Papers by Budiman Minasny
Water-stable soil aggregates are generally formed following the addition of organic materials in ... more Water-stable soil aggregates are generally formed following the addition of organic materials in soils. This process is mediated by the interaction between microbes and soil organic matter in ways that are still not completely understood. To get insight into the effects of decomposing plant residues on aggregate dynamics, a clay soil with an inherently low soil organic carbon (SOC) content, was amended with two different sources of organic matter (alfalfa, C:N = 16.7 and barley straw, C:N = 95.6) at different input levels (0, 10, 20, & 30 g C kg À1 soil). These were incubated for a period of 3 months over which soil respiration was assessed using the NaOH capture method, water aggregate stability was determined with the mean weight diameter (MWD) by wet sieving, and the relative strength of aggregates exposed to ultrasonic agitation was modelled using the aggregate disruption characteristic curve (ADCC) and soil dispersion characteristic curve (SDCC). As expected, the quality and quantity of organic matter added controlled the respiration rate, with alfalfa (0.457 g CO 2 À ÀC g À1 C for total respiration rate) being greater than barley amended samples (0.178 g CO 2 À ÀC g À1 C) at any C input rate. Both residue quality and quantity of organic matter input also influenced the amount of aggregates formed and their relative strength. The MWD of soils amended with alfalfa residues was greater than that of barley straw at lower input rates and early in the incubation (e.g. at 28 days of incubation and at a rate of 10 g C kg À1 soil, MWD was 575 mm and 731 mm for barley straw and alfalfa, respectively). However, in the longer term (84 days of incubation), the use of ultrasonic energy revealed that barley straw resulted in stronger aggregates, especially at higher input rates despite showing similar MWD as alfalfa. The use of ultrasonic agitation, where we quantify the energy required to liberate and disperse aggregates allowed us to differentiate the effects of C inputs on the size of stable aggregates and their relative strength.
We present an alternative equation for estimating carbon density of tropical peatlands. We compil... more We present an alternative equation for estimating carbon density of tropical peatlands. We compiled a dataset of tropical peatlands with various land uses and found that when carbon content is greater than 0.5 g g −1 , there is no relationship between carbon content and bulk density. Thus, carbon density can be estimated from an average carbon content (0.5501 ± 0.0225 g g −1) multiplied by the measured bulk density. This simple model is in contrast with previous studies, where a linear regression model is fitted to the carbon density and bulk density data. We tested the model to the data and demonstrated its high accuracy and applicability across land uses.
Tropical peatlands have an important role in the global carbon cycle. In order to quantify carbon... more Tropical peatlands have an important role in the global carbon cycle. In order to quantify carbon stock for peatland management and conservation, the knowledge of the spatial distribution of peat and its depth is essential. This paper proposed a cost-effective and accurate methodology for mapping peat depth and carbon stocks in Indonesia. The method, based on the scorpan spatial soil prediction function framework, was tested in Ogan Komering Ilir, South Sumatra and Katingan, Central Kalimantan. A peat hydrological unit, where a peatland is bounded by at least two rivers, is defined as the mapping area or extent. Peat depth is modelled as a function of topography and spatial position. Four machine learning models were evaluated to model and map peat depth: Cubist regression tree, Random Forests (RF), Quantile Regression Forests (QRF) and Artificial Neural Network (ANN). Covariates representing topography and spatial position were derived from the 1 arc-second digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) (resolution of 30.7 m). The spatial models were calibrated from field observations. For model calibration and uncertainty analysis, the k-fold cross validation approach was used. Three models: Cubist, Random Forests, and Quantile Regression Forests models showed excellent accuracies of peat depth prediction for both areas where the coefficient of determination values range from 0.67 to 0.92 and root mean squared error (RMSE) values range from 0.6 to 1.1 m. ANN showed inferior results. In addition, QRF and Cubist showed the best account of the uncertainty of prediction, in terms of percentage of observations that fall within the defined 90% confidence interval. In terms of the best predictor, elevation comes first. Using the spatial prediction functions, peat depth maps along with their 90% confidence interval were generated. The estimated mean carbon stock for Ogan Komering Ilir is 0.474 Gt and for Katingan is 0.123 Gt. Our estimate for Ogan Komering Ilir is twice larger than a previous study because we mapped the peatland hydrological unit, while the previous study only delineated peat domes. Finally, we recommend a sampling method for peat depth mapping using numerical stratification of elevation to cover both the geographical and covariate space. We expect that the combination of an improved sampling strategy, machine learning models, and kriging will increase the accuracy of peat depth mapping.
