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FARMERS’ PERCEPTION ABOUT CLIMATE CHANGE AND ITS IMPACT ON AGRICULTURE AND ALLIED
ACTIVITIES IN CHHATTISGARH PLAINS
Ph.D. Thesis
by
Omprakash Parganiha
DEPARTMENT OF AGRICULTURAL EXTENSION COLLEGE OF AGRICULTURE, RAIPUR
FACULTY OF AGRICULTURE INDIRA GANDHI KRISHI VISHWAVIDYALAYA
RAIPUR (Chhattisgarh)
2016
FARMERS’ PERCEPTION ABOUT CLIMATE CHANGE AND ITS IMPACT ON AGRICULTURE AND ALLIED
ACTIVITIES IN CHHATTISGARH PLAINS
Thesis
Submitted to the
Indira Gandhi Krishi Vishwavidyalaya, Raipur
by
Omprakash Parganiha
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF
Doctor of Philosophy
in
Agricultural Extension
Roll No. 15435 ID No. Ag./96/36
January, 2016
i
ACKNOWLEDGEMENT First of all, I bow my head and offer flowers of reverence to the supreme
almighty “God” whose blessings enabled me to be so today and I dedicate my every effort and achievement to my father Late Shri Aparbal Singh Parganiha who inspires, loved, cared and blessed in every moment of my life.
It is an unique opportunity to express my heartiest and deep sense of gratitude, indebtedness, profound etiquette and sincere thanks to Dr. M.L. Sharma, Professor and Head, Department of Agricultural extension, College of Agriculture, I.G.K.V., Raipur and chairman of my advisory committee, for his keen interest, meticulous supervision, scholastic guidance, sustained inspiration, valuable advice, constructive criticism, vigilant supervision and encouragement throughout the course of investigation and preparation of this manuscript.
I owe indebtedness to Dr. (Major) G.K. Shrivastava, Professor, Department of Agronomy and member of my advisory committee, for extending his generous help and able guidance throughout the ups and downs during my research work. With a deep sense of gratitude and zeal, I extend my warmest thanks to the members of my advisory committee Dr. M.L. Lakera, Professor (Agricultural Statistics) and Dr. M.A. Khan, Associate Professor (Agril. Extension), IGKV, Raipur for their valuable suggestion, constant guidance, and cooperation in carrying out the work during the entire course of investigation.
I am highly obliged to Prof. S.K. Patil, Hon’ble Vice Chancellor, Indira Gandhi Krishi Vishva Vidylaya, Raipur, Chhattisgarh, for allowing me one and half years study leave with full paid salary to pursue Ph.D. I owe a deep sense of reverence to Dr. S.S. Shaw, Director Instructions, Dr. S.S. Rao, Dean, College of Agriculture, Dr. J.S. Urkurkar, Director Research Services, Dr. M.P. Thakur, Director Extension services, Dr. O.P. Kashyap, Dean Student Welfare, IGKV, Raipur.
I extend my sincere regards and heartiest gratitude to Dr. R.N. Ganguli, Dean and all the faculty members of SKS CARS, Rajnandgaon and CHRS, Jagdalpur for their valuable advice, kind cooperation, timely help and providing necessary facilities during the course of the study.
With a deep sense of gratitude and zeal, I extend my warmest thanks to Dr. K.N.S. Banafar, Professor and Head (Agril. Economics), Dr. J.D. Sarkar, Professor (Agril. Extension), Dr. K.K. Shrivastava, Dr. R.S. Sengar, Dr. H.K. Awashthi
iii
TABLE OF CONTENTS
Chapter Title Page
ACKNOWLEDGEMENT iii
TABLE OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES x
LIST OF ABBREVIATIONS xi
ABSTRACT xii I INTRODUCTION 1-8
II REVIEW OF LITERATURE 9-43 2.1 Socio-personal characteristics 9 2.2 Socio-economic characteristics 13 2.3 Communicational characteristics 19 2.4 Psychological characteristics 24 2.5 Perception of farmers about climate change 29 2.6 Impact of climate change on agriculture and allied activities
34
2.7 Coping mechanism/adaptation in response to climate change
38
2.8 Crop diversification in response to climate change 40 2.9 Relationship between dependent and independent variables
41
2.10 Factors affecting adaptation 41 2.11 Constraints in adaptation 42 2.12 Suggestions 43
III MATERIALS AND METHODS 44-76
3.1 Location of the study area 44 3.2 Sample and sampling procedure 44 3.3 Variables of the study 48
3.3.1 Independent variables 48 3.3.2 Dependent variables 48
iv
Chapter Title Page
3.4 Operationalization of independent variables and their measurement
48
3.4.1 Socio-personal characteristics 48 3.4.2 Socio-economic characteristics 52 3.4.3 Communicational characteristics 58 3.4.4 Psychological characteristics 62
3.5 Operationalization of dependent variables and their measurement
68
3.5.1 Perception of farmers about climate change 68 3.5.2 Impact of climate change on agriculture and allied
activities 68
3.6 Coping mechanism/adaptation in response to climate change
69
3.7 Relationship between dependent and independent variables
70
3.8 Constraints faced by farmers in coping mechanism/adaptation
72
3.9 Suggestions given by farmers to overcome the constraints 72 3.10 Type of data 72 3.11 Developing the interview schedule 72
3.11.1 Validity 73 3.11.2 Reliability 73
3.12 Method of data collection 74 3.13 Statistical analysis 74
IV RESULTS AND DISCUSSION 77-159
4.1 Independent Variables 77 4.1.1 Socio-personal characteristics 78 4.1.2 Socio-economic characteristics 83 4.1.3 Communicational characteristics 99 4.1.4 Psychological characteristics 110
4.2 Dependent variables 120 4.2.1 Perception of farmers about climate change 120 4.2.2 Impact of climate change on agriculture and allied activities
124
v
Chapter Title Page
4.2.2.1 Impact of long term climate change 125 4.2.2.2 Impact of short term climate change 131
4.3 Coping mechanism/adaptation to climate change 141 4.4 Relationship between dependent and independent variables
145
4. 5 Constraints faced by farmers in adaptation to climate change and their suggestions to minimize the constraints
153
4.5.1 Constraints in coping/adaptation to climate change 153 4.5.2 Suggestions given by farmers to overcome the constraints
154
V SUMMARY AND CONCLUSIONS 160-171 BIBLIOGRAPHY 172-190 APPENDICES 191-219 Appendix A – Interview schedule 191 Appendix B – Monthly average maximum & minimum temperature, rainfall and sunshine hour of Raipur district of Chhattisgarh Plain
212
Appendix C – Annual rainfall trends in different districts of Chhattisgarh Plains
216
Appendix D – Paper cutting of climate change related news 219 VITA 220
vi
LIST OF TABLES
Table Title Page
3.1 List of selected blocks, villages and number of respondents in different districts of Chhattisgarh Plains
46
3.2 Scales used for measuring the variables 47
4.1 Distribution of respondents according to their socio-personal characteristics
79
4.2 Distribution of respondents according to their land holding 84
4.3 Distribution of respondents according to availability of irrigation 85
4.4 Distribution of respondents according to availability of irrigation and source wise irrigated area among the respondents
86
4.5 Season wise crops grown by respondents with average area and productivity
87
4.6 Distribution of respondents according to their occupation 89
4.7 Distribution of respondents according to their annual income 90
4.8 Credit acquisition pattern of the respondents 93
4.9 Distribution of respondents according to their availability of farm implements
94
4.10 Distribution of respondents according to their distance to market for seasonal farm inputs
96
4.11 Distribution of respondents according to their crop insurance institution
98
4.12 Distribution of respondents according to their socio-economic status 99
4.13 Extent of contact of the respondents with extension personnel 101
4.14 Extant of participation of respondents in extension activities 101
4.15 Extent of mass media participation 105
4.16 Extent of utilization of information sources for weather forecast 105
4.17 Distribution of respondents according to their cosmopoliteness 110
4.18 Distribution of respondents according to their psychological characteristics
111
4.19 Distribution of respondents according to their awareness about climatic variability
114
vii
Table Title Page
4.20 Distribution of respondents according to their level of awareness about climate change
115
4.21 Natural disasters faced by respondents during last 15 years along with their coping mechanisms
117
4.22 Other disasters faced by respondents during last 15 years along with their coping mechanisms
118
4.23 Disasters faced by respondents along with extent of losses during last 15 years
119
4.24 Distribution of respondents according to their extent of vulnerability 120
4.25 Distribution of respondents according to their perception about climatic variability
121
4.26 Distribution of respondents according to their extent of perception about climatic variability
123
4.27 Perception of respondents about impact of long term climate change on agriculture
126
4.28 Perception of respondents about impact of long term climate change on allied activities
127
4.29 Perception of respondents about overall impact of long term climate change
128
4.30 Impact of long term climate change on various crops grown by respondents
130
4.31 Impact of short term climate change on area under various varieties of paddy
132
4.32 Impact of short term climate change on area under various crops 134
4.33 Impact of short term climate change on infestation of weeds, insects and diseases in paddy crop
136
4.34 Impact of short term climate change on other selected cultural operations of paddy
139
4.35 Distribution of respondents according to their coping mechanism against excess rainfall
142
4.36 Distribution respondents according to their coping mechanism against deficit rainfall
144
4.37 Correlation matrix of selected independent and dependent variables 146
viii
Table Title Page
4.38 Multiple regression analysis of best fit model among selected independent variables with perception of farmers’ about climate change
148
4.39 Multiple regression analysis of selected model among independent variables with perception of farmers’ about climate change
149
4.40 Multiple regression analysis of best fit model among selected independent variables with perception of farmers’ about impact of climate change on agriculture and allied activities
151
4.41 Multiple regression analysis of selected model among independent variable with perception of farmers’ about impact of climate change on agriculture and allied activities
152
4.42 Distribution of respondents according to constraints faced by them in coping to climate change
154
4.43 Distribution of respondents according to their suggestions to minimise the constraints in coping to climate change
155
ix
LIST OF FIGURES
Figure Title Page
3.1 Map of study area 45
3.2 Conceptual model of study 50
3.3 Empirical model of vulnerability 67
4.1 Distribution of respondents according to their participation in social organisation
82
4.2 Income and expenditure patterns of respondents 91
4.3 Distribution of respondents according to their availability of farm implements
95
4.4 Distribution of respondents according to availability of market 97
4.5 Distribution of respondents according to their contact with extension personnel
100
4.6 Distribution of respondents according to their participation in extension activities
102
4.7 Distribution of respondents according to their use of mass media 104
4.8 Utilization pattern of information sources for weather forecast 107
4.9 Distribution of respondents according to their cosmopoliteness 109
4.10 Distribution of respondents according to their decision making pattern
113
4.11 A strategies suggested for sustainable agriculture against climate change
