Orientador: José Eduardo Corá / Banca: Célia Regina Paes Bueno / Banca: Waldir de Carvalho Junior / Banca: Antonio Sérgio Ferraudo / Resumo: O presente estudo buscou desenvolver um modelo capaz de compreender as relações solo-paisagem para a predição de classes de solo das folhas do IBGE de Ribeirão Preto, Serrana, Cravinhos e Bonfim Paulista, que constituem a quadrícula de Ribeirão Preto. Para isto, foram utilizadas informações contidas em um mapa pedológico convencional semidetalhado na escala 1:100.000, um Modelo Digital de Elevação (MDE) com resolução espacial de 30 metros, além do mapa geológico na escala 1:50.000. Do mapa geológico foi obtida a litologia e do MDE, foram obtidas as variáveis geomorfométricas por meio de técnicas de geoprocessamento. Todas essas informações foram relacionadas em uma matriz, de onde foram selecionadas três amostragens estratificadas de acordo com a área das classes, extraindo-se dados para treino e teste, que foram utilizados para aplicação em modelos do método Random Forest e avaliação da acurácia. Foram testados diferentes ajustes, com aplicação dos modelos nas classes no segundo e terceiro nível categórico. Com uma amostragem que compreende apenas 0,43% do total da área, o modelo para o segundo nível categórico apresentou uma exatidão global de 62,5%, com o mapa digital de solos apresentando uma persistência de 70,63% das classes do mapa original, valores maiores do que os apresentados para o terceiro nível categórico, com exatidão global de 57,1% e persistência de 44,24%. As variáveis mais importantes na compreensão das relações solo-paisagem foram Litologia, Elevação, Declividade e Distância da rede de drenagem. O estudo mostrou que a metodologia empregada é capaz de contribuir para criação de mapas de solo, com a possibilidade de ser empregado em áreas onde não há informações de solos pré-existentes, de maneira rápida e menos onerosa, auxiliando o trabalho dos pedólogos / Abstract: This study aimed to develop a model to understand the soil-landscape relationships to predict soil classes of topographic sheets of IBGE from Ribeirão Preto, Serrana, Cravinhos and Bonfim Paulista, constituting the grid Ribeirão Preto. For this, we used information included in a conventional semi-detailed soil map at 1:100,000 scale, a Digital Elevation Model (DEM) with a spatial resolution of 30 meters, in addition to the geological map at 1: 50,000 scale. From geological map was obtained lithology and from MDE were obtained the geomorphometric variables through geoprocessing techniques. All this information was linked in a matrix, from which they were selected three stratified sampling according to the area of classes, extracting data for training and testing, which were used for use in models of Random Forest method and evaluation of accuracy. Adjustments were tested with application of models in classes on the second and third categorical level. With a sample comprising only 0.43% of the total area, the model for the second categorical level had an overall accuracy of 62.5%, with the digital soil map showing a persistence of 70.63% of classes from original map, higher values than those presented for the third categorical level, with an overall accuracy of 57.1% and persistence of 44.24%. The most important variables in understanding the soil-landscape relationships were Lithology, Elevation, Slope Distance and drainage network. The study showed that the method is able to contribute to the creation of soil maps, with the possibility of being employed in areas where there is no pre-existing soil information quickly and less costly way, assisting the work of soil scientists / Mestre

Orientadores: Paulo Sérgio Graziano Magalhãe, Armando Zaupa Remacre / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola / Made available in DSpace on 2018-08-27T21:34:19Z (GMT). No. of bitstreams: 1 Sanches_GuilhermeMartineli_M.pdf: 13551439 bytes, checksum: 1aa534b602f92044d4ea34bee5e63027 (MD5) Previous issue date: 2015 / Resumo: Umas das ferramentas utilizadas na Agricultura de Precisão (AP) é a geoestatística, cujo principal objetivo é a descrição dos padrões espaciais e a estimativa de dados em locais não amostrados. Um dos fatores limitantes para se fazer um adequado mapeamento do solo e atender os requisitos mínimos dos métodos de interpolação é a necessidade de uma amostragem densa da área, o que inviabiliza muitas vezes o mapeamento do solo, devido ao demorado e custoso processo de retirada de amostras. Dentro deste contexto os métodos de interpolação geoestatísticos vislumbram uma solução para o presente desafio, tornando possível a descrição da variabilidade espacial do solo com uma pequena amostragem da variável a