999 resultados para Crop Simulation
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In this paper, a hybrid simulation-based algorithm is proposed for the StochasticFlow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation to the latter. In order to achieve this goal, we rely on Monte Carlo Simulation and an adapted version of a deterministic heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.
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The objective of this work was to determine the sensitivity of maize (Zea mays) genotypes to water deficit, using a simple agrometeorological crop yield model. Crop actual yield and agronomic data of 26 genotypes were obtained from the Maize National Assays carried out in ten locations, in four Brazilian states, from 1998 to 2006. Weather information for each experimental location and period were obtained from the closest weather station. Water deficit sensitivity index (Ky) was determined using the crop yield depletion model. Genotypes can be divided into two groups according to their resistance to water deficit. Normal resistance genotypes had Ky ranging from 0.4 to 0.5 in vegetative period, 1.4 to 1.5 in flowering, 0.3 to 0.6 in fruiting, and 0.1 to 0.3 in maturing period, whereas the higher resistance genotypes had lower values, respectively 0.2-0.4, 0.7-1.2, 0.2-0.4, and 0.1-0.2. The general Ky for the total growing season was 2.15 for sensitive genotypes and 1.56 for the resistant ones. Model performance was acceptable to evaluate crop actual yield, whose average errors estimated for each genotype ranged from -5.7% to +5.8%, and whose general mean absolute error was 960 kg ha-1 (10%).
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The objective of this study was to improve the simulation of node number in soybean cultivars with determinate stem habits. A nonlinear model considering two approaches to input daily air temperature data (daily mean temperature and daily minimum/maximum air temperatures) was used. The node number on the main stem data of ten soybean cultivars was collected in a three-year field experiment (from 2004/2005 to 2006/2007) at Santa Maria, RS, Brazil. Node number was simulated using the Soydev model, which has a nonlinear temperature response function [f(T)]. The f(T) was calculated using two methods: using daily mean air temperature calculated as the arithmetic average among daily minimum and maximum air temperatures (Soydev tmean); and calculating an f(T) using minimum air temperature and other using maximum air temperature and then averaging the two f(T)s (Soydev tmm). Root mean square error (RMSE) and deviations (simulated minus observed) were used as statistics to evaluate the performance of the two versions of Soydev. Simulations of node number in soybean were better with the Soydev tmm version, with a 0.5 to 1.4 node RMSE. Node number can be simulated for several soybean cultivars using only one set of model coefficients, with a 0.8 to 2.4 node RMSE.
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In this paper, a hybrid simulation-based algorithm is proposed for the StochasticFlow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation to the latter. In order to achieve this goal, we rely on Monte Carlo Simulation and an adapted version of a deterministic heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.
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The Crop Progress and Condition Report is made possible by the dedication of our volunteer Crop Progress Reporters. A review of Iowa crop land with charts and graphs.
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The objective of this work was to assess the effects of integrated crop-livestock systems, associated with two tillage and two fertilization regimes, on the abundance and diversity of the soil macrofauna. Four different management systems were studied: continuous pasture (mixed grass); continuous crop; two crop-livestock rotations (crop/pasture and pasture/crop); and native Cerrado as a control. Macrofauna was sampled using a modified Tropical Soil Biology and Fertility method, and all individuals were counted and identified at the morphospecies level for each plot. A total of 194 morphospecies were found, distributed among 30 groups, and the most representative in decreasing order of density were: Isoptera, Coleoptera larvae, Formicidae, Oligochaeta, Coleoptera adult, Diplopoda, Hemiptera, Diptera larvae, Arachnida, Chilopoda, Lepidoptera, Gasteropoda, Blattodea and Orthoptera. Soil management systems and tillage regimes affected the structure of soil macrofauna, and integrated crop-livestock systems, associated with no-tillage, especially with grass/legume species associations, had more favorable conditions for the development of "soil engineers" compared with continuous pasture or arable crops. Soil macrofauna density and diversity, assessed at morphospecies level, are effective data to measure the impact of land use in Cerrado soils.