159
5.1 Empirical model of the study 171
x
LIST OF ABBREVIATIONS
ADO Agriculture Development officer C.G. Govt. Chhattisgarh Government CBS Central Bureau of Statistics CEEPA Center for Environmental Economics and Policy in Africa CIMMYT International Maize and Wheat Improvement Center FAO Food and Agricultural Organization of the United Nations EDP Entrepreneurship Development Programme GDP Gross Domestic Product IARI Indian Agriculture Research Institute IFAD International Fund for Agricultural Development IPCC Intergovernmental Panel for Climate Change IMD Indian Meteorological Department INC Intergovernmental Negotiating Committee NEST Nigerian Environmental Study Team NGO Non-Governmental Organization RAEO Rural Agriculture Extension officer RCDC Regional Centre for Development Cooperation SAGUN Strengthened Actions for Governance in Utilization of Natural
Resources SMS Subject Matter Specialist SAE Supervised Agricultural Experience TERI Tata Energy research Institute TV Television UAE Unsupervised Agricultural Experience UNEP United Nations Environment Programme UNFCCC United Nation Framework Convention on Climate Change USA United States of America WHO World Health Organization
xii
were possessing 1.1 to 2 ha of land including leased land and belonged to small farmers’ category. They were engaged in agriculture along with labour as their main occupation followed by agriculture alone with average annual income of Rs. 87534.62/- and belonged within lower to lower middle class. Majority of them had acquired credit up to Rs. 25001 to 50000 as crop loan from cooperative societies and repaid in kind. They insured their crop from government institution like cooperative society as compulsory insurance. About half of the respondents had low level of contact with extension personnel, they had contacted regularly with input dealers and occasionally with RAEO. Among them friends/relatives/etc., news paper and mobile were most credible sources of information with low level of utilization for collecting weather information. They were having low level of cosmopoliteness, medium level of risk orientation, innovativeness, scientific orientation, awareness about climate change and low level of decision making and vulnerability. Majority of the respondents perceived high changes in climatic conditions in rainy season, winter season and summer season. They agreed that investment in agriculture has increased, cropping pattern has changed and use of traditional crop varieties decreased as major impact of long term climate change on agriculture. The study revealed that area under long duration variety like Swarna increased in case of early arrival of monsoon, whereas, area under medium duration varieties like MTU-1010 and Mahamaya increased in case of late arrival of monsoon in kharif. Moreover, area under broadcasting/biasi method of sowing of paddy decreased and lehi method & transplanting method of sowing increased in case of early arrival of monsoon in kharif. As an adaptation to excess rainfall at the time of sowing the majority of them delayed sowing dates and used short duration variety of paddy. On other hand they were opted late harvesting in case of excess rainfall at the time of maturity of crop. They delayed sowing dates and increased seed rate of paddy as an adaptation to deficit rainfall at the time of sowing. The major constraints to adapting to climate change faced by the respondents were lack of information about accurate weather forecast, irregularity of extension services and lack of knowledge about need based improved agriculture technologies with rank of I, II and III, respectively. To overcome the above constraints, the majority of the respondents suggested that weather forecast should be more accurate and timely, whereas, another suggestion were effective extension services should be available to the farmers and proper information should be provided about climate change which might be enable them to adapt against climate change.
xiv
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Introduction
CHAPTER – I
INTRODUCTION
Climate change is one of the biggest environmental challenges in all
countries in the world. Climate change refers to any change in climate over time,
whether due to natural variability or/and as a result of human activity (IPCC,
2007a). It has become a major concern to society because of its potentially adverse
impacts worldwide. There are already increasing concerns globally regarding
changes in climate that are threatening to transform the livelihoods of the
vulnerable population segments. The average annual temperature of the Earth’s
surface has risen over the last century. Not only is the temperature rising, but the
rate of warming itself is increasing too. The earth’s climate has warmed on average
by about 0.70C over the past 100 years with decades of the 1990s and 2000s being
the warmest in the instrumental record (Watson, 2010). In ecological terms, this is
a very rapid change. Most of the countries are facing the problems of rising
temperature, melting of glaciers, rising of sea-level leading to inundation of the
coastal areas, changes in precipitation patterns leading to increased risk of
recurrent droughts and devastating floods.
Climate change impacts and associated vulnerability are of particular
concern to developing countries, where large parts of the population depend on
climate sensitive sectors like agriculture and forestry for livelihood. By adversely
affecting freshwater availability and quality, biodiversity and desertification,
climate change tends to disproportionately affect the poorest in the society,
exacerbating inequities in access to food, water and health. India is considered to
be especially vulnerable to the impacts of climate change with an extraordinary
variety of climatic regions, ranging from tropical in the south to temperate and
alpine in the Himalayan north, where elevated regions receive sustained winter
snowfall. The north of the country has a continental climate with severe summer
conditions that alternates with cold winters when temperatures plunge to freezing
point. In contrast are the coastal regions of the country, where the warmth is
unvarying and the rains are frequent. Climate change is likely to affect all the
1
natural ecosystems as well as socio-economic systems as shown by the National
Communications Report of India to the UNFCCC (INC, 2004). Various studies
have indicated a probability of 10 to 40 per cent loss in crop production in the
country due to the anticipated rise in temperature by 2080.
Climate change and agriculture are interrelated processes, both of which
take place on a global scale (Parry et al., 2007). Global warming is projected to
have significant impacts on conditions affecting agriculture, including temperature,
precipitation and glacial run-off (Funk et al., 2008 and McCarthy et al., 2001).
Agriculture places heavy burden on the environment in the process of providing
humanity with food and fiber, while climate is the primary determinant of
agricultural productivity. Given the fundamental role of agriculture in human
welfare, concern has been expressed by federal agencies and others regarding the
potential effects of climate change on agricultural productivity. Interest in this
issue has motivated a substantial body of research on climate change and
agriculture over the past decade (Lobell et al., 2008, Wolfe et al., 2005 and Fischer
et al., 2002).
In India, climate change is putting additional stress on ecological and socio-
economic systems that are already facing tremendous pressures due to rapid
urbanization, industrialization and economic development. With its huge and
growing population, a 7500-km long densely populated and low-lying coastline
and an economy that is closely tied to its natural resource base, India is considered
to be especially vulnerable to the impacts of climate change.
Like most other developing countries, people in India are dependent to a
large extent on its natural resources for livelihood and economy. Any adverse
impacts on these natural resources will have repercussion on the nation’s
livelihood security and economy and widen the gap between the rich and the poor.
Climate change is predicted by scientists to have the main impact on agriculture,
economy and livelihood of the populations of developing countries and India is
one of them, where large parts of the population depend on climate sensitive
sectors like agriculture and forestry for livelihood. By adversely affecting
freshwater availability and quality, biodiversity and desertification, climate change
tends to disproportionately affect the poorest in the society, exacerbating inequities
2
in access to food, water and health. The capacity to adapt is a function of access to
wealth, scientific and technical knowledge, information, skills, infrastructure,
institutions and equity and therefore varies among regions and socio-economic
groups. Climate change therefore is intrinsically linked to other environmental
issues and to the challenge of sustainable development.
In order to understand how human beings would respond to climate change,
it is essential to study people's perceptions of climate and the environment in
general (Vedwan and Rhoades, 2001). Human expectations regarding weather and
climate sometimes lead to perceptions of climate change which are not supported
by observational evidences (Rebetcz, 2000). A better understanding of how
farmers’ perceive climate change, ongoing adaptation measures, and the factors
influencing the decision to adapt farming practices is needed to craft policies and
programmes aimed at promoting successful adaptation of the agricultural sector
(Bryan et al., 2009).
As the understanding on global climate and its change is pre requisite to
take appropriate initiatives to combat climate change. The only solution for these
huge populations seems to be adequate and relevant adaptation strategies. It has
been reported that there is a large deficit of information and knowledge in this
vulnerable region which impedes decision making and assessment of climate
related risks, and adaptation (McSweeney et al., 2010). Adaptation to climate
change requires that farmers first notice that the climate has altered. Farmers then
need to identify potentially useful adaptations and implement them.
With regards to Chhattisgarh, the state is major producer of rice and is
mainly dependent on monsoon rainfall as the irrigation facilities are limited to very
small part of the region. The water supply for domestic purpose, water storage in
dams, ground water table, Hydro-electric generation, planning of government
policies and schemes etc. are also dependent on monsoon rainfall. Recent studies
in climate change in Chhattisgarh indicated that the rainfall pattern has changed
during 20th century, fluctuations in the onset and offset of monsoon rainfall,
decreasing pattern of rainfall in many districts and also the deficit rainfall years
increased during the global worming period. Climate is getting hotter in the state
due to increasing trend for both maximum and minimum temperature, which, has
3
been showed by many of the studies. With this climatic variability, farmers in the
state are vulnerable because their livelihood is totally dependent on agriculture.
Chhattisgarh is gradually progressing since its inception in the year 2000.
