The Chinese Academy of Agricultural Sciences (CAAS) and the International Food Policy Research Institute (IFPRI) jointly hosted the International Conference on Climate Change and Food Security (ICCCFS) November 6-8, 2011 in Beijing, China. This conference provided a forum for leading international scientists and young researchers to present their latest research findings, exchange their research ideas, and share their experiences in the field of climate change and food security. The event included technical sessions, poster sessions, and social events. The conference results and recommendations were presented at the global climate talks in Durban, South Africa during an official side event on December 1.

1. Crop yields impacted by ENSO episodes
on the North China Plain: 1956–2006
Yuan LIU Xiaoguang YANG
IEDA, CAAS CAU
7th Nov 2011 IEDA,CAAS CAU CSIRO

2. Outline
• Background
• Data and Methods
• Results
• Summery

3. Background
• ENSO (El Niño - Southern Oscillation) is the most prominent driver for inter-
annual variability of climate around the world, which affects regional crop
production through its impacts on regional climate.
• Climate anomalies: drought, flooding and hurricanes
• Agriculture worldwide: Plants, Fishing, Agricultural economics
• The North China Plain (NCP) (350,000 km2) is one of the largest agricultural
production areas in China. There is still lack of detailed studies on the
relationship among ENSO, regional climate and crop production in the NCP.
• Well, the linkage of ENSO and crop yield in the NCP will potentially provide
the insight to climate change impact on the food security subject in China.

4. Objectives
• Case study: winter wheat and summer maize
• to analyse the relationships between ENSO and regional climate
anomalies
• to ascertain whether the grain yield of the two main crops in the NCP, is
affected by the different phases of ENSO

5. What is ENSO
• ENSO is a planetary scale
phenomenon, involving the
coupling of the ocean and the
atmosphere.
• SOI and SST have been used as
indicator to describe ENSO events.
• The rainfall region is different in El
Niño and La Niña years near the
equator.

6. ENSO and climate in China
Relationship between ENSO and summer precipitation in China
• ENSO is a significant climatic signal to the summer precipitation fluctuation in the NCP;
• Rainfall tends to be low in El Nino years in the region, the reverse pattern is occur in La
Nina years
• Temperature response is not as strong as rainfall
Is it completely opposite conditions in the El Niño and La Niña years?
The correlation between the summer rain fall in
China and the SST in the eastern equatorial Pacific Enso precipitation anomalies in relative percentage
in the developing stage of ENSO. Shaded areas distribution in ENSO year
indicate the coefficient of correlation above 0.4. (Liu YQ and Ding YH, 1995)
(Huang RH and Wu YF, 1989)

7. ENSO and yield in the world
• ENSO also negatively or positively impacted
the crop yield in the world:
• Negative: maize in Zimbabwe,
rice in the Philippines and Indonesia,
cereals production in Indian…
• Positive: corn in U.S.,
sorghum and soybean in Argentina…

8. Study Sites
Nanyang : 33.0oN, 112.6oE, 80.0m
Zhengzhou : 34.8oN, 113.7oE, 129.2m
Luancheng : 37.9oN, 114.6oE, 52.5m

9. Weather and Crop Data
Item Nanyang Zhengzhou Luancheng
Daily temperature
Weather
1956—2006
Data Daily sunshine hour
Daily precipitation
Crop Data Grain yield 1956—2006
All data were obtained from China Meteorological Agency and China Statistics Press.

10. Categorization of ENSO
Warm event years Cold event years
Neutral years
(El Niño) (La Niña)
1957 1958 1984 1956
1963 1959 1985 1964
1965 1960 1989 1967
1969 1961 1990 1970
1972 1962 1992 1971
1976 1966 1993 1973
1982 1968 1994 1975
1986 1974 1995 1988
1987 1977 1996 1998
1991 1978 2000 1999
1997 1979 2001
2002 1980 2003
2006 1981 2004
1983 2005
Based on Japan Meteorological Agency (JMA).

