Have Extractable Phosphorus and Potassium Contents Evolved In

Total Page:16

File Type:pdf, Size:1020Kb

Have Extractable Phosphorus and Potassium Contents Evolved In Have extractable phosphorus and potassium contents evolved in French agricultural soils since 2004? Blandine Lemercier, Nicolas Saby, Laëtitia Gouny, Philippe Eveillard, Pascal Denoroy To cite this version: Blandine Lemercier, Nicolas Saby, Laëtitia Gouny, Philippe Eveillard, Pascal Denoroy. Have ex- tractable phosphorus and potassium contents evolved in French agricultural soils since 2004?. Pedo- metrics 2017, Jun 2017, Wageningen, Netherlands. 298 p. hal-01605162 HAL Id: hal-01605162 https://hal.archives-ouvertes.fr/hal-01605162 Submitted on 2 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - ShareAlike| 4.0 International License Abstract Book Pedometrics 2017 Wageningen, 26 June – 1 July 2017 2 Contents Evaluating Use of Ground Penetrating Radar and Geostatistic Methods for Mapping Soil Cemented Horizon .................................... 13 Digital soil mapping in areas of mussunungas: algoritmos comparission .......... 14 Sensing of farm and district-scale soil moisture content using a mobile cosmic ray probe (COSMOS Rover) .................................... 15 Proximal sensing of soil crack networks using three-dimensional electrical resistivity to- mography ......................................... 16 Using digital microscopy for rapid determination of soil texture and prediction of soil organic matter ...................................... 17 Analysis of complementarities of different spectral analytics to sense soil properties ... 18 Long-term diachronic series for soil carbon saturation evidence. A case study on volcanic soils of reunion island under sugarcane crops. ..................... 19 Concept of entropy in spatial distribution of vegetation in satellite images ........ 20 Digital Soil Mapping Method Based on the Similarity of Environmental Covariates in the Spatial Neighborhood .................................. 21 Multivariate and multi-layer soil mapping using structural equation modelling ...... 22 Incorporating infrared spectroscopic data, land management, soil drainage and soil erosion observations into Bayesian framework for modelling soil erosion risk ........ 23 Comparing airborne and terrestrial laser scanning DTMs for high resolution topsoil pH modelling ......................................... 24 Multi-sensor data fusion for supervised land-cover classification through a Bayesian set- ting coupling multivariate smooth kernel for density estimation and geostatistical techniques ......................................... 25 Uncertainty in soil properties from the hydrological point of view: a call for new types of soil maps? ......................................... 26 Detecting soil microbial community shifts via field spectroscopy .............. 27 Using Near Infrared Spectroscopy in determining the mineralogical variations of the Lon- don Clay Formation, Whitecliff Bay, Isle of White, UK. ............... 28 Standardization of world soil profile data to support global mapping and modelling ... 29 Soil and Environment software, a tool for soil management ................ 30 App Soil Calculator ...................................... 31 Algorithms for quantitative pedology ............................. 32 Saskatchewan Soils: Access and improvements to soil information ............. 33 Soil Spectral Library of Ethiopia (SSL-ETH), Version-I .................. 34 Detailed predictive mapping of acid sulfate soil occurrence using electromagnetic induc- tion data ......................................... 35 Supplementing predictive mapping of acid sulfate soil occurrence with Vis-NIR spectroscopy 37 3D prediction of soil moisture using data from varying horizontal and vertical supports . 38 Joint multifractal analysis of the influence of topography and soil texture on soil water storage ........................................... 39 3 Multitemporal Soil Pattern Analysis for Organic Matter Estimation on Arable Fields using Multispectral Satellite Data ............................ 40 Spectral mixing for vis-NIR diffuse reflectance spectroscopy ................ 41 Predictive mapping of the acidifying potential for acid sulfate soils ............ 42 Can soil spatial prediction models from different areas be similar? ............ 44 Thermal remote sensing for digital soil mapping ....................... 45 Optimal stratification for validation of digital soil maps .................. 