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