Agriculture and Climate Change Revisited

Agriculture and Climate Change Revisited Anthony Fisher 1 Michael Hanemann 1 Michael Roberts 2 Wolfram Schlenker 3 1 University California at Berkeley 2 North Carolina State University 3 Columbia University and NBER World Bank - October 16, 2009

Outline 1 Motivation 2 Model and Data Summary 3 Empirical Results 4 Climate Change Impacts 5 Comparison to Other Studies 6 Conclusions

Setting the Stage Background - Agriculture and Climate Change Mounting evidence that climate is changing Emerging consenus that temperature will increase Several studies focus on agricultural sector Climate / weather directly impacts agricultural production Green revolution drastically increased yields In 1800: 75-80% of population worked in agriculture Food less scare today (recent run up in prices) Agriculture - large share of GDP in developing countries Agriculture - small share of GDP in the US, but US produces 40% of all corn in the world (38% of all soybeans, 20% of all cotton) Impacts in the US will influence world supply and prices Discussion whether US will be net beneficiary or net loser

Setting the Stage Background - Agricultural Production Five Largest Producers

Setting the Stage Background - Agricultural Production Caloric Production (Corn, Rice, Soybeans, and Wheat)

Setting the Stage Background - Agricultural Production Caloric Production (Corn, Rice, Soybeans, and Wheat) We focus on US agriculture

Motivation Model/Data Results Impacts Comparisons Conclusions Setting the Stage Background - Agriculture in the US Elevation Map

Setting the Stage Background - Agriculture in the US Agricultural Area (2.5x2.5mile grids)

Setting the Stage Literature Review Early studies of agricultural productivity Ronald Fisher: Studies in Crop Variation I-VI Developed Maximum Likelihood Estimator More recent studies of agricultural productivity Crop simulation models Daily temperature and precipitation values Too many parameters to estimate (calibrated instead) Other inputs are held constant Reduced-form statistical studies Large geographic extend (entire US) Average weather variables (spatial or temporal)

Setting the Stage Statistical Studies: Cross Section versus Panel Cross-section analysis of farmland values Value of land if put to best use Climate varies across space (south is hotter) Pro: measures how farmers adapt to various climates Con: omitted variables problem Panel of yields or profits Link year-to-year fluctuations in weather to profits/yield Pro: panel allows for use of fixed effects mitigates omitted variables problem Con: Short-run response different from long-run response difference between weather and climate

Setting the Stage Statistical Studies: Cross Section versus Panel Weather Variable Nonlinear Temperature Average Temperature Effects Cross- Section slightly positive large negative Panel Data insignificant

Setting the Stage Statistical Studies: Cross Section versus Panel Weather Variable Nonlinear Temperature Average Temperature Effects Cross- slightly positive Section large negative large negative Panel insignificant large negative Data

Main Findings The Importance of Extreme Temperatures Nonlinear relationship between yields and temperature Yields increasing in temperature until upper threshold 29 C for corn, 30 C for soybeans, and 32 C for cotton Yields decreasing in temperature above threshold Slope of decline much steeper than slope of incline Extreme heat measured by degree days 30 C Degrees above 30C, e.g., 34C is 4 degree days 30C Degree days 30 C Explain 45% of variation in aggregate corn yields Similar relationship in cross section and time series Similar relationship in cross section of farmland values

Main Findings Implication for Climate Change Both panel and cross-section give similar results If extreme temperatures are included in regression equation Difficulty to adapt to extreme temperatures Different results in previous studies Not driven by different sources of identification Cross section versus panel But difference in data / specification / climate model Large predicted damages in all models Extreme temperatures become more frequent

Outline 1 Motivation 2 Model and Data Summary 3 Empirical Results 4 Climate Change Impacts 5 Comparison to Other Studies 6 Conclusions

Model Model Specification - SR (2009) Log yields y it are additively influenced by temperature h y it = h h g(h)φ it (h)dh + z it δ + c i + ɛ it where y it : log yield in county i in year t h: heat / temperature g(): growth as a function of heat φ it : time crop is exposed to heat t in county i in year t z it : other controls (precipitation, quadratic time trend by state) c i : county fixed effect ɛ it : error (we adjust for spatial correlation)

