Digital soil mapping: evaluation of sampling systems for soil surveys and refinement of soil maps at lower cost using legacy data

In soil surveys, several sampling systems can be used to define the most representative sites for sample collection and description of soil profiles. In recent years, the conditioned Latin hypercube sampling system has gained prominence for soil surveys. In Brazil, most of the soil maps are at small scales and in paper format, which hinders their refinement. The objectives of this work include: (i) to compare two sampling systems by conditioned Latin hypercube to map soil classes and soil properties; (II) to retrieve information from a detailed scale soil map of a pilot watershed for its refinement, comparing two data mining tools, and validation of the new soil map; and (III) to create and validate a soil map of a much larger and similar area from the extrapolation of information extracted from the existing soil map. Two sampling systems were created by conditioned Latin hypercube and by the cost-constrained conditioned Latin hypercube. At each prospection place, soil classification and measurement of the A horizon thickness were performed. Maps were generated and validated for each sampling system, comparing the efficiency of these methods. The conditioned Latin hypercube captured greater variability of soils and properties than the cost-constrained conditioned Latin hypercube, despite the former provided greater difficulty in field work. The conditioned Latin hypercube can capture greater soil variability and the cost-constrained conditioned Latin hypercube presents great potential for use in soil surveys, especially in areas of difficult access. From an existing detailed scale soil map of a pilot watershed, topographical information for each soil class was extracted from a Digital Elevation Model and its derivatives, by two data mining tools. Maps were generated using each tool. The more accurate of these tools was used for extrapolation of soil information for a much larger and similar area and the generated map was validated. It was possible to retrieve the existing soil map information and apply it on a larger area containing similar soil forming factors, at much low financial cost. The KnowledgeMiner tool for data mining, and ArcSIE, used to create the soil map, presented better results and enabled the use of existing soil map to extract soil information and its application in similar larger areas at reduced costs, which is especially important in development countries with limited financial resources for such activities, such as Brazil.

The use of agricultural soil maps and the status of agricultural soil mapping in the United States. Presented at the Twenty-Eighth annual meeting, 1948.

Cover title: Engineering use of agricultural soil maps.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area

A new approach to soil classification mapping based on the spatial distribution of soil properties

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Spatial disaggregation of land systems mapping in the Burnett catchment (South-East Queensland)

This project examined the potential for historical mapping of land resources to be upgraded to meet current requirements for natural resource management. The methods of spatial disaggregation used to improve the scale of mapping were novel and provide a method to rapidly improve existing information. The thesis investigated the potential to use digital soil mapping techniques and the multi-scale identification of areas within historical land systems mapping to provide enhanced information to support modern natural resource management needs. This was undertaken in the Burnett Catchment of South-East Queensland.

Incorporating Legacy Soil pH Databases into Digital Soil Maps

Soil data and reliable soil maps are imperative for environmental management. conservation and policy. Data from historical point surveys, e.g. experiment site data and farmers fields can serve this purpose. However, legacy soil information is not necessarily collected for spatial analysis and mapping such that the data may not have immediately useful geo-references. Methods are required to utilise these historical soil databases so that we can produce quantitative maps of soil propel-ties to assess spatial and temporal trends but also to assess where future sampling is required. This paper discusses two such databases: the Representative Soil Sampling Scheme which has monitored the agricultural soil in England and Wales from 1969 to 2003 (between 400 and 900 bulked soil samples were taken annually from different agricultural fields); and the former State Chemistry Laboratory, Victoria, Australia where between 1973 and 1994 approximately 80,000 soil samples were submitted for analysis by farmers. Previous statistical analyses have been performed using administrative regions (with sharp boundaries) for both databases, which are largely unrelated to natural features. For a more detailed spatial analysis that call be linked to climate and terrain attributes, gradual variation of these soil properties should be described. Geostatistical techniques such as ordinary kriging are suited to this. This paper describes the format of the databases and initial approaches as to how they can be used for digital soil mapping. For this paper we have selected soil pH to illustrate the analyses for both databases.