46 Spatial modeling of geomorphometric variables for natural hazard valuation to desertifi- cation in tropical zones .................................. 47 Use of drone high resolution images to quantify soil erosion ................ 48 Predicting Scottish soil properties using X-ray powder diffraction ............. 49 Digital Soil Mapping of soil properties across GB: case studies from Scotland and England 50 A routine chemometrics approach to estimate soil organic carbon in croplands exploiting LUCAS topsoil database. ................................ 51 Integration of GPR measurements with sparse textural data for characterizing forest soils: an application of data fusion in southern Italy (Calabria) .............. 52 Determination of naturally occurring concentrations of trace elements in New Zealand soils 53 Mapping spatial variability of soil organic carbon, phosphorus and soil acidity in Zambia 54 A Method Research on Digital Soil Mapping Using ES-RS-GIS in Semi-arid Sandy Land: A Case Study of Horqin Left Back Banner ....................... 55 Soil classification of multi-horizontal profiles using support vector machines and vis-NIR spectroscopy ....................................... 56 Using new sparsity genomic methods to improve soil chemometric models ........ 57 Transferring and spiking of soil spectral models between two south Indian villages ... 58 Mapping the Impact of Zero Tillage on the Biophysical Properties of Soil ........ 59 Analysis of total carbon in soils from Itatiaia National Park: relationship with profile attributes and terrain covariates ............................ 60 Proximal sensing of soil surface properties in relation to crusting, and rainfall-runoff processes: from portable to UAV-based platforms ................... 61 The spatial variability of soil’s plant-available water capacity, and its implications for site-specific management ................................. 62 Evaluating recent and sub-recent magnetic impact records of air pollution by combined soil and bio-magnetic monitoring ............................ 63 Ecosystem services provided by groundwater dependent wetlands in karst areas: carbon storage and sequestration ................................ 64 Spatial explicit prediction of soil organic matter using a hybrid model composed of random forest and ordinary kriging ............................... 65 Identifying soil management zones in a sugarcane field using proximal sensed electromag- netic induction and gamma-ray spectrometry data .................. 66 Evaluating the potential of simulated soil clay content by SoilGen2 model as soft data in Regression Kriging in sparsely sampled areas ..................... 67 Orthogonalisation and standardisation as alternatives to improve predictions of soil prop- erties and lime requirement using on-the-go Vis-NIR-SWIR spectroscopy ...... 68 Use of GPR in evaluation of iron ore tailings deposition characteristics in the River Doce Basin - Brazil ....................................... 69 A graphical user interface in R to perform preprocessing, multivariate modeling and prediction using spectroscopic data ........................... 70 Rapid detection of alkanes and polycyclic aromatic hydrocarbons (PAH) in oil-contaminated soils using visible near-infrared spectroscopy and chemometrics ........... 71 A combination of soil sensors provides useful and efficient landscape genesis information for archaeological prospection .............................. 72 4 Assessment of soil ecosystem services at landscape scale by direct soil monitoring and modelling. ......................................... 73 Measuring functional pedodiversity using spectroscopic information ............ 74 Slakes: A soil aggregate stability android application .................... 75 Soil NIR-spectra and high-resolution satellite images to monitor the characteristics of active layer most related to permafrost thermal behaviour, Crater Lake CALM site, Deception Island, Marine Antarctica. .......................... 76 Large scale modelling of soil organic matter using DTM variables and geographically weighted regression .................................... 77 Laser scanner technologies to monitoring mountain peatlands recovering ......... 78 Using a Portable XRF for Classifying Volcanic Paddy Soils of West Sumatra, Indonesia 79 Quantifying the uncertainty in a model reconstruction of a soilscape for archaeological land evaluation ...................................... 80 Soil hydrological classification mapping in Scotland using DSM and Random Forests .. 81 GIS-based multivariate predictive models for gully erosion susceptibility