Model Model Specification - SR (2009) Let Φ it (h) be the total time temperatures are below h Dummy-variable approach (discretize integral) y it = 39 j=0,3,6,9,... Chebyshev polynomials (mth-order) γ j [Φ it (h + 3) Φ it (h)] +z itδ + c i + ɛ it }{{} x it,j y it = = 39 h= 1 j=1 m j=1 m γ j T j (h + 0.5) [Φ it (h + 1) Φ it (h)] + z it δ + c i + ɛ it 39 γ j h= 1 T j (h + 0.5) [Φ it (h + 1) Φ it (h)] +z it δ + c i + ɛ it } {{ } x it,j

Data - Dependent Variables Descriptive Statistics - Dependent Variables Average Corn Yields in Bushels / Acre (1950-2005)

Data - Dependent Variables Descriptive Statistics - Dependent Variables Average Soybean Yields in Bushels / Acre (1950-2005)

Data - Dependent Variables Descriptive Statistics - Dependent Variables Average Cotton Yields in Pounds / Acre (1950-2005)

Data - Weather Fine-scaled Weather Data Set Daily minimum / maximum temperature and precipitation 2.5x2.5 mile grid for entire US Constructed from individual weather stations PRISM interpolation procedure Time temperatures are in each 1 C interval Sinusoidal curve between minimum and maximum temp. Sum over days in growing season March-August for corn and soybeans April-October for cotton Weather in county Satellite scan of agricultural area Weighted average of all 2.5x2.5 mile grids in county

Data - Weather Descriptive Statistics - Weather Average Weather in Sample (1950-2005)

Outline 1 Motivation 2 Model and Data Summary 3 Empirical Results 4 Climate Change Impacts 5 Comparison to Other Studies 6 Conclusions

Panel of Crop Yields Link between Temperature and Yields Panel of Corn and Soybean Yields

Panel of Crop Yields Link between Temperature and Yields Panel of Cotton Yields

Comparison of Temperature Variables The Importance of Extreme Temperatures Model comparison tests Horse race: which specification does best? Estimate model using 85% of data Predict observations for remaining 15% of data Check how close predictions are to actual outcomes How much do weather variables reduce error

Comparison of Temperature Variables The Importance of Extreme Temperatures Notes: Table compares various temperature specifications for corn, soybeans, and cotton according to the root mean squared out-of sample prediction error.

Comparison of Temperature Variables The Importance of Extreme Temperatures Assessment of extreme heat by futures market New information about expected yields will move prices Weekly corn futures returns 1950-2006 Extreme temperatures move prices up significantly No significant relationship with average temperature Next steps Check various sources of identification How robust are results?

Robustness and Sensitivity Checks Various Sources of Identification Cross-Section versus Time Series

Robustness and Sensitivity Checks Adaptation and Technology Progress Limited Adaptation in Warmer Climates

Robustness and Sensitivity Checks Adaptation and Technology Progress Corn: Limited Progress in Heat Tolerance

Outline 1 Motivation 2 Model and Data Summary 3 Empirical Results 4 Climate Change Impacts 5 Comparison to Other Studies 6 Conclusions

Impact on Yields Impact on Crop Yields Predicted damages large and significant Driving Force: extreme heat predicted to increase Especially by end of century Extreme temperature are highly damaging to crop Caveats Does not allow for CO 2 fertilization Keeps crops, growing area, and planting dates fixed Will present sensitivity checks below

Data - Climate Change IPCC Emission Scenarios Slowest Warming (B1), Fastest Warming (A1FI)

Data - Climate Change Changes in Crop Yields (Percent) Medium Term (2020-2049)

Data - Climate Change Changes in Crop Yields (Percent) Long Term (2070-2099)

Data - Climate Change Impact on Crop Yields Examining adaptation possibilities Limited effect of shift in planting dates Corn: Shift planting dates one month forward (Feb-July) Damages (A1FI - long term) decrease from 79% to 64% Less extreme heat in February than August But: Also less solar radiation Limited potential for adaptation within species Comparable results for various subsets (north, south, etc) Comparable results in time series and cross section

Impact on Farmland Values Farmland Values Cross section analysis of farmland values Value of land reflects profitability of land Allows for adaptation (land is put to best use) Compares values across climatic regions Schlenker, Hanemann and Fisher (2006) Counties east of 100 degree meridian Farmland values linked to degree days (8-32 C, 34 C) Controls for income, population density, soil controls Extreme heat (degree days 34 C) very damaging Omitted variable bias? Robust to inclusion/exclusion of controls if model uses degree days!

