The Experts below are selected from a list of 254931 Experts worldwide ranked by ideXlab platform
Alexander B. Mcbratney - One of the best experts on this subject based on the ideXlab platform.
-
DIGISoil: An Integrated System of Data Collection Technologies for Mapping Soil Properties
Proximal Soil Sensing, 2010Co-Authors: Gilles Grandjean, R Maftei, G. Richard, Alexander B. Mcbratney, A. Stevens, Florence Carre, Isabelle Cousin, Thomas Hermann, Olivier Cerdan, M Thornelof, Alain Tabbagh, Sandro Moretti, P. Lagacherie, Leandro Chiarantini, Bas Van Wesemael, Sébastien Lambot, Eyal Ben-dorAbstract:The multidisciplinary DIGISoil consortium intends to integrate and improve in situ proximal measurement technologies for assessing Soil Properties and Soil degradation indicators, moving from the sensing technologies themselves to their integration and application in (digital) Soil mapping (DSM). The core objective of the project is to explore and exploit new capabilities of advanced geophysical technologies for answering this societal demand. To this aim, DIGISoil addresses four issues covering technological, Soil science, and economic aspects: (i) development and validation of hydrogeophysical technologies and integrated pedogeophysical inversion techniques; (ii) the relation between geophysical parameters and Soil Properties; (iii) the integration of derived Soil Properties for mapping Soil functions and Soil threats; and (iv) the evaluation, standardisation, and industrialisation of the proposed methodologies, including technical and economic studies.
-
Soil Properties and processes
2009Co-Authors: Alfred E. Hartemink, Alexander B. Mcbratney, R. E. WhiteAbstract:This four-volume set, edited by leading experts in Soil science, brings together in one collection a series of papers that have been fundamental to the development of Soil science as a defined discipline. Tis volume 2 on Soil Properties and Processes covers: - Soil physics - Soil (bio)chemistry - Soil biology - Soil mineralogy - Soil chemical, physical and biological interfacial reactions
-
Regression rules as a tool for predicting Soil Properties from infrared reflectance spectroscopy
Chemometrics and Intelligent Laboratory Systems, 2008Co-Authors: Budiman Minasny, Alexander B. McbratneyAbstract:Abstract Pedometrics is the use of quantitative methods for the study of Soil distribution and genesis and as a sustainable resource. A common research area in pedometrics and chemometrics is the calibration and prediction of Soil Properties from diffuse infrared reflectance spectra. The most common method is using partial least-squares regression (PLS). In this paper we present an alternative method in the form of regression rules. The regression-rules model consists of a set of rules, in which each rule is a linear model of the predictors. It is also analogous to piecewise linear functions. The accuracy is tested for prediction of Soil Properties from their mid-infrared (2500–25000 nm) diffuse reflectance spectra. In addition, we also tested it with the Chimiometrie 2006 challenge data which used the near-infrared spectra to predict Soil Properties. The results showed that, in comparison with PLS with spectra pretreatment and another data-mining technique, the regression-rules model provides greater accuracy, is simpler and more parsimonious, produces comprehensible equations, provides an optimal variable selection, and respects the upper and lower limits of the data.
-
spatial prediction of Soil Properties using eblup with the matern covariance function
Geoderma, 2007Co-Authors: Budiman Minasny, Alexander B. McbratneyAbstract:Spatial prediction with the presence of spatially dense ancillary variables has attracted research in pedometrics. While Soil survey and analysis of Soil Properties are still expensive and time consuming, the secondary data can be made available on a dense grid for the whole area of interest. The main aim of using the ancillary data is to enhance prediction of Soil Properties by making use of the ancillary variables as covariates. Methods that can be used for this purpose are kriging with external drift, cokriging, regression kriging, and REML-EBLUP (Residual Maximum Likelihood-Empirical Best Linear Unbiased Predictor). Regression kriging is a sub-optimal method that has been utilised extensively because it is easy to use and has been shown empirically to perform as well as other methods. A statically sound method is REML-EBLUP. This paper examines the use of REML-EBLUP in combination with the Matern covariance function for spatial prediction of Soil Properties. Methods for estimating parameters of the Matern variogram using REML, and prediction with EBLUP are described. The prediction capability of REML-EBLUP, regression kriging, and ordinary kriging is compared for four datasets. Results show that although REML-EBLUP generally improves the prediction, the improvement is small compared with regression kriging. Thus, for practical applications regression kriging appears to be a robust method. REML-EBLUP is useful when the trend is strong, and the number of observations is small (< 200). We concluded that improvement in the prediction of Soil Properties does not rely on more sophisticated statistical methods, but rather on gathering more useful and higher quality data.
