Hydropedology: Linking Dynamic Soil Properties with Soil Survey Data

2014 ◽  
pp. 23-50
Author(s):  
Henry Lin ◽  
Weihua Zhang ◽  
Haoliang Yu
Keyword(s):  
Soil Properties ◽  
Survey Data ◽  
Soil Survey ◽  
Dynamic Soil Properties

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.;

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.

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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