Modelled soil organic carbon stocks and changes in the Indo-Gangetic Plains, India from 1980 to 2030

The Global Environment Facility co-financed Soil Organic Carbon (GEFSOC) Project developed a comprehensive modelling system for predicting soil organic carbon (SOC) stocks and changes over time. This research is an effort to predict SOC stocks and changes for the Indian, Indo-Gangetic Plains (IGP), an area with a predominantly rice (Oryza sativa) - wheat (Triticum aestivum) cropping system, using the GEFSOC Modelling System and to compare output with stocks generated using mapping approaches based on soil survey data. The GEFSOC Modelling System predicts an estimated SOC stock for the IGP, India of 1.27, 1.32 and 1.27 Pg for 1990, 2000 and 2030, respectively, in the top 20 cm of soil. The SOC stock using a mapping approach based on soil survey data was 0.66 and 0.88 Pg for 1980 and 2000, respectively. The SOC stock estimated using the GEFSOC Modelling System is higher than the stock estimated using the mapping approach. This is due to the fact that while the GEFSOC System accounts for variation in crop input data (crop management), the soil mapping approach only considers regional variation in soil texture and wetness. The trend of overall change in the modelled SOC stock estimates shows that the IGP, India may have reached an equilibrium following 30-40 years of the Green Revolution. This can be seen in the SOC stock change rates. Various different estimation methods show SOC stocks of 0.57-1.44 Pg C for the study area. The trend of overall change in C stock assessed from the soil survey data indicates that the soils of the IGP, India may store a projected 1.1 Pg of C in 2030. (C) 2007 Elsevier B.V. All rights reserved.

Discrimination of geomorphic surfaces with multivariate analysis of soil attributes in sandstone - basalt lithosequence

O conceito de superfície geomórfica permite uma interligação entre os diferentes ramos da ciência do solo, tais como geologia, geomorfologia e pedologia. Esta associação favorece a compreensão da distribuição espacial dos solos na paisagem, e torna possível compreender o comportamento dos atributos do solo, que estão principalmente relacionadas com a estratigrafia e formas do relevo. Assim, este estudo visa à aplicação da estatística multivariada para categorizar superfícies geomórficas em uma litossequência arenito-basalto, de modo a fornecer uma base para a avaliação do solo em áreas afins. A área de estudo está localizada no município de Pereira Barreto, São Paulo, Brasil. A área escolhida possui 530 hectares, onde foram localizadas e mapeadas três superfícies geomórficas (I, II e III). Na área, 134 amostras foram coletadas nas profundidades de 0,0-0,2 m e 0,8-1,0 m, foram determinados os conteúdos de areia, silte e argila, pH em CaCl2, conteúdo de MO, P, Ca, Mg, K, Al e H+Al. Com base nos resultados, foram realizadas a análise univariada e multivariada de variância, clusters e principal componente, a fim de comparar as três superfícies geomórficas. A análise estatística univariada dos atributos do solo não foi eficiente na identificação das três superfícies geomórficas. Utilizando-se os atributos físicos e químicos do solo, as técnicas estatísticas multivariada permitiram à separação dos três grupos de corpos naturais do solo que foram equivalentes as três superfícies geomórficas mapeadas. Estes resultados são interessantes, pois demonstram a viabilidade da utilização de classificação numérica das superfícies geomórficas para ajudar no mapeamento de solo.

Soil and land information: How to support decision-making?

Soils are fundamental to ensuring water, energy and food security. Within the context of sus- tainable food production, it is important to share knowledge on existing and emerging tech- nologies that support land and soil monitoring. Technologies, such as remote sensing, mobile soil testing, and digital soil mapping, have the potential to identify degraded and non- /little-responsive soils, and may also provide a basis for programmes targeting the protection and rehabilitation of soils. In the absence of such information, crop production assessments are often not based on the spatio-temporal variability in soil characteristics. In addition, uncertain- ties in soil information systems are notable and build up when predictions are used for monitor- ing soil properties or biophysical modelling. Consequently, interpretations of model-based results have to be done cautiously. As such they provide a scientific, but not always manage- able, basis for farmers and/or policymakers. In general, the key incentives for stakeholders to aim for sustainable management of soils and more resilient food systems are complex at farm as well as higher levels. The same is true of drivers of soil degradation. The decision- making process aimed at sustainable soil management, be that at farm or higher level, also in- volves other goals and objectives valued by stakeholders, e.g. land governance, improved envi- ronmental quality, climate change adaptation and mitigation etc. In this dialogue session we will share ideas on recent developments in the discourse on soils, their functions and the role of soil and land information in enhancing food system resilience.

(Tables 2-7) Soil characteristics and geochemistry of marshland in the North Frisian mainland, Schleswig-Holstein, Germany

Results from a large scale soil mapping on the North Frisian mainland indicate, that field characteristics, particularly the grain-size, bedding, and degree of compaction, with in general determine the soil units mapped, are closely correlated with each other and with other field and laboratory data. Exchangable ions and the Ca/Mg-ratio, however, indicate no explainable connections with the soil units and with most of the other field characteristics but are determined postsedimentarily by processes of the development of soil and landscape, such as desalting and decalcification, silicate weathering, fresh- and salt-water innundations, salty precipitations, salty groundwater and fertilization. Therefore the Ca/Mg-ratio is not suitable to differentiate between more clayey compacted Knick-marsh soils and less clayey permeable Klei-marsh soils. The results confirm that marsh-soils may only be classified and mapped by means of all available field-data which have to be supplemented by laboratory investigations.

