PICTURE PLACEHOLtttDERPICTURE PLACEHOLtttDERPICTURE PLACEHOLtttDERPICTURE PLACEHOLtttDER

PICTURE PLACEHOLDER

PICTURE PLACEHOLDER

PICTURE PLACEHOLDER

PICTURE PLACEHOLDER

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PICTURE PLACEHOLDER

PICTURE PLACEHOLDER

PICTURE PLACEHOLDER

References

1. Jin, X., and V. Sridhar, Impact of climate change on hydrology and water resources in the Boise and Spokane River Basins, Journal of the American Water Resources Association, accepted under revision Dec 2010.2. Williams, J.R., C.A. Jones, J.R. Kiniry and D.A. Spanel. 1989. The EPIC crop growth model. Trans ASAE 32(2):497-5113. Gerik, T.J., W.L. Harman, J.R. Williams, L. Francis, J. Greiner, M. Magre, A. Meinardus, and E. Steglich. 2006 Researcher's Guide: WinEPIC model, version 3.0. Blackland Research and Extension Center, Temple, Texas.4. Wilson, L. T., Y. Yang, P. Lu, J. Wang, J.W. Nielsen-Gammon, N. Smith, and C.J. Fernandez.2007. Integrated Agricultural Information and Management System(iAIMS):World Climatic Data. August 2007. http://beaumont.tamu/edu/ClimaticData/5. Yang, Y., L.T. Wilson, and J. Wang. 2010. Development of an Automated Climatic Data Scraping, Filtering and Display System. Computers and Electronics in Agriculture 71:77-87. http://beaumont.tamu.edu/ClimaticData/

5. Results and Conclusions

1. Introduction Climate change is expected to alter the timing, duration, and quantity of natural

precipitationAs temperatures increase, precipitation becomes more variable and follows a decreasing trend

Changes in precipitation will impact the decisions made by agricultural producers

We examine producer incentives to grow different crops and whether they will produce those crops with or without irrigation

We focus on the Western Snake River Basin in Idaho Ada, Canyon, Elmore CountiesForage hay is the top commodity produced in all three counties (growing dairy industry)Wheat for grain, barley and sugar-beets are also commonly producedDue to the drier climate conditions in the three counties, only about 10% of the land harvested

for each crop is rain-fed

2.ObjectiveEstimate the distribution of yield and revenue for field crops using down-scaled climate projection data from 2010-2090

Step 1: Examine rain-fed crops produced in the study region: Alfalfa, Barley, Spring Wheat, Winter Wheat

Step 2: Examine irrigated crops produced in the study region, compare with rain-fed

Evaluate changes in cropping choices over time based on economic criteria

We assume the producers are risk-averse and they update their expectations about a seasonal precipitation based on historical observation (with a moving average)We compare the mean and variance of yield for crops and assess which crop(s) stochastically dominate one another with changing conditions

Stochastic dominance examines the trade-off between mean and variance

A crop with a higher mean may have a greater variance, so growing that crop entails greater risk (and a greater reward) than growing other crops

We estimate whether that mean-variance trade-off changes among the crops considered with changing precipitation

3. Climate ModelClimate projection data for the Snake River Basin made available to us through the Idaho EPScoR office

Climate predictions made by adding a spatially corresponding temperature factor to the observed monthly mean temperature and by multiplying a spatially corresponding precipitation factor to the observed monthly mean precipitation

How will Idaho's Agricultural Producers Adapt to Changing Precipitation Patterns?

Evidence from an Integrated Hydrologic-Economic ModelGretchen L. Beebe, Graduate Student, Boise State University (GretchenBeebe@u.boisestate.edu)

Professor Kelly M. Coburn, Department of Economics, Boise State University (kellycoburn@boisestate.edu)Professor Alejandro N.Flores,Department of Geosciences, Boise State University (lejoflores@boisestate.edu)

The authors gratefully acknowledge support by the Idaho EPSCoR Program and the National Science Foundation under award EPS-0814387.

Figure 2.

Properties of Projected Climate Data for Ada County

Figure 3.

Figure 4.

4. Crop ModelTexas AgriLife Research and Extension Center’s EPIC model was used to simulate crop yields in response to the climate projection data

Each simulation was calibrated to fit historical yield valuesSoil input reflects common soil parameter values for southwest IdahoPlanting and harvesting dates reflect average planting and harvesting dates in Idaho for each crop

Alfalfa harvested 4 times per yearBarley, Spring Wheat, Winter Wheat harvested one time per year

Other management practices (fertilizing, plowing, etc.) set to default and generated by EPIC programWe are not as concerned with the fine-tuning of our crop model as we are in piecing together the crop model and it’s economic impact, thus producing an integrated hydro-economic model that reflects the adaptation of agricultural producers to changing conditions of risk over

time

5. Results and ConclusionsIn general yields and revenue/acre increased

Ambiguities may be due to variability taking effect until later in the time frame (after 2040)

Alfalfa becomes first-order stochastically dominant over all other crops as time progresses in all three countiesAlthough alfalfa shows the greatest variability and therefore holds the most risk, it is the dominant crop choice moving forward despite the inherent risk.

Figure 2.

Figure 9-11.

Calibration of Generated Yield (for years 1973-2007) to Observed Yield (for all years available)

Figure 5. Figure 6. Figure 7. Figure 8.

6. Future WorkExamine alfalfa, barley, spring wheat and winter wheat under irrigated conditions, compare with rain-fed crop modelsConsider more crops common to southwestern Idaho and more counties within the Snake River PlainTake farmer’s decisions on crop choice and whether crops should be irrigated or rain-fed as input into the model Update the model after each harvest with the farmer’s new input for the next growing season