qual se deseja conhecer, utilizando para isto outros atributos que são mais facilmente mensuráveis e a um custo menor. Uma das técnicas possíveis para otimizar a quantidade de pontos amostrais consiste na utilização de dados obtidos através de sensores de solo para orientação da amostragem. Este trabalho tem como hipótese que, utilizando dados provenientes de sensores de Condutividade Elétrica Aparente (CEa) do solo em conjunto com técnicas de geoestatística, é possível, através de uma amostragem direcionada e reduzida, conhecer a descrição da variabilidade espacial da fertilidade e do estado físico dos solos com adequada precisão. A presente pesquisa teve como objetivo obter mapas da variabilidade espacial dos atributos químicos e físicos do solo utilizando um número reduzido de amostras e aplicando métodos de interpolação geoestatísticos (krigagem ordinária e com deriva externa), tendo como base dados de condutividade elétrica aparente do solo. A metodologia utilizada para a obtenção dos mapas de variabilidade espacial dos atributos do solo indicam que é possível prever mapas que podem ser utilizados para recomendação de fertilizantes à taxa variável. Esta abordagem abre novas possibilidades para que atributos agronômicos importantes possam ser estimados em grandes áreas a partir de um número reduzido de amostras, auxiliando os agricultores no manejo da cultura e tomada de decisão / Abstract: One of the tools used in precision agriculture (PA) is geostatistics, which main objective is to describe the spatial patterns and the estimated data on non-sampled locations. One of the limiting factors for making a proper soil mapping and meet the minimum requirements of interpolation methods is the need for a dense sampling grid, which often makes it impossible, as the process are time consuming and expensive. Within this context, the geostatistical interpolation methods envision a solution for this challenge, making it possible to describe the soil spatial variability with a small sampling of the primary variable (which you want to know), using other attributes that are easily measured. One of the possible techniques to optimize the number of soil sampling is the use of data obtained from soil sensors. This work the assumption that, using data from the Apparent Electrical Conductivity (ECa) together with geostatistical techniques, it is possible, through a targeted and reduced sampling, know the spatial variability of soils fertility and physical condition with adequate precision. Therefore, this research aims to obtain maps of the spatial variability of chemical and physical soil properties using a reduced number of samples and applying geostatistical interpolation methods (ordinary kriging and kriging with external drift), based on data of apparent electrical conductivity. The methodology used to obtain the maps of spatial variability of soil attributes indicate that it is possible to provide maps that can be used for fertilizer recommendation to the variable rate. This approach opens new possibilities for important agronomic attributes be estimated in large areas from a small number of samples, assisting farmers in crop management and decision-making / Mestrado / Maquinas Agricolas / Mestre em Engenharia Agrícola

Procedures were developed for a computer-based geographic information system to map the probable soils of a county in southwestern Virginia having no soil map. To facilitate the association of known soil characteristics and interpretations with derived soil units, the procedures were based on a soil map available for a similar, mapped county in the region. Using a grid-cell size of 1/81 km² (1.2 ha or 3.0 acres), data were collected for geology, slope, aspect, topographic shape, distance to ridge, and distance to stream in both counties, and for soils in the county having a soil map. A hierarchical, monothetic, divisive classification program was written to combine these data and produce a dichotomous key to soil types. About 36,000 cells belonging to 26 soil types were classified with 55 percent accuracy using data available for the entire study area. Twenty- three of the 26 types could not be distinguished in the classification. Using more detailed data available for an 11 km² (4.3 sq mi) subarea, 900 cells belonging to 19 soil types were classified with 69 percent accuracy. Eleven soil types in the subarea could not be distinguished in the classification. High variability within some of the larger soil types mapped in the study area and inaccuracies in the topographic data vere the most probable causes of the apparent inability to classify many of the soil types, and the results were not applied to the mapped county. / M.S.

