Assessing Future Agricultural Vulnerability in Kashmir Valley: Mid- and Late-Century Projections Using SSP Scenarios

Abstract

The fragile environment of the Himalayan region is prone to natural hazards, which are intensified by climate change, leading to food and livelihood insecurity for inhabitants. Therefore, building resilience in the most dominant livelihood sector, i.e., the agricultural sector, has become a priority in development and planning. To assess the perils induced by climate change on the agriculture sector in the ecologically fragile region of Kashmir Valley, a study has been conducted to evaluate the risk using the Intergovernmental Panel on Climate Change (IPCC) framework. The risk index has been derived based on socioeconomic and ecological indicators for risk determinants, i.e., vulnerability, hazard, and exposure. Furthermore, the study also evaluated the future risk to the agriculture sector under changing climatic conditions using Shared Socioeconomic Pathways (SSPs) for SSP2-4.5 and SSP5-8.5 at mid- and late-century timescales. It was observed that districts such as Bandipora (0.59), Kulgam (0.56), Ganderbal (0.56), and Kupwara (0.54) are most vulnerable due to drivers like low per capita income, yield variability, and areas with >30% slope. Shopian and Srinagar were found to be the least vulnerable due to adaptive capacity factors like livelihood diversification, crop diversification, percentage of tree crops, and percentage of agriculture labor. In terms of the Risk index, the districts found to be at high risk are Baramulla (0.19), Pulwama (0.16), Kupwara (0.15), and Budgam (0.13). In addition, the findings suggested that the region would experience a higher risk of natural hazards by the mid- (MC) and end-century (EC) due to the projected increase in temperature with decreasing precipitation, which would have an impact on crop yields and the livelihoods of farmers in the region.

1. Introduction

Climate change is widely recognized as the most critical global issue and the greatest challenge facing humanity in the twenty-first century [1]. Its effects are pervasive, transcending political boundaries and impacting all regions and populations indiscriminately [2]. Environmental damage in one part of the world can have significant repercussions in distant, seemingly unrelated areas [3]. Researchers have long been studying these interconnected dynamics and their influence on the Earth’s atmosphere [4]. Observable evidence, such as the accelerated melting of polar ice caps [5], rising sea levels, and increasing global temperatures [6], provides clear indicators of shifting environmental conditions. Climate change manifests in a variety of forms, including a rise in the frequency and intensity of extreme weather events, record-breaking temperatures [7], heavy rainfall, hailstorms, floods, droughts [8], and the proliferation of pests and diseases [9]. Such phenomena can have both immediate and long-term consequences on economies and livelihoods [10], underscoring the urgent need for comprehensive and coordinated global action.

Changing weather patterns are expected to significantly impact agriculture [11]. These effects include increased variability in productivity, regional declines, and shifts in the geography of production. Agricultural economics will be affected by climate change through variations in farm productivity, costs, supply and demand, trade, and regional resource availability [12,13,14]. Climatic variability has already been observed to adversely influence crop efficiency and production [15,16], heightening concerns about food and livelihood security. Long-term climatic stress could further impact agriculture and horticulture by altering average temperatures, precipitation patterns, atmospheric carbon dioxide levels [17], and water availability. These changes may also degrade soil quality and affect the nutritional value and growth of essential crops. IPCC has also reported on the consequences of climate change, regarding the prediction of reduced crop yields by 2030, focusing on two issues, viz., agriculture and food security [18].

Ensuring food security to meet the anticipated needs of a growing global population is essential. In India, agriculture remains a critical economic activity, supporting 65% of the working population, even as its contribution to the country’s GDP continues to decline. The combination of moderate developments in the agrarian sector and the impacts of climate change must be given significant attention, as they are closely tied to food security and poverty levels for a large portion of the population [19,20]. The heavy reliance on farming practices, coupled with the risks posed by increasing climatic stress, heightens the vulnerability of farmers and poses a serious threat to the economy, development, and sustainability. Over the past few decades, the impacts of climate change in the Himalayas, including the Jammu and Kashmir regions, have become increasingly evident. These changes are marked by rising temperatures, retreating glaciers, and altered precipitation patterns [21,22,23,24].

The agriculture sector, which is crucial to the economy of Jammu and Kashmir, is particularly vulnerable to these changes. Approximately 70% of the population depends on agriculture and related activities, either directly or indirectly. The region’s key crops, such as paddy, maize, oilseeds, pulses, vegetables, fodder, and wheat, face growing risks due to shifting climate conditions. Additionally, Kashmir, renowned for cultivating high-quality saffron, is experiencing the strain of climate change, which threatens the sustainability of this unique and valuable crop.

The Kashmir Valley exhibits significant variations across its districts in terms of socioeconomic and ecological indicators, necessitating the development of a specific Inherent Vulnerability Index. These disparities arise from differences in factors such as population density, literacy rates, economic reliance on agriculture, access to healthcare, and the availability of natural resources like water bodies and forests. Ecological factors—including susceptibility to climate change, the presence of fragile ecosystems, and the degree of environmental degradation—further contribute to this variability. The Inherent Vulnerability Index aims to capture and quantify these differences, offering a more precise assessment of each district’s capacity to withstand and recover from environmental and socioeconomic challenges. This tailored approach ensures that vulnerability assessments and subsequent interventions are context-specific, addressing the unique challenges faced by each district in the valley.

This study, aligned with Sustainable Development Goals (SDGs) 2 and 13, focuses on the initial phases of analyzing climate change in the region with three primary objectives: characterizing the region’s vulnerability and risk to climate change, observing forecasted trends in climate variables, and evaluating the performance of agriculture under current and future climatic conditions. SDG 2 seeks to end hunger, achieve food security, and promote sustainable agriculture, while SDG 13 emphasizes urgent action to combat climate change and its impacts. By integrating these goals into current research framework, the study aims to provide insights that not only enhance an understanding of climate vulnerability but also contribute to sustainable agricultural practices and resilience-building strategies in the region.

