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. How climate change
matters 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. Outline
• Background
• Conceptual Model
• Data and methodology
• Results
• Discussion

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. Location of the studied province
•An inland
province
•An main rice
producer
•3 harvest
per year

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. Conceptual model

7. Data source
• National Meteorological Information
Centre
• Official statistics of Jiangxi province
• National Geo-database

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. • 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. Calculating eta square
Sources: Wenjuan Li et al. Attractive Vicinities, Population, Space and
Place 15, 1–18 (2009) DOI: 10.1002/psp.505

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. Average temperature and precipitation
during paddy season(April-October)
Interpolation based on meteo data
Precipitation
60s 70s 80s 90s 2010s
Average temperature

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. 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 Coefficients
Model 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. 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 Coefficients
Model 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. Results
η2 = 0.1938
Meaning: climate variables contribute about 2
percent to rice production in Jiangxi Province.

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. 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. 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