Utility of the factual key and soil taxonomy in the Lower Macquarie Valley, NSW

Soil Research ◽  
1989 ◽  
Vol 27 (2) ◽  
pp. 289 ◽  
Author(s):  
NJ Mckenzie ◽  
MP Austin
Keyword(s):  
Soil Properties ◽  
Survey Data ◽  
Soil Survey ◽  
Numerical Classification ◽  
Dryland Agriculture ◽  
Soil Taxonomy ◽  
Classification Schemes ◽  
Local Classification ◽  
Soil Variation ◽  
National Systems

The utility of the Factual Key and Soil Taxonomy was tested by using comprehensive soil survey data from the lower Macquarie Valley, N.S.W. The aim was to assess whether the two classification schemes partitioned soil variation efficiently and to establish their usefulness for predicting variables not used during profile allocation. A numerical taxonomic method was used to generate a local classification which served as a benchmark to assess the two national systems. The effectiveness of the three classifications was determined by comparing the proportion of variation accounted for in a range of soil properties of direct relevance to irrigated and dryland agriculture. The Factual Key and Soil Taxonomy were found to be equally poor for predicting relevant soil properties. Both systems arbitrarily subdivided important local modalities. The variation accounted for by the numerical classification was 20-30% greater. The result demonstrates the practical advantages of a local classification and the reality of Butler's taxonomic hiatus.

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

scholarly journals An Environmental Approach to Understanding the Expansion of Future Vineyards: Case Study of Soil Developed on Alluvial Sediments

Environments ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 96
Author(s):  
Raimundo Jiménez-Ballesta ◽  
Sandra Bravo ◽  
Jose Angel Amorós ◽  
Caridad Pérez-de-los-Reyes ◽  
Jesus García-Pradas ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Type ◽  
Salt Content ◽  
Organic Matter Content ◽  
Matter Content ◽  
Soil Survey ◽  
Final Conclusion ◽  
Soil Taxonomy ◽  
Age Related ◽  
La Mancha

The importance of soil properties in wine grape production is generally treated as secondary to climate and canopy management. This study was undertaken to characterize and classify a singular soil resource for a vineyard in a traditional viticultural region: Castilla-La Mancha, central Spain. The soil under study was described and sampled using standard soil survey procedures as outlined by FAO, and served as a pedologic window for Gleyic Fluvisol (Calcaric, Humic), according to the FAO System, or Fluventic Haploxerept, according to the Soil Taxonomy System. This soil, developed on alluvial materials of Holocene age related to the Gigüela river (either carbonatic or gypsiferous) has, in addition to obvious hydromorphic features (that reduce its use), high organic matter content (5.5% in the Ap horizon) and moderate salt content (between 1.14 and 2.39 dS/m). Other properties are common to most vineyard soils in Castilla-La Mancha, such as alkaline reactivity (pH between 7.6 and 8.2); calcium and magnesium as the dominant cations followed by sodium and potassium; finally, some deficiency in N (0.11%) and P (12.3 mg/kg). The most restricting soil factors for vineyard growth of this soil type were waterlogging, which can affect vine roots, and the appearance of certain salinity problems. The final conclusion of this study was that the use of the studied soil type for vineyard cultivation could be recommended to farmers only in the case of improving soil properties—for example, draining the river level.

Serpentine Soil Distributions and Environmental Influences

2007 ◽  
Author(s):  
Earl B. Alexander ◽  
Roger G. Coleman ◽  
Todd Keeler-Wolfe ◽  
Susan P. Harrison
Keyword(s):  
Soil Properties ◽  
Soil Classification ◽  
Serpentine Soil ◽  
Soil Survey ◽  
Aluminum Silicate ◽  
Serpentine Soils ◽  
Soil Taxonomy ◽  
Parent Materials ◽  
Exchange Complex ◽  
Cation Status