Recommended publications
  • Soils Section
    Soils Section 2003 Florida Envirothon Study Sections Soil Key Points SOIL KEY POINTS • Recognize soil as an important dynamic resource. • Describe basic soil properties and soil formation factors. • Understand soil drainage classes and know how wetlands are defined. • Determine basic soil properties and limitations, such as mottling and permeability by observing a soil pit or soil profile. • Identify types of soil erosion and discuss methods for reducing erosion. • Use soil information, including a soil survey, in land use planning discussions. • Discuss how soil is a factor in, or is impacted by, nonpoint and point source pollution. Florida’s State Soil Florida has the largest total acreage of sandy, siliceous, hyperthermic Aeric Haplaquods in the nation. This is commonly called Myakka fine sand. It does not occur anywhere else in the United States. There are more than 1.5 million acres of Myakka fine sand in Florida. On May 22, 1989, Governor Bob Martinez signed Senate Bill 525 into law making Myakka fine sand Florida’s official state soil. iii Florida Envirothon Study Packet — Soils Section iv Contents CONTENTS INTRODUCTION .........................................................................................................................1 WHAT IS SOIL AND HOW IS SOIL FORMED? .....................................................................3 SOIL CHARACTERISTICS..........................................................................................................7 Texture......................................................................................................................................7
    [Show full text]
  • World Reference Base for Soil Resources 2014 International Soil Classification System for Naming Soils and Creating Legends for Soil Maps
    ISSN 0532-0488 WORLD SOIL RESOURCES REPORTS 106 World reference base for soil resources 2014 International soil classification system for naming soils and creating legends for soil maps Update 2015 Cover photographs (left to right): Ekranic Technosol – Austria (©Erika Michéli) Reductaquic Cryosol – Russia (©Maria Gerasimova) Ferralic Nitisol – Australia (©Ben Harms) Pellic Vertisol – Bulgaria (©Erika Michéli) Albic Podzol – Czech Republic (©Erika Michéli) Hypercalcic Kastanozem – Mexico (©Carlos Cruz Gaistardo) Stagnic Luvisol – South Africa (©Márta Fuchs) Copies of FAO publications can be requested from: SALES AND MARKETING GROUP Information Division Food and Agriculture Organization of the United Nations Viale delle Terme di Caracalla 00100 Rome, Italy E-mail: [email protected] Fax: (+39) 06 57053360 Web site: http://www.fao.org WORLD SOIL World reference base RESOURCES REPORTS for soil resources 2014 106 International soil classification system for naming soils and creating legends for soil maps Update 2015 FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS Rome, 2015 The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned. The views expressed in this information product are those of the author(s) and do not necessarily reflect the views or policies of FAO.
    [Show full text]
  • Diagnostic Horizons
    Exam III Wednesday, November 7th Study Guide Posted Tomorrow Review Session in Class on Monday the 4th Soil Taxonomy and Classification Diagnostic Horizons Epipedons Subsurface Mollic Albic Umbric Kandic Ochric Histic Argillic Melanic Spodic Plaggen Anthropic Oxic 1 Surface Horizons: Mollic- thick, dark colored, high %B.S., structure Umbric – same, but lower B.S. Ochric – pale, low O.M., thin Histic – High O.M., thick, wet, dark Sub-Surface Horizons: Argillic – illuvial accum. of clay (high activity) Kandic – accum. of clay (low activity) Spodic – Illuvial O.M. accumulation (Al and/or Fe) Oxic – highly weathered, kaolinite, Fe and Al oxides Albic – light colored, elluvial, low reactivity Elluviation and Illuviation Elluviation (E horizon) Organic matter Clays A A E E Bh horizon Bt horizon Bh Bt Spodic horizon Argillic horizon 2 Soil Taxonomy Diagnostic Epipedons Diagnostic Subsurface horizons Moisture Regimes Temperature Regimes Age Texture Depth Soil Taxonomy Soil forming processes, presence or Order Absence of major diagnostic horizons 12 Similar genesis Suborder 63 Grasslands – thick, dark Great group 250 epipedons High %B.S. Sub group 1400 Family 8000 Series 19,000 Soil Orders Entisols Histosols Inceptisols Andisols Gelisols Alfisols Mollisols Ultisols Spodosols Aridisols Vertisols Oxisols 3 Soil Orders Entisol Ent- Recent Histosol Hist- Histic (organic) Inceptisol Incept- Inception Alfisol Alf- Nonsense Ultisol Ult- Ultimate Spodosol Spod- Spodos (wood ash) Mollisol Moll- Mollis (soft) Oxisol Ox- oxide Andisol And- Ando (black) Gelisol