Digital soil mapping (DSM) is a successful sub discipline of soil science with an active research... more Digital soil mapping (DSM) is a successful sub discipline of soil science with an active research output. The success of digital soil mapping is a confluence of several factors in the beginning of 2000 including the increased availability of spatial data (digital elevation model, satellite imagery), the availability of computing power for processing data, the development of data-mining tools and GIS, and numerous applications beyond geostatistics. In addition, there was an increased global demand for spatial data including uncertainty assessments, and a rejuvenation of many soil survey and university centres which helped in the spreading of digital soil mapping technologies and knowledge. The theoretical framework for digital soil mapping was formalised in a 2003 paper in Geoderma. In this paper, we define what constitutes digital soil mapping, sketch a brief history of it, and discuss some lessons. Digital soil mapping requires three components: the input in the form of field and laboratory observational methods, the process used in terms of spatial and non-spatial soil inference systems, and the output in the form of spatial soil information systems, which includes outputs in the form of rasters of prediction along with the uncertainty of prediction. We also illustrate the history with a number of sleeping beauty papers that seem too precocious and consequently the ideas were not taken up by contemporaries and largely forgotten. It took another 30 to 40 years before the ideas were rediscovered and then flourished. Examples include proximal soil sensing that was developed in the 1920s, soil spectroscopy in 1970s, and soil mapping based on similarity of environmental factors in 1979. In summary, the coming together of emerging topics and timeliness greatly assists in the development of paradigm. We learned that research and ideas that are too precocious are largely ignored — such work warrants (re)discovery.
Soil is central in the terrestrial ecosystem, linking and providing feedback responses to the oth... more Soil is central in the terrestrial ecosystem, linking and providing feedback responses to the other components, i.e., water, atmosphere, and vegetation. However, the role of soil in landscape evolution is usually not well acknowledged. In modeling landscape evolution, soil is only treated as a residue of weathering that is transported and redistributed along the hillslope. Weathering is considered as a process that produces clays and generates unconsolidated materials available for erosion. While pedology has been debating the form of qualitative factorial models for 75 years; models for soil water, heat, solute, gas and chemical reactions in a profile have matured. As soils are distributed continuously in three dimensions across landscapes, the profile models need to consider lateral fluxes. This review outlines the role of soil in landscape modeling. First, we review the role of soil in the current landscape evolution models. We then review data and models on soil weathering rates and transport processes. We discuss soil profile models that simulate soil formation processes, and combined soil–landscape evolution models. Finally we discuss how the models can be tested and validated in the real world and suggest how both soil scientists and landscape modelers can work together to address the grand challenges in modeling earth surface processes.
A soil hydraulic model that considers capillary hysteretic and adsorptive water retention as well... more A soil hydraulic model that considers capillary hysteretic and adsorptive water retention as well as capillary and film conductivity covering the complete soil moisture range is presented. The model was obtained by incorporating the capillary hysteresis model of Parker and Lenhard into the hydraulic model of Peters-Durner-Iden (PDI) as formulated for the van Genuchten (VG) retention equation. The formulation includes the following processes: capillary hysteresis accounting for air entrapment, closed scanning curves, nonhysteretic sorption of water retention onto mineral surfaces, a hysteretic function for the capillary conductivity, a nonhysteretic function for the film conductivity, and a nearly nonhysteretic function of the conductivity as a function of water content (θ) for the entire range of water contents. The proposed model only requires two additional parameters to describe hysteresis. The model was found to accurately describe observed hysteretic water retention and conductivity data for a dune sand. Using a range of published data sets, relationships could be established between the capillary water retention and film conductivity parameters. Including vapor conductivity improved conductivity descriptions in the very dry range. The resulting model allows predictions of the hydraulic conductivity from saturation until complete dryness using water retention parameters.