Serpentine soils occur in all but one of the twelve orders (Alexander 2004b), which is the highest level in Soil Taxonomy (Soil Survey Staff 1999), the primary system of soil classification utilized in this book (appendix C). They occur in practically every environment from cold arctic to hot tropical and from arid to perhumid (always wet). Thus the variety of serpentine soils is very great even though they occupy only a small fraction of the earth. Serpentine soils have been found in all states and provinces that are adjacent to the Pacific Ocean from Baja California to Alaska. They are most concentrated in the California Region, where they have been mapped in 34 counties in California and in 5 counties in southwestern Oregon. Serpentine lateritic (or “nickel laterite”) soils, which have not been mapped separately from other soils, are economically significant in California and southwest Oregon, even though they are not widely distributed in western North America. A representative serpentine soil is shown in figure 6-1. Serpentine soils, or soils in magnesic (serpentine) families, are represented in 11 of the 12 soil orders. Spodosols and Histosols in magnesic families occur only where there is a thin cover of nonserpentine materials over the serpentine materials, and there are no serpentine Andisols. Andisols contain amorphous and poorly ordered aluminum-silicate minerals, which are responsible for andic soil properties of these soils. Serpentine soil parent materials do not contain enough aluminum for the development of andic soil properties that are definitive of Andisols. Alfisols are soils with argillic (or natric) horizons having more than 35% exchangeable bases (Ca2+, Mg2+, Na+, and K+) on the cation exchange complex. Al3+ and H+ are the common nonbasic (acidic) cations on the exchange complex. The Mg2+ that serpentine soil parent materials release upon weathering keeps the basic cation status of soils high, unless they are leached intensively. Some of the soil horizon sequences are A-Bt, A-Btn, and A-Bt-Btk in Alfisols. Soils of Dubakella Series and other moderately deep Mollic Haploxeralfs with a mesic soil temperature regime are the most extensively mapped serpentine Alfisols in California and southwestern Oregon. Figure 6-1 is representative of the Mollic Haploxeralfs.

INTERPRETATION OF SOIL DRAINAGE GROUPS FROM SOIL TAXONOMY—SOUTHERN ONTARIO

1984 ◽  
Vol 64 (3) ◽  
pp. 383-393 ◽  
Author(s):  
P. S. CHISHOLM ◽  
R. W. IRWIN ◽  
C. J. ACTON
Keyword(s):  
Surface Water ◽  
Survey Data ◽  
Parent Material ◽  
Soil Survey ◽  
Soil Series ◽  
Soil Taxonomy ◽  
Ordered Groups ◽  
Symbol Code ◽  
Particle Size Classes ◽  
Principle Source

Ontario Soil Survey data for 278 soil series were interpreted to describe relationships between soil characteristics and the movement of water in saturated zones of the soil profile. Based on family particle size classes, groups of soil families, similar in profile and parent material, were formed. Groups were ordered in terms of increasing ability of soil to transmit water, as interpreted by Soil Conservation Service guidelines. The ordered groups were separated into two parts using the European concepts for surface water gley and groundwater gley soils. Six groups of surface water gley soils were differentiated in which surface water was interpreted as the principle source of saturation. Groundwater was interpreted to be the principle source of saturation for three groups of groundwater gley soils. Principles applied to grouping and ordering were augmented by data for soil texture and structure to develop a generalized profile description for each group. The generalized profile description was translated into a five symbol code by which the interpretation is applied to individual soil series within a group. The code is intended to enhance application of soil survey data to design of buried agricultural drainage systems. Key words: Soil physical characteristics, drainage characteristics, hydrologic soil groups, surface water Gleysols, groundwater Gleysols, pseudogleysols

scholarly journals Soil Diversity (Pedodiversity) and Ecosystem Services

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 288 ◽  
Author(s):  
Elena A. Mikhailova ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Mark A. Schlautman ◽  
Gregory C. Post
Keyword(s):  
Decision Making ◽  
Ecosystem Services ◽  
Sustainable Use ◽  
Taxonomic Diversity ◽  
Soil Survey ◽  
Original Research ◽  
Soil Taxonomy ◽  
Extrinsic Factors ◽  
Business Decision ◽  
Soil Diversity