Vulnerability assessment, which estimates the extent of climate change hazards, is defined as the region’s susceptibility and degree of risk from long-term climatic variability. This assessment depends on a range of geographic, economic, cultural, social, demographic, governance, institutional, and environmental factors [25]. The methodologies and determinant variables used to estimate vulnerability vary across studies [26,27]. A comprehensive assessment of a system or region’s vulnerability and risk to climate change is widely recognized [28] as involving key components like: sensitivity, adaptive capacity, exposure and hazard [29,30].

This study aims to provide a comprehensive analysis of how the diverse districts within the Kashmir Valley are likely to experience and respond to the challenges posed by climate change. The investigation focuses on assessing the risk in the agriculture sector of the Kashmir Valley by utilizing vulnerability, adaptive capacity, sensitivity, and hazard indices. The primary objective is to determine the susceptibility of Kashmir’s agricultural sector to climate change, identify the key indicators contributing to the region’s vulnerability, and simulate potential risk outcomes based on Shared Socioeconomic Pathways (SSPs) for the middle and later parts of this century. The findings of this study are crucial for supporting India’s national efforts to achieve SDGs 2 and 13 and emphasize the need for urgent action to adapt to the evolving climate patterns in the Himalayan region of Kashmir.

2. Materials and Methods

2.1. Study Area

The study area (Kashmir Valley), is located in the far north-western corner of the country, within the northernmost state of India, Jammu and Kashmir. The valley is elliptical and bowl-shaped, stretching between 32°22′–34°43′ N latitude and 73°52′–75°42′ E longitudes, covering an area of 15,948 square kilometers at an altitude of 1577 m above mean sea level (Figure 1). Surrounded by the Himalayan ranges, the region exhibits the characteristics of a semi-closed ecosystem and is drained by the river Jhelum, a tributary of the larger Indus. The Kashmir Valley experiences a temperate climate, which is suitable for both horticultural and agricultural crops. The valley receives 60% of its annual precipitation in the form of rain and snow during December and January. The temperature varies significantly from the valley floor to the rim region, often decreasing with elevation. This variation contributes to the highly varied temperature and precipitation conditions at the meso and micro scales.

Agriculture is the primary livelihood for most of the people in the valley. Any changes in agriculture can significantly affect their livelihood patterns. The region is fragile and highly susceptible to climate change, particularly in the mountainous areas. Rice is the staple crop of the region, with other crops including maize, apple, and pear. The Kashmir Valley comprises ten districts: Budgam, Bandipora, Anantnag, Baramulla, Ganderbal, Kulgam, Pulwama, Kupwara, Shopian, and Srinagar.

2.2. Methods

2.2.1. Assessment of Inherent Vulnerability

The Inherent Vulnerability Index has been calculated by utilizing an indicator-based approach as a function of sensitivity and adaptive capacity. When creating a composite index, it is crucial to use indicators/variables that accurately represent the fundamental concept, as this will ensure the validity of the vulnerability index [31,32]. The selection of indicators for this study was based on the availability of data across time and space (districts), a review of the pertinent literature, and consultation with experts in agriculture, water resources, and climate change. A set of 32 indicators pertaining to ecological (biophysical) and socioeconomic variables were divided into sixteen indicators of sensitivity and sixteen measures of adaptive capacity. The rationale for use and derivation methods are described below and summarized in Figure 2.

The overview of the major indicators related to agricultural vulnerability, including descriptions and sources/methods for data derivation, is given in

Table 1. Key agricultural vulnerability indicators and data sources for Kashmir.

