949 resultados para soil data requirements


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Soil and subsoil pollution is not only significant in terms of environmental loss, but also a matter of environmental and public health. Solid, liquid and gaseous residues are the major soil contamination agents. They originate from urban conglomerates and industrial areas in which it is impossible to emphasize the chemical, petrochemical and textile industry; thermoelectric, mining, and ironmaster activities. The contamination process can thus be defined as a compound addition to soil, from what qualitative and or quantitative manners can modify soil's natural characteristics and use, producing baneful and deteriorative effects on human health. Studies have shown that human exposition to high concentration of some heavy metals found on soil can cause serious health problems, such as pulmonary or kidney complications, liver and nervous system harm, allergy, and the chronic exposition that leads to death. The present study searches for the correlation among soil contamination, done through a geochemical baseline survey of an industrial contamination area on the shoreline of Sao Paulo state. The study will be conducted by spatial analysis using Geographical Information Systems for mapping and regression analysis. The used data are 123 soil samples of percentage concentration of heavy metals. They were sampled and spatially distributed by geostatistics methods. To verify if there is a relation between heavy metals soil pollution and morbidity an executed correlation and regression analysis will be done using the pollution registers as the independent variables and morbidity as dependable variables. It is expected, by the end of the study, to identify the areas relation between heavy metals soil pollution and morbidity, moreover to be able to provide assistance in terms of new methodologies that could facilitate soil pollution control programs and public health planning. © 2010 WIT Press.

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The objective of this study was to develop and evaluate a mathematical model used to estimate the daily amino acid requirements of individual growing-finishing pigs. The model includes empirical and mechanistic model components. The empirical component estimates daily feed intake (DFI), BW, and daily gain (DG) based on individual pig information collected in real time. Based on DFI, BW, and DG estimates, the mechanistic component uses classic factorial equations to estimate the optimal concentration of amino acids that must be offered to each pig to meet its requirements. The model was evaluated with data from a study that investigated the effect of feeding pigs with a 3-phase or daily multiphase system. The DFI and BW values measured in this study were compared with those estimated by the empirical component of the model. The coherence of the values estimated by the mechanistic component was evaluated by analyzing if it followed a normal pattern of requirements. Lastly, the proposed model was evaluated by comparing its estimates with those generated by the existing growth model (InraPorc). The precision of the proposed model and InraPorc in estimating DFI and BW was evaluated through the mean absolute error. The empirical component results indicated that the DFI and BW trajectories of individual pigs fed ad libitum could be predicted 1 d (DFI) or 7 d (BW) ahead with the average mean absolute error of 12.45 and 1.85%, respectively. The average mean absolute error obtained with the InraPorc for the average individual of the population was 14.72% for DFI and 5.38% for BW. Major differences were observed when estimates from InraPorc were compared with individual observations. The proposed model, however, was effective in tracking the change in DFI and BW for each individual pig. The mechanistic model component estimated the optimal standardized ileal digestible Lys to NE ratio with reasonable between animal (average CV = 7%) and overtime (average CV = 14%) variation. Thus, the amino acid requirements estimated by model are animal- and time-dependent and follow, in real time, the individual DFI and BW growth patterns. The proposed model can follow the average feed intake and feed weight trajectory of each individual pig in real time with good accuracy. Based on these trajectories and using classical factorial equations, the model makes it possible to estimate dynamically the AA requirements of each animal, taking into account the intake and growth changes of the animal. © 2012 American Society of Animal Science. All rights reserved.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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A large volume of generated sewage sludge makes its disposal a problem. The usage of sludge in agriculture is highlighted by a number of advantages. However, heavy metals and other toxic compounds may exercise harmful effects to soil organisms. This study evaluated the possible toxic effects of a biosolid sample, under laboratory conditions, for 30 days, using diplopods Rhinocricus padbergi and plants Allium cepa (onion) as test organisms. The data obtained demonstrated that the biosolid raw sample had genotoxic potential for Allium cepa root tip cells. In the diplopods exposed to biosolid sample, epithelium disorganization in the midgut and a reduction of the volume of the hepatic cells were observed after 7 days of exposure. After 30 days, the animals still showed a reduction of the volume of the hepatic cells, but in minor intensity. Allium cepa analysis showed genotoxicity, but this effect was reduced after 30 days of bioprocessing by diplopods. This study was important to know the effects as well as to determine how this waste could be applied concerning the soil living organisms and plants. © 2012 Cintya Ap. Christofoletti et al.

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Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

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Increasing human demands on soil-derived ecosystem services requires reliable data on global soil resources for sustainable development. The soil organic carbon (SOC) pool is a key indicator of soil quality as it affects essential biological, chemical and physical soil functions such as nutrient cycling, pesticide and water retention, and soil structure maintenance. However, information on the SOC pool, and its temporal and spatial dynamics is unbalanced. Even in well-studied regions with a pronounced interest in environmental issues information on soil carbon (C) is inconsistent. Several activities for the compilation of global soil C data are under way. However, different approaches for soil sampling and chemical analyses make even regional comparisons highly uncertain. Often, the procedures used so far have not allowed the reliable estimation of the total SOC pool, partly because the available knowledge is focused on not clearly defined upper soil horizons and the contribution of subsoil to SOC stocks has been less considered. Even more difficult is quantifying SOC pool changes over time. SOC consists of variable amounts of labile and recalcitrant molecules of plant, and microbial and animal origin that are often operationally defined. A comprehensively active soil expert community needs to agree on protocols of soil surveying and lab procedures towards reliable SOC pool estimates. Already established long-term ecological research sites, where SOC changes are quantified and the underlying mechanisms are investigated, are potentially the backbones for regional, national, and international SOC monitoring programs. © 2013 Elsevier B.V.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

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Pós-graduação em Agronomia (Produção Vegetal) - FCAV

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)