Post on 25-Dec-2014
description
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, PhDInst. of Agricultural Resources and Regional
Planning, CAAS
Outline
• Background• Conceptual Model• Data and methodology• Results• Discussion
Background (1)• China 973 project (National Basic
Research Program of China): Impact ofClimate Change on Grain Production inChina (2010CB951502-04)
• Purpose : to develop a new approach to identify climate change impact and its share of contribution which shapes grainproduction
• Start point: Paddy rice, Jiangxi province
Location of the studied province
•An inlandprovince•An main riceproducer•3 harvestper year
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 riceproducers in China
Conceptual model
Data source
• National Meteorological Information Centre
• Official statistics of Jiangxi province• National Geo-database
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
Sources: Wenjuan Li et al. Attractive Vicinities, Population, Space and Place 15, 1–18 (2009) DOI: 10.1002/psp.505
Calculating eta square
Link spatial data with statistic data
Totally 1720 5km*5kmsquares (paddy land)50 years data(1960-2009)
Average temperature andprecipitation duringpaddy rice growingseason
Statistic data
A data table with 1720*5rows, 11 variables
Average temperature and precipitationduring paddy season(April-October)
Interpolation based on meteo data
60s 70s 80s 90s 2010s
Precipitation
Average temperature
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
Results: full modelANOVAb
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
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
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
Results: partial modelANOVAb
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
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
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
Results
η2 = 0.1938
Meaning: climate variables contribute about 2percent to rice production in Jiangxi Province.
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?
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
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