Indicator	Rationale	Method	Source
SENSITIVITY
Ecological Indicators
Mean Elevation	Higher elevation limits agricultural land utilization and is correlated with remoteness and marginalization.	Derived from High Mountain Asia 8 m DEM	https://nsidc.org/ Accessed on 23 May 2023
Mean Slope	Steep slopes are more prone to soil erosion, negatively affecting the cultivation.	Derived from High Mountain Asia 8 m DEM	https://nsidc.org/ Accessed on 23 May 2023
Mean Aspect	Aspect determines the amount of sunlight important for crop production.	Derived from HMA 8 m DEM using the ArcGIS 10.8.	from https://nsidc.org/ Accessed on 23 May 2023
Barren Land	Barren land is not suitable for agriculture owing to its soil characteristics (weather to coarse sand). Research also highlights that barren lands are more exposed to landslides.		[33]
Net Sown Area	The proportion of agricultural land area in a region represents the area that would be exposed to environmental risks. Hence, the greater the area under cultivation, the more sensitive the region.	On-screen digitization land use/landcover	Cartosat Satellite data (2.5 m).
Current Fallow	Represents agricultural areas not under cultivation at present. Land with reduced fertility is often left fallow. Linkages exist between fallow land and higher dependence on unreliable monsoons for irrigation.		[33]
Tree Crop	Tree crop areas are a proxy for areas under horticulture. Environmental changes threaten horticulture production due to its high sensitivity. These areas are highly exposed to climate stress and more susceptible to damage.	Derived from Sentinel 2A using Object Based Image Analysis (OBIA) in eCognition Software 10.3.	https://scihub.copernicus.eu/ Accessed on 13 January 2022
Culturable Wasteland	Culturable waste lands area is suitable for cultivation but has not been cultivated in the last 5 years. The presence of such a land-use type reflects that cultivation in these areas is economically redundant owing to adverse site conditions and a propensity for degradation.		[33]
Drainage Density	An increasing drainage density results in increased flooding of agricultural fields. Drainage density coupled with steep slopes makes the topsoil vulnerable to erosion.	Derived from HMA 8 m DEM using the Hydrology tool in ArcGIS	https://nsidc.org/ Accessed on 23 May 2023
Variability in Food Grain Crop Production	High variability in yield indicates fluctuations in agro-climatic conditions over time. The agriculture sector has a high contribution to the State Domestic Products and employment for the states in IHR. High yield variability reflects a lack of adaptive capacity. In J and K, the yield variability of food grains has significantly decreased over 10 years.	Yield Variability = (Standard Deviation/Average of Total Yield)	https://data.gov.in/ Accessed on 29 April 2022
Cropping Intensity	Cropping intensity denotes the number of crops grown in a year on one piece of land. The agricultural land will be more vulnerable if it is not much used for growing crops. In other words, the vulnerability is inversely proportional to the cropping intensity of that land.		[33]
% Unirrigated Area	If % of rainfed agriculture is higher, the sector becomes more vulnerable to rainfall variability.		https://inputsurvey.dacnet.nic.in/. Accessed on 29 April 2022
Socioeconomic Indicators
Population Density	It determines the pressure on available natural resources.		[33]
% of Landholdings below 1 Hectare	Farmers with large landholdings stand a better chance of diversifying their farming practices to adapt to climate change than those with small landholdings		[34]
% of Agricultural Labor	Cultivators are involved for more than 6 months in agriculture on their own land. Most of the farmers in J and K are owner–cultivators. Though cultivators have more access to resources, they face enhanced risks of income losses owing to their complete dependence on agriculture-based activities.		[33]
% of BPL Population	A higher percentage of BPL households indicates lesser adaptive capacity.		[33]
ADAPTIVE CAPACITY
Ecological Indicators
NDVI	NDVI is generally used as a proxy for productivity. Higher NDVI values are an indicator of good vegetation health.	$NDVI={\displaystyle \frac{\left(NIR-RED \right)}{\left(NIR+RED\right)}}$	https://bhuvan-app3.nrsc.gov.in/ Accessed on 29 April 2022
Soil Moisture	Soil moisture identifies water and irrigated areas. A higher availability of soil moisture enhances agriculture production. Soil moisture is a key input to several precision agricultural applications such as irrigation scheduling, identifying crop health, and pests and diseases.	$MSI={\displaystyle \frac{\left(\rho swir \right)}{\left(\rho nir\right)}}$	https://www.mosdac.gov.in/ Accessed on 29 April 2022
Forest Area (in ha)/1000 Population	Forests are an important resource for agriculture. They check soil erosion and maintain soil moisture and water levels. Also, dense forest cover plays a major role in maintaining the hydrological regime. This is an important source of alternative livelihood and a source of food in case of crop failure. Approximately 10–15 ha of forest land is needed for every hectare of cultivated land to maintain agriculture stability. Therefore, locations with higher forest availability per unit of agriculture area would have better agriculture yields.	Data from the district census book of Jammu and Kashmir	[34]
Soil Organic Matter	Soil Organic Carbon (SOC) is also a fundamental determinant of fertility, contributing to the biological, chemical, and physical aspects of the soil and its capability to sustain plant growth.		https://bhuvan-app3.nrsc.gov.in/ Accessed on 29 April 2022
Groundwater Availability	Groundwater is used outside irrigation districts or when surface water from reservoirs is scarce.		https://bhuvan-app3.nrsc.gov.in/ Accessed on 29 April 2022
Socioeconomic Indicators
Literacy	Literacy is considered to be an important factor in determining access to information. Moreover, literacy reduces poverty and provides wider social benefits. The skills required to organize and manage natural resources in mountains are enhanced through higher literacy, along with a higher capacity for adaptive learning.	Viewshed analysis	[33]
Communication Network	The availability of communication facilities provides several benefits for agricultural communities living in isolated mountain regions. Improved communication of agrometeorological information has synergies for agriculture development and adaptation processes.		Point data with location and height of tower obtained from BSNL, Jio, Airtel Communications
% Electricity	Power is an important input for agriculture development. In mountain areas, access to electricity enables the usage of water pumps required for irrigation, substantially reducing manual labor.		[33]
Road Density	Availability of roads is crucial for connectivity with markets and for access to basic necessities. The presence of roads increases the opportunities for non-farm economic activities.		Derived from Carosat 2.5 m
Crop Diversification Index	Crop diversification and improvement in cropping patterns are common adaptation strategies at the farm level.	$Simpson Diversification Index \left(SID\right)=1-{\displaystyle \sum _{n=1}^{\infty }}{\left(a_j/A\right)}^2$	[34]
Livestock to Human Ratio	The livestock sector contributes to the livelihoods of one billion of the poorest population in the world and employs close to 1.1 billion people. This reflects adaptive capacity through alternative livelihood/diversity of livelihood.		[34]
MGNREGA	This indicates means of access to information, empowerment, alternate sources of livelihood, and building climate resilience in the area through MNREGA projects.		https://www.nrega.nic.in Accessed on 29 April 2022
Per Capita Income	High income and expenditure, a measure of wealth, provides better access to markets, technology, and other agricultural inputs, increasing the capacity of agricultural communities to cope with any stress.		[34]
Agriculture Credit	Agriculture credit societies provide microcredits to meet the requirement of funds for agriculture development in times of crisis.		[34]
Banks/10,000 People	Banks/financial institutions add to the capacity of a community. These collective institutions not only empower agricultural communities but also provide financial and social benefits. The presence of banks also indicates the active involvement of the population in sustaining livelihoods.		[34]
Livelihood Diversification	Climate variability, associated with farm income variability, is recognized as one of the main drivers of livelihood diversification strategies in developing countries.		[34]

. Each row lists an indicator, a description of how it influences agricultural practices or sustainability, and the method/source used to derive the data.

The calculation of the vulnerability index involves four major steps, viz., data segregation, the processing of data, the computation of the index, and reclassification. During the initial phase of data processing, all indicators were systematically classified into two dimensions, sensitivity and adaptive capacity, throughout the region. The subsequent step included establishing the functional association of each indicator with vulnerability, identifying whether it had a positive or negative impact on vulnerability. Following this, processing was carried out using the linear minimum–maximum scaling method, which is often employed in hierarchical systems and brings all indications onto a common, dimensionless measuring scale. Since there is a positive relationship between sensitivity and vulnerability and a negative relationship with adaptive capacity, the following equations were used to standardize the indicators using Equations (1) and (2):

$$Normalized Value (NV)={\displaystyle \frac{\left(Actual Value-Minimum Value\right)}{\left(Maximum value-Minimum Value\right)}}$$

(1)

$$Normalized Value (NV)={\displaystyle \frac{\left(Maximum value-Actual Value\right)}{\left(Maximum value-Minimum value\right)}}$$

(2)

Each indicator’s normalized value was relative, falling between 0 and 1, with 0 being the least vulnerable and 1 being the most. After normalizing the chosen indicators, they were each given the same weight in the final analysis [35]. In the absence of data or consensus regarding distinct weights, giving all assessment markers the same importance is possible [36].

