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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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Climate Induced Changes in Maize Potential Productivity in Heilongjiang Province of China
Wang Xiufen, Institute of Agriculture Resources and Regional Planning,[email protected], Institute of Geographical Sciences and Natural Resources ResearchYou Fei, Institute of Agriculture Resources and Regional Planning, [email protected] Wenjuan, Institute of Agriculture Resources and Regional Planning
Outline
Background1
Data and models2
Results3
Discussion and Conclusion4
1 BackgroundGlobal 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.
2 Data and modelsStudy area
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
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 xia is constantb is the regression coefficienta and b are estimated by the least squaresThe 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.
•Climate change scenarios
mean daily temperature increase(℃) mean daily rainfall decrease(%)
Baseline(1980-2009) — —
Scenarios1 0.5 5Scenarios2 1.0 10Scenarios3 1.5 15
Three climate change scenarios used in this study
Potential Productivity Model (Agro-Ecological zones Model)
•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:
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)
● 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]
f(p) =1 P>ETm1-Ky×(1-P/ETm) P<ETm
Main parameters of AEZ modelSymbol Definition Values
cL correction crop development and leaf area 0.5
cN correction for dry matter production, 0.6 for cool and0. 5 for warm conditions 0.6
cH correction for harvest index 0.45G total growing period (days) CalculatedF fraction of the daytime the sky is clouded. Calculated
ym maximum leaf gross dry matter production rate of acrop for a given climate, kg/ha/day Calculated
y0gross dry matter production of a standard crop for agiven location on a completely overcast (clouded) daykg/ha/day
, Calculated
ycgross dry matter production rate of a standard cropfor a given location on a clear (cloudless) daykg/ha/day
, Calculated
ky yield response factor 1.25kc 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
The climate change during last 30 years in Heilongjiang province
3 Results
Temporal Change
* p < 0.05;** p < 0.1.
The tendency rate of mean temperature and cumulated precipitation
Mean temperature (℃ per decade)
cumulated precipitation (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
Spatial Change
The performance of FAO-AEZ model for regional simulation
LTPP and CPP of Maize in Heilongjiang province from 1980 to 2009
The impact of climate change on maize potential productivity
linear linear
Response of LTPP and CPP to future climate change scenarios
Simulated LTPP and CPP responses to different climatic scenarios in future
Scenarios Temperaturincrease(℃
e )
Precipitation decrease(%)
LTPP increase(%)
CPP 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
4 Discussion and Conclusion
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.
Discussion
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
Conclusion