Footings are often founded on multi-layered soil profiles. Real soil profiles are often multi-layered with material constantly varying with depth, which affects the footing response significantly. Furthermore, the properties of the soil are known to vary with location. The spatial variability of soil can be described by random field theory and geostatistics. The research presented in this thesis focuses on quantifying the effect of soil variability on the bearing capacity of rough strip footings on single and two layered, purely-cohesive, spatially variable soil profiles. This has been achieved by using Monte Carlo analysis, where the rough strip footings are founded on simulated soil profiles are analysed using finite element limit analysis. The simulations of virtual soil profiles are carried out using Local Average Subdivision (LAS), a numerical model based on the random field theory. An extensive parametric study has been carried out and the results of the analyses are presented as normalized means and coefficients of variation of bearing capacity factor, and comparisons between different cases are presented. The results indicate that, in general, the mean of the bearing capacity reduces as soil variability increases and the worst case scenario occurs when the correlation length is in the range of 0.5 to 1.0 times the footing width. The problem of estimating the bearing capacity of shallow strip footings founded on multi-layered soil profiles is very complex, due to the incomplete knowledge of interactions and relationships between parameters. Much research has been carried out on single- and two-layered homogeneous soil profiles. At present, the inaccurate weighted average method is the only technique available for estimating the bearing capacity of footing on soils with three or more layers. In this research, artificial neural networks (ANNs) are used to develop meta-models for bearing capacity estimation. ANNs are numerical modelling techniques that imitate the human brain capability to learn from experience. This research is limited to shallow strip footing founded on soil mass consisting of ten layers, which are weightless, purely cohesive and cohesive-frictional. A large number of data has been obtained by using finite element limit analysis. These data are used to train and verify the ANN models. The shear strength (cohesion and friction angle), soil thickness, and footing width are used as model inputs, as they are influencing factors of bearing capacity of footings. The model outputs are the bearing capacities of the footings. The developed ANN-based models are then compared with the weighted average method. Hand-calculation design formulae for estimation of bearing capacity of footings on ten-layered soil profiles, based on the ANN models, are presented. It is shown that the ANN-based models have the ability to predict the bearing capacity of footings on ten-layered soil profiles with a high degree of accuracy, and outperform traditional methods. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1368281 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

It is well recognised that the inherent soil variability and the effect of vegetation, in particular the effect of tree root reinforcement, have a significant effect on the stability of a natural slope. However, in practice, these factors are not commonly considered in routine slope stability analysis. This is due mainly to the fact that the effects of soil variability and vegetation are complex and difficult to quantify. Furthermore, the available slope stability analysis computer programs used in practice, which adopt conventional limit equilibrium methods, are unable to consider these factors. To predict the stability of a natural slope more accurately, especially the marginally stable one, the effects of soil variability and vegetation needs to be taken into account. The research presented in this thesis focuses on investigating and quantifying the effects of soil variability and vegetation on the stability of natural slopes. The random finite element method (RFEM), developed by Griffiths and Fenton (2004), is adopted to model the effect of soil variability on slope stability. The soil variability is quantified by the parameters called the coefficient of variation (COV) and scale of fluctuation (SOF), while the safety of a slope is assessed using probability of failure. In this research, extensive parametric studies are conducted, using the RFEM, to investigate the influence of COV and SOF on the probability of failure of a cohesive slope (i.e. undrained clay slope) with different geometries. Probabilistic stability charts are then developed using the results obtained from the parametric studies. These charts can be used for a preliminary assessment of the probability of failure of a spatially random cohesive slope. In addition, the effect of soil variability on c'–ϕ' slopes is also studied. The available RFEM computer program (i.e. rslope2d) is limited to analysing slopes with single-layered soil profile. Therefore, in this research, this computer program is modified to analyse slopes with two-layered soil profiles. The modified program is then used to investigate the effect of soil variability on two-layered spatially random cohesive slopes. It has been demonstrated that the spatial variability of soil variability has a significant effect on the reliability of both single and two-layered soil slopes. Artificial neural networks (ANNs), which are a powerful data-mapping tool for determining the relationship between a set of input and output variables, are used in an attempt to predict the probability of failure of a spatially random cohesive slope. The aim is to provide an alternative tool to the RFEM and the developed probabilistic stability charts because the RFEM analyses are