Vulnerability of Dairy-Based Livelihoods to Climate Variability And
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RESEARCH COMMUNICATIONS Vulnerability of dairy-based mate fluctuates yearly above or below a long-term aver- age value. It is not as noticeable as weather variability livelihoods to climate variability and because it happens over seasons and years. Climate change: a study of Western Ghats change is a long-term continuous change (increase or region in Wayanad, Kerala decrease) to average weather conditions or the range of weather and climatological normal is 30-years average of weather variables10. Aparna Radhakrishnan* and Jancy Gupta Climate change impact differs from region to region, National Dairy Research Institute, Karnal 132 001, India country to country, sector to sector and community to community12,13. Its vulnerability is dynamic and depends The study assesses the livelihood vulnerability of dairy on both biophysical and social processes14,15. The Inter- farmers to climate variability and change (CVC) in governmental Panel on Climate Change (IPCC) defines Wayanad district of the Western Ghats region in vulnerability to climate change as ‘the degree to which a Kerala. For this purpose, a Livelihood Vulnerability system is susceptible, or unable to cope with adverse ef- Index (LVI) was developed underlying the definition of Intergovernmental Panel on Climate Change con- fect of climate change, including climate variability and sisting of 28 indicators and 7 LVI components. A extremes. Vulnerability to climate change depends on the fussel framework was used for conceptualizing the rate of change of the climate and the extent to which a vulnerable situation. Participatory rural appraisal system is exposed, its sensitivity, and adaptation capa- and personal interviews were used to collect house- city’10. ‘Sensitivity is the degree to which a system is hold data of 180 dairy farmers of three taluks com- affected, either adversely or beneficially by climate- plemented by thirty years of gridded weather data. related stimuli’. ‘Adaptive capacity is the ability of a sys- The normalized data were then combined into three tem to adjust to climate change including climate vari- indices, i.e. sensitivity, exposure and adaptive capa- ability and extremes, to moderate the potential damage city, which were then averaged with weights given from it to take advantage of its opportunities or to cope using principal component analysis, to obtain the with its consequences2’. Observed positive effects for overall index. LVI indicated that the dairy farmers of all the taluks of Wayanad are vulnerable to CVC with poor and marginalized people, which are restricted, Pulpally taluka being the most vulnerable with include examples such as diversification of social 11 48.33% farmers under the high level vulnerability networks and of agricultural practices . In India, the re- category with wide variation in LVI components silience of agricultural system is closely linked to the across the taluks. For the sustenance of dairy farming livestock system and the livestock’s contribution towards of small and marginal farmers of the region and for risk reduction and adaptation to climate variability is sig- mitigating risks, policies are required for incentivizing nificantly higher than their negative impacts16. The esti- the livelihood infrastructure and promotion of grass mated annual loss at present due to heat stress among root level innovations. cattle and buffaloes at the all-India level is 1.8 million tonnes of milk, about 2% of the total milk production in Keywords: Climate change, livelihoods, livelihood vul- the country, amounting to a whopping Rs 2661 crore17. nerability, participatory rural appraisal, milk. This communication briefly assesses the overall house- hold level livelihood vulnerability to climate change of REDUCING rural poverty, achieving global food and nutri- the dairy farmers of Wayanad region, Kerala. tional security and mitigating climate change, are the The present study is based on contextual vulnerability three most critical and interrelated problems encountered 1 approach that usually centres on the present socio-economic by the global community . Economically poor farmers are factors or determinants of vulnerability, i.e. economic, the most vulnerable group to the long-term impacts of 2–5 social and institutional conditions. Fussel framework climate change and the impact is detrimental to a de- defines four fundamental dimensions to describe a vulner- veloping country like India where the main source of the able situation18, concepts describing the vulnerability livelihood population is agriculture and allied sectors6–8. 