The state having total 138 lakh ha of geographical area, in which, about 64 lakh ha
is under forest, quite higher than the cultivated area of about 46 lakh ha. Area
under second crop is only one third of the cultivated area. The irrigation
availability in the state during kharif season is only for about 30 per cent of total
cultivated area, out of which about 61 per cent is irrigated by canal, 27 per cent by
tube well and remaining is by other sources. The disparity also prevails in the
holding size of farm families, out of total 32 lakh farm families residing in the
state, about 76 per cent comes under marginal and small farmers having less than 2
ha of land for cultivation. In the year 2011, share of production of important crops
like rice was about 65 lakh tons, gram 2.5 lakh tons and wheat 1.3 lakh tons out of
the total food grain production of 76 lakh tons in the state (Directorate of
Agriculture, Raipur, C.G. Govt., 2013).
Chhattisgarh plain zone comprising 15 districts out of 27 districts in the
state. It has a tropical wet and dry climate, temperature remain moderate
throughout the year, except from march to June. It has a mixed climate which is
more towards hotter side, summer are extremely hot and at times the mercury may
rise up to 47˚C. The zone receives about 1250-1300 mm of annual rainfall, in
which, share of monsoon rainfall from June to September is about 85-90 per cent.
Winters last from December to February and are mild, at times minimum
temperature dips up to 5˚C (IMD, Lalpur, Raipur). As for as agriculture is
concerned, out of total cultivated area in the state, 65 per cent is shared by the plain
zone. Rice is the principle crop of the zone and about 75 per cent of the total
production of rice in the state is produced by this zone. Area under second crop is
only one third of total cultivated area of the zone. The irrigation availability in the
zone during kharif season is only for about 45 per cent of total cultivated area and
rest of the area under cultivation is dependent on monsoon rainfall (Directorate of
Agriculture, Raipur, C.G. Govt., 2013).
The people and their livelihoods are inextricably entwined with their
climate and a very small change can affect them directly as well as indirectly. The
4
impact of climate change is not directly visible in the plains as compared to hilly
regions, but there is no doubt that there are some potential impacts that are still
unknown, that can adversely affect the plains regions as well. The present work
entitled “Farmers’ perception about climate change and its impact on
agriculture and allied activities in Chhattisgarh plains” was an attempt to
understand the impacts of climate change in the Chhattisgarh plain by taking into
account farmers’ perception, and to know how and to what extent they are
adjusting and/or not adjusting to these changes. The investigation was carried out
in Plain Zone of Chhattisgarh State during the years 2013-14 and 2014-15 with the
following objectives:
1. To study the profile of the farmers,
2. To determine the awareness and perception about climate change among
the farmers,
3. To assess the farmers vulnerability due to climatic variability,
4. To find out the impacts of climate change on various agriculture and allied
activities,
5. To find out adaptation/mitigation measures being taken by farmers in
response to climate change,
6. To ascertain the association between perception of farmers about climate
change and impact of climate change with selected independent variables,
and
7. To find out the constraints faced by farmers in various adaptation activities
in response to climate change and obtained suggestions from them to
minimise the constraints.
Importance of the StudyAgriculture is the most important sector of the economy in India provides
food and livelihood security to much of the Indian population. It plays a crucial
role in the country’s development contributing 16 per cent of India’s Gross
Domestic Product (GDP). Climate is one of the key components influencing
agricultural production and has large-scale impacts on food production and overall
economy. Agriculture in India suffers a lot from erratic weather patterns such as
heat stress, longer dry seasons and uncertain rainfall, since about 65 per cent of the
5
cultivated area fully depends on monsoon rainfall. Declined yield due to
unfavorable weather and climate will lead to vulnerability in the form of food
insecurity, hunger and shorter life expectancies. There are some impacts for which
adaptation is the only available and appropriate response.
India demonstrates diverse geo-physical and climatic conditions within
relatively small areas. It is, therefore, an ideal place to study climate change
impacts on natural and socio-economic spheres. Such a study would contribute
towards a better understanding of the intensity and impacts of global changes.
Studies on perceptions, local knowledge, and adaptive strategies at the household
and community levels, as well as lessons learned, can provide the basis for
concepts and methods of assessing climate change impacts, vulnerability, and
adaptation activities of the local farmers. In this context the present research seeks
to investigate impacts of climate change on agriculture and adaptation activities
carried out by the local people. Based on the case of the local peoples of
Chhattisgarh plain zone, this investigation intends to capture the extent of local
peoples‘awareness and perceptions of climate variability and change and the types
of adjustments they have made in their farming practices in response to these
change
Limitations of the StudyWith the limited knowledge and perception of farmers about climate
change, it was an attempt to quantify the impact of climate change on agriculture.
Large number of factors may be responsible for changes in climatic conditions and
its adverse effect on agriculture. However, with some limitations of the scholar,
efforts were taken to consider most of the important variables for investigation, so
that all the objectives of the study can be justified. It is not easy to evidently say
that particular change is occurring and agriculture is being directly impacted by
that, therefore, the present study has been carried out under a set of following
limitations:
1. The scientific knowledge on impacts of climate change is increasing all the
time, as are practical experiences in responding to adaptation needs. But,
this knowledge has not been fully exploited, which, imposed lot of problem
in collecting reviews.
6
2. In India lack of research and credible evidence on the impacts of climate
change was major challenge to find out the correctness of findings.
3. Selected issues were considered under study due to limited understanding
of local farmers on such basic issues as the nature and scale of impacts of
climate change on agriculture and livelihood aspect.
4. The resource constraints compelled the researcher to restrict the study in 4
districts out of 15 districts of plain zone of Chhattisgarh.
5. The investigation was confined to 240 respondents, selected from 8 blocks
of 4 selected districts, which may not be provide valid results of whole state
divided into three agro-climatic zones where climatic conditions are not
similar.
6. Complete findings of the study are based on past experiences, memories
and verbally expressed opinions of the farmers.
7. All necessary efforts were made to select and use of standardized tools and
techniques of data collection and analysis of data, yet their accuracy may
not guaranteed.
Terminologies usedClimate Change
United Nation Framework Convention on Climate Change (UNFCCC) has
defined climate change as a change of climate that is attributed directly or
indirectly to human activity that alters the composition of the global atmosphere
and that is in addition to natural climate variability observed over comparable time
periods.
Perception
As Ban et al. (2000) define perception: it is the process by which we
receive information or stimuli from our environment and transform it into
psychological awareness. It is interesting to see that people infer about a certain
situation or phenomenon differently using the same or different sets of
information. Knowledge, interest, culture and many other social processes that
shape the behaviour of an actor who uses the information and tries to influence that
particular situation or phenomenon (Banjade, 2003).
7
Impact
The effects of climate change on natural and human systems (IPCC, 2007a).
Depending on the consideration of adaptation, one can distinguish between
potential impacts and residual impacts:
Potential impacts: all impacts that may occur given a projected change in
climate, without considering adaptation.
Residual impacts: the impacts of climate change that would occur after
adaptation.
Adaptation
Initiatives and measures to reduce the vulnerability of natural and human
systems against actual or expected climate change effects (IPCC, 2007a). Various
types of adaptation exist, e.g. anticipatory and reactive, private and public, and
autonomous and planned.
Adaptation is the adjustment in natural or human systems in response to
actual or expected climate stimuli or their effects, which moderated harm or
exploits beneficial opportunities (SAGUN, 2009).
Adaptive capacities
Is the ability of a system to adjust to climate change (including climate
variability and extremes) to moderate potential damages, to take advantage of
opportunities, or to cope with the consequences.
There are individuals and groups within all societies that have insufficient
capacity to adapt to climate change. The capacity to adapt is dynamic and
influenced by economic and natural resources, social networks, entitlements,
institutions and governance, human resources, and technology (IPCC, 2007a).
Vulnerability
Vulnerability to climate change is the degree to which geophysical,
biological and socio-economic system are susceptible to, and unable to cope with,
adverse impacts of climate change, including climate variability and extremes
(IPCC, 2007a). Vulnerability is a function of the character, magnitude, and rate of
climate change and variation to which a system is exposed, its sensitivity, and its
adaptive capacity. The term vulnerability may therefore refer to the vulnerable
system itself, the impact to this system, or the mechanism causing these impacts.
8
Review of Literature
CHAPTER-II
REVIEW OF LITERATURE
A brief review of past literature is an integral and essential part of any
investigation. Review of literature provides information to the researcher regarding
the previous works done in their area of research and thereby helps them in
identifying the theoretical framework and methodological issues relevant to the
study. It provides the researchers a proper direction to carry out their research work
and enable them to arrive at a meaningful result. This chapter consists of salient
research findings directly or indirectly related with the present research, conducted
on climate change. As far as possible the most recent reviews from researches
conducted in India and abroad only are incorporated. The entire reviews have been
chronologically organized and presented under different heads as given below.
2.1 Socio-personal characteristics
2.2 Socio-economic characteristics
2.3 Communicational characteristics
2.4 Psychological characteristics
2.5 Perception of farmers about climate change
2.6 Impact of climate change on agriculture and allied activities
2.7 Coping mechanism/adaptation in response to climate change
2.8 Crop diversification in response to climate change
2.9 Relationship between dependent and independent variables
2.10 Factors affecting adaptation
2.11 Constraints in adaptation
2.12 Suggestions
2.1 Socio-personal characteristics
2.1.1 Age Shiferaw and Holden (1998) argued that age of the head of household can
be used to capture farming experience. On the other hand, a negative relationship
between age and implementation of improved soil conservation practices can be
identified.
9
Kumar and Gowda (1999) indicated that 38 per cent of the farmers
belonged to the young age group, whereas, 45 per cent of them belonged to middle
age group and only 17 per cent of them belonged to old age group. Maddison
(2006), Nhemachena and Hassan (2007) and Deressa et al. (2009) stated that age
might often mean better experience, access to information, and knowledge, but
also other things like a weaker health, and consequently age and experience might
give both positive and negative outcomes. Experience in farming increases the
likelihood of uptake of adaptations to climate changes.
More (2000) found that about 20 percent of respondents were from young
age group, while 68 percent were from middle age group and about 22 per cent
were belonged to old age group. Suresh (2004) observed that 64.58 per cent of
respondents belong to middle age followed by 17.92 per cent in young age and
17.50 per cent in old age. Sorhang and Kristiansen (2011) reported that those over
69 years old seems to be less adaptive, however, younger people seem to some
extent to be more likely to adopt adaptation strategies.
