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Agriculture Sector Climate Change Related

Vulnerability Assessment of Rural Communities

of Sikkim Himalaya, India

Brijendra Swaroop

Managing Director, Sikkim State Cooperative Supply and Marketing

Federation (SIMFED), Sonam Gyamtso Marg, Gangtok – 737101, Sikkim, India

Email: 1. brijendra_swaroop@rediffmail.com

2. md@simfed.in

Telephone: +913592-203432 (o), Cell: +919434169208

Fax: +913592-203451

Corresponding Address: Forest Colony, Baluwakhani,

Gangtok, Sikkim – 737101, India

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ABSTRACT

With impacts of climate change becoming increasingly visible locally, identification of areas vulnerable to climate change risks is emerging as an urgent policy need. This assessment responds to the requirement by identifying the most vulnerable villages in agriculture sector using a rapid, cost effective and high resolution methodology. The study provide a preliminary, village level, climate related agriculture sector vulnerability assessment of the rural communities in the Sikkim state of India. It has been observed and validated through participatory appraisal also that climate change impacts have resulted in a reduction in the temporal spread of rainfall; increase in the intensity; with a marked decline in

winter rain having enhanced impact on rural communities mainly dependent on agriculture for sustenance and livelihood needs.

It has been observed that the agriculture of subtropical villages (less than 1000m) especially in the drought prone zone is more vulnerable due to an increased outbreak of pest, disease and infestation of invasive species. Increase in temperature has adverse impact on traditional spring water sources used for drinking and irrigation purposes. Communities during participatory appraisal have felt that warmer winters are challenging for preservation of seeds. A high variation was found in the agriculture sector sensitivity and adaptive capacity due to the diverse developmental profile of the villages.

Development of sector specific vulnerability indices at Gram Panchayat level will bring sharp focus on policy level interventions to meet challenges of climate change and prioritizes implementation also. This approach will provide geographical specificity in developing adaptive capacity of rural communities to combat adverse effects of climate change as well as to avail new opportunities made available due to climate change. Analytical framework adopted at village level has replication potential for other fields like vulnerability assessment of forest fringe dwellers dependent on natural resources. The methodology as per IPCC prescribe frame work can act as prioritizing tool for resource allocation for policy makers and implementing agencies looking after management of natural resources. One of the main expected outcomes of this study will be to provide a scientific basis for designing climate change adaptation policy and programs.

KEYWORDS: exposure, sensitivity, adaptive capacity, coping mechanism, policy

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1.0 INTRODUCTION

Sikkim is the least populous and the second-smallest state of India. Despite its small area, it is geographically diverse due to its location in the Himalayas, with a high variation in elevation ranging from 300 to 8598 m. Mt. Khangchendzonga, the world's third-highest peak, is the guardian deity of the state. It is not only the highest but also the steepest landscape in the country since the width of the Himalaya across its entire length is narrowest here1. It is a part of the Eastern Himalaya global biodiversity hotspot with 47% forest cover2, 3. In terms of country-level vulnerability to climate change, India ranks very high globally4. Within India, Sikkim state shows high resilience as compared to the other states5. The relative climate change vulnerability rank of Sikkim amongst the mountain ecosystems in the Eastern Himalayas was found to be 51 out of 896.

Mitigation and adaptation are two responses to offset climate change. Mitigation requires global efforts whereas adaptation is localized and site specific and gaining much of recognition due to inevitability of +20 C to +40 C global warming scenario. Adaptation to climate change is necessary, in addition to mitigation of climate change, to avoid unacceptable impacts of anthropogenic climate change7. First-generation vulnerability assessments focused on climate change related drivers (temperature, rainfall, submergence etc) and off late second-generation assessments are being explored, which adopt a multi-disciplinary approach where non-climatic drivers (economic, education, infrastructure etc) are also taken into consideration8.

