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Climate Change Impact Assessment on Maize Production in Jilin, China

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Climate Change Impact Assessment on Maize Production in Jilin, China Climate change impact assessment on maize production in Jilin, China Meng Wang, Wei Ye and Yinpeng Li 1 Backgrounds APN CAPaBLE project with focus on integrated system development for food security assessment Bio-physical & Economic Uncertainties: e.g. GCMs, CO2 emission scenarios Adaptation measures (cross multi-scales) 2 SimCLIM model Greenhouse gas MAGICC emission scenarios Data Global Climate Projection Scenario selections Climate and GCM pattern import Local Climate toolbox average, variability, extremes IPCC CMIP (GCMs) (present and future) USER -Synthetic changes - GCM patterns “Plug-in” Models Biophysical Impacts on: Agriculture, Coastal, - Land data Human Health, Water - Other spatial data Impact Model 3 Case Study: Jilin Province 4 Climate Scenario Baseline Climate CRU global climatology dataset, 1961-1990 (New, 2000) Climate change scenarios • Pattern scaling (Santer, 1990; Mitchell, 2003) • 20 GCMs change patterns (Covey et al., 2003) • 6 SRES emission scenarios (IPCC, 2000) 5 DSSAT model – to simulate maize growth CERES-Maize model (Jones, 1986) • Site-based, daily time step • Input – weather, soil, cultivating strategies, cultivar parameters • Output – yield, phenological parameters (e.g. growing season, growing phase date), etc. 6 DSSAT – weather generator SIMMETEO (Geng & Auburn, 1986) • Input – monthly Tmax, Tmin, Rs, Prec. • Random seed sensitive 9.5 Ensemble 1 (b) 8.5 Ensemble 2 ) Ensemble 3 -1 7.5 Ensemble 4 6.5 Yield (t ha Yield (t 5.5 4.5 3.5 0 20 40 60 80 100 120 Random seed So, the average result of 100-seed simulations 7 Climate change projections Precipitation (mm) Baseline 2080 ensemble 8 Climate change projections Mean temperature (degC) Baseline 2080 ensemble 9 DSSAT – Soil ISRIC-WISE soil dataset (Batjes, 2006) • 5 × 5 minutes • 14 FAO soil categories 10 DSSAT – cultivating strategies Scheme Planting manner density: 6 plants m-2 date: controlled by soil temperature Fertilization nitrogen fertilizer (kg ha-1) applied by county Irrigation controlled by the total irrigation quota (350mm) and frequency 11 Calibration – cultivar parameters The initial ranges of 5 gene coefficients (P 1, P 2, P 5, G 2, G3 ) Produce 30 trial geno-types by the uniform design method Cultivar parameters P1, P2, P5 Yield Simulation G2, G3 Calculate Ie ; Find out the genotypes with the two smallest Ie s The i -th ranges of gene coefficients 11-site observations If (the differences between the ranges of these two genotypes are smaller than 5% of the initial No yield, upper range) then sowing date, Yes harvest date The final range of gene coefficients The coefficients are the average of the final range 12 Calibration – cultivar selection Late Early P1 280 270 Late P2 0.3 0.3 Early P5 790 700 G2 720 720 G3 8.5 8.5 13 Calibration – 11 sites (1996- 2000) Site Cultivar Ysim/Yobs Msim/Mobs Changling Late 0.55 0.94 Nong’an Late 0.92 1.03 Yushu Late 0.87 1.06 Lishu Late 1.05 1.05 Ji’an Late 0.94 0.96 Shulan Early 0.87 1.06 Yongji Early 0.99 0.95 Dunhua Early 1.23 1.13 Liaoyuan Early 1.05 1.01 Meihekou Early 0.94 0.94 Huadian Early 0.90 1.00 14 Validation – Compare with the county census yield (1990-2002) Average Standard deviation a b 15 Climate scenarios Climate change in Jilin Province • Growing season (Apr.