No abstract

No abstract

No abstract

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 394Ð6-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 3Ð03 ð 10 6 m 3 . Also, area percentages of crops within UCA#2 command area are 70Ð1%, 19% and 10Ð9% for alfalfa, barley and corn, respectively, and within UCA#4 command area are 41Ð5%, 38Ð9%, 14Ð4% and 5Ð2% 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.

Paddy field storage of rainfall by bunds is like a rainwater cistern system during the rainy season. Deepwater management practice in paddy fields might be good for rice production and water storage capacity at the same time. The study was conducted in an experiment testing two water management treatments with six rice variety treatments using two lysimeter experiments.The results revealed that deepwater management significantly affected rice production. On the other hand, deepwater management increased the effective rainfall (70 mm) and percolation (225 mm). The runoff coefficient under deepwater management decreased from 0.67 to 0.53. It was reasonable to conclude that the deepwater management provided more water storage capacity during the growing season. Finally, water productivity results point out that even though the volume of input water under deepwater management had increased, deepwater management still can generate higher water productivity.It looks as if it is pointless to save water during the rainy season. On the contrary, if excess water is available in rivers, it should be delivered in timely fashion to the wetland rice fields to increase effective rainfall and the excess saved by maintaining adequate percolation to replenish groundwater, without having to follow strict water conservation measures. Copyright # 2007 John Wiley & Sons, Ltd. RÉ SUMÉ Le stockage des précipitations dans les rizières au moyen de diguettes est analogue à la récupération de l'eau de pluie par citerne. La pratique de la gestion de plan d'eau dans les rizières pourrait être une bonne chose à la fois pour la production de riz et la capacité de stockage de l'eau. L'étude porte sur une expérience combinant deux types de gestion de l'eau, six variétés de riz et deux sortes de lysimètre.Les résultats montrent que la gestion de plan d'eau a affecté de manière significative la production de riz. Par ailleurs elle a augmenté les précipitations efficaces (70 millimètres) et la percolation (225 millimètres). Le coefficient d'écoulement a diminué de 0,67 à 0,53. Il était donc raisonnable de conclure que la gestion de plan d'eau a augmenté la capacité de stockage de l'eau pendant la saison de croissance. Et finalement, les résultats de productivité de l'eau montrent que, en dépit de l'augmentation du volume d'eau entré dans la rizière grâce à la gestion de plan d'eau, cette dernière permet une productivité de l'eau plus élevée.

This study presents three optimization techniques for on‐farm irrigation scheduling in irrigation project planning: namely the genetic algorithm, simulated annealing and iterative improvement methods. The three techniques are applied to planning a 394.6 ha irrigation project in the town of Delta, Utah, for optimizing economic profits, simulating water demand, and estimating the crop area percentages with specific water supply and planted area constraints. The comparative optimization results for the 394.6 ha irrigated project from the genetic algorithm, simulated annealing, and iterative improvement methods are as follows: (1) the seasonal maximum net benefits are $113,826, $111,494, and $105,444 per season, respectively; and (2) the seasonal water demands are 3.03*103 m3, 3.0*103 m3, and 2.92*103 m3 per season, respectively. This study also determined the most suitable four parameters of the genetic algorithm method for the Delta irrigated project to be: (1) the number of generations equals 800, (2) population size equals 50, (3) probability of crossover equals 0.6, and (4) probability of mutation equals 0.02. Meanwhile, the most suitable three parameters of simulated annealing method for the Delta irrigated project are: (1) initial temperature equals 1,000, (2) number of moves equal 90, and (3) cooling rate equals 0.95.

No abstract

The study compares the prediction performances of evapotranspiration by the FAO56 PenmanMonteith method and the pan evaporation method using the artificial neural network. A backpropagation neural network was adopted to determine the relationship between meteorological factors and evapotranspiration or evaporation. The evapotranspiration in the ChiaNan irrigated area of Tainan was considered. Weather data compiled by Irrigation Experiment Station of ChiaNan Irrigation Association were the input layer variables, including (1) the highest temperature, (2) the lowest temperature, (3) the average temperature, (4) the relative humidity, (5) the wind speed, (6) hours of sunlight, (7) amount of solar radiation, (8) the dew point, (9) morning ground temperature and (10) afternoon ground temperature. The importance of the ten weather factors was ranked by the general influence (GI) factor. Results show that the correlation coefficient between the evapotranspiration in 2004 calculated by FAO56 Penman-Monteith method and the one predicted by the neural network model with a hidden layer of ten nodes is 0.993. The actual evapotranspiration is 911.6 cm, and value prediction by the neural network is 896.4 cm, between which two values the error is 1.67%. The results reveal that the backpropagation neural network based on the FAO56 Penman-Monteith method can accurately predict evapotranspiration. However, the correlation coefficient between the actual evaporation in 2004 and the value prediction by the neural network with a hidden layer of ten nodes and an output layer with the pan evaporation as its target output is 0.708. The pan evaporation is 1,673.1 cm, while the value predicted by the backpropagation neural network is 1,451.7 cm, between which values the error is 13.23%. The backpropagation neural networks with pan evaporation as target outputs predict the evaporation with large errors. Moreover, the use of four agricultural weather factors (determined by the GI) including wind speed, average temperature, dew point and maximum temperature as input variables, and a hidden layer of three nodes in the backpropagation neural network model can successfully predict evapotranspiration based on the FAO56 PenmanMonteith method (R = 0.98, error = 1.35%).