Spatial Targeting of Sustainable Development: GIS Based Framework for Assessment of Vulnerability...

1. Introduction

The overriding objective of this study is to develop a sufficiently general, analytical, and knowledge based decision-support framework for identifying strategic opportunities for sustainable development in disadvantaged regions. Sustainable systems – crop production systems, farming systems, livelihood systems, communities or regional and national economies – are those that can cope with or are able to recover from external shocks or stresses, while maintaining or improving their resource base (FAO, 1995). In recent years, assessments of vulnerability and adaptive capacity of biophysical and social systems have become the key components of assessments for planning for sustainable development (IPCC, 2001; TERI, 2003; Gregory et al, 2005; Clark, et al, 2005). While vulnerability is the degree to which a system is likely to experience harm due to

an external stress, adaptive capacity is the degree to which the system can modify its circumstances to move to a less vulnerable condition (IPCC, 2001; Turner et al, 2003a). High vulnerability and lack of adaptive capacity are indicative of a more general lack of sustainability in the system. Vulnerability and adaptive capacity of systems are therefore related concepts that need to be considered together when planning for sustainable development.

Quantitative assessments of vulnerability and adaptive capacity of systems, which can be useful for prioritizing investments and evaluating responses, are ambiguous and continue to evolve (Nelson et al, 2010). Most current measures are based on a limited number of arbitrarily derived indicators of vulnerability and adaptive capacity, for relatively large planning units like districts (eg. TERI, 2003). For more effective planning, such measures need to

2013 AARS, All rights reserved.* Corresponding author: [email protected] Tel: 091-040-24581333 Fax: 091-040-24581484

Spatial Targeting of Sustainable Development: GIS Based Framework for Assessment of Vulnerability and Adaptive

Capacity of Rural Livelihood Systems

NH Rao1*, MN Reddy1, KV Kumar1 and MRK Murthy1

1National Academy of Agricultural Research Management Rajendranagar, Hyderabad, India 500030

Abstract

Assessment of vulnerability and adaptive capacity of livelihood systems is important in planning for sustainable development. Planning in India, and many other countries, is generally based on arbitrarily chosen indicators of development, for relatively large regional units like districts. The actual investments and interventions, however, take place at a much smaller spatial scale of blocks, watersheds, or villages. The significant spatial variability at this level in natural resources, infrastructure and socio economic conditions, leads to problems in identifying relevant indicators and targeting investments and actions for sustainable development. This paper presents the development of a decision-support system (DSS) that integrates the sustainable rural livelihoods (SRL) framework and Geographic Information Systems (GIS) to quantify and map vulnerability and adaptive capacity indicators in multiple dimensions, for smaller planning units (blocks) within a region.. The DSS design and application are illustrated using data from a case study area. The framework is scalable, and enables better judgment, insight, and accountability in planning for sustainable development.

Key words: GIS, Development, Vulnerability, Adaptive capacity, Sustainable development.

Spatial Targeting of Sustainable Development: GIS Based Framework for Assessment of Vulnerability and Adaptive Capacity of Rural Livelihood Systems

be derived from a verifiable and acceptable framework that allows systematic selection, design and integration of appropriate measures, identification of relevant data and relationships, and criteria for quantifying vulnerability and adaptive capacity of the systems of interest. The framework also needs to be available at a larger spatial scale for greater relevance, and on a dynamic interactive decision-support platform to enable better interpretation, insight and judgment for targeting of investments. The availability of such a platform will lead to better science-policy engagement, and more robust and accountable planning processes and decisions. The objective of this paper is to develop such a framework and interactive platform for vulnerability and adaptive capacity assessments of rural livelihood systems

The widely accepted sustainable rural livelihoods (SRL) framework of DFID (DFID, 2001; Turrall, 2011) is used as the basic framework for identifying vulnerability and adaptive capacity measures for rural livelihood systems. The SRL framework was specially developed for characterizing livelihood systems to explore the links between poverty, natural resources and rural livelihoods. It allows characterization and analysis of rural livelihood systems by integrating the vulnerability and adaptive capacity concepts. In this framework, ‘a livelihood comprises capabilities, assets (stores, resources, claims and access) and activities required for a means of living: a livelihood is sustainable which can cope with and recover from stress and shocks, maintain or enhance its capabilities and assets, and provide sustainable livelihood opportunities for the next generation; and which contributes net benefits to other livelihoods at the local and global levels and in the long and short term’ (Chambers and Conway, 1992; definition adopted by DFID). The SRL framework was developed to address multiple needs of designing both policy and technology interventions that can help to reduce poverty in rural communities (DFID). The framework simultaneously allows assessing the vulnerability that is intrinsic to the system, as well as its adaptive capacity which is dependent both on the intrinsic vulnerability of the system and exogenous socio-economic determinants like available technology, institutions, human capital, policies, etc., that determine access to risk reducing processes (IPCC, 2001). It also provides an effective means to quantify and characterize vulnerability and adaptive capacity through a set of composite indicators in multiple dimensions (natural, physical, social, human and financial). The indicators can also be aggregated at different scales – household, village, block, district, region, nation, etc. The framework has been widely applied in food security and rural development studies (eg. Downing and Ziervogel, 2005 for vulnerability to food insecurity; O’Brien et al, 2004 for district level vulnerability indicators for climate and market stresses on Indian agriculture; Rao and Rogers, 2006 for agricultural sustainability).