Like in other developing countries, there is a lack of spatially disaggregated meteorological records. Long term, reliable data is available only for one station - Gangtok. Climate change related studies based on the analysis of the data for this station month-wise, season wise and annually from 1957 to 2005 indicates a trend towards warmer nights and cooler days, with increased rainfall except in winter. The maximum temperature in Gangtok has been rising at the rate of 0.2 0C per decade and the annual rainfall is increasing at the rate of nearly 50 mm per decade9, 10, and 11. Comparison of long term meteorological data available for Gangtok station (1957 to 2005) with the trend over the last few years (2006-09), shows an acceleration of these patterns, with winters becoming increasingly warmer and drier now (Table 1). The National Action Plan on Climate Change which incorporates the country’s vision on climate change was prepared in 200812. The above factors have created a need for reliable and high resolution vulnerability assessment reports, which can act as decision support systems for developmental planning.

The state is administratively divided into four districts, namely North, East, South and West. Climate change related vulnerability studies taken up in the state at the district level have found the South and West districts to be the most vulnerable11, 13. Inspite of being a small state, there is a high variation in exposure (temperature and rainfall), sensitivity (water, livelihoods and health) and adaptive capacity (poverty, literacy, environment and connectivity) indicators over short distances. So far only macro studies had been conducted, and there is an absence of studies at the village level. For the first time a micro scale study was taken up to address the issue of high variability typical of mountain areas.

Agriculture is vital to the progress of Sikkim, because more than 64 per cent

of population depend on agriculture for their livelihood. It has been the backbone

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of our economy, representing about a third of State’s Gross Domestic Product. The government has decided to adopt the policy of growth with sustainability, making Horticulture a priority sector for higher income generation to farming community as well as to concentrate more on securing maximum crop production of agricultural crops and managing primary agro-resources like soil, water and bio-diversity. Sikkim is a hilly State in the Eastern Himalayas where agricultural practices and adaptations are highly variable in time and space due to varying altitudes and agro-climatic situations. The surveyed arable land in Sikkim is estimated to be around 1, 09,000 hectares, i.e., 15.36 % of the total geographical area, and of which, about 9.50 % is the net sown area. The land holdings of the majority of the farmers are small. Mixed farming, an ideally suited system, is commonly followed by farmers in the State, and which fits well in the developmental process of making Sikkim an Organic State.

Cash and commercial crops like large cardamom, ginger, orange, seed potato, flowers and off-season vegetables along with other horticultural crops which are dealt by the Horticulture Department and cereals like rice, wheat, maize, finger-millet, barley and buckwheat, pulses like urd, rice bean, rajma, field-pea, cowpea and cluster-bean, oilseeds like rapeseed, mustard, soybean and sunflower, and agricultural miscellaneous crops which are dealt by the Agriculture and Food Security Department are the main crops grown in Sikkim.

The purpose of this study was to undertake a comparative preliminary assessment of the climate-related agriculture vulnerability of the rural communities at the village level. The questions for the study are identification of the most vulnerable villages and perception of the villagers on nature of climate change, its impacts and current coping mechanisms. The results of this analysis are intended to provide to the Government, voluntary sector and international donors a basis for improved developmental planning and policy, whereby the results are used to devise strategies that support in managing vulnerability and to raise awareness on climate related impact on agriculture sector of mountain ecosystem to a broader audience.

2.0 METHODOLOGY

The Intergovernmental Panel on Climate Change7 defines Climate-related vulnerability as a function of climate change exposure, sensitivity, and adaptive capacity. Exposure is defined by the magnitude, character and rate of climate change in a given geographical area. Sensitivity to climate change of sector is the

degree to which a community is adversely or beneficially affected by climate-related stimuli. The adaptive capacity of a community is its ability to adjust to climate change, to moderate or cope with the impacts. Data analysis adequately indicates in certain cases of high level of exposure values is being offseted due to high adaptive capacity of the community in respect to a particular sector or overall. Communities of developing economies in comparison with developed economies having lesser adaptive capacity are considered to be more vulnerable to climate change.

Development of indexes for agriculture sector exposure, sensitivity, and adaptive capacity at Gram Panchayat Level use of disaggregated attribute datasets from various secondary sources was done. The conceptual framework of IPCC was used for preliminary assessment of the current agriculture sector vulnerability of all the villages of Sikkim. The study is preliminary in the sense that a rapid approach was used, and is dependent on available data from public sources.

