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AMARNATH TRIPATHI INSTITUTE OF ECONOMIC GROWTH, DELHI EMAIL: [email protected] FARMERS’ VULNERABILITY TO CLIMATE CHANGE IN UTTAR PRADESH: MEASUREMENT AND DETERMINATES less than 15 percent? domestic products has declined to contribution in country’s gross sectors in India, as its been more important than other © Whether agriculture sector has not agriculture sector đ The majority of poor population comes from in the country agriculture is still main source of employment đ Though declining contribution in GDP, security is still main challenge for India đ Despite many developments, food and nutrition vulnerable to climate change and variability @ Agriculture in India is expected to be highly production Š India has comparative advantage in agriculture 2. To assess factors affecting climate change vulnerability. 1. To identify districts most vulnerable to climate change and variability in Uttar Pradesh state of India OBJECTIVES A study based on a large and heterogeneous region always has a wider perspective because it provides a range of outcomes, which can also be used for other parts of the country climate sensitivity to agriculture is very high in the state (O’Brien et al., 2004) Significant role in the country’s food and nutrition security programme WHY UTTAR PRADESH Climate change vulnerability was regressed on a set of explanatory variables which may affect farmers’ vulnerability to climate change and variability. A regression model to examine the correlates of farmers’ vulnerability to climate change and variability 17 environmental and socio-economic indicators were first selected, and then vulnerability index is constructed by aggregating them Each component is represented with several indicators Adopted IPCC definition of vulnerability which states that …. METHODS Physical Capital Human Capital Financial Capital Social Capital Irrigated Land Small & marginal farming Diversification Population Agriculture Share 1. Frequency of drought and flood 1. Frequency of warming years (temperature above to long term average temperature ) 1. Inter-annual variation in rainfall 1. Variation in diurnal temperature Extreme climate events in last 40 years (from 1970 to 2010) Variability in climatic variables No unit No unit Unit of Measurement Number Number Percent Percent Percent Percent Percent Number of farmer members of primary cooperative societies Literacy rate 1. Farm income 2. Percent of people below poverty 3. Average farm holding 4. Access to credit Infrastructure index Cropping intensity Hectare Rs No unit Percent Percent Rs Percent Number - + - - + + + - + Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Census Jila Sankhyaki Patrika Jila Sankhyaki Patrika Jila Sankhyaki Patrika Hypothetical Data Source Relationship 1. India Water Portal + 1. India Meteorology Department, Pune ADAPTIVE CAPACITY INDICATORS Irrigation ratio Percentage of small & marginal holdings in total holdings Diversification index Rural population density Percent of agriculture GDP SENSITIVITY INDICATORS Variables Indicators EXPOSURE INDICATORS Control Variables Economic Development by consumption of fertilizer (in Kg per hectare ) technological progress measured Regional dummies, & Infant Mortality Rate (in number) forest to total reported area Per capita income (in Rs.) , & Forestry (in %) labour in total workforce number of livestock per 1000 population percentage of land under Livestock (in number) Feminisation(in number) Share of non-farm employment (in %) percentage of urban Urbanization (in %) population to total population Sex Ratio share of non-agriculture Measurements Variables LIST OF EXPLANATORY VARIABLES - - - - + + Expected Relationship Non-climatic data were first pulled together for three consecutive years (2007-08, 2008-09, and 2009-10) and converted into the form of the above three years average Climatic variables were collected for the period from 1970 to 2010 to observe the frequency of extreme climate events and interannual variability over the past 40 years All data used are either on climatic variables or on non-climatic or socio-economic variables Cross-section data of 70 districts of UP DATA & DATA TRANSFORMATION low adaptive capacity and high exposure to climate change and variability are mainly responsible Mixed pattern in the central, eastern, and north-eastern plains. The districts in the western plains, mid-western plains, Bhabhar and Tarai zones, and the south-western semiarid regions. All the districts in the Bundelkhand and Vindya regions, Kaushambi from the central plain and Shravasti and Balrampur from the north-eastern plains SPATIAL PATTERN OF CLIMATE CHANGE VULNERABILITY IN UTTAR PRADESH Model (a. Dependent variable: Farmers’ Vulnerability Index) Constant Urbanization Sex Ratio Live Stock Forestry Fertilizer use Per capita income Infant Mort. Rate Non-farm activity Model Summary R2 Adjusted R2 F-stat p-value Observation 0.52 0.45 8.12 0.00 69 Coefficient s 11.229 -0.0134 -0.005 -0.007 0.045 -0.006 -0.000003 -0.034 -0.003 -0.487 -1.131 -3.186 1.945 -2.091 -1.374 -2.295 -0.104 T-stat 2.453 pvalue 0.01 0.63 0.26 0.00 0.05 0.04 0.17 0.02 0.92 The above positive relationship is observed because forestry has been used either little or not at all in adaptation to climate change. Cannot infer that higher the area under forests, higher the farmers’ vulnerability to climate change, because it is well established that trees on farms protect the soil and regulate water and microclimate, and protect crops and livestock from climate variability Except FOR, the sign of all variables was as expected. The coefficient of FOR was expected negative but found positive. URB, SR, NFE, and PCI were statistically non-significant, while LS, FOR, COF, and IMR were statistically significant CORRELATES Very High Medium Low Very Low Very Low High Medium Low Low High High Very High Low Medium 21.4 21.1 18.2 17.8 17.6 17.4 15.0 Low Medium Low Low Very Low Medium Medium Sensitivity Very Low Medium Low Highly forested districts, vulnerability is high but climate sensitivity is low, because adaptive capacity is low. District Sonbhadra Chandauli Mirzapur Lakhimpur khere Pelebhit Balrampur Shrawasti Chitrakoot Marajganj Etawah Adaptive Vulnerability capacity Very High Very Low Low High High Very Low Percent of area under forest 47.8 30.5 24.1 DISTRICTS WITH THE HIGHEST PERCENT AREA UNDER FOREST IN DESCENDING ORDER These indicators were finally aggregated using PCA to estimate a VI Seventeen environmental and socioeconomic indicators were identified to reflect these three components In this paper IPCC definition of vulnerability was adapted which states that........ In deciding where adaptation efforts are the most required, vulnerability mapping is instrumental. Adaptation to climate change may reduce the vulnerability, but a common adaptation strategy will not help because .......... CONCLUSIONS AND POLICY IMPLICATIONS Livestock, forestry, consumption of fertiliser, PCI, and IMR are observed to be important correlates of farmers’ vulnerability to climate change and therefore ...... Further, to observe its correlates, the VI is regressed on a set of explanatory variables Infrastructurally and economically developed districts are found less vulnerable to climate change, it means ........ Bundelkhand and Vindyachal districts were found the most vulnerable to climate change, the reasons are ............ CONCLUSIONS AND POLICY IMPLICATIONS