The SRL framework, when integrated into a GIS platform, can provide the basis to interactively and dynamically design

and map indicators of vulnerability and adaptive capacity of systems in multiple dimensions and at different scales. The integrated spatial framework can provide for more informative vulnerability and adaptive capacity assessments, and policy and technology interventions. The GIS based framework will also provide an interactive interface for exploring linkages between global and local-scale changes in stresses that affect the sustainability of rural livelihoods. Together the two, SRL framework and GIS, when integrated on a single platform can constitute a dynamic decision support framework for development policy and planning.

The development of such GIS based integrated framework for vulnerability and adaptive capacity assessments is presented. A case study based approach is adopted to illustrate the development and application of the framework at the block/mandal (local name for an administrative Block) level for a district in India. The framework is scalable and can be adapted at village level or larger district and state levels. Commonly available data from public sources for the Nalgonda district in Andhra Pradesh State, India is used. The district comprises 59 Administrative Blocks or Mandals and is principally rainfed. Agricultural production and the livelihoods of the vast majority of the population in the district are vulnerable to a variety of external stressors. It is also one of the 150 districts identified by the Government of India as backward and vulnerable.

2. Analytical Framework for Assessment of Vulnerability and Adaptive Capacity

The sustainable rural livelihoods (SRL) framework has been used effectively in the past in rural development and poverty alleviation studies as it provides a means to quantify, characterize, structure and analyze rural livelihood systems. For this reason, the framework is also adapted in this study to assess vulnerability and adaptive capacity of livelihood systems through appropriately designed indicators and indices.

2.1 Sustainable Rural Livelihoods Framework

In the SRL framework (Figure.1), sustainable livelihood strategies of individuals and households depend on access, use and development of five different types of assets or capital – natural capital (land, water, biodiversity), physical capital (infrastructure, machinery), human capital (labour, skills), financial capital (savings, disposable assets), and social capital (rights, support systems) from which rural livelihoods are derived. Households with a greater diversity of assets and activities are likely to be less vulnerable and have greater adaptive capacity. Balance between the five capitals, and the various components of each type of asset, is also important, because minimum levels of one asset may be necessary to effectively make use of another.

Characterizing these assets is critical to assessing intrinsic vulnerability of the system and identifying available options

14

Asian Journal of Geoinformatics, Vol.13,No.2 (2013)

Figure 1. The sustainable livelihoods framework.(key: H = Human Capital; S= Social Capital ; N = Natural Capital; P = Physical Capital; F= Financial capital)

(Adapted from: DFID; http://www.livelihoods.org )

to increase its adaptive capacity. The framework also identifies two distinct categories, which govern livelihood strategies. The first is the vulnerability context in which the assets exist (trends, shocks and local cultural practices that affect livelihoods). The second is structures and processes, which define livelihood options. Structures include organizations (government, private) and processes include the policies, laws, and incentives. Access, control and use of assets are influenced by structures and processes. The assets, and existing structures and processes, guide development of livelihood strategies which lead to outcomes and which in turn impact the assets. Three types of strategies have been identified: agricultural intensification or extensification; livelihood diversification; and migration. The strategies can be both natural resource based and non-natural resource based. The framework permits a more desegregated analysis of changes in environment and socio economic conditions in response external shocks and stresses.

The SRL framework has been widely used in past decade by various development agencies to derive strategies for improving livelihoods in terms of the five types of capital. Though it has been applied specifically for individual, household, or village level development strategies, the outcomes can be effectively understood only in the total context which is visible at the wider spatial extent level of a block, district, region, or agro ecological zone. It is essential, therefore, that the full range of scale over which the outcomes can be visualized is kept in sight while applying the framework at any scale.

2.2 Adaptation of SRL Framework to Vulnerability and Adaptive Capacity Assessment

Vulnerability is the degree to which human and environmental systems (including livelihood systems) are likely to experience harm due to an external perturbation or stress (Turner et al., 2003). The primary objective of vulnerability

assessment is to identify people or places that are most susceptible to external shocks and to identify vulnerability-reducing actions (Stephen and Downing, 2001; Downing et al., 2001; Clark et al., 2000; Polsky et al., 2003). This requires not only an analysis of the external stressors and their corresponding impacts on a system, but also of the system’s ability to cope or respond to such stresses. Vulnerability assessment therefore requires an intensive examination of the system being stressed with respect to both its vulnerability to stresses (sensitivity to exposure) and its adaptive capacity (ability to cope). When applied to livelihood systems, vulnerability is a dynamic outcome of both environmental and social processes occurring at multiple scales (O.Brien et al. 2004). The SRL framework of DFID (Figure 1) provides a convenient starting point for assessing the vulnerability of livelihood systems as it:

1. Permits quantitative characterization of vulnerability (vulnerability context) and adaptive capacity through appropriately designed indicators for the five different types of capital (natural, human, physical, financial and social) that characterize a livelihood system,

2. Permits integration of structures and livelihood processes that determine the adaptive capacity of the system which enables it to cope with external stressors,

3. Integrates both natural and social processes in a dynamic framework, and

4. Is scalable from household to regional levels,

5. Permits comparisons between corresponding indicators of vulnerability at a given scale, and therefore allows to prioritize and plan investments for reducing vulnerability

The vulnerability context for the predominantly agriculture based livelihood systems is defined by the stresses imposed

15


by external factors like climate (droughts, climate change) and markets (globalization, liberalization of trade).

