Impacts of Climate Change on Corn and Soybean Yields in China Jintao Xu With Xiaoguang Chen and Shuai Chen June 2014
Dec 23, 2015
Impacts of Climate Change on Corn and Soybean Yields in China
Jintao XuWith 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 (1950-2000)
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
Yr,t: crop yields
Zr,t :weather and technology variables
LUCr,t: land use change variables
Pr,t: price ratio
Ar,t: adaptation to climate change
cr: county-fixed effect
Weather variables: Zr,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 (LUCr,t) may affect crop yields
Year T
Year T+1
Marginal land
10 ha
Corn60 ha
Soybean30 ha
Marginal land5 ha
Corn75 ha
Soybean20 ha
Marginal acre: 5 ha Substitution acre: 10 ha
Price ratio: Pr,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: Ar,t
• Farmers may take adaptation behaviorsInvest new technology Use ground or surface irrigationAdopt 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 2001-2009
• 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 (2005-2009)
Corn Soybean
Weather stations in China
Daily measures of minimum and maximum temperatures, precipitation and radiation
Results: Impacts of temperature on crop yields
1 1.5 2 2.5 3 3.5
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Log
Yie
ld
Corn
1 1.5 2 2.5 3 3.5
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Log
Yie
ld
Soybean
Model 1
Model 2
Model 3Model 4
Model 5
Sample mean
Model 1
Model 2
Model 3Model 4
Model 5
Sample mean
Corn Soybean
Growing Degree Days (8-32°C) (thousand °C)
The optimal numbers of GDDs: Corn 2300-2700; Soybean 1600-1800
Results: Impacts of precipitation on crop yields (thousand mm)
Corn Soybean0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
Log
Yie
ld
Corn
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
Log
Yie
ld
Soybean
Model 1
Model 2
Model 3Model 4
Model 5
Sample mean
Model 1
Model 2
Model 3Model 4
Model 5
Sample mean
The optimal numbers of precipitation: Corn 74cm; Soybean 54cm
Results: Impacts of solar radiation on crop yields (1000 hours)
Corn Soybean0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.05
0.1
0.15
Log
Yie
ld
Corn
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.05
0.1
0.15
Log
Yie
ld
Soybean
Model 2
Model 3
Model 4Model 5
Sample mean
Model 2
Model 3
Model 4Model 5
Sample mean
The optimal numbers of radiation: Corn 1000-1200 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 $117-250 million in China’s corn and soybean sectors in 2009.
Baselin
e
Distance
matri
x(6-)
Distance
matri
x(4-)
Year f
ixed
effec
t
Non-irrig
ated su
b...
Inter
val GDDs
-100
-50
0
50
100
150
200
250
300
350
Temperature Precipitation Solar Radiation
$ M
illio
n
Climate Change Impacts by Temperature6 IPCC Scenarios
Mid-term (2040-2060)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 (2090-2099)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.
-25
-20
-15
-10
-50
Impa
cts
by 2
040-
2060
(P
erce
nt)
Corn Soybean
B1 B2 A1B A2 A1FI B1 B2 A1B A2 A1FI
-25
-20
-15
-10
-50
Impa
cts
by 2
090-
2099
(P
erce
nt)
Corn Soybean
B1 B2 A1B A2 A1FI B1 B2 A1B A2 A1FI
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 $117-250 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
-1-.
8-.
6-.
4-.
20
.2Im
pact
s on
Cro
p Y
ield
s (P
erce
nt)
-40 -30 -20 -10 0 10 20 30 40Change in Precipitation (Percent)
Corn Soybeans
-1-.
8-.
6-.
4-.
20
.2Im
pact
s on
Cro
p Y
ield
s (P
erce
nt)
-20 -10 0 10 20Change in Radiation (Percent)
Corn Soybeans
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.0
51
0E
xpo
sure
(D
ays
)
-.0
75
-.0
5-.
02
50
.02
5
Lo
g Y
ield
(T
on
/Ha
)
0 5 10 15 20 25 30 35 40
Temperature (Celsius)
Corn
05
10
Exp
osu
re (
Da
ys)
-.0
75
-.0
5-.
02
50
.02
5
Lo
g Y
ield
(T
on
/Ha
)
0 5 10 15 20 25 30 35 40Temperature (Celsius)
Soybeans
-35
-30
-25
-20
-15
-10
-50
Imp
act
s o
n C
rop
Yie
lds
(Pe
rce
nt)
Corn Soybeans
B1 B2 A1B A2 A1FI B1 B2 A1B A2 A1FI
Descriptive Statistics
Variable Mean Minimum Maximum Std. Dev.
