The dryland ecosystems of the US Desert Southwest (SW) are dependent on soil moisture for aboveground productivity; the generation of soil moisture in the SW is dependent on both soil physical properties and climate forcing. This study is one of the first regional point-scale analyses that explores the role of soil physical properties in modulating aboveground vegetation dynamics in response to climate forcing in the SW. Soil texture accounted for significant differences in average aboveground primary productivity across the SW. However, soil texture could not account for differences in inter-annual aboveground productivity variation across the SW. Subsurface soil texture was tightly coupled with precipitation seasonality in accounting for differences in long-term average seasonal aboveground productivity in the Mojave and Sonoran Deserts. The results of this study indicate that the subsurface is a significant factor in modulating aboveground primary productivity, and needs to be included in future modeling exercises of dryland ecosystem response to climate forcing and change.

Made available in DSpace on 2014-06-11T19:23:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-04-28Bitstream added on 2014-06-13T18:50:29Z : No. of bitstreams: 1 almeida_cx_me_jabo.pdf: 1204259 bytes, checksum: 236a0af5f42cb1b639ed5de10c3cc693 (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / A estimativa da curva de resistência do solo à penetração (CRP), a partir de variáveis de fácil obtenção, como o conteúdo de água (Ug), representa uma medida muito útil não só para a quantificação do estado de compactação, mas também para facilitar a interpretação da resistência do solo à penetração (RP), obtida em diferentes condições de campo. O objetivo foi estimar a CRP em solos de diferentes granulometrias e densidades, a partir de dados obtidos com o penetrômetro de impacto. O experimento foi realizado em laboratório, na Faculdade de Ciências Agrárias e Veterinárias (UNESP), Jaboticabal, SP. Foram utilizadas quatro classes de solos: Neossolo Quartzarênico, Argissolo Vermelho-Amarelo, Latossolo Vermelho Distrófico e Latossolo Vermelho Acriférrico, os quais foram amostrados na camada de 0-0,20 m. Colunas de PVC foram preenchidas de forma a se obter diferentes condições de densidade: solo não compactado e compactado. O Ug, inicialmente elevado até o ponto de saturação, foi monitorado diariamente através de um medidor eletrônico composto pelo Profile Probe PR2 acoplado ao Moisture Meter HH2. A RP foi mensurada através de um penetrômetro de impacto adaptado para vaso. Os pares de dados entre a RP e o Ug foram ajustados e as CRP submetidas ao teste de significância. A relação entre a RP e o Ug foi descrita pelo modelo exponencial decrescente, representado pela seguinte equação: RP = a+be Ug/c em que: RP representa a resistência do solo à penetração (MPa); Ug o conteúdo gravimétrico de água (kg kg-1) e a, b e c são os parâmetros da equação. Foram obtidos coeficientes de determinação que variaram de 0,57 a 0,96. / The estimate of soil resistance to penetration curve (RPC) from easy attainment variables, like the water content (Wc), represents a very useful measure not only in the evaluation of soil compaction, but also to simplify the interpretation of the soil resistance to penetration (SR) obtained in different field conditions. The objective was to estimate the RPC in soils of different textures and bulk densities, from data obtained with the impact penetrometer. The experiment was carried out in Faculdade de Ciências Agrárias e Veterinárias (UNESP), Jaboticabal, SP, and four classes of soil were used: Entissol, Alfissol, Oxisol medium texture and clayey Oxisol, which were collected at 0-0.20 m soil depth. The PVC columns filled in manner to obtain a condition of non compacted and compacted soil. The Wc, initially elevated to the saturation point, was daily monitored through an electronic measurer composed by Profile Probe (PR2) connected to a Moisture Meter HH2. The SR was measured by an impact penetrometer adapted to the vase. The pairs of data between SR and Wc were adjusted and the RPC submitted to the significance test. The equation that best described the relationship between the SR and Wc was the exponentially decreasing, as follows: SR = a+be Wc/c where SR is the soil resistance to penetration (MPa), Wc soil water content (kg kg-1) and a, b and c are the parameters of the equation. There was a significant relation between soil resistance to penetration and water content and the equations fit the data with coefficients of determination ranging from 0.57 to 0.96.