2.2.1 Integration in GIS

Indicator based methods are data intensive and inclusion of spatial dimension is important for sustainability assessments. Both inclusion of the spatial dimension and data management are effectively carried out in a GIS environment as it has features to manage relational databases and map data from

36

Fig 2. Framework for assessment of vulnerability of livelihood systems

Figure 2. Framework for assessment of vulnerability of livelihood systems.

such databases. In addition spatial analysis with data is also possible in a GIS

2.2.2 Variables and indicators of vulnerability and adaptive capacity

The SRL framework is used to identify the variables that characterize vulnerability and adaptive capacity of livelihood systems (Figure 2). For each asset, a set of variables or attributes are identified. Each variable/attribute may be

16


22

Table 1: Variables for characterizing vulnerability and adaptive capacity of livelihood assets

Livelihood/ Capital Assets

Variables for characterizing Intrinsic vulnerability

Variables for characterizing Adaptive capacity

Natural Capital Soil Quality Rainfall (average) Ground Water Crop diversity Livestock density Livestock diversity Land use

Social Capital Workers Population density (per sqr km.) Agricultural Enterprises Non- Workers Landholding size Landless labour Cultivators Agricultural labour Fair price shops

Human Capital Literacy Female literacy

Financial Capital Per capita agricultural Income Credit Societies Government Expenditure

Physical Capital Irrigated area Irrigation Source diversity Net irrigated area Hospitals (no. of beds) Area irrigated more than once Agricultural Machinery Road facilities (Transportation) Bus facilities Postal facilities (Communication) Telegraph facilities Banking facilities Primary school facilities (Educational) Upper primary school facilities High school facilities Junior college facilities Drinking Water facilities Source of water Medical facilities (Health) Veterinary facilities Well density

Table 1. Variables for characterizing vulnerability and adaptive capacity of livelihood assets.

characterized either by a single indicator or by a set of indicators. All the indicators are normalized to be in the range of 1-100, and classified on a scale of 1-5 to allow comparisons. Next, the classified indicators for each variable and for all the variables representing an asset are summarized into an index. For example, for the livelihood asset Natural Capital, the variables are defined by soils, rainfall, and groundwater. For each variable, specific indicators are identified; eg for soils the indicators are soil depth, soil type, available water holding capacity, slope, and erosion status. These are integrated into a Soil Quality Index. All the indicators are normalized to have values only in the range of 0 to 100. The normalized indicators for each attribute are then summarized into an index for that variable. Finally the indices for each type of asset (natural, physical, capital, human, social) are summarized into a corresponding asset index. A similar process is adapted for variables characterizing the adaptation capacities (Figure 2 and Tables 1, 2, and 3).

2.2.3 Weighting of indicators

The vulnerability indicators are to be derived for the regional units represented by mandals/blocks in a district. Data for

some livelihood indicators like average rainfall for natural capital, population density, literacy, etc for social capital and human capital, and financial capital and physical capital indicators are available as average values for the Block. These can be directly used to derive the relevant livelihood indicator for the Block. Others like the indicators for natural resources variables soils and groundwater vary spatially across different locations within the same mandal. Similarly, values of some social indicators of livelihood variables like land holdings, incomes etc are also distributed within the local population in the same mandal. For obtaining a representative indicator value for the block, the indicators need to be weighted. For natural resources area weights are used for deriving the weighted indicator. For the social variables the weight factors are given by the proportionate weights for each class of the indicator.

2.2.4 Normalization of indicators

The method adapted for normalization of indicators is similar to that used by the UNDP for deriving the Human Development Index (UNDP, 2002). The values of each indicator are normalized to the range of values in the data set

17


Variables

Relative Vulnerability Indicators (score) 1. Very low 2. Low 3. Moderate 4. High 5. Very high

I. Natural capital 1. Soil Quality Texture

Loamy Clay Loam, Gravelly Loam

Loam Calcarious, Gravelly Loam Calcarious, Silky Clay

Gravelly Clay, Sandy loam to Loam

Clay, Cracking, Clay Calcarious, Cracking Clay, Sandy

Depth > 150 cm 100 - 150 cm 50 - 100 cm 25 - 50 cm 10 - 25 cm

Slope

0 - 1 % Level

1 - 8 % Gentle slope

8 - 15% Moderate Slope

15 - 30 % Moderate Steep slope

>30% Steep slope

Drainage

-

-

Moderately well drained

Somewhat excessive, well drained

Excessively drained

Erosion status

Slight Erosion Moderate erosion

Severe erosion

2. Rainfall (average, mm)

> 1100 900 - 1100 700 - 900 500 - 700 < 500

3. Groundwater Depth to Water level (Pre-Monsoon)

< 4 (m. bgl)

4 - 7 (m. bgl)

7 - 12 (m. bgl)

12 - 20 (m. bgl)

> 20 (m. bgl)

Depth to Water level (Post-Monsoon)

< 2 (m. bgl)

2 - 5 (m. bgl)

5 - 10 (m. bgl)

10 - 15 (m. bgl)

> 15 (m. bgl)

Water level Fluctuations (Pre-Post)

-

< 2 (m)

2 - 4 (m)

> 4 (m)

Hills

Ground water resources

- Safe Semi critical Critical Over exploited

Ground water availability (mm/ha)

<200

200 - 300

300 - 400

400 - 500

>500

Ground water utilization (mm/ha)

<50

50 - 100

100 - 150

150 - 200

>200

4. Livestock Livestock density (number/area)

0 - 20

21 - 40

41 - 60

61 - 80

81 - 100

5. Land use Land put to non-agriculture capital(% of total area)

> 20%

20 - 15%

15 - 10%

10 - 5%

< 5%

Permanent pasture capital(% of total area)

> 20%

20 - 15%

15 - 10%

10 - 5%

< 5%

Net sown area capital

> 60%

50 - 60%

40 - 50 %

30 - 40%

< 30%

II. Social Capital 1. Workers > 60% 50 - 60% 40 - 50 % 30 - 40% < 30% 2. Population (Density)

< 200

200 - 300

300 - 400

400 - 500

> 500

3. Non-workers < 30% 30 - 40% 40 - 50 % 50 - 60% > 60%

Table 2. Vulnerability Indicators of livelihood assets.

