The need for improving nutrient use efficiency and increase savings demand a better knowledge of soils and production systems. The objective of this study was to evaluate the soil P and K budget and its relationships with their temporal evolution on site-specific management areas. The experiments were carried out in four cropland areas from the Medium Plateau region of the Rio Grande do Sul state, located at the cities of Almirante Tamandaré do Sul, Tio Hugo, Não-Me-Toque and Vitor Graeff. Soil tests were used to verify the spatial variability, budget and temporal evolution of soil P and K levels by the comparison of the years of 2005 and 2009. Regression analysis was performed by the software JMP IN v.3.2.1 to evaluate the relationship between P and K budget and the temporal evolution of soil P and K levels. Cropland areas showed high spatial variability for P and K soil test levels at the beginning of the study, which was decreased by applying precision farming techniques. Relationships between P and K budget and the temporal evolution of soil P and K test levels were positive with the exception of the Vitor Graeff area which showed negative P and K budget. P and K fertilization were 30 and 25% higher in relation to their exportation, respectively. On average, soybean extracted 44 kg P2O5 ha-1 and 63 kg K2O ha-1, while the maize exported 78,5 kg P2O5 ha-1 and 59 kg K2O ha-1. Temporal evolution of soil P and K test levels was high when P and K budget was low and there was a low temporal evolution when the P and K budget was high. On the average of the four cropland areas there were necessary the input of 12,0 kg P2O5 ha-1 to increase 1 mg dm-3 of soil P test levels and the input of 4,1 kg K2O ha-1 to increase 1 mg dm-3 of soil K test levels. / A necessidade de aproveitar melhor os insumos e economizá-los na maior medida possível leva à obrigação de conhecer a fundo os solos e sistemas de produção. O objetivo deste trabalho foi avaliar o balanço nutricional de fósforo (P) e potássio (K) no solo e sua relação com a evolução temporal, em áreas manejadas com auxilio da agricultura de precisão. Os experimentos foram conduzidos no Planalto Médio do Rio Grande do Sul, em quatro áreas situadas nos municípios de Almirante Tamandaré do Sul, Tio Hugo, Não-Me-Toque e Vitor Graeff. Por meio da análise das amostras de solo foi verificada a evolução dos teores de P e K comparando os anos de 2005 a 2009. Análises de regressão realizadas pelo software JMP IN v.3.2.1 foram utilizadas para avaliar a relação entre saldo e evolução de P e K. Determinaram-se a variabilidade espacial, o balanço nutricional e a evolução temporal de P e K no solo. As áreas apresentaram no inicio do estudo uma grande variabilidade espacial, as quais foram reduzidas mediante uso de práticas de agricultura de precisão. As relações entre os saldos e as exportações foram positivas em todos os locais com exceção da área situada em Vitor Graeff onde também se obtiveram saldos negativos. As fertilizações fosfatadas e potássicas foram 30 e 25 % superiores em relação as exportações, respectivamente. A soja extraiu em média 44 kg ha-1 de P2O5 e 63 kg ha-1 de K2O já o milho exportou 78,5 kg ha-1 de P2O5 e 59 kg ha-1 de K2O. A evolução dos teores foi alta quando os saldos eram baixos e, baixa quando o saldo se encontrava em níveis altos. Em média para os quatro locais foram necessários aplicar 12,0 kg ha-1 de P2O5 para elevar 1 mg dm- 3 de P no solo, assim como 4,1 kg ha-1 de K2O para elevar 1 mg dm-3 de K no solo.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-06-15T19:05:07Z No. of bitstreams: 1 ARMINDO BEZERRA LEÃO - DISSERTAÇÃO PPGEA 2006..pdf: 18036151 bytes, checksum: 3c7202f8b76aca90d252e37405bcede3 (MD5) / Made available in DSpace on 2018-06-15T19:05:07Z (GMT). No. of bitstreams: 1 ARMINDO BEZERRA LEÃO - DISSERTAÇÃO PPGEA 2006..pdf: 18036151 bytes, checksum: 3c7202f8b76aca90d252e37405bcede3 (MD5) Previous issue date: 2006-05 / O solo pode apresentar ampla variação dos seus atributos, estando esta relacionada a fatores ligados à sua formação ou manejo de práticas agrícolas, a exemplo da irrigação que tem, como efeito nocivo se mal empregada, problemas de salinidade. A estatística clássica considera que a variabilidade do solo ocorre de forma inteiramente aleatória; entretanto, vários estudos têm revelado que seus atributos apresentam grande dependência espacial necessitando, portanto, de uma análise geoestatística Neste trabalho se propôs como objetivo geral, estudar as variáveis que caracterizam a salinidade do solo através da estatística clássica e geoestatística e, como metas específicas avaliar a variabilidade espacial, a existência de dependência espacial entre amostras, a validação cruzada do método de interpolação utilizado (krigagen) e construir mapas de isolinhas das variáveis da área em estudo. O estudo foi conduzido no perímetro Irrigado Engenheiro Arcoverde, localizado no Município de Condado, PB, o qual se situa na Bacia do Alto Piranhas, cuja área experimental tinha 42 hectares e se constituía de solos Neossolos Flúvicos, onde se coletaram 53 amostras, nas profundidades de 0-20, 20-40 e 40-60 cm. A malha de amostragem foi irregular, os pontos de coleta se distanciaram de lOOm entre si. De início se utilizaram para avaliação da variabilidade espacial, métodos de estatística clássica a fim de se verificar medidas de posição, dispersão e geostatística, analisando-se os variogramas e se realizando, também, a validação cruzada como meio de conhecer os melhores ajustes para os modelos encontrados. Após a realização de interpolação por krigagem, construíram-se mapas de isolinhas revelando os problemas de salinidade