    [Show full text]
  • Sustaining the Pedosphere: Establishing a Framework for Management, Utilzation and Restoration of Soils in Cultured Systems
    Sustaining the Pedosphere: Establishing A Framework for Management, Utilzation and Restoration of Soils in Cultured Systems Eugene F. Kelly Colorado State University Outline •Introduction - Its our Problems – Life in the Fastlane - Ecological Nexus of Food-Water-Energy - Defining the Pedosphere •Framework for Management, Utilization & Restoration - Pedology and Critical Zone Science - Pedology Research Establishing the Range & Variability in Soils - Models for assessing human dimensions in ecosystems •Studies of Regional Importance Systems Approach - System Models for Agricultural Research - Soil Water - The Master Variable - Water Quality, Soil Management and Conservation Strategies •Concluding Remarks and Questions Living in a Sustainable Age or Life in the Fast Lane What do we know ? • There are key drivers across the planet that are forcing us to think and live differently. • The drivers are influencing our supplies of food, energy and water. • Science has helped us identify these drivers and our challenge is to come up with solutions Change has been most rapid over the last 50 years ! • In last 50 years we doubled population • World economy saw 7x increase • Food consumption increased 3x • Water consumption increased 3x • Fuel utilization increased 4x • More change over this period then all human history combined – we are at the inflection point in human history. • Planetary scale resources going away What are the major changes that we might be able to adjust ? • Land Use Change - the world is smaller • Food footprint is larger (40% of land used for Agriculture) • Water Use – 70% for food • Running out of atmosphere – used as as disposal for fossil fuels and other contaminants The Perfect Storm Increased Demand 50% by 2030 Energy Climate Change Demand up Demand up 50% by 2030 30% by 2030 Food Water 2D View of Pedosphere Hierarchal scales involving soil solid-phase components that combine to form horizons, profiles, local and regional landscapes, and the global pedosphere.
    [Show full text]
  • Soil Properties Database of Spanish Soils. Volumen
    65°^ e 0 6,2.1 Centro de Investigaciones Energeticas, Medioambientales y Tecnoldgicas Miner A, is /X Base de Dates de Propiedades Edafoldgicas de los Suelos Espanoles. VolumenXI. CASTHIA-LEON(b): Palentia, Valladolid y Avila C. Trueba RMillan T. Schmid received C. Lago JUL 121999 C. Roquero M. Magister OSTl MormesTecnicosCiemat 898 julio,1999 1. -a;:' ' > DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. Informes T ecnicos Ciemat 898 julio, 1999 Base de Dates dePropiedades Edafologicas de los Suelos Espanoles. VohnnenXL CASHLLA-LEON(b): Palencia, Valladolid y Avila C. Trueba (*) R. Millan(*) T. Schmid (*) C. Lago (*) C. Roquero (**) M.Magister (**) (*) CIEMAT (**)UPM Departamento de Impacto Ambiental de la Energia Toda correspondenica en relation con este trabajo debe dirigirse al Servicio de Information y Documentation, Centro de Investigaciones Energeticas, Medioambientales y Tecnoldgicas, Ciudad Universitaria, 28040-MADRID, ESPANA. Las solicitudes de ejemplares deben dirigirse a este mismo Servicio. Los descriptores se ban seleccionado del Thesauro del DOE para describir las materias que contiene este informe con vistas a su recuperation. La catalogacidn se ha hecho utilizando el documento DOE/TIC-4602 (Rev. 1) Descriptive Cataloguing On-Line, y la clasificacion de acuerdo con el documento DOE/TIC.4584-R7 Subject Categories and Scope publicados por el Office of Scientific and Technical Information del Departamento de Energia de los Estdos Unidos. Se autoriza la reproduction de los resumenes analiticos que aparecen en esta publication. Deposito Legal: M -14226-1995 ISSN: 1135-9420 NIRO: 238-99-003-5 Editorial CIEMAT CLASIFICACION DOE Y DESCRIPTORES 540230 SOILS; SOIL CHEMISTRY; SOIL MECHANICS; RADIONUCLIDE MIGRATION; DATA BASE MANAGEMENT; DATA COMPILATION; SPAIN; “Base de Dates de Propiedades Edafologicas de los Suelos Espanoles.