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Sikkim being characterized with low population density and scattered habitation is having a total of 163 Gram Panchayat’s (or villages) in the State with an average population of approximately 3000 and an extent of 10 km2. This study is based on secondary information in the form of reliable government sourced datasets and primary information collected from village consultations. A total of thirteen indicators available in a disaggregated manner at the village level were used to measure agriculture sector vulnerability. In order to develop climate change related exposure index data for drought (absence of rainfall), mean annual temperature, slope and aspect are used. For developing sensitivity index datasets for elevation, population dependent on farming, area under wasteland and % of area under rainfed farming has been used. In order to fathom comparative adaptive capacity of various Gram Panchayat’s various parameters/datasets like % BPL population, literacy, and distance from town, population density and availability /non availability of land resources.

Ground truthing in the form of participatory approaches is also integrated. The whole study was completed in six months from April to October 2010.

2.1 Data sources

Table II shows the thirteen indicators used and their source, grouped into sectors and into exposure, sensitivity and adaptive capacity components for assessment of agriculture vulnerability of gram panchayats of Sikkim. 2.1.1 Exposure indicators

Four exposure indicators mean annual temperature, mean annual rainfall, average slope of Gram Panchayat and predominant aspect of Gram Panchayat having relevance with agriculture sector were chosen.

Long term, reliable meteorological data is available only for Gangtok station and was sourced from the Indian Meteorological Department, Gangtok office. Hence the disaggregated annual mean temperature data was obtained from the website www.worldclim.org. However the annual mean rainfall pattern indicated here was not found to be accurate and consequently this was sourced from the rainfall distribution map of the State14. Slope and Aspect of a Gram Panchayat were obtained from the Shuttle Radar Topographic Mission (SRTM) produced by NASA originally. The SRTM 90m DEM file (srtm_54_07.zip) was downloaded from the CGIAR-CSI GeoPortal15. This file in Geotiff format was processed in Erdas Imagine software (version 8.5) to subset the study area from this image and exported to grid format.

2.1.2 Sensitivity indicators

Agriculture climate change related sensitivity component includes % of rainfed farming as proxy indicator of availability of water resources, to represent the agriculture sensitivity regarding livelihoods two indicators were selected namely elevation and percentage of population mainly dependent on farming and for a measure of resources proxy indicator of % cultivable waste land in Gram Panchayat was used.

Elevation was chosen since the crops in the subtropical belt (less than 1000m) have been impacted the most by pests, disease and weed. Elevation was obtained from the Shuttle Radar Topographic Mission (SRTM) produced by NASA originally. The SRTM 90m DEM file (srtm_54_07.zip) was downloaded from the CGIAR-CSI GeoPortal15. This file in Geotiff format was processed in Erdas Imagine software (version 8.5) to subset the study area from this image and exported to grid

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format. The percentage of population mainly dependent on farming was obtained from the Census of India16.

2.1.3 Adaptive capacity indicators

The agriculture related adaptive capacity of Gram Panchayat includes economic capacity represented by population below poverty line, human capacity represented by % of class 10 pass population in a Gram Panchayat, environmental capacity as land resource shortage and population density physical connectivity(as access to market) by distance of nearest town from Gram Panchayat. These socio-economic data at village level was sourced from the Census of India16 and State Socio-economic Household Census17.

2.1.4 Limitations

The framework of analysis is limited due to the choice and representativeness of the indicators and many of the indicators like poverty, literacy, road access etc are not strictly independent. Chances are there for degree of arbitrariness in any set of indicators; however what has to be ensured is that the dataset must be as meaningful as possible.

Agriculture exposure index needs augmentation with extreme events like high intensity rainfall, landslides etc and also by using the weather data from recently installed automatic weather stations.

Agriculture sensitivity index formulation should include frequency and number of occurrence of pest and disease outbreak occurrence.

The sample size of the villages where PRA was conducted needs to be further enlarged, especially by including a few villages of North district to make the sample representative.