e sodicidade, em que os resultados obtidos indicaram grande variabilidade justificando, assim, o uso da geoestatística, principalmente para os atributos CE e PST do solo, reconhecendo-se a geoestatística como uma poderosa ferramenta indicadora da dependência espacial entre amostras e a distância, a partir dos quais elas se tornam independentes. Os atributos estudados, indicadores de salinidade, apresentaram estrutura de dependência espacial, o que permitiu o seu mapeamento. A validação cruzada demonstrou forte relação entre os valores observados e os estimados, comprovando que a Krigagem ordinária é um bom interpolador. A identificação de compartimentos como sub-regiões da paisagem na área de estudo, mostrou-se eficiente na caracterização da salinidade do solo. A técnica de interpolação por krigagem permitiu verificar que a salinidade do solo aumentou em profundidade, além de distribuição diferenciada dos sais na área, com maiores teores em sub-região específica. / The soil atributes may present a great variability, due to formation and management factores, such as an inadequate irrigation that coutj produce salinity problems. The classical statistics consider that soil variability occurs in a randomized form, however several studies have shown that the attributes present an strong spatial dependence, nedding thus geoestatical analises. The objective of this work was to study the variables that characterize the soil salinity by classical statistics and geostatistics and also to evaluate the spatial variability, the spatial dependence amongst samples, to verify the crossed validation of the interpolation method (Kriging) and to build maps of isolines of the ali variables. The present work was conducted at the Engenheiro Arcoverde irrigated perimeter located at Condado, Paraíba, aiming to measure the soil parameters that define the soil salinity, to study the spatial variability of them throughout geoestatical analyses, to study the liability of the Kriging interpolation method and to construct salinity and sodicity maps, furnishing subsides for an adequate soil management which could allows their reclamation. For this, 159 soil samples were collected at 0-20, 20-40 and 40-60 cm depth intervals, on an irregular grid with the sampling points separated at approximately 100 m. To evaluate the spatial variability initially it was used classical statistics to verify position and dispersion measures and afterwards geoestatistics to analise the semivariograms, crossed validation and to obtain the best adjusted models. After interpolation by kriging, isoline maps were constructed showing the salinity and sodicity situation of the area. The results obtained allowed to observe a great spatial variability of eletrical conductivity and exchange sodium percentage. The pH variable showed a low spatial variability. The spatial dependence of the studied attributes permitted their mapping using kriging techniques. The crossed validation offered an excellent precision to estimate data. The compartments identification, sub-regions of the studied area, was excellent to characterize the soil salinity. Small variations on the soil relief, on the soil formation and management conditioned the variability found for the studied attributes. Finally, the geoestatistics showed the possibility to obtain a greater volume of information from a small volume of data, which means efficience, saving of time and resource

Understanding the spatial and temporal dynamics of soil water and crop water stress within a field is critical for effective Variable Rate Irrigation (VRI) management. Proper VRI can result in improved protection of the crop from early onset of crop water stress while minimizing runoff and drainage losses. The objectives of this study are (1) to examine zone delineation for informing irrigation recommendations from volumetric water content (VWC) and field capacity (FC) to grow similar or greater wheat yields with less water, (2) evaluate the ability to model soil and crop water dynamics within a season and within a field of irrigated winter wheat, and evaluate the sensitivity of crop water stress, evapotranspiration and soil water depletion outputs within a water balance model with Penman-Monteith evapotranspiration (ET) in response to adjusted soil properties, spring volumetric water content (VWC), and crop coefficient model input values. Five irrigation zones were delineated from two years of historical yield and evapotranspiration (ET) data. Soil sensors were placed at multiple depths within each zone to give real time data of the VWC values within each soil profile. Soil samples were taken within a 22 ha field of winter wheat (Triticum aestivum ‘UI Magic’) near Grace, Idaho, USA multiple times during a growing season to describe the spatial variation of VWC throughout the field, and to assist in modeling soil water dynamics and crop water stress through energy balance and water balance equations. Spatial variation of VWC was observed throughout the field, and on a smaller scale within each zone, suggesting the benefit of breaking portions of the field into zones for irrigation management purposes. Irrigation events were triggered when soil sensors detected low values of VWC, with each zone receiving unique rates intended to refill to zone specific FC. Cumulative irrigation rates varied among zones and the VRI approach saved water when compared to an estimated uniform Grower Standard Practice (GSP) irrigation approach. This method of zone management with soil sampling and sensors approximately represented the VWC within each zone and proved beneficial with effective reduction of irrigation rates