18


4. Land holding > 20 Ha. 10 - 20 Ha. 5 - 10 Ha. 2 - 5 Ha. < 2 Ha. 5. Landless labour (% in workforce)

> 60%

50 - 60%

40 - 50 %

30 - 40%

< 30%

6. Cultivators (% in total population)

> 20%

20 - 15%

15 - 10%

10 - 5%

< 5%

7. Agricultural labour (% in total population)

> 25%

25 - 20%

20 - 15%

15 - 10%

< 10%

8. Fair price shops (no. of house hold /no. of shops)

<300

300 - 400

400 - 500

500 - 600

>600

III. Human Capital 1. Literacy > 60% 50 - 60% 40 - 50 % 30 - 40% < 30% 2. Female literacy

> 60% 50 - 60% 40 - 50 % 30 - 40% < 30%

IV. Financial Capital 1. Per capita agricultural Income (in Rs.)

>12000

8000 - 12000

4000 - 8000

1000 - 4000

< 1000

2. Government Expenditure (in lakhs)

< 50

50 - 100 100 - 200 200 - 400 > 400

V. Physical Infrastructure 1. Irrigated area (% net sown area)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

2. Net irrigated area

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

3. Area irrigated more than once

> 60 % 50 - 60 % 40 - 50 % 30 - 40 % < 30 %

4. Agricultural Machinery (no.)

>30000 20000 -30000 15000 - 20000 10000-15000 <10000

5. Road facilities (% no. of villages on main road)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

6. Bus transport facilities (% no. of villages having bus stops)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

7. Postal facilities (% no. of villages having post office)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

8. Telegraph facilities (% no. of villages having Telegraph office)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

9. Banks (no. of house hold /no. of banks)

< 3000

3000 - 5000

5000 - 7000

7000 - 9000

> 9000

10. Primary school facilities (% no. of

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

19


villages having Primary school) 11. Upper Primary school facilities (% no. of villages having Upper Primary school)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

12. High school facilities (% no. of villages having High school)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

13. Junior college facilities % no. of villages having Junior college)

> 20%

20 - 15%

15 - 10%

10 - 5%

< 5%

14. Drinking Water facilities (% no. of villages having PWS)

> 60%

50 - 60%

40 - 50 %

30 - 40%

< 30%

15. Source of water (no.of HH/drinking water facilities)

< 300

300 - 500

500 - 700

700 - 900

>900

16. Medical facilities (% no. of villages having medical facilities)

> 60 %

50 - 60 %

40 - 50 %

30 - 40 %

< 30 %

17. Veterinary facilities (% no. of villages having Veterinary facilities)

> 60%

50 - 60%

40 - 50 %

30 - 40%

< 30%

18. Well density Per Sq. Km (number/area)

0 - 20

21 - 40

41 - 60

61 - 80

81 - 100

27

Table 3. Indicators of adaptive capacity of livelihood systems

Relative Adaptive capacity Indicators (score) Variables Very low (1) Low (2) Moderate (3) High (4) Very high (5) I. Natural capital Crop diversity < 0.3 0.3 - 0.5 0.5 - 0.7 > 0.7 - Livestock diversity < 0.3 0.3 - 0.5 0.5 - 0.7 > 0.7 - II. Social capital Agricultural Enterprises

> 60 % 50 - 60 % 40 - 50 % 30 - 40 % < 30 %

III. Human capital IV. Financial capital Credit societies (Loans outstanding per Household)

< 20

20 - 40

40 - 60

60 - 80

>80

V. Physical Infrastructure Irrigation Source diversity

< 0.3 0.3 - 0.5 0.5 - 0.7 > 0.7 -

Hospital (no. of beds)

> 150

100 - 150

50 - 100

25 - 50

< 0 - 10

Table 3. Indicators of adaptive capacity of livelihood systems.

20


by applying the following general formula.

Normalized Value of Indicator = (actual value of the Indicator – minimum value) / (maximum value of the indicator – minimum value) * 100 %

In some cases, the normalized values were reversed by subtracting them from 100 (100 – normalized vale). This is done to ensure that high index values indicate high vulnerability in all cases. In the case of literacy rates, for example, where a higher literacy level indicates higher degrees of human capital and lower levels of vulnerability, the index value is reversed, so that a high index value corresponds to low levels of literacy (i.e., high vulnerability). Each indicator was evaluated in this manner, prior to construction of the composite indices.

2.2.5 Indicator scores and classification

For the normalized score of each indicator of a variable of the corresponding livelihood asset, indicator scores are assigned as follows:

for very low vulnerability (normalized values in the range, 0-20), Indicator score = 1

for low vulnerability (normalized values in the range, 21-40), Indicator score = 2

for moderate vulnerability (normalized values in the range, 41-60), Indicator score = 3

for high vulnerability (normalized values in the range, 61-80), Indicator score = 4

for very high vulnerability (normalized values in the range, 81-100), Indicator score = 5

For example, for the indicator, land holdings: less than 2 ha (marginal farmers) are assigned an indicator score of 5

corresponding to very high vulnerability. Similarly if land holdings are >20 ha their vulnerability would be relatively low and are assigned an indicator value of 1. The corresponding indicators for all the assets and variables included in the study are given in Table 2.