The Inherent Vulnerability Index was computed using Equation (3).

$$Vulnerability \left(V\right)=f[Sensitivity \left(S\right), Adaptive Capacity \left(AC\right)]$$

(3)

Additionally, two composite indices were computed to examine the distribution of these characteristics throughout the districts, viz., the sensitivity and adaptive capacitance index.

After applying the criteria, the districts showed varying levels of vulnerability. The vulnerability index and rankings were calculated by summing the normalized scores for each indicator and adjusting them based on their relative importance. This process helps pinpoint the specific factors contributing to a region’s vulnerability, providing decision-makers and stakeholders with clear insights into the extent and distribution of associated hazards. This information aims to support effective decision-making and targeted interventions by highlighting the most critical vulnerabilities.

$$Driver of Vulnerability (V_d)={\displaystyle \frac{S_i}{{\sum }_{i=1}^{n}Si}}$$

(4)

2.2.2. Assessment of Hazards

A hazard is defined as “a process, phenomena, or human activity that has the potential to result in loss of life, injury or other health impacts, property damage, social and economic disruption, or environmental degradation” [37]. The assessment of hazards was conducted with a focus on key environmental threats, including drought, flood, and landslides (

Table 2. Key hazard indicators and data sources for Kashmir.

Indicator	Rationale	Method	Source
Flood	Floods are more significant in terms of the global hydrological cycle because of the devastation they cause to human life. The risk of flooding poses a significant threat to the lives, property, and farmland of those who reside in the floodplains.	The highest flood level extent was derived from the GeoEye satellite image of 9th September 2014 and further analysis for districtwise estimations was carried out using ArcGIS	Department of Ecology, Environment, and Remote Sensing
Landslide	Evidence indicates that the primary factors contributing to frequent landslides that cause harm to mountainous agricultural areas are steep hill slopes, seasonally dry periods, extreme rainfall intensities, and unstable soils.	Landslide data prepared under the Disaster Risk Mapping Project through ANN was used in the analysis	Disaster Management, Relief, Rehabilitation, and Reconstruction Department
Drought	One of the biggest risks to water supply systems is drought, which can severely impair agricultural productivity.	The drought index for every district was calculated using the average yearly loss for each crop as a result of the drought	Disaster Management, Relief, Rehabilitation, and Reconstruction Department

), to evaluate their potential impacts and risks using Equation (5),

The total hazard for the district was calculated using the following formula:

$$H=f (Flood, Landslides, Drought)$$

(5)

2.2.3. Assessment of Exposure

“Exposure” refers to the degree of climate stress that a community or region experiences, indicating the susceptibility of resources, infrastructure, and people as consequences of climate change. The exposure indicators are given in

Table 3. Exposure indicators and data sources for Kashmir.

Indicator	Rationale	Method	Source
Tmax	Tmax, Tmin, and rainfall are among the most significant and directly measurable indicators of climate change. They represent the core elements of climate variability, which directly influence various environmental and socioeconomic factors. Changes in these parameters can have profound effects on ecosystems, agriculture, water resources, and human health. These three factors are critical for agriculture, which is highly sensitive to changes in temperature and precipitation patterns. Tmax and Tmin affect the growing season, crop yields, and the timing of agricultural activities, while rainfall directly impacts soil moisture and water availability.	The data in .netcdf format was processed and decoded in ArcGIS Pro using Multidimensional tools and extracted to Excel 365 Version.	https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6 Accessed on 28 June 2023
Tmin
Precipitation

.

The exposure indicators included Temperature (Max), Temperature (Min), and Precipitation. For studying the future climate, the bias-corrected data of 13 models within the latest generation of the coupled model inter-comparison project (CMIP6) were utilized, using empirical quantile mapping (EQM) over the South Asian domain at a daily temporal scale and 0.25° spatial resolution [38].

Over the Indian region, bias correction was carried out using IMD’s gridded rainfall and temperature datasets. The future analysis was carried out for two Shared Socioeconomic Pathways (SSP), viz., SSP2-4.5 (“middle of the road”) and SSP5-8.5 “fossil-fueled development” [39,40,41]. Districtwise composite exposure was calculated using Equation (6).

$$E=f (Tmax, Tmin, Precipitation)$$

(6)

2.2.4. Assessment of Risk

The term “risk” can be used in two different ways, depending on the context: (a) as a synonym for the likelihood that an adverse event will occur, and (b) as a synonym for the mathematical expectation of the severity of that occurrence [37]. The assessment of risk involved analyzing the exposure, hazard, and vulnerability associated with key threats such as drought, flood, and landslide. This comprehensive approach was essential for understanding the potential impacts and identifying areas most at risk to develop targeted mitigation strategies. The risk was calculated using Equation (7):

$$Risk \left(R\right)=f\left[Vulnerability \left(V\right), Hazard \left(p\right), Exposure \left(E\right)\right]$$

(7)

The overall approach to arrive at the risk is shown in Figure 3.

3. Results

3.1. Vulnerability

The 10 Districts in Kashmir Valley have been categorized and mapped into different vulnerability groups using indicator of Sensitivity and Adaptive Capacity shown in Figure 4 &

Table 4. Sensitivity, adaptive capacity, and vulnerability across districts.

District	Sensitivity	Adaptive Capacity	Vulnerability
Bandipora	0.558	0.633	0.59
Kulgam	0.546	0.592	0.56
Ganderbal	0.597	0.527	0.54
Kupwara	0.46	0.621	0.54
Anantnag	0.478	0.514	0.5
Budgam	0.442	0.526	0.48
Baramulla	0.458	0.493	0.48
Pulwama	0.524	0.423	0.47
Shopian	0.446	0.469	0.43
Srinagar	0.37	0.446	0.42

. There are differences amongst the districts of Kashmir Valley in terms of socioeconomic and ecological indicators. Bandipora was found to be the most vulnerable district with a vulnerability index of 0.59, followed by Kulgam, Ganderbal, and Kupwara with vulnerability indices ranging from 0.56, 0.54, and 0.54, respectively. Srinagar (0.42) and Shopian (0.43) were among the least venerable districts.