Orientador: José Frederico Centurion / Banca: Elias Nascentes Borges / Banca: Itamar Andrioli / Resumo: A estimativa da curva de resistência do solo à penetração (CRP), a partir de variáveis de fácil obtenção, como o conteúdo de água (Ug), representa uma medida muito útil não só para a quantificação do estado de compactação, mas também para facilitar a interpretação da resistência do solo à penetração (RP), obtida em diferentes condições de campo. O objetivo foi estimar a CRP em solos de diferentes granulometrias e densidades, a partir de dados obtidos com o penetrômetro de impacto. O experimento foi realizado em laboratório, na Faculdade de Ciências Agrárias e Veterinárias (UNESP), Jaboticabal, SP. Foram utilizadas quatro classes de solos: Neossolo Quartzarênico, Argissolo Vermelho-Amarelo, Latossolo Vermelho Distrófico e Latossolo Vermelho Acriférrico, os quais foram amostrados na camada de 0-0,20 m. Colunas de PVC foram preenchidas de forma a se obter diferentes condições de densidade: solo não compactado e compactado. O Ug, inicialmente elevado até o ponto de saturação, foi monitorado diariamente através de um medidor eletrônico composto pelo Profile Probe PR2 acoplado ao Moisture Meter HH2. A RP foi mensurada através de um penetrômetro de impacto adaptado para vaso. Os pares de dados entre a RP e o Ug foram ajustados e as CRP submetidas ao teste de significância. A relação entre a RP e o Ug foi descrita pelo modelo exponencial decrescente, representado pela seguinte equação: RP = a+be Ug/c em que: RP representa a resistência do solo à penetração (MPa); Ug o conteúdo gravimétrico de água (kg kg-1) e a, b e c são os parâmetros da equação. Foram obtidos coeficientes de determinação que variaram de 0,57 a 0,96. / Abstract: The estimate of soil resistance to penetration curve (RPC) from easy attainment variables, like the water content (Wc), represents a very useful measure not only in the evaluation of soil compaction, but also to simplify the interpretation of the soil resistance to penetration (SR) obtained in different field conditions. The objective was to estimate the RPC in soils of different textures and bulk densities, from data obtained with the impact penetrometer. The experiment was carried out in Faculdade de Ciências Agrárias e Veterinárias (UNESP), Jaboticabal, SP, and four classes of soil were used: Entissol, Alfissol, Oxisol medium texture and clayey Oxisol, which were collected at 0-0.20 m soil depth. The PVC columns filled in manner to obtain a condition of non compacted and compacted soil. The Wc, initially elevated to the saturation point, was daily monitored through an electronic measurer composed by Profile Probe (PR2) connected to a Moisture Meter HH2. The SR was measured by an impact penetrometer adapted to the vase. The pairs of data between SR and Wc were adjusted and the RPC submitted to the significance test. The equation that best described the relationship between the SR and Wc was the exponentially decreasing, as follows: SR = a+be Wc/c where SR is the soil resistance to penetration (MPa), Wc soil water content (kg kg-1) and a, b and c are the parameters of the equation. There was a significant relation between soil resistance to penetration and water content and the equations fit the data with coefficients of determination ranging from 0.57 to 0.96. / Mestre