2.2.6 Derivation of composite indices from indicators

Indicators are integrated into indices at 3 levels:

1. variables – if they are represented by more than one indicator (eg soils, please see below)

2. livelihood assets – the index is derived based on corresponding indicators/index for each type of asset separately (that is one index each for natural capital, social capital financial capital, human capital, financial capital and physical infrastructure)

3. region/block/mandal – by integrating the indices of all five types of livelihood assets for each block

2.3 Derivation of Area Weighted Soil Indicators and Index

Five indicators are used to characterize the soil assets: soil texture, soil depth, soil drainage, soil slope, and soil erosion status. Thematic maps for each of these indicators are derived from the soil map of the district in GIS. By overlay of the five themes in GIS, a new layer is generated which maps regions homogenous with respect to the five indicators. For each region, a composite Soil Quality Index, represented by the geometric mean of the five indicators for the region can be derived. Since each block/mandal may comprise several such homogenous regions, area weighting of this index would give a reasonable representation of the soil quality index of the Mandal. Thematic maps can be derived for each indicator as well as the index. The procedure is summarized in Figure 3.

37

Fig 3. Derivation of the Soil Quality Index

Figure 3. Derivation of the Soil Quality Index.

21


A similar procedure can be adapted for integrating the different indicators corresponding to variables of each livelihood asset. Subsequently the same process is extended to the indices of each asset to arrive at a vulnerability index for the block/mandal.

2.3.1 GIS databases for mapping of indicators and indices and spatial analysis

The indicators of variables, and the indices at all the 3 levels described above need to be mapped and analyzed in a GIS framework. To facilitate this, the Block/mandal map of the district is linked to an external database comprising tables with basic and derived data relevant to the derivation of the indicators and indices. The database in MS Access enables independent computation and mapping of any indicator or index and its analysis in GIS.

3. Application and Results

3.1 Assessment of Vulnerability

Vulnerability is assessed at the Block/mandal level for the Nalgonda district based on indicators and indices of variables and assets, representing the five capital assets of the SRL framework, namely, natural, social, human, financial and physical infrastructure.

3.1.1 Vulnerability of Natural Capital

Natural capital is represented in this study by 5 variables and their corresponding indicators (Table 4). Some variables like soil resources have been grouped into a set of indicators. The others, like average annual rainfall, are also indicators by themselves. The indicators are calculated by normalizing the values of each variable in the range 0-100. Vulnerability scores in the range of 1-5 are assigned to each indicator based on the classification scheme defined in Table 2. The set of indicators for a variable are integrated into a corresponding index for the variables (with more than one indicator), and for the natural capital as a whole as given in Table 4.

3.1.2 Vulnerability index for natural resources (NRI)

Since all the indices for the different natural resource variables have been computed, the Natural Resource Index can be derived from their geometric mean as

NRI = (S*R*GW*LS*LU)1/5

The distribution of NRI and its components over the blocks of the district is shown in Figure 4. This distribution for NRI is relatively uniform and includes only three classes for the index, 1 2 and 3. However, the individual components of the index show wider variation over the district. This is more so

28

Table 4: Variables, indicators and index of natural resources vulnerability

Variables Indicator Index Soil resources Soil texture, soil depth, soil drainage, soil

slope, soil erosion status Soil Quality Index, S (Geometric mean of indicators for homogeneous units after GIS overlay; area weights for the block level index)

Rainfall Annual average rainfall Rain fall index (=indicator), R Ground water availability

Depth to water level (pre-monsoon); Depth to water level (Post monsoon), Fluctuations (pre & post monsoon), Ground water resources, Ground water availability, Ground water utilization

Groundwater index (geometric mean of indicators), (G)

Livestock Livestock number Livestock index (= indicator), L Land use Land put to non-agriculture capital,

Permanent pasture capital, Net sown area Land utilization index (= indicator), LU

Natural capital Natural resources index, (NRI) (geometric mean above 5 indices)

N = (S*R*G*L*LU)1/5

29

Table 5: Variables, indicators and index of social vulnerability

Variables Indicator Workers % of district workers employed in agriculture , W Population Population density, P Non-workers % of non workers in population, NW Land holdings % of land holdings by size classes, LH (table 2) Landless labour % of population in agricultural workforce, LL Cultivators % of cultivators in population, C Agricultural Labour % of agricultural labour in population, AL Fair price shops Ratio of fair price shops in households, FP Social Capital Index (SCI) (Geometric mean of indicators)

S = (W* P* NW *LH* LL* C*AL*FP)1/8

Table 4. Variables, indicators and index of natural resources vulnerability.

Table 5. Variables, indicators and index of social vulnerability.

22


for rainfall, soils and groundwater. It is interesting that regions relatively more vulnerable with respect to these are better endowed with livestock resources. The relative distribution of the six natural assets considered here allows for spatial prioritizing between specific investments towards development of the assets.

3.2 Vulnerability of Social Capital

Social capital is represented by 8 variables and their corresponding indicators (Table 5). Data on each of these indicators is either directly available or can be derived from the data in the District handbook. The data are first normalized

38

12345

N

c. Depth to water level - Pre monsoon

12345

N

d. Depth to water level - Post monsoon

12345

N

e. Water level fluctuations - pre, post monsoon

12345

N

f. Ground water resources

12345

N

g. Ground water availability

12345

N

h. Ground water utilization

12345

Ni. Ground water (G)

12345

N

j. Livestock (L)

12345

N

k. Land put to non-agricultural capital

12345

N

l. Permanent pasture

12345

N

b. Rainfall (R)

12345

Na. Soil Quality (S)

39

Fig 4. Relative vulnerability of natural resources in Nalgonda district (on a scale of 1-5; 1 very low to 5 very high)

12345

Nm. Net sown area

12345

Nn. Land use (LU)

12345

No. Natural Capital Index (N)

Figure 4. Relative vulnerability of natural resources in Nalgonda district (on a scale of 1-5; 1 very low to 5 very high).

