Thesis (Ph. D.)--University of Canterbury, 2007. / Typescript (photocopy). Accompanied by CD-ROM: Appendix 1: Electronic copy of source code and input data. Includes bibliographical references (leaves 173-179). Also available via the World Wide Web.

No description available.

In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of optimizing nonlinear crop models and describe application requirements and implementation of the technique. GAs were found to converge quickly to near-optimal solutions. Second, we analyze the relationship between GA control parameters (population size, crossover rate, and mutation rate) and performance. We identify a combination of population, mutation, and crossover which searched the fitness landscape efficiently. The results suggest that smaller populations are able to provide better performance at relatively low mutation rates. More stable outcomes were generated using low mutation rates. Without crossover the quality of solutions were generally impaired, and the search process was lengthened. Aside from crossover rate zero, no other crossover rates significantly differed. The behaviors observed for best, online, offline, and average performances were sensitive to the combined influences control parameters. Interaction among control parameters was strongly indicated. Finally, several adaptive penalty techniques are presented for handling constraints in GAs, and their effectiveness is demonstrated. The constant penalty function suffered from sensitivity to settings of penalty coefficients, and was not successful in satisfying constraints. The adaptive penalty functions utilizes violation distance based metrics and search time based scaling using generation or trials number, and fitness values to penalize infeasible solutions, as the distance from the feasible region or number of generations increases so does the penalty. They were quite successful in providing solutions with minimal effort. They adapt the penalty as the search continues, encouraging feasible solutions to emerge over the time. Adaptive approaches presented here are flexible, efficient, and robust to parameter settings. / Graduation date: 1997

The Mississippi River Valley Alluvial Aquifer (MRVAA) is declining precipitously due to irrigation withdrawal for row-crops. The development of scientific irrigation scheduling techniques and for soybean (Glycine max L.) will reduce withdrawal from the MRVAA. The objective of this research was to determine if soybean grain yield, irrigation water use efficiency (IWUE), and net return above irrigation cost could be optimized using a static irrigation threshold or if the irrigation threshold should be changed as a function of plant growth stage.

No description available.

An optimal stochastic multi-crop irrigation scheduling algorithm was developed which was able to incorporate complex farm system models, and constraints on daily and seasonal water use, with the objective of maximising farm profit. This scheduling method included a complex farm simulation model in the objective function, used decision variables to describe general management decisions, and used a custom heuristic method for optimisation. Existing optimal schedulers generally use stochastic dynamic programming which relies on time independence of all parameters except state variables, thereby requiring over-simplistic crop models. An alternative scheduling method was therefore proposed which allows for the inclusion of complex farm system models. Climate stochastic properties are modelled within the objective function through the simulation of several years of historical data. The decoupling of the optimiser from the objective function allows easy interchanging of farm model components. The custom heuristic method, definition of decision variables, and use of the Markov chain equation (relating an irrigation management strategy to mean water use) considerably increases optimisation efficiency. The custom heuristic method used simulated annealing with continuous variables. Two extensions to this method were the efficient incorporation of equality constraints and utilisation of population information. A case study comparison between the simulated annealing scheduler and scheduling using stochastic dynamic programming, using a simplistic crop model, showed that the two methods resulted in similar performance. This demonstrates the ability of the simulated annealing scheduler to produce close to optimal schedules. A second case study demonstrates the ability of the simulated annealing scheduler to incorporate complex farm system models by including the FarmWi$e model by CSIRO in the objective function. This case study indicates that under conditions of limited seasonal water, the simulated annealing scheduler increases pasture yield returns by an average of 10%, compared with scheduling irrigation using best management practice. Alternatively expressed, this corresponds to a 20-25% reduction in seasonal water use (given no change in yield return).

Thesis (Ph.D.)(Agricultural Economics)--University of Pretoria, 2006. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

An estimate of potential evapotranspiration is acquired to implement an irrigation scheduling program. Four equations, which estimate potential evapotranspiration (evaporation) were evaluated for prediction of Class "A" Pan evaporation. The equations used were Penman's combination equation, the Jensen-Haise temperature and radiation equation, and two of the Christiansen-Hargreaves polynomial regression type equations. Measured and calculated radiation was used together with two values of albedo. Four years of daily climate data from Rhodesia was evaluated by general linear regression methods. Despite the significant variations between the four annual regression lines of each equation, the use of confidence intervals indicate that the Penman and Christiansen-Hargreaves equation adequately predict Class "A" pan evaporation for irrigation control purposes. Using class "A" pan evaporation as the measure of potential evapotranspiration for sugarcane, a computerized irrigation scheduling model was developed. Controlled plant moisture stress was incorporated in the program with an irrigation coefficient related to the limiting effects of low soil moisture on plant transpiration. Five soil moisture regimes and two levels of irrigation were studied. Within the limits of the defined soil moisture assumptions, the program exhibited considerable flexibility in computation and the control of desired plant moisture stress.

6 pp. / Water use, critical soil water depletion, and irrigation scheduling for wheat and barley are explained in this publication.

Crop coefficients (K(c)) are a useful means of predicting how much water is needed for irrigating a crop. The crop water stress index (CWSI), on the other hand, is a means of knowing when to irrigate. Two field experiments were conducted during the summers of 1990 and 1991 at Maricopa Agricultural Center and Marana Agricultural Center, respectively, to evaluate water use (evapotranspiration, ET) of different cotton varieties, to develop crop coefficients for cotton grown in the state of Arizona, and to evaluate empirical and theoretical crop water stress indices under field conditions. For the 1990 experiment, ET from the cotton variety DPL 77 was obtained using soil water balance (SWB) and steady state heat balance (SSHB) techniques. For the 1991 experiment, ET from two cotton varieties (DPL 20 and Pima S-6) was estimated using the Bowen ratio energy balance (BREB) method and the steady state heat balance method. Reference evapotranspiration (ETᵣ) was obtained from weather stations located close to the experimental plots. Average daily ET from the SSHB measurements ranged from 8.24 to 15.13 mm and from 10.34 to 12.12 mm for the 1990 and 1991 experiments, respectively. Total ET from the SWB was approximately 19% less than the total ET estimated by the SSHB. Total ET from individual plants was well correlated with average stem area over the evaluation periods. Daily ET from the two cotton varieties (DPL20 and Pima S-6) was approximately similar when irrigation conditions were the same, but differed later by as much as 48.4% as irrigation continued for the variety Pima S-6 only. Daily ET from the BREB measurements and ETᵣ were used to develop a crop coefficient curve for cotton grown at Marana, Arizona, which had a maximum smoothed value of 1.21. A critical value of CWSI equal to 0.3 was obtained by observing the pattern of the CWSI values over well-watered and drier conditions, and from previous research. Using the developed crop coefficient curve and the CWSI should provide a useful means of scheduling irrigation for cotton grown under climatic conditions similar to those at Marana, Arizona.