computationally intensive and time consuming. The results obtained from the parametric studies of a spatially random cohesive slope are used as the database for the ANN model development. It has been demonstrated that the ANN models developed in this research are capable of predicting the probability of failure of a spatially random cohesive slope with high accuracy. The developed ANN models are then transformed into relatively simple formulae for direct application in practice. The effect of root reinforcement caused by vegetation is modelled as additional cohesion to the soils, known as root cohesion, cr. The areas affected by tree roots (i.e. root zone) are incorporated in the finite element slope stability model. The extent of the root zone is defined by the depth of root zone, hr. Parametric studies are conducted and the results are used to develop a set of stability charts that can be used to assess the contribution of root reinforcement on slope stability. Furthermore, ANN models and formulae are also developed based on the results obtained from the parametric studies. It has been demonstrated that the factor of safety of a slope increase linearly with the values cr and hr, and the contribution of root reinforcement to a marginally stable slope is significant. In addition, probabilistic slope stability analysis considering both the variability of the soils and root cohesion are conducted using the modified RFEM computer program. It has been demonstrated that the spatial variability of root cohesion has a significant effect on the probability of slope failure. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1349971 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

Footings are often founded on multi-layered soil profiles. Real soil profiles are often multi-layered with material constantly varying with depth, which affects the footing response significantly. Furthermore, the properties of the soil are known to vary with location. The spatial variability of soil can be described by random field theory and geostatistics. The research presented in this thesis focuses on quantifying the effect of soil variability on the bearing capacity of rough strip footings on single and two layered, purely-cohesive, spatially variable soil profiles. This has been achieved by using Monte Carlo analysis, where the rough strip footings are founded on simulated soil profiles are analysed using finite element limit analysis. The simulations of virtual soil profiles are carried out using Local Average Subdivision (LAS), a numerical model based on the random field theory. An extensive parametric study has been carried out and the results of the analyses are presented as normalized means and coefficients of variation of bearing capacity factor, and comparisons between different cases are presented. The results indicate that, in general, the mean of the bearing capacity reduces as soil variability increases and the worst case scenario occurs when the correlation length is in the range of 0.5 to 1.0 times the footing width. The problem of estimating the bearing capacity of shallow strip footings founded on multi-layered soil profiles is very complex, due to the incomplete knowledge of interactions and relationships between parameters. Much research has been carried out on single- and two-layered homogeneous soil profiles. At present, the inaccurate weighted average method is the only technique available for estimating the bearing capacity of footing on soils with three or more layers. In this research, artificial neural networks (ANNs) are used to develop meta-models for bearing capacity estimation. ANNs are numerical modelling techniques that imitate the human brain capability to learn from experience. This research is limited to shallow strip footing founded on soil mass consisting of ten layers, which are weightless, purely cohesive and cohesive-frictional. A large number of data has been obtained by using finite element limit analysis. These data are used to train and verify the ANN models. The shear strength (cohesion and friction angle), soil thickness, and footing width are used as model inputs, as they are influencing factors of bearing capacity of footings. The model outputs are the bearing capacities of the footings. The developed ANN-based models are then compared with the weighted average method. Hand-calculation design formulae for estimation of bearing capacity of footings on ten-layered soil profiles, based on the ANN models, are presented. It is shown that the ANN-based models have the ability to predict the bearing capacity of footings on ten-layered soil profiles with a high degree of accuracy, and outperform traditional methods. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1368281 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

It is well recognised that the inherent soil variability and the effect of vegetation, in particular the effect of tree root reinforcement, have a significant effect on the stability of a natural slope. However, in practice, these factors are not commonly considered in routine slope stability analysis. This is due mainly to the fact that the effects of soil variability and vegetation are complex and difficult to quantify. Furthermore, the available slope stability analysis computer programs used in practice, which adopt conventional limit equilibrium methods, are unable to consider these factors. To predict the stability of a natural slope more accurately, especially the marginally stable one, the effects of soil variability and vegetation needs to be taken into account. The research presented in this thesis focuses on investigating and quantifying the effects of soil variability and vegetation on the stability of natural slopes. The random finite element method (RFEM), developed by Griffiths and Fenton (2004), is