23


and indicator scores are assigned as per the procedure have already been given under weighting of indicators and indicator score and classification. Each variable is represented by only one indicator. The eight indicators can be integrated into an index (Table 5)

A high population density will increase the pressure on the limited land water resources and increase vulnerability. A high percentage of population dependent on agriculture will increase vulnerability of the population to climate and market stresses. Similarly, high percentage of landless labour

will also indicate high vulnerability, as they are the first to lose incomes in agricultural distress. Farmers with small landholdings will be more vulnerable compared to those with large holdings. High ratio of agricultural labourers to cultivators indicates that a high proportion of the population might have problems when drought or floods occur, since agricultural labourers will have problems finding employment in such periods.

Among the above indicators, land holdings data is available as number or percentage within specified size classes (5

40

Fig 5. Relative vulnerability of social capital (on a scale of 1-5; 1 very low to 5 very high)

12345

Na. Workers (W)

12345

N

b. Population density (P)

12345

N

c. Non-workers (NW)

12345

N

d. Land holding (LH)

12345

N

e. Landless labour (LL)

12345

Nf. Cultivators (C)

12345

N

g. Agricultural labour (AL)

12345

N

h. Fair price shops (FP)

12345

N

i. Social Capital Index (S)

Figure 5. Relative vulnerability of social capital (on a scale of 1-5; 1 very low to 5 very high).

41

Fig 6. Relative vulnerability of human capital (on a scale of 1-5; 1 very low to 5 very high)

12345

Na. Total literates (L1)

12345

N

b. Female literacy (FL)

12345

Nc. Human Capital Index (H)

Figure 6. Relative vulnerability of human capital (on a scale of 1-5; 1 very low to 5 very high).

24


classes in Table 2). A weighted average is taken using the percentages as weights to obtain one indicator value for the block. The indicators and the Social capital index are mapped in Figure 5. The regions differ significantly in the vulnerability levels with respect to each indicator. For example blocks that are highly vulnerable with respect to employment (workers) are less so with respect to land holdings. The integrated social capital index is distributed over 3 classes and has greater variability within the district compared to the natural resources index which had only 2 classes.

3.3 Vulnerability of Human Capital

Human capital is measured by two indicators: the literacy level (L1) in the adult population (>7 years) and female literacy (FL). Increased overall literacy levels reduce vulnerability by increasing people’s capabilities and access to information and thus their ability to cope with adversities. Female literacy and child survival chances are measured by female literacy rate. Empirical studies have shown that increased female literacy has a significant impact on child mortality rates and fertility rates. The composite index for vulnerability of human capital is computed from the indicators (Table 6, Figure 6). All the five classes of vulnerability are present in the human capital index, which indicates higher variability across the district compared to

30

Table 6: Variables, indicators and index of human capital

Variables Indicator Literacy (L1) Literate population (%) Female literacy (FL) Female literacy rate (%) Human capital Index (HCI) H = (L1*FL)1/2

31

Table 7: Financial Vulnerability Index

Variables Indicator

Agricultural income Income from commodities per head (AI)

Government expenditure Expenditure per capita (GE) Financial capital index (FCI) F = (AI*GE)1/2

Table 6. Variables, indicators and index of human capital.

Table 7. Financial Vulnerability Index.

42

Fig 7. Relative vulnerability of financial capital (on a scale of 1-5; 1 very low to 5 very high)

12345

Na. Per capita agricultural income (AI)

12345

N

b. Government expenditure (GE)

12345

N

c. Financial Capital Index (F)

Figure 7. Relative vulnerability of financial capital (on a scale of 1-5; 1 very low to 5 very high).

the natural and social capital indices. Low general level of literacy indicates that a high proportion of the population may have problems accessing relevant information and exerting political rights over resources.

3.4 Vulnerability of Financial Capital

Financial capital is represented by 2 indicators – agricultural income and government expenditure (per capita). The normalized indicators and index are listed in Table 7 and mapped in Figure 7. The index is distributed over 3 classes, and indicates that where per capita income is low higher government expenditure can compensate to reduce relative vulnerability.

3.5 Vulnerability of Physical Capital

Physical capital is represented by 18 variables and their corresponding indicators (Table 8). Each variable is represented by only one indicator. The data are first normalized and indicator scores are assigned as per the procedure have already been given under weighting of indicators and indicator score and classification. The eighteen indicators are then integrated into an index and mapped (Table 8, Figure 8). The physical capital index is relatively uniformly distributed over the district, reflecting the relatively more uniform distribution of the irrigated area.

3.6 Vulnerability Index of Nalgonda District

The vulnerability index (VI) is determined by taking the Geometric mean of the following five indices:

1. Vulnerability index of natural resources (NRI)

2. Vulnerability index of social capital (SCI)

3. Vulnerability index of human capital (HCI)

4. Vulnerability index of financial capital (FCI)

5. Vulnerability index of physical capital (PCI)

25


32

Table 8: Physical vulnerability indicators and index

Variables Indicator Irrigated area Irrigated area as percent of cultivated area (IA) Net sown area Net sown area as percent of geographical area (NS) Area irrigated more than once

Area irrigated more than once as percent of geographical area (IM1)

Agricultural machinery % of agricultural machinery usage (AM) Road facilities % number of villages on main road (X) Bus transport facilities % number of villages having bus stop (BS) Postal facilities % number of villages having post office (PO) Telephone facilities % number of villages having telegraph office (T) Banking facilities Number banks per household (B) Primary schools % number of villages having primary schools (PS) Upper primary schools % number of villages having upper primary schools (UPS) High schools % number of villages having high schools (HS) Junior colleges % number of villages having junior colleges (JC) PWS water facilities % number of villages having PWS (W) Drinking water Source Number of household per source of water (SW) Veterinary facilities % number of villages having veterinary services (V) Medical facilities % number of villages having Govt. medical facilities (M) Well density Well density per square kilometer, (WD) Physical Capital Index (PCI) (Geometric mean of indicators)

P = (IA*NS*IM1*AM*X*BS*PO*T*B*PS*UPS*HS* JC*W*SW*V*M*WD)1/18

Table 8. Physical vulnerability indicators and index.

