Impacts of Climate Change on Corn and Soybean Yields in China Jintao Xu With Xiaoguang Chen and Shuai Chen June 2014

-In past 100 years: 0.5~0.8°C; past 50 years: 1.1°C -In next 100 years: China: +3.9°C~6.0°C; World: +1.1°C~6.4°C Climate change: More Acute in China

-Less precipitation in drier north -More precipitation in the south where water is abundant Skewed trend in annual precipitation ( ) Source: China’s weather bureau

Background Agriculture is vulnerable to climate change Temperature, precipitation, and solar radiation are direct inputs for agricultural production There is a growing body of literature examining the impacts of climate change on agriculture in the developed world Mendelsohn et al. (1994), Schlenker et al. (2006) and Schlenker and Roberts (2009) This line of studies can guide or misguide climate policy in developing country Influential studies in China believe positive impacts from climate change

Significance China’s agriculture – employs more than 300 million farmers – supports over 20% of the world’s population with only 8% of the global sown area – the world’s largest agricultural economy Corn and Soybean  Important sources of feed grains for livestock production  China is a major importer of corn and soybeans  About 80% of domestic soybean consumption from international markets  Increasing share of corn imports from the world markets

Objectives Estimate the linkage between weather variables and corn and soybean yields Predict corn and soybean yields based on IPCC scenarios

Empirical estimation strategy: A spatial error model Y r,t: crop yields Z r,t :weather and technology variables LUC r,t : land use change variables P r,t : price ratio A r,t : adaptation to climate change c r : county-fixed effect

Weather variables: Z r,t Growing Degree-Days (GDDs 8-32°C) is used to represent the relationship between temperature and crop yields Extremely high temperatures (GDD, 34+) Cumulative precipitation and radiation over crops’ growing seasons Both linear and quadric forms to capture the nonlinear effect of weather variables on crop yields A time trend and quadric time trend to capture the nonlinear effect of exogenous technology changes on crop yields

Regional land use change (LUC r,t ) may affect crop yields Year T Year T+1 Marginal land 10 ha Corn 60 ha Soybean 30 ha Marg inal land 5 ha Corn 75 ha Soybean 20 ha Marginal acre: 5 ha Substitution acre: 10 ha

Price ratio: P r,t Price ratio= Expected crop price/input prices Capture the effects of relative price changes in output and input prices Higher input prices, less input use

Climate adaptation behaviors: A r,t Farmers may take adaptation behaviors  Invest new technology  Use ground or surface irrigation  Adopt drought-tolerant seeds A proxy for farms’ climate adaptation behaviors (Greenstone 2007)

Data County-level panel on crop yields, historical planted (irrigated) acres of major crops for years Daily measures of minimum and maximum temperatures, precipitation and radiation from 820 weather stations Province-level socioeconomic data

Five-year average planted acres of corn and soybean ( ) Corn Soybean

Weather stations in China Daily measures of minimum and maximum temperatures, precipitation and radiation

Results: Impacts of temperature on crop yields Corn Soybean Growing Degree Days (8-32°C) (thousand °C) The optimal numbers of GDDs: Corn ; Soybean

Results: Impacts of precipitation on crop yields (thousand mm) Corn Soybean The optimal numbers of precipitation: Corn 74cm; Soybean 54cm

Results: Impacts of solar radiation on crop yields (1000 hours) Corn Soybean The optimal numbers of radiation: Corn hours; Soybean 1000 hours

Robustness checks Results are robust to Alternative spatial weighting matrices Year-fixed effects Alternative approach to calculate growing degree days Rainfed regions only

Regression findings Finding 1: Nonlinear and asymmetric relationships between corn and soybean yields and weather variables Finding 2: Extreme high temperatures above 34°C are always harmful for crop growth Finding 3: Expansion of corn and soybean production areas had detrimental effects on corn and soybean yields Finding 4: Climate adaptation behavior was actively undertaken for corn production; not significant for soybeans

Economic loss due to climate change Climate change led to a net economic loss of $ million in China’s corn and soybean sectors in 2009.