Impact on Farmland Values Farmland Values Including / Excluding Controls (1,048,576 Permutations)

Impact on Farmland Values Farmland Values versus Corn/Soybeans Yields Long Term (2070-2099) Area-weighted Impact by County Variable Impact (t-val) Mean Min Max Std Farmland Values HCM3 - B1-27.37-78.77 44.15 22.58 HCM3 - B2-31.61-88.28 52.37 26.57 HCM3 - A2-61.64-94.72 27.87 20.25 HCM3 - A1FI -68.54-96.95 39.61 21.79 Corn HCM3 - B1-43.35 (19.20) -45.70-83.76 18.11 18.18 HCM3 - B2-50.76 (21.13) -53.51-90.03 18.16 18.08 HCM3 - A2-69.83 (16.01) -71.15-96.59 0.19 16.39 HCM3 - A1FI -78.67 (14.61) -79.83-98.45-7.70 14.35 Soybeans HCM3 - B1-35.03 (22.25) -34.27-82.53 25.01 19.61 HCM3 - B2-42.46 (24.54) -42.15-87.53 26.09 20.42 HCM3 - A2-62.79 (20.56) -61.33-94.56 19.72 19.54 HCM3 - A1FI -72.87 (18.96) -71.36-96.79 11.87 17.32

Outline 1 Motivation 2 Model and Data Summary 3 Empirical Results 4 Climate Change Impacts 5 Comparison to Other Studies 6 Conclusions

Deschenes and Greenstone - Summary Summary of Paper Paper pioneered the use of panel data Authors focus predominantly on profits One sensitivity check using corn and soybean yields Find no significant relationship between weather and profit Agriculture is predicted to benefit from warming Potential concerns Data quality issues Yield regression does not account for extreme heat Profit uses sales in a given year Omits storage / short-run price response Assume weather is bad in a year price increases and storage is depleted (as price is high) sales will not necessarily decrease!

Deschenes and Greenstone - Data Data Concerns

Deschenes and Greenstone - Data Data Concerns Variable dd89 in DG Replication of dd89 R 2 σ e e > 1F R 2 σ e e > 1F (1a) (1b) (1c) (2a) (2b) (2c) No Fixed Effects (F.E.) 6.82F 91.1% 6.07F 89.8% County F.E. 0.845 2.69F 56.5% 0.940 1.49F 64.7% County + Year F.E. 0.867 2.48F 54.8% 0.979 0.88F 24.2% County + State-by-Year F.E. 0.879 2.38F 50.5% 0.997 0.35F 1.3%