The detailed vulnerability analysis carried out shows that the key drivers of vulnerability are large percentage of cultivators and agricultural laborers, fewer land holdings, lack of crop diversification, high dependency on the agriculture sector for primary sector production, a low number of agriculture credit societies, and a smaller number of livestock per household. The districts with low vulnerability had adaptive capacity factors such as livelihood diversification, crop diversification, a higher percentage of tree crops, and a higher percentage of agricultural labor. Therefore, there is a need to focus more on the vulnerable districts and the indicators contributing to increased vulnerability.

3.2. Hazard

(a)

Flood Hazard

The peak September 2014 flood level has been mapped using Digital Globe World View-2. District Srinagar has the highest area/infrastructure (29.07 sqkm) under flood hazard, which indicates that the district is more prone to flood hazard, making the infrastructure of the capital city risk-prone. It is followed by District Pulwama (13.05 sqkm). District Shopian has the least area (0.20) under flood risk, while District Kupwara has shown no flooding (

Table 5. Districtwise hazard assessment in Kashmir.

S No.	District	Flood Hazard (sqkm)	Flood Normalized	Landslide Hazard (%)	Landslide Normalized	Drought Hazard (Rs)	Drought Normalized	Total Hazard
1.	Anantnag	2.20	0.08	33	0.64	0.77	0.77	0.49
2.	Kulgam	2.00	0.07	24	0.45	0.00	0.00	0.17
3.	Pulwama	13.05	0.45	14	0.23	0.69	0.69	0.46
4.	Shopian	0.20	0.01	3	0.00	0.85	0.85	0.28
5.	Srinagar	29.07	1.00	13	0.21	0.15	0.15	0.46
6.	Ganderbal	3.45	0.12	49	0.98	0.08	0.08	0.39
7.	Budgam	5.80	0.20	7	0.09	1.00	1.00	0.43
8.	Baramulla	2.77	0.10	31	0.60	0.92	0.92	0.54
9.	Bandipora	10.37	0.36	50	1.00	0.31	0.31	0.55
10.	Kupwara	0	0.00	44	0.87	0.54	0.54	0.47

and Figure 5a).

(b)

Landslide Hazard

Landslide susceptibility maps have been made possible by the availability of diverse and easily accessible remote sensing data, as well as thematic layers of GIS-based contributing-causes data. District Bandipora has the highest percentage of area (50%) prone to landslides, followed by districts Ganderbal (49%) and Kupwara (44%). District Shopian has the lowest percentage (3%) of area prone to landslides (Table 5 and Figure 5b).

(c)

Drought Hazard

Drought statistics were gathered from the State Disaster Management Department in Jammu and Kashmir. The average annual loss due to drought for each crop was used to determine the drought index for each district. District Budgam shows the highest impact of droughts (1.00 Cr), and District Kulgam has shown the least impact of drought among all other districts (Table 5 and Figure 5c).

Based on the findings, the hazard index ranged from 0.17 to 0.55 (Table 5). Two districts show the highest hazard index, viz., Bandipora (0.55) is the most vulnerable, followed by Baramulla (0.54). The following five districts were moderately prone to hazard: Anantnag (0.49), Kupwara (0.47), Pulwama (0.46), Srinagar (0.46), and Budgam (0.43). Ganderbal (0.39), Shopian (0.28 and Kulgam (0.17) were the least hazard-prone districts (Table 5). In order to facilitate efficient decision-making, the GIS environment was also used to create the hazard ranking and index maps, which are displayed in Figure 5 and Figure 6, respectively.

3.3. Exposure

Tmax, Tmin, and precipitation were used as exposure (climatic) indicators. The outcomes of each indicator are discussed below:

(a)

T(max)

The maximum projected temperatures for Kashmir Valley and its districts have been examined using ensemble means of CMIP6 South Asia climate data for IPCC AR6 SSP2-4.5 and SSP5-8.5 scenarios. According to these scenarios,

Table 6. Districtwise Tmax (°C) under different climate scenarios in Kashmir.

District	Mid Century		End Century
	SSP2-4.5	SSP5-8.5	SSP2-4.5	SSP5-8.5
Anantnag	14.54	15.17	15.55	18.74
Kulgam	16.51	17.19	17.65	21.09
Pulwama	20.32	20.98	21.38	24.70
Shopian	20.54	21.23	21.68	25.11
Srinagar	19.29	19.95	20.35	23.70
Ganderbal	12.95	13.59	13.95	17.20
Budgam	18.81	19.50	19.96	23.46
Baramulla	20.19	20.91	21.40	25.05
Bandipora	12.81	13.45	13.81	17.11
Kupwara	18.69	19.39	19.87	23.57

shows the expected changes in annual maximum temperatures towards the mid- and end-century with respect to baseline (BL). Figure 7a–d depict the shift in yearly peak temperature from BL to MC and EC (Table 6). The following is a synopsis of the expected maximum temperature change for the SSP2-4.5 and SSP5-8.5 scenarios:

• The average annual maximum temperature in Kashmir Valley is expected to rise by approximately 1.4 °C and 2.5 °C by MC and EC under the SSP2-4.5 scenario respectively. Under SSP5-8.5 the temperature is expected to increase by 1.6 °C and 5.0 °C by MC and EC respectively. As a result, the predicted rise in temperature at the end of the century is more than that for the middle of the century.
• The projected increase in maximum temperature towards MC varies from 1.5 °C in the Kupwara sub-mountain and low-hills zone to 2.0 °C in the Bandipora, Ganderbal, and Anantnag belt lying in the very-high-hills temperate zone for the SSP2-4.5 scenario. Under SSP5-8.5 scenario the temperature varies from 1.8 °C in the Kupwara to 2.5 °C in Bandipora, Ganderbal, and Anantnag belt.
• The projected increase in maximum temperature towards EC varies from 4.4 °C in the Kupwara to 5.8 °C in the Bandipora, Ganderbal, and Anantnag in the SSP2-4.5 scenario, and from 2 °C in the Kupwara to 6 °C in the Bandipora, Ganderbal, and Anantnag in the SSP5-8.5 scenario.
• The northeastern districts of the valley show a higher projected increase than the southwestern districts.

