1.2 Scope of Application Portfolio In this portfolio project, I will compare three models: 1) a standard large irrigation project with deterministic inputs, 2) an inflexible standard large irrigation project that accounts for uncertaintyand 3) a flexible smaller irrigation project with the option to expand that also accounts for uncertainty. The comparison will be evaluated on the following metrics: Expected net present Value (ENPV) Probability of losing money (P<0) Range of ENPV, 5th and 95th percentiles and Benefit-Cost (B/C) ratio. The flexible approach may be mutually beneficial to Egypt and Ethiopia since a standard large scale irrigation may not be easily acceptable to Egypt, however a smaller project with the option to expand in future may be more tolerable. The conventional thought with irrigation projects is that projects just do better than small “big projects.” This tends to occur because of economies of scale, particularly when laying pipes, excavating and building the reservoirs. (IWMI, 2007) The analysis in this portfolio project will use tools from flexibility analysis to determine whether a large irrigation project (80,000hectares) orthe small irrigation schemes (20,000 Hectares) with option to increase capacity by the same amount over time is preferable. Defining the System The problem is complex but in this report we will make simplifying assumptions. Although this may come at a loss of accuracy, it is still possible to glean some insight about the preferred project. The system will focus on the irrigation project for single crop cultivation. The principle design levers or variables are the aspects of the project that can be controlled to improve performance. In a potential irrigation system, some of the principle design levers that would affect the design are identified as follows: Project Size (land area to be irrigated) Type of crop irrigated Demand and price of crop Availability of water and forecasted shortages Crop yield The last three of these are stochastic (andexogenously determined) in nature. In order to incorporate uncertainty, and for the purposes of this analysis, the historical data is fitted to a
1.2 Scope of Application Portfolio In this portfolio project, I will compare three models: 1) a standard large irrigation project with deterministic inputs, 2) an inflexible standard large irrigation project that accounts for uncertaintyand 3) a flexible smaller irrigation project with the option to expand that also accounts for uncertainty. The comparison will be evaluated on the following metrics: Expected net present Value (ENPV) Probability of losing money (P<0) Range of ENPV, 5th and 95th percentiles and Benefit-Cost (B/C) ratio. The flexible approach may be mutually beneficial to Egypt and Ethiopia since a standard large scale irrigation may not be easily acceptable to Egypt, however a smaller project with the option to expand in future may be more tolerable. The conventional thought with irrigation projects is that “big projects just do better than small projects.” This tends to occur because of economies of scale, particularly when laying pipes, excavating and building the reservoirs. (IWMI, 2007) The analysis in this portfolio project will use tools from flexibility analysis to determine whether a large irrigation project (80,000hectares) orthe small irrigation schemes (20,000 Hectares) with option to increase capacity by the same amount over time is preferable. Defining the System The problem is complex but in this report we will make simplifying assumptions. Although this may come at a loss of accuracy, it is still possible to glean some insight about the preferred project. The system will focus on the irrigation project for single crop cultivation. The principle design levers or variables are the aspects of the project that can be controlled to improve performance. In a potential irrigation system, some of the principle design levers that would affect the design are identified as follows: Project Size (land area to be irrigated) Type of crop irrigated Demand and price of crop Availability of water and forecasted shortages Crop yield The last three of these are stochastic (andexogenously determined) in nature. In order to incorporate uncertainty, and for the purposes of this analysis, the historical data is fitted to a