adopted to model the effect of soil variability on slope stability. The soil variability is quantified by the parameters called the coefficient of variation (COV) and scale of fluctuation (SOF), while the safety of a slope is assessed using probability of failure. In this research, extensive parametric studies are conducted, using the RFEM, to investigate the influence of COV and SOF on the probability of failure of a cohesive slope (i.e. undrained clay slope) with different geometries. Probabilistic stability charts are then developed using the results obtained from the parametric studies. These charts can be used for a preliminary assessment of the probability of failure of a spatially random cohesive slope. In addition, the effect of soil variability on c'–ϕ' slopes is also studied. The available RFEM computer program (i.e. rslope2d) is limited to analysing slopes with single-layered soil profile. Therefore, in this research, this computer program is modified to analyse slopes with two-layered soil profiles. The modified program is then used to investigate the effect of soil variability on two-layered spatially random cohesive slopes. It has been demonstrated that the spatial variability of soil variability has a significant effect on the reliability of both single and two-layered soil slopes. Artificial neural networks (ANNs), which are a powerful data-mapping tool for determining the relationship between a set of input and output variables, are used in an attempt to predict the probability of failure of a spatially random cohesive slope. The aim is to provide an alternative tool to the RFEM and the developed probabilistic stability charts because the RFEM analyses are computationally intensive and time consuming. The results obtained from the parametric studies of a spatially random cohesive slope are used as the database for the ANN model development. It has been demonstrated that the ANN models developed in this research are capable of predicting the probability of failure of a spatially random cohesive slope with high accuracy. The developed ANN models are then transformed into relatively simple formulae for direct application in practice. The effect of root reinforcement caused by vegetation is modelled as additional cohesion to the soils, known as root cohesion, cr. The areas affected by tree roots (i.e. root zone) are incorporated in the finite element slope stability model. The extent of the root zone is defined by the depth of root zone, hr. Parametric studies are conducted and the results are used to develop a set of stability charts that can be used to assess the contribution of root reinforcement on slope stability. Furthermore, ANN models and formulae are also developed based on the results obtained from the parametric studies. It has been demonstrated that the factor of safety of a slope increase linearly with the values cr and hr, and the contribution of root reinforcement to a marginally stable slope is significant. In addition, probabilistic slope stability analysis considering both the variability of the soils and root cohesion are conducted using the modified RFEM computer program. It has been demonstrated that the spatial variability of root cohesion has a significant effect on the probability of slope failure. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1349971 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

A study of 12 expansive soils in four major physiographic provinces in Virginia was initiated to examine and quantify the relationship between shrink-swell potential, shrink-swell indices, and soil properties. The mineralogy classes, soil series, and (physiographic provinces, parent materials) examined include smectitic -- Jackland and Waxpool (Triassic, diabase), Iredell (Piedmont, hornblende); vermiculitic -- Kelly (Triassic, thermal shale); kaolinitic -- Cecil (Piedmont, granite gneiss), Davidson (Triassic, diabase); and mixed -- Carbo and Frederick (Valley and Ridge, limestone), Craven and Peawick (Coastal Plain, fluvial and marine sediments), and Mayodan and Creedmoor (Triassic, sandstones). Three sites in each of the 12 map units were described and major horizons sampled for physical, chemical, and mineralogical laboratory analysis. An expansive soil rating system, termed the Expansive Soil Index (ESI), was developed using the soil properties best correlated with shrink-swell potential. The sum of swelling 2:1 minerals, swell index, liquid limit, and CEC gave expansive soil potential ratings (ESI) for each soil series. The higher the ESI, the greater the shrink-swell potential. Smectite distributions within the soil profiles were investigated. Smectite concentration in the clay fraction increases with depth in soils formed from diabase and thermally altered shale. Smectite weathers to kaolinite and hydroxy-interlayered vermiculite with increasing proximity to the soil surface thus accounting for the observed decrease in smectite toward the soil surface. The highest amount of smectite from the granite gneiss, limestone, sandstones and shales, and Coastal Plain sediments were in the Bt2 horizon where maximum expression of the argillic horizon occurs. Smectite contents decrease away (upwards and downwards) from the maximum in the Bt2 horizon. A satellite study focused on locating and quantifying the variability within five map units in the Culpeper (Triassic) Basin in northern Virginia. Variability of the shrink-swell indices and related properties are high in all map units. Dissimilar inclusions could adversely affect foundations if a home is sited on both moderate and high shrink-swell soils. Although there is extreme variability in the map units, the variability occurs within the delineations of each map unit. Each delineation within an individual map unit contains similar levels of variability. / Ph. D.

