Author ORCID Identifier

https://orcid.org/0000-0002-1618-7119

Date Available

3-24-2019

Year of Publication

2019

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Agriculture, Food and Environment

Department/School/Program

Agricultural Economics

First Advisor

Dr. David Freshwater

Abstract

Over the past five decades, the Saudi government has adopted many agricultural policies aimed to: achieve self-sufficiency of food, increase the participation of the agricultural sector in the economy, and reduce the consumption of irrigation water. Due to conflicts among government objectives and the incompatibility of farmers' objectives with those of some agricultural policies, the government has not been able to fully achieve its objectives.

To accomplish its goals the government, or decision maker needs to understand the farmer, or follower, reaction when s/he adopts a new decision. The dissertation aims to build a model that achieves government goals of minimizing the total irrigation water used while improving the total revenue from agricultural production, while incorporating farmers’ objective of maximizing their profit. To do this, linear programming and bi-level multi-objective multi-follower models are developed and applied to six regions of Saudi Arabia, which account for around 70 percent of cropland and consume about 13.131 BCM of irrigation water per year.

The result of the linear programming model applied to the Riyadh region shows there is an unobserved factor effect on the farmers’ decisions, including irrigation water demand that comes from the presence of indirect subsidies. On the other hand, the bi-level multi-objective, multi-follower model shows there is the possibility to minimize irrigation water consumption while maintaining current total revenue from crop production through reallocating irrigation water among regions, while applying a variety of crop specific tax and subsidy policies among the regions to alter planting decisions.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2019.040

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