Soil ecosystem services (ES) (e.g., provisioning, regulation/maintenance, and cultural) and ecosystem disservices (ED) are dependent on soil diversity/pedodiversity (variability of soils), which needs to be accounted for in the economic analysis and business decision-making. The concept of pedodiversity (biotic + abiotic) is highly complex and can be broadly interpreted because it is formed from the interaction of atmospheric diversity (abiotic + biotic), biodiversity (biotic), hydrodiversity (abiotic + biotic), and lithodiversity (abiotic) within ecosphere and anthroposphere. Pedodiversity is influenced by intrinsic (within the soil) and extrinsic (outside soil) factors, which are also relevant to ES/ED. Pedodiversity concepts and measures may need to be adapted to the ES framework and business applications. Currently, there are four main approaches to analyze pedodiversity: taxonomic (diversity of soil classes), genetic (diversity of genetic horizons), parametric (diversity of soil properties), and functional (soil behavior under different uses). The objective of this article is to illustrate the application of pedodiversity concepts and measures to value ES/ED with examples based on the contiguous United States (U.S.), its administrative units, and the systems of soil classification (e.g., U.S. Department of Agriculture (USDA) Soil Taxonomy, Soil Survey Geographic (SSURGO) Database). This study is based on a combination of original research and literature review examples. Taxonomic pedodiversity in the contiguous U.S. exhibits high soil diversity, with 11 soil orders, 65 suborders, 317 great groups, 2026 subgroups, and 19,602 series. The ranking of “soil order abundance” (area of each soil order within the U.S.) expressed as the proportion of the total area is: (1) Mollisols (27%), (2) Alfisols (17%), (3) Entisols (14%), (4) Inceptisols and Aridisols (11% each), (5) Spodosols (3%), (6) Vertisols (2%), and (7) Histosols and Andisols (1% each). Taxonomic, genetic, parametric, and functional pedodiversity are an essential context for analyzing, interpreting, and reporting ES/ED within the ES framework. Although each approach can be used separately, three of these approaches (genetic, parametric, and functional) fall within the “umbrella” of taxonomic pedodiversity, which separates soils based on properties important to potential use. Extrinsic factors play a major role in pedodiversity and should be accounted for in ES/ED valuation based on various databases (e.g., National Atmospheric Deposition Program (NADP) databases). Pedodiversity is crucial in identifying soil capacity (pedocapacity) and “hotspots” of ES/ED as part of business decision making to provide more sustainable use of soil resources. Pedodiversity is not a static construct but is highly dynamic, and various human activities (e.g., agriculture, urbanization) can lead to soil degradation and even soil extinction.

Use of Soil Survey Data for Regional Soil Water Simulation Models

1985 ◽  
Vol 49 (5) ◽  
pp. 1238-1244 ◽  
Author(s):  
J. H. M. Wösten ◽  
J. Bouma ◽  
G. H. Stoffelsen
Keyword(s):  
Soil Water ◽  
Survey Data ◽  
Simulation Models ◽  
Soil Survey ◽  
Water Simulation

Deep Learning-based Soil Property Prediction for Remediation of Radioactive Contamination in Agriculture

2021 ◽  
Author(s):  
Franck Albinet ◽  
Gerd Dercon ◽  
Tetsuya Eguchi
Keyword(s):  
Deep Learning ◽  
Soil Properties ◽  
Radioactive Contamination ◽  
The United States ◽  
Soil Survey ◽  
United States Department ◽  
Least Squares Regression ◽  
Transfer Factors ◽  
Wide Range ◽  
Plant Transfer

