Improved representation of uncertainty in farm-level financial cost-benefit analyses of supplemental irrigation in humid regions

J. Mitchell Paoletti and Julie E. Shortridge

Agricultural Water Management, 2020, vol. 239, issue C

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.

Keywords: Monte Carlo simulation; Decision support; Epistemic uncertainty; Variability; Aleatory uncertainty (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377419311497
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:239:y:2020:i:c:s0378377419311497

DOI: 10.1016/j.agwat.2020.106245

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().