2.1.5 Ground truthing

Participatory Rural Appraisal (PRA) was conducted with support from German Technical Cooperation (GIZ) in five villages covering all the eco-regions in three of the four districts. Four tools namely resource based seasonal dependency matrix, seasonal activity calendar, weather event chart and hazard ranking using spider web were used. These PRAs took place in July-Aug 2010 and were attended by a group size of around 35, with representation of all the stakeholders.

2.2 Data analysis

Climate change related agriculture vulnerability index was formulated by three components exposure, sensitivity and adaptive capacity. These three integral component of Vulnerability Index were separately calculated on the basis of proxy indicators of each having equal weightage. In order to make them comparable normalization was done with following formula:

In = (I – Imin) / (Imax – Imin)

where In is the standardized value of the indictor and I is the non-standardized

value. Imax is the maximum value of the indicator and Immiinn is the minimum value of

the indicator. The simple average of the standardized indicators is to calculate the value of each of the components.

C = Average (In)

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Agriculture sector climate-related vulnerability (V) of each Gram Panchayat is calculated as:

V = (Exposure – Adaptive capacity) x Sensitivity

This value is standardized again to obtain the vulnerability index (VI) so that the scale used is 0-1, indicating the lowest vulnerability level (0) to the highest vulnerability level (1). To identify the vulnerable areas, we ranked the regions according to the index and divided the list into four equal parts or quintiles. Those villages falling were further classified as least vulnerable, mildly vulnerable, moderately vulnerable and or highly vulnerable.

2.3 Data integration

The developmental units in the state comprise of 163 Gram Panchayat Units (GPU), 24 developmental blocks and 4 districts namely North, East, South and West. The village is defined as the GPU, and its administrative boundaries were marked on the Survey of India 1:25000 scale topographical sheets. These boundaries for all the GPUs were then digitized and the village maps prepared. The census data was integrated on a Geographic Information System (GIS) platform. ArcGIS software (version 9) was used for integration of the various layers on a GIS platform.

3.0 RESULTS

Components of agriculture exposure index- temperature, rainfall, slope and aspect have shown large variations in short distances due to rugged and difficult geography of Sikkim. The annual mean temperature varied from 50C to 22.50C, the annual mean rainfall varied from 1200 mm to 3200 mm. Agriculture related exposure index vary from 0.2 to 0.78 among 163 Gram Panchayats with mean at 0.52+0.13. The agriculture exposure index is more in drought prone/rain shadow areas of Darjeeling Himalayas. This has positive correlation with increased slope and harsher aspect like eastern and southern aspect. PRA exercise results of table III have corroborated the results of scientific studies as provided table I.

The agriculture sensitivity indicator varied from 0.23 to 0.64 with the mean being 0.51+0.07. Cash crops like ginger, oranges and cardamom of subtropical zone have a dampening effect on productivity due to prolonged droughts and infestation of pest, diseases and invasive species. It has been found that cropping pattern is now shifting towards cultivation of maize, broom grass and turmeric due

to resilience. Due to reduced winter rains farmers have shifted from multiple crops to single crop dependent on monsoon. Seed storage and preservation is difficult due to pest, disease and dry winter. Climate change is providing new opportunities for growing off season vegetables with enhanced productivity in middle and upper hills (elevation ranging from 1,000 mts to 2,500 mts).

Adaptive capacity varied from 0.17 to 0.53 with the mean being 0.34+0.06. Remote Gram Panchayats with lack of physical connectivity also showed high illiteracy and poverty resulting in weak adaptive capacity. Gram Panchayats adjacent to urban centers with good connectivity and diverse opportunities displayed high adaptive capacity.

The climate-related agriculture sector vulnerability index of the 163 Gram Panchayats had a mean of 0.55+0.19 (Chart 5). The agriculture sector related vulnerability has pronounced impact on rain-shadow/ drought prone areas of South and West Sikkim. These Gram Panchayats are mainly in subtropical zone or in middle hills of South and West Sikkim. Mainly vulnerable Gram Panchayats are

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in Sikkip, Melli, Namchi, Jorethang, Soreng and Kaluk Blocks of South and West Sikkim. These villages face highest exposure with low adaptive capacity coupled with high level of sensitivity regarding agriculture sector. In case of district wise analysis of results it was observed that South Sikkim has maximum vulnerability followed by West District. East and North Sikkim are much more resilient towards climate related agriculture vulnerability.