2.1.2 Education Norris and Batie (1987) argued that higher level of education is believed to
be associated with access to information on improved technologies and higher
productivity.
Smith and Lenhart (1996) inferred that countries with higher levels of
stores of human knowledge are considered to have greater adaptive capacity than
are developing nations and those in transition. Jadhav (2000) found that 60 per cent
of respondents were illiterate, while 19.17 per cent of respondents had primary and
middle school education and only 1.66 per cent of respondents had high school and
above levels of education. Manay and Farzana (2000) in their study on socio-
economic characteristics of rural families revealed that, 33.33 per cent of the
family heads had education up to high school followed by middle school (22.17%)
and illiterates (18.67%).
Igoden et al. (1990) and Maddison (2006) reported that there is a positive
relationship between the education level of the head of household, the
implementation of improved technologies, and adaptation to climate changes.
10
Deressa et al. (2009) and Akponikpe et al. (2010) revealed that farmers
with higher levels of education are more likely to adapt better to climate changes
(the average age of interviewed farmers was 52 (± 16) with the majority between
35-70 years. Most of the farmers have no formal education (58 %), followed by
primary or literacy levels (29%) and secondary education levels (10%). Dhaka et
al. (2010) reported that the respondent’s level of education greatly increases the
probability of adaptation to cope with effect of climate change.
2.1.3 Size of family
Croppenstedt et al. (2003) inferred that households with a larger pool of
labour are more likely to implement agricultural technology and use it more
intensively because they have fewer labour shortages at peak times. Karjagi
(2006), it was found that 62.96 per cent of the respondents belonged to small
family with less than five members followed by 5-8 members and more than eight
members accounting to 31.11 per cent and 5.93 per cent, respectively.
Yirga (2007) stated that the influence of household size on adaptation
methods can be seen from two perspectives. The first assumption is that
households with large families may be forced to divert part of the labour force to
off-farm activities in an attempt to earn income in order to ease the consumption
pressure imposed by a large family. The other assumption is that large family size
is normally associated with a higher labour endowment, which would enable a
household to accomplish various agricultural tasks.
Deressa et al. (2009) reported that households with large families are more
likely to adapt to climate changes. Sorhang and Kristiansen (2011) revealed that
larger household size is related to higher adaptive capacity.
2.1.4 Farming experienceKebede et al. (1990) studies have shown a positive relationship between
numbers of years of experience in agriculture and the implementation of improved
agricultural technologies. Krishnamurthy (1997) reported that there was positive
and significant relationship between farming experience and adoption of dry land
technologies by small and marginal dry land farmers.
11
Sumathi and Annamalai (1993) found that farmers having more years of
experience were found to be the highest adopter of technologies, there is positive
and significant correlation with the level of adoption. Experience helps an
individual to think in a better way and makes a person more mature to take right
decision. Maddison (2006) notes that perception of climate change appears to
hinge on farmer experience and the availability of free extension advice
specifically related to climate change.
Nhemachena and Hassan (2007) reported that highly experienced farmers
are likely to have more information and knowledge on changes in climatic
conditions and crop and livestock management practices. Smithers and Smit (2009)
observed that adaptations can either be planned or autonomous with the latter
being done without awareness of climate change predictions but based on
experience and prevailing conditions.
Dhaka et al. (2010) reported that more experienced farmers are more likely
take up an adaptation measure. Sarkar and Padaria (2010) revealed that farmers
with the greatest farming experience were more likely to notice changes in climatic
conditions.
2.1.5 Social participation
Smith and Lenhart (1996) indicated that countries with well developed
social institutions are considered to have greater adaptive capacity than those with
less effective institutional arrangements. Khan et al. (1997) observed that majority
(81.81%) of paddy cultivators had medium social participation followed by 18.19
per cent having high social participation. Kumar (2001) reported that majority
(45.84%) of the respondents fell under category of medium social participation
followed by 41.66 and 12.50 per cent of low and high social participation,
respectively.
Jasudkar (2000) in the socio-economic study of beneficiaries of tribal sub-
plan programme with reference to agriculture in Ambegaon block of Pune district
mentioned that most of tribal farmer beneficiary (84.80%) did not participated in
any social organization. Gaikwad (2000) in his study on tribal farmers with
12
reference to their knowledge and benefits derived from different agricultural tribal
development schemes reported that 70 per cent of the tribal farmers had not
participated in any social organization.
2.2 Socio-economic characteristics
2.2.1 Land holding
Adger et al. (2003) stated that climate change will have greater negative
impacts on poorer farm households as they have the lowest capacity to adapt to
changes in climatic conditions. Adaptation measures are therefore important to
help these communities to better face extreme weather conditions and associated
climatic variations.
Suresh (2004) observed that majority of the respondents (68.75%) were
having medium size of land holding followed by high (19.17%) and low (12.08%)
size of land holding. Bradshaw et al. (2004) studied on adaptation of agricultural
technologies and indicated that farm size has both negative and positive effects on
adaptation, showing that the effect of farm size on technology adaptation is
inconclusive.
Maddison (2007) study shows that subsistence farmers are more capable of
perceiving the changes. Nhemachena and Hassan (2007) pointed out that farmers
who own their farm have a higher propensity to invest in adaptation options
compared to no ownership. Karjagi (2006) revealed that 62.22 per cent of the
respondents have belonged to small holding (<2 ha) followed by medium (2.1 to
8.0 ha) and large holding (> 8 ha), which accounts for 24.44 per cent and 13.34 per
cent, respectively.
2.2.2 Occupation
Patange et al. (2001) observed from his study conducted in Solapur district
of Maharashtra state that 70.62 per cent of respondents had farming as main
occupation and animal husbandry and dairy as subsidiary occupation. It also seen
that 11.87 and 11.64 per cent of the respondents participated in dairy business
along with service and other business with farming, respectively.
13
Jhamtani et al. (2003) revealed that more than half of the respondents
(52.82%) were engaged in farming as their main occupation. Whereas, 20.44 per
cent of them were engaged in service, followed by 12.00 per cent who were
engaged in more than one occupation, while 11.55 per cent of them were engaged
in labour work and only 3.11 per cent of them were engaged in business.
Maddison (2007) study shows that dependence on non-farm income does
not necessarily hinder the ability to perceive some changes in climate. Deressa et
al. (2009) found that farm and nonfarm income and livestock ownership are
hypothesized to increase adaptation to climate changes. Akponikpe et al. (2010)
stated that farm land endowment of respondents was 5.9(±3.2) ha dominated (71%)
by cereals (maize, sorghum and millet). Farmers’ main activity was rainfed
farming which they often combined with animal husbandry (60%).
Patwal (2010) argued that adapting to climate change, people have started
cultivation of crop varieties that required less water. Villagers have also adopted
new enterprises for income generation. Pande and Akermann (2010) stated that
farmers in the study area almost completely depend on agriculture for their
livelihoods. This economic activity is closely linked to the natural resource base
and is therefore highly sensitive to changes in climatic conditions, especially in the
absence of irrigation facilities.
2.2.3 IncomeAccording to CIMMYT (1993), higher income farmers may be less risk
averse and have more access to information, a lower discount rate, and a longer
term planning horizon. Franzel (1999) reported that the impact of income on
adaptation found a positive correlation between income and adaptive capacity.
Furthermore, farm and nonfarm income, farm size, and livestock ownership,
represent wealth. According to Kandlinkar and Risbey (2000), with income and
resource limitations, farmers fail to meet transaction costs necessary to acquire
adaptation measures and at times farmers cannot make beneficial use of the
available information they might have.
Karjagi (2006) stated that annual income of majority of the respondents
(66.67%) was below Rs. one lakh followed by Rs. 1-3 lakh group and less than Rs.
14
3 lakh group accounted for 31.11 per cent and 2.22 per cent, respectively. World
Bank (2008), under a modest to harsh climate change scenario, a substantial rise in
temperatures (2.3˚C – 3.4˚C) and a modest but erratic increase in rainfall (4% to
8%) - small farmer incomes could decline by as much as 20%.
Knowler and Bradshaw (2007) hypothesized that the implementation of
new agricultural technologies requires sufficient financial wellbeing. Binkadakatti
(2008) found that, 45.00 per cent of trained and 41.25 per cent of untrained
respondents were belonged to semi-medium income category. Rawat (2010)
revealed that the problem was sighted by everyone in the village but they had little
income and resources or expertise to adapt to the changing situation.
Pande and Akermann (2010) reported that some of the farmers claimed that
their income, mainly due to crop losses and high input prices, has fallen by one
third. Many farmers are not able to live solely from the income gained through
agricultural activities anymore and are compelled to look for other livelihood
options. Deressa et al. (2011) study shows that higher farm income positively
affects the perception of climate change while non-farm income has negative
effects. Sorhang and Kristiansen (2011) reported that 5 per cent of the respondents
in Hagere Selam mentioned reduced income or increased poverty due to climate
changes. No respondents in Kofele said directly that climate changes leads to
increased poverty.
2.2.4 Annual expenditureBellwinkel (1973) revealed that on an average 56. 00 per cent of the total
wages of contract workers of Delhi was spent on food and clothes and 16. 00 per
cent on items not specified by workers, presumably alcohol and status articles.
Halim (1984) noticed that the major portion of expenditure (97. 00%) in the
families of women labourers of Bangladesh was spent on food items. Only
negligible amount of money per year was spent on buying cloths.
Biradar (1992) conducted a study in Hubli-Dharwad, Karnataka, and
revealed that the average monthly expenditure pattern of the women workers
whose income was less than Rs. 11500 per annum was food item (70.00%)
followed by clothing (14.00%), miscellaneous items (5.00%), house work (3.00%),
medical expenses (2. 505), recreation (1.60%) and education (0.50%). Whereas,
15
the average monthly expenditure pattern of the respondents whose income was
above Rs. 11,500 per annum was that they spent maximum proportion of their
income on food items (63.00%) followed by clothing (12.00%).