in every zone compared to an estimated GSP. As such, there was a delay in the premature onset of crop water stress throughout some areas of the field. Variability in soil properties and spring soil moisture were key in giving accurate values to the model in order to make proper VRI management decisions. When assessing the model sensitivity, changing the inputs such as FC, wilting point (WP), total available water (TAW), spring VWC and crop coefficient (Kc) by -4 to +4 standard deviations away from their spatially average values, impacted the outputs of the model, with Kc having a large impact all three of the outputs. Further work is needed to improve the accuracy of representing VWC throughout a field, thus improving VRI management, and there is potential benefit in using a variable crop coefficient could to more accurate VRI management decisions from a soil water depletion model.

The properties of natural soil are inherently variable and influence design decisions in geotechnical engineering. Apart from the inherent variability of the soil, the variability may arise due to measurement of soil properties in the field or laboratory tests and model errors. These wide ranges of variability in soil are expressed in terms of mean, variance and autocorrelation function using probability/reliability based models. The most common term used in reliability based design is the reliability index, which is a probabilistic measure of assurance of performance of structure. The main objective of the reliability based design is to quantify probability of failure/reliability of a geotechnical system considering variability in the design parameters and associated safety. In foundation design, reliability based design is useful compared to deterministic factor of safety approach. Several design codes of practice recommend the use of limit state design concept based on probabilistic models, and suggest that, development of reliability based design methodologies for practical use are of immense value. The objective of the present study is to propose reliability based design methodologies for pile foundations under static and seismic loads. The work presented in this dissertation is subdivided into two parts, namely design of pile foundations under static vertical and lateral loading; and design of piles under seismic loading, embedded in non-liquefiable and liquefiable soil. The significance of consideration of variability in soil parameters in the design of pile foundation is highlighted. A brief review of literature is presented in Chapter 2 on current pile design methods under vertical, lateral and seismic loads. It also identifies the scope of the work. Chapter 3 discusses the methods of analysis which are subsequently used for the present study. Chapter 4 presents the reliability based design methodology for vertically and laterally loaded piles based on cone penetration test data for cohesive soil. CPT data from Konaseema area in India is used for analysis. Ultimate limit sate and serviceability limit state are considered for reliability based design using CPT data and load displacement curves. Chapter 5 presents the load resistance factor design (LRFD) of vertically and laterally loaded piles based on load test data. Reliability based code calibrated partial factors are determined considering bias in failure criteria, model bias and variability in load and resistance. Chapter 6 illustrates a comprehensive study on the effect of soil spatial variability on response of vertically and laterally loaded pile foundations in undrained clay. Two-dimensional finite difference program, FLAC2D (Itasca 2005) is used to model the soil and pile. The response of pile foundations due to the effect of variance and spatial correlation of undrained shear strength is studied using Monte Carlo simulation. The influence of spatial variability on the propagation and formation of failure near the pile foundation is also examined. Chapter 7 describes reliability based design methodology of piles in non-liquefiable soil. The seismic load on pile foundation is determined from code specified elastic design response spectrum using pseudo-static approach. Variability in seismic load and soil undrained shear strength are incorporated. The effects of soil relative densities, pile diameters, earthquake predominant frequencies and peak acceleration values on the two plausible failure mechanisms; bending and buckling are examined in Chapter 8. The two-dimensional finite difference analysis is used for dynamic analysis. A probabilistic approach is proposed to identify governing failure modes of piles in liquefiable soil in Chapter 9. The variability in the soil parameters namely SPT-N value, friction angle, shear modulus, bulk modulus, permeability and shear strain at 50% of modulus ratio is considered. Monte Carlo simulation is used to determine the probability of failure. A well documented case of the failed pile of Showa Bridge in 1964 Niigata earthquake is considered as case example. Based on the studies reported in this dissertation, it can be concluded that the reliability based design of pile foundations considering variability and spatial correlation of soil enables a rational choice of design loads. The variability in the seismic design load and soil shear strength can quantify the risk involved for pile design in a rational basis. The identification of depth of liquefiable soil layer is found to be most important to identify failure mechanisms of piles in liquefiable soil. Considerations of soil type, earthquake intensity, predominant frequency of earthquake, pile material, variability of soil are also significant.