Recent researches on the effects of environmental degradation on food security suggest that a better understanding of the relationship between agricultural intensification and pollutant transfer is urgently required to support the implementation of sustainable agricultural policies, globally. Poor understanding of the hydrological behaviour of clay-rich soils in intensively managed agricultural regions is highlighted as an important problem. The study therefore evaluated precipitation-soil water chemistry relationships, soil variability and concentration-discharge relationships at the farm-scale based on datasets from the North Wyke Farm Platform between 2011 and 2013. The three main hypothesis were that (1) precipitation and soil water chemistry are significantly related (2) significant relationships exists between the distribution of soil physiochemical characteristics and the managments of the fields, and that (3) hydrological behaviour of fields underlain by certain dominant soils in the study area are different from that of other fields. The basis of this work was to elucidate links between sources of pollutants and water quality, further understanding of the effect that management of the soil may have upon the quality of the water and improve understanding of the pathways of pollutants within intensively managed landscapes. Precipitation chemistry of the study area was chemically different from that of the other regions in the United Kingdom, and was influenced by contributions from sea salts and terrestrial dusts. The soil chemistry was rich in organic matter which contributed significantly (r2>0.60; p<0.05) to the distribution of total carbon and total nitrogen in the fields. Mean total carbon and nitrogen stocks ranged 32.4 - 54.1 t C ha-1, and 4 - 6.2 t Na ha-1, respectively in the entire farm platform while runoff coefficient at four selected fields (Pecketsford, Burrows, Middle and Higher Wyke Moor, and Longlands East) varied between 0.1 and 0.28 in January and November, 2013. The study rejected the first and third hypotheses, and concluded that the study area is largely influenced by contributions from the surface runoff mechanisms. The study also noted that sodium and chloride ions were dominant in the precipitation chemistry, and therefore suggests their further investigation as conservative tracers in the soil and runoff chemistry.

Two wild blueberry fields were selected in central Nova Scotia, to characterize and quantify the spatial pattern of variability in soil properties, leaf nutrients and fruit yield, identification of yield influencing soil properties, and to develop management zones for site-specific fertilization. A combination of classical statistics, geostatistical analysis and mapping in Arc GIS 9.3 indicated substantial variation within field. The stepwise regression suggested that the soil EC, horizontal co-planar geometry (HCP), inorganic nitrogen and moisture content were major yield influencing factors. The cluster analysis of the soil variables with the fruit yield also indicated that HCP, inorganic nitrogen, EC, SOM, and ?v were closely grouped with the fruit yield at a similarity level greater than 70%. Based on the results of this study the wild blueberry fields can be divided into different management zones for variable rate fertilization to improve crop production, increase revenue, and reduce potential environmental contamination.

Precision management of cotton production can increase profitability by decreasing inputs. The overall objective of this project is to improve cotton production by minimizing seeding rates while still maximizing yields and lint quality in water-limited soils. The research for this study was conducted at the Texas AgriLife Research IMPACT Center located in the Brazos River floodplain. In 2008 and 2009, 27 measurement locations were selected in production-sized center-pivot irrigated fields and planted in cotton variety Deltapine 164 roundup ready flex / bollgard II. Sites were selected based on soil apparent electrical conductivity (ECa) values, in a low, medium, and high ECa zones. Three seeding rates (74,100; 98,800; and 123,500 seeds ha-1) were established in each of the three ECa zones with three replications. In 2009, an additional seeding rate was added at 49,400 seeds ha-1. At each measurement location, soil texture, soil moisture (weekly), lint quantity and quality (High Volume Instrument) were measured. An additional replication for each ECa zone and seeding rate was selected for lint quantity and quality (HVI) measurements. Results indicated that cotton lint yield increased as ECa values, clay content, and water holding capacity of the soil increased. The seeding rates did not consistently affect cotton lint yield or quality. Seeding rates of 74,100 and 49,400 seeds ha-1 in a low and medium ECa zone for IMPACT-08 and -09 yielded more lint (300 kg ha-1), respectively. HVI lint quality parameters, such as, micronaire, fiber length, strength, uniformity, and elongation were significantly better in ECa zone 3. While the seeding rates did not affect the amount of soil water used throughout the season, lint yield variations between ECa zones can be explained by the rate at which soil water was used. Lower rates at which soil water was used within ECa zone 3 resulted in higher lint yields when compared to ECa zones 1 and 2, which used soil water faster and at greater depths. The findings suggest that irrigation applied to the low ECa zone was not sufficient to meet the plants demand, while in a high ECa zone, irrigation could have been reduced, resulting in cost savings through reduced inputs.