    [Show full text]
  • Adsorption and Availability of Phosphorus in Response to Humic Acid Rates in Soils Limed with Caco3 OR Mgco3 9
    Ciência e Agrotecnologia, 42(1):7-20, Jan/Feb. 2018 http://dx.doi.org/10.1590/1413-70542018421014518 Adsorption and availability of phosphorus in response to humic acid rates in soils limed with CaCO3 or MgCO3 Adsorção e disponibilidade de fósforo em resposta a doses de ácido húmico em solos corrigidos por CaCO3 ou MgCO3 Henrique José Guimarães Moreira Maluf1*, Carlos Alberto Silva1, Nilton Curi1, Lloyd Darrell Norton2, Sara Dantas Rosa1 1Universidade Federal de Lavras/UFLA, Departamento de Ciência do Solo/DCS, Lavras, MG, Brasil 2Purdue University, Department of Agricultural and Biological Engineering, West Lafayette, Indiana, USA *Corresponding author: [email protected] Received in May 18, 2017 and approved in July 24, 2017 ABSTRACT Humic acid (HA) may reduce adsorption and increase soil P availability, however, the magnitude of this effect is different when 2+Ca prevails over Mg2+ in limed soils. The objective of this study was to evaluate the effects of HA rates and carbonate sources on the adsorption, phosphate maximum buffering capacity (PMBC), and P availability in two contrasting soils. Oxisol and Entisol samples were firstly incubated with the -1 following HA rates: 0, 20, 50, 100, 200 and 400 mg kg , combined with CaCO3 or MgCO3, to evaluate P adsorption. In sequence, soil samples were newly incubated with P (400 mg kg-1) to evaluate P availability. The least P adsorption was found when 296 mg kg-1 of HA was added to Oxisol. Applying HA rates decreased maximum adsorption capacity, increased P binding energy to soil colloids and did not alter PMBC of Entisol.
    [Show full text]
  • Meta-Analysis of the Effects of Soil Properties, Site Factors And
    Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Hydrol. Earth Syst. Sci. Discuss., 8, 10007–10052, 2011 Hydrology and www.hydrol-earth-syst-sci-discuss.net/8/10007/2011/ Earth System doi:10.5194/hessd-8-10007-2011 Sciences © Author(s) 2011. CC Attribution 3.0 License. Discussions This discussion paper is/has been under review for the journal Hydrology and Earth System Sciences (HESS). Please refer to the corresponding final paper in HESS if available. Meta-analysis of the effects of soil properties, site factors and experimental conditions on preferential solute transport J. K. Koestel, J. Moeys, and N. J. Jarvis Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), P.O. Box 7014, 750 07 Uppsala, Sweden Received: 27 October 2011 – Accepted: 28 October 2011 – Published: 15 November 2011 Correspondence to: J. K. Koestel ([email protected]) Published by Copernicus Publications on behalf of the European Geosciences Union. 10007 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Abstract Preferential flow is a widespread phenomenon that is known to strongly affect solute transport in soil, but our understanding and knowledge is still poor of the site factors and soil properties that promote it. To investigate these relationships, we assembled a 5 database from the peer-reviewed literature containing information on 793 breakthrough curve experiments under steady-state flow conditions. Most of the collected experi- ments (642 of the 793 datasets) had been conducted on undisturbed soil columns, although some experiments on repacked soil, clean sands, and glass beads were also included. In addition to the apparent dispersivity, we focused attention on three poten- 10 tial indicators of preferential solute transport, namely the 5 %-arrival time, the holdback factor, and the ratio of piston-flow and average transport velocities.