-
Boundary-line analysis of field-scale yield response to Soil Properties
The Journal of Agricultural Science, 2004Co-Authors: T. M. Shatar, Alexander B. McbratneyAbstract:An algorithm to fit boundary lines, using cubic smoothing splines, was written and used to identify yield responses to changes in Soil Properties. This method involves fitting a curve that represents the maximum yield response to each predictor value, which represents the yield potential at each Soil property value. Boundary-line yield responses to individual Soil Properties were found to differ from responses found by fitting curves through the data scatter. The effects of correlated variables appeared to be lessened using the boundary line approach. Multivariate boundary-line models, based on the Law of the Minimum, were found to be useful for the identification of site-specific causes of yield variation and yield potentials. The boundary line was found to be a useful complement to more traditional data analysis techniques.
Gebreyesus Brhane Tesfahunegn - One of the best experts on this subject based on the ideXlab platform.
-
Assessing Soil Properties and Landforms in the Mai-Negus Catchment, Northern Ethiopia
Pedosphere, 2016Co-Authors: Gebreyesus Brhane Tesfahunegn, Lulseged Tamene, Paul L. G. VlekAbstract:Abstract Soil degradation is a serious environmental problem in Ethiopia. However, little information is documented on indicators such as variations in Soil Properties across different landforms in a catchment. This study was aimed to assess Soil Properties and their changes across sites with different erosion statuses, and identify landscape positions that require prior management attention in the Mai-Negus catchment, northern Ethiopia. Three types of erosion-status sites (stable, eroding and aggrading) were identified using reconnaissance surveys, and then the corresponding Soil samples were collected and analyzed. The major Soil Properties were significantly varied (P ≤ 0.05) among the three erosion-status sites. The highest Soil pH, organic carbon, total nitrogen, cation exchange capacity, iron and zinc were recorded from the aggrading sites in the reservoir and valley landforms of the study catchment. A higher bulk density was generally recorded in the eroding sites, whereas a lower value was observed in the aggrading sites. The highest sand content was observed in the eroding sites of the mountain followed by the central ridge landform. The paired mean difference and the correlation matrix of most Soil Properties between the different erosion statuses also showed significant differences. About 95% of the erosion-status sites were correctly classified by the discriminant function, indicating that the field survey-based classification was acceptable for decision making. On the basis of this study, suitable interventions should thus be introduced to the prioritized landforms, which are the mountain and central ridge, and eroding sites with severely degraded Soil Properties across the catchment.
-
catchment scale spatial variability of Soil Properties and implications on site specific Soil management in northern ethiopia
Soil & Tillage Research, 2011Co-Authors: Gebreyesus Brhane Tesfahunegn, Lulseged Tamene, Paul L. G. VlekAbstract:Abstract Scientific information on the spatial variability and distribution of Soil Properties is critical for understanding ecosystem processes and designing sustainable Soil–crop and environmental management decisions. However, little is known on spatial distribution and variability of Soil Properties at catchment-scale in many tropical developing regions including Ethiopia. This study aims to examine catchment-scale spatial dependence and variability of Soil Properties using classical and geostatistical methods to indicate for site-specific Soil management in the Mai-Negus catchment, northern Ethiopia. Soil samples were collected based on sampling zones identified by the knowledge of local farmers and field observation and analyzed following standard laboratory procedures for selected Soil Properties. The coefficient of variation of the Soil Properties ranged from 8.6% (pH) to 73.4% (clay) at catchment-scale. The mean Soil organic carbon (OC) (1.21%), total nitrogen (TN) (0.12%), and available phosphorus (Pav) (7.8 mg kg −1 ) of the Soils in the catchment were low, whereas high in exchangeable potassium (Ex K) (0.77 cmol c kg −1 ), and medium in cation exchange capacity (CEC) (23.4 cmol c kg −1 ) compared to the rate for African Soils reported in literature. The results of semivariograms indicated a strong (8%) to moderate (63%) degree of spatial dependence for the Soil Properties. In addition, the goodness-of-prediction criterium ( G ) are higher than zero indicating that spatial Soil Properties mapped based on kriging interpolation are more accurate than the catchment average value (classical statistics) for site-specific management decisions. This study indicates a wide range of variability in the Soil Properties as the kriged maps of the Soil Properties at catchment-scale showed for sand (15–70%), silt (18–77%), clay (3–51%), bulk density (1.00–2.00 Mg m −3 ), OC (0.20–4.5%), TN (0.05–1.0%), Pav (1–26 mg kg −1 ), Ex K (0.10–1.30 cmol c kg −1 ), exchangeable calcium, Ex Ca (5–28 cmol c kg −1 ), exchangeable magnesium, Ex Mg (2–15 cmol c kg −1 ), CEC (8–51 cmol c kg −1 ), and iron (3–45 mg kg −1 ). The lowest Soil nutrients and fine Soil particles were measured on the sub-sampling zones such as low Soil quality, eroded sites, and marginal land Soils. Introducing appropriate interventions such as conservation tillage, fertilizer rates, agro-forestry practices, crop rotation, exclosure degraded lands, and conservation measures based on the kriged Soil Properties maps produced is crucial for sustainable production and environmental services.
