Climate change and Indo-Gangetic Basin (IGB)

Transcription

1 Assessment of Climate sensitivity to present production system and evaluation of use of adaptation strategies to improve the livelihood security of small and marginal farmers of Indo- Gangetic Plains of India A multi-crop-climateeconomic modeling approach N. Subash et al

2 Climate change and Indo-Gangetic Basin (IGB) Ø Rice-wheat system is spread over 13.5 mha in Indo- Gangetic Plains of South Asia Ø Climate change impacts are increasingly visible in IGB with greater variability of the monsoon Ø Increase in the occurrence of extreme weather events such as heat waves and intense/unseasonal rainfall that affect agricultural production drastically and thereby the food security and livelihoods of many small and marginal farmers, particularly in the more stress-prone regions of the central and eastern IGB Ø If current trends continue until 2050, the yields of irrigated crops in IGB are projected to decrease significantly maize by 17 %, wheat by 12 % and rice by 10 % - as a result of climate change induced water stress (ADB, 2009)

3 Indo-Gangetic Basin Food Basket of South Asia Low productivity (Rice-Wheat 4-5 t/ha) Poor investment in infrastructure Medium-high precipitation ( to > 2000 mm) High potential for cold water fisheries and livestock Degradation of Land and water resources Low human capital - high out-migration Downstream environmental constraints

4 Low Productivity (R-W:4-8 t/ha) - Food deficit region Low investment in infrastructure Medium - High rainfall ( to > 2000 mm) Underutilization of ground water (< 20 %) Less developed irrigation network High risk of flooding, poor drainage and moderate drought Out-migration of laborers High Productivity (R-W: 8-12 t/ha) - Food surplus region High investment in infrastructure Higher inputs of agro-chemicals Low - Medium rainfall ( to mm) Over exploitation of ground water (>80 %) Well developed irrigated network Severe to moderate drought prone areas In-migration of labour

5 Scale down to 23 GCMs % rainfall as the criteria to remove biased GCMs, Meerut A = ACCESS1-0 B = bcc-csm1-1 C = BNU-ESM D = CanESM2 E = CCSM4 F = CESM1-BGC G = CSIRO-Mk3-6-0 H = GFDL-ESM2G I = GFDL-ESM2M J = HadGEM2-CC K = HadGEM2-ES L = inmcm4 M = IPSL-CM5A-LR N = IPSL-CM5A-MR GCM Selection 29 GCMs Used O = MIROC5 P = MIROC-ESM Q = MPI-ESM-LR R = MPI-ESM-MR S = MRI-CGCM3 T = NorESM1-M U = FGOALS-g2 V = CMCC-CM W = CMCC-CMS X = CNRM-CM5 Y = HadGEM2-AO Z = IPSL-CM5B-LR 1 = GFDL-CM3 2 = GISS-E2-R 3 = GISS-E2-H

6 GCM selection RCP4.5 & RCP8.5 Mid-term century ( ) 6

7 RCP4.5 Mean monthly/growing seasonal mean maximum temperature (RCP4.5 & ) of selected GCMs compared to baseline ( ) RCP8.5 Rice Wheat Annual Baseline M hot/wet S cool/wet L cool/dry Q hot/dry Y Middle C hot/wet T cool/wet P cool/dry Q hot/dry A Middle

8 RCP4.5 Mean monthly/growing seasonal mean minimum temperature (RCP ) of selected GCMs compared to baseline ( ) RCP8.5 Rice season wheat season Annual Baseline M hot/wet S cool/wet L cool/dry Q hot/dry Y Middle C hot/wet T cool/wet P cool/dry Q hot/dry A Middle 8

9 Change in mean monthly/growing seasonal mean Rainfall (RCP ) of selected GCMs compared to baseline ( ) Jan Feb Mar Apr May June July M S L Q Y C T P Q A Aug Sep Oct Nov Dec Rice Wheat Annual M hot/wet S cool/wet L cool/dry Q hot/dry Y Middle C hot/wet T cool/wet P cool/dry Q hot/dry A Middle

10 Boxplot for RCP4.5 & RCP8.5 -Wheat Percent change in wheat yield across the farms for Q1- CM2- in Meerut District. RCP4.5 RCP8.5 Baseline wheat yield across the farms for Q1 CM1 in Meerut District

11 Boxplot for RCP4.5 & RCP8.5 -Rice Percent change in rice yield across the farms for Q1- CM2- in Meerut District. RCP8.5 Baseline rice yield across the farms for Q1 CM1 in Meerut District

12 Q1 Climate sensitivity C1 C2 C3 C4 C5 M1 M2 M1 M2 M1 M2 M1 M2 M1 M2 observed mean yield (Rice) (kg/farm) mean yield change (Rice) (%) [defined as: (mean relative yield -1)*100] observed mean yield (Wheat) (kg/farm) mean yield change (Wheat) (%) [defined as: (mean relative yield -1)*100] observed mean yield (Livestock) (kg/farm) mean yield change (Livestock) (%) [defined as: (mean relative yield -1)*100] losers (%) gains (% mean net returns) losses (% mean net returns) observed net returns without climate change (Rs/ farm) observed net returns with climate change (Rs/ farm) observed per-capita income without climate change (Rs) observed per-capita income with climate change (Rs) observed poverty rate without climate change (%) observed poverty rate with climate change (%)

13 Key messages of integrated assessment of current production system vthe average rice-wheat system productivity may decrease by 9 % in 2050s. vaverage net returns per farm and percapita income may decrease by 10 % and 6 %, respectively in 2050 under current production system. vthe poverty rate may increase by 2 % in 2050.

14 Tested Adaptation strategies for rice-wheat (Q2) Timely sowing of wheat Introduction of improved and heat tolerant varieties PBW502, HD 2967, HD 2733 and HD 3118 Higher yield compared to existing PBW343 with higher inputs and efficiency regimes Residue management zero tillage along with rice residue management Potash application inclusion of K in fertilizer scheduling Planting density more fertilizer application yield increase

15 Introduction of new variety PR109, Pusa 1509, Pusa 1221 (short duration) with high yield level 2 days AWD Direct seeded rice SRI Zero tillage K scheduling Tested adaptation strategies available for rice-wheat system Introduction of legumes in rice-wheat system

16 Challenges and way forward Diversity of farming situation bunch of adaptation strategies are needed Uncertainty in GCMs and Crop Model projections convince the policy makers Assessment of all adaptation strategies are not possible through modeling approach

17 Thank You