Research on digital soil mapping (DSM) in Brazil is subject to the existence of legacy soil data obtained through conventional methods for extrapolation of soil-landscape relations. In areas with no available soil maps, it is necessary to develop methodologies for the acquisition of these data in a scale compatible with the needs of the users. In this context, the main objective of this study was to develop a methodology, through techniques of DSM, to predict soil classes at semidetailed level, in a region of gently undulating relief delimited by a topographic map in a scale of 1:50,000. Techniques for relief modeling were used to elaborate and evaluate the quality of the digital elevation models used in the area covered by the topographic map and in the reference area (RA). The RA technique was used to establish soil-landscape relations in the DSM. A basemap in a scale of 1:5,000 was created to support the implementation of soil mapping by conventional methods for the RA. Decision Tree (DT) technique was used to build the prediction model based on the soil map and eight terrain attributes of the RA. Two DSM strategies were tested in order to obtain the data to create the classification rules (DSM 1 and DSM 2). Each strategy employed the eight terrain attributes as predictor variables: elevation (ele), distance to channel network (dis), slope (dec), aspect (asp), topographic wetness index (twi), profile curvature (per), plan curvature (pla), and landform classes (lan). The previous selection of digital elevation models to extract the terrain attributes aggregated quality to the use of the predictor variables that participated in the production of the model. The use of RA in areas with limitation of data proved to be an efficient strategy to improve the understanding of soil-landscape relations for prediction of occurrence of soil classes through the DSM method. The comparison between field data and the digital soil map resulted in a global accuracy (GA) of 66.15% and Kappa of 0.35 for the DSM 1, and GA of 65.58% and Kappa of 0.27 for the DSM 2. The approach of soil survey through the conventional method in the RA proved appropriate, since it contributed to the knowledge of predominant soil categories, as well as reduced the number of field observations in the area covered by the topographic map. / As pesquisas em mapeamento digital de solos (MDS) realizadas no Brasil são dependentes da existência de dados legados de solos obtidos por métodos convencionais para extrapolação das relações solo-paisagem. Em áreas onde não há disponibilidade de mapas de solos, torna-se necessário desenvolver metodologias para aquisição desses dados em escala compatível com a necessidade dos usuários. Nesse contexto, o objetivo geral desta pesquisa foi desenvolver uma metodologia, por intermédio de técnicas de MDS para predizer classes de solos em nível semidetalhado, numa região de relevo suave ondulado, tendo como limite uma carta topográfica na escala 1:50.000. Foram utilizadas técnicas de modelagem do relevo para a elaboração e a avaliação da qualidade dos modelos digitais de elevação utilizados na área de abrangência da carta topográfica e na área de referência (AR). A técnica de AR foi empregada para estabelecer as relações solo-paisagem no MDS. Foi construída uma base cartográfica, na escala 1:5.000, que serviu de apoio para executar o mapeamento de solos com técnicas convencionais para a AR. A técnica de árvore de decisão (AD) foi utilizada para construção do modelo preditivo com base no mapa de solos e em oito atributos de terreno da AR. Duas estratégias de MDS foram testadas com o objetivo de obter os dados para gerar as regras de classificação (MDS 1 e MDS 2), sendo que cada estratégia empregou os oito atributos de terreno como variáveis preditoras: elevação (ele), distância da hidrografia (dis), declividade (dec), orientação de vertente (asp), índice de umidade topográfica (twi), curvatura em perfil (per), curvatura planar (pla) e classes de geoformas (lan). A seleção prévia de modelos digitais de elevação para extrair os atributos de terreno agregou qualidade no uso das variáveis preditoras que participaram da construção do modelo. O uso da AR em locais com limitação de dados mostrou-se uma estratégia eficiente para aprimorar o conhecimento referente às relações solo-paisagem com vistas à predição de ocorrência de classes de solos geradas pelo método de MDS. A comparação entre a verdade de campo e o mapa digital de solos resultou numa exatidão global (EG) de 66,15% e Kappa de 0,35 para o MDS 1 e uma EG de 65,58% e Kappa de 0,27 para o MDS 2. A abordagem de levantamento de solos pelo método convencional na AR demonstrou ser apropriada, visto que contribuiu para o conhecimento dos tipos de solos predominantes, assim como reduziu a quantidade de observações de campo na área de abrangência da carta topográfica.

