Abstract

In recent years, there has been increasing interest in and adoption of supplemental irrigation in humid regions that have traditionally relied on rainfed agriculture. In these regions, irrigation typically supplements rainfall and serves as a risk management strategy to avoid losses in years with low precipitation. However, the higher returns achieved with irrigation may not be sufficient to offset its investment and operational costs. The question of whether supplemental irrigation is profitable for a given farm is subject to many sources of uncertainty, such as system costs, energy requirements, and yield response, as well as year-to-year variability in weather, energy, and commodity prices. Previous research on financial outcomes of irrigation rarely considers this uncertainty and variability in a comprehensive manner, limiting their practical use for agricultural decision making. The objective of this work is to present a novel approach to representing uncertainty and variability in farm-specific cost-benefit analyses of supplemental irrigation based on two levels of Monte Carlo simulation. The first level estimates annual returns for each year of an irrigation system’s useful life based on multiple realizations of historic weather, crop prices, and energy prices to demonstrate year-to-year variability in financial returns. The second level repeats this process under different assumptions regarding system investment and operational costs to represent epistemic uncertainty in these factors. This approach is demonstrated with a simple decision-support tool that estimates financial costs and benefits of irrigation for four commodity crops in Virginia and shows how this uncertainty and variability can be presented for a general audience. This approach can be used to assess irrigation profitability for different crops and irrigation systems, and highlight how factors such as the fuel source or irrigation scheduling method used can impact profitability.

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