Thesis (M.Sc. (Soil Science)) -- University of Limpopo, 2022 / In South Africa, land use planning has received limited attention in areas perceived as suitable for agricultural production. In the lack of reliable soil type and fertility status information, crop yields remain lower than the land’s potential, with subsequent land degradation. Despite this, studies that focused on land capability and soil suitability to date have not considered the spatial variability of the soil nutrients and factors influencing their variability. However, this information is key for site-specific soil management. Therefore, it is vital to link land capability and soi suitability with the spatial variability of soil nutrients as it opens opportunities for more rational management of the soil resources since soil nutrients directly affect crop growth and consequently yield. To address this issue, a study was conducted on a 12 ha banana plantation portion of the Makuleke farm. The main objectives of this study were to (1) survey, classify and characterise soils in order to derive and map land capability classes of Makuleke farm, (2) quantify the physical and chemical properties of the soils in order to derive and map the soil suitability of Makuleke farm for banana production, (3) assess the spatial variability and structure of soil nutrients across the Makuleke farm and (4) Identify the factors of control of the spatial variability of the soil nutrients across the Makuleke farm. To begin with, a field soil survey was conducted using transect walks complemented by auger observations to sub-divide the 12 ha banana plantation portion of the farm into varied soil mapping units. Thereafter, soil classification was done to group soils based on their morphological properties and pedological processes. During soil classification, a total of 12 representative profile pits (1.5 m × 1.5 m long × 2 m deep/limiting layer) were excavated, studied, described, and sampled. At each profile pit, three replicates samples were collected at 0 – 30 cm depth intervals giving rise to 36 bulk soil samples. From the gathered soil profile information, four soil units were thus delineated and identified across the 12 ha banana plantation. For soil fertility assessment, a grid sampling strategy at 50 × 50 m was adopted to collect the samples across the 12 ha banana plantation. A total of 27 composite samples were collected at the nodes of the grid, and thereafter bagged, labelled, and transported to the laboratory. In the laboratory, all collected samples were air-dried and sieved using a 2 mm sieve in preparation for soil physical and chemical properties analysis. The land capability assessment of Makuleke farm was done using the concepts and principles of the FAO framework for Land Evaluation (FAO, 1976), but adapted to South African conditions by Smith (2006). Soil suitability assessment was done using the FAO framework for Land Evaluation (FAO, 1976) coupled with the guidelines for rainfed agriculture (FAO, 1983) and the criteria proposed by Sys et al. (1993) and Naidu et al. (2006). To assess the spatial variability and structure of the soil nutrients across the farm, classical and geostatistical techniques were employed respectively. A correlation matrix was employed to identify key factors influencing the spatial variability of soil nutrients across the farm. For interpolation, ordinary kriging was used to generate soil nutrient spatial distribution maps. In this study, four soil forms were identified and classified as Hutton, Westleigh, Glenrosa, and Valsrivier, which are broadly distinguished as Lixisols, Plinthosols, Leptosols, and Cambisols. Land capability results revealed that 17% of the 12 ha portion of the farm has very high arable potential (I), 60% of the farm has medium arable potential (III), 6% has low arable potential (IV) and 17 % is non-arable (VI), which might explain the varied banana yields in the farm. Soil suitability analysis revealed that 12% of the 12 ha farm is highly suitable (S1), 34% is moderately suitable (S2), 38% is marginally suitable (S3) and 16% is permanently not suitable (N2) for banana production. The low arable and marginally suitable portion of the farm was under Valsrivier soils which were limited by its shallow depth, shallow rooting depth, acidic soil pH, low organic carbon (OC), and the fact that it was located on a steeper slope gradient. The non-arable and not suitable portion of the farm for banana production was under Glenrosa and it was limited by its location on a steep slope gradient and was characterised by shallow effective rooting depth, low OC, low clay content, and acidic soil pH. Classical statistical techniques revealed that phosphorus (P), potassium (K), calcium (Ca), zinc (Zn), manganese (Mn), and copper (Cu) content varied highly across the banana plantation, while magnesium (Mg) and total nitrogen (TN) varied moderately. In addition, the geostatistical analysis revealed that spatial dependency was weak (Ca, Cu, and TN), moderate (Mg and Zn), and strong (P, K, and Mn) for the different soil nutrients across the 12 ha banana plantation. Soil nutrients with strong spatial dependency have a good spatial structure and are easily manageable (in