Thesis (MScAgric)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: Increased pressure on groundwater sources due to increased population size and threats of climate change is driving research to better understand the process of aquifer recharge. Soil pattern is of interest as it serves to partition rainwater into different flowpaths destined for surface runoff, evapotranspiration and deep percolation. The challenges inherent to studying these flowpaths are almost universal as uncertainties concerning spatial and temporal heterogeneity in catchments make the upscaling of models complex. This research addresses these challenges as it aims to improve the catchment scale hydrological models of two aquifer systems: One a fractured bedrock system at the Kogelberg Nature Reserve, Kleinmond, and the other a cover sand system in Riverlands Nature Reserve, Malmesbury. This study focussed on strengthening the link between what is known about a given soil form and the hydrological assumptions that can be drawn from that classification, and formulating the results so that they may ultimately be used to calibrate the recharge prediction models for the respective catchments. The research was done in two parts: The first phase was to conduct soil surveys in both reserves during which soils were classified according to South African Soil Classification. Samples were collected at representative observation points which provided textural data for use in pedotransfer functions (PTFs). These PTFs were used to estimate plant available water (PAW) and hydraulic conductivity (K) for the observed profiles. Infiltration experiments were subsequently done to investigate the infiltration patterns of distinctly different soil forms at two sites from each reserve. The experiments included double ring and mini disc infiltration, volumetric water content determination and flow path visualisation using a staining dye. A statistical comparison between the hydrological properties (K and PAW) of the different soil forms suggest that hydraulic properties differed between the deep sandy soil forms (Fernwood, Pinegrove and Witfontein in Kogelberg and Witfontein, Concordia and Lamotte in Riverlands) and the shallow rocky soil forms (Cartref and Glenrosa in Kogelberg). Thus grouping of hydrological similar units (HSUs) could be done on the basis of the soil forms present within the given catchments. The infiltration study showed that shallow, rocky soils that grade into bedrock would have infiltration rates far greater than those estimated using PTFs in Kogelberg. This is due to the prevalence of continuous preferential flow (PF) of water between coarse fragments in these profiles. Recharge estimates would thus be inaccurate in such soils and calibration using locally derived data is recommended. On the contrary, PTFs produced accurate infiltration estimates relative to measured infiltration rates in deep sandy soils in Kogelberg and Riverlands. The Lamotte soil form is an example of such a soil form. It should however be noted that an increase in PF in these soils had subsequently higher K values than estimated, thus illustrating the link between PF and accelerated infiltration rates. These results confirm that using soil survey information, in the form of a soil map, and calibrated hydrological properties, one can delineate HSUs that encompass a large degree of heterogeneity in a given catchment. / AFRIKAANSE