Cécile Gomez - One of the best experts on this subject based on the ideXlab platform.
-
Predictive ability of Soil Properties to spectral degradation from laboratory Vis-NIR spectroscopy data
Geoderma, 2017Co-Authors: K.r.m. Adeline, Cécile Gomez, N. Gorretta, J.m. RogerAbstract:Laboratory Visible-Near Infrared (Vis-NIR) spectroscopy is a good alternative to costly physical and chemical Soil analysis to estimate a wide range of Soil Properties. Various statistical methods relate Soil Vis-NIR spectra to Soil Properties including partial least-squares regression (PLSR), the most common multivariate statistical technique in Soil science. Most efforts are generally dedicated to the comparison of methodologies and their optimization for the estimation of Soil Properties. Instead, the focus of this paper is to assess the prediction of Soil Properties from laboratory Vis-NIR spectroscopy data in regards to spectral degradation. Consecutively, both spectra quality and PLSR models quality are analyzed across the definition of different spectral configurations, each one characterized by three parameters: the number of spectral bands, the spectral resolution and the spectral sampling interval. The originality of this work is to perform this study on four Soil Properties with different spectral absorption features due to their various physico-chemical interactions with Soil substrate, namely: clay, free iron oxides, calcium carbonate (CaCO3) and pH. The initial database is composed of 1961 spectral bands, spectral resolutions of 3 and 10 nm in the 400'1000 nm and 1000'2500 nm ranges, respectively, with a resampled spectral interval of 1 nm. Seven degraded spectral configurations were built from this reference database with a number of spectral bands decreasing from 328 to 10, a spectral resolution decreasing from 3 nm to 200 nm, and a spectral sampling interval equaling the spectral resolution (i.e., uniform interval sampling). All of these databases were composed of 148 Soil samples collected at a Mediterranean site. PLSR predicted the four selected Soil Properties, and the results were as follows: (1) the prediction performances of the PLSR models were accurate and globally stable with a spectral resolution between 3 and 60 nm regardless of the Soil Properties (R2 decreased from 0.8 to 0.77 for clay, from 0.88 to 0.84 for CaCO3, from 0.66 to 0.58 for pH and remained constant at 0.78 for iron), (2) the prediction performances decreased, but remained acceptable for clay, iron oxides and CaCO3 at spectral resolutions between 60 and 200 nm (R2 > 0.7), (3) the sensitivity of a given Soil property to spectral configurations depended on its spectral features and correlations with other Soil Properties.
