Incorporating Climate into a Decision-Support Tool for Specialized Agriculture Jenni van Ravensway, Julie Winkler, Jeanne Bisanz, Krerk Piromsopa, Galina Guentchev, Johnathan Clark, Haryono Prawiranata, Hai Kyung Min, Ryan Torre, Jeffrey Andresen, Costanza Zavalloni, J. Roy Black 1 Department of Geography, Michigan State University, East Lansing, MI 1 Department of Agricultural Economics, Michigan State University, East Lansing, MI The web-based tool suite for specialized agriculture consists of four separate tools: 1) a historical climate tool that provides users with access to over 50 years of observations for selected stations in the Great Lakes region, 2) a future climate tool that allows users to evaluate how climate may change in the future, 3) a tart cherry development and production tool that employs phenological and yield models to evaluate climate impacts, and 4) a tart cherry investment tool that is used to calculate average annualized net returns. All of the tools have a uniform “look” in order to facilitate learning and usability. In addition, each tool is accompanied by a set of user cases that illustrate how the tool may be used by decision makers. Brief, illustrated descriptions of the methods used to develop the tool are also provided. A WEB-BASED TOOL SUITE Figure 2. Example images of the primary agricultural tools (excluding the future climate tool). The Future Climate Tool allows users to evaluate how climate may change in the future. Simulations from four GCMs (CCSM1.2, CGCM2, ECHAM4, HadCM3) driven with two emissions scenarios (A2, B2) were downscaled for 15 locations in the Great Lakes region, using multiple downscaling methods. The resulting daily temperature and precipitation scenarios were converted to stakeholder-relevant indices and threshold variables (such as the date of last spring frost and growing degree day accumulation). The graphical output displays the projections from the entire ensemble of scenarios. The range of projected changes is a proxy indicator of the degree of uncertainty surrounding the projections. The tool displays the projected change (or “delta”) in the median value of the indices and threshold variables. Medians were calculated for 20-year periods, and the deltas are the difference between a future period and a control ( ) period. Users are provided with four different display options. The first displays the values for each index or threshold variable for an observed reference period defined as The second display type allows the user to compare the scenario ensembles of the projected change in the median value for early century (defined as ), mid-century (defined as ), and a late-century (defined as ) periods. The third display shows the differences between the scenarios developed using the A2 and B2 emission scenarios. Finally, the fourth display type illustrates the projected change in the median value for 20-year overlapping decades beginning with and continuing to Below are example displays for one index, annual growing degree day accumulation (base 41°F). FUTURE CLIMATE TOOL This example shows the number of growing degree days for each year from Users can evaluate the observed interannual variation. The median value for the 20-year reference period is provided in the box on the right-hand side of the display. The observed median is a reference point for evaluating the projected changes from the scenario ensembles. REFERENCE CLIMATE Figure 4. Reference climate display for annual growing degree days (base 41°F). Figure 5. Display of projected changes for 20-year early-, mid-, and late-century periods. EARLY-CENTURY, MID-CENTURY, AND LATE-CENTURY SCENARIOS In this example, the scenario ensembles for the 20-year early-, mid-, and late-century periods display considerable uncertainty. All scenarios suggest an increase in annual growing degree day accumulation, although the magnitude of the projected change differs, with greater uncertainty for the late-century period. TREND This display shows the projected trend in annual growing degree accumulation for overlapping 20-year periods and the uncertainty surrounding the trend. A gradual increase in heat accumulation is suggested, although the uncertainty range increases with time. Figure 7. Example display of the trend in the projected change of the median value for 20-year overlapping periods. The A2 and B2 scenarios of the projected change in annual growing degree accumulation are similar for the early- and mid-century period, but for the late-century period the A2 scenarios generally suggest a larger projected change. Figure 6. Comparison of A2 and B2 emissions scenarios. A2 AND B2 SCENARIOS Figure 3: Future scenarios input interface. One objective of the Pileus Project is to better understand the potential impacts of climate variability and change on specialized agriculture in the Great Lakes region. The tart (sour) cherry industry is the primary focus, as all major aspects of the industry and the value chain are located within a small geographic area. Consequently, this commodity is a useful model system and provides a unique opportunity to link expertise in agricultural production, economics, and climatology. INTRODUCTION Figure 1. Home page of Pileus Project web-based tools. The specific goals are to 1) identify, with the assistance of stakeholders, the information needed in decision making that is influenced by climate, 2) create quantitative models to simulate the impact of climate, and 3) develop web- based decision support tools for risk management. ONGOING ACTIVITIES The tools are accompanied by user cases and an audiovisual interpretation guide demonstrating how stakeholders can incorporate the tools into their decision-making process. For the future climate tool, the interpretation guides help users to better understand the uncertainty surrounding projected future climate and recognize that climate scenarios should not be considered deterministic projections of the future but rather a series of plausible outcomes. In the initial tool development, stakeholder input was received regarding the type of climate indices and threshold variables to include in the tool and the readability of the displays. Ongoing stakeholder input is being sought to improve the usability of the tools and to critique the interpretation guides and user cases. The Pileus Project is funded by the U.S. Environmental Protection Agency, project number R This poster has not been subjected to peer review by this agency. The authors are solely responsible for any errors or omissions. We thank John Furlow and Jordan West, our EPA project managers, for their help and guidance. We also thank the many stakeholders who have provided background information, data, and shared their invaluable experience with us. ACKNOWLEDGEMENTS