OPSOMMING: Verhoogde druk op grondwaterhulpbronne weens die groeiende bevolking en klimaatsverandering dryf tans navorsing om akwifeer hervulling beter te verstaan. Die grondlaag is van belang sienend dat dit reënwater verdeel in oppervlak afloop, evapotranspirasie en diep dreinering. Die uitdagings in hidrologiese navorsing is universeel as gevolg van onsekerhede oor ruimtelike en tydelike variasie wat lei tot komplekse grondwatermodelle. Diè navorsing mik om die tekortkominge in akwifeer hervulling aan te vul deur groundwatermodelle van twee akwifeersisteme te verbeter: Die een is 'n gebroke rots sisteem in die Kogelberg Natuur Reservaat, Kleinmond, en die ander is 'n sand-bedekde sisteem in Riverlands Natuur Reservaat, Malmesbury. Die navorsing streef om die verhouding tussen 'n spesifieke grondvorm en sy hidroliese vloeipaaie te bestudeer en om die gevolgtrekkings so te formuleer dat dit kan gebruik word om die onderskeie grondwatermodelle te kalibreer. Die eerste fase van die navorsing was om 'n grondopname van die onderskeie reservate te doen waartydens die gronde geklassifiseer was volgens die Suid Afrikaanse Grondklassifikasie Sisteem. Grondmonsters is by verteenwoordigende observasiepunte geneem en geanaliseer om tekstuurdata vir pedo-oordraagbare-funksies (PTFs) te kry. Die PTFs was gebruik om plant beskikbare water (PBW) en hidrouliese geleiding (K) te voorspel vir die verskeie observasiepunte. Infiltrasie eksperimente was daarna gedoen om die infiltrasie patroon van twee verskillende grondvorms van elke reservaat te bestudeer. Die eksperimente sluit dubbel- en minidisk-infiltrasie, volumetriese waterinhoud bepaling en vloeipad visualisering met die gebruik van 'n kleurstof in. Die statistiese vergelyking van die hidrouliese eienskappe (K en PBW) en grondvorm dui aan dat die hidrouliese eienskappe verskil tussen die diep, grondvorms met 'n oorwegende sand tekstuur (Fernwood, Pinegrove en Witfontein in Kogelberg en Witfontein, Concordia en Lamotte in Riverlands) en die vlakker, klipperige grondvorms (Cartref en Glenrosa in Kogelberg). Groepering van hidrologies soortgelyke eenhede (HSE's) kan dus op die basis van die teenwoordige grondvorms in 'n opvangsgebied gedoen word. Die infiltrasie studie het bewys dat vlak, klipperige gronde wat tot die rotsbodem gradueer 'n baie hoër infiltratsie tempo sal hê as die PTF voorspelde waardes. Dit is as gevolg van die voorkoms van aaneenlopende voorkeurvloei (VV) van water tussen die growwe materiaal in die profiele, veral die gebroke rots ondergorond. Voorspellings van akwifeer hervulling sal dus onakkuraat wees en kalibrasie met plaaslike data word dus aanbeveel. In teendeel met die begenoemde, het die PTFs akkurate voorspellings gemaak relatief tot die gemete infiltrasie tempo's in die diep sanderige grondvorms in Kogelberg en Riverlands. Dit was duidelik met metings dat 'n toename in aaneenlopende VV hoër gemete K waardes getoon as die voorspelde waardes. Die verband tussen VV en verhoogde infiltrasie tempo word dus hiermee geillustreer. Die resultate bevestig dus dat grondopname data, in die vorm van 'n grondkaart en gekalibreerde hidrouliese eienskappe gebruik kan word om hidrologies soortgelyke eenhede uiteen te sit wat die meerderheid van die variasie in 'n gegewe opvangsgebied insluit. Die HSE's kan gebruik word om grondwatermodelle meer akkuraat te laat funksioneer en dus beter voorspellings te genereer.