(b)

Tmin

In

Table 7. Districtwise Tmin (°C) under different climate scenarios of the Kashmir Valley.

District	Mid Century		End Century
	SSP2-4.5	SSP5-8.5	SSP2-4.5	SSP5-8.5
Anantnag	4.93	6.05	6.46	11.42
Kulgam	6.64	7.76	14.60	13.15
Pulwama	9.59	10.74	11.15	16.20
Shopian	9.93	11.06	11.48	16.49
Srinagar	9.01	10.18	10.59	15.72
Ganderbal	3.38	4.56	4.97	10.12
Budgam	8.44	9.6	10.03	15.13
Baramulla	10.20	11.39	11.83	17.06
Bandipora	3.25	4.45	4.87	10.12
Kupwara	9.17	10.40	10.84	16.21

, shows that annual minimum temperatures are expected to shift toward MC and EC relative to BL in Kashmir Valley and its districts under the SSP2-4.5 and SSP5-8.5 scenarios. Annual minimum temperatures shift from BL to MC and EC in Figure 8a–d under the SSP2-4.5 and SSP5-8.5 scenarios, as below:

• In the valley, annual minimum temperatures are expected to rise by an average of 1.4 °C and 2.7 °C by MC and EC respectively under the SSP2-4.5 scenario. Under SSP5-8.5 scenario the average minimum temperature is expected to rise by 1.8 °C. As a result, the predicted temperature rise heading toward EC is greater than MC.
• The projected increase in minimum temperature towards MC varies from 1.4 °C in the Bandipora, Ganderbal, and Anantnag belt to 1.7 °C in Pulwama, Kulgam, Baramulla zone for SSP2-4.5 scenario, and from 1.8 °C in the Bandipora, Ganderbal, and Anantnag to 2.5 °C in Pulwama, Kulgam, Baramulla zone for the SSP5-8.5 scenario.
• The projected increase in minimum temperature towards EC varies from 2.5 °C in Bandipora, Ganderbal, and Anantnag to 1.7 °C in Pulwama, Kulgam, Baramulla zone for SSP2-4.5 scenario, and from 1.8 °C in Bandipora, Ganderbal, and Anantnag to 2.5 °C in Pulwama, Kulgam, Baramulla zone for the SSP5-8.5 scenario.
• The southwestern districts show a higher projected increase than the northeastern districts of the valley.
• The rise is greater in the SSP5-8.5 scenario compared to the SSP2-4.5 scenario.

(c)

Precipitation

The annual precipitation in Kashmir Valley and its districts evaluated using the ensemble mean of the CMIP6 SSP2-4.5 and SSP5-8.5 scenarios shown in

Table 8. Districtwise precipitation under different climate scenarios of the Kashmir Valley.

District	Mid Century		End Century
	SSP2-4.5	SSP5-8.5	SSP2-4.5	SSP5-8.5
Anantnag	83.26	79.14	78.41	83.09
Kulgam	99.79	99.30	97.52	104.64
Pulwama	81.59	81.30	80.49	85.09
Shopian	94.89	94.64	93.11	99.03
Srinagar	75.26	75.04	74.36	78.63
Ganderbal	65.53	65.27	64.88	68.53
Budgam	83.26	83.10	81.73	87.11
Baramulla	79.01	79.12	77.68	82.76
Bandipora	69.11	69.04	68.42	72.14
Kupwara	77.16	77.35	76.26	80.79

and Figure 9a–d depicting the expected annual changes in MC and EC in relation to BL as given below:

• In the SSP2-4.5 scenario, average annual rainfall is anticipated to increase by 5.9 % by MC and roughly 13.8 % by EC for the valley, whereas, in the SSP5-8.5 scenario, it is forecasted to increase by about 14 % by MC and EC. This means that both the MC and EC projections for increased rainfall are not significant.
• Districts namely Kulgam, Shopian, and Anantnag, show the highest projected increase (18%) in rainfall towards MC, while the Srinagar, Baramulla, and Kupwara districts in the south show the lowest projected increase (16%) in annual rainfall as compared to the other districts of valley towards EC with respect to BL for the SSP2-4.5 scenario. The Ganderbal district shows a moderate projected increase towards both MC and EC.
• It is observed that the northeastern districts show a higher projected increase than the western districts of the valley.
• The rise is greater in the SSP5-8.5 scenario compared to the SSP2-4.5 scenario.

3.3.1. Inherent Exposure (Baseline Data)

The exposure index ranged from 0.29 to 0.82 as shown in Figure 10. Baramulla District has the highest exposure (0.82), followed by Pulwama and Srinagar (0.76) and Kupwara (0.71). These are the four districts with the highest exposure indices. Shopian (0.68) and Budgam were the two districts with the moderate exposure. For baseline (BL) data, the least amount of exposure was found in Kulgam, Anantnag, Bandipora (0.33), and Ganderbal (0.29) (

Table 9. Inherent exposure of the evaluated districts of the Kashmir region.

District	Exposure (w.r.t. Baseline)
Baramulla	0.82
Pulwama	0.76
Srinagar	0.76
Kupwara	0.71
Shopian	0.68
Budgam	0.65
Kulgam	0.33
Anantnag	0.33
Bandipora	0.33
Ganderbal	0.29

).

3.3.2. Exposure (SSP2-4.5 MC) and (SSP2-4.5 EC)

According to SSP2-4.5 MC, District Shopian has the highest exposure (0.95), followed by Pulwama (0.80) and Baramulla (0.76). Districts Budgam (0.69), Srinagar (0.65), Kulgam (0.65), and Kupwara (0.62) are moderately exposed, while districts Anantnag (0.32), Bandipora (0.03), and Ganderbal (0.01) fall in low-exposure zones (Figure 10c).

Under SSP2-4.5 EC, District Shopian again has the highest exposure (0.955), followed by Pulwama (0.774) and Baramulla (0.768). Districts Budgam (0.693), Srinagar (0.651), Kulgam (0.659), and Kupwara (0.630) are moderately exposed, while districts Anantnag (0.286), Bandipora (0.036), and Ganderbal (0.012) fall in low-exposure zones (Figure 10d).