    [Show full text]
  • Sugar Cane Response to Potassium Fertilization on Andisol, Entisol, and Mollisol Soils of Guatemala
    Guatemala Sugar Cane Response to Potassium Fertilization on Andisol, Entisol, and Mollisol Soils of Guatemala By Ovidio Pérez and Mario Melgar This research examines the potential for improved sugar cane yield and quality in Guatemala through increased potassium (K) use in combination with adequate nitrogen (N) and phosphorus (P) rates. A critical soil test K level for the major sugar cane-producing region of Guatemala is also suggested. Guatemala’s sugar cane region is located on its southern coastland in volcanic lowlands and coastal valleys. In the upper and middle parts of the region, high amounts of precipitation are common. Andisols and sandy soils with low K levels dominate. In contrast, the alluvial soils of the coastal valley generally have moderate levels of K. Most sugar cane growers do not consider application of K in their fer- tilization program, although K is extract- ed in large amounts by the crop. In fact, Andisols with low soil K sugar cane requires more K than N. levels are common in the Preliminary results from a regional study on N, P, and K use in sugar cane production area sugar cane showed a small but consistent crop response to K fertiliza- of Guatemala. tion on soils with low K levels (Pérez and Melgar, 1998). However, these experiments were characterized by addition of low rates of N and P. Unpublished data from local sugar cane companies have shown large Table 1. Effect of K on sugar cane yield, sugar content, application rates of N, P and K increase and juice purity. sugar production in some of these areas.
    [Show full text]
  • Engineering Values from Soil Taxonomy
    ENGINEERING VALUES FROM SOIL TAXONOMY W. R. Philipson, R. W. Arnold, and D. A. Sangrey, Cornell University Many properties of engineering interest can be deduced from the connota­ tive nomenclature of Soil Taxonomy, the pedological soil classification sys­ tem adopted for use by the National Cooperative Soil Survey in 1965. The system is based on soil properties that have been defined quantitatively and named. Each soil class name, in turn, is composed of word elements taken from the names of those properties that are diagnostic for the soil class. The major features of a soil placed within any class are signified directly by the specific formative elements appearing in the soil class name. The descriptions and formative elements of six surface horizons (epipedons ), 17 subsurface horizons, 56 special features, and the 10 soil classes at the highest level of the system (orders), are provided. Because the basis of this paper is use of Soil Taxonomy by engineers, emphasis is placed on the nomenclature rather than the structure of philosophy of the system. The 39 formative elements considered to be most informative for general engineering analysis are highlighted for handy reference. •SINCE 1965, all U.S. Department of Agriculture soil survey reports have correlated soils into the new Soil Taxonomy (11, 12), until recently referred to as the 7th Approxi­ mation (10). In contrast to the qualitative nature of the previous pedological classifi­ cation system (2, 8, 14), Soil Taxonomyprovides aquantitative framework in which to place soils of tne United States as well as many soils of the world. Authors, attempt­ ing to point out the engineering value of the previous system, have been forced to treat the general merits of pedological soil surveys (4, 5, 6) or to confine themselves to specific soil series encountered in their particular project (7, 13).
    [Show full text]
  • NRCS Introduction to Soils
    Soils Intro USDA Natural Resources Conservation Service Take an apple Have it represent the Earth Cut out and save … ¼ of the apple This much represents ldland area. Now cut that slice in half, and keep one piece. This much represents where people live. Cut that piece in quarters and keep one ¼. This represents the amount of soil where food can be grown. This is 3% (1/32) of the Earth’s surface. We Study Soil Because It’s A(n) Great integrator Snapshot of Medium of geologic, crop climatic, production biological, and human history Producer and Waste decomposer absorber of gases MdiMedium Source for plant material for growth construction, medicine, art, Home to etc. organisms (plants, animals Filter of and others) water and Essential natural resource wastes Five Soil Forming Factors Biota Parent Material Topography Climate (The first four factors over) Time Glacial Till Parent Material Sutton Series Glaciofluvial Parent Material Manchester Series Alluvium Parent Material Hadley Series Glaciolacustrine Parent Material Scitico Series Organic Parent Material Natchaug Series Disturbed Parent Material Soil Parent Material Soil Color Munsell Notation Hue VlValue Chroma Redox Concentrations Pore linings on root cha nn el and Fe mass ped surface in matrix Nodule in matrix Soft Fe/Mn Hard Fe/Mn accumulations accumulations Modules Concretions Redox Depletions B. Fe depletions along ped surfaces. A. No redox depletions. Fe(II) Clay in in Matrix Matrix C. Clay dep let ions along ped surfaces. A Soil Profile Soil Structure - With Structure Granular Blocky (Su bangu lar ) (Angu lar ) Platy Prismatic Columnar Wedge Soil Structure – Without Structure Single Grain Massive USDA Textural Triangle Landscape Factors • Depth to bedrock • Depth to water table • Flooding vs.
