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Academic literature on the topic 'Soil maps'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 May 2022

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Journal articles on the topic "Soil maps"

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Mueller, T. G., and F. J. Pierce. "Soil Carbon Maps." Soil Science Society of America Journal 67, no. 1 (2003): 258. http://dx.doi.org/10.2136/sssaj2003.0258.

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Mueller, T. G., and F. J. Pierce. "Soil Carbon Maps." Soil Science Society of America Journal 67, no. 1 (January 2003): 258–67. http://dx.doi.org/10.2136/sssaj2003.2580.

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Grodzynskyi, M. "Representation of soils in the landscapes maps." Visnyk of the Lviv University. Series Geography, no. 39 (December 11, 2011): 113–21. http://dx.doi.org/10.30970/vgg.2011.39.2169.

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Series (succession sequences) of soils that change each other over time and within the landscape units are proper objects for landscape mapping. The soil series give an idea of both retrospective state of a soil before its anthropogenic transformations and of tendencies of soil development in landscape complexes of various types. The names of soils as they are appeared in soil nomenclature of Soil science should not be duplicated in the legends of landscape maps. "Landscape" names for soils have to stress on their features and attributes that are of primary importance for vegetation, water, thermal and other ecological regimes of landscapes. The "landscape" names for different types of Albeluvisols and Phaeozems of Ukraine are suggested. Key words: soil, landscape, landscape map, landscape science.

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Hartemink, Alfred E., Birl Lowery, and Carl Wacker. "Soil maps of Wisconsin." Geoderma 189-190 (November 2012): 451–61. http://dx.doi.org/10.1016/j.geoderma.2012.05.025.

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Bogaert, Patrick, and Dimitri D'Or. "Estimating Soil Properties from Thematic Soil Maps." Soil Science Society of America Journal 66, no. 5 (September 2002): 1492–500. http://dx.doi.org/10.2136/sssaj2002.1492.

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Kondratyeva, Maria, and Natalya Bazhukova. "Experience of regional soil-geochemical mapping." InterCarto. InterGIS 26, no. 1 (2020): 584–94. http://dx.doi.org/10.35595/2414-9179-2020-1-26-584-594.

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The developed series of soil-geochemical maps reveals the ecological functions of soils and the soil cover associated with the processes of migration, transformation, and accumulation of chemicals substances in landscapes. The thematic basis for the maps was the electronic version of the soil map of the Russian Federation with a scale of 1 : 2 500 000 and the Unified State Register of Soil Resources of the Russian Federation developed on its basis, as well as regional sources and the database of soil properties of the Perm Territory. Prepared maps represented by two main blocks — basic and applied, each of which, in turn, includes constituent and assessment maps. The article discusses the methodological foundations, the compilation methodology and the content of the presented maps. Baseline maps reveal the most common soil-geochemical patterns of migration and accumulation of substances in soils. The block includes: maps of the thicknesses of organogenic and humus soil horizons, cation exchange capacities, and sorption capacity of soils. These maps make it possible to characterize the sorption properties of surface soil horizons as the most important geochemical barriers for technogenic substances. An analysis of the content of these maps allows us to conclude that the sorption capacity of the most common soils in the region is estimated to be very low and low, due to the low thickness of the humus horizons and low cation exchange capacity of podzolic soils. Podzolized chernozems, soddy-gley and soddy-carbonate soils have an increased sorption ability, but their distribution area is small. The high sorption capacity of soils is associated with a significant thickness of organogenic horizons in hydromorphic soils. The application block is devoted to the analysis of soil properties in relation to heavy metals as a priority group of pollutants for which the soil is a depositing medium. Two maps are included in this block — “Conditions for the migration of heavy metals in soils” and “Sensitivity of soils”. The conditions for the migration of heavy metals highlighted on the map of the same name are represented by 12 options. In the northern and central parts of the region, conditions prevail that combine constant or seasonal recovery conditions and low pH values. In the southern part of the region they are replaced by oxidative weakly acidic. The assessment of the sensitivity of soils to heavy metal pollution is given on the basis of expert assessment. The soils of the region are defined as sensitive and very sensitive, that is, they relatively quickly change their properties to a negative side under the influence of anthropogenic load.

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Yang, Lin, You Jiao, Sherif Fahmy, A.-Xing Zhu, Sheldon Hann, James E. Burt, and Feng Qi. "Updating Conventional Soil Maps through Digital Soil Mapping." Soil Science Society of America Journal 75, no. 3 (May 2011): 1044–53. http://dx.doi.org/10.2136/sssaj2010.0002.

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Oke, Satoshi, Masaharu Ookado, Takuo Kokuryu, and Sakae Shibusawa. "Soil Maps in Precision Farming." Agricultural Information Research 13, no. 1 (2004): 69–78. http://dx.doi.org/10.3173/air.13.69.

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Hartemink, Alfred E., and Marthijn P. W. Sonneveld. "Soil maps of The Netherlands." Geoderma 204-205 (August 2013): 1–9. http://dx.doi.org/10.1016/j.geoderma.2013.03.022.

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Hartemink, Alfred E., Pavel Krasilnikov, and J. G. Bockheim. "Soil maps of the world." Geoderma 207-208 (October 2013): 256–67. http://dx.doi.org/10.1016/j.geoderma.2013.05.003.

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Dissertations / Theses on the topic "Soil maps"

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Reed, Seann Mischa. "Use of digital soil maps in a rainfall-runoff model /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Kraft, Jason Edmund. "Development of liquefaction susceptibility and hazard maps for the islands of Jamaica and Trinidad." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47676.

