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Assessment of Climate Change Impact on Eastern Washington Agriculture Claudio O. Stöckle Biological Systems Engineering, Washington State University USA Objective Assess the potential impact of climate change and elevated atmospheric CO2 concentration on selected crops in eastern Washington, region that produces most of the state’s agricultural output value. Correlation between CO2 Concentrations and Temperature The current concentration is the highest in 800,000 years, as determined by ice core data a The 800,000-year records of atmospheric carbon dioxide (red; parts per million, p.p.m.) and methane (green; parts per billion, p.p.b.) from the EPICA Dome C ice core together with a temperature reconstruction (relative to the average of the past millennium) based on the deuterium– hydrogen ratio of the ice, reinforce the tight coupling between greenhouse-gas concentrations and climate observed in previous, shorter records. The 100,000-year ‘sawtooth’ variability undergoes a change about 450,000 years ago, with the amplitude of variation, especially in the carbon dioxide and temperature records, greater since that point than it was before. Concentrations of greenhouse gases in the modern atmosphere are highly anomalous with respect to natural greenhouse-gas variations (present-day concentrations are around 380 p.p.m. for carbon dioxide and 1,800 p.p.b. for methane). b The carbon dioxide and methane trends from the past 2,000 years. Ed Brook, Nature 453, 291 (2008). Global Greenhouse Gas Trends Source: IPCC General Circulation Models Four GCMs were selected for this study: PCM1, CCSM3, ECHAM5, and CGCM3. PCM1 projects less warming and CCSM3 more warming for eastern WA. The other two GCMs are intermediate. The GCMs project an increase in precipitation (3 to 9% by 2020 and 2080, respectively) with some differences in distribution, and with a larger relative increase in the winter. Annual Precipitation and Potential Evapotranspiration ETo (mm) 1400 1400 Pullman (CCSM3) 1200 1000 1000 800 Precipitation 600 ETo 800 600 400 400 200 200 0 0 Baseline 1400 1200 Pullman (PCM1) 1200 2020 2040 2080 Sunnyside (CCSM3) Baseline 1400 1200 1000 1000 800 800 600 600 400 400 200 200 0 0 Baseline 2020 2040 2080 2020 2040 2080 Sunnyside (PCM1) Baseline 2020 2040 2080 Seasonal (April 1- Sept 30) Precipitation and Potential Evapotranspiration ETo (mm) 1400 1400 Pullman (CCSM3) 1200 1000 1000 800 Precipitation 600 ETo 800 600 400 400 200 200 0 0 Baseline 1400 1200 Pullman (PCM1) 1200 2020 2040 2080 Baseline 1400 Sunnyside (CCSM3) 1200 1000 1000 800 800 600 600 400 400 200 200 0 Baseline 2020 2040 2080 2020 2040 2080 Sunnyside (PCM1) 0 Baseline 2020 2040 2080 Annual and Season Mean Temperature (oC) Annual Mean Temperature (oC) Pullman 20 15 CCSM3 10 PCM1 5 0 Annual Mean Temperature (oC) 25 25 Sunnyside 20 15 10 5 0 Baseline 25 Season Mean Temperature (oC) Season Mean Temperature (oC) 25 Pullman 20 15 10 2020 2040 2080 Sunnyside 20 15 10 5 5 0 0 Baseline 2020 2040 2080 Baseline 2020 2040 2080 Annual Temperature Difference with Baseline (oC) Sunnyside Annual 2020 2040 2080 CCSM3 1.9 2.8 3.6 PCM1 1.3 2.2 3.0 2020 2040 2080 CCSM3 1.8 2.9 3.8 PCM1 1.3 1.9 2.8 Sunnyside Seasonal Change in frostfree period (days) Probability Distribution of Tmax (June/July) 45 40 CCSM3 CGCM3 ECHAM5 PCM1 Historical Pullman Tmax (oC) 35 30 25 20 15 10 0.0 0.2 0.4 0.6 0.8 Probability of equal or higher temperature 1.0 Probability Distribution of Tmin (April) 15 Sunnyside Tmin (oC) 10 5 0 CCSM3 CGCM3 ECHAM5 PCM1 Historical -5 -10 0.0 0.2 0.4 0.6 0.8 Probability of equal or lesser temperature 1.0 Schlenker and Roberts (2008) National Bureau of Economic Research Corn Schlenker and Roberts (2008) National Bureau of Economic Research What about Atmospheric CO2 increase? Atmospheric CO2 Concentration (PPM) 900 800 700 600 500 B1 400 A1B A2 300 200 100 0 1900 1950 2000 2050 2100 Year IPCC Projections Relative change of Radiation-use efficiency for wheat and maize simulated with the CTP model (Stockle and Kemanian, 2009) Free-Air CO2 Enrichment (FACE) Experiments Long et al. (2004) Annual Rev. Plant Biol. 55 Long et al. (2004) Annual Rev. Plant Biol. 55 Sour Orange Trees (13 years of data) Idso and Kimball BA (2001) Env Exp Bot 46 Assessment Approach Relied on crop simulation modeling with interpretation based on literature and