Binkadakatti (2008) revealed that slightly more than half of the trained and
untrained farmers were belonged to nuclear family. So because of nuclear family
their family expenditure was more leading to fewer saving as compared to joint
family.
2.2.5 IrrigationO’Brien et al. (2004) revealed that irrigation potential and literacy rate are
other important factors contributing to adaptation to climate change. Irrigation
potential was selected because of the assumption that places with more potentially
irrigable land are more adaptable to adverse climatic conditions.
Molua (2008) also conducted a study on the impact of climate change on
Cameroon’s agriculture and the results indicated that 3.5 per cent increase in
temperature and 4.5 per cent increase in precipitation in the absence of irrigation
facilities would be detrimental to Cameroon’s agriculture, leading to a loss of 46.7
per cent in output value.
According to Bhusal (2009), because of access to water for irrigation
increases the resilience of farmers to climate variability, irrigation investment
needs should be reconsidered to allow farmers increased water control to
counteract adverse impacts from climate variability and change. Pande and
Akermann (2010) inferred that farmers in the study area almost completely depend
on agriculture for their livelihoods. This economic activity is closely linked to the
natural resource base and is therefore highly sensitive to changes in climatic
conditions, especially in the absence of irrigation facilities. Most of the
respondents of Uttarakhand (90%) claimed a scarcity of drinking and irrigation
water in the villages.
Akponikpe et al. (2010) stated that crop management strategies (change in
sowing date and crop cultivar) were more adopted than soil fertility and soil water
managements due to constraints attached to the latter ones. Soil fertility is
16
restricted by fertilizer availability and cost; and soil water managements by
irrigation equipment, labour or water availability.
2.2.6 Access to Credit
Sundarambal and Annamalai (1995), while studying the Jowar growers of
both categories of small and big farmers, established a positive and significant
association between credit acquisition and adoption. On the contrary, Mishra
(2006) observed that credit acquisition had a non-significant relationship with
extent of adoption of recommended sugarcane production technology.
Yirga (2007), Pattanayak et al. (2003) and Deressa et al. (2009) reported
that research on adaptation of agricultural technologies indicates that there is a
positive relationship between the level of adaptation and the availability of credit.
Gbetibouo (2006) found out that approximately half of the farmers studied had
adjusted their farming practices to alleviate climate change effects. The only
inhibiting element to adaptation was lack of access to credit as cited by farmers.
Maddison (2007) reported a large number felt that lack of credit or savings
represented a barrier to adaptation. Bhusal (2009) stated that government policies
should ensure that farmers have access to affordable credit to increase their ability
and flexibility to change production strategies in response to the forecasted climate
conditions. Availability of credit eases the cash constraints and allows farmers to
buy inputs such as fertilizer, improved crop varieties, and irrigation facilities.
2.2.7 Distance to Market Maddison (2006) hypothesized that as distance to output and input markets
increases, adaptation to climate changes decreases. Proximity to market is an
important determinant of adaptation, presumably because the market serves as a
means of exchanging information with other farmers. Sarkar and Padaria (2010)
reported that farmer experience, access to free extension service and availability of
markets were important determinants of adaptation against climate change.
IPCC (2007c) revealed that proximity to supplies of agricultural inputs is
identified as an indicator of technology. For instance, drought-tolerant or early
maturing varieties of crops as technology packages usually require access to
17
complementary inputs, such as fertilizers or pesticides. Thus, the supplies of such
inputs positively contribute to successful adaptation.
Arya (2010) reported that 10 to 15 years ago, food grains and vegetables
were available for 10-12 moths of the year. People would exchange their cash crop
(potato, choulai and rajma) with rice, sugar and wheat in the market. But now the
farmers are totally depend on the markets for food grains and other necessary item.
2.2.8 Crop insuranceCutter et al. (2000) stated that wealth enables communities to absorb and
recover from losses more quickly due to insurance, social safety nets, and
entitlement programs.
According to Pande and Akermann (2010), the provision of an effective
crop insurance against weather-induced risks could improve the livelihood of
small-scale farmers considerably. In interviews, farmers repeatedly emphasized the
need for such insurance. They are willing to pay, within their means, for such
services when they are effectively serving their needs.
2.2.9 Socioeconomic statusOloruntoba and Fakoya (2000) studied the socio-economic indicators such
as income and pattern of expenditure, education, occupation and household size in
assessing statutes of rural adult female. Descriptive statistics was used to analyse
the data obtained from cross sectional survey of adult females in six selected rural
communities in Ifeldun local Government area of Kawara state in Nigeria.
However, findings suggest that rural adult females exhibit variables typical poor
status because majority of them have low average monthly income, high
expenditure on food consumption which fueled low savings. They are also mostly
petty traders with large family size of eight persons sourced informal credit to
boost income generating activities and have low education.
Kandlinkar and Risbey (2000) revealed that adaptation has the potential to
significantly contribute to reductions in negative impacts from changes in climatic
conditions as well as other changing socio-economic conditions, such as volatile
short-term changes in local and international markets.
Rao and Rupkumar (2005) studied the socio agro-economic characteristics
of trained agripreneurs in Maharashtra by considering various variables such as
18
age, education, sex, social group, land holding, annual income etc. It was found
that 74 per cent of the sample SAEs were below 30 years while 25 per cent of
UAEs in the range of 31-40 years. As many as 69 per cent of UAEs had only
graduation. Out of 16 SAEs 38 per cent belong to weaker sections while 3 per cent
UAEs hail from other category, vast majority of SAUs are self employed and that
of UAEs are either dependents or employed and 75 per cent of them are small and
marginal farmers. However, analysis of their annual income show that 50 per cent
of sample SAEs have Rs. 1 to 3 lakhs, while, 69 per cent UAEs have annual
income less than Rs. 1 lakh.
Nhemachena and Hassan (2007) opined that with more financial and other
resources at their disposal farmers are able to change their management practices
in response to changing climatic and other factors and are better able to make use
of all the available information they might have on changing conditions both
climatic and other socio-economic factors
2.3 Communicational characteristics
2.3.1 Extension ContactMarkad (1996) revealed that 42.50 and 32.50 per cent respondents had
medium and low extension contact, respectively. Only 13 per cent had high
extension contact whereas, 12 per cent of tribals had no extension contact. Dixit
and Bhople (2001) reported that 66.00 per cent of tribals had contact with
extension officers of panchayat samiti, and about 28.00 per cent of tribal farmers
had contacted sometimes with block development officers, while only 15.34 per
cent had rare contacts with tribal development officers.
Rathod (2001) in his study on Korku tribals reported that 80 per cent of
respondents had regular contact with village extension workers, out of them 72 per
cent of farmers occasionally contacted agriculture extension officers (Panchayat
samiti). Whereas, more than 80 per cent of farmers had no contact with agriculture
officers, subject matter specialist and extension specialist/scientist of agricultural
universities.
Nhemachena and Hassan (2007) stated that farmers who have significant
extension contacts have better chances to be aware of changing climatic conditions
and also of the various management practices that they can use to adapt to changes
19
in climatic conditions. Gbetibouo (2009) argues that farmers with access to
extension services are likely to perceive changes in the climate because extension
services provide information about climate and weather.
2.3.2 Participation in extension activitiesGupta (1999) reported that about 74.00 per cent respondents were aware of
training programmes of which only 36.00 per cent respondents had participated in
the training programmes, whereas, 56.00 per cent of respondents were aware of
demonstrations and only 4.66 per cent of farmers had participated, but none of the
respondents had participated in field days and field visits.
Angadi (1999) stated that majority of the respondents had not participated
in various extension activities viz., discussions with extension personnel (98.76%),
group meeting (75.23%) and training programmes (72.50%). Only 43.75 per cent
and 38.13 per cent of the respondents participated regularly in extension activities
like method demonstrations and Krishimela, respectively. Kumar (2004) from his
study on tomato growers of Belgaum district revealed that nearly 23.00 per cent of
respondents participated regularly in agricultural exhibitions followed by 20.83 per
cent in demonstrations. Majority of them never participated in activities like
trainings (66.67%), educational tours (94.17%) and field visits (92.05%).
Anitha (2004) reported that 17.50 per cent of respondents had high
extension participation, 44.20 per cent had medium and 38.30 per cent had low
extension participation. The FAO (2006) indicated that the effects would be felt by
both developed and developing countries, but developing countries would be most
affected because of their lack of resources, knowledge, veterinarian services,
extension services and research technology.
Hassan and Nhemachena (2008), Bryan et al. (2009), Deressa et al. (2010)
and Apata et al. (2009) indicated that access to and use of extension services had a
strong positive influence on adapting to climate change. Dhaka et al. (2010)
revealed that being in receipt of extension advice relating about either livestock or
crop production strongly increases the probability of the farmer adapting.
2.3.3 Mass Media participationShashidhar (2003) in his study on drip irrigation farmers in Shimoga and
Davanagere district of Karnataka reported that 41.11 per cent of the respondents
20
belonged to medium level of mass media participation, followed by low (35.56%)
and high level (23.33%) mass media users.
Kumar (2004) from his study on tomato growers of Belgaum district
revealed that 59.17 per cent of the respondents were occasionally listening
agricultural programmes in radio. Whereas, 30.00 per cent of them viewed
agricultural programmes in television occasionally and 70.86 per cent and 85.00
per cent of them never used to read the newspapers and farm magazines,
respectively.
Nirban (2006) in his study conducted in Konkan region reported that only
9.09 per cent respondents were reading newspapers regularly and hardly 5.45 per
cent read the agricultural publications regularly. Majority of respondents (56.34%)
possessed radio and apparently less number of them (22.50%) listened to it
regularly. About 14 per cent of respondents had televisions and only 4 per cent of
them were watching it regularly.
2.3.4 Sources to Information Jyothi (2000) reported that input dealers were the most frequently consulted
information sources followed by progressive farmer, TV, Extension personnel of
private organization, friends, radio and Assistant Agriculture Officers. Jones
(2003) and Kandlinkar and Risbey (2000) revealed that lack of and or limitations
in information (seasonal and long-term climate changes and agricultural
production) increases high downside risks from failure associated with uptake of
new technologies and adaptation measures. Maddison (2006) reported that
information received by respondents about climate change to improve livestock
production significantly affected adaptation to climate change. Although access to
information is quite limited (i.e. 30.70%) in the study.
