Li Wenjuan — How climate change matters to our rice bowl

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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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Li Wenjuan — How climate change matters to our rice bowl

  1. 1. How climate changematters to our rice-bowl? Analysis on climate impact and its share of contribution to paddy rice production in Jiangxi, China Li, Wenjuan, PhD Inst. of Agricultural Resources and Regional Planning, CAAS
  2. 2. Outline• Background• Conceptual Model• Data and methodology• Results• Discussion
  3. 3. Background (1)• China 973 project (National Basic Research Program of China): Impact of Climate Change on Grain Production in China (2010CB951502-04)• Purpose : to develop a new approach to identify climate change impact and its share of contribution which shapes grain production• Start point: Paddy rice, Jiangxi province
  4. 4. Location of the studied province •An inland province •An main rice producer •3 harvest per year
  5. 5. Background(2)• China has been the largest rice producer all over the world since 1961.• The rice production in China accounts nearly 30 percent of world rice production (FAO 2011).• In China paddy rice accounts 37 percent total grain production while only 27 percent grain planting area• Jiangxi Province is one of the biggest rice producers in China
  6. 6. Conceptual model
  7. 7. Data source• National Meteorological Information Centre• Official statistics of Jiangxi province• National Geo-database
  8. 8. Methodology• Link spatial dataset with statistic data• Rice production model• Y = rice production per 5km*5 km square• 10 X variables – average temperature of paddy season, total precipitation of paddy season, cultivated land area, agri-machinary, chemical fertilizer, agri- electricity, machine ploughed farming land, population, agricultural population (purchase price of rice, techn)• Full model and partial model
  9. 9. • OLS models (Full and partial models) Y=a+b1x1 +…+bixi• Partial F test – to test if a single X variable gives a significant contribution in the model• η2 -- the explanatory power of X variable (s) to the Y variable 9
  10. 10. Calculating eta squareSources: Wenjuan Li et al. Attractive Vicinities, Population, Space andPlace 15, 1–18 (2009) DOI: 10.1002/psp.505
  11. 11. Link spatial data with statistic data Totally 1720 5km*5km squares (paddy land) 50 years data(1960-2009) Average temperature and precipitation during paddy rice growing season Statistic data A data table with 1720*5 rows, 11 variables
  12. 12. Average temperature and precipitation during paddy season(April-October) Interpolation based on meteo data Precipitation 60s 70s 80s 90s 2010s Average temperature
  13. 13. Results• Full model (with climate factors) – R square = 0.885 – Adjust R square = 0.885• Partial Model (with out climate factors) – R square = 0.868 – Adjust R square = 0.868
  14. 14. Results: full model ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.290E9 10 1.290E8 6.151E3 .000a Residual 1.677E8 7995 20971.665 Total 1.458E9 8005 Coefficientsa Standardized Unstandardized Coefficients CoefficientsModel B Std. Error Beta t Sig.1 (Constant) -1871.547 54.275 -34.483 .000 水稻生长其均温60年代 .449 .018 .161 24.261 .000 水稻生长期60年代均温 4.860 .141 .198 34.564 .000 年末耕地面积公顷 .268 .006 .290 45.205 .000 农业机械总动力万瓦特 9.287E-7 .000 .048 9.958 .000 化肥施用折纯量吨 .331 .018 .249 18.319 .000 农村用电量万千瓦小时 .000 .000 -.491 -42.161 .000 机耕面积千公顷 .557 .009 .600 59.145 .000 化肥施用量实物量吨 .054 .006 .130 8.419 .000 总人口万人 .018 .002 .382 11.363 .000 农业人口万人 -.004 .002 -.074 -2.347 .019
  15. 15. Results: partial model ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.265E9 8 1.581E8 6.560E3 .000a Residual 1.927E8 7997 24102.115 Total 1.458E9 8005 Coefficientsa Standardized Unstandardized Coefficients CoefficientsModel B Std. Error Beta t Sig.1 (Constant) -136.929 7.824 -17.501 .000 年末耕地面积公顷 .227 .006 .246 41.034 .000 农业机械总动力万瓦特 9.668E-7 .000 .049 9.828 .000 化肥施用折纯量吨 .420 .019 .315 22.494 .000 农村用电量万千瓦小时 .000 .000 -.513 -42.213 .000 机耕面积千公顷 .547 .010 .590 54.198 .000 化肥施用量实物量吨 .050 .007 .119 7.194 .000 总人口万人 .010 .002 .211 5.927 .000 农业人口万人 .006 .002 .116 3.477 .001
  16. 16. Results η2 = 0.1938Meaning: climate variables contribute about 2percent to rice production in Jiangxi Province.
  17. 17. Discussion• How to view the 2 percent contribution share?• Is the 2 percent contribution independent or interactive?• How to identify independent contribution from interactive contribution?• Does climate change really threatens our rice-bowl?
  18. 18. Next step…• Contribution effect: independent contribution of each variable• For identifying independent contribution of one climate factor, one partial model is needed in which the variable is excluded. η2t = effect t• When identifying the contribution of two variables, t and p, three partial models are needed. One is a partial model excluding group t; another is excluding group p and the third is excluding group t and p. η2 t+p = effect t + effect p + effect t+p
  19. 19. Thanks to my team members• Dr. You Fei, IARRP, CAAS• Dr. Liu Xiumei, Jiangxi Academy of Agricultural Sciences• Mr. Ji Jianhua, Jiangxi Academy of Agricultural Sciences• Mr. Chen Changli, Inst. Of Crop Science, CAAS• Dr. Wang Xiufen, IARRP, CAAS

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