Download F A R M E R S ` V U L N E R A B IL IT Y T O C L IM A T E C H A N G E

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