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Concern for soil quality has acquired greater significance in the agricultural context. Therefore, we have sought indices assessing levels of physical deterioration suffered by the soil, depending on the management applied. The "S" index, a measure of soil physical structure, was proposed by Dexter (2004a,) and is defined by the slope of the tangent line to the inflection point of the soil water retention curve and, according to the author, is indicative of the porosity of the soil, especially its pore distribution by size and geometry. The porosity is divided into two parts: textural porosity (influenced by soil primary particles), usually related to poor water infiltration, poor workability and low physical quality; and structural porosity (determined by soil structural units), sensitive to management factors and whose presence indicates soil with good physical quality. This study aims to determine the index "S" taking into account the influence of texture and structure for different soil types from Cearà state, Brazil. Soil samples deformed structure and undeformed were collected in three distinct textural classes of soils (Ultisol, Cambisol, Entisol) in the 0-0.15 m layer The S showed sensitivity to variations in the structure and texture of soils. By multivariate analysis it was observed that the S has a greater interaction with structural variables (porosity, aggregate stability, average diameter). We conclude that the S is an indicator of physical quality of the soil and condition reflects its structural soil. / A preocupaÃÃo com a qualidade do solo tem tido grande destaque dentro do contexto agrÃcola. Diante disso, tem-se procurado Ãndices que avaliem os nÃveis de degradaÃÃes fÃsicas sofridas pelos solos, em funÃÃo do manejo aplicado. O Ãndice âSâ, parÃmetro de avaliaÃÃo da estrutura do solo, foi proposto por Dexter (2004 a), sendo definido pela inclinaÃÃo da reta tangente ao ponto de inflexÃo da curva de retenÃÃo de Ãgua no solo e, segundo o autor, à um indicativo da porosidade do solo, principalmente de sua distribuiÃÃo de poros por tamanho e geometria. A porosidade à dividida em duas partes: porosidade textural (influenciada pelo tamanho das partÃculas primÃrias do solo), geralmente associada a baixos valores de infiltraÃÃo de Ãgua, pouca trabalhabilidade e qualidade fÃsica; e porosidade estrutural (determinada pelas unidades estruturais do solo), sensÃvel a fatores de manejo. Este trabalho teve como objetivo determinar à influÃncia da textura e da estrutura no Ãndice âSâ em diferentes solos do estado do CearÃ. As amostras de solo com estrutura deformada e indeformada foram coletadas em trÃs solos de classes texturais distintas (Argissolo Vermelho, Cambissolo HÃplico, Neossolo FlÃvico) na profundidade de 0-0,15 m. O Ãndice S mostrou sensibilidade Ãs variaÃÃes na estrutura e textura dos solos analisados. Por meio da analise multivariada foi possÃvel observar que o Ãndice S apresenta uma maior interaÃÃo com variÃveis estruturais (porosidade, estabilidade de agregados, diÃmetro mÃdio ponderado). Concluiu-se que o Ãndice S à um indicador da qualidade fÃsica do solo e reflete a sua condiÃÃo estrutural do solo.

This study looks at relationships along the hydrologic gradient between and within six pools; including the vegetation community, soil characteristics and hydrology. Pool conditions were monitored weekly throughout the 2011 and 2012 growing seasons. Each pool was equipped with permanent platinum-tipped redox probes to quantify the severity and duration of soil reduction. We described and analyzed 12 soil profiles in each pool, distributed in summit/upland, basin, and rim/transition positions as defined by the high water line. The pools were systematically surveyed for understory vegetation during the 2012 growing season. Vegetation patterns varied between study areas. No clear pattern of unique vegetation was evident from an ordination of the gradient communities. Time series redox potential data showed a visual relationship to water table fluxuation, but also a dampening effect from soil organic matter content in the basin positions.

This thesis presents an automatic soil texture classification system using hyperspectral soil signals and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, which use images for training and testing. Although using image-based algorithms is a straightforward way to conduct soil texture classification, our research shows that it does not provide reliable and consistent results. Rather, we develop a novel system using hyperspectral soil textures, better known as hyperspectral soil signals, which provide rich information and intrinsic properties about soil textures. Hyperspectral soil textures, in their very nature, are nonstationary and time-varying. Therefore, the wavelet transform, which is proven to be successful in such applications, is incorporated. In this study, we incorporate two wavelet-domain statistical models, namely, the maximum likelihood (ML) and the hidden Markov model (HMM) for the classification task. Experimental results show that this method is reliable and robust. It is also more effective and efficient in terms of practical implementation than the traditional image-based methods.

No description available.

