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. Climate Induced Changes in Maize
Potential Productivity in Heilongjiang
Province of China
Wang Xiufen, Institute of Agriculture Resources and
Regional Planning,wangxf@mail.caas.net.cn
YangYanzhao, Institute of Geographical Sciences and
Natural Resources Research
You Fei, Institute of Agriculture Resources and Regional
Planning, yofae@sina.com
Li Wenjuan, Institute of Agriculture Resources and
Regional Planning

2. Outline
1 Background
2 Data and models
3 Results
4 Discussion and Conclusion

3. 1 Background
Global climate change is unequivocal.
Many natural systems are being affected by regional
climate changes, including crop production system.
The likely impacts of climate change on crop production
have been studied widely either by experimental data or
by crop growth simulation models.
However, studies of potential crop production capabilities
affected by climate change in long time series remains
relatively rare.

4. 2 Data and models
Study area

5. Data sources
Meteorological data were obtained from the National
Climatic Centre of the China Meteorological
Administration
The land use map and administrative boundary maps of
Heilongjiang province were collected from Institute of
Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences

6. Models
Climate change analysis : the least squares linear model
xi＝a+bti i=1,2,…,n
xi is one of the climate variables(temperature or precipitation)
ti is the time corresponding to xi
a is constant
b is the regression coefficient
a and b are estimated by the least squares
The positive and negative sign of b represent the change trend
of the climate variable , when b＞0, the climate variable
increase with the time rise, vice versa.
b×10 are the climate tendency rates, units are ℃ per decade or
mm per decade.

7. •Climate change scenarios
Three climate change scenarios used in this study
mean daily temperature increase(℃) mean daily rainfall decrease(%)
Baseline(1980-2009) — —
Scenarios1 0.5 5
Scenarios2 1.0 10
Scenarios3 1.5 15

8. Potential Productivity Model (Agro-Ecological zones Model)
● The Formula for calculating LTPP is as follows:
When ym≥20kg/ha/h，
YT=cL·cN·cH·G·[F(0.8+0.01ym)y0+(1-F)(0.5+0.025ym)yc]
when ym＜20kg/ha/h，
YT=cL·cN·cH·G·[F(0.5+0.025ym)y0+(1-F)(0.05ym)yc]
•On the basis of the calculation of LTPP, the obtained relative yield decrease
factor f(p) is then applied to the calculation of CPP.
● The formula for calculating the CPP is as follows:
YC= YT · f(p)
YC = the climatic potential productivity (CPP) of maize[kg/ha],
YT = the light-temperature potential productivity (LTPP) of maize [kg/ha],
f(p)= precipitation effective coefficient, f(p) is defined as follows:
1－Ky×(1－P/ETm) P＜ETm
f(p) =
１ P＞ETm
Ky = yield response factor, P =effective precipitation, ETm = Kc ×ET0,
Kc=crop coefficient, ET0=Reference Evapotransyiration，ET0 was
calculated from daily ground-based agro-meteorological data substituted into
the Penman-Monteith equation (Allen PG 1998)

9. Main parameters of AEZ model
Symbol Definition Values
cL correction crop development and leaf area 0.5
cN correction for dry matter production, 0.6 for cool and 0.6
0. 5 for warm conditions
cH correction for harvest index 0.45
G total growing period (days) Calculated
F fraction of the daytime the sky is clouded. Calculated
maximum leaf gross dry matter production rate of a
ym crop for a given climate, kg/ha/day Calculated
gross dry matter production of a standard crop for a
y0 given location on a completely overcast (clouded) day, Calculated
kg/ha/day
gross dry matter production rate of a standard crop
yc for a given location on a clear (cloudless) day, Calculated
kg/ha/day
ky yield response factor 1.25
kc crop coefficient 0.825
Reference: Doorenbos J, AH Kassam (1979) Crop Yields Response to Water. FAO Irrigation
and drainage paper No. 33. Food and Agriculture Organization of the United Nations, Rome

10. 3 Results
The climate change during last 30 years in Heilongjiang province
Temporal Change

11. The tendency rate of mean temperature and cumulated precipitation
Mean temperature cumulated precipitation
(℃ per decade) (mm per decade)
Annual 0.55* -23.1**
Maize growing season (May.-Sep.) 0.42* -27.6**
Spring (Mar.-May.) 0.53* 5.62
Summer (Jun.-Aug.) 0.38* -25.09**
Autumn (Sep.-Nov.) 0.45* -12.86*
Winter (Dec.-Feb. of next year) 0.76* 1.23
January 0.86 1.41
February 0.76 0.44
March 0.59 3.43*
April 0.51 -0.60
May 0.53* 1.93
June 0.45 -1.04
July 0.31 -2.84
August 0.17 -14.26
September 0.69* -11.41*
October 0.77* -1.41
November 0.08 -0.37
December -0.02 1.05
* p < 0.05;** p < 0.1.

12. Spatial Change

13. The performance of FAO-AEZ model for regional simulation
LTPP and CPP of Maize in Heilongjiang province from 1980 to 2009

14. The impact of climate change on maize potential productivity
linear linear

15. Response of LTPP and CPP to future climate change scenarios
Simulated LTPP and CPP responses to different climatic scenarios in future
Scenarios Temperature Precipitation LTPP CPP
increase(℃) decrease(%) increase(%) decrease(%)
Scenarios1 0.5 5 7.5 5.0
Scenarios2 1.0 10 13.7 8.1
Scenarios3 1.5 15 23.1 8.7

16. 4 Discussion and Conclusion
Discussion
Our analysis of climate-change impacts in maize potential
productivity only consider daily mean temperature and
precipitation change scenario. Other factors will be considered
in next studies.
The outcome of this presentation will be used to analyze the
contribution rate of climate change to maize production
formation. The preliminary research result showed that the
contribution rate of climate change is lesser.

17. Conclusion
The climate was becoming warm-dry in maize growth period in
Heilongjiang province from 1980 to 2009
The LTPP increased with the increasing trend of mean
temperature, and the CPP decreased with the decreasing trend of
precipitation
The water is the main restricted factor to the maize potential
productivity of Heilongjiang province. If the water is enough,
the climate warming has positive contribution to the maize
production