<p>Soil residual herbicides are a vital component of the best management practices (BMPs), to provide early-season weed control in most cropping systems. The availability of a biologically effective dose of a soil residual herbicide in the soil solution is dependent on several soil parameters including soil texture, organic matter (OM), and pH.  Soil residual herbicides are currently applied as a uniform application rate over an individual field; yet soil properties can vary spatially within agricultural fields. Therefore, areas of the field are being over- and under-applied when using a uniform application rate. By integrating variable-rate (VR) technology with soil residual herbicides, the correct rate could be applied based on the intra-field soil variability. However, the extent of spatial soil variability within a field and the impact on herbicide application rates has not been well-characterized to inform whether soil residual herbicide applications should move towards variable rate applications. Therefore, the objectives of this research were to 1) determine the extent of intra-field variability of soil texture and organic matter in ten commercial Indiana fields, 2) quantify the reliability of five different combinations of spatial soil data sources, 3) determine the impact of soil sample intensity on map development and the classification accuracy for VR applications of soil residual herbicides, 4) quantify the impact of VR herbicide application on the total amount and spatial accuracy of herbicide applied according to product labels, and 5) determine if the intensive spatial characterization of soil properties is related to weed seedbank abundance and species richness to improve predictive weed management using soil residual herbicides.</p> <p><br></p> <p>Commercial soil data was generated by intensively collecting 60 soil samples in a stratified random sampling pattern in 10 agricultural fields across Indiana. Analysis of this data from commercial fields confirmed inherent field variability that would benefit from multiple management zones according to the labeled rate structures of pendimethalin, s-metolachlor, and metribuzin. Therefore, further research was conducted to determine an accurate and reliable method to delineate the fields into management zones for variable-rate residual herbicide applications based on the spatial soil variability and herbicide labels. </p> <p><br></p> <p>A modified Monte Carlo cross-validation method was used to determine the best source of spatial soil data and sampling intensity for delineating management zones for variable-rate applications of pendimethalin, s- metolachlor, and metribuzin. These sources of spatial soil data included: Soil Survey Geographic database (SSURGO) data, intensive soil samples, electrical resistivity sensors, and implement mounted optical reflectance sensors using VNIR reflectance spectroscopy. The mean management zone classification accuracy for maps developed from soil samples with and without electrical conductivity was similar for 75% of all maps developed across each field, herbicide, and sampling intensity. The method of using soil sampling data combined with electrical conductivity (SSEC) maps was most frequently the top performing source of spatial soil data. The most reliable sampling intensity was one sample per hectare which resulted in lower root mean squared error (RMSE) OM values, higher management zone classification accuracy, and more reliable predictions for the number of management zones within each field. </p> <p><br></p> <p>Using VR maps developed from SSEC with one sample per hectare sampling intensity, additional research was conducted to compare the amount of herbicide and field area that was over-or under-applied with a uniform application rate compared to a VR application for 10 corn and soybean residual herbicides. Although research from our previous study documented that spatial soil variability was extensive enough to require two or more management zones for all fields, the same labeled herbicide dose defined for multiple soil conditions led to 20% of all maps not requiring a variable rate application (VRA). Additionally, no difference was shown in the total amount applied of herbicide in an individual field between a variable and uniform application rate for all herbicides. Nonetheless, nearly half of all VR maps had 10% or more of the field area misapplied with a uniform application rate and justifies further research to determine if the proper placement of residual herbicide adds value through increased weed control in the field areas being under-applied. </p> <p><br></p> <p>Similar to soil residual herbicides, weed seedbank abundance and species richness were impacted by the variable soil conditions present within the field area. The seedbanks favor the establishment in areas of the field that promote vigorous germination, growth, and reproduction next to the competing crop. Therefore, soil sampling and weed seedbank greenhouse grow-outs were conducted in four fields to gain a better understanding in the relationship between the spatial soil and weed seedbank variability. All weed seedbank characteristics were shown to be spatially aggregated. Even though no individual or combination of soil parameters consistently explained the variability of weed seedbank abundance, species richness, or individual weed species across all four fields. However, clay content was the most persistent soil parameter to negatively impact (lower seedbank values) the soil weed seedbank.