9 concepts and nomenclature of vulnerable conditions. Fus- The farmers often have limited capacity to adapt . In sel describes climate-related vulnerability assessments India climate change significantly impacts agriculture as based on ‘characteristics of the vulnerable system, the the west coast and southern India are projected to shift to type and number of stressors and their root causes, their a new high temperature climatic regime under 4C warm- 10 effects on the system, and the time horizon of the assess- ing . In India, the livelihood of small farm holders (those ment’18. The system of analysis is the first fundamental owning less than 2.0 ha of farmland) that comprise 78% dimension that combines natural or human systems18. For of the country’s farmer population, is mostly affected by 11 the present study, the major component of the dairy farm- climate change . Climate variability is the way the cli- ing system comprises the sample dairy farmers and the dairy animals of Wayanad, Western Ghats region. The *For correspondence. (e-mail: [email protected]) second dimension is the attribute of concern: the valued CURRENT SCIENCE, VOL. 113, NO. 1, 10 JULY 2017 123 RESEARCH COMMUNICATIONS attribute(s) of the vulnerable system that is/are threatened 650 lakh litres25. From Wayanad district, three taluks by its exposure to a hazard18. The present study finds out were randomly selected namely Sulthan Bathery, Man- how the livelihood of dairy farmers is affected by CVC. anthavady and Pulpally. Sixty dairy farmers were ran- The study is based on LVI by Hahn et al.19, that includes domly selected from each taluk from the dairy farmer seven major components (Table 1). The analysis of LVI population in the taluk, and thus constituting a total of components and their progress further permits discussing 180 respondents that owned a minimum of two dairy the livelihood vulnerability of farmers. Fussel defines the animals and a maximum of ten and who were dairy farm- third dimension – the hazard as ‘a potentially damaging ers since 1994. influence on the system of analysis’, ‘some influence that Econometric and indicator approaches are two tech- may adversely affect a valued attribute of a system’. The niques commonly employed to measure vulnerability to present study considers the dynamic perspective of the CVC19,26. This study adopts the indicator approach in system, while studying the effects of the multiple factors measuring the vulnerability of dairy farmers of the West- of CVC on changes in average milk production. The ern Ghat region to climate change and involves selection fourth dimension of vulnerable situations is the temporal of indicators that largely account for vulnerability27. Sta- reference. For the present study, the different dynamics of tistical Package for the Social Sciences (SPSS 20)28, R CVC is considered for the period 1994 to 2014 in addi- Core Team (2012)29, Microsoft Excel 2007 are the major tion to the current vulnerability assessment. software packages used for analysis. Participatory rural appraisal (PRA) techniques like The indicators are selected to provide animal hus- transect walk, focus group discussions, time lines, sea- bandry department, development organizations and policy sonal calendar, actor system mapping, key informant makers a practical tool to assess the contributions of live- interview, Venn diagram, problem tree analysis and semi- stock, social and climatic factors that suits the need of structured personal interview schedule were used to col- each geographical location. Mathematical approach to lect primary data of dairy farmers. Three PRA were con- LVI comprises of seven major components26: socio- ducted at taluk level, comprising of progressive farmers, demographic profile, livelihood strategies, social net- dairy development officers and officers of milk coopera- works, health, food, livestock, natural disasters and climate tive societies. The primary data collected from farmers variability. Each component comprises several indicators were validated with the members of respective milk or sub-components. These were developed based on a re- cooperative societies. Secondary data sources were Natio- view of the literature on each major component, as well nal Centers for Environmental Prediction (NCEP)/National as the practicality of collecting the needed data through Center for Atmospheric Research (NCAR) Reanalysis me- household surveys (Tables 1 and 2). After normalization, teorological data, from National Oceanic and Atmospheric the testing of suitability of indicators and elimination of Administration (NOAA)20 Physical Sciences Division non-significant indicators and assigning weightage were (PSD) data sources, India Meteorological Department carried out using principal component analysis (PCA) (IMD) data21,22 and CRU 3.23 (Climate Research Unit, following similar studies30–32. For the present study the UK)23 was taken and climatic indicators were calculated