3.3.3. Exposure (SSP5-8.5 MC) and (SSP5-8.5 EC)

As per the analysis, under SSP5-8.5 MC District Shopian again has the highest exposure (0.954), followed by Pulwama (0.802) and Baramulla (0.775). Districts Budgam (0.693), Kulgam (0.654), Srinagar (0.653), and Kupwara (0.627) are moderately exposed, while districts Anantnag (0.283), Bandipora (0.037), and Ganderbal (0.012) fall in low-exposure zones (Figure 10e).

For SSP5-8.5 EC, District Shopian again has the highest exposure (0.948), followed by Baramulla (0.793), Pulwama (0.785), and Budgam (0.701). Districts Kulgam (0.665), Kupwara (0.652), and Srinagar (0.642) are moderately exposed, while districts Anantnag (0.270), Bandipora (0.033), and Ganderbal (0.007) fall in low-exposure zones (Figure 10f).

3.4. Risk

The BL, MC and EC Risk scenarios were calculated risk using IPCC (2014) risk model which is the function of vulnerability, hazard and exposure and is presented in

Table 10. Overall districtwise values of vulnerability, hazard, exposure, and risk of the Kashmir region.

District	Vulnerability	Hazard	Exposure					Risk
			BL	SSP2- 4.5 M	SSP5- 8.5 M	SSP2- 4.5 E	SSP5- 8.5 E	BL	SSP2- 4.5. M	SSP5- 8.5. M	SSP2- 4.5. E	SSP5- 8.5. E
Kulgam	0.569	0.17	0.339	0.652	0.654	0.659	0.665	0.033	0.064	0.06	0.06	0.07
Pulwama	0.473	0.46	0.77	0.804	0.802	0.801	0.785	0.167	0.174	0.17	0.17	0.18
Shopian	0.457	0.28	0.689	0.952	0.954	0.955	0.948	0.09	0.124	0.12	0.12	0.127
Srinagar	0.408	0.46	0.762	0.654	0.653	0.651	0.642	0.122	0.131	0.13	0.13	0.139
Ganderbal	0.562	0.39	0.294	0.012	0.012	0.012	0.007	0.065	0.066	0.06	0.06	0.07
Budgam	0.484	0.43	0.656	0.69	0.693	0.693	0.701	0.136	0.144	0.14	0.14	0.149
Baramulla	0.476	0.54	0.825	0.768	0.775	0.774	0.793	0.193	0.198	0.20	0.20	0.207
Bandipora	0.595	0.55	0.333	0.035	0.037	0.036	0.033	0.01	0.012	0.01	0.01	0.019
Kupwara	0.541	0.47	0.712	0.62	0.627	0.63	0.652	0.158	0.16	0.16	0.16	0.168
Anantnag	0.496	0.49	0.336	0.286	0.270	0.283	0.268	0.082	0.283	0.281	0.319	0.268

BL: baselinel; MC: mid-century; EC: end-century; SSP: Shared Scepresentative Concentration Pathway.

. This projected calculation of climate risks provides planners and stakeholders with information that may be beneficial in preparing for the future.

3.4.1. Risk (BL)

Based on the assessment, the risk index ranged from 0.010 to 0.193 (Figure 11a,b). Five districts show the highest risk index, viz., District Baramulla has the highest risk (0.193), followed by Pulwama (0.167), Kupwara (0.158), Budgam (0.136), and Srinagar (0.122). The following three districts were moderately prone to risk: Shopian (0.090), Ganderbal (0.065), and Anantnag (0.062). Kulgam (0.033) and Bandipora (0.010) have the least risk for BL.

3.4.2. Risk (SSP2-4.5 MC) & (SSP2-4.5 EC)

As per SSP2-4.5 MC, Baramulla has the highest risk (0.198), followed by Pulwama (0.174), Kupwara (0.160), Budgam (0.144), Srinagar (0.131), and Shopian (0.124). The: Anantnag (0.070) and Ganderbal (0.066) districts were moderately prone to risk. Kulgam (0.064) and Bandipora (0.012) were least at risk for the SSP2-4.5 mid-century scenario (Figure 11c).

Under SSP2-4.5 EC, Baramulla has the highest risk (0.203), followed by Pulwama (0.176), Kupwara (0.166), Budgam (0.145), Srinagar (0.133), and Shopian (0.124). The Anantnag (0.073) and Ganderbal (0.067) districts were moderately prone to risk. Kulgam (0.065) and Bandipora (0.014) were least at risk (Figure 11d).

3.4.3. Risk (SSP5-8.5 MC) & (SSP5-8.5 EC)

According to SSP5-8.5 MC, Baramulla has the highest risk (0.198), followed by Pulwama (0.174), Kupwara (0.159), Budgam (0.144), Shopian (0.124), and Srinagar (0.121). The Anantnag (0.069) and Kulgam (0.064) districts were moderately prone to risk. Bandipora (0.012) and Ganderbal (0.003) were least at the risk (Figure 11e).

Under SSP5-8.5 EC, District Baramulla has the highest risk (0.197), followed by Pulwama (0.174), Kupwara (0.158), Budgam (0.143), Shopian (0.124), and Srinagar (0.121). The Anantnag (0.079) and Kulgam (0.064) districts were moderately prone to risk. Bandipora (0.011) and Ganderbal (0.003) were least at risk (Figure 11f).

In both of the climate scenarios, the risk to the Kashmir Valley and its districts will rise from the BL by the EC. By EC, the SSP5-8.5 scenario is more likely to result in a more risk in districts than the SSP2-4.5 scenario.

4. Discussion

The assessment of vulnerability, hazard, exposure, and risk in the Kashmir Valley districts provides crucial insights into climate-related challenges and their implications for Sustainable Development Goals (SDGs) 2 (Zero Hunger) and 13 (Climate Action). These findings highlight the interplay between climate impacts and development objectives, underscoring the need for targeted strategies to address food security and climate resilience.

