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Soil Data (soil + data)
Selected AbstractsMultivariate calibration of hyperspectral ,-ray energy spectra for proximal soil sensingEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2007R. A. Viscarra Rossel Summary 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 ,-ray energy spectra to predict various surface and subsurface soil properties. The spectra were collected with a proximal, on-the-go ,-ray spectrometer. We surveyed two geographically and physiographically different fields in New South Wales, Australia, and collected hyperspectral ,-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 ,-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 ,-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 ,-ray spectrometry. Proximally sensed ,-ray spectrometry proved to be a useful tool for predicting soil properties in different soil landscapes. [source] Neural network models to predict cation exchange capacity in arid regions of IranEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 4 2005M. Amini Summary Design and analysis of land-use management scenarios requires detailed soil data. When such data are needed on a large scale, pedotransfer functions (PTFs) could be used to estimate different soil properties. Because existing regression-based PTFs for estimating cation exchange capacity (CEC) do not, in general, apply well to arid areas, this study was conducted (i) to evaluate the existing models and (ii) to develop neural network-based PTFs for predicting CEC in Aridisols of Isfahan in central Iran. As most researches have found a significant correlation between CEC and soil organic matter content (OM) and clay content, we also used these two variables for modelling of CEC. We tested several published PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples. The data set was divided into two subsets for calibration and testing of the models. In general, the neural network-based models provided more reliable predictions than the regression-based PTFs. [source] Soil organic carbon stocks in China and changes from 1980s to 2000sGLOBAL CHANGE BIOLOGY, Issue 9 2007ZUBIN XIE Abstract The estimation of the size and changes of soil organic carbon (SOC) stocks is of great importance for decision makers to adopt proper measures to protect soils and to develop strategies for mitigation of greenhouse gases. In this paper, soil data from the Second State Soil Survey of China (SSSSC) conducted in the early 1980s and data published in the last 5 years were used to estimate the size of SOC stocks over the whole profile and their changes in China in last 20 years. Soils were identified as paddy, upland, forest, grassland or waste-land soils and an improved soil bulk density estimation method was used to estimate missing bulk density data. In the early 1980s, total SOC stocks were estimated at 89.61 Pg (1 Pg=103 Tg=1015 g) in China's 870.94 Mha terrestrial areas covered by 2473 soil series. In the paddy, upland, forest and grassland soils the respective total SOC stocks were 2.91 Pg on 29.87 Mha, 10.07 Pg on 125.89 Mha, 34.23 Pg on 249.32 Mha and 37.71 Pg on 278.51 Mha, respectively. The SOC density of the surface layer ranged from 3.5 Mg ha,1 in Gray Desery grassland soils to 252.6 Mg ha,1 in Mountain Meadow forest soils. The average area-weighted total SOC density in paddy soils (97.6 Mg ha,1) was higher than that in upland soils (80 Mg ha,1). Soils under forest (137.3 Mg ha,1) had a similar average area-weighted total SOC density as those under grassland (135.4 Mg ha,1). The annual estimated SOC accumulation rates in farmland and forest soils in the last 20 years were 23.61 and 11.72 Tg, respectively, leading to increases of 0.472 and 0.234 Pg SOC in farmland and forest areas, respectively. In contrast, SOC under grassland declined by 3.56 Pg due to the grassland degradation over this period. The resulting estimated net SOC loss in China's soils over the last 20 years was 2.86 Pg. The documented SOC accumulation in farmland and forest soils could thus not compensate for the loss of SOC in grassland soils in the last 20 years. There were, however, large regional differences: Soils in China's South and Eastern parts acted mainly as C sinks, increasing their average topsoil SOC by 132 and 145 Tg, respectively. In contrast, in the Northwest, Northeast, Inner Mongolia and Tibet significant losses of 1.38, 0.21, 0.49 and 1.01 Pg of SOC, respectively, were estimated over the last 20 years. These results highlight the importance to take measures to protect grassland and to improve management practices to increase C sequestration in farmland and forest soils. [source] The spatiotemporal dynamics of a primary successionJOURNAL OF ECOLOGY, Issue 2 2008N. A. Cutler Summary 1Conceptual models of ecosystem development commonly predict a phase of initial colonization characterized by the nucleation, growth and coalescence of discrete patches of pioneer plants. Spatiotemporal dynamics during subsequent development may follow one of three different models: the classical model, in which initially discrete patches of competitive dominant (secondary) colonists coalesce to form a homogeneous cover; the patch dynamics model, in which renewal mechanisms such as disturbance create a shifting mosaic of patches at different stages; and the geoecological model, in which the vegetation gradually differentiates along edaphic gradients related to the underlying physical template. 2These models of spatiotemporal dynamics were tested using vegetation and soil data from an 850-year chronosequence, comprised of seven lava flows on Mt Hekla, Iceland. The scale and intensity of spatial pattern were quantified on each flow using spatial analyses (mean-variance ratios, quadrat variance techniques and indices of autocorrelation). Changes in spatial pattern with increasing terrain age were compared with predicted trajectories, in order to identify which of the models of spatiotemporal dynamics was most consistent with the observations. 