-
Mapping of primary Soil Properties using optical visible and near infrared (Vis-NIR) remote sensing
2016Co-Authors: Cécile Gomez, Philippe LagacherieAbstract:Information regarding Soils and their variability in different landscapes is increasingly sought after to improve decision-making regarding a wide range of global issues, such as agricultural production, climate change and the problems of environmental degradation. This information comes in the form of a group of Soil Properties, indicated by field observations or laboratory analyses (i.e. organic matter (OM), particle size, calcium carbonate, iron, pH, humidity, etc.), the list and the determination procedures were the subjects of standardization. This group of Soil Properties, also called “primary” Soil Properties, is what makes up current Soil databases and the resulting Soil maps. These primary Soil Properties are used as input data for pedotransfer functions to estimate Properties called “functional” Soil Properties (i.e. available water content, structural Soil stability, a pesticide’s adsorption coefficient, etc.). These functional Properties are then used to help decision about Soil management, and also as input parameters of crop models, carbon dynamics models, hydrological models and erosion models. The main factor limiting the use of these models is the lack or low density and accuracy of the determination of primary Soil Properties needed to organize them. In fact, the existing Soil databases in the world are neither sufficiently comprehensive nor sufficiently precise to meet the demands of Soil data, especially to organize agri-environmental models as mentioned above. Therefore, there is a major challenge to develop alternative methods for mapping Soil Properties over large areas, with high spatial resolution, while presenting acceptable cost implementation.
-
Regional predictions of eight common Soil Properties and their spatial structures from hyperspectral Vis-NIR data
Geoderma, 2012Co-Authors: Cécile Gomez, Philippe Lagacherie, Guillaume CouloumaAbstract:The potential of the visible-near infrared (Vis-NIR; 400-2500 nm) laboratory spectroscopy for the estimation of Soil Properties has been previously demonstrated in the literature, and the Vis-NIR spatial spectroscopy is expected to provide direct estimates of these Properties at the Soil surface. The aim of this work was to examine whether Vis-NIR airborne spectroscopy could be used for mapping eight of the most common Soil Properties, including clay, sand, silt, calcium carbonate (CaCO3), free iron, cation-exchange capacity (CEC), organic carbon and pH, without mispredicting the local values of these Properties and their spatial structures. Our study was based on 95 Soil samples and a HyMap hyperspectral image available over 192 bare Soil fields scattered within a 24.6 km(2) area. Predictions of Soil Properties from HyMap spectra were computed for the eight Soil Properties using partial least squares regression (PLSR). The results showed that 1) four out of the eight Soil Properties (CaCO3, iron, clay and CEC) were suitable for mapping using hyperspectral data, and both accurate local predictions and good representations of spatial structures were observed and 2) the application of prediction models using hyperspectral data over the study area provided statistical characterizations within Soilscape variations and variograms that describe in details the short range Soil variations. All results were consistent with the previous pedological knowledge of the studied region. This study opens up the possibility of more extensive use of hyperspectral data for digital Soil mapping of these successfully predicted Soil Properties.
Ying Zhao - One of the best experts on this subject based on the ideXlab platform.
-
spatial variability of Soil Properties affected by grazing intensity in inner mongolia grassland
Ecological Modelling, 2007Co-Authors: Ying Zhao, Stephan Peth, Julia Krummelbein, Rainer Horn, Zhongyan Wang, Markus Steffens, Carsten Hoffmann, Xinhua PengAbstract:Analysis of the spatial variability of Soil Properties is important to interpret the site-specific ecosystems not only with respect to process investigations but also to model upscaling. This paper aims to study the effects of the grazing intensity on Soil physical and mechanical Properties and their interactions in a Leymus chinensis steppe of the Xilin River Basin, Inner Mongolia, China. The investigated sites were subjected to five grazing intensities (ungrazed since 1979, ungrazed since 1999, winter grazing, continuous grazing and heavy grazing). Soil water content (SWC), hydraulic conductivity (K), water drop penetration time (WDPT), shear strength (SS), Soil organic carbon (SOC) concentration, bulk density (BD), and Soil texture were measured at a grid with 15 m sampling distance on the surface Soil during the period of 2004–2005. The data were analyzed using descriptive statistics and geostatistics. The correlation and interaction between Soil Properties were analyzed by the methods of Pearson correlation, partial correlation and multiple regression analysis. The results showed that spatial distributions of Soil Properties could be well described by spherical or exponential models. The ranges of spatial dependence were the highest for WDPT and the lowest for SS. Grazing decreased SWC, SOC and WDPT but increased BD and SS. Multiple regression analysis showed significant correlations among SWC, K, WDPT, SOC and BD; as well as between SS and silt content. Soil compaction induced by sheep trampling, especially in the heavily grazed site, inclined to a homogenous spatial distribution of Soil Properties, which will possibly enhance Soil vulnerability to water and nutrient loss, and consequently reduce the plant available water and thus grassland productivity.
Paul L. G. Vlek - One of the best experts on this subject based on the ideXlab platform.