Climate Change Impacts by Temperature 6 IPCC Scenarios Mid-term ( ) Corn yields decrease by 1.5-2% under B1 and by 1.5-4% under A1F1. Soybean yields decrease by 3-4.5% under B1 and 4-8% under A1F1. Long-term ( ) Corn yields decrease by 2-5% under B1 and by 5-15% under A1F1. Soybean yields decrease by 5-10% under B1 and 8-22% under A1F1. Note: Blue: Effect for change in GDD 8-32; Red: GDD 34+; Black: Aggregate Effect

Concluding remarks Nonlinear and asymmetric relationships between corn and soybean yields and weather variables Extreme high temperatures are always harmful for crop growth Expansion of corn and soybean acres had negatively affected corn and soybean yields Climate change has led to a net economic loss of $ million in 2009 in China’s corn and soybean sectors Corn and soybean yields in China are expected to decrease by 2-15% and 5-22%, respectively, by the end of this century

Climate Change Impacts by Precipitation and Radiation Changes in precipitation and solar radiation are expected to yield negligible effects on corn and soybean yields (less than 1%) Precipitation Radiation

Comparison with Roberts-Shlenker Methods Corn optimal temperature 30 centi., for soybeans 29 centi. Slightly over-estimates (Average nearly 5%) by RS methods in prediction.

Descriptive Statistics

Table 4: Spatial Error Estimations (Dependent Variable: Log Corn Yield) Model Model (1): GDD and precipitation only Model (2): add solar radiation Model (3): add LUC variables Model (4): add economic variables Model (5): add climate adaptation variable GDD (8-32°C) *** *** *** *** *** (2.84)(2.98)(3.18)(2.95)(2.93) GDD (8-32°C) squared ** *** *** *** *** (-2.53)(-2.68)(-2.91)(-2.67)(-2.65) Square root of GDD ( 34°C) *** (-2.94) *** (-3.66) *** (-3.53) *** (-4.14) *** (-4.15) Precipitation *** *** *** *** *** (2.95)(3.02)(3.04)(3.15)(3.13) Precipitation squared *** *** *** *** *** (-3.47)(-3.42)(-3.41)(-3.45) Radiation *** *** *** *** (5.17)(5.11)(4.81)(4.87) Radiation squared *** *** *** *** (-5.04)(-4.84)(-4.64)(-4.68) LUC: marginal acre *** *** *** (-7.68)(-7.88)(-8.01) LUC: substitution acre *** *** *** (-5.19)(-5.17)(-5.25) Ratio: corn price/fertilizer price index (1.37)(1.15) Ratio: corn price/wage ** *** (2.09)(2.06) Irrigation ratio *** (3.04) Spatial correlation *** *** *** *** *** (37.57)(37.16)(35.73)(35.03)(35.09) N16840 R2R

Table 5: Spatial Error Estimations (Dependent Variable: Log Soybean Yield) Model Model (1): GDD and precipitation only Model (2): add solar radiation Model (3): add LUC variables Model (4): add economic variables Model (5): add climate adaptation variable GDD (8-32°C) *** *** *** *** *** (3.57)(3.59)(3.54)(3.09)(3.14) GDD (8-32°C) squared *** *** *** *** *** (-4.57)(-4.56)(-4.53)(-4.02)(-4.05) Square root of GDD ( 34°C) (-0.19) (-0.78) (-0.79) (-1.28) (-1.24) Precipitation *** *** *** ** ** (2.64)(2.68)(2.73)(2.56)(2.55) Precipitation squared *** *** *** *** *** (-3.52)(-3.49)(-3.53)(-3.50)(-3.47) Radiation *** *** *** *** (4.16)(4.14)(4.49)(4.45) Radiation squared *** *** *** *** (-4.26)(-4.21)(-4.64)(-4.62) LUC: marginal acre *** *** (-4.62)(-4.74)(-4.75) LUC: substitution acre *** *** (-2.63)(-2.59)(-2.58) Ratio: soybean price/fertilizer price index *** *** (2.77)(2.89) Ratio: soybean price/wage *** *** (4.34)(4.39) Irrigation ratio (1.11) Spatial correlation *** *** *** *** *** (25.46)(25.90)(25. 27)(24.93)(24.82) N17400 R2R