<p>The Joint IAEA/FAO Division of Nuclear Techniques in Food and Agriculture, through its Soil and Water Management & Crop Nutrition Laboratory (SWMCNL), launched in October 2019, a new Coordinated Research Project (D15019) called “Monitoring and Predicting Radionuclide Uptake and Dynamics for Optimizing Remediation of Radioactive Contamination in Agriculture''. Within this context, the high-throughput characterization of soil properties in general and the estimation of soil-to-plant transfer factors of radionuclides are of critical importance.</p><p>For several decades, soil researchers have been successfully using near and mid-infrared spectroscopy (MIRS) techniques to estimate a wide range of soil physical, chemical and biological properties such as carbon (C), Cation Exchange Capacities (CEC), among others. However, models developed were often limited in scope as only small and region-specific MIR spectra libraries of soils were accessible.</p><p>This situation of data scarcity is changing radically today with the availability of large and growing library of MIR-scanned soil samples maintained by the National Soil Survey Center (NSSC) Kellogg Soil Survey Laboratory (KSSL) from the United States Department of Agriculture (USDA-NRCS) and the Global Soil Laboratory Network (GLOSOLAN) initiative of the Food Agency Organization (FAO). As a result, the unprecedented volume of data now available allows soil science researchers to increasingly shift their focus from traditional modeling techniques such as PLSR (Partial Least Squares Regression) to classes of modeling approaches, such as Ensemble Learning or Deep Learning, that have proven to outperform PLSR on most soil properties prediction in a large data regime.</p><p>As part of our research, the opportunity to train higher capacity models on the KSSL large dataset (all soil taxonomic orders included ~ 50K samples) makes it possible to reach a quality of prediction for exchangeable potassium so far unsurpassed with a Residual Prediction Deviation (RPD) around 3. Potassium is known for its difficulty of being predicted but remains extremely important in the context of remediation of radioactive contamination after a nuclear accident. Potassium can help reduce the uptake of radiocaesium by crops, as it competes with radiocaesium in soil-to-plant transfer.</p><p>To ensure informed decision making, we also guarantee that (i) individual predictions uncertainty is estimated (using Monte Carlo Dropout) and (ii) individual predictions can be interpreted (i.e. how much specific MIRS wavenumber regions contribute to the prediction) using methods such as Shapley Additive exPlanations (SHAP) values.</p><p>SWMCNL is now a member of the GLOSOLAN network, which helps enhance the usability of MIRS for soil monitoring worldwide. SWMCNL is further developing training packages on the use of traditional and advanced mathematical techniques to process MIRS data for predicting soil properties. This training package has been tested in October 2020 with thirteen staff members of the FAO/IAEA Laboratories in Seibersdorf, Austria.</p>

scholarly journals Distribution of phosphorus fractions of different plant availability in German forest soils and their relationship to common soil properties and foliar P concentrations

2018 ◽  
Author(s):  
Jörg Niederberger ◽  
Martin Kohler ◽  
Jürgen Bauhus
Keyword(s):  
Soil Properties ◽  
Forest Soil ◽  
Soil Survey ◽  
Soil Conditions ◽  
Organic Carbon Content ◽  
P Fractions ◽  
P Pools ◽  
P Content ◽  
Labile P ◽  
Total P

Abstract. Repeated, grid-based forest soil inventories such as the nationwide German forest soil survey (GFSI) aim, among other things, at detecting changes in soil properties and plant nutrition. In these types of inventories, the only information on soil phosphorus (P) is commonly the total P content. However, total P content in mineral soils of forests is usually not a meaningful variable to predict the availability of P to trees. Here we tested a modified sequential P extraction ac-cording to Hedley to determine the distribution of different plant available P fractions in soil samples (0–5 and 10–30 cm depth) from 146 GFSI sites, capturing a wide variety of soil conditions. In addition, we analyzed relationships between these P fractions and common soil proper-ties such as pH, texture, and organic Carbon content (SOC). Total P content among our samples ranged from approximately 60 up to 2800 mg kg−1. The labile, moderately labile, and stable P fractions contributed to 27 %, 51 % and 22 % of total P content, respectively, at 0–5 cm depth. At 10–30 cm depth, the labile P fractions decreased to 15 %, whereas the stable P fractions in-creased to 30 %. These changes with depth were accompanied by a decrease in the organic P fractions. High P contents were related with high pH-values. Whereas the labile P pool increased with decreasing pH in absolute and relative terms, the stable P pool decreased in absolute and relative terms. Increasing SOC in soils led to significant increases in all P pools and in total P. In sandy soils, the P content across all fractions was lower than in other soil texture types. Multiple linear regressions indicated that P pools and P fractions were moderately well related to soil properties (r2 mostly above 0.5), and sand content of soils had the strongest influence. Foliage P concentrations in Pinus sylvestris were reasonably well explained by the labile and moderately labile P pool (r

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