Nhemachena and Hassan (2007) and D’Emden et al. (2008) argued that
access to information through extension increases the likelihood of adapting to
climate changes. Yirga (2007) stated that studies in developing countries, including
Ethiopia, reported a strong positive relationship between access to information and
the adaptation behavior of farmers. Deressa et al. (2009) reported that access to
information might increase the likelihood of adapting to climate changes.
Extension on crop and livestock production and information on climate represent
21
access to the information required to make the decision to adapt to climate
changes.
Mandleni (2011) revealed that access to information seemed to be an
important element that motivated adaptation to climate change among farmers. In
the Eastern Cape area of study also indicated that extension attendance had
significant effect on adoption of conservation tillage in the cropping regions of
Australia. Luni et al. (2012) reported that only 11.8 per cent of the respondents
replied that they have heard about it. The source of information was cited as radio
by 6.9 per cent, staffs of NGOs by 2.5 per cent and teachers at school by 1.5 per
cent of the respondents.
2.3.5 Access to Weather Forecasts Athimuthu (1982) conducted study in Tamila nadu state on content analysis
of agricultural news in two Tamil dailies and indicated that farmers perceived
agricultural news followed by news on marketing of agricultural products and
weather forecast as the most useful news in selected Tamil dailies.
Pettengell (2010) reported that access to weather forecasts is important for
the farmers to be able to plan what to do on the field. Erratic rainfall patterns and
changing seasons are upsetting farming cycles in many parts of the world. Many
Ethiopian communities are experiencing changes in seasons, with rainfall being
concentrated into fewer, more extreme events, or the delayed onset of rainy
seasons. With traditional farming calendars becoming less reliable, farmers need
interventions to help them to plan and prepare, including weather forecasts for
deciding when to sow and when to harvest, and seasonal forecasts for what to sow
and how to manage risk.
UNEP (2006), there, exist a number of constraints which, needs to be
addressed before the potential of climate forecasts can be fully exploited for the
local communities. The regional nature of seasonal forecasts may limit their
relevance for planning at the national or local level. Knowing whether seasonal
rainfall will be above or below normal alone will not necessarily make a weather
forecast useful to potential end-users. One needs to be careful when delivering
output information to avoid “finger pointing” in case of bad decisions by farmers.
22
Moreover, the confrontation of weather forecast outputs with traditional indicators
of rainfall, together with a sound discussion on the possibilities of different
outcomes, is likely to make seasonal weather forecasts more acceptable by farmers.
It is important that the weather forecasts often are correct, if the farmers are to
make agricultural decisions based on the weather forecasts. A forecast will be
meaningful only if it allows enough lead-time for decision making (UNEP 2006:
28-29). Furthermore, models can now predict the number of rainy days with great
accuracy, but it is difficult to predict if any dry spells is coming or not. The
analysis of the number of modeled rainy days against a defined baseline may
therefore give an indication on the likely occurrence of dry spells during the
growing season (UNEP 2006: 28).
2.3.6 CosmopolitenessKumar (1989) conducted study on “A comparative study of farm financing
by a Regional Rural Bank and an Agricultural Development Research of a
Commercial bank” and found that majority of the respondents had medium urban
contact and visit occasionally to the nearby cities, taluka places and district.
Manjunath and Balasubramanya (2002) reported that majority (44.66%) of
the Kannada farm magazine readers had medium level of urban contact followed
by low level (32.00%) of urban contact. Patel et al. (2003) observed that majority
of respondents (74.00%) had medium cosmpoliteness, whereas, 14.50 per cent of
them had high cosmopoliteness.
Anitha (2004) indicated that more than one fourth of farm women (28.30%)
had high cosmopoliteness, followed by medium (44.20%) and low (27.50%)
cosmopoliteness groups. Suresh (2004) reported that 45.00 per cent of respondents
had low level of cosmopoliteness, 44.17 per cent of them had medium level and
10.83 per cent had high level of cosmopliteness.
Keshavamurthy (2005) revealed that 37.50 per cent of the respondents
visited the nearest town once in fortnight followed by 33.33 per cent and 20.84 per
cent respondents were noticed to visit once in a month and once in a week,
respectively. The remaining 8.33 per cent visited to nearest town occasionally.
Chandramouli (2005), in his study indicated that among the rice growing farmers,
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42.50 per cent had medium level of cosmopoliteness, 39.17 per cent had low and
remaining 18.33 per cent had high level of cosmopoliteness.
2.4 Psychological characteristics
2.4.1 Awareness and Environment cautiousnessNhemachena and Hassan (2007) stated that raising awareness of changes in
climatic conditions among farmers would have greater impact in increasing
adaptation to changes in climatic conditions. Kotei et al. (2007) observed that the
lack of sufficient knowledge about climate changes and the impact on agricultural
production is a setback to long term sustainable agriculture in most developing
countries, including Ghana.
Dietz et al. (2007) studied on climate change and reported that very few
people i.e. only 9 per cent had lot of knowledge about climate change. Semenza et
al. (2008) studied on public perceptions of climate change in the USA and
indicated that a vast majority (92%) was aware of climate change. Ishaya and
Abaje (2008) reported that constraining factors to the adoption of modern
techniques of combating climate changes in the area were observed to include lack
of improved seeds, lack of access to water for irrigation, lack of current knowledge
of modern adaptation strategies, lack of capital, lack of awareness and knowledge
of climate change.
Aggarwal (2009) revealed that majority of the farmers (above 80%) of all
the centers except Raipur stated that they are fully aware about various
environmental hazards. At Raipur center, 42 per cent of the farmers only declared
that they are aware of the risks involved in excess use of agro-chemicals. Mertz et
al. (2009) conducted study in the Sahel region and found that farmers are aware of
climate variability. Bhushal et al. (2009) reported that most of the coping activities
were found to be event specific based on local knowledge and innovations, because
most of the respondents were not aware about actual impacts of climate changes.
Sharma (2010) reported that two third of the respondents were aware about the
global warming and knew that there was a change in the climate. However, about
one third of population was still not aware of global warming. Similarly, a
significant majority of them had knowledge about various types of changes in the
24
climate such as increasing pollution, melting glaciers, cyclones incidents, increased
crop failure and rise in sea-level.
IFAD (2010) reported that neither adaptation nor mitigation can avoid all
climate change impacts. To respond to this threat, it will be necessary to focus on
awareness of climate change and adaptation in order to support local communities
in dealing with the impacts of climate change. Sarkar and Padaria (2010)
investigation revealed that nearly 38 per cent of the respondents had heard about
climate change, equal percentages of the respondents (43.3%) were fully aware and
not aware at all about rise in sea level and reduction in the availability of fresh
water.
Sorhang and Kristiansen (2011) reported that in Hagere Selam, 95 per cent
of the respondents said they have experienced changes in climatic conditions
during the last twenty years. The majority of respondents (91.7%) in Hagere Selam
said they had experienced negative climatic changes. Mandleni (2011) reported
that 57 per cent of a total of 250 livestock farmers were more aware of climate
change and 43 per cent were not aware during the study period.
2.4.2 Risk orientationRavishankar (1995) in his study observed that 65 per cent of the
respondents had medium level of risk bearing capacity followed by high (20. 00%)
and low (15.00%) level of risk orientation. Sawant (1999) conducted a study on
different modes of presentation of information on mushroom cultivation in
Maharashtra and observed that majority of the respondents (75.00%) had medium
risk bearing capacity, while, 17.00 per cent of them had high risk bearing capacity.
Venkataramalu (2003) reported that majority of the farmers had medium level of
risk bearing (73. 33%) capacity.
Bhagyalaxmi et al. (2003) revealed that majority of the respondents
(75.56%) had medium risk orientation followed by low (15.56%) and high
(13.33%) risk orientation categories. Suresh (2004) in his study on entrepreneurial
behaviour of milk producers in Chittoor district of Andhra Pradesh indicated that
majority of respondents had medium level of risk taking ability followed by low
and high level at the rate of 62.02, 24.58 and 13.34 per cent, respectively.
25
Pandeti (2005) reported that majority of small farmers (47.50%) had low
risk taking ability, whereas, 47.50 per cent of medium and 37.50 per cent of big
farmers had medium and high risk taking ability, respectively. Reddy (2005)
reported that 56 per cent belonged to medium risk orientation category followed by
high (28%) and low (19.33%) risk orientation categories. Sushma (2007) in her
study on analysis of entrepreneurship development in women through EDP
trainings revealed that majority of the trained women entrepreneurs (61.55%) had
medium level of risk bearing ability while 10.76 per cent and 27.69 per cent of
them had high and low level of risk taking ability, respectively.
2.4.3 InnovativenessRaghupathi (1994) reported that 42.50 per cent of command area farmers
were in the medium innovative proneness category, whereas, only 15.00 per cent
were in low innovativeness category. Kumar (2001) conducted a study in Ranga
Reddy district of Andhra Pradesh and indicated that 47.50 per cent of the
respondents fell in low category followed by 31.66 per cent in medium category
and 20.84 per cent to high category.
Bhagyalaxmi et al. (2003) observed that majority of the respondents
(69.44%) belonged to medium innovativeness category, followed by 15.56 and
15.00 per cent of them belonged to high and low innovativeness category,
respectively. Shashidhar (2004) reported that higher percentage (47.50%) of the
respondents was in medium innovativeness category followed by low (31.66%)
and high (20.83%) innovativeness category.
Pandeti (2005) reported that majority of small farmers (47.50%) belonged
to low innovativeness category, while, 42.50 per cent of medium farmers had
medium innovativeness and 37.50 per cent of big farmers belonged to high
innovativeness category. On the whole, majority of the farmers (43.34%) belonged
to medium innovativeness category.