-Sept.) • Temp. • Prec. 25 1000 Prec (mm) ) 20 800 ℃ • Prec/PET 15 600 10 400 5 200 0 0 Baicheng SongyuanChangchun Siping Jilin Liaoyuan Baishan Tonghua Yanji 2.0 1.6 1.2 P/PET=1.0 P/PET Temp ( P/PET 0.8 0.4 0 Baicheng SongyuanChangchun Siping Jilin Liaoyuan Baishan Tonghua Yanji P/PET Prec Temp 16 Results – spatial pattern of yield change Baseline 2020 2050 2070 17 Results – uncertainties among scenarios 8 8 Baicheng Jilin Baishan 7 7 6 6 5 5 4 4 3 3 2 2 1 2020s 2050s 2070s 2020s 2050s 2070s 2020s 2050s 2070s 1 0 0 ) 8 8 -1 Songyuan Liaoyuan Tonghua 7 7 6 6 5 5 4 4 Yield (t ha (t Yield 3 3 2 2 1 2020s 2050s 2070s 2020s 2050s 2070s 2020s 2050s 2070s 1 0 0 8 8 Changechun Siping Yanji 7 7 6 6 5 5 4 4 3 3 2 2 1 2020s 2050s 2070s 2020s 2050s 2070s 2020s 2050s 2070s 1 0 0 A1B A1FI A1T A2 B1 B2 Baseline Median 18 Results – the probability of yield reduction 5 10 15 20 30 40 50 5 10 15 20 30 40 50 100% 100% 80% Baicheng Jilin 80% 60% 60% 40% 40% 20% 20% 0% 0% 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 100% 100% 80% Songyuan Liaoyuan 80% 60% 60% 2020 Probability 40% 40% 20% 20% 2050 0% 0% 2070 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 100% 100% 80% Changechun Siping 80% 60% 60% 40% 40% 20% 20% 0% 0% 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 Yield reduction % 19 Results – changes in sowing date Baseline 2020 2050 2070 20 Results – changes in flowering date Baseline 2020 2050 2070 21 Results – changes in growing season Baseline 2020 2050 2070 22 Results – yield and growing phase 8 160 8 200 Baicheng Dunhua 7 140 7 180 47 160 6 34 32 120 6 22 28 60 30 29 140 5 100 5 120 4 43 80 4 71 68 61 100 44 41 41 54 3 60 3 80 Yield (t ha-1)Yield (t ha-1)Yield Duration (days) 60 Duration (days) 2 40 2 40 54 59 56 53 66 67 70 66 1 20 1 20 0 0 0 0 Baseline 2020s 2050s 2070s Baseline 2020s 2050s 2070s 8 160 8 200 Tongyu Huadian 7 140 7 180 160 6 34 120 6 33 30 28 140 30 37 51 65 5 100 5 120 4 43 43 43 43 80 4 100 65 3 60 3 61 55 53 80 Yield (t ha-1)Yield (t ha-1)Yield Duration (days) 60 Duration (days) 2 40 2 58 56 56 54 40 1 20 1 51 50 51 49 20 0 0 0 0 Baseline 2020s 2050s 2070s Baseline 2020s 2050s 2070s Sowing - Tasseling begins Tasseling begins - Filling begins Filling begins - Filling ends Auto Irr Control Irr 23 Results – adaptation analysis: irrigation 10 Baicheng 8 ) -1 6 Baseline 4 Yield (t ha (t Yield 2 2020 2050 2070 0 350 450 550 650 750 350 450 550 650 750 350 450 550 650 750 Irrigation quota (mm) 10 Tongyu 8 ) -1 Baseline 6 4 Yield (t ha (t Yield 2 2020 2050 2070 0 350 450 550 650 750 350 450 550 650 750 350 450 550 650 750 Irrigation quota (mm) 25-75th Percentile 10-90th percentile Median 24 Conclusion Impacts on maize yield west & central areas eastern mountain areas Impacts on phenology shortened growing season in the west and central (especially the reduced grain filling phase) Adaptation measures Increase in total irrigation (20- 50 years) New cultivar with longer growing season (>50 years) 25 Thank You 26.
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