Crop yields
Corn yield (MT per ha) 5.19 0.04 16.92 1.95
Soybean yield (MT per ha) 2.15 0.03 10.81 1.03
Weather variables for corn
GDD (8-32°C) (thousand D) 2.12 0.90 3.55 0.34
GDD ( 34°C) (D) 6.33 0 225.22 9.78
Solar radiation (thousand hours) 0.89 0.41 2.08 0.33
Precipitation (thousand mm) 0.57 0.025 2.07 0.28
Weather variables for soybeans
GDD (8-32°C) (thousand D) 2.12 0.67 3.40 0.37
GDD ( 34°C) (D) 6.08 0 104.86 8.61
Solar radiation (thousand hours) 0.90 0.40 2.08 0.33
Precipitation (thousand mm) 0.58 0.026 1.98 0.27
Table 4: Spatial Error Estimations (Dependent Variable: Log Corn Yield)
ModelModel (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) 0.3509*** 0.3703*** 0.3888*** 0.3673*** 0.3646***
(2.84) (2.98) (3.18) (2.95) (2.93)
GDD (8-32°C) squared -0.0824** -0.0871*** -0.0932*** -0.0874*** -0.0868***
(-2.53) (-2.68) (-2.91) (-2.67) (-2.65)
Square root of GDD( 34°C)
-0.0093***
(-2.94)-0.0120***
(-3.66)-0.0113***
(-3.53)-0.0135***
(-4.14)-0.0135***
(-4.15)
Precipitation 0.0900*** 0.0927*** 0.0921*** 0.0968*** 0.0958***
(2.95) (3.02) (3.04) (3.15) (3.13)
Precipitation squared -0.0666*** -0.0653*** -0.0642*** -0.0658*** -0.0657***
(-3.47) (-3.42) (-3.41) (-3.45) (-3.45)
Radiation 0.3165*** 0.3089*** 0.2960*** 0.2996***
(5.17) (5.11) (4.81) (4.87)
Radiation squared -0.1492*** -0.1417*** -0.1373*** -0.1383***
(-5.04) (-4.84) (-4.64) (-4.68)
LUC: marginal acre -0.0051*** -0.0053*** -0.0054***
(-7.68) (-7.88) (-8.01)
LUC: substitution acre -0.0059*** -0.0058*** -0.0059***
(-5.19) (-5.17) (-5.25)
Ratio: corn price/fertilizer price index 0.1568 0.1325
(1.37) (1.15)
Ratio: corn price/wage 0.4818** 0.4742***
(2.09) (2.06)
Irrigation ratio 0.0439***
(3.04)
Spatial correlation 0.3819*** 0.3809*** 0.3729*** 0.3699*** 0.3689***
(37.57) (37.16) (35.73) (35.03) (35.09)
N 16840 16840 16840 16840 16840
R2 0.8087 0.8095 0.8105 0.8110 0.8110
Table 5: Spatial Error Estimations (Dependent Variable: Log Soybean Yield)
ModelModel (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) 0.3942*** 0.3936*** 0.3873*** 0.3417*** 0.3442***
(3.57) (3.59) (3.54) (3.09) (3.14)
GDD (8-32°C) squared -0.1413*** -0.1406*** -0.1396*** -0.1241*** -0.1250***
(-4.57) (-4.56) (-4.53) (-4.02) (-4.05)
Square root of GDD( 34°C)
-0.0007(-0.19)
-0.0028(-0.78)
-0.0028(-0.79)
-0.0046(-1.28)
-0.0044(-1.24)
Precipitation 0.0927*** 0.0946*** 0.0960*** 0.0900** 0.0892**
(2.64) (2.68) (2.73) (2.56) (2.55)
Precipitation squared -0.0783*** -0.0768*** -0.0775*** -0.0770*** -0.0763***
(-3.52) (-3.49) (-3.53) (-3.50) (-3.47)
Radiation 0.2891*** 0.2866*** 0.3111*** 0.3081***
(4.16) (4.14) (4.49) (4.45)
Radiation squared -0.1418*** -0.1399*** -0.1545*** -0.1534***
(-4.26) (-4.21) (-4.64) (-4.62)
LUC: marginal acre -0.0038*** -0.0039*** -0.0039***
(-4.62) (-4.74) (-4.75)
LUC: substitution acre -0.0048*** -0.0047*** -0.0047***
(-2.63) (-2.59) (-2.58)
Ratio: soybean price/fertilizer price index 0.1360*** 0.1419***
(2.77) (2.89)
Ratio: soybean price/wage 0.0771*** 0.0779***
(4.34) (4.39)
Irrigation ratio 0.0190 (1.11)
Spatial correlation 0.2869*** 0.2799*** 0.2819*** 0.2749*** 0.2719***
(25.46) (25.90) (25. 27) (24.93) (24.82)
N 17400 17400 17400 17400 17400
R2 0.8128 0.8132 0.8136 0.8139 0.8139