We present a novel method (Ospats) to optimize spatial stratification and allocation for stratifi... more We present a novel method (Ospats) to optimize spatial stratification and allocation for stratified random sampling of points in the plane. Our quality criterion is the sampling variance under Neyman allocation given a sample size. The method uses a grid of points with uncertain predictions of the target variable. The difference with existing techniques is that we account for the uncertainties. From the quality criterion, we derive an objective function defined by generalized distances between pairs of grid points, determined by the difference between the predictions, the variances of the prediction errors, and their covariance as a function of the geographical distance. Iterative reallocation is used to minimize the function. Resulting stratifications typically represent solutions on a continuous scale between two extremes: for errorless predictions, a stratification close to those by the cum-root-f method, and for entirely uninformative or missing predictions, a compact geographical stratification based only on the locations of the grid points. From a simulation study, we conclude that Ospats performs as expected: the stratifications that it produces are more efficient than the two extreme solutions. A case study showed that the method can be successfully applied at farm scale. Extensions to larger-scales and 1D or 3D spaces are straightforward.
Soil water retention curves are an important parameter in soil hydrological modeling. These curve... more Soil water retention curves are an important parameter in soil hydrological modeling. These curves are usually represented by the van Genuchten model. Two approaches have previously been taken to predict curves across a field – interpolation of field measurements followed by estimation of the van Genuchten model parameters, or estimation of the parameters according to field measurements followed by interpolation of the estimated parameters. Neither approach is ideal as, due to their two-stage nature, they fail to properly track uncertainty from one stage to the next. In this paper we address this shortcoming through a spatial Bayesian hierarchical model that fits the van Genuchten model and predicts the fields of hydraulic parameters of the van Genuchten model as well as fields of the corresponding soil water retention curves. This approach expands the van Genuchten model to a hierarchical modeling framework. In this framework, soil properties and physical or environmental factors can be treated as covariates to add into the van Genuchten model hierarchically. Consequently, the effects of covariates on the hydraulic parameters of the van Genuchten model can be identified. In addition, our approach takes advantage of Bayesian analysis to account for uncertainty and overcome the shortcomings of other existing methods. The code used to fit these models are available as an appendix to this paper. We apply this approach to data surveyed from part of the alluvial plain of the river Rhône near Yenne in Savoie, France. In this data analysis, we demonstrate how the inclusion of soil type or spatial effects can improve the van Genuchten model’s predictions of soil water retention curves.
El proyecto GlobalSoilMap nace con el propósito de proveer información acerca de las propiedades ... more El proyecto GlobalSoilMap nace con el propósito de proveer información acerca de las propiedades del suelo, producidas en forma consistente, que
sirva de soporte para la toma de deciciones en temas medioambientales y sociales claves como seguridad alimentaria, cambio climático, degradación
de suelo y captura de carbono. El carboo orgánico es un componente clave de ecosistemas funcionales y es crucial para la seguridad alimentaria, de
agua y energÃa, por lo que es una de las propiedades a ser mapeadas en GlobalSoilMap. El objetivo de este trabajo es realizar una predicción espacial
del contenido de CO en el área de estudio, usando técnicas de mapeo digital de suelos, siguiendo las especificaciones del proyecto GlobalSoilMap.