3The early stages of ecosystem development were characterized by colonization of the pioneer species, especially Racomitrium mosses, in discrete patches (,Pioneer colonization stage', < 20 years), which then grew laterally and coalesced to form a continuous, homogeneous carpet (,Pioneer expansion stage', 20,100 years). Later in the sequence, higher plants established in discrete patches within this pioneer matrix (,Higher plant colonization stage', 100,600 years). Over time, heterogeneity re-emerged at a larger spatial scale as the vegetation differentiated according to topographic variations in the underlying substrate (,Differentiation stage', > 600 years). 4Synthesis. The spatiotemporal dynamics observed in the early stages of this succession were consistent with a model of pioneer nucleation in micro-scale safe sites, followed by growth, coalescence and eventual fragmentation of pioneer patches. The spatial patterns which emerged later in development support the geoecological model, with spatial differentiation of vegetation related to meso-scale substrate topography. The findings provide insight on how vegetation patterns emerge at different stages of ecosystem development in response to differing scales of heterogeneity in the underlying physical environment. [source] Digital soil mapping in Germany,a reviewJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 3 2006Thorsten Behrens Abstract Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high-resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time-consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states. This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives. Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital,soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction. An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil-class mapping based on correlations to environmental covariates within the scope of the German classification system. [source] Carbon stock assessment and soil carbon management in agricultural land-uses in ThailandLAND DEGRADATION AND DEVELOPMENT, Issue 3 2008N. Gnanavelrajah Abstract The organic carbon pool in agricultural land-uses is capable of enhancing agricultural sustainability and serving as a potential sink of atmospheric carbon dioxide. A study was carried out to estimate and map carbon stock of different agricultural land-uses in a sub-watershed of Thailand and to assess the land-use sustainability with respect to carbon management. A quadrat sampling methodology was adopted to estimate the biomass and its carbon content of 11 different land-uses in the study area. Existing soil data were used to calculate the soil carbon. GIS was used for integrating biomass carbon, soil carbon and carbon stock mapping. Roth carbon model was used to project the soil carbon of present land-uses in the coming 10 years and based on which the sustainability of land-uses was predicted. The total carbon stock of agricultural land-uses was estimated to be 20·5,Tg, of which 41·49 per cent was biomass carbon and 58·51 per cent was soil carbon. Among the land-uses, para rubber had the highest average biomass C (136·34,Mg,C,ha,1) while paddy had the lowest (7·08,Mg,C,ha,1). About four-fifths of agricultural land-uses in the watershed are sustainable in maintaining the desired level of soil carbon in coming 10 years while one-fifths are unstable. Such information on carbon stock could be valuable to develop viable land-use options for agricultural sustainability and carbon sequestration. Copyright © 2007 John Wiley & Sons, Ltd. [source] Climate for crops: integrating climate data with information about soils and crop requirements to reduce risks in agricultural decision-makingMETEOROLOGICAL APPLICATIONS, Issue 4 2006D. S. Wratt Abstract Locally applicable information about climate and soil properties can help farmers identify opportunities and reduce risks associated with changing to new land uses. This article describes techniques for preparing high-resolution regional maps and GIS surfaces of agriculturally relevant climate parameters. Ways of combining these climate surfaces with soil data and information about the physical requirements of crops to identify areas likely to be the most suitable for new high-value crops are then outlined. Innovative features include methods for merging observations from temporary climate stations installed for one to two years in conjunction with longer-term climate station observations to improve input data for the maps, and techniques for mapping quantiles of climatic factors that may constrain agricultural operations. Examples are the expected ,one-in-five year' first and last frost dates, and the ,one-in-five year' lowest and highest seasonal rainfalls. The use of night-time satellite infrared observations to improve spatial resolution of frost hazard maps is also described. Typical standard errors of these climate mapping techniques are summarised. The benefits of ongoing consultation with local farmers and local government staff during the design and implementation of climate/soil/crop potential studies are described. These include optimising products to meet local needs, quality control of the resulting maps and GIS surfaces through local knowledge, and improved uptake of information by users. Further applications of techniques described in this paper include products useful to the energy sector, preparation of daily gridded climate data estimates for use in water quality and plant growth modelling, and development of regional climate change scenarios. Copyright © 2006 John Wiley & Sons, Ltd. [source] | |||||||||||||