Graduate Thesis Or Dissertation
 

An irrigation efficiency model for optimum irrigation management

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9z9032509

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  • Improved water use efficiencies in irrigated agriculture are necessary to assist in coping with the accelerating demand and economic competition for the world's fresh water supply and increasing ecological concerns. This will likely rely in part on optimum irrigation, which implies regulated deficit irrigation. While conventional irrigation management is intended to maximize crop yield, optimum irrigation takes an economic approach, focusing on irrigation plans that maximize total benefits. Optimum irrigation management is inherently more complex than conventional irrigation management, requiring advanced analytical tools for identifying the best irrigation design and management strategies. As part of a larger project to initiate an optimum irrigation advisory service in Oregon, an irrigation efficiency model (IEM) was developed to evaluate the effects of spatially variable field properties, non-uniform irrigation applications, random weather and crop factors in combination with alternative irrigation strategies. IEM integrates all of these elements to represent the relationship between water use and crop yield. The logic of deficit irrigation is predicated on the curvilinear shape of the relationship between applied water and crop yield. The shape of a crop production function derives from a complex of interacting factors, including: (i) spatial variability of soil properties, (ii) timing, rate and uniformity of irrigation water applications, (iii) evaporative rates, transpiration and the relationship between transpiration and crop water stress, and (iv) yield response to water stress or excess water at various growth stages. The basic premise of this thesis is that these factors must be considered in totality if we are to develop a realistic crop production function. That is the primary objective of IEM. Additional objectives for IBM are to minimize the complexity of user inputs and maximize computational efficiency to make the model a viable tool for practical applications.
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