11. Statistical Analysis
1. Trend analysis:
• using Student’s t-test to examine the slope confidence (statistically significant at 95%
and 99% levels)
• using Kolmogorov–Smirnov (KS) test to examine the contrasting distributions
2. Data Standardization:
• removing such non-climate-related influences as improved varieties, better
management, more irrigation, and higher doses of fertilizers introduced since 1956.
Raw (Grey dots) and smoothed (black line) time series (a) and the detrended residual (b) of wheat yield around whole China, 1954-2008

12. 3. APSIM model
APSIM = (Agricultural Production Systems Simulator)
Structure: Processes represented as modules
Ecological Manager Report
Environmental
section
section
Crop
Soil pH
Crop A
Soil water
Crop B E
Soil N
N
￤￤
G Soil P
Pasture I
N Soil erosion
Surface residue
E
Meteorological data

13. Model Calibration
Yucheng, 2000-2001 Treatment1,Beijing, 2004-2005
NRMSE: 36% NRMSE: 28%
(Li Yan, 2006)

14. Model Validation
Yucheng, 2000-2001 Treatment1,Beijing, 2004-2005
Yield
NRMSE: 21%
APSIM model can be applied under the monsoon climate
conditions in the NCP to simulate the crop growth dynamics
and grain yields of wheat and maize.
(Li Yan, 2006)

15. Simulated Scenario
• The validated APSIM model was used to simulate the growth and
grain yield of wheat and maize crops using historical climate data
(1956-2006)
• One variety of wheat and maize (from 1981) was used for the
simulations during the whole period to eliminate impact of varietal
changes
• Potential/Rain-fed yields simulated (different of irrigation regimes)
were conducted under conditions of fully water and fertilizer supply
to eliminate impact of water and nutrient stresses

16. Climatic Background
Annual precipitation had
slightly declined over the
past 50yr without significant.
Warming trends were
occurred , especially
significant increase in
minimum temperature
after 1980.
Sunshine hour had
decreased significantly at
three sites.
Red nodes are for El Niño event, blue nodes are for La Nina event, black for normal year.

17. Climate Trends on ENSO phases
%/decadee.g. Precipitation
Climatic Nanyang Zhengzhou Luancheng
Phases
Parameters Trend Trend Trend
El Niño ‐22 ‐22 ‐8
P Neutral 8 6 11
La Niña ‐57* ‐47 ‐53
El Niño 1.4 3.1 5.3
SH Neutral ‐0.9 ‐1.3 ‐1.3
La Niña 6.9 5.5 7.1
El Niño 2.6 3.0 2.1
T_avg Neutral ‐1.3 ‐1.2* ‐1.4
La Niña 6.6* 7.5* 9.7
El Niño 2.3 2.8 0.6
T_max Neutral ‐1.3 ‐1.3* ‐1.2
La Niña 9.4* 10.3 10.9
El Niño 1.6 3.7 5.9
T_min Neutral ‐1.2 ‐1.1 ‐1.4
La Niña 1.0 2.1 4.6
Precipitation had declined in both Ell Nino and
The 5 climatic variables had the same
La Nina year and increased in Neutral year.
trend in both El Nino and La Nino year.

18. Probability of exceedance
Probability of Climatic parameters on ENSO years
Probability exceedance of precipitation in La Niña years are higher than the El
Nino phases, the Neutral years had the similar trends with total years.

19. Probability of potential yields on ENSO years
Under full of water
regime in crop growth,
there was no significant
difference in both El
Niño and La Niña years
and even Neutral years,

20. Probability of rainfed yields on ENSO years
Under no irrigation regime in
the crop growth, for wheat the
probability is lower in La Niña
years than that in El Niño
years at Nanyang and
Zhengzhou sites.
For maize, the probability in
El Niño years is lower than
other phases at Luancheng
and Zhengzhou.

21. Probability of statistical yields on ENSO years
At the provincial level, the
categories had little impact on
actual yields in well-managed
fields.
Maize production was more
vulnerable in El Nino and La
Nina years than wheat
production was.

22. Summary
• With all the years together over the past 50 years, no significant
trend in annual climatic variables at three sites.
• But there seems to be a trend of decrease in annual
precipitation in both El Niño and La Niña years, while a trend of
increase in neutral years. In general, the probability of
exceeding certain amount of rainfall was higher in La Niña
years than in El Niño years
• ENSO events affect maize yield more than wheat yield,
particularly under conditions of insufficient irrigation water
supply.
• The yield was lower in El Nino and La Nina years because of
lower precipitation and higher in the Neutral year because of
longer sunshine hours and additional irrigation.

23. IEDA, CAAS
Yuan LIU
Email: Liuyuan@ieda.org.cn
Thank you for your attention!