terms of fertilisation, liming, and irrigation) across the farm compared to the ones with weak spatial dependency which have a poor structure. This study also found that land attributes, which are soil type and topographic position were the main factors driving the spatial variability of the soil nutrients across the farm. In terms of soil type, soils such as Valsrivier and Glenrosa with 2:1 clay-type smectite were the ones that had nutrient content compared to soils with 1:1 clay-type kaolinite (e.g., Westleigh and Hutton). Higher nutrient contents were also observed in the footslope position compared to the middleslope of the farmland. Correlation analysis revealed that Mn was the key polyvalent cation influencing the spatial variability of P, K, and Zn. Soil pH and effective cation exchanges capacity (ECEC) were the key soil factors driving the spatial variability of Ca, while ECEC was the key factor affecting the spatial variability of Mg. Moreover, the spatial variability of soil Mn and Cu was driven by soil Cu and clay content, respectively. The kriged maps showed that P, Mg, Zn, and Mn were high in the northeast part and low in the northwest part of the farm. Similarly, K and Ca were low in the northwest part, but they were high in the south to the southwest part of the study area. Total nitrogen was high in the west part and low in the east-northeast part, while Cu was evenly distributed across the plantation. This study highlights the importance of prior land use planning (i.e., land capability and soil suitability) and fertility assessment for agricultural production. The research results obtained provide the actual reference state of the capability of the land for arable farming and soil suitability for banana production at Makuleke farm. Moreover, the research results provide the spatial variability and structure of the soil nutrients which have a greater impact on the growth and yield of bananas. The results obtained in this study will be useful for site-specific management of soil nutrients and other soil management practices (e.g., irrigation, fertilisation, liming, etc.), developing appropriate land use plans, and quantifying anthropogenic impacts on the soil system and thus improving land productivity. / National Research Foundation (NRF)

La conception des ouvrages en Génie Civil se fait habituellement de manière semi-probabiliste en employant des valeurs caractéristiques auxquelles sont associées des facteurs partiels de sécurité. Toutefois, de telles approches ne permettent pas de garantir la robustesse de l’ouvrage conçu vis-à-vis des sources d’incertitudes susceptibles d’affecter ses performances au cours de sa réalisation et de son fonctionnement. Nous proposons dans ce mémoire une méthodologie d’aide à la décision pour la conception robuste des ouvrages qui est appliquée à une structure métallique reposant sur des fondations superficielles. La conception de cet ouvrage est conduite en intégrant le phénomène d’interaction sol-structure qui implique que les choix de conception faits sur la fondation influencent ceux faits sur la structure supportée (et réciproquement). La démarche de conception proposée fait appel à des outils d’optimisation multi-objectif et d’aide à la décision afin d’obtenir une solution qui offre le meilleur compromis entre l’ensemble des préférences énoncées par le décideur sur chaque critère de conception. Des analyses de sensibilité sont menées parallèlement dans le but d’identifier et de quantifier les sources d’incertitude les plus influentes sur la variabilité des performances de l’ouvrage. Ces sources d’incertitude représentées sous une forme probabiliste sont intégrées dans la procédure de conception et propagées à l’aide d’une méthode d’échantillonnage par hypercube latin. Une partie du mémoire est consacrée à l’analyse des effets de l’incertitude relative à la modélisation des paramètres géotechniques sur la réponse de l’ouvrage et sur la démarche plus globale d’optimisation. / Design in Civil Engineering is usually performed in a semi-probabilistic way using characteristic values which are associated with partial safety factors. However, this approach doesn’t guarantee the structure robustness with regard to uncertainties that could affect its performance during construction and operation. In this thesis, we propose a decision aid methodology for robust design of steel frame on spread foundations. Soil-structure interaction is taken into consideration in the design process implying that the design choices on foundations influence the design choices on steel frame (and vice versa). The proposed design approach uses multi-objective optimization and decision aid methods in order to obtain the best solution with respect to the decision-maker’s preferences on each criterion. Furthermore, sensitivity analyzes are performed in order to identify and quantify the most influencing uncertainty sources on variability of the structure performances. These uncertainties are modeled as random variables and propagated in the design process using latin hypercube sampling. A part of this dissertation is devoted to the effects of uncertainties involved in soil properties on the structure responses and on the design global approach.