Deschenes and Greenstone - Data Data Concerns

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Corn Soybeans (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 12.6% 21.3% 26.3% Model comparison tests (J-test) DG against other weather (t-value) Other weather against DG (t-value) Climate change impact (Percent) Hadley II-IS92a scenario (s.e.) [s.e. Conley] Hadley III-B2 scenario (s.e.) [s.e. Conley] Observations 6862 6862 6862 5141 5141 5141 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Corn Soybeans (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 12.6% 21.3% 26.3% Model comparison tests (J-test) DG against other weather (t-value) 15.97 21.08 Other weather against DG (t-value) 1.85 1.66 Climate change impact (Percent) Hadley II-IS92a scenario (s.e.) [s.e. Conley] Hadley III-B2 scenario (s.e.) [s.e. Conley] Observations 6862 6862 6862 5141 5141 5141 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Corn Soybeans (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 12.6% 21.3% 26.3% Model comparison tests (J-test) DG against other weather (t-value) 15.97 21.08 Other weather against DG (t-value) 1.85 1.66 Climate change impact (Percent) Hadley II-IS92a scenario -0.98-11.50-14.03 (s.e.) (1.23) (1.71) (1.66) [s.e. Conley] [2.12] [5.57] [5.18] Hadley III-B2 scenario (s.e.) [s.e. Conley] Observations 6862 6862 6862 5141 5141 5141 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Corn Soybeans (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 12.6% 21.3% 26.3% Model comparison tests (J-test) DG against other weather (t-value) 15.97 21.08 Other weather against DG (t-value) 1.85 1.66 Climate change impact (Percent) Hadley II-IS92a scenario -0.98-11.50-14.03 (s.e.) (1.23) (1.71) (1.66) [s.e. Conley] [2.12] [5.57] [5.18] Hadley III-B2 scenario -44.46-65.64 (s.e.) (3.69) (3.97) [s.e. Conley] [13.09] [11.76] Observations 6862 6862 6862 5141 5141 5141 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Corn Soybeans (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 12.6% 21.3% 26.3% 15.3% 31.0% 38.1% Model comparison tests (J-test) DG against other weather (t-value) 15.97 21.08 19.98 25.80 Other weather against DG (t-value) 1.85 1.66 1.59 1.16 Climate change impact (Percent) Hadley II-IS92a scenario -0.98-11.50-14.03-3.01-16.49-18.36 (s.e.) (1.23) (1.71) (1.66) (1.38) (1.73) (1.64) [s.e. Conley] [2.12] [5.57] [5.18] [1.96] [4.93] [4.39] Hadley III-B2 scenario -44.46-65.64-53.90-75.71 (s.e.) (3.69) (3.97) (3.87) (3.92) [s.e. Conley] [13.09] [11.76] [11.94] [10.60] Observations 6862 6862 6862 5141 5141 5141 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Corn Soybeans (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 7.2% 13.9% 18.5% 12.6% 20.3% 23.4% Model comparison tests (J-test) DG against other weather (t-value) 10.71 15.19 11.45 14.26 Other weather against DG (t-value) 2.04 1.71 1.12 0.91 Climate change impact (Percent) Hadley II-IS92a scenario 0.15-22.24-20.55 1.06-19.04-14.93 (s.e.) (1.08) (3.31) (3.05) (0.95) (2.74) (2.67) [s.e. Conley] [1.36] [7.24] [6.01] [0.88] [5.22] [4.21] Hadley III-B2 scenario -65.12-78.28-57.73-63.51 (s.e.) (7.23) (6.83) (6.05) (5.79) [s.e. Conley] [16.54] [13.93] [12.73] [9.85] Observations 6862 6862 6862 5141 5141 5141 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes State-By-Year FE Yes Yes Yes Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Profit Profit (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 0.5% 1.4% 1.5% Model comparison tests (J-test) DG against other weather (t-value) 4.89 4.13 Other weather against DG (t-value) 0.44 0.63 Climate change impact (Percent) Hadley II-IS92a scenario -6.08-34.49-29.53 (s.e.) (3.03) (6.90) (6.63) [s.e. Conley] [4.07] [12.25] [12.14] Hadley III-B2 scenario -52.91-41.70 (s.e.) (11.24) (11.30) [s.e. Conley] [20.04] [21.54] Observations 9024 9024 9024 9024 9024 9024 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes No No No State-By-Year FE No No No Yes Yes Yes

Deschenes and Greenstone - Yield and Profit Comparison of Yield/Profit Regressions Profit Profit (1a) (1b) (1c) (2a) (2b) (2c) Regression diagnostics Variance explained by weather 0.5% 1.4% 1.5% 0.5% 0.7% 0.9% Model comparison tests (J-test) DG against other weather (t-value) 4.89 4.13 2.97 3.90 Other weather against DG (t-value) 0.44 0.63 0.72 0.65 Climate change impact (Percent) Hadley II-IS92a scenario -6.08-34.49-29.53 3.92 0.52 3.86 (s.e.) (3.03) (6.90) (6.63) (2.87) (12.85) (12.86) [s.e. Conley] [4.07] [12.25] [12.14] [2.56] [12.53] [12.80] Hadley III-B2 scenario -52.91-41.70-4.47 5.50 (s.e.) (11.24) (11.30) (20.24) (20.72) [s.e. Conley] [20.04] [21.54] [19.76] [20.85] Observations 9024 9024 9024 9024 9024 9024 Soil controls Yes Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes No No No State-By-Year FE No No No Yes Yes Yes

