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

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

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  Climate Induced Changesin Maize Potential Productivity in Heilongjiang Province of China Wang Xiufen, Institute of Agriculture Resources and Regional Planning,[email protected] YangYanzhao, Institute of Geographical Sciences and Natural Resources Research You Fei, Institute of Agriculture Resources and Regional Planning, [email protected] Li Wenjuan, Institute of Agriculture Resources and Regional Planning
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  Outline 1 Background 2 Data and models 3 Results 4 Discussion and Conclusion
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  1 Background Global climatechange 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.
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  2 Data andmodels Study area
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  Data sources Meteorological datawere 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
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  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.
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  •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
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  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)
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  Main parameters ofAEZ 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
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  3 Results The climatechange during last 30 years in Heilongjiang province Temporal Change
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  The tendency rateof 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.
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  Spatial Change
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  The performance ofFAO-AEZ model for regional simulation LTPP and CPP of Maize in Heilongjiang province from 1980 to 2009
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  The impact ofclimate change on maize potential productivity linear linear
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  Response of LTPPand 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
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  4 Discussion andConclusion 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.
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  Conclusion The climate wasbecoming 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