The rural communities have already started coping with the agricultural impacts of climate change using indigenous methods. Ginger seed is now being protected from dry winter by storing it underground. Seeds of pulses, beans and soyabean are preserved by mixing with kerosene, ash, camphor etc. The seed bank has also been enlarged to account for replanting when young crops get damaged by hail. Fruit trees are protected from increased incidences of insect borers by applying kerosene, petrol and diesel in the holes. Crop residue is being stored for use as winter fodder for livestock.

4.0 DISCUSSIONS

Recent impacts of climate change indicate a warmer and dry winter which has resulted in a decline in the production of the winter crops in the sub tropical regions of Sikkim which forms major areas of habitation also. Annual mean rainfall showed high variation due to the geography, with the rain shadow areas in the subtropical zone receiving only half the rainfall compared to East District. Chart 1 and 6 shows the value of exposure indicators for the five most agriculture sector vulnerable villages. The result clearly indicates those agriculture areas belonging to sub-tropical zone having southern or eastern aspect are more exposed to climate change. The GP’s are Sumbuk Kartikey, Wak Omchu and Mamley Kamrang etc. The agriculture of these villages is suffering due to lesser water availability, changes in cropping pattern and increased incident of pest and diseases.

The rain shadow areas of South & West Sikkim are found to be having high degree of agriculture related sensitivity due to high dependence on farming coupled with rainfed farming. Sensitivity (chart 2 & 7) enhances due to existence of wasteland and belonging to subtropical zone. GPs like Sumin Lingzey, Sanganath, and Sangadorji etc are having very high agriculture sector sensitivity index. These areas require livelihood diversification with irrigation facilities to meet the climate change related challenges.

In terms of a comparison of the adaptive capacity (chart 3 & 8) it was found that GPs like Tharpu, Shipgeyer having high adaptive capacity are better geared to tackle climate change despite having high exposure index and sensitivity. Whereas GPs like Lachen, Lachung, Gnathang etc are having low adaptive capacity but are not agriculturally vulnerable due to low exposure and sensitivity. Similarly, Rawatey-Rumtek village had a low poverty rate (8%), high education level (25% have passed class ten) and is well connected resulting in a high adaptive capacity.

As per chart 4 and 5 it has been found that villages/GPs having high adaptive capacity with moderate exposure and moderate sensitivity have shown lesser agriculture sector related vulnerability such as Sayari, Samali Marchak, Naitam Nadak etc. Similarly Ranka, Luing Parbing had the least vulnerability owing to the low exposure, low sensitivity and high adaptive capacity. The agriculture sector vulnerability was stark since the communities that practiced the most climate sensitive livelihoods, had least adaptive capacity and also faced high exposure. Communities practicing sensitive livelihoods of rainfed farming were found to have the least adaptive capacity (poverty, education, infrastructure etc) and also faced high exposure of climate-related change (drought, pests).

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To counter the agriculture related climate change impacts, diverse development interventions with multi pronged strategy is needed. Like GPs having high exposure indicator require emphasis on climate resilient crops, reduced dependence on rainfed farming, measures for fighting pests and diseases having organic origin, better linkage to agriculture markets. In order to reduce agriculture vulnerability in general focus on production of market favored crops, better linkages with market and protected farming will provide the best strategy. Post harvest management with improved transit & storage efficiency will provide sufficient safeguards to farming community of Sikkim Himalaya. Due smaller land holdings with less productivity agriculture of mountain ecosystem is highly vulnerable to climate change; this requires paradigm shift in approach like contract farming, primary level processing of cash crops at village level.

Significant variation in agriculture vulnerability was found within a district. This high intra-district variation in vulnerability calls for high resolution

vulnerability assessment studies to ensure that errors of inclusion and exclusion are minimized and climate change adaptation funds are efficiently targeted especially in mountain areas.