2.4.4 Scientific orientationSakharkar (1995) observed that majority (65.00%) of the soybean farmers
of Nagpur district of Maharashtra state belonged to medium category of scientific
orientation, 17.33 per cent equal of the farmers belonged to low and high scientific
orientation category, respectively.
26
Chandran (1997) in her study reported that 31.67 per cent of the
respondents belonged to the low scientific orientation category, while, 30.00 per
cent and 38.33 per cent of them were found to have medium and high scientific
orientation, respectively.
Karpagam (2000) reported that majority of the respondents (75.00%) were
in medium category followed by low category (13.33%) and high category
(11.67%) with respect to scientific orientation.
2.4.5 Decision making abilityMather (1992) revealed that in practice, farmers take decisions in the
context of their own environment, and differences may exist between perceived
and real environments. Baethgen et al. (2003) stated that availability of better
climate and agricultural information helps farmers make comparative decisions
among alternative crop management practices and this allows them to better
choose strategies that make them cope well with changes in climatic conditions.
Sidram (2008) in his study on analysis of organic farming practices in
pigeon pea in Gulbarga district of Karnataka state found that 46.67 per cent of the
respondents belonged to high decision making ability category with mean score of
10.55 followed by 34.17 and 19.17 per cent of respondents belonged to medium
and low decision making ability categories with mean scores of 7.46 and 5.69,
respectively.
2.4.6 VulnerabilityAllen (2003) and Kelly and Adger (2000) argued that individuals in a
community often vary in terms education, gender, wealth, health status, access to
credit, access to information and technology, formal and informal (social) capital,
political power, and so on. These variations are responsible for the variations in
vulnerability levels. In this case, vulnerability is considered to be a starting point or
a state (i.e., a variable describing the internal state of a system) that exists within a
system before it encounters a hazard event.
Fischer et al. (2005), Thomas and Twyman (2005) and Morton (2007)
stated that a consensus has emerged that developing countries are more vulnerable
to climate change than developed countries because of the predominance of
agriculture in their economies and scarcity of capital for adaptation measures.
27
Desalegn et al. (2006) found that selling of livestock were a common
coping strategy during drought periods amongst farmers in the Upper Awash Basin
in Ethiopia. Tadege (2007) revealed that low level of socio-economic
development, inadequate infrastructure and lack of institutional capacity is often
making subsistence farmers more vulnerable to climatic changes. Ebi et al. (2007)
reported that declined yield due to unfavorable weather and climate will lead to
vulnerability in the form of food insecurity, hunger and shorter life expectancies.
NEST (2004), IPCC (2007b) and Apata et al. (2009) reported that climate
change will have a strong impact on Nigeria, particularly in the areas of
agriculture, land use, energy, biodiversity, health and water resources. Nigeria, like
all the countries of Sub-Saharan Africa, is highly vulnerable to the impacts of
climate change. Senbeta (2009) and Smith et al. (2001) found that agriculture is
one of the sectors most vulnerable to climate change impacts. The impacts are
often strongest in Africa, because agriculture here is important for the daily
subsistence, and adaptive capacity is often low.
According to FAO (2009), livelihood systems are vulnerable to climate
change. These systems include small-scale rain-fed farming systems, pastoralist
systems and forest-based systems in locations, where, productivity declines are
projected as a consequence of climate change. Sharma (2010) reported that 36 per
cent of the respondents had positive/affirmative perception, that is, they had
perceived that the change in climate certainly had an effect on them. Whereas, 24
per cent had perceived that it had no effect in the area. However, those who were
ambiguous in their responses were found to be 40 per cent.
Sarkar and Padaria (2010) reported that increased incidences of pests
(Blast in seedbed of paddy, yellowing of leaves, angari disease in betel vine,
curling of leaves and rooting of seedlings etc. of different crops were reported in
the area), reduction in acreage of some crops (Cultivation of some vegetables like
tomato, potato has been decreased due to high temperature in winter season as
these crops require low temperature), reduction in yield, increased cost of
cultivation etc. were important perceived risks in agriculture that increase
vulnerability among farmers.
28
Pettengell (2010) argued that populations dependent on agriculture are
particularly vulnerable to climate changes. Krishna et al. (2011) reported that
empowering communities with information, technological skills, education and
employment is the best way to address vulnerability.
2.5 Perception of farmers about climate change
2.5.1 General perception of farmers Vedwan and Rhoades (2001) stated that in order to understand how human
beings would respond to climate change, it is essential to study people's
perceptions of climate and the environment in general. Eriksson (2006) reported
that as in other regions of the world, climatic and ecological changes caused by
global warming have resulted in several negative consequences for people‘s health,
the economy and livelihoods in Nepal.
Ishaya and Abaje (2008) findings indicated that the threat of climate change
is perceived to be more on health, food supply, biodiversity loss and fuel wood
availability than on businesses and instigating of disaster. Gbetibouo (2008),
Mubaya et al. (2010) and Deressa et al. (2011) studied in several developing
countries and found that most farmers perceive temperatures to have become
warmer and rainfall reduced over the past decade or two.
Akponikpe et al. (2010) reported that more proportion of farmers in the
Sahel identified the change of climate to have started between 20-30 years ago or
more, while the majority of them mentioned it to be less than 10 years ago. This
difference between climatic zones was pronounced in Guinean Ghana where more
than 50 per cent said the change began less than 10 years ago and the Sahelian
Niger where more than 55 per cent perceived it to have begun 20-30 years ago
(10% more than 30 years ago).
Lyngdoh and Baishya (2010) mentioned that rice and wheat production
declined due to a reduction in fog and lesser cold over the years. Respondents also
observed a reduction in the production of barley and oil crops such as mustard and
linseed. This observation was similar to the observation of the people of Tangmang
village in the Meghalaya state of the Himalayas, who also realised noticeable
changes in temperature accompanied by erratic rainfall patterns resulting in
29
reductions in crop yields. Another observation was a significant shift in the sowing
season of wheat between 1977 and 1995.
Pande and Akermann (2010) stated that according to farmers the weather
situation has changed drastically compared to some decades ago. In interviews,
farmers reported experiencing recent changes in climate in terms of increasing
temperatures and generally in terms of a decrease in precipitation during the
monsoon season.
Arya (2010) conducted a study about perceptions on climate change in
village communities of Garhwal Himalaya and stated that respondents perceived
unseasonable rainfall, decreasing moisture and increasing heat. They also observed
drought, low crop production, snowfall and fluctuations in temperatures. Increased
soil erosion due to heavy rainfall in the rainy season, and decreased water level due
to high temperatures were other observations.
Rawat (2010) also conducted study on climate change and reported that a
large number of people believed changes in temperature, precipitation and
depletion of natural resources have been taking place since the last 3-4 years.
However, the reduction in snowfall had been observed for 10-12 years. Osbahr et
al. (2011) conducted study in Uganda, where farmers perceived the regional
climate to have changed in the past 20 years. Farmers also felt that temperatures
had increased and seasonality and variability had changed.
Krishna et al. (2011) argued that more than 50 per cent respondents
believed, warming days have been increasing, rainfall pattern has become more
unpredictable, seasons may have been changing, frequency of drought has
increased, warmer wind flows these days, decreasing natural water sources,
windstorm is getting stronger, changes in flowering and fruiting time.
Johnsen and Aune (2011) reported that most farmers experienced changes
in the onset of the cold season (59.4%), the hot season (56.5%) and the rainy
season (80.5%). Similar trend was observed on the offset of seasons. Most farmers
felt that the rainy season started later and stopped earlier in the recent past as
compared to a long time ago.
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2.5.2 Perception of farmers about rainfallPashupalak (2009) found that rainfall has become irregular and more
unpredictable in Orissa over the last decade. The intensity of rainfall is also
increasing. Out of 1500 mm rainfall, 500 mm to 700 mm precipitation falls within
a span of 3-4 days, which, sometimes causes severe floods. Dev (2010) reported
that according to villagers feel the amount of winter precipitation has decreased
significantly.
Dhaka et al. (2010) revealed that majority of farmers believed that the
rainfall levels had decreased. Similarly, the overall perception on changes in
precipitation is that the region is getting drier and that there are pronounced
changes in the timing of rains and frequency of droughts. Krishna et al. (2011)
reported that more than 80 per cent of the respondents were perceived rainfall
variability with untimely, late monsoon start, no winter rain and high intensity
pattern with short periods. Furthermore, they have been experiencing an
unpredictable rainfall patterns over the past 10 years.
Bhushal et al. (2009) stated that 97 per cent of the respondents observed an
unpredictable rainfall patterns over the past 10 years and 3 per cent noticed a
predictable and constant rainfall patterns. Almost 72 per cent of the respondents
said that the incidents of drought has been increasing and link it with the untimely
and unusual rainfall patterns over the past few years. Key informants also shared
their experience that in recent year (2009) there was less or no rainfall in the
monsoon season, similar findings were reported by Patwal (2010) and Tripathi
(2010).
Sontakke et al. (2008) and Pande and Akermann (2010) reported that
monsoon rainfall at all India level does not show any trend but there are some
regional patterns. Areas of increasing trend in monsoon rainfall are found along the
west coast, north Andhra Pradesh and north-west India, and those of decreasing
trend over east Madhya Pradesh and adjoining areas, north-east India and parts of
Gujarat and Kerala (-6 to -8% of normal over 100 years). Moreover, a recent study
indicates that the intensity and frequency of heavy to very heavy rainfall events is
showing an increasing trend during the past 50 years over the region covering parts
of Andhra Pradesh, Orissa and Chhattisgarh and Madhya Pradesh.
31
Patwal (2010) found that there has been less or negligible winter rains in
the village for the last 4 or 5 years and the rain cycle has also shifted by 2 to 3
months. This basically means that it has resulted in low/loss of production of the
winter crops and erratic rainfall during the monsoon period.