Four fields of 117 ha area located at the University of Arizona's Maricopa Agricultural Center were selected for this study. Two soil series, the Casa Grande sandy clay loam and Trix clay loam occur. Surface samples (0-25 cm) were collected on a 98 m interval and 3 rows providing 47 sites per field. Sites were classified either as surveying (32) or testing (15) in each of the four fields. Additional samples at 25-50, 50-75, 75-100, and 100-125 cm were obtained with duplicate surface undisturbed cores at 5 sites per field. Soil parameters include bulk density, saturated hydraulic conductivity, moisture retention, particle size analysis, pH, EC, soluble cations, SAR, and ESP. A quantification of the spatial interdependence of samples was developed based on the variogram of soil parameters. A linear model was best fitted to the clay, EC, Ca²⁺, Mg²⁺, Na⁺, SAR and ESP, and a spherical model to the sand, silt, pH, and K⁺ observed variograms. A comparison of variograms obtained conventionally and with the robust estimation of Cressie and Hawkins (1980) for sand and Ca²⁺ were performed with a fixed couples number per class and with a fixed class size. Additionally, a negative log-likelihood function along with cross-validation criteria were used with the jackknifing method to validate and determine variogram parameters. Three interpolation techniques have been compared for estimating 11 soil properties at the test sites. The techniques include Arithmetic Mean, Inversely Weighted Average, and Kriging with various numbers of neighbor estimates. Using 4 point estimates resulted in nearly identical results, but the 8 point estimates gave more contrast for results among the alternative techniques. Jackknifing was used with 4, 8, 15, 25 neighbors for estimating 188 points of sand and Ca²⁺ with the three techniques. Sand showed a definite advantage of Kriging by lowering the Mean Square Error with increasing neighbor number. The simple interpolator Arithmetic Mean was comparable and sometimes even better than the other techniques. Kriging, the most complex technique, was not the absolute best interpolator over all situations as perhaps expected. The spatial dependence for the 11 soil variables was studied by preparing contour maps by punctual Kriging. Sand and Ca²⁺ were also mapped by block Kriging estimates.

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high-resolution soil properties. Remote sensing and spatial modeling techniques can bridge the gap between site-specific soil properties and landscape variability, thereby improving predictions of soil attributes. Three studies were completed to advance soil prediction models in semiarid areas. The first study developed a soil pre-mapping technique using automated image segmentation that utilized soil-landscape relationships and surface reflectance to produce an effective map unit design in a 160,000 ha soil survey area. Overall classification accuracy of soil taxonomic units at the suborder was 58 % after including soil temperature regime. Physical soil properties were not significantly different for individual transects; however, properties were significantly different between soil pre-map units when soils from the entire study area were compared. Other studies used a raster approach to predict physical soil properties at a 5 m spatial resolution for a 6,265 ha area using digital soil mapping. The second study utilized remotely-sensed auxiliary data to develop a sampling design and compared three geostatistical techniques for predicting surface soil properties. Ordinary kriging had the smallest prediction error; however, regression kriging preserved landscape features present in the study area and demonstrated the potential of this technique for quantifying variability of soil components within soil map units. The third study applied quantitative data from soil prediction models in study 2 and additional models of subsurface properties to a pedotransfer function for predicting hydraulic soil parameters at the landscape scale. Saturated hydraulic conductivity and water retention parameters were used to predict water residence times for loss to gravity and evapotranspiration across the landscape. High water residence time for gravitational water corresponded to both low drainage density and high clay content, whereas high residence of plant available water was related to increased vegetation response. These studies illustrate the utility of digital soil mapping techniques for improving soil information at landscape scales, while reducing required resources. Resulting soil information is useful for quantifying landscape-scale processes that require constraint of spatial variability and prediction error of soil properties to better model hydrological and ecological responses to climate and land use change.

Soil property variability is a large component of the overall environmental variability that Precision Agriculture practices seek to address. Thus, the creation of accurate field soil maps from field soil samples is of utmost importance to practitioners of Precision Agriculture, as understanding and characterizing variability is the first step in addressing it. Today, growers often interpolate their soil maps in a “black-box” fashion, and there is a need for an easy to use, accurate method of interpolation. In this study, current interpolation practices are examined as a benchmark, a Random Forest (RF) based prediction framework utilizes public data to aid predictions, and the RF framework is exposed via a webtool. A high density (0.20 ha/sample) field soil sample dataset provides 28 training points and 82 validation points to be used as a case study. In the prediction of soil percent organic matter (OM), the grid and ordinary kriging interpolations both had higher Mean Absolute Error (MAE) scores than a field average prediction, though the difference was not statistically significant at a 5\% confidence level. A RF framework interpolation utilizing a high resolution (1.52 m) DEM and distances to known points as the feature set had a significantly lower MAE score than the field average, grid, and ordinary kriging interpolations. The results suggest that for the study site, RF framework performed better compared to a field average, a grid based, and an ordinary kriging interpolation methods.