</p> <p><br></p> <p>Further field studies should be conducted across multiple sites to determine if variable-rate residual herbicide applications aid farmers by reducing the risk of crop injury in over-applied field areas and increased weed control in the areas being under-applied.  These studies should also access whether earlier emergence and/or greater weed densities occur in field areas receiving sublethal herbicide doses compared to areas receiving the optimal application rate. Additional research should investigate the utility of VR residual herbicide applications when tank-mixing multiple products during an application. Particularly, when the soil parameters used for selecting the herbicide rate are not defined the same across herbicide labels </p>

The agriculture of precision has been an important tool for the characterization of the spatial variability of soil attributes and crops. Thus, there is many options to elaborate thematic digital maps that can improve the soil and crop management. In this work, two croplands that has been managed with precision agriculture during several years were selected. One of them was called field Lagoa, it is a 132 ha, and the other, is field Schmidt, it is a 125 ha. In both croplands soil samples were collected with regular grid of 100 x 100 m. In the field Lagoa three soil sampled were done while in the field Schmidt two soil sampled were done along four years of evaluation. In order to investigate the crop yield variability, the yields data were collected with a combine MF34, equipped with yield sensor, GPS (Global Position System), board computer (Datavision). In the field Talhao, the yield maps were made to corn 2004/05, wheat 2005, soybean 2005/06, soybean 2006/07, corn 2007/08 and, in the field Schmidt, crop data were collected to soybean 2004/05, soybean 2005/06, soybean 2006/07 and corn 2007/08. The yield data and soil data were processed in the softwares SGIS® and Campeiro 6.0. The variable rate fertizer was done with equipment Hércules 10.000 equipped with disks system to immediate change the fertilizer rate . The evolution of soil fertility indicate that, in areas with high phosporus and potassium levels, the range of variation was maintained through the years, although, with a decrease in extreme values. The correlations of soil attributes with crop yield maps were higher in the field Schmidt mainly to CEC, Mg, organic matter, Ca, and, in the field Lagoa there was correlation, mainly, with organic matter. The match distribution and potassium the varied rate allowed that the nutrient supply was enlarged e/ou above below the export of the cultures, not interfering in the revenue. The crop yield map was an efficient tool to define yield zones driving the site specific management of fertilizers and soil management. / A agricultura de precisão tem se mostrado uma ferramenta importante, principalmente, para a caracterização da variabilidade espacial de atributos de solo e das culturas. Assim, há varias possibilidades de elaboração de mapas temáticos que poderão melhorar as intervenções de manejo no solo e ou na cultura. Neste trabalho foram avaliadas duas áreas que vem sendo manejadas com técnicas de AP ao longo dos anos, sendo uma delas, denominada talhão Lagoa, contendo 132 ha, e a outra, talhão Schmidt, contendo 125 ha. Em ambos os talhões foram coletadas amostras de solo com grade regular de 100 x 100 m, sendo que, no talhão lagoa foram três coletas de solo e no talhão Schmidt foram duas coletas de solo ao longo de quatro anos de avaliação. Para a caracterização da variabilidade das culturas, foi utilizada a coleta de dados através de uma colhedora MF34, equipada com sensor de rendimento, GPS (Sistema de Posicionamento Global), computador de bordo (Datavision). No talhão Lagoa, foram confeccionados mapas de colheita nas culturas de milho 2004/05, trigo 2005, soja 2005/06, soja 2006/07, milho 2007/08 e, no talhão Schmidt, foram coletados dados de colheita nas culturas de soja 2004/05, soja 2005/06, soja 2006/07 e milho 2007/08. Os resultados de colheita e solo foram processados nos softwares SGIS® e Campeiro 6.0. As aplicações a taxa variada foram realizadas com o equipamento Hércules 10.000 equipado com sistema de disco para distribuição a lanço dos fertilizantes com variação instantânea das doses. Os resultados de fertilidade indicaram que, em áreas com altos teores de fósforo e potássio, a amplitude de variação foi mantida ao longo dos anos, porém, com a diminuição de valores extremos. As correlações de atributos de solo com mapas de colheita foram maiores no talhão Schmidt para os atributos Schmidt para os atributos CTC, magnésio, matéria orgânica e cálcio e, no talhão Lagoa houve correlação positiva, principalmente, com matéria orgânica, ao longo das safras agrícolas. A distribuição de fósforo e potássio a taxa variada permitiu que fosse ampliado o fornecimento de nutriente acima e/ou abaixo da exportação das culturas, não interferindo no rendimento. Com isso, o mapeamento do rendimento da cultura foi uma ferramenta eficiente na definição de zonas com potencial produtivo, orientando as intervenções localizadas de fertilizantes e de manejo do solo.