expert opinions. CropSyst, a cropping systems model developed at WSU was used for the assessment. Insect and disease models were used to complement the evaluation. CropSyst has been tested and applied in all continents and under a wide range of climatic conditions ClimGen CropSyst has been used for climate change assessment in studies elsewhere. The WSU weather generator ClimGen was used to generate daily series of projected weather. Assessment Approach Focus on the major agricultural commodities in terms of economic value: apples, potatoes, and wheat. Wheat is the dominant dryland crop. Potato is the main irrigated annual crop. Apple is the main irrigated tree fruit crop. Assessment Approach Daily weather data for the years 1975-2005 were used to establish a baseline for change. Projections of daily precipitation and temperature from the four GCMs were used to define three climate change scenarios: 2020 (2010 – 2039) 2040 (2030 – 2059) 2080 (2070 – 2099) Assessment Approach The following locations (crops) were included in the analysis: o o o o o Pullman (winter and spring wheat, high precipitation) Saint John (winter and spring wheat, intermediate precipitation) Lind (winter wheat, low precipitation) Othello (potatoes, irrigated) Sunnyside (apples, irrigated) Assessment Approach Computer simulations of crop growth and yield assumed adequate supply of water and nutrients and good control of pests and diseases. The only variables were climate change and CO2 elevation. The impact of possible irrigation water shortages was assessed in a complementary effort (hydrology sector). Assessment Approach to project atmospheric CO2 concentration. Atmospheric CO2… The A1B IPCC emission scenario was used 800 700 600 500 400 300 200 100 0 1900 1950 2000 2050 2100 Crop biomass productivity (a parameter that affects several simulated processes including crop water use) was assumed to increase 20% with a CO2 change from 370 to 600 PPM (FACE experiments). Results Winter Wheat 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Pullman (6.2 Mg/ha) No CO2 CO2 2020 2040 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 2080 Lind (4.3 Mg/ha) 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 St. John (5.1 Mg/ha) 2020 2020 2040 2080 2040 2080 1.6 Pullman (4.4 Mg/ha) 1.4 Spring Wheat 1.2 1 No CO2 0.8 CO2 0.6 CO2 + Adaptation 0.4 0.2 0 2020 2040 2080 1.6 St. John (3.7 Mg/ha) 1.4 1.2 1 0.8 0.6 0.4 0.2 0 2020 2040 2080 Potatoes 1.6 Othello (81 Mg/ha) 1.4 1.2 1 No CO2 CO2 CO2 + Adaptation 0.8 0.6 0.4 0.2 0 2020 2040 2080 Apples 1.6 Sunnyside (61 Mg/ha) 1.4 1.2 1 No CO2 CO2 CO2 + Adaptation 0.8 0.6 0.4 0.2 0 2020 2040 2080 Codling Moth 250 Day of the Year 200 150 First Flight First Generation Second Generation Fraction Third Generation 100 50 0 Historical 2020 2040 2080 Average Number of Days of Powdery Mildew Risk 14 Grapes 12 Low Medium High 8 6 4 16 2 14 Cherries 0 Historical 2020 2040 12 2080 10 Days Days 10 8 6 4 2 0 Historical 2020 2040 2080 Conclusions It is projected that the impact of climate change alone on selected but economically important crops in eastern WA would be generally mild in the short term (i.e., next couple of decades), but increasingly detrimental with time (potential yield losses reaching 25% for some crops by the end of the century). Conclusions However, the projected CO2 increase is expected to provide significant mitigation to the effect of warming. In fact, if the projected beneficial effect of CO2 elevation are fully realized, some crops may obtain important yield gains. Adaptation based on changes in management (e.g., planting dates) or on new research (e.g., better adapted varieties) can provide additional mitigation or further enhance CO2 effects. Conclusions Caveats to consider: o Possible changes in the frequency and persistence of extreme temperature effects are not well represented in current climate projections o We have assumed good control of pests and diseases, but these could affect crops in ways not described here o Availability of irrigation water may become a significant limiting factor in some areas. Conclusions Caveats to consider: o Focus of the study is on yields, but quality can be affected even when yields increase. o The economic cost of adaptation (e.g., management for increased pest control or greater nitrogen fertilization requirements) should be accounted for in future studies. Thank you.