Monitoring the spatial and temporal changes in soil organic carbon (SOC) brought about by climate... more Monitoring the spatial and temporal changes in soil organic carbon (SOC) brought about by climate change and agricultural practices is challenging because existing SOC monitoring methods are very time and resource consuming. This study examined the use of visible near-infrared spectroscopy (Vis-NIR) as a speedy method to predict SOC and to monitor spatial and temporal changes in SOC compared with labor-intensive traditional laboratory (TL) measurements. For SOC prediction, topsoil (0-25 cm) and subsoil (25-50 cm) samples in the Danish soil spectral library for the years 1986 and 2009 were used. Empirical Bayesian Kriging was used to map SOC. The Vis-NIR predictions indicated that average topsoil and subsoil SOC had decreased slightly in Denmark from 1986 to 2009, and this was confirmed by TL measurements of SOC. In East Denmark, Vis-NIR predictions differed significantly from the measured SOC values. For subsoil samples, the ability of Vis-NIR to predict SOC levels varied. In West Jutland, Central Jutland, North Jutland, and Thy, Vis-NIR-predicted SOC levels did not differ from TL-measured levels, showing good predictive ability. For topsoil samples, the spatial pattern of change in TLmeasured and predicted SOC was consistent during the 23-year study period, but there were significant discrepancies in the corresponding SOC change patterns for subsoil samples. To conclude, Vis-NIR is a promising method for monitoring spatial and temporal changes in SOC at the national scale, especially in the topsoil. Some difficulties can arise in low SOC subsoils, so more systematic work is needed to improve the method for practical applications.
Using Vis-NIR Spectroscopy for Monitoring Temporal Changes in Soil Organic Carbon
Citation metrics and h indices differ using different bibliometric databases. We compiled the num... more Citation metrics and h indices differ using different bibliometric databases. We compiled the number of publications, number of citations, h index and year since the first publication from 340 soil researchers from all over the world. On average, Google Scholar has the highest h index, number of publications and citations per researcher, and the Web of Science the lowest. The number of papers in Google Scholar is on average 2.3 times higher and the number of citations is 1.9 times higher compared to the data in the Web of Science. Scopus metrics are slightly higher than that of the Web of Science. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus is on average 1.1 times larger than Web of Science. Over time, the metrics increase in all three databases but fastest in Google Scholar. The h index of an individual soil scientist is about 0.7 times the number of years since his/her first publication. There is a large difference between the number of citations, number of publications and the h index using the three databases. From this analysis it can be concluded that the choice of the database affects widely-used citation and evaluation metrics but that bibliometric transfer functions exist to relate the metrics from these three databases. We also investigated the relationship between journal's impact factor and Google Scholar's h5-index. The h5-index is a better measure of a journal's citation than the 2 or 5 year window impact factor.
Arid Land Research & Management, 2014
There is a demand for soil spatial information for improving natural resource management outcomes... more There is a demand for soil spatial information for improving natural resource management outcomes through the development of soil suitability maps. In response to this demand, Digital Soil Mapping (DSM) techniques have been proposed as an efficient way to deliver soil information. DSM involves acquisition of field soil observations and matching them with environmental variables that can explain the distribution of soils. The harmonization of these data sets, through computer-based methods, are increasingly being found to be as reliable as traditional soil mapping practices, but without the prohibitive costs. Therefore, the present research developed decision tree models for spatial prediction of soil classes in a 720 km 2 area located in an arid region of central Iran, where traditional soil survey methods are difficult to undertake. Using the conditioned Latin hypercube sampling method, the location of 187 soil profiles were selected, which were then described, sampled, analysed and allocated to six great groups and eight sub-great groups according to the USDA Soil Taxonomy classification. Auxiliary data representing the soil forming factors were derived from a digital elevation model (DEM), Landsat 7 ETM + images and a map of geomorphology. The accuracy of the decision tree models was evaluated using overall, user and producer accuracy based on an independent validation data set. Our results showed some auxiliary variables had more influence on the prediction of soil classes which included: topographic wetness index, geomorphological map, multiresolution index of valley bottom flatness, elevation and principal components of Landsat 7 ETM + images. Furthermore, the results have confirmed the DSM model successfully predicted great groups and sub-great groups with overall accuracy up to 86%. Our results suggest that the developed methodology could be used to predict soil classes in the arid region of Iran.