The correlation analysis (Figure 12) among vulnerability, hazard, exposure, and risk reveals intricate relationships that challenge common assumptions about risk factors. Notably, there is a moderate negative correlation between vulnerability and risk (−0.617), suggesting that higher vulnerability in agriculture sector in Kashmir valley does not necessarily equate to higher risk based on the dataset and indicators used [42]. This finding is contrary to typical risk assessment frameworks, where vulnerability is often considered a direct driver of risk. In this context, high vulnerability districts like Bandipora maintain low risk in the agriculture sector primarily due to low exposure [43] as the district is having less area under cropland/plantation hence the influence of climatic factors leading to droughts and floods is minimal, underscoring the critical role exposure plays in amplifying risk. The relationship between hazard and risk (0.378) is weakly positive, indicating that while an increase in hazard levels tends to raise risk, the impact is not pronounced. This could be attributed to the variability in how districts are exposed or prepared for potential hazards, demonstrating that hazard intensity alone is insufficient to determine overall risk without considering exposure and vulnerability [44].

Exposure, with a strong positive correlation to risk (0.873), emerges as the most significant factor contributing to the overall risk levels. This strong association underscores that districts with higher exposure, such as Baramulla and Pulwama, as the districts are having maximum area under agriculture/horticulture, which faces substantial risk regardless of their vulnerability or hazard levels. Climate variability and extreme weather events pose substantial threats to agricultural productivity, potentially leading to crop failures and increased food insecurity [45]. During the recent years the extreme weather events like untimely and heavy rainfall/snowfall, hailstorms, high temperatures, etc have severely damaged the standing crop in these districts [46]. Conversely, districts such as Kupwara and Srinagar show elevated risk levels due to the combined effect of moderate hazards and high exposure. Hazard itself has a varying influence, as seen in districts like Anantnag, where moderate hazard and vulnerability do not translate into very high risk due to relatively lower exposure. This is attributed to Kupwara district’s increasing precipitation trends [47], coupled with its extensive vegetative cover and cultivated land, which collectively elevate its risk profile. In contrast, Srinagar district’s low-lying topography makes it inherently vulnerable to frequent flood events, particularly during periods of heavy precipitation. The flat terrain and inadequate drainage capacity in Srinagar exacerbate its susceptibility to flooding, posing persistent risks to agriculture sector and livelihoods [48]. These contrasting conditions highlight the need for differentiated risk management approaches, where Kupwara’s land use and precipitation trends necessitate enhanced soil and water conservation measures, while Srinagar requires robust flood mitigation strategies to address its chronic exposure to flood hazards.

The analysis highlights that managing exposure is crucial for effective risk mitigation, as it consistently exacerbates the impacts of hazards, independent of the district’s baseline vulnerability. This underscores the necessity of incorporating climate resilience into agricultural practices [49]. Enhancing soil health, diversifying crop varieties, and optimizing water management strategies are critical for bolstering food security and advancing the objectives of Sustainable Development Goal 2 (SDG 2).

Institutional mechanisms and mainstream initiatives are essential for implementing climate-resilient adaptation measures [50]. Reviewing national-level plans reveals elements that support water and agricultural management. Although achieving institutional convergence for development is challenging, testing and implementing mainstream solutions can help overcome financial limitations and improve adaptation efforts.

5. Conclusions and Future Scope

The study comprehensively assessed agricultural vulnerability and risk in the Kashmir Valley using the IPCC framework and SSP based climate scenarios for Mid and End Century. The results revealed significant disparities in vulnerability across different districts. Bandipora emerged as the most vulnerable district followed closely by Kulgam and Ganderbal. These districts are particularly susceptible due to factors such as low per capita income, high yield variability, and large areas with slopes greater than 30%. In contrast, Shopian and Srinagar were identified as the least vulnerable districts, benefiting from adaptive capacities like livelihood diversification, a higher percentage of tree crops, and a significant proportion of agricultural labor. In terms of risk, Baramulla, Pulwama, Kupwara, and Budgam were identified as the highest-risk districts, primarily due to their high exposure levels to hazards like floods, landslides, and droughts. The analysis highlighted that while vulnerability and hazard levels play crucial roles, exposure emerged as a significant determinant of overall risk. For instance, Baramulla and Pulwama, with high agricultural and horticultural land areas, are particularly exposed to climate variability, increasing their risk levels regardless of their inherent vulnerability. Projected future climate scenarios indicate a marked increase in temperature and variability in precipitation, which could exacerbate the region’s risk profile, significantly impacting crop yields and agricultural sustainability. The findings underline the necessity for district-specific adaptation strategies to mitigate these risks, align with Sustainable Development Goals 2 and 13, and enhance the resilience of the agricultural sector in the Kashmir Valley.

Future research on agricultural vulnerability and risk in the Kashmir Valley should aim to incorporate a broader range of climate scenario models, including more extreme conditions, to better capture potential future impacts on the region. Additionally, integrating future socioeconomic changes such as population growth, economic development, and land-use changes could provide a more dynamic understanding of how these factors might influence vulnerability and risk over time. There is also a need for more localized assessments at the village or panchayat level, as this would enable a finer resolution in identifying specific at-risk communities and allow for more targeted and effective adaptation strategies. Advanced statistical methods like Principal Component Analysis (PCA), factor analysis, and cluster analysis could enhance future studies by providing deeper insights into the interrelationships among various vulnerability indicators and refining assessment models. Furthermore, expanding the scope of research to include other sectors such as health, water resources, and forestry would offer a more comprehensive view of climate vulnerability and aid in developing holistic adaptation strategies.

One major uncertainty is the limitation of data quality and availability, which can affect the accuracy of vulnerability and risk assessments. Additionally, the inherent variability in climate models and the assumptions made in projecting future climate conditions can lead to differing outcomes, thereby influencing the reliability of risk predictions. There is also uncertainty regarding the adaptive capacity of local communities, as this can vary widely depending on future policy interventions, economic changes, and technological advancements. Despite these uncertainties, vulnerability and risk assessments are invaluable tools for policymakers. They provide crucial insights that can guide the design of adaptation strategies aimed at reducing climate risks and enhancing resilience. By identifying the most vulnerable areas and understanding the factors contributing to their risk, these assessments help prioritize resource allocation and inform targeted interventions. This, in turn, supports the development of policies that are proactive and responsive to the specific needs of different regions, ultimately contributing to sustainable development and climate resilience in the Kashmir Valley.