liu yuan — crop yields impacted by enso episodes on the north china plain 1956–2006
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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.TRANSCRIPT
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Crop yields impacted by ENSO episodes on the North China Plain: 1956–2006
Yuan LIU Xiaoguang YANGIEDA, CAAS CAU7th Nov 2011 IEDA,CAAS CAU CSIRO
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Outline
• Background
• Data and Methods
• Results
• Summery
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Background
• ENSO (El Niño - Southern Oscillation) is the most prominent driver for inter-annual variability of climate around the world, which affects regional cropproduction 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 agriculturalproduction areas in China. There is still lack of detailed studies on therelationship among ENSO, regional climate and crop production in the NCP.
• Well, the linkage of ENSO and crop yield in the NCP will potentially providethe insight to climate change impact on the food security subject in China.
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Objectives
• Case study: winter wheat and summer maize
• to analyse the relationships between ENSO and regional climateanomalies
• to ascertain whether the grain yield of the two main crops in the NCP, isaffected by the different phases of ENSO
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What is ENSO• ENSO is a planetary scale
phenomenon, involving thecoupling of the ocean and theatmosphere.
• SOI and SST have been used asindicator to describe ENSO events.
• The rainfall region is different in ElNiño and La Niña years near theequator.
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ENSO and climate 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 LaNina 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
in the developing stage of ENSO. Shaded areas indicate the coefficient of correlation above 0.4.
(Huang RH and Wu YF, 1989)
Enso precipitation anomalies in relative percentage distribution in ENSO year
(Liu YQ and Ding YH, 1995)
Relationship between ENSO and summer precipitation in China
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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…
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Study Sites
Nanyang : 33.0oN, 112.6oE, 80.0mZhengzhou : 34.8oN, 113.7oE, 129.2mLuancheng : 37.9oN, 114.6oE, 52.5m
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Weather and Crop Data
Item Nanyang Zhengzhou Luancheng
WeatherData
Daily temperature1956—2006Daily sunshine hour
Daily precipitationCrop Data Grain yield 1956—2006
All data were obtained from China Meteorological Agency and China Statistics Press.
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Categorization of ENSOWarm event years
(El Niño) Neutral years Cold event years(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).
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Statistical Analysis1. 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
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Manager Report
Soil pH
Environmentalsection
Ecologicalsection
Crop
Crop A
Crop B
¦¦
Surface residue
Soil water
Soil N
Soil P
Soil erosion
Meteorological data
ENGINE
Pasture
3. APSIM modelAPSIM = (Agricultural Production Systems Simulator)Structure: Processes represented as modules
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Model CalibrationYucheng, 2000-2001 Treatment1,Beijing, 2004-2005
NRMSE: 36% NRMSE: 28%
(Li Yan, 2006)
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Model ValidationYucheng, 2000-2001 Treatment1,Beijing, 2004-2005
Yield
NRMSE: 21%
(Li Yan, 2006)
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.
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Simulated Scenario
• The validated APSIM model was used to simulate the growth andgrain yield of wheat and maize crops using historical climate data(1956-2006)
• One variety of wheat and maize (from 1981) was used for thesimulations during the whole period to eliminate impact of varietalchanges
• Potential/Rain-fed yields simulated (different of irrigation regimes)were conducted under conditions of fully water and fertilizer supplyto eliminate impact of water and nutrient stresses
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Climatic Background
Red nodes are for El Niño event, blue nodes are for La Nina event, black for normal year.
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.
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Climate Trends on ENSO phasesClimatic
Parameters PhasesNanyang Zhengzhou Luancheng
Trend Trend Trend
P
El Niño ‐22 ‐22 ‐8
Neutral 8 6 11
La Niña ‐57* ‐47 ‐53
SH
El Niño 1.4 3.1 5.3
Neutral ‐0.9 ‐1.3 ‐1.3
La Niña 6.9 5.5 7.1
T_avg
El Niño 2.6 3.0 2.1
Neutral ‐1.3 ‐1.2* ‐1.4
La Niña 6.6* 7.5* 9.7
T_max
El Niño 2.3 2.8 0.6
Neutral ‐1.3 ‐1.3* ‐1.2
La Niña 9.4* 10.3 10.9
T_min
El Niño 1.6 3.7 5.9
Neutral ‐1.2 ‐1.1 ‐1.4
La Niña 1.0 2.1 4.6
%/decadee.g. Precipitation
The 5 climatic variables had the sametrend in both El Nino and La Nino year.
Precipitation had declined in both Ell Nino and La Nina year and increased in Neutral year.
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Probability of Climatic parameters on ENSO yearsP
roba
bilit
y of
exc
eeda
nce
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.
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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,
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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.
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Probability of statistical yields on ENSO years
At the provincial level, thecategories had little impact onactual yields in well-managedfields.Maize production was morevulnerable in El Nino and LaNina years than wheatproduction was.
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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 annualprecipitation in both El Niño and La Niña years, while a trend ofincrease in neutral years. In general, the probability ofexceeding certain amount of rainfall was higher in La Niñayears than in El Niño years
• ENSO events affect maize yield more than wheat yield,particularly under conditions of insufficient irrigation watersupply.
• The yield was lower in El Nino and La Nina years because oflower precipitation and higher in the Neutral year because oflonger sunshine hours and additional irrigation.