Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing

摘要

As water resources are limited and the demand for agricultural products increases, it becomesincreasingly important to use irrigation water optimally. At a farm scale, farmer’s have a particularlystrong incentive to optimize their irrigation water use when the volume of water available over a seasonis production limiting. In this situation, a farmer’s goal is to maximize farm profit, by adjusting when andwhere irrigation water is used. However, making the very best decisions about when and where toirrigate is not easy, since these daily decisions require consideration of the entire remaining irrigationseason. Future rainfall uncertainty further complicates decisions on when and which crops should besubjected to water stress. This paper presents an innovative on-farm irrigation scheduling decisionsupport method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal wateravailability is limited. Previous optimal scheduling methods generally use stochastic dynamicprogramming, which requires over-simplistic plant models, limiting their practical usefulness. TheCIS method improves on previous methods because it accommodates realistic plant models. Future farmprofit (the objective function) is calculated using a time-series simulation model of the farm. Differentirrigation management strategies are tested using the farm simulation model. The irrigation strategiesare defined by a set of decision variables, and the decision variables are optimized using simulatedannealing. The result of this optimization is an irrigation strategy that maximizes the expected futurefarm profit. This process is repeated several times during the irrigation season using the CIS method, andthe optimal irrigation strategy is modified and improved using updated climate and soil moistureinformation. The ability of the CIS method to produce near optimal decisions was demonstrated by acomparison to previous stochastic dynamic programming schedulers. A second case study shows the CISmethod can incorporate more realistic farm models than is possible when using stochastic dynamicprogramming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Resultsshow that when seasonal water limit is the primary constraint on water availability, the CIS couldincrease pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared withscheduling irrigation using current state of the art scheduling practice.