4 resultados para Field Matching Method

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An essential step in the development of products based on biotechnology is an assessment of their potential economic impacts and safety, including an evaluation of the potential impact of transgenic crops and practices related to their cultivation on the environment and human or animal health. The purpose of this paper is to provide an assessment method to evaluate the impact of biotechnologies that uses quantifiable parameters and allows a comparative analysis between conventional technology and technologies using GMOs. This paper introduces amethod to performan impact analysis associatedwith the commercial release and use of genetically modified plants, the Assessment SystemGMPMethod. The assessment is performed through indicators that are arranged according to their dimension criterion likewise: environmental, economic, social, capability and institutional approach. To perform an accurate evaluation of the GMP specific indicators related to genetic modification are grouped in common fields: genetic insert features, GMplant features, gene flow, food/feed field, introduction of the GMP, unexpected occurrences and specific indicators. The novelty is the possibility to include specific parameters to the biotechnology under assessment. In this case by case analysis the factors ofmoderation and the indexes are parameterized to perform an available assessment.

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This paper presents the results of four field experiments carried out with the objetive of evaluating the feaibility of irrigation by porous capsule method, from 1979 to 1983, at Bebedouro Experiment Station, EMBRAPA-CPATSA, Petrolina, PE, Brazil. The irrigation system consisted of fulcrum of cone shaped porous capsules, interconnected with conduit pipe and installed in the soil at equidistance and 0.10 m deep along contour lines. The hydrostatic pressures studies did not significantly influence the crop yield, but influenced, at 0.10 level, the daily water release from porous unit. The mean yields for watermelon (Citrullus vulgaris Shard), var. Charleston Gray, for muskmelon (Cucumis melo L.) var. Valenciano Amarelo, and for maize (Zea mays L.), var. Centralmex, estimated in ton/2,500 units/ha or in cobs/2,500 units/ha, were 28.5, 10 and 17,500, respectively. The water consumption for watermelon, musk melon and maize was, respectively, 60 mm, 60 mm and 100 mm in a deep sandy yellow-red latosol. The cost of the system was US$ 1.677,41/ha.

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Despite the large applicability of the field capacity (FC) concept in hydrology and engineering, it presents various ambiguities and inconsistencies due to a lack of methodological procedure standardization. Experimental field and laboratory protocols taken from the literature were used in this study to determine the value of FC for different depths in 29 soil profiles, totaling 209 soil samples. The volumetric water content (θ) values were also determined at three suction values (6 kPa, 10 kPa, 33 kPa), along with bulk density (BD), texture (T) and organic matter content (OM). The protocols were devised based on the water processes involved in the FC concept aiming at minimizing hydraulic inconsistencies and procedural difficulty while maintaining the practical meaning of the concept. A high correlation between FC and θ(6 kPa) allowed the development of a pedotransfer function (Equation 3) quadratic for θ(6 kPa), resulting in an accurate and nearly bias-free calculation of FC for the four database geographic areas, with a global root mean squared residue (RMSR) of 0.026 m3·m-3. At the individual soil profile scale, the maximum RMSR was only 0.040 m3·m-3. The BD, T and OM data were generally of a low predicting quality regarding FC when not accompanied by the moisture variables. As all the FC values were obtained by the same experimental protocol and as the predicting quality of Equation 3 was clearly better than that of the classical method, which considers FC equal to θ(6), θ(10) or θ(33), we recommend using Equation 3 rather than the classical method, as well as the protocol presented here, to determine in-situ FC.

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Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.