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Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report
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We present a novel numerical algorithm for the simulation of seismic wave propagation in porous media, which is particularly suitable for the accurate modelling of surface wave-type phenomena. The differential equations of motion are based on Biot's theory of poro-elasticity and solved with a pseudospectral approach using Fourier and Chebyshev methods to compute the spatial derivatives along the horizontal and vertical directions, respectively. The time solver is a splitting algorithm that accounts for the stiffness of the differential equations. Due to the Chebyshev operator the grid spacing in the vertical direction is non-uniform and characterized by a denser spatial sampling in the vicinity of interfaces, which allows for a numerically stable and accurate evaluation of higher order surface wave modes. We stretch the grid in the vertical direction to increase the minimum grid spacing and reduce the computational cost. The free-surface boundary conditions are implemented with a characteristics approach, where the characteristic variables are evaluated at zero viscosity. The same procedure is used to model seismic wave propagation at the interface between a fluid and porous medium. In this case, each medium is represented by a different grid and the two grids are combined through a domain-decomposition method. This wavefield decomposition method accounts for the discontinuity of variables and is crucial for an accurate interface treatment. We simulate seismic wave propagation with open-pore and sealed-pore boundary conditions and verify the validity and accuracy of the algorithm by comparing the numerical simulations to analytical solutions based on zero viscosity obtained with the Cagniard-de Hoop method. Finally, we illustrate the suitability of our algorithm for more complex models of porous media involving viscous pore fluids and strongly heterogeneous distributions of the elastic and hydraulic material properties.
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The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.
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Abstract
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Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report
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Visualization is a relatively recent tool available to engineers for enhancing transportation project design through improved communication, decision making, and stakeholder feedback. Current visualization techniques include image composites, video composites, 2D drawings, drive-through or fly-through animations, 3D rendering models, virtual reality, and 4D CAD. These methods are used mainly to communicate within the design and construction team and between the team and external stakeholders. Use of visualization improves understanding of design intent and project concepts and facilitates effective decision making. However, visualization tools are typically used for presentation only in large-scale urban projects. Visualization is not widely accepted due to a lack of demonstrated engineering benefits for typical agency projects, such as small- and medium-sized projects, rural projects, and projects where external stakeholder communication is not a major issue. Furthermore, there is a perceived high cost of investment of both financial and human capital in adopting visualization tools. The most advanced visualization technique of virtual reality has only been used in academic research settings, and 4D CAD has been used on a very limited basis for highly complicated specialty projects. However, there are a number of less intensive visualization methods available which may provide some benefit to many agency projects. In this paper, we present the results of a feasibility study examining the use of visualization and simulation applications for improving highway planning, design, construction, and safety and mobility.
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Visualization is a relatively recent tool available to engineers for enhancing transportation project design through improved communication, decision making, and stakeholder feedback. Current visualization techniques include image composites, video composites, 2D drawings, drive-through or fly-through animations, 3D rendering models, virtual reality, and 4D CAD. These methods are used mainly to communicate within the design and construction team and between the team and external stakeholders. Use of visualization improves understanding of design intent and project concepts and facilitates effective decision making. However, visualization tools are typically used for presentation only in large-scale urban projects. Visualization is not widely accepted due to a lack of demonstrated engineering benefits for typical agency projects, such as small- and medium-sized projects, rural projects, and projects where external stakeholder communication is not a major issue. Furthermore, there is a perceived high cost of investment of both financial and human capital in adopting visualization tools. The most advanced visualization technique of virtual reality has only been used in academic research settings, and 4D CAD has been used on a very limited basis for highly complicated specialty projects. However, there are a number of less intensive visualization methods available which may provide some benefit to many agency projects. In this paper, we present the results of a feasibility study examining the use of visualization and simulation applications for improving highway planning, design, construction, and safety and mobility.
Resumo:
Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October. Formally titled: Iowa Crop and Weather Report