    [Show full text]
  • Soil Physical Properties
    Soil Horizons Master Horizons O A O horizon E A horizon E horizon B B horizon C horizon C R horizon W horizon R W Master Horizons O horizon – predominantly organic matter (litter and humus) A horizon – zone of organic matter accumulation E horizon – zone of eluviation (loss of clay, Fe, Al) B horizon – zone of accumulation (clay, Fe, Al, CaC03, salts…) – forms below O, A, or E horizon Master Horizons C horizon – little or no pedogenic alteration, unconsolidated parent material, soft bedrock R horizon – hard, continuous bedrock O horizon A horizon E horizon B horizon B horizon B horizon B horizon C horizon R horizon Transitional Horizons contains properties of horizon above and horizon below – AB – mostly A horizon, but some B horizon – BA – mostly B horizon, but some A horizon – BE – mostly B horizon, but some E horizon – BC – mostly B horizon, but some C horizon – CB – mostly C horizon, but some B horizon – C/B – intermingled bodies of C and B horizon material, majority is C horizon material Transitional Horizons EB BC Transitional Horizons BE BC Horizon Suffixes Horizon Criteria Suffix a highly decomposed organic matter (O) b buried genetic horizon (any) c concretions or nodules (any) e moderately decomposed organic matter (O) g strong gleying (any) h illuvial organic matter accumulation (B) i slightly decomposed organic material (O) k pedogenic carbonate accumulation (B or C) Horizon Suffixes Horizon Criteria Suffix o residual sesquioxide accumulation (B) p plow layer or other artificial disturbance (A) r weathered or
    [Show full text]
  • Chapter 3 Chapter 3 Learning Objectives
    Chapter 3 Learning Objectives Chapter 3 • Describe the current USDA soil classification system • List the six categories of classification in Soil Soil Taxonomy Taxonomy • Describe the major characteristics, the general degree of weathering and soil development, and the worldwide distribution and uses of the 12 soil orders • List key features of a particular soil and its environment given the soil name (e.g., Hapludalf) Diagnostic Subsurface Horizons Diagnostic Subsurface Horizons • 18 of them – Albic: light-colored elluvial horizon (leached) – Cambic: weakly developed horizon, some color • Six we will focus on (and the assoc. genetic change label): – Spodic: illuvial horizon with accumulations of O.M. – Argillic: subsurface accumulations of silicate clays – Albic (E) -Argg(illic (Bt) – CliCalcic: accumu ltilation o f car bona tes, o ften as – Cambic (Bw) - Fragic (Bx) white, chalk-like nodules – Spodic (Bhs) - Calcic (Bk) – Fragipan: cemented, dense, brittle pan Light colored horizon Albic Argillic Weakly developed horizon Cambic No significant accumulation silicate clays Unweathered Accumulation of Acid weathering, material organic matter Fe, Al oxides Spodic Calcic Fragipan Modified from full version of Figure 3.3 in textbook (page 62). 1 Levels of Description Levels of Description • Order Most general • Order – One name, all end in “-sol” There are 12. Differentiated by presence or absence of • Suborder diagnostic horizons or features that reflect • Great group soil-forming processes. EXAMPLE: ENTISOL • Subgroup • Suborder • Family • Great group • Subgroup •Series •Family Most specific •Series Levels of Description Levels of Description • Order – One name, all end in “-sol” There are 12 . • Order – One name, all end in “-sol” There are 12 .
    [Show full text]