Deschenes and Greenstone - Storage The Importance of Storage

Deschenes and Greenstone - Storage Do Yield Shocks Translate Into Sales? Regressing Sales on Value of Production (Quantity times Price) Using County and Year Fixed Effects Combined Corn Soybeans Wheat Cotton (1) (2) (3) (4) (5) Using all farms in a county Coefficient 0.718 0.640 0.702 0.862 0.819 Std. Error (0.012) (0.012) (0.007) (0.009) (0.025) Observations 11595 10268 8241 10540 2573 Using farms with no livestock Coefficient 0.792 0.693 0.721 0.864 0.823 Std. Error (0.016) (0.013) (0.008) (0.010) (0.029) Observations 11083 9303 7569 9490 2207

Deschenes and Greenstone - Storage Do Yield Shocks Translate Into Sales? Regressing Sales on Value of Production (Quantity times Price) Using County and State-by-Year Fixed Effects Combined Corn Soybeans Wheat Cotton (1) (2) (3) (4) (5) Using all farms in a county Coefficient 0.780 0.738 0.770 0.868 0.923 Std. Error (0.010) (0.012) (0.007) (0.010) (0.019) Observations 11595 10268 8241 10540 2573 Using farms with no livestock Coefficient 0.821 0.772 0.793 0.871 0.931 Std. Error (0.016) (0.015) (0.009) (0.012) (0.021) Observations 11083 9303 7569 9490 2207

Deschenes and Greenstone - Storage Do Yield Shocks Translate Into Sales? Regressing State-level Log Storage on Extreme Heat Using County and State-by-Year Fixed Effects Corn Soybeans (1a) (1b) (2a) (2b) Extreme Heat -1.57e-3-2.57e-3-4.60e-3-4.38e-3 (s.e.) (7.65e-4) (7.73e-4) (9.41e-4) (8.98e-4) Extreme Heat (Lag 1) 4.02e-4-2.86e-3 (s.e.) (7.85e-4) (9.15e-4) Extreme Heat (Lag 2) 7.36e-4-2.89e-3 (s.e.) (7.50e-4) (9.44e-4) Observations 1535 1453 1002 942 Year F.E. Yes Yes Yes Yes County F.E. Yes Yes Yes Yes

Deschenes and Greenstone - Robustness of Hedonic Regression Robustness of Hedonic Regression

Mendelsohn, Nordhaus and Shaw Summary of Paper Paper pioneered hedonic analysis of farmland values Link farmland values in the entire US to climate Authors use two sets of weights Cropland weights: large damages from global warming Croprevenue weights: modest benefits from global warming Potential concerns Access to highly subsidized irrigation water in the West Subsidy higher than average farmland value in east Subsidy capitalizes into farmland values Regression equates higher temperature with subsidies!

Mendelsohn, Nordhaus and Shaw Irrigated vs Non-irrigated Test: Is East different from West Chow test with p-value less than 0.0001 Focus on East only Large damages under both set of weights

Mendelsohn, Nordhaus and Shaw Irrigated vs Non-irrigated

Outline 1 Motivation 2 Model and Data Summary 3 Empirical Results 4 Climate Change Impacts 5 Comparison to Other Studies 6 Conclusions

Main Findings Conclusions Agricultural output directly linked to weather Nonlinear relationship between weather and yields Yields increasing in temperature until upper threshold 29 C for corn, 30 C for soybeans, and 32 C for cotton Yields decreasing in temperature above threshold Slope of decline much steeper than slope of incline Extreme temperatures have dominating effect Accounting for extreme temperatures gives superior out-of-sample forecasts Comparable results using Panel of yields Time series of aggregate (national yields) Cross section of average yields in a county Futures market returns Various subsets (geographic / temporal) Cross section of average farmland value in a county