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Caribbean nations lie within a zone of distinct seismic hazard. While ground motion in the region has been analyzed, the potential for liquefaction has not been evaluated in most cases. In order to evaluate liquefaction, data describing soil composition, surficial geology, and seismic hazard analyses were collected and applied. This allowed for expansion of previously localized liquefaction analysis to be expanded to the extents of two island nations in the Caribbean. This thesis utilizes the Youd and Perkins (1978) qualitative liquefaction susceptibility and Holzer et al. (2011) liquefaction probability methodologies to evaluate the possibility of liquefaction in Trinidad and Jamaica during major seismic events. Maps were developed using geographic information system (GIS) data to compare susceptibility and hazard across the islands at varying levels of magnitude. In this way, the distribution of liquefiable deposits is displayed in a manner that can be used quickly and easily to motivate further study of susceptible regions and mitigation activities to reduce the risk posed by liquefaction in the countries.

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Eldridge, Simon Michael, and n/a. "The impact of the scale of mapping on soil map quality." University of Canberra. Resource, Environmental & Heritage Sciences, 1997. http://erl.canberra.edu.au./public/adt-AUC20060707.102807.

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It is generally assumed that increased map precision (ie map unit homogeneity) and map purity (map unit accuracy) should result from increasing the scale of mapping of the soil resource, since it should enable a more intricate breakdown of the landscape into landform facet based units. This study compared the predictive success of a 1:1 OK scale soil association map with the 1:25K and 1:1OOK scale soil landscape maps within the Birrigai area of the Paddy's river catchment, south west of Canberra, A.C.T. The 1:25K and the 1:100K scale soil landscape maps were also evaluated in a second larger evaluation area in the Paddy's river catchment which allowed more of the larger soil landscape map units to be evaluated. The 1:25K scale soil map was produced by another author for the A.C.T Government, and was surveyed at a substantially lower survey intensity than that for the 1:100K and 1:10K scale soil maps (ie only 0.05 observation sites / cm2 of published map). These maps were evaluated using a set of randomly located independent evaluation sites in each evaluation area, and from these calculating and comparing standard Marsman & de Gruijter(1986) measures of Map Purity. The strength of soil-landscape relationships within this catchment were determined from a Fixed One Way Analysis of Variance, and from more simplistic graphical comparisons of the means and standard deviations of the discrete soil data within these landform based map units. Soil-landscape relationships for the Nominal scale soil data (ie class type data) were evaluated by comparing the Marsman & de Gruijter(1986) Homogeneity index ratings among the soil map units. Intensive survey traverses were also carried out in selected soil landscapes to further evaluate the strength of soil landscapes present. The results revealed obvious improvements in map quality associated with increasing map scale from 1:100,000 to 1:10,000, and these included increases in the predictive success (Map Purity), reductions in the extent of map unit impurities, and planning advantages associated with having individual land facets delineated on the 1:10,000 scale map. The respectable purity ratings achieved by the 1:100,000 scale soil landscape map (ie average purity rating of 63%) was largely attributed to the flexibility of the "soil material" approach to soil landscape mapping. The relatively poor performance of the 1:25K consultancy soil landscape map demonstrated the fact that; any benefit gained from the improved intricacy in the representation of map unit delineation's with increased mapping scale, will be drastically reduced if it is not matched by an associated increase in the intensity of field investigations. Evaluations of the soil-landscape relationships found that the land facets of the Paddy's river catchment generally failed to delineate areas that were both uniform and unique in respect of their soil properties. Soil-landscape relationships were instead found to be quite complex, applying to only certain land facets, and in regards to only certain soil properties. Soil maps with units based on landsurface features were recommended on the basis of the importance of other landscape factors other than soils to land capability ratings, as well as on the useability of such maps. This study recommended the adoption of a " >2 detailed soil profile observations / land facet in each map unit " mapping standard to ensure a reasonable estimate of the variability and modal soil conditions present, as well as a reliable confirmation of the perceived soil-landscape relationships. The error usually associated with small scale mapping was effectively reduced by rapid ground truthing, involving driving along the major roads dissecting the map area and making brief observations of soil exposures on road batters, despite the bias of the road network making such mapping improvements uneven across the map. The major point to come from this study was the re-emphasising of the point that soil spatial variability has to be accepted as a "real landscape attribute" which needs to be accurately described and communicated to land users, and must not be considered as some sort of soil mapping failure. The fact that individual facets of the landscape rarely coincide with unique pockets of uniform and unique soils and soil properties must be considered simply an on the ground reality of nature, and not some mapping failure. It was thought that since other landscape factors (eg hillslope gradient) most often dominate the determination of land use suitability and capability, it is better to effectively describe the range and modal state of the soil conditions within such facets, then to attempt to extrapolate possible soil boundaries using geostatistical techniques which cut across such land facets, and may or may not correlate with real groupings of soil properties, depending on the spatial resolution of the soil variability distribution in the landscape. Even so the results of this investigation do put the validity of the physiographic terrain class mapping model as a predictor of soil traits under question, at least for the more complex landscape settings.

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Guan, Zhi Wei 1953. "Soil moisture approximation using thermal inertia maps : verification study on the relationship between HCMM observations and antecedent precipitation index for St. Lawrence Lowland of Southern Quebec." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=55601.

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Lowman, Amelia M. "Creation of soil liquefaction susceptibility maps for San Luis Obispo & Marin counties using geographic information systems /." Click here to view, 2009. http://digitalcommons.calpoly.edu/erscsp/2.

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Thesis (B.S.)--California Polytechnic State University, 2009.
Project advisor: Lynn E. Moody. Title from PDF title page; viewed on Jan. 13, 2010. Includes bibliographical references. Also available on microfiche.

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Amichev, Beyhan Y. "Comparison of Techniques for Estimation of Forest Soil Carbon." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/9915.

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Soil organic carbon represents the largest constituent of the global C pool and carbon budgets are studied by researchers and modelers in C cycling, global climate change, and soil quality studies. Pedon and soil interpretation record databases are used with soil and ecological maps to estimate regional SOC even though these databases are rarely complete for surface litter and mineral subsurface horizons. The first main objective of the project is to improve the ability to produce soil organic carbon estimates from existing spatial soils datasets, such as STATSGO. All records in the STATSGO Layer table that were incomplete or appeared to be incorrectly filled with a null or zero value were considered invalid. Data sorting procedures and texture lookup tables were used to identify exiting correct (valid) data entries that were used to substitute invalid records. STATSGO soil property data were grouped by soil order, MLRA, layer number, and texture to produce replacement values for all invalid data used to calculate mass SOC. Grouping criteria was specific to each variable and was based on texture designations. The resulting filled and unfilled tables were used with procedures assuming Normal and Lognormal distribution of parameters in order to analyze variation of mass SOC estimates caused by using different computation techniques. We estimated mass SOC to 2 m in Maine and Minnesota using filled and unfilled STATSGO data tables. Up to 54% of the records in Maine and up to 80% of the records in Minnesota contained null or zero values (mostly in fields related to rock fragments) that were replaced. After filling, the database resulted in 1.5 times higher area-weighted SOC. SOC calculated using the Normal distribution assumption were 1.2 to 1.5 times higher than those using the Lognormal transformation. SOC maps using the filled tables had more logical geographic SOC distribution than those using unfilled tables. The USDA Forest Service collects and maintains detailed inventory data for the condition and trends of all forested lands in the United States. A wide range of researchers and landowners use the resulting Forest Inventory and Analysis (FIA) database for analytical and decision making tasks. FIA data is available to the public in transformed or aggregate format in order to ensure confidentiality of data suppliers. The second main objective of this project was to compute SOC (kg m-2) results by FIA forest type and forest type group for three depth categories (25 cm, 1 m, and 2 m) at a regional scale for the 48 contiguous United States. There were four sets of results derived from the filled STATSGO and FIA datasets for each depth class by region: (1) SOC computed by the Lognormal distribution approach for (1a) all soil orders, (1b) without Histosols; and (2) SOC computed by the Normal distribution approach for (2a) all soil orders, (2b) without Histosols. Two spatial forest cover datasets were relevant to this project, FIA and AVHRR. We investigated the effects of FIA inventory data masking for Maine and Minnesota, such as plot coordinates rounding to the nearest 100 arc-second, and the use of 1 km resolution satellite-derived forest cover classes from AVHRR data, on SOC estimates to 2 m by forest type group. SOC estimates by soil mapping unit were derived from fixed STATSGO database tables and were computed by the Lognormal distribution approach including all soil orders. The methods in this study can be used for a variety of ecological and resource inventory assessments and the automated procedures can be easily updated and improved for future uses. The procedures in this study point out areas that could benefit the most during future revisions of STATSGO. The resulting SOC maps are dynamic and can be rapidly redrawn using GIS whenever STATSGO spatial or tabular data undergo updating. Use of pedon data to define representative values for all properties in all STATSGO layers and correlation of STATSGO layers to soil horizons will lead to vast improvement of the STATSGO Layer table and promote its use for mass SOC estimation over large regions.
Master of Science

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CARVALHO, Claudia Csek? Nolasco de. "Infer?ncia fuzzy para predi??o de classes de solo em ?reas de assentamentos na microrregi?o da Mata Alagoana." Universidade Federal Rural do Rio de Janeiro, 2015. https://tede.ufrrj.br/jspui/handle/jspui/2399.

