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Sheng-Feng Kuo

Bio: Sheng-Feng Kuo is an academic researcher from University of Kang Ning. The author has contributed to research in topics: Irrigation scheduling & Irrigation management. The author has an hindex of 7, co-authored 8 publications receiving 338 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the reference and actual crop evapotranspiration, derived the crop coefficient, and collected requirements input data for the CROPWAT irrigation management model to estimate the irrigation water requirements of paddy and upland crops at the ChiaNan Irrigation Association, Taiwan.

118 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented a model based on on-farm irrigation scheduling and the simple GA method for decision support in irrigation project planning, which is applied to an irrigation project located in Delta, Utah of 394.6 ha in area, for optimizing economic profits, simulating the water demand, crop yields, and estimating the related crop area percentages with specified water supply and planted area constraints.

107 citations

Journal ArticleDOI
TL;DR: In this article, a simulation and optimization model was developed and applied to an irrigated area in Delta, Utah to optimize the economic benefit, simulate the water demand, and search the related crop area percentages with specified water supply and planted area constraints.
Abstract: A simulation and optimization model was developed and applied to an irrigated area in Delta, Utah to optimize the economic benefit, simulate the water demand, and search the related crop area percentages with specified water supply and planted area constraints. The user interface model begins with the weather generation submodel, which produces daily weather data, which is based on long-term monthly average and standard deviation data from Delta, Utah. To simulate the daily crop water demand and relative crop yield for seven crops in two command areas, the information provided by this submodel was applied to the on-farm irrigation scheduling submodel. Furthermore, to optimize the project benefit by searching for the best allocation of planted crop areas given the constraints of projected water supply, the results were employed in the genetic algorithm submodel. Optimal planning for the 394·6-ha area of the Delta irrigation project is projected to produce the maximum economic benefit. That is, projected profit equals US$113 826 and projected water demand equals 3·03 × 106 m3. Also, area percentages of crops within UCA#2 command area are 70·1%, 19% and 10·9% for alfalfa, barley and corn, respectively, and within UCA#4 command area are 41·5%, 38·9%, 14·4% and 5·2% for alfalfa, barley, corn and wheat, respectively. As this model can plan irrigation application depths and allocate crop areas for optimal economic benefit, it can thus be applied to many irrigation projects. Copyright © 2003 John Wiley & Sons, Ltd.

40 citations

Journal ArticleDOI
TL;DR: In this paper, the authors estimate the extent of infiltration in a paddy field in Yun-Lin, Taiwan by adopting a one-dimensional Darcy-based soil/water balance model SAWAH and two sets of empirical equations used by the Taiwan Provincial Water Conservancy Bureau.

33 citations

Journal ArticleDOI
TL;DR: In this article, a model based on the on-farm irrigation scheduling and the simulated annealing (SA) optimization method for agricultural water resource management is presented, which is applied to an irrigation project located in Delta, Utah of 394·6 ha area for optimizing economic profits.

20 citations


Cited by
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Journal ArticleDOI
TL;DR: With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture.

242 citations

Journal ArticleDOI
TL;DR: The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided and conclusions are drawn where gaps exist and more research needs to be focused.

194 citations

Journal ArticleDOI
TL;DR: Although winter wheat/spring maize intercropping system does not improve WUE, it may significantly raise yield, which is helpful to ensure food safety in northern China.

159 citations

Journal ArticleDOI
TL;DR: The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000, while particle swarm optimization (PSO) can obtain better parameter solutions than other algorithms given fewer number of model runs.
Abstract: With the popularity of complex hydrologic models, the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimization algorithms for calibrating computationally intensive hydrologic models is becoming a nontrivial issue. In this study, five global optimization algorithms (genetic algorithms, shuffled complex evolution, particle swarm optimization, differential evolution, and artificial immune system) were tested for automatic parameter calibration of a complex hydrologic model, Soil and Water Assessment Tool (SWAT), in four watersheds. The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000, while particle swarm optimization (PSO) can obtain better parameter solutions than other algorithms given fewer number of model runs (less than 2000). Given limited computational time, the PSO algorithm is preferred, while GA should be chosen given plenty of computational resources. When applying GA and PSO for parameter optimization of SWAT, small population size should be chosen. Copyright © 2008 John Wiley & Sons, Ltd.

155 citations

Journal ArticleDOI
TL;DR: The MOPECO model as discussed by the authors is a tool for identifying optimal production plans and water irrigation management strategies, which can be used to determine an optimum cropping pattern and irrigation strategy to maximize the gross margin on a farm in a specific scenario.
Abstract: Water is a natural, sometimes scarce, and fundamental resource for life, essential both for agriculture in many regions of the world and also to achieve sustainability in production systems. Maximizing net returns with the available resources is of the utmost importance, but doing so is a complex problem, owing to the many factors that affect this process (e.g. climatic variability, irrigation system configuration, production costs, subsidy policies). The MOPECO model is a tool for identifying optimal production plans, and water irrigation management strategies. The model estimates crop yield, production and gross margin as a function of the irrigation depth. Finally, these gross margin functions are used to determine an optimum cropping pattern and irrigation strategy to maximize the gross margin on a farm in a specific scenario. Since the relationships between the variables are generally non-linear and the number of alternative strategies is quite large, the optimum process is complex and computationally intensive. Genetic algorithms are therefore used to identify optimal strategies. This paper describes the MOPECO model, which comprises three computing modules: (1) estimation of net water requirements; (2) derivation of the relationship between gross margin and irrigation depth; and (3) identification of the crop planning and the water volumes to be applied. The results obtained by applying the MOPECO model to a specific irrigable area in a semi-arid area of Spain, with great deficits and high water costs, are also included and discussed. These results usually show that the irrigation depth for maximum benefits is lower than that necessary to obtain maximum production. In some areas of Spain, horticultural crops are nearly always part of the optimum alternative. The crops that become part of the optimum alternative are mainly horticultural crops with a high gross margin and low water requirements. The irrigation depths selected for the ideal crop rotation are included among the irrigation depth of maximum economic efficiency and the maximum gross margin irrigation depth. Both are lower than that necessary for the maximum yield. This model helps farmers, extension services, and other agents to analyse, make decisions and optimize water management.

123 citations