55..00 CCOONNCCLLUUSSIIOONNSS

Impacts of climate change are becoming conspicuous locally and thus

identification of sector related areas vulnerable is an imperative policy tool. The sector related vulnerability assessment responds to the requirement by identifying the most vulnerable villages using a rapid, cost effective and high resolution methodology. GIS maps were also prepared to show the climate change related vulnerability of rural communities in the state.

Climate change related exposures are global issues whereas sensitivity and adaptive capacity which are locality specific may be addressed with local interventions. A high variation was found in the sensitivity and adaptive capacity due to the diverse developmental profile of the villages. In order to offset the same, the adaptation strategy needs to be fine scale and multi-sectoral, to encompass the diverse sectors of agriculture, education, health, environment, roads, irrigation, water, poverty alleviation, skill development, non-farm employment etc.

It is expected that the outputs of this analysis will be used by policy makers and donor agencies for better designing and targeting of the climate change adaptation policy and programs.

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References

1. Schaller, G. B., Mountain Monarchs: Wild Goat and Sheep of the Himalaya. Chicago, IL: University of Chicago Press, 1977.

2. Mittermeier, R. A., Gils, P. R., Hoffman, M., Pilgrim, J., Brooks, T., Mittermeier,

C.G., Lamoreaux and J., Da-Fonseca, G. A. B., Hotspots Revisited. Earth’s Biologically Richest and Most Endangered Terrestrial Eco-regions. Arlington, VA: Conservation International, 2004.

3. Forest Survey of India, State of forest report, Forest Survey of India, 2007. 4. Moss, R. H., Brenkert, A. L. and Malone, E. L., Vulnerability to Climate Change:

A Quantitative Approach, PNNL-SA-33642, Pacific Northwest National Laboratory, Washington, DC, http://www.pnl.gov/globalchange/projects/ vul/index.htm, 2001.

5. Malone, E.L. and Brenkert, A.L., Modeling Vulnerability and Resilience to

Climate Change: A Case Study of India and Indian States, Climatic Change, 2005, 72, 57-102.

6. Tse-ring, K., Sharma, E., Chettri, N. and Shrestha, A., Climate change

vulnerability of mountain ecosystems in the Eastern Himalayas; Climate change impact an vulnerability in the Eastern Himalayas – Synthesis report. Kathmandu: ICIMOD, 2010.

7. Intergovernmental Panel on Climate Change (IPCC), Climate Change 2007:

Climate Change Impacts, Adaptation, and Vulnerability. Cambridge University Press, Cambridge, 2007.

8. Füssel, H. and Klein, J. T., Climate Change Vulnerability Assessments: An

Evolution of Conceptual Thinking, Climatic Change, 2006, 75, 301–329. 9. Seetharaman, K., Climate change scenario over Gangtok. Letter to the editor.

Meteorological center, Gangtok. Indian Meteorological Department, India. Mausam, 2008, 59(3), 361-366.

10. Ravindranath, N. H., Joshi, N. V., Sukumar R. and Saxena, A., Impact of

climate change on forests in India, Current Science, 2006, 90(3), 354-361. 11. Indian Institute of Science (IISC), Climate Related Vulnerability and Adaptation

for North East India. Indian Institute of Science, Bangalore, 2010. 12. Anonymous, National Action Plan on Climate Change. Prime Ministers Council

on Climate Change, GOI, New Delhi. 2008, p-49.

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13. WWF-India, An ecosystems approach to Climate Change Vulnerability

Assessment in Sikkim and Western Arunachal Pradesh, WWF-India report, 2010, p-72.

14. NBSSLUP, Sikkim soils prepared and published by National Bureau of Soil

Survey and Land Use Planning (ICAR), Nagpur, Regional Centre, Calcutta, in cooperation with Department of Agriculture, Department of Forest, Government of Sikkim, 2010.

15. Jarvis, A., Reuter, H. I., Nelson, A. and Guevara, E., Hole-filled SRTM for the

globe Version 3, available from the CGIAR-CSI SRTM 90m Database: http://srtm.csi.cgiar.org, 2006.