Drought, salinity, and low soil fertility have negative impacts on agricultural productivity, resulting in food scarcity and nutritional insecurity, particularly in Sub-Saharan Africa. Mungbean [Vigna radiata (L.) R. Wilczek] has seen increased interest as a short-duration and drought tolerant legume crop, capable of atmospheric N₂ fixation. Mungbean is a protein and iron-rich legume and can be used as vegetable or grain for human consumption or multipurpose crop. At present, few studies have simultaneously explored the best agronomic practices for mungbean cultivation and evaluated its potential for increasing crop yields via intercropping systems and improving soil fertility through biological N₂ fixation. To understand the agronomic practices and soil physical properties limiting mungbean production, the impacts of two mungbean cultivars (Berken and OK2000) with and without inoculation with Bradyrhizobium spp. grown in loamy sand and silt loam soils on mungbean growth and yield were investigated under glasshouse conditions. Promising results from this study led to the introduction of mungbean into pearl millet systems in Senegal and evaluation of the effects of intercropping on growth, yields, land equivalent ratio (LER), canopy cover estimates, and normalized difference vegetation index (NDVI). Finally, we evaluated plant growth and N₂ fixation of five mungbean genotypes grown in two soil textures using the ¹⁵N natural abundance technique leading to recommendations for those with the greatest overall benefit to the cropping system. The literature review shows mungbean often proposed as a strategic crop for increasing legume diversification within current cropping systems and providing increased food security as well as market diversification and economic sustainability. The greenhouse study revealed that OK2000 cultivar produced significantly higher yield when inoculated and planted on a silt loam soil than other treatments, indicating the importance of inoculation and soil texture in mungbean establishment. Intercropping mungbean and millet significantly (p≤ 0.05) increased combined yields (35% to 100% increase) and LER compared to sole millet cropping systems. Canopy cover estimates and NDVI values significantly increased up to 60% and 30%, respectively, in millet-mungbean intercropping over millet alone. The N2 fixation study showed that %Ndfa of mungbean was higher when grown in the loamy sand soil (27% increase). However, soil N uptake (235 mg plant⁻¹) and amount of N fixed (67 mg plant⁻¹) were greater in the silt loam soil. Among genotypes, IC 8972-1 significantly (p≤ 0.05) derived less N from the atmosphere (23%) but took more soil N (155 mg plant⁻¹) which yielded significantly greater dry biomass (7.85 g plant⁻¹) and shoot N content (200 mg plant⁻¹). The results from the N₂ fixation study indicated that choice of mungbean genotype can contribute to reducing N needs of agricultural systems. Overall, this research project demonstrated that mungbean has the potential for diversifying smallholder agriculture and adding biologically fixed N into soils, in line with transformative adaptation strategies being promoted for sustainable agriculture. Further research and development programs on good cultural practices, adaptation to cropping systems, and nutritional benefits for human consumption can promote mungbean cultivation in SSA. / Doctor of Philosophy / Global population growth is expected to reach 9.8 billion in 2050 while climate change is predicted to reduce food production. Sustainable solutions are needed for increasing food availability and satisfying nutritional needs under changing climatic conditions. Mungbean is a viable option because it is a legume crop capable of restoring soil fertility and has low water requirements. Mungbean also contains high levels of protein and iron and can, therefore, provide a nutritious and healthy food. Although the agronomic benefits of mungbean have been studied, best cultural practices and its impact on farming systems and soil fertility are scattered. The objectives of this research were to identify the best agronomic practices for mungbean production, assess its effects when grown together with millet, and measure its nitrogen contribution to the soil. The results showed that selecting the best genotypes to be grown in a particular soil texture can significantly increase mungbean growth and yield. In addition, incorporation of mungbean into cereal-based farming systems demonstrated its capacity for improving agricultural production in a low-input environment. Assessment of nitrogen fixation by mungbean showed that it can naturally add nitrogen into the soils, the most limiting plant nutrient, reducing nitrogen application needs. Thus, the ability of mungbean to diversify farming systems, improve soil fertility, and deliver nutritious food will provide agronomic, environmental, and economic benefits to farmers, especially in food-insecure households. However, exploitation of the full potential of mungbean won't be achieved without understanding the major factors influencing mungbean cultivation and production.