Main Findings Conclusions - Impacts Large damages from global warming Extreme temperatures become more frequent Heat waves have strong negative effects Yields by the end of the century are predicted to decrease 30%-46% under slow-warming (B1) scenario 63%-82% under fast-warming (A1FI) scenario Limited potential for adaptation Cross-section of yields has same shape as time-series Similar relationship for farmland values wider set of adaptations Analysis first step More structural model of crop choice, planting dates, etc Need to account for extreme temperatures

Model/Data Results Impacts Outline 7 Model and Data Summary 8 Empirical Results 9 Climate Change Impacts

Model/Data Results Impacts Northern and Southern Counties Geographic Subgroups of Counties

Model/Data Results Impacts Sinusoidal Interpolation Time in Each 1 C Interval

Model/Data Results Impacts Spatial Interpolation Spatial Interpolation of Weather Station Data

Model/Data Results Impacts Outline 7 Model and Data Summary 8 Empirical Results 9 Climate Change Impacts

Model/Data Results Impacts Panel of Crop Yields Other Regression Results Precipitation variable Significant inverted U-shape for corn and soybeans Optimum: 25 inches for corn / 27.2 inches for soybeans Not significant for cotton (highly irrigated) Quadratic time trend by state Almost threefold increase in average yields 1950-2005 Summary statistics Corn: R-squared of 0.77 using 105,981 observations Soybeans: R-squared of 0.63 using 82,385 observations Cotton: R-squared of 0.37 using 31,540 observations Weather explains roughly one third of variance

Model/Data Results Impacts Model Comparison The Importance of Extreme Temperatures Model comparison tests Horse race: which specification does best? Estimate model using 48 of 56 year (86% of data) Predict observations for remaining 8 years Check how close predictions are to actual outcomes New models gives best forecasts Nonlinear effects of temperatures Extreme temperatures drive down yields significantly Welch test of 1000 repetitions None significant difference among new models All new models significantly better than previous models

Model/Data Results Impacts Model Comparison The Importance of Extreme Temperatures Notes: Table compares various temperature specifications for corn, soybeans, and cotton according to the root mean squared out-of sample prediction error.

Model/Data Results Impacts Model Comparison The Importance of Extreme Temperatures RMS Welch Test for Equal Forecast Accuracy (RMS) Step Piecewise Monthly Degree Days Degree Days Function Linear Averages (Thom) (Daily Mean) Polynomial (8th-order) 0.2259 0.10 0.68 13.94 23.58 25.76 Step Function 0.2260 0.58 13.90 23.57 25.75 Piecewise Linear 0.2265 13.49 23.24 25.44 Monthly Averages 0.2387 9.22 12.02 Degree Days (Thom) 0.2481 3.18 Degree Days (Daily Mean) 0.2516 County F.E. (No Weather) 0.2688

Model/Data Results Impacts Growing Season Additive Effects of Heat Corn: Temperature Effects are Additive

Model/Data Results Impacts Growing Season Additive Effects of Heat Soybeans: Temperature Effects are Additive

Model/Data Results Impacts Growing Season Additive Effects of Heat Cotton: Temperature Effects are Additive

Model/Data Results Impacts Growing Season Various Definitions of Growing Season Corn

Model/Data Results Impacts Growing Season Various Definitions of Growing Season Soybeans

Model/Data Results Impacts Growing Season Various Definitions of Growing Season Cotton

Model/Data Results Impacts Outline 7 Model and Data Summary 8 Empirical Results 9 Climate Change Impacts

Model/Data Results Impacts Data - Construction Climate Change Predictions Hadley HCM3 model (216 grid points covering the US) Change in climatic variables (2020-2049) and (2070-2099) compared to (1960-1989) Absolute change in minimum and maximum temperature Relative change in precipitation Distance-weighted change at each 2.5x2.5mile grid Using four surrounding Hadley grids Add predicted temperature change to historic baseline Mean shift with constant variance Multiply historic precipitation with predicted change Variance increase if predicted change >1

Model/Data Results Impacts Data - Descriptive Statistics Descriptive Statistics - Climate Change Hadley 3 Model - B1 Scenario

Model/Data Results Impacts Data - Descriptive Statistics Descriptive Statistics - Climate Change Hadley 3 Model - A1FI Scenario