Climate change and economic changes in India: The impacts on agriculture

1. Climate change and economic changes in India: The impacts on agriculture Partners: CICERO, IISD, TERI, RUTGERS Supported by Canadian International Development Agency and Government of Norway Lunch seminar CICERO 20.3.02

2. Overall goal : To contribute to India's capacity to promote environmentally sound development throughimproved environment practices and policies Project purpose: To assess the vulnerability of the Indian agriculture sector to climate change and economic globalization, and enhance the long-term stability of Indian agriculture, thereby contributing to improvement of food security and reduction of rural poverty

3. Double exposure Both climate change and economic globalization are ongoing processes with uneven impacts. Indian agriculture will be confronted by both processes simultaneously, leading to changing patterns of vulnerability.

4. Main objectives • Identify highly vulnerable areas and social groups • Identify key economic factors influencing vulnerability • Assess how domestic policies enhance or constrain farmers’ ability to adapt to climate change in the context of economic change • Suggest measures to reduce vulnerability of farmers

5. Work packages • Vulnerability profile (macro-scale) CICERO • Policy survey and recommendations IISD • Case studies (micro-scale) TERI • Integration of macro- and micro- scale analyses TERI / CICERO / IISD

6. Vulnerability Function of two components: • Capacity or social vulnerability: • The negative effect that an event may have on humans. • Exposure : • The risk that such an event may occur.

7. Economic Vulnerability Composite Vulnerability Index Biophysical Vulnerability Social Vulnerability

8. Bio-physical vulnerability maps Describe current and expected regional patterns of agricultural vulnerability to climate events • Agro-ecological zones • Agricultural regions (linked to cropping patterns) • Rainfed / irrigated • Soil type and degradation • Climatic regions (temperature and precipitation) • Climate scenarios

9. Economic vulnerability maps • Develop district-level trade sensitivity index as a function of: • Percent cropping area in export- and import- sensitive crops, • Average productivity of these crops weighted by cropping area, • Extent of irrigation, • Density of road / transportation infrastructure, • Proximity to international ports

10. Selection criteria: climate sensitivity Importance for economy or livelihood staple food crop internationally traded / export potential data availability Current data sources: FAO, Ministry of Commerce, Ministry of Agriculture Rice Wheat Maize Gram Sugarcane Oilseeds Apples Coffee Tea Onions Tobacco Selected crops

11. Social vulnerability maps Three overlapping dimensions: • Poverty dimension • Empowerment dimension • Technological dimension Indicators for each dimension has been chosen

12. Poverty dimension • Group of indicators that tries to capture the poorer areas where large parts of the population due to absolute poverty constantly face the risk of having to sell off productive resources and fall further down the poverty ratchet.

13. Social Vulnerability Indicators: Poverty

14. Empowerment dimension • Group of indicators that tries to capture the aspects of social organisation that make some groups less capable of accessing resources and exerting political rights.

15. Social Vulnerability Indicators: Empowerment

16. Technological dimension • Group of indicators of the technological development of an agricultural region (here district level). • It is assumed that higher level of technological development in a region will make its agriculture more adaptible to changing economic incentives and climate variability and extremes.

17. Social Vulnerability Indicators: Technology

18. Policy survey Examine key policies at the central and state levels which have the greatest impact on Indian agriculture: • Agriculture • Poverty eradication • Transport • Energy • Water • Trade

19. Case studies (TERI) • Use of Participatory Rural Appraisal (PRA) • 4 case studies (including 1 pilot study) • Identify study sites on basis of macro-level vulnerability profile (hotspots)

20. Case studies (TERI) • Ground truth macro vulnerability profile • Identify factors influencing vulnerability at the micro scale • Impacts of economic policies and changes on coping strategies and response options • Changing patterns of climate vulnerability (using recall method/ time lines (10 years ago, causes of change))

21. Integration analysis • Integrate case study results and policy analysis with macro-level vulnerability profile • Develop matrix relating globalization, policies, and climate vulnerability for agriculture sector • Recommendations for adaptation • Areas for further research

22. Classification Issues: How to Display Social Vulnerability Optimally Three Options: • Natural Breaks- Minimizes the sum of the variance between the classes (finds natural groupings and patterns). • Standard Deviation- Shows the standard deviation values. • Quantile (~ Percentile)- Each class contains the same number of features. Well suited for linear data. • Normalize each variable to a score of 1 -100, then sum the score for each district along several normalized variables. Then use quantiles for the composite indicator. Also, how many classes should we use? The question of 3 versus 5 classes.

23. Natural Breaks: 3 Divisions Quantile: 3 Divisions Comparison of Differing Legend Classifications: Child Sex Ratio Natural Breaks: 5 Divisions Quantile: 5 Divisions

24. Natural Breaks: 3 Divisions Quantile: 3 Divisions Comparison of Differing Legend Classifications: Ratio of Agricultural Laborers to Cultivars Natural Breaks: 5 Divisions Quantile: 5 Divisions

25. Legend Classification- Child Sex Ratio Comparison with Standard Deviation

26. Classification Histogram using Standard Deviations Mean

27. Classification Histogram using Natural Breaks

28. Classification Histogram using Quantiles

29. Classification Issues: How to Display Social Vulnerability Optimally Conclusions so far: • Three classes does not illustrate variability sufficiently. • Want a standardized way of viewing the classifications • Natural Breaks are too random. • Standard Deviation highlights extremes and the mean is not necessarily a good benchmark. • Quantiles are the most appropriate way to demonstrate variability • Ex: Class 1 includes values in the top 20% etc • Have the same number of districts in each quantile. • Involves ranking values • The normalisation procedure will retain most information.

30. Some examples of maps Social indicators

31. Adult Sex Ratio Lowest= 632 Chandigarh, a city UT Gujarat: Tribal districts stand out

32. Access to Drinking Water Himachal Pradesh Kerala: Exceptional State

33. Rural Homes without Toilets and Electricity

34. Some examples of maps: Bio-physical indicators

35. Biophysical Data • Data from the CRU, East Anglia • Organized data in monthly grids • Average • Standard Deviation • 1931-1960, 1961-1990 • Preliminary Calculations

36. Biophysical Data: Average Rainfall for the month of August (1961-1990) Mean 138 mm -1 Std Dev=-20 to -138 mm +1 Std Dev=139 to 298 mm *Mean refers to the whole of India

37. D r o u g h t a n d f l o o d p r o n e d i s t r i c t s o f I n d i a D r o u g h t p r o n e F l o o d p r o n e F l o o d a n d d r o u g h t p r o n e

38. W a t e r e r o s i o n W a t e r l o g g i n g S a l i n i z a t i o n W i n d e r o s i o n Soil degradation ”Wheat Basket” Sea level Rise

39. 1 1 4 9 1 6 2 4 1 7 1 3 1 5 1 0 1 5 5 1 1 1 2 6 1 8 7 2 0 0 0 2 0 0 4 0 0 K l o m e t e r s 3 1 9 8 L e g e n d S O I L D E P T H ( c m s ) 2 0 0 - 2 5 2 5 - 5 0 5 0 - 1 0 0 1 0 0 - 3 0 0 > 3 0 0 l Soil depth in agroecological regions i

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