-
Assessing Soil Properties and Landforms in the Mai-Negus Catchment, Northern Ethiopia
Pedosphere, 2016Co-Authors: Gebreyesus Brhane Tesfahunegn, Lulseged Tamene, Paul L. G. VlekAbstract:Abstract Soil degradation is a serious environmental problem in Ethiopia. However, little information is documented on indicators such as variations in Soil Properties across different landforms in a catchment. This study was aimed to assess Soil Properties and their changes across sites with different erosion statuses, and identify landscape positions that require prior management attention in the Mai-Negus catchment, northern Ethiopia. Three types of erosion-status sites (stable, eroding and aggrading) were identified using reconnaissance surveys, and then the corresponding Soil samples were collected and analyzed. The major Soil Properties were significantly varied (P ≤ 0.05) among the three erosion-status sites. The highest Soil pH, organic carbon, total nitrogen, cation exchange capacity, iron and zinc were recorded from the aggrading sites in the reservoir and valley landforms of the study catchment. A higher bulk density was generally recorded in the eroding sites, whereas a lower value was observed in the aggrading sites. The highest sand content was observed in the eroding sites of the mountain followed by the central ridge landform. The paired mean difference and the correlation matrix of most Soil Properties between the different erosion statuses also showed significant differences. About 95% of the erosion-status sites were correctly classified by the discriminant function, indicating that the field survey-based classification was acceptable for decision making. On the basis of this study, suitable interventions should thus be introduced to the prioritized landforms, which are the mountain and central ridge, and eroding sites with severely degraded Soil Properties across the catchment.
-
catchment scale spatial variability of Soil Properties and implications on site specific Soil management in northern ethiopia
Soil & Tillage Research, 2011Co-Authors: Gebreyesus Brhane Tesfahunegn, Lulseged Tamene, Paul L. G. VlekAbstract:Abstract Scientific information on the spatial variability and distribution of Soil Properties is critical for understanding ecosystem processes and designing sustainable Soil–crop and environmental management decisions. However, little is known on spatial distribution and variability of Soil Properties at catchment-scale in many tropical developing regions including Ethiopia. This study aims to examine catchment-scale spatial dependence and variability of Soil Properties using classical and geostatistical methods to indicate for site-specific Soil management in the Mai-Negus catchment, northern Ethiopia. Soil samples were collected based on sampling zones identified by the knowledge of local farmers and field observation and analyzed following standard laboratory procedures for selected Soil Properties. The coefficient of variation of the Soil Properties ranged from 8.6% (pH) to 73.4% (clay) at catchment-scale. The mean Soil organic carbon (OC) (1.21%), total nitrogen (TN) (0.12%), and available phosphorus (Pav) (7.8 mg kg −1 ) of the Soils in the catchment were low, whereas high in exchangeable potassium (Ex K) (0.77 cmol c kg −1 ), and medium in cation exchange capacity (CEC) (23.4 cmol c kg −1 ) compared to the rate for African Soils reported in literature. The results of semivariograms indicated a strong (8%) to moderate (63%) degree of spatial dependence for the Soil Properties. In addition, the goodness-of-prediction criterium ( G ) are higher than zero indicating that spatial Soil Properties mapped based on kriging interpolation are more accurate than the catchment average value (classical statistics) for site-specific management decisions. This study indicates a wide range of variability in the Soil Properties as the kriged maps of the Soil Properties at catchment-scale showed for sand (15–70%), silt (18–77%), clay (3–51%), bulk density (1.00–2.00 Mg m −3 ), OC (0.20–4.5%), TN (0.05–1.0%), Pav (1–26 mg kg −1 ), Ex K (0.10–1.30 cmol c kg −1 ), exchangeable calcium, Ex Ca (5–28 cmol c kg −1 ), exchangeable magnesium, Ex Mg (2–15 cmol c kg −1 ), CEC (8–51 cmol c kg −1 ), and iron (3–45 mg kg −1 ). The lowest Soil nutrients and fine Soil particles were measured on the sub-sampling zones such as low Soil quality, eroded sites, and marginal land Soils. Introducing appropriate interventions such as conservation tillage, fertilizer rates, agro-forestry practices, crop rotation, exclosure degraded lands, and conservation measures based on the kriged Soil Properties maps produced is crucial for sustainable production and environmental services.