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Integrated Assessment of Climate Change Impacts on Principle Crops and Farm Household Incomes in Southern India 1.Introduction / Overview 2. Farming System 3. Climate Analysis 4. Crop Analysis-Model calibration 5. Crop Analysis-Model simulations 6. Economic Analysis 7. Summary 8. Future Activities In Tamil Nadu Maize-sunflower/ Tomato – Maize/ Maize-cotton/ Pulse – Maize is grown in annual rotation. In Andhra Pradesh Maize + redgram , Maize- groundnut, maize- horse gram, maize-fallow are the major crop rotations Livestock such as goat, cow and poultry plays a significant role as an alternative livelihood option, regular employment and income generation activity. Farm household, agriculture and livestock components are complimentary and depend on each other on day-to-day basis The DSSAT model was calibrated using fertilizer trials conducted at TNAU, Coimbatore and ANGRAU, Hyderabad. Farm survey data was collected and compared with simulated yields using DSSAT and APSIM in the study area location of TN & AP Acknowledgements Paramasivam Ponnusamy, Geethalakshmi Vellingiri, Lakshmanan Arunachalam, Raji Reddy Danda, Dakshina Murthy Kadiyala, Sonali McDermid, Sunandini and Mahendran Kandaswamy Climate impact studies predict changes in atmospheric temperature and uncertainties in precipitation in Southern states of India. Impact of climate change on agriculture in these parts of the world would act largely through water availability to crops at their critical growth stages. To quantify these impacts a study was undertaken to assess the impact of climate change on agricultural production in Tamil Nadu and Andhra Pradesh in South India and its implications for farm household income and food security through integrated climate crop and economic models. Future climate data generated for RCP 8.5 mid century through twenty GCMs indicate increase in both maximum and minimum temperatures and possibility for increase in rainfall during the Northeast monsoon (Oct–Dec) in Tamil Nadu and during south west monsoon (June- Sep) in Andhra Pradesh. The GCMs display more uncertainty in rainfall than in temperature changes (the models agree on warmer future conditions). The scatter plot also shows most of the GCMs cluster at one place with few GCMs predicting higher precipitation and temperatures (J, K, G) compared to the baseline 0 2000 4000 6000 8000 10000 12000 0 2000 4000 6000 8000 10000 Simulated yield Observed yield Objectives To assess vulnerability of current production systems and economic status of farm households in irrigated and rainfed farming ecosystems to climate change, To select and apply suitable climate scenarios from different global and regional climate models for assessing the impact on productivity of principal irrigated and rainfed crops using crop simulation models To derive impact indicators of the regions/crops to changing climate and to identify key adaptation measures dynamic crop simulation and socio economic models Fig. 1 Study area Fig. 2. Farming system diagram Fig 3. RCP8.5 Mid-century temperature and precipitation scenarios for all GCMs in TN & AP Fig 4. GCM Scatter plots showing precipitation and temperatures during crop period in TN & AP compared to base period 0 1000 2000 3000 4000 5000 6000 7000 0 1000 2000 3000 4000 5000 6000 Simulated yield Observed yield Fig 5. Calibration of CERES-Maize model using sentinel site data Site : Coimbatore cultivar : CoM5 Site : Hyderabad cultivar : DeKalb-900M 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 1000 2000 3000 4000 5000 6000 7000 Maize Yields (kg/ha) Observed DSSAT APSIM Fig 6. Cumulative frequency distribution showing distribution of DSSAT, APSIM simulated yields and the observed farm survey yields for the farm survey sites- year 2012 ï10 ï5 0 5 10 15 20 25 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Farm Number % Change in Yield for 20 GCMs Range of Percent Change in Rice Yields for 20 GCMs at 60 Farms near Coimbatore, India (RCP8.5 Mid Century) The DSSAT and APSIM models predicted considerable changes in maize yields due to climate change as predicted by 20 GCMs Fig. 7 Percent change in maize yields as predicted by the DSSAT & APSIM crop models Site : Hyderabad, Mahabubnagar Site: Coimbatore Depending the inputs used, management conditions practiced, the yield of rainfed maize is expected to deviate from the current conditions by -12 to +32 %. The reason for increased yield in many farm conditions might be due to increased rainfall and CO 2 conditions expected in the future -60000 -40000 -20000 0 20000 40000 60000 0 10 20 30 40 50 60 70 80 90 100 OPPCOST ADOPT_A GFDL-ESM2G HadGEM2-CC HadGEM2-ES IPSL-CM5A-LR Tamil Nadu: Compared to base farm survey yields simulated future farm yields per hectare were higher by about 10 percent on average using 20 GCM and crop model simulated yields. Net gainers ranged from 52 to 72 percent of the sample farmers across the GCM scenarios with an average of 65 percent Andhra Pradesh: Compared to base farm survey yields , simulated rainfed maize future farm yields per hectare were lower by 7-11 % as simulated by DSSAT model but are higher by 17-20% as simulated by APSIM model . Net gainers ranged from 50 to 70 percent of the sample farmers across the GCM scenarios with an average of 38 percent. RAP narratives identified for this regions include: increase in crop diversity; decreased water quality and water availability for agriculture; increased water use efficiency; increased farm size; wage rates; and mechanization Fig 8 . Gainers and losers with different GCMs in rainfed maize in AP Scaling-up of integrated analyses to obtain a regional assessment of climate change impact Interpretation of the results , development of farmers friendly adaptation package for the change climate scenarios duly involving all the stakeholders Using RAP narratives as base, developing economically viable and environmentally safe mitigation strategies Derive and disseminate principles and policy recommendations that will enable a more effective design and implementation of adaptation programmes at multiple scales Dissemination of technologies and knowledge to selected areas beyond immediate project study sites We appreciate the University authorities of TNAU & ANGRAU for their support in successful implementation of this project. We also thank the funding agency for providing financial support . The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. Increase in both maximum , minimum temperatures and rainfall is expected in both the regions as per the future climate scenarios. The increase in rainfall is mainly noticed in South West Monsoon period in AP and North East Monsoon period in TN DSSAT and APSIM models predicted decrease in rainfed maize yields in future compared to base period in Mahabubnagar region of AP. But in few locations where soils are mostly shallow rainfed maize yields may increase due to increased rainfall activity In Tamil Nadu the models predicted mostly increased maize yields in future compared to base period, the reasons for this may be attributed to increased rainfall and CO 2 conditions expected in the future climate In Tamil Nadu across GCMs future farm yields varied from 3 to 17 per cent. Net gainers ranged from 52 to 72 percent of the sample with an average of 65 percent. While in AP simulated rainfed maize future farm yields per hectare were lower by 7-11 % as simulated by DSSAT model but are higher by 17-20% as simulated by APSIM model 0.00 0.20 0.40 0.60 0.80 1.00 0 1000 2000 3000 4000 5000 6000 Exceedance probability Maize yield (kg ha -1 ) Observed DSSAT Simulated APSIM Simulated
