Publication Date

8-2017

Date of Final Oral Examination (Defense)

5-19-2017

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Hydrologic Sciences

Department

Geosciences

Major Advisor

Alejandro N. Flores, Ph.D.

Advisor

Bangshuai Han, Ph.D.

Advisor

Rebecca Som Castellano, Ph.D.

Abstract

The impacts of climate change have significant implications for agricultural yields and water use. Previous studies have focused on impacts of climatic factors on crop phenology and yields, with little consideration of local farm management strategies that might mitigate some of these negative effects. Further, the inclusion of stakeholders is commonly left out of many biophysical studies of agricultural landscapes. Therefore, there is considerable uncertainty in the future of regional agroecosystems. In this study, we adopt a social-ecological systems perspective to develop an intellectual framework for assessing agricultural climate adaptation. With research questions focused in both biophysical and social science, we utilize a process-based crop simulation model and stakeholder meetings to examine agricultural response to climate change and adaptations that mitigate for climate change effects. This study advances our understanding of future climate effects on local agriculture, and provides a framework to include local variables into process-based modelling methods.

A regional assessment of baseline (1980–2015) and future (2015–2099) yields and water use for four irrigated crops in the Lower Boise River Basin (LBRB) of southwestern Idaho was conducted using a stakeholder informed model. Six different future climate scenarios, ranging in precipitation and temperature, were applied to our model to understand the potential degree to which climate change might affect yields, hydrologic fluxes, and planting date. Analysis of crop yields in most climate scenarios show a slight to moderate decrease in wheat and corn yields by 2100, while alfalfa and sugarbeets stay the same or moderately increase in more mild scenarios. Next, we identify potential concerns with the current irrigation season, which starts on April 1. Under all climate scenarios, our model predicts the growing season to start earlier in the year based on ET estimates and planting dates. This has major implications for future water policy, as the current irrigation season may need to be redefined to allow for early season irrigation in the coming decades. Our results, along with continued communication and iterative stakeholder engagement in the LBRB, can lead to adaptive solutions and policy changes in the agricultural sector. This research highlights the usefulness of combining local information with biophysical models that aim to understand agricultural systems, and can therefore be adjusted to other regions.

DOI

https://doi.org/10.18122/B2JX31

Download

Share

COinS