Orientador: Célia Regina Lopes Zimback / Banca: Carlos Alberto Oliveira de Matos / Banca: Andrea Bogatti Guimarães Tomazella / Banca: Ivana Furio Batista / Banca: Leticia Colares Vilela / Resumo: O presente trabalho teve como objetivo determinar a variabilidade espacial da fertilidade e de propriedades físicas do solo, em área irrigada sob sistema de pivô central cultivado com culturas anuais em plantio direto, com o intuito de verificar as conseqüências do uso intensivo do solo com irrigação, buscar soluções para melhor utilização de insumos e da água de irrigação, e subsidiar o mapeamento em diferentes zonas de manejo. O estudo foi realizado no município de Colômbia (SP), num pivô central de 65ha, cujas coordenadas geográficas são: latitude 20º16’09-S e longitude 48º40’43-W, em LATOSSOLO VERMELHO Distrófico, textura média. As amostras de solos georreferenciadas foram coletadas em duas profundidades (0-0,2 e 0,2-0,4m) e analisadas quanto ao pH em CaCl2, H + Al, matéria orgânica, Presina, cálcio, magnésio e potássio, e micronutrientes: boro, cobre, ferro, enxofre, manganês e zinco; sendo calculados: soma de bases (SB), CTC, V%. Foram realizadas também análises de granulometria e densidade de partículas. Nas profundidades de 0-0,05 e 0,20-0,25m analisou-se: densidade do solo, porosidade total, micro e macroporosidade; sendo a umidade atual realizada somente na profundidade de 0-0,05m. A análise da dependência espacial foi realizada por meio do ajuste dos dados ao variograma experimental e da interpolação de dados através da krigagem ordinária, visando definir o padrão espacial das variáveis estudadas. Com base nos mapas obtidos foram estabelecidas zonas de manejo da fertilidade do solo e dos parâmetros físico-hídricos para manejo da irrigação. De acordo com os resultados obtidos, verificou-se que mesmo em área restrita como a do pivô central, ocorre variabilidade espacial tanto das características químicas quanto físicas. Os atributos químicos e físicos do solo apresentaram dependência espacial de média a forte.. (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The aim of this work was to determine the spatial variability of fertility and physical properties of the soil, in an irrigated area under central pivot system cultivated with annual cultures in no tillage system, to verify which are the possible influences that the intensive soil use with irrigation causes, to looking for solutions to improve the input and the irrigation water use, and to enhanced the plotting in different control zones. The study was accomplished in Colombia's Municipal district (SP), with geographical coordinates: latitude 20º16’09-S and longitude 48º40’43-W, in a loamy dystrophic Red Latosol. The georeferencing soil samples were collected at two depths (0- 0.20 and 0.20-0.40 m) and were analyzed by pH (CaCl2), H + Al, MO-organic matter, Presin, Ca, Mg, K, SB, CTC, V%, B, Cu, Fe, S, Mn, and Zn. Were determined granulometry, soil density and particles density, total porosity, micro and macroporosity. The spatial analysis dependence was realized through the data adjustment to experimental variogram and ordinary kriging interpolation, aiming to define the standard space of the studied variables. Based in this maps, were established soil fertility handling control zones and of the physics parameters for irrigation handling. Using obtained results, it is possible to conclude that: even in a restricted area as the central pivot occurs spatial variability as much the chemical how much the physical characteristics; the chemical and physical attributes of the soil presented strong spatial dependence; the spatial dependence of the chemical attributes varied from 86 to 700m superficially and from 113 to 533m subsuperficially, to concluding itself that in the fertility handling it should considering the distance from 86m; the spatial dependence of the physical attributes varied from 207 to 714m, concluding itself that in the soil physical variables handling... (Complete abstract click electronic access below) / Doutor

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