Equivalent soil mass (ESM) methods have been increasingly used for assessing soil OC masses in pl... more Equivalent soil mass (ESM) methods have been increasingly used for assessing soil OC masses in place of "fixed depth" assessments. In fixed depth assessments, soil OC is evaluated in fixed depth increments (e.g., 0-0.1, 0.1-0.2, and 0.2-0.3 m). pointed out that such assessments are invalid when soil bulk density differences exist between treatments or over monitoring time periods. This is because the soil mass (calculated as the product of the bulk density and depth, and expressed in units of mass per unit area such as Mg m -2 or Mg ha -1 ) will differ when bulk density differs. Comparisons of soil C masses in different soil masses are not valid. For example, for two soils with bulk densities of 1.0 and 1.2 Mg m -2 and sampled to 0.10 m, one would be comparing soil C masses in 1000 Mg ha -1 in one soil vs. soil C masses in 1200 Mg ha -1 in the other soil. This comparison will always result in a relative over-assessment of soil C in the higher bulk density soil, because it contains a greater soil mass. The premise of ESM comparisons is that changes in OC masses that occur over time can be accurately assessed if one compares OC masses in the same (or equivalent) soil masses at all sampling times. Equivalent soil mass (ESM) methods address errors in fixed depth assessments by correcting for bulk density differences at various sampling times, such that all comparisons are made in the same, or equivalent, soil mass.
that it is in fact just inversely related to R 2 . RPD = Sd/SEP, and R 2 = 1 -SS res /SS tot wher... more that it is in fact just inversely related to R 2 . RPD = Sd/SEP, and R 2 = 1 -SS res /SS tot where SEP = standard error of prediction, which is
Journal of Geophysical Research - Earth Surface
Soil Science Society of America Journal, Apr 2013
We describe in this paper, a broad overview of spatial scale concepts and scaling procedures that... more We describe in this paper, a broad overview of spatial scale concepts and scaling procedures that are specifically relevant for digital soil mapping (DSM). Despite the recent growth and operational status of DSM, one existing and foreseeably growing issue for users of digital soil information is the inequality of spatial scales between what is required and what is actually available to adequately address soil-related questions posed from within and from outside the soil science community. In the absence of conducting new soil survey or not being able to acquire the original legacy soil information (soil point data) as a means of creating user-specified soil information products, spatial scaling provides a useful solution. Spatial scaling for DSM involves changes in map extent, grid-cell resolution, and prediction support. We review in this paper the different forms of spatial scaling, which are described in terms of changes to grid spacing and prediction support. Fine-gridding and coarse-gridding are operations where the grid spacing changes but support remains unchanged. Deconvolution and convolution are operations where the support always changes which may or may not involve changing the grid spacing. While disseveration and conflation operations occur when the support and grid size are equal and both are then changed equally and simultaneously. Some possible and existing pedometric methods are described for implementation of each scaling process, as is an extended example for performing convolution where the support changes yet the resolution remains the same.
Uploads
Papers by Budiman Minasny
sirva de soporte para la toma de deciciones en temas medioambientales y sociales claves como seguridad alimentaria, cambio climático, degradación
de suelo y captura de carbono. El carboo orgánico es un componente clave de ecosistemas funcionales y es crucial para la seguridad alimentaria, de
agua y energÃa, por lo que es una de las propiedades a ser mapeadas en GlobalSoilMap. El objetivo de este trabajo es realizar una predicción espacial
del contenido de CO en el área de estudio, usando técnicas de mapeo digital de suelos, siguiendo las especificaciones del proyecto GlobalSoilMap.