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Wang Xiufen — Climate induced changes in maize potential productivity in heilongjiang province of china
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Wang Xiufen — Climate induced changes in maize potential productivity in heilongjiang province of china

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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 …

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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  • 1. Climate Induced Changes in MaizePotential Productivity in HeilongjiangProvince of ChinaWang Xiufen, Institute of Agriculture Resources andRegional Planning,wangxf@mail.caas.net.cnYangYanzhao, Institute of Geographical Sciences andNatural Resources ResearchYou Fei, Institute of Agriculture Resources and RegionalPlanning, yofae@sina.comLi Wenjuan, Institute of Agriculture Resources andRegional Planning
  • 2. Outline1 Background2 Data and models3 Results4 Discussion and Conclusion
  • 3. 1 BackgroundGlobal climate change is unequivocal.Many natural systems are being affected by regionalclimate changes, including crop production system.The likely impacts of climate change on crop productionhave been studied widely either by experimental data orby crop growth simulation models.However, studies of potential crop production capabilitiesaffected by climate change in long time series remainsrelatively rare.
  • 4. 2 Data and modelsStudy area
  • 5. Data sourcesMeteorological data were obtained from the NationalClimatic Centre of the China MeteorologicalAdministrationThe land use map and administrative boundary maps ofHeilongjiang province were collected from Institute ofGeographic Sciences and Natural Resources Research,Chinese Academy of Sciences
  • 6. ModelsClimate 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 trendof the climate variable , when b>0, the climate variableincrease with the time rise, vice versa.b×10 are the climate tendency rates, units are ℃ per decade ormm 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 decreasefactor 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>ETmKy = yield response factor, P =effective precipitation, ETm = Kc ×ET0,Kc=crop coefficient, ET0=Reference Evapotransyiration,ET0 wascalculated from daily ground-based agro-meteorological data substituted intothe Penman-Monteith equation (Allen PG 1998)
  • 9. 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 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 0.825Reference: Doorenbos J, AH Kassam (1979) Crop Yields Response to Water. FAO Irrigationand drainage paper No. 33. Food and Agriculture Organization of the United Nations, Rome
  • 10. 3 ResultsThe 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 scenariosSimulated 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 ConclusionDiscussionOur analysis of climate-change impacts in maize potentialproductivity only consider daily mean temperature andprecipitation change scenario. Other factors will be consideredin next studies.The outcome of this presentation will be used to analyze thecontribution rate of climate change to maize productionformation. The preliminary research result showed that thecontribution rate of climate change is lesser.
  • 17. ConclusionThe climate was becoming warm-dry in maize growth period inHeilongjiang province from 1980 to 2009The LTPP increased with the increasing trend of meantemperature, and the CPP decreased with the decreasing trend ofprecipitationThe water is the main restricted factor to the maize potentialproductivity of Heilongjiang province. If the water is enough,the climate warming has positive contribution to the maizeproduction

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