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There has been a growing interest in the improvement of irrigation water management by addressing decision making imperfections resulting from the fluctuation of supplies and the expansion of conflicts due to increase of demand. In addition to the emphasis on demand management, policy makers are becoming highly concerned with the adoption of decentralized decision making and the acquisition and sharing of real time information to facilitate the engagement of stakeholders. Such a concern has been facilitated by the emergence of different technological platforms and infrastructure that improved sensing, acquiring, storage, processing and sharing of irrigation management information. Taken under the umbrella of hydro-informatics, different conventional, intelligent and web based decision support systems and applications are being widely used for irrigation water management. In addition to the integration of information about the dynamics of the physical water system, demand functions and supply indicators, the use of hydro-informatics applications provide effective computational models that can be used for the simulation and optimization of water availability and allocation. Such models can be used at different levels of analysis (watercourse, tributary, river and basin) and under different administrative boundaries (administrative, hydrological or mixed).
Decisions in irrigated agriculture can be taken at three levels (Pereira, 1987). Firstly, at the farm’s level where farmers need to decide on crops, cropping systems, irrigation methods and on-farm irrigation management practices. Such decisions are crucial for the management of irrigation projects. Secondly, at the level of the irrigation project, operation authorities decide on the amount and schedule of deliveries which significantly effects on-farm water management decisions. To enable best irrigation and farming practices such decisions need to be highly integrated. Thirdly, at the basin’s level decisions reflect trans- boundary decisions are related to country or regional water resource policies and influence agriculture through water allocation and water quality criteria. In managing irrigation water resources, decision making is viewed as a multi-phase (i.e., decision making steps) multi-layer (local, regional and state) and multi-duration (short, medium and long terms). However, according to Becu et al (2003), taking these decisions demands the adoption of an integrated framework because of (a) the interactions between natural resources and other resources (such as local goods market, local labor market and land tenure systems) which requires large scale representation of several irrigation schemes and (b) the growing importance of understanding the behavior of farmers and simulating “complex” emerging rules on the basis of simple individual behaviors. Taking effective decisions in irrigated agriculture tends to be challenged with different complexities. The intensity and rigidity of supply-related change agents; the shift of stakeholders’ preferences (mainly of farmers) and the lack and inaccessibility of real time information are, among others, the main decision-related obstacles.