VI is computed as the geometric mean of the five indices as:

VI = (NRI * SCI * HCI * FCI * PCI)1/5

The index is mapped in Figure 9. The distribution of the lumped index VI (Figure 9) appears to be relatively uniform (with a large proportion of the district with index class 1, and a few blocks under index class 2), though there is significant spatial variability in the index for each type of capital. The different types of capital appear to neutralize deficiencies in one type of capital with better endowments of other types of capital. This has significant implications for prioritizing investments and interventions for regional development. More effective planning will result from differential and spatial assessments of vulnerability at the disaggregated indicator levels for different types of capital, than using a lumped index. .

4. Adaptive Capacity

Adaptive capacity is assessed by the variables and indicators

33

Table 9. Adaptive capacity variables and indicators

Variables Indicator/Index Crop diversity Crop diversity index Livestock diversity Live stock diversity index Agricultural Enterprises % number of agricultural enterprises Credit Societies Loans outstanding per household Irrigation sources Irrigation diversity index Hospital services Number of hospital beds Adaptive Capacity Index (ACI) (Geometric mean of indicators)

A = (CDI*LDI*AE*LO*IDI*NB) 1/6

Table 9. Adaptive capacity variables and indicators.

identified in Table 9 and mapped (Figure 10). Unlike in the case of vulnerability, there is wide variability in the lumped adaptive capacity index across the district reflecting the differential distribution of the indicators of adaptive capacity.

5. Discussion and Conclusion

Much of the planning for development in India and most developing countries is top down, with large regions like districts or larger units forming the basic planning units for assessing resources and livelihoods, and their development requirements. For example district level development plans have been available in India for a long time (Planning Commission, Government of India, 2006). The plans, priorities, investments and interventions are based on a few lumped indicators. The actual implementation of plans, however, takes place at the much smaller spatial scale of blocks, watersheds, and villages and can involve many more indicators.

A recent example is that of the National Rainfed Area

26


43

Fig 8. Relative vulnerability of physical capital (on a scale of 1-5; 1 very low to 5 very high)

12345

Na. Irrigated area (IA)

12345

Nb. Agricultural machineries (AM)

12345

N

c. Distance to main road (no. of villages) (X)

12345

N

d. Primary schools (no. of villages) (PS)

12345

N

e . D rink ing w ater (n o . o f v illages hav ing P W S ) (W )

12345

N

f. Veterinary (no. of villages) (V)

12345

N

g. Well density (WD)

12345

Nh. Physical Capital Index (P)

Figure 8. Relative vulnerability of physical capital (on a scale of 1-5; 1 very low to 5 very high).

Authority of India (NRAA, 2012) which used two composite indices, natural resources index and livelihood index at district level to prioritize the rainfed areas of the country for development interventions. The Natural Resource Index was constructed from nine variables, viz., rainfall, frequency of drought, available water content, extent and per cent of degraded and wastelands, irrigation intensity, extent and per cent of rainfed areas and ground water status. Similarly, the Livelihoods Index was derived by combining socio-economic index, health and sanitation index and infrastructure index. The choice of variables was by a relatively arbitrary judgment and consultation process. This is unlike the more systematic sustainable livelihoods framework used in this study to derive the indicators from commonly available development data time series. Also, the adaptive capacities were not specifically addressed in the NRAA study. There is significant spatial variability at this level in all natural and other resources and consequently in local vulnerability and adaptive capacities. This leads to ambiguities and problems in translating the district based priorities and investments to blocks and villages. Further, in a majority of prioritized districts, natural resources status and livelihoods status were

found to be inversely related. This is similar to the balancing effects of the different types of capital observed in this study at the more micro level.

This study presented a more generalized and systematic GIS based framework for assessment and mapping of sustainability of livelihood systems as a function of their vulnerability and adaptive capacity. The sustainable livelihoods framework was used to define a system of variables, indicators and indices for vulnerability and adaptive capacity of rural livelihood systems using commonly available natural resource and development data. The indicators and indices were derived at block level after aggregating the values of corresponding variables at smaller spatial units of villages and soil units. Weighting procedures were developed based on the type of indicators, scale of data and type of data available and the weighted indices were normalized to a five point scale. The identification and mapping of indicators in GIS enabled dynamic comparisons between the relative vulnerabilities and adaptive capacities of different blocks with respect to different indicators and indices. The GIS framework also provided a platform for systematic storage of the relevant data as tables in a relational

27


database. This enables dynamic and interactive decision support by querying and visualizing the variations in the variables and indicators as maps. Additional variables and indicators can be included without significant changes to the basic framework, and integrated into the model and mapped as required. The framework also enables tracking the vulnerability and adaptive capacity indicators using the time series data of the different variables at different times enabling the assessment of impact of interventions.

