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.

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Liu Yuan — Crop yields impacted by enso episodes on the north china plain 1956–2006

  1. 1. Crop yields impacted by ENSO episodeson the North China Plain: 1956–2006Yuan LIU            Xiaoguang YANGIEDA, CAAS        CAU7th Nov 2011 IEDA,CAAS CAU CSIRO
  2. 2. Outline• Background• Data and Methods• Results• Summery
  3. 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. 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. 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. 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 inChina 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. 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. 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. 9. Weather and Crop Data Item Nanyang Zhengzhou Luancheng Daily temperatureWeather 1956—2006 Data Daily sunshine hour Daily precipitationCrop Data Grain yield 1956—2006 All data were obtained from China Meteorological Agency and China Statistics Press.
  10. 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. 11. 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 distributions2. 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. 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. 13. Model Calibration Yucheng, 2000-2001 Treatment1,Beijing, 2004-2005 NRMSE: 36% NRMSE: 28% (Li Yan, 2006)
  14. 14. Model Validation Yucheng, 2000-2001 Treatment1,Beijing, 2004-2005 Yield NRMSE: 21%APSIM model can be applied under the monsoon climateconditions in the NCP to simulate the crop growth dynamicsand grain yields of wheat and maize. (Li Yan, 2006)
  15. 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. 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. 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. 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. 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. 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. 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. 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. 23. IEDA, CAAS Yuan LIU Email: you for your attention!