The majority of outdoor sports are played on natural turf pitches. Throughout the playing season, a pitch has continual player interaction, which, during periods of sustained unfavourable conditions, can cause the turf to tear up (shear) under player contact. This is most evident in Rugby Union scrummages, which create deep divots in the turf and rootzone that reduce player safety and are criticised by the media. However, little is known of the turf/rootzone strength to depth, termed 'shear stability' in this thesis, and there is currently no appropriate means to test this property. In order to explore the shear stability of turf, a device was designed and developed. The prototype device, termed the 'Shear Tester', underwent trial, validation and several redesigns until it was deemed suitable to investigate turf shear stability. A range of natural and hybrid constructions and laboratory-controlled samples were investigated, and the key variables found to influence the shear stability were grass rooting, water content and rootzone density.

Soil is extremely important for providing food, biomass and raw materials, water and nutrient storage; supporting biodiversity and providing foundations for man-made structures. However, its health is threatened by human activities, which can greatly affect the potential of soils to fulfil their functions and, consequently, result in environmental, economic and social damage. These issues require the characterisation of the impact and spatial extent of the problems. This can be achieved through the creation of detailed and comprehensive soil maps that describe both the spatial and vertical variability of key soil properties. Detailed three-dimensional (3D) digital soil maps can be readily used and embedded into environmental models. Three-dimensional soil mapping is not a new concept. However, only with the recent development of more powerful computers has it become feasible to undertake such data processing. Common techniques to estimate soil properties in the three-dimensional space include geostatistical interpolation, or a combination of depth functions and geostatistics. However, these two methods are both partially flawed. Geostatistical interpolation and kriging in particular, estimate soil properties in unsampled locations using a weighted average of the nearby observations. In order to produce the best possible estimate, this form of interpolation minimises the variance of each weighted average, thus decreasing the standard deviation of the estimates, compared to the soil observations. This appears as a smoothing effect on the data and, as a consequence, kriging interpolation is not reliable when the dataset is not sampled with a sampling designs optimised for geostatistics. Depth function approaches, as they are generally applied in literature, implement a spline regression of the soil profile data that aims to better describe the changes of the soil properties with depth. Subsequently, the spline is resampled at determined depths and, for each of these depths, a bi-dimensional (2D) geostatistical interpolation is performed. Consequently, the 3D soil model is a combination of a series of bi-dimensional slices. This approach can effectively decrease or eliminate any smoothing issues, but the way in which the model is created, by combining several 2D horizontal slices, can potentially lead to erroneous estimations. The fact that the geostatistical interpolation is performed in 2D implies that an unsampled location is estimated only by considering values at the same depth, thus excluding the vertical variability from the mapping, and potentially undermining the accuracy of the method. For these reasons, the literature review identified a clear need for developing, a new method for accurately estimating soil properties in 3D – the target of this research, The method studied in this thesis explores the concept of soil specific depth functions, which are simple mathematical equations, chosen for their ability to describe the general profile pattern of a soil dataset. This way, fitting the depth function to a particular sample becomes a diagnostic tool. If the pattern shown in a particular soil profile is dissimilar to the average pattern described by the depth function, it means that in that region there are localised changes in the soil profiles, and these can be identified from the goodness of fit of the function. This way, areas where soil properties have a homogeneous profile pattern can be easily identified and the depth function can be changed accordingly. The application of this new mapping technique is based on the geostatistical interpolation of the depth function coefficients across the study area. Subsequently, the equation is solved for each interpolated location to create a 3D lattice of soil properties estimations. For this way of mapping, this new methodology was denoted as top-down mapping method. The methodology was assessed through three case studies, where the top-down mapping method was developed, tested, and validated. Three datasets of diverse soil properties and at different spatial extents were selected. The results were validated primarily using cross-validation and, when possible, by comparing the estimates with independently sampled datasets (independent validation). In addition, the results were compared with estimates obtained using established literature methods, such as 3D kriging interpolation and the spline approach, in order to define some basic rule of application. The results indicate that the top-down mapping method can be used in circumstances where the soil profiles present a pattern that can be described by a function with maximum three coefficients. If this condition is met, as it was with key soil properties during the research, the top-down mapping method can be used for obtaining reliable estimates at different spatial extents.