The results show that an aggregated index of vulnerability may not be an adequate measure of the relative vulnerability of different blocks in the district. It would be necessary to examine the indicators at a more disaggregated level to differentiate between the types of vulnerability and adaptive capacity of different blocks to different stresses. This is because regions that may relatively be more vulnerable with respect to one type of livelihood assets (for example natural

44

Fig 9. Relative vulnerability of livelihood assets and net vulnerability in Nalgonda district

12345

NNatural Capital Index

12345

NSocial Capital Index

12345

NHuman Capital Index

12345

N

Financial Capital Index

12345

NPhysical Capital Index

12345

N

Vulnerability Index (VI)

Figure 9. Relative vulnerability of livelihood assets and net vulnerability in Nalgonda district.

capital), may be less so with respect to other assets (physical, financial, human or social capital). On the other hand, the aggregate measure of adaptive capacity captures the differences between blocks to enable differentiation and prioritization.

The framework developed is sufficiently general and scalable for use in development planning that aims at enhancing sustainable livelihoods security. It can be used adaptively by policy makers to develop insights into the multiple dimensions of vulnerability and adaptive capacity and deployed for decision-making at the following three levels:

(i) issue identification: for generating awareness about vulnerability and adaptive capacity of communities and regions; and prioritizing vulnerable groups, sectors and regions to different stresses, like climate and market stresses

28


(ii) strategic planning: long-term location-specific plans to reduce vulnerability and increase adaptive capacity to climate change

(iii) operational planning: developing location specific technologies and guidelines for prioritizing investments and interventions.

45

Fig 10. Relative adaptive capacity in Nalgonda district

54321

N

a. Crop diversity index (CDI)

54321

Nb. Livestock diversity index (LDI)

54321

Nc. Agriculrutal enterprises (AE)

54321

N

d. Credit societies loans outstanding per househo ld (LO)

54321

N

e. Irrigation source diversity index (IDI)

54321

N

f. Medical facilities total no. of beds (NB)

54321

Ng. Adaptive Capacity Index (A)

Figure 10. Relative adaptive capacity in Nalgonda district.

References

Chambers, Robert and Gordon R. Conway,1992. Sustainable Rural Livelihoods: Practical Concepts for the 21st Century, IDS Discussion Paper 296, 42 pp.

Clark, W.C., Crutzen P.J., and Schellnhuber. H>J., 2005. “Science for Global Sustainability: Toward a New Paradigm.” in Earth System Analysis for Sustainability. Edited by Hans Joachim Schellnhuber, Paul J. Crutzen, William C. Clark, Martin Claussen, and Hermann Held.Cambridge, MA: MIT Press.

Clark,WC and Dickson NM, 2003. Sustainability science: the emerging research program, Proc. National Academy of Sciences, 100(14), 8059-61.

DFID, 2001. Sustainable Livelihoods Guidance Sheets, Numbers 1–8, Department for International Development (also available on http://www.livelihoods.org )

Downing,,TE and Ziervogel, G., 2005. Food system scenarios: exploring global and local linkages, Working paper, Poverty and Vulnerability programme, Stockholm Environment Institute,, Feb, 2005, 28pp

FAO, 1995. Sustainability issues in agricultural and rural development policies, in Trainers Manual, Vol 1 (http://www.fao.org/sd)

Gregory, PJ, Ingram JSI, and Brklacich M., 2005. Climate change and food security, Philosophical Transactions of Royal Society B, 360, 2139-2148

IPCC , 2001. Climate Change 2001. Impacts adaptation and vulnerability, Intergovernmental Panel on Climate Change; Cambridge University Press, 1032 pp

Luers A.L., Lobell D.B., Sklard, L.C. Addams, L. ,. Matson P.A., 2003. A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico, Global Environmental Change 13, 255–267

29


Nelson, R., Kokic, P., Crimp S.,Meinke, H., and Howden,S.M. 2010. The vulnerability of Australian rural communities to climate variability and change: Part I—Conceptualising and measuring vulnerability, Environmental Science and Policy, 13, 8-17.

NRAA.2012, Prioritization of Rainfed Areas in India, Study Report 4, NRAA, New Delhi, India, 100pp

O’Brien K., Leichenko R., Kelkar U., Venema H., Aandhal G., Tompkins H., Javed A., Bhadwal S., Barg S., Nygaard L. and West J., 2004, Mapping vulnerability to multiple stressors: climate change and globalization in India, Global Environmental Change,14, 303-313.

Planning Commission, Government of India (2006) Guidelines for district planning in XI Five Year Plan, 25pp.

Rai Anil, Sharma, S.D., Misra P,. Malhotra P.K, 2008. Development of livelihood index fopr different agroclimatic zomes of India, Agriultural Economics Research Review, 21, 173-182.

Rao N.H. and Rogers P.P., 2006, Assessment of agricultural

sustainability, Current Science, 91, .439 – 447.

Sharma U. and Patwardhan S. 2008. Methodology for identifying vulnerability hotspots

to tropical cyclone hazard in India, Mitigation and Adaptation Strategies for adapting to global change, 13, 703-17.

TERI. 2003. Coping with global change: vulnerability and adaptation in Indian Agriculture, 26 pp

Turner, B.L., II, Kasperson, R.E., Matson, P.A., McCarthy, J.J., Corell, R.W., Christensen L., Eckley, N., Kasperson, J.X., Luers, A., Martello, M.L., Polsky, C., Pulsipher, A., Schiller, A., 2003. A framework for vulnerability analy sis in sustainability science. Proceedings of the National Academy of Sciences, 100 (14), 8074–8079

Turrall S. (ed) 2011. Sustainable livelihoods approaches: past, present and...future?, Knowledge Services, IDS, 2011, 8 pp

World Food Programme. 2006. Comprehensive food security and vulnerability analysis – an internal review, May 2006, 112 pp.

30

Spatial Targeting of Sustainable Development: GIS Based Framework for Assessment of Vulnerability...

Documents

Transcript of Spatial Targeting of Sustainable Development: GIS Based Framework for Assessment of Vulnerability...