16. Census of India, Provisional Population Totals: Paper 1 of 2001. Census of India, Gangtok, 2001.

17. DESME, State Socio-Economic Household Census, Directorate of Economics,

Statistics, Monitoring and Evaluation (DESME), Government of Sikkim, Gangtok, 2006.

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ANNEXURE

Table I:

Variation of monthly rainfall in percentage, maximum and minimum temperature averaged for the years 2006-2009, in comparison with long period average (LPA) for the period 1957-2005 (Source Seetharaman K. pers. Communication, 2010)

TABLE II:

Month Rainfall Max Temp Min Temp

Jan -73% -0.1% 2.1%

Feb -19% 0.3% 2.0%

Mar -25% -0.3% 1.5%

Apr 7% -0.6% 1.4%

May -26% 0.1% 1.4%

Jun -8% -0.4% 0.9%

Jul -10% -0.2% 1.4%

Aug 0% -0.3% 1.0%

Sep 2% -0.2% 1.0%

Oct -40% -0.3% 1.5%

Nov -24% -1.0% 1.6%

Dec -39% -0.7% 2.1%

Sr No Component Sector Proxy indicators Sources

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Indicators used and their source, grouped into sectors and into exposure, sensitivity and adaptive capacity components

Table III:

Community observations on climate change and associated impacts on agriculture sector (use of PRA techniques)

Climate

events

Community observations on climate-

change

Associated impacts on

Agriculture sector

Snow fall In last 4 years reduction in

snowfall and frost in villages

located in temperate zones.

Warm dry winter and

less frost

Changes in crop composition and

cropping pattern

1 Exposure Temperature Annual mean temperature

worldclim.org

2 Rainfall Mean annual rainfall NBSSLUP

2000

3 Slope Average slope of Gram

Panchayat Unit

SRTM DEM

4 Aspect Predominant aspect of

Gram Panchayat Unit

SRTM DEM

5 Agricultural

Sensitivity

Water resources

(Drought)

% Rainfed farming Census 2001

6 Agriculture

livelihood

% farming population Census 2001

7 Elevation SRTM DEM

8 Availability of

farming land

resource

% Cultivable Waste land

in GPU

DESME 2005

9 Adaptive capacity

Economic capacity Poverty rate DESME 2005

10 Human capacity % of class ten pass population

DESME 2005

11 Environmental

capacity

Population density DESME 2005

12 Land resource shortage DESME 2005

13 Physical connectivity Rural connectivity Census 2001

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Rainfall Temporal spread of rainfall has

decreased (month-wise spread)

with accentuated intensity. The spread of rainfall had declined

from nine months in a year to

five to seven months, with the

sub-tropical zone being

adversely impacted.

There is heavy downpour during monsoons compared to

constant low intensity rainfall

in the past.

The winter rainfall has

decreased significantly over the last five years specially in lower

belt

Frequent landslides,

unreliable connectivity,

floods, longer dry period in winter

Decline in productivity

Crop damage

Reduced access to

market

Hail Storm Enhanced degree of

unpredictability with bigger

sized hail.

Severe damage to

tender maize, potato

and orange crop

Cold Temperatur

e

Winters have become warmer and duration has reduced from six

months to three to four months

with the sub-tropical zone being

adversely impacted

Increased population of insects and pest attacks

on crops.

High

Temperatur

e

The high temperature has

increased from reduced from five

months to six to eight months with

the sub-tropical zone being

adversely impacted.

Decrease of agricultural

yield with increase in

outbreaks of pest,

diseases and weeds

resulting in decrease in

yield of crops like ginger, fruits and

tomatoes.

Farmers in middle hill

belt have started

growing these crops.

Dry season Hardly any rainfall for continuous

six months from October to March

Drying of spring water

sources, decline in

production of winter

crops and vegetables

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CHART

1: CHART 2:

16

CHART

3: CHART 4:

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CHART 5: Five most & least vulnerable agriculture related Gram Panchayat’s of Sikkim

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CHART 6: Five gram Panchayat’s having highest Exposure Index for agriculture sector.

CHART 7: Agriculture related sensitivity index of gram Panchayat’s

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CHART 8: Gram Panchayat’s having high adaptive capacity regarding agriculture related adaptive capacity