Akponikpe et al. (2010) found that the later onset of the first rainy season
and the earlier cessation were reported by a higher proportion of farmers in Benin
(65-90%) than Togo and Ghana (35-50%) and by higher proportion of Sahelian
farmers (70-90%) than Sudanian and Guinean ones (40-67%). Further, he
mentioned that the number of rainfall events during the first rainy season was
perceived to have decreased consistently with the number of dry spells perceived
to have increased. A higher proportion of farmers in Benin, Burkina Faso and
Niger (70-95%) mentioned this change compared to Ghana (25-50%) and Togo
(25-35%). Exceptionally in Togo, more farmers even said that the numbers of
rainfall events have increased (53%).
Sorhang and Kristiansen (2011) reported that 71.7 per cent of the
respondents in Hagere Selam said rainfall had decreased. 5 per cent said rainfall
had decreased and was also more irregular, while, 3.3 per cent said rainfall was
more irregular, 98 per cent of the respondents in Kofele can remember negative
changes in rainfall and 2 per cent of the respondents said there had been no
changes in rainfall.
2.5.3 Perception of farmers about temperatureMathon et al. (2002) mentioned that temperature was reduced during the
rainy season periods. In fact they explained that rainfall events usually came after
one to three very hot days and this was a way for them to predict rains. But over
the last few years, those very hot days had not been as common which may explain
the decrease in rainfall events. According to IPCC (2007c), air temperature near
the earth surface rose by 0.74 °C from 1906 to 2005 and scientists estimated it
could be increased as much as 6.4 °C on average during the 21st century.
Funk et al. (2008) and McCarthy et al. (2001) argued that global warming
is projected to have significant impacts on conditions affecting agriculture,
including temperature, precipitation and glacial run-off. Pashupalak (2009)
mentioned that the Orissa state is expected to experience a further 0.72˚C increase
32
in mean annual temperature by 2020. The maximum increase of 1.36˚C is
projected to happen in the post monsoon season.
Bhushal et al. (2009) revealed that 92 per cent of the local people
interviewed perceived long-term changes in temperature. While, most of them
(90%) perceive the temperature has been increased. Only 2 per cent noticed the
contrary, a decrease in temperature. Dhaka et al. (2010) and Kemausuar et al.
(2011) indicated that most farmers perceived the temperature distribution has
undergone a significant shift in addition to an overall increase in temperatures. By
contrast almost none believed they had decreased.
Tripathi (2010) argued that the people in the Indo-Gangetic Region indeed
perceived a significant change in temperature distribution and a definite reduction
in the number of winter months, which then lasted for only two months. Almost
100 per cent of the respondents perceived the changes in winter. These perceptions
were not in line with traditional weather descriptions because temperatures were
way above the normal temperatures.
Pande and Akermann (2010) stated that mean surface temperature rise by
the end of the century, ranging from 3 to 5°C under A2 scenario and 2.5 to 4°C
under B2 scenario, with warming more pronounced in the northern parts of India.
A 20 per cent rise in all India summer monsoon rainfall and further rise in rainfall
is projected over all states except Punjab, Rajasthan and Tamil Nadu, which show
a slight decrease.
McSWeeney et al. (2010) reported that temperature (>60%) and the
number of hot days (>50%) have increased. The rate of increase was reported to be
highest in April-June. Daily temperature data also indicated that the frequency of
‘hot’ days has increased significantly in all seasons. Akponikpe et al. (2010)
mentioned that generally in the year, the number of hot days had increased, but it
had reduced during the rainy season periods. Farmer reported that temperatures
(>60%) and the number of hot days (>50%) have increased. Sorhang and
Kristiansen (2011) reported that 38.3 per cent of the respondents in Hagere Selam
said temperatures have increased over the last thirty years. The respondents in
Kofele were very clear when it came to temperature, 86 per cent of them said that
they think the temperature has increased over the last twenty years.
33
Luni et al. (2012) studied regarding the changes in temperature and argued
that majority of the respondents have noticed the rising summer temperature
(47.5%), while, nearly 9.5 per cent of the respondents perceived that summer has
become cooler. For the inter temperature, nearly 21.8 per cent perceived that
winter is becoming colder, while, nearly equal percentage of the respondents
(22.6%) perceived that winter is getting warmer.
2.6 Impact of climate change on agriculture and allied activities
2.6.1 Impact of long term climate changePearce et al. (1996) and McCarthy et al. (2001) studies indicated that
Africa’s agriculture will be negatively affected by climate change. The estimate for
Africa is that 25-42 per cent of species habitats could be lost, affecting both food
and non-food crops. Kinuthia (1997) reported that climate change with expected
long-term changes in rainfall patterns and shifting temperature zones are expected
to have significant negative effects on agriculture, food and water security and
economic growth in Africa; and increased frequency and intensity of droughts and
floods is expected to negatively affect agricultural production and food security.
Adger et al. (2003) reported that negative impacts of extreme events such
as floods and droughts are expected to be high in developing countries especially
in rural areas. Jones and Thornton (2003) and Thornton et al. (2009) argued that
the tropics and subtropics in general, crop yields have been predicted to fall by 10
to 20 per cent in 2050 because of warming and drying, but there are places where
yield losses may be much more severe. According to Rischkowsky et al. (2004),
recent climate change scenarios showed that most of the Near East region would
face a decrease in water availability by up to 40 mm per annum.
FAO (2005) has predicted that in developing countries, 11 per cent of
arable land would be affected by climate change, including a reduction of cereal
production in up to 65 countries, about 16 per cent of agricultural Gross Domestic
Product (GDP). Aggarwal and Mall (2002) provided an excellent review of climate
change impact studies on Indian agriculture, mainly from the perspective of
physical impact, while, yields of important cereal crops like rice and wheat are
expected to drop significantly with impacts of projected climate change,
34
biophysical impacts on some of the important crops like sugarcane, cotton and
sunflower are yet to be studied adequately.
CBS (2006) mentioned that about 96 per cent of the total water use in the
country suffers a lot from erratic weather patterns such as heat stress, longer dry
seasons and uncertain rainfall, since 64 per cent of the cultivated area fully
depends on monsoon rainfall. Food and Agriculture Organisation (FAO) in (2007)
estimated that approximately 20 to 30 per cent of plant and animal species were
expected to be at risk of being extinct by 2100. Higher temperatures were
envisaged as well as changes in rainfall patterns, which were expected to result in
increased spread of existing vector-borne diseases and macro parasites of animals
as well as the emergence and spread of new diseases.
FAO (2007) and Jianchu et al. (2007) reported that as climatic patterns
change, so also do the spatial distribution of agro-ecological zones, habitats,
distribution patterns of plant diseases and pests which can have significant impacts
on agriculture and food production. Naerstad (2007) wrote that “if the rain starts
four weeks later than normally, in the wrong period of growth, or if the amount
changes drastically, the impact on food production can be tremendous”. The
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report
(2007a) forecasted that by 2100, the increase in average surface temperature would
be between 1.8ºC and 4.0ºC globally. Further, projected that the increase in
temperature has both negative and positive impact on agriculture, the potential
food production to increase with increase in local average temperature over a range
of 1 to 30C, but above this it is projected to decrease.
IPCC (2007b) reported that climate change imposes constraints to
development especially among smallholder farmers whose livelihoods mostly
depend on rain-fed agriculture. Moreover, IPCC in 2007 projected for India an
acceleration of warming above that observed in the 20th century, a decrease in
precipitation, and an increase in the occurrence of extreme weather events. Climate
change is expected to have adverse effects on agriculture, the eradication of
poverty, food security, and the water supply.
RCDC (2008) indicated that the coastal districts received progressively
more rainfall; the opposite was true for the interior districts. Rainfall is gradually
35
increasing in May and October, but declining from November to March. Patil
(2008) found that due to deviation in rainfall patterns, the flow of many rivers in
Orissa has been reduced. Bhushal et al. (2009) reported that erratic rainfall patterns
and hailstorm contributing to soil erosion, soil fertility loss, and crop damage are
having an adverse impact on livelihoods of most of these communities, thus
increasing risk to food security.
Aggarwal (2009) argued that a 1°C increase in temperature with no
associated CO2 increase could lead to a decrease of 6 million tonnes of wheat
production. This loss is projected to increase to 27.5 million tonnes at 5°C increase
in mean temperature. It was estimated that yield loss would be 3.9 million tonnes
due to climate change by 2020, 11.7 million tonnes by 2050 and 23.5 million
tonnes by 2080. It is also estimated that India loses 1.8 million tonnes of milk
production at present due to climatic stresses in different parts of the country.
Regmi et al. (2009) stated that global climate change will also likely shift monsoon
precipitation patterns in ways that will threaten Nepal’s current agricultural
practices, infrastructure, bio-diversity, especially in mountain regions where
migration of species is physically restricted.
Tripathi (2010) noticed a significant shift in the sowing season of wheat,
between 1977 and 1995, sowing of wheat was done in the first 13 days of the
Kartik month (1-13 October), which was harvested in Falgun month (15 Feb – Mar
15). After 1995 however, climate was conducive for wheat sowing in December as
well and sometimes even in Falgun (Feb). Mubaya et al. (2010), in Zambia and
Zimbabwe, indicated that 80 per cent of famers perceived a change in climate as
they had noticed droughts and excessive rains in the past five years, which had
both positive and negative impacts on farming.
Patwal (2010) said that a positive change observed by villagers is that the
ripening period of wheat has reduced by some days. According to Pande and
Akermann (2010) various studies have indicated a probability of 10 to 40 per cent
loss in crop production in the country due to the anticipated rise in temperature by
2080. Studies conducted by Indian Agricultural Research Institute (IARI) have
pointed to a possible loss of 4 to 5 million tonnes in the overall wheat production
with every 1oC increase in temperature throughout the growing period of the crop.
36
Arya (2010) argued that according to people’s perception, climate change
has effect the phenological event in all plants, like flowering, fruiting and fruit
size, their quality and quantity. Rawat (2010) reported that almost every one
interviewed suggested that there is a change in cropping pattern and animal
keeping since the last 5-6 years. Pettengell (2010) and Owusu-Sekyere et al. (2011)
saw that the main impact of climate changes was in decreasing crop and animal
yields.
Sorhang and Kristiansen (2011) reported that 96 per cent of the respondents
in Kