil and subsoil structure are desirable for a wide range of models that aim to predict erosion, solute transport, or flow of water through the soil. Also such maps would be useful to precision farmers when deciding how to apply nutrients and pesticides in a site-specific way, and to target subsoiling and soil structure stabilization procedures. Typically, soil structure is inferred from bulk density or penetrometer resistance measurements and more recently from soil resistivity and conductivity surveys. To measure the former is both time-consuming and costly, whereas observations made by the latter methods can be made automatically and swiftly using a vehicle-mounted penetrometer or resistivity and conductivity sensors. The results of each of these methods, however, are affected by other soil properties, in particular moisture content at the time of sampling, texture, and the presence of stones. Traditional methods of observing soil structure identify the type of ped and its degree of development. Methods of ranking such observations from good to poor for different soil textures have been developed. Indicator variograms can be computed for each category or rank of structure and these can be summed to give the sum of indicator variograms (SIV). Observations of the topsoil and subsoil structure were made at four field sites where the soil had developed on different parent materials. The observations were ranked by four methods and indicator and the sum of indicator variograms were computed and modelled for each method of ranking. The individual indicators were then kriged with the parameters of the appropriate indicator variogram model to map the probability of encountering soil with the structure represented by that indicator. The model parameters of the SIVs for each ranking system were used with the data to krige the soil structure classes, and the results are compared with those for the individual indicators. The relations between maps of soil structure and selected wavebands from aerial photographs are examined as basis for planning surveys of soil structure.

2. Lark, R.M. 2007. Inference about soil variability from the structure of the best wavelet packet basis. European Journal of Soil Science, 58, 822–831. [.pdf]

The plausibility of the assumption that soil variation can be treated as a realization of a random spatial process that is stationary in the variance can break down in various ways. It is possible to test the assumption using methods based on the wavelet transform. To date these approaches have been applied using the discrete wavelet transform. A drawback of this approach is that it uses a partition of the spatial frequencies represented in the data into intervals (scales) that are arbitrarily defined in advance and are not necessarily suitable for the representation of the variation of the data in question. A solution to this problem is to identify the best basis for the data from a wavelet packet library. An interesting question is whether the structure of this best basis is in itself informative about the plausibility of the stationarity assumption. In this paper, I show that this is indeed the case. The best basis for a stationary random variable from some packet library is the basis on the maximum dilation of the mother wavelet, which gives the finest resolution in the frequency domain. I propose the ratio of the entropy cost functional for this basis to that of the empirical best basis as a measure of evidence against the null hypothesis of stationarity in the variance. Critical values of this statistic may be obtained by Monte Carlo methods. I demonstrate the method using data on the clay content of soil on a transect in central England. The null hypothesis of stationarity in the variance may be rejected. Tests for the uniformity of variance can then be applied to wavelet packets in the best basis. The dominant local feature that is reflected in this behaviour is the unique pattern of variation in alluvium around a drainage channel that crosses the transect. This variation contrasts with that seen at most positions on the transect, variation that arises from a more or less regular pattern of boundaries between contrasting Jurassic strata.

3. Li, W. 2007. Transiograms for characterizing spatial variability of soil classes. Soil Science Society of America Journal, 71, 881–893. [.pdf]

The characterization of complex autocorrelations and interclass relationships among soil classes call for effective spatial measures. This study developed a transition probability-based spatial measure— the transiogram—for characterizing spatial heterogeneity of discrete soil variables. The study delineated the theoretical foundations and fundamental properties, and explored the major features of transiograms as estimated using different methods and data types, as well as challenges in modeling experimental transiograms. The specifi c objectives were to: (i) provide a suitable spatial measure for characterizing soil classes; (ii) introduce related knowledge for understanding spatial variability of soil types described by transiograms; and (iii) suggest methods for estimating and modeling transiograms from sparse sample data. Case studies show that (i) cross-transiograms are normally asymmetric and unidirectionally irreversible, which make them more capable of heterogeneity characterization, (ii) idealized transiograms are smooth curves, of which most are exponential and some have a peak in the low-lag section close to the origin, (iii) real-data transiograms are complex and usually have multiple ranges and irregular periodicities, which may be regarded as “non-Markovian properties” of the data that cannot be captured by idealized transiograms, and (iv) experimental transiograms can be approximately fi tted using typical mathematical models, but sophisticated models are needed to effectively fi t complex features. Transiograms may provide a powerful tool for measuring and analyzing the spatial heterogeneity of soil classes.

4. Viscarra Rossel, R.A., Taylor, H.J. & McBratney, A.B. 2007. Multivariate calibration of hyperspectral g-ray energy spectra for proximal soil sensing. European Journal of Soil Science, 58, 343–353. [.pdf]

The development of proximal soil sensors to collect fine-scale soil information for environmental monitoring, modelling and precision agriculture is vital. Conventional soil sampling and laboratory analyses are time-consuming and expensive. In this paper we look at the possibility of calibrating hyperspectral g-ray energy spectra to predict various surface and subsurface soil properties. The spectra were collected with a proximal, on-the-go g-ray spectrometer. We surveyed two geographically and physiographically different fields in New South Wales, Australia, and collected hyperspectral g-ray data consisting of 256 energy bands at more than 20 000 sites in each field. Bootstrap aggregation with partial least squares regression (or bagging-PLSR) was used to calibrate the g-ray spectra of each field for predictions of selected soil properties. However, significant amounts of pre-processing were necessary to expose the correlations between the g-ray spectra and the soil data. We first filtered the spectra spatially using local kriging, then further de-noised, normalized and detrended them. The resulting bagging-PLSR models of each field were tested using leave-one-out cross-validation. Bagging-PLSR provided robust predictions of clay, coarse sand and Fe contents in the 0–15 cm soil layer and pH and coarse sand contents in the 15–50 cm soil layer. Furthermore, bagging-PLSR provided us with a measure of the uncertainty of predictions. This study is apparently the first to use a multivariate calibration technique with on-the-go proximal g-ray spectrometry. Proximally sensed g-ray spectrometry proved to be a useful tool for predicting soil properties in different soil landscapes.

5. Weller, U., Zipprich, M., Sommer, M., Zu Castell, W. & Wehrhan, M. 2007. Mapping clay content across boundaries at the landscape scale with electromagnetic induction. Soil Science Society of America Journal, 71, 1740–1747. [.pdf]

Detailed information on soil textural heterogeneity is essential for land management and conservation. It is well known that in individual fields, measurement of the soil’s apparent electrical conductivity (ECa) offers an opportunity to map the clay content of soils with free drainage under a humid climate. At the catchment scale, however, units of different land management and differing sampling dates add variation to ECa and constrain the mapping across field boundaries. We analyzed their influence and compared three approaches for applying electromagnetic induction (EMv) to clay-content mapping at the landscape scale across the boundaries of individual fields and different sampling dates. In the study region, a separate calibration of the relation between clay and ECa for each field and sampling date (fieldwise calibration) yielded satisfactory clay-content predictions only if the costly precondition of sufficient calibration points for each field was fulfilled. We propose a method (nearest- neighbors ECa correction) for unifying ECa across boundaries based only on the ECa data themselves, and the assumption of continuity of textural properties at field boundaries, which was fulfilled in the landscape studied. Prediction is calibrated once for the entire landscape, which allows a reduced set of calibration points. The coefficient of determination for predicting clay content (here, including silt <4 ?m) was improved from R2 = 0.66 (no correction for land use and sampling date) to R2 = 0.85 (n = 46). With the method developed, ECa offers a powerful and cheap method of clay-content mapping in agricultural landscapes.

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