The effect of preferential sampling on sampling variance

We examine some variations of standard probability designs that preferentially sample sites based on how easy they are to access. Preferential sampling designs deliver unbiased estimates of mean and sampling variance and will ease the burden of data collection but at what cost to our design efficiency? Preferential sampling has the potential to either increase or decrease sampling variance depending on the application. We carry out a simulation study to gauge what effect it will have when sampling Soil Organic Carbon (SOC) values in a large agricultural region in south-eastern Australia. Preferential sampling in this region can reduce the distance to travel by up to 16%. Our study is based on a dataset of predicted SOC values produced from a datamining exercise. We consider three designs and two ways to determine ease of access. The overall conclusion is that sampling performance deteriorates as the strength of preferential sampling increases, due to the fact the regions of high SOC are harder to access. So our designs are inadvertently targeting regions of low SOC value. The good news, however, is that Generalised Random Tessellation Stratification (GRTS) sampling designs are not as badly affected as others and GRTS remains an efficient design compared to competitors.

Aerofotos convencionais e imagens orbitais TM/LANDSAT no mapeamento morfopedológico em Santa Bárbara D'Oeste (SP)

Foram estudados, com o auxílio de fotografias aéreas, aspectos qualitativos e quantitativos do relevo e da rede de drenagem de solos de uma área de Santa Bárbara D'Oeste, SP. Esta região compreende 14.625 ha, onde foram selecionadas bacias hidrográficas de 3ª ordem de ramificação e amostras circulares de 5km². As unidades de mapeamento simples ou associações de solos são: Latossolo Vermelho Escuro, Podzólico, Litossolo + Podzólico, Terra Roxa Estruturada + Latossolo Roxo distrófico. Após a caracterização das feições fisiográficas, da área de ocorrência desses solos, foram realizados dois mapas morfopedológicos. No primeiro utilizou-se fotografias aéreas verticais pancromáticas na escala 1: 35.000 (data de 25/6/78) e no segundo imagens orbitais do sensor Thematic Mapper do LANDSAT-5, nas bandas 3, 4 e 5 e composição colorida 3/4/5 na escala 1: 100.000 (data de 12/9/91). As análises qualitativas e quantitativas do relevo (índice de declividade média) e rede de drenagem (densidade de drenagem, freqüência de rios, razão de textura) mostraram-se eficientes na diferenciação das unidades de solo estudadas, tanto em bacias hidrográficas como em amostras circulares. A utilização de fotografias aéreas, permitiu maior riqueza de detalhes na precisão dos limites das unidades de mapeamento e no maior número de unidades de mapeamento discriminadas em relação as imagens orbitais. A composição colorida 3/4/5 permitiu diferenciar os Latossolos argilosos dos Latossolos de textura média, assim como o Latossolo Húmico.

Produtividade de madeira do eucalipto correlacionada com atributos do solo visando ao mapeamento de zonas específicas de manejo

The perfect spatial intervention to manage the cultivation of the land, deriving from specific regions of the soil mapping, increases the agricultural productivity, as well as its clear gain. The relationships, spatial and Pearson, between eucalyptus data plant with some physical and chemical attributes of soil, in the growing season of 2011, in Selviria County, Mato Grosso do Sul State, Brazil (20 degrees 20' S lat.; 51 degrees 24' W long.), were studied in order to obtain the one that could have the best relationship in order to improve the wood productivity. Thus, a geostatistical grid was installed for the soil and plant data collection, it contained 120 sample points, in plantation of Eucalyptus camaldulensis of 2 years old. The soil was a Dystrophic Red Latosol (Haplic Acrustox) whit slope of 0.025m m(-1). Due to their excellent relationship with wood productivity, the basis diameter of the crown and the bulk density showed management specific regions of soil whit high relation to eucalyptus wood productivity.

Mapeamento de solos atraves de imagens de radar e de satelite.

The objective of the present work was the improvement of the methodology for soil mapping through the drainage network with the aid of Landsat data. The drainage network of the area studied was delineated and the soils identified. The main problem of the method has been the characterization of the contact between soil units when it is not abrupt, and in this case field trips and the analysis of the relief has been used to solve doubts.-from English summary