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Sustainable development requires more detailed soil maps with representative information of local reality, helping to conciliate increasing agriculture production and environmental impacts reduction. It is a hard task to achieve considering the Brazil territorial extension and the absence of governmental programs and resources. The Digital Soil Mapping (DSM), through methodologies implementation, has been consolidated as support technique for conventional mapping. Using DSM techniques, we aimed to evaluate a methodological procedure to generate soil maps with refined scale by fuzzy inference, and to map simpler units, which means more adequate areas for the rural settlement development plans. We carried out the study on an area at the microregion of Mata of Alagoas State and the following attributes were integrated by pedologist knowledge: elevation, slope, curvature and profile, topographic wetness index ? derived from a Digital Elevation Model (built by aerophotogrammetric restitution at scale 1:8 000), and geological data at scale 1:50 000. In a first moment, we integrate the thematic layers of 5m resolution pixel related to the attributes. Possibility maps of predefined classes were generate based on rules established by pedological expertise using fuzzy inference. Then, we integrate the evidence maps of classes by weighted mean inference to produce the soils digital map. The predicted digital soil map was opposed to a map elaborated traditionally, and validated by field observation of transects and pre-existent profiles. The comparison between the digital soil map and the conventional indicated a accuracy of 81.2% between the predicted classes of the digital map and the ones on the conventional map units (described on the legend, however not spatialized). Confronting the predicted classes of the digital soil map and the ones identified on field, the observed accuracy was of 74.65%. The Kappa index found was very good, with 0.696 values for the comparison between the digital map and the conventional map and 0.705 between the digital map and field observations. The results demonstrate that legacy maps data may, through the pedologists knowledge and more detailed attribute data, aggregate information and produce more detailed soil maps. Considering the concepts of minimum mappable area, the definitions of pixel sizes suggested for the digital cartographic scale, and the rules for soil survey, the applied methodology allowed, on the used cartographic base, the amplification of the mapping scale from 1:25 000 to 1:15 000.
O desenvolvimento sustent?vel requer mapas de solo mais detalhados com informa??es representativas da realidade local que permitem conciliar aumento da produ??o agropecu?ria com minimiza??o de impactos ambientais. Essa ? uma tarefa dif?cil considerando a extens?o territorial do Brasil e a falta de programas e recursos governamentais. O Mapeamento Digital de Solos-MDS, atrav?s da implementa??o de metodologias, vem se consolidando como t?cnica de apoio ao mapeamento convencional. Usando t?cnicas de MDS, esse trabalho objetivou avaliar um procedimento metodol?gico para gerar mapa de solos com refinamento de escala usando infer?ncia fuzzy para mapear unidades mais simples, portanto mais adequadas aos planos de desenvolvimento dos assentamentos rurais. A ?rea escolhida para teste est? localizada na microrregi?o da Mata do Estado de Alagoas. Para execu??o do trabalho os atributos do terreno eleva??o, declividade, curvatura em perfil, plano de curvatura e ?ndice de umidade topogr?fica derivados de Modelo Digital de Eleva??o-MDE (gerado por restitui??o aerofotogram?trica na escala 1:8.000) e dados geol?gicos em escala 1:50:000, foram integrados atrav?s do conhecimento de ped?logos. Os planos de informa??es, com pixel de 5 m, a partir de regras estabelecidas pela expertise de ped?logos foram fuzzificados e integrados para gerar os mapas de possibilidades de ocorr?ncia para as classes de solo predefinidas. O mapa digital de solos foi produzido pela integra??o dos mapas de possibilidades das classes por infer?ncia m?dia ponderada. O mapa digital de solos predito foi confrontado com mapa elaborado por m?todo tradicional e validado por observa??es em campo ao longo de transectos e por perfis preexistentes. A an?lise comparativa entre o mapa digital de solos e o convencional obteve acur?cia de 81,2 % entre as classes preditas no mapa digital e as contidas nas unidades do mapa convencional (descritas na legenda, por?m n?o espacializadas). Confrontando as classes preditas no mapa digital e as identificadas no campo a acur?cia encontrada foi de 74,65%. A aferi??o das classes pelo ?ndice de Kappa foi considerada muito boa com valores de 0,696 para a compara??o entre o mapa digital versos mapa convencional e 0,705 entre o mapa digital e as observa??es de campo. A pesquisa mostrou que dados contidos em mapas legados de solo podem atrav?s do conhecimento de ped?logos e de dados detalhados de atributos do terreno agregar informa??es e produzir mapa digital de solo com amplia??o da escala e que neste trabalho ? compat?vel com a de levantamentos detalhados (1:15.000).

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Dellamea, Ricardo Batista Cerezer. "Eficiência da adubação a taxa variável em áreas manejadas com AP no Rio Grande do Sul." Universidade Federal de Santa Maria, 2008. http://repositorio.ufsm.br/handle/1/5484.

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This research aim to evaluate the efficiency of variable fertilizer rate in croplands under precision agriculture (PA) in Rio Grande do Sul State with different level of technologies adoption. In the first chapter was evaluated the PF performance in a small farm with 19 ha in which was cropped soybean in 2005/06 and 2006/07 located in central of Rio Grande do Sul State. In this cropland were used the equipments and technology available in this farm. It was determined the spatial variability of soil attributes by grid sampling with 100 x 100 m also the yield map was done by manual sampling of crop and variable fertilizer rates by soil management zones. The PA increased in average 8% the soybean yield.The soil attribute that had better correlation with yield was soil organic matter. The economic analysis show a return of R$ 72,05 ha-1 due inputs save and increase yields. Also, the PA had a 29% increase of fertilizer use efficiency. In the second chapter were evaluated 6 farms totalizing 138 ha of soybean and 96 ha of maize linked to Aquarius Project of PA located in different counties of Alto Jacui, RS. The main activities evaluated were yield map with a Massey Ferguson combine, model MF 34, fertilizer variable rate with centrifuge force distribution Hercules 10.000 from STARA. The average of result shows decrease of 33.1 and 36.8% in fertilizer inputs and increase of 10 and 14% in soybean and maize yields, respectively. The efficiency use of fertilizer was increased in 47.2 and 62.8% allowing a return of R$ 143.34 ha-1 to soybean and R$ 312.44 ha-1 to maize.
Este trabalho teve por objetivo avaliar a eficiência da adubação a taxa variável em áreas manejadas com agricultura de precisão (AP) no Rio Grande do Sul, com diferentes níveis tecnológicos. No capítulo 1 foram analisados 19 ha cultivados com soja nas safras 2005/06 e 2006/07, localizados em uma pequena propriedade no município de Silveira Martins, região central do estado do Rio Grande do Sul. Nessa área foram utilizados os recursos tecnológicos disponíveis na propriedade. Com isso, foram determinadas as variabilidades espaciais dos atributos do solo através de um grid com 100 x 100 m, confeccionados mapas de produtividade de forma manual, aplicados os insumos em diferentes doses por zonas de manejo. O manejo localizado proporcionou uma economia de 17% na quantidade total dos insumos e um aumento de 8% na produtividade da soja, o que representou um retorno financeiro bruto de R$ 72,05 ha-1 e um aumento na eficiência de 29% em relação ao manejo tradicional. No capítulo 2 foram analisados 138 ha com a cultura da soja e 96 ha com a cultura do milho na safra 2006/07, distribuídos em 6 áreas pertencentes a produtores ligados ao Projeto Aquarius de AP e localizadas em diferentes municípios na região do Alto Jacuí RS. A colheita georreferenciada foi realizada com o auxílio de uma colhedora marca Massey Ferguson modelo MF34 e as aplicações, a taxas variáveis de insumos, foram realizadas através do distribuidor centrífugo Hércules 10000, marca Stara. Para realizar a análise da eficiência do uso de fertilizantes foram comparadas as entradas e saídas de fertilizantes e grãos nos talhões com AP, com a estimativa média de entradas e saídas dos talhões com AC que serviram de testemunhas dentro das propriedades correspondentes. Os resultados evidenciam uma redução média de 33,1 e 36,8% no uso dos insumos e um aumento de 10 e 14% no rendimento das culturas da soja e milho, respectivamente. A eficiência do uso de fertilizantes foi incrementada em 47,2 e 55,1% com o uso da AP, quando consideradas as quantidades totais de fertilizantes, passando para 31,3 e 62,8%, quando considerados os recursos financeiros, proporcionando um retorno de R$ 143,34 ha-1 para a cultura da soja e R$ 312,44 ha-1para a cultura do milho.

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Miranda, Elka Élice Vasco de [UNESP]. "Modelagem e mapas de capacidade suporte de carga de solos cultivados com cana-de-açúcar." Universidade Estadual Paulista (UNESP), 2006. http://hdl.handle.net/11449/101733.

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New studies must consider and quantify the load level that a determined soil can receive and in what management condition this soil can be handled without suffering physical alterations and additional compaction. The objectives of this work were to quantify the preconsolidation pressure, suggesting it as a quantitative indicator of the soil structural sustentabilidade cultivated with sugar cane; to get models that allow to predict the behavior of the soil structure cultivated with sugar cane in function of the preconsolidation pressure and the water content; to get agricultural traffic maps in function of water content and the preconsolidation pressure of the soil using geostatistics resources; and to evaluate the soil mechanical resistance, based on cone index and water content variation under several types of soils. Soil samples from several soil classes were collected cropped with sugar cane and areas that varied from 4.88 and 17.86 ha. The maps that illustrated the spatial variability of the several physical attributes were made using statistical and geostatistical resources. The models of the soil load capacity satisfactory modeled the behavior of the soil structure in function of the water content and depth, helping the maximum load prediction that this soil can receive without compromising its structure. The obtained models were sensible to identify the consolidation structure, identifying the layers and grids with huge mechanical resistance. The use of geostatistical tools and mapping the studied attributes showed a promising tool to take decisions about the system, aiming at, over all the soil structure sustentation. The maps of the soil load capacity constituted an efficient tool to locate 4 areas into the field with compromised structure, making possible to plan the correct dimension of the machinery... (Complete abstract, click electronic access below)

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Cherubin, Maurício Roberto. "EFICIÊNCIA DE MALHAS AMOSTRAIS UTILIZADAS NA CARACTERIZAÇÃO DE ATRIBUTOS QUÍMICOS EM LATOSSOLOS MANEJADOS COM AGRICULTURA DE PRECISÃO." Universidade Federal de Santa Maria, 2013. http://repositorio.ufsm.br/handle/1/4915.

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The georeferenced soil sampling using sampling grids is the main strategy of precision agriculture (PA) to recognize the spatial variability of soil attributes, allowing manage them in site-specific. In this context, the objective of the work was to evaluate the efficiency of using different sizes of sampling grids in the characterization of spatial variability of chemical attributes in Oxisols managed with PA in Southern Brazil. Therefore, the work consists of three studies. In studies one and two were used 60 areas (6,046.55 ha), located in 23 counties of North region of Rio Grande do Sul (RS). The areas were sampled using regular grids of 100 x 100 m (20 areas), 142 x 142 m (20 areas) and 173 x 173 m (20 areas), being that 10 areas of each grid were collected from 0.00-0.10 m and 10 areas from 0.00-0.15 m. The spatial variability of acidity attributes (pHwater and base saturation) and bases related (Ca and Mg) (study one) and the levels of P and K (study two) were analyzed by descriptive statistics and geostatistics. In study three, was used an area of 41.96 ha, located in Boa Vista das Missões - RS, where was made the collect of soil in seven sizes of sample grids, 50 x 50 m, 75 m x 75, 100 x 100 m, 125 m x 125, 150 x 150 m, 175 m and 200 x 175 x 200 m, at depth of 0.00-0.10 m. The levels of P and K were analyzed by descriptive statistics and geostatistics, and the similarity of thematic maps was compared by the coefficient of relative deviation (CRD) and by Pearson correlation matrix (p <0.05). The areas of Oxisols managed with PA in RS, show moderate acidity and high fertility, with the exception of subareas that presents limiting P levels. Independently of the depth sampled, the grid sample sizes used in RS state, in general, are not efficient in capturing the different scales of variability in soil chemical properties. According as increase the sample grid the thematic maps of P (CRD: 24.0 to 36.2%) and K (CRD: 11.7 to 19.4%) show more dissimilar to those obtained in smaller grid (50 x 50 m) considered as reference. Thus, the reduction of sampling grid sizes increases the accuracy of the information generated through thematic maps, allowing to do prescriptions of lime and fertilizers on site-specific with greater efficiency, and should be recommended in future sampling plans of soil adopted in PA areas in Southern Brazil.
A amostragem georreferenciada de solo utilizando malhas regulares é a principal estratégia da agricultura de precisão (AP) para reconhecer a variabilidade espacial dos atributos de solo, possibilitando manejá-los em sítio-específico. Nesse contexto, o objetivo do trabalho foi avaliar a eficiência do uso de diferentes dimensões de malhas amostrais na caracterização da variabilidade espacial de atributos químicos em Latossolos Vermelhos manejados com AP no Sul do Brasil. Para tanto, o trabalho constitui-se de três estudos. Nos estudos um e dois utilizaram-se 60 áreas (6.046,55 ha), localizadas em 23 municípios da região Norte do Rio Grande do Sul (RS). As áreas foram amostradas utilizando malhas regulares de 100 x 100 m (20 áreas); 142 x 142 m (20 áreas) e 173 x 173 m (20 áreas), sendo que 10 áreas de cada malha foram coletadas de 0,00-0,10 m e 10 áreas de 0,00-0,15 m. A variabilidade espacial dos atributos de acidez (pHágua e saturação por bases) e bases relacionadas (Ca e Mg) (estudo um) e dos teores de P e K (estudo dois) foram analisadas por meio da estatística descritiva e geoestatística. No estudo três, utilizou-se uma área de 41,96 ha, localizada em Boa Vista das Missões RS, onde foi realizada a coleta de solo em sete dimensões de malha amostral, 50 x 50 m, 75 x 75 m, 100 x 100 m, 125 x 125 m, 150 x 150 m, 175 x 175 m e 200 x 200 m, na profundidade de 0,00-0,10 m. Os teores de P e K foram analisados por meio da estatística descritiva e da geoestatística, e a similaridade dos mapas temáticos foi comparada pelo coeficiente de desvio relativo (CDR) e pela matriz de correlação de Pearson (p <0,05). As áreas de Latossolos Vermelhos manejadas com AP no estado do RS apresentam moderada acidez e alta fertilidade, com exceção de locais que apresentam teores limitantes de P. Independente da profundidade amostrada, as dimensões das malhas amostrais utilizadas no RS, em geral, não são eficientes em captar as diferentes escalas de variabilidade dos atributos químicos do solo. À medida que aumenta a malha amostral os mapas temáticos de P (CDR: 24,0 a 36,2%) e K (CDR: 11,7 a 19,4%) mostram-se mais dissimilares aos obtidos na menor malha (50 x 50 m) considerada como referência. Desta forma, a redução da dimensão da malha amostral aumenta a acurácia das informações geradas por meio de mapas temáticos, permitindo realizar prescrições de corretivos e fertilizantes em sítio-específico com maior eficiência, e devem ser preconizadas em futuros planos de amostragem de solo adotados nas áreas de AP no Sul do Brasil.

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Books on the topic "Soil maps"

1

Case, Roger J. Soil survey of Columbia County, New York. [Washington, D.C.]: The Service, 1989.

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Case, Roger J. Soil survey of Columbia County, New York. [Washington, D.C.?]: The Service, 1989.

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McCowan, Carlie. Soil survey of Macon County, Tennessee. [Washington, D.C.?]: Natural Resources Conservation Service, 2004.

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Purkey, Thomas H. Soil survey of Newaygo County, Michigan. [Washington, D.C.?]: U.S. Dept. of Agriculture, Soil Conservation Service, 1995.

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Nyberg, Paul R. Soil survey of Itasca County, Minnesota. [Washington, D.C.?]: The Service, 1987.

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Awalt, F. L. Soil survey of Crawford County, Illinois. [Washington, D.C.?]: The Service, 1996.

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Windhorn, Roger D. Soil survey of Knox County, Illinois. [Washington, D.C.?]: The Service, 1986.

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Hammer, Roger R. Soil survey of Cherry County, Nebraska. [Washington, D.C.]: U.S. Dept. of Agriculture, Natural Resources Conservation Service, 2005.

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Windhorn, Roger D. Soil survey of McLean County, Illinois. [Washington, D.C.?]: The Service, 1998.

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Perkis, William E. Soil survey of Hillsdale County, Michigan. [Washington, D.C.?]: The Service, 1997.

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Book chapters on the topic "Soil maps"

1

Marto, Aminaton, and Safiah Yusoff. "Major Soil Type, Soil Classification, and Soil Maps." In Soils of Malaysia, 69–101. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315277189-5.

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Marto, Aminaton, and Safiah Yusmah Mohd Yusoff. "Major Soil Type, Soil Classification, and Soil Maps." In Soils of Malaysia, 69–101. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/b21934-4.

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Ferguson, Richard B., Joe D. Luck, and Rachel Stevens. "Developing Prescriptive Soil Nutrient Maps." In Practical Mathematics for Precision Farming, 149–66. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2018. http://dx.doi.org/10.2134/practicalmath2016.0109.

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Gobin, Anne, Gérard Govers, and Mike Kirkby. "Pan-European Soil Erosion Assessment and Maps." In Soil Erosion in Europe, 659–74. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470859202.ch47.

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Diezma Iglesias, Belén. "Soil sensors." In Manuali – Scienze Tecnologiche, 15. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-044-3.15.

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Sensors for estimation of soil properties will be explained in this topic. Principles about soil sensors based on different technologies (electroconductivity, magnetic response, NIR optical signals, mechanical resistance…) will be presented. Relation between these sensors and soil attributes related to fertility are important in order to extract relevant agronomical information out of soil maps.

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D’or, D., and P. Bogaert. "Fine Scale Soil Texture Estimation Using Soil Maps and Profile Descriptions." In Quantitative Geology and Geostatistics, 453–62. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0810-5_39.

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Jacobsen, J. S., A. E. Plantenberg, G. A. Nielsen, and J. M. Caprio. "MAPS Mailbox - A Land and Climate Information System." In Proceedings of Soil Specific Crop Management, 361. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1993.soilspecificcrop.c36.

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Schaetzl, R. J., and B. A. Miller. "Use of Soil Maps and Surveys to Interpret Soil-Landform Assemblages and Soil-Landscape Evolution." In Geopedology, 251–64. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19159-1_15.

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Bacon, S. N., E. V. McDonald, G. K. Dalldorf, S. E. Baker, D. E. Sabol, T. B. Minor, S. D. Bassett, S. R. MacCabe, and T. F. Bullard. "Predictive Soil Maps Based on Geomorphic Mapping, Remote Sensing, and Soil Databases in the Desert Southwest." In Digital Soil Mapping, 411–21. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8863-5_32.

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Dmytruk, Yuriy, and Olga Stuzhuk. "Making Better Soil Maps Using Models of Tangential Curvature." In Soil Science Working for a Living, 101–7. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45417-7_8.

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Conference papers on the topic "Soil maps"

1

Miller, Gerald A., and Robert C. Mortensen. "Digitized Soil Maps for Iowa." In Proceedings of the First Annual Crop Production and Protection Conference. Iowa State University, Digital Press, 1990. http://dx.doi.org/10.31274/icm-180809-334.

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Miller, Gerald A., and Robert C. Mortensen. "Digital Soil Maps for Iowa." In Proceedings of the 1992 Crop Production and Protection Conference. Iowa State University, Digital Press, 1992. http://dx.doi.org/10.31274/icm-180809-414.

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Doolittle, J., R. Dobos, S. Waltman, E. Benham, W. Tuttle, and S. Peaslee. "Ground-penetrating Radar Soil Suitability Maps." In 22nd EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems. European Association of Geoscientists & Engineers, 2009. http://dx.doi.org/10.3997/2214-4609-pdb.157.sageep097.

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Doolittle, James, Robert Dobos, Sharon Waltman, Ellis Benham, Wes Tuttle, and Steve Peaslee. "Ground‐Penetrating Radar Soil Suitability Maps." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2009. Environment and Engineering Geophysical Society, 2009. http://dx.doi.org/10.4133/1.3176780.

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Font, J., C. Gabarro, J. Ballabrera, A. Turiel, J. Martinez, M. Umbert, F. Perez, et al. "SMOS CP34 soil moisture and ocean salinity maps." In 2012 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad). IEEE, 2012. http://dx.doi.org/10.1109/microrad.2012.6185236.

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Andrews, William. "APPLICATIONS OF NEW SEISMIC SOIL MAPS IN KENTUCKY." In GSA Annual Meeting in Indianapolis, Indiana, USA - 2018. Geological Society of America, 2018. http://dx.doi.org/10.1130/abs/2018am-324736.

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Cramer, Chris H. "Incorporating the Effects of Site Geology in CEUS Hazard Maps." In Geotechnical Earthquake Engineering and Soil Dynamics Congress IV. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40975(318)36.

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Tsibart, A., A. Postelmans, J. Dillen, A. Elsen, H. Vandendriessche, G. Van De Ven, and W. Saeys. "73. Deriving application maps for precision fertilization from soil scans and historical NDVI maps." In 13th European Conference on Precision Agriculture. The Netherlands: Wageningen Academic Publishers, 2021. http://dx.doi.org/10.3920/978-90-8686-916-9_73.

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Hnatushenko, Volodymyr, Igor Garkusha, and Volodymyr Vasyliev. "Creating soil moisture maps based on radar satellite imagery." In Active and Passive Microwave Remote Sensing for Environmental Monitoring, edited by Claudia Notarnicola, Nazzareno Pierdicca, and Emanuele Santi. SPIE, 2017. http://dx.doi.org/10.1117/12.2278040.

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Piikki, K., and M. Söderström. "Validating and Adapting Regional Soil Maps for Precision Agriculture." In First Conference on Proximal Sensing Supporting Precision Agriculture. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201413845.

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Reports on the topic "Soil maps"

1

Haselow, L. A., V. A. Rogers, C. J. Riordan, G. W. Eidson, and M. K. Herring. Analysis of water and soil from the wetlands of Upper Three Runs Creek. Volume 3, Wetlands survey maps. Office of Scientific and Technical Information (OSTI), August 1992. http://dx.doi.org/10.2172/10163527.

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Wells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown, and Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287466.

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This study was conducted to inventory, classify, and map soils and vegetation within the ecosystems of Katmai National Park and Preserve (KATM) using an ecological land survey (ELS) approach. The ecosystem classes identified in the ELS effort were mapped across the park, using an archive of Geo-graphic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, surficial geology, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified in the ELS work provides tools to support the design and implementation of future field- and RS-based studies, facilitates further analysis and contextualization of existing data, and will help inform natural resource management decisions. We collected information on the geomorphic, topographic, hydrologic, pedologic, and vegetation characteristics of ecosystems using a dataset of 724 field plots, of which 407 were sampled by ABR, Inc.—Environmental Research and Services (ABR) staff in 2016–2017, and 317 were from existing, ancillary datasets. ABR field plots were located along transects that were selected using a gradient-direct sampling scheme (Austin and Heligers 1989) to collect data for the range of ecological conditions present within KATM, and to provide the data needed to interpret ecosystem and soils development. The field plot dataset encompassed all of the major environmental gradients and landscape histories present in KATM. Individual state-factors (e.g., soil pH, slope aspect) and other ecosystem components (e.g., geomorphic unit, vegetation species composition and structure) were measured or categorized using standard classification systems developed for Alaska. We described and analyzed the hierarchical relationships among the ecosystem components to classify 92 Plot Ecotypes (local-scale ecosystems) that best partitioned the variation in soils, vegetation, and disturbance properties observed at the field plots. From the 92 Plot Ecotypes, we developed classifications of Map Ecotypes and Disturbance Landscapes that could be mapped across the park. Additionally, using an existing surficial geology map for KATM, we developed a map of Generalized Soil Texture by aggregating similar surficial geology classes into a reduced set of classes representing the predominant soil textures in each. We then intersected the Ecotype map with the General-ized Soil Texture Map in a GIS and aggregated combinations of Map Ecotypes with similar soils to derive and map Soil Landscapes and Soil Great Groups. The classification of Great Groups captures information on the soil as a whole, as opposed to the subgroup classification which focuses on the properties of specific horizons (Soil Survey Staff 1999). Of the 724 plots included in the Ecotype analysis, sufficient soils data for classifying soil subgroups was available for 467 plots. Soils from 8 orders of soil taxonomy were encountered during the field sampling: Alfisols (<1% of the mapped area), Andisols (3%), Entisols (45%), Gelisols (<1%), Histosols (12%), Inceptisols (22%), Mollisols (<1%), and Spodosols (16%). Within these 8 Soil Orders, field plots corresponded to a total of 74 Soil Subgroups, the most common of which were Typic Cryaquents, Typic Cryorthents, Histic Cryaquepts, Vitrandic Cryorthents, and Typic Cryofluvents.

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Thomas, Douglas, and Mellon Michael. Sublimation of terrestrial permafrost and the implications for ice-loss processes on Mars. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41244.

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Sublimation of ice is rate-controlled by vapor transport away from its outer surface and may have generated landforms on Mars. In ice-cemented ground (permafrost), the lag of soil particles remaining after ice loss decreases subsequent sublimation. Varying soil-ice ratios lead to differential lag development. Here we report 52 years of sublimation measurements from a permafrost tunnel near Fairbanks, Alaska, and constrain models of sublimation, diffusion through porous soil, and lag formation. We derive the first long-term in situ effective diffusion coefficient of ice-free loess, a Mars analog soil, of 9.05 × 10⁻⁶ m² s⁻¹, ~5× larger than past theoretical studies. Exposed ice-wedge sublimation proceeds ~4× faster than predicted from analogy to heat loss by buoyant convection, a theory frequently employed in Mars studies. Our results can be used to map near-surface ice-content differences, identify surface processes controlling landform formation and morphology, and identify target landing sites for human exploration of Mars.

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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Бабець, Євген Костянтинович, Ірина Петрівна Антонік, Ірина Євгенівна Мельникова, and Антон Всеволодович Петрухін. nfluence of Mining and Concentration Works Activity on Land Resources. Petroșani, 2019. http://dx.doi.org/10.31812/123456789/3120.

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The research provides assessment of current and longer-term consequences of iron ore open pit mining for land resources of adjacent areas. There are applied methods of analysis of fund materials; comparison of topographic sheets and special maps, visual observation, soil testing, laboratory analyses and statistic processing of data obtained. It is revealed that facilities of iron ore mining and concentration waste accumulation (dumps and tailing ponds) are destructive factors for the local lithosphere, dust chemical contamination being the basic one. The steps aimed at reducing negative impacts of technogenic objects of the mining and raw material complex on the environment are under study.

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Schlack, Trevor, Samuel Beal, Elizabeth Corriveau, and Jay Clausen. Detection limits of trinitrotoluene and ammonium nitrate in soil by Raman spectroscopy. Engineer Research and Development Center (U.S.), February 2022. http://dx.doi.org/10.21079/11681/43302.

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The detection limit of 2,4,6-trinitrotoluene (TNT) and ammonium nitrate (AN) in mixtures of Ottawa sand (OS) was studied using a Raman microscope applying conventional calibration curves, Pearson correlation coefficients, and two-sample t-tests. By constructing calibration curves, the conventionally defined detection limits were estimated to be 1.9 ± 0.4% by mass in OS and 1.9 ± 0.3% by mass in OS for TNT and AN. Both TNT and AN were detectable in concentrations as low as 1% by mass when Pearson correlation coefficients were used to compare averaged spectra to a library containing spectra from a range of soil types. AN was detectable in concentrations as low as 1% by mass when a test sample of spectra was compared to the same library using two-sample t-tests. TNT was not detectable at a concentration of 1% by mass when using two-sample t-tests.

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Conrady, Morgan, Markus Bauer, Kyoo Jo, Donald Cropek, and Ryan Busby. Solid-phase microextraction (SPME) for determination of geosmin and 2-methylisoborneol in volatile emissions from soil disturbance. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42289.

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A method is described here for the concentration and determination of geosmin and 2-methylisoborneol (2-MIB) from the gaseous phase, with translation to field collection and quantification from soil disturbances in situ. The method is based on the use of solid-phase microextraction (SPME) fibers for adsorption of volatile chemicals from the vapor phase, followed by desorption into a gas chromatograph-mass spectrometer (GC-MS) for analysis. The use of a SPME fiber allows simple introduction to the GC-MS without further sample preparation. Several fiber sorbent types were studied and the 50/30 μm DVB/CAR/PDMS was the best performer to maximize the detected peak areas of both analytes combined. Factors such as extraction temperature and time along with desorption temperature and time were explored with respect to analyte recovery. An extraction temperature of 30 ◦C for 10 min, with a desorption temperature of 230 ◦C for 4 min was best for the simultaneous analysis of both geosmin and 2-MIB without complete loss of either one. The developed method was used successfully to measure geosmin and 2-MIB emission from just above disturbed and undisturbed soils, indicating that this method detects both compounds readily from atmospheric samples. Both geosmin and 2-MIB were present as background concentrations in the open air, while disturbed soils emitted much higher concentrations of both compounds. Surprisingly, 2-MIB was always detected at higher concentrations than geosmin, indicating that a focus on its detection may be more useful for soil emission monitoring and more sensitive to low levels of soil disturbance.

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Chen, Wei-Yao, and Arvind Chaturvedi. Engineering Soils Maps of Randolph County, Indiana : Final Report. West Lafayette, IN: Purdue University, 1992. http://dx.doi.org/10.5703/1288284314614.

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Chen, Wei-Yao. Engineering Soils Maps of Johnson County, Indiana : Final Report. West Lafayette, IN: Purdue University, 1992. http://dx.doi.org/10.5703/1288284314615.

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Chen, Wei-Yao. Engineering Soils Maps of Wells County, Indiana : Final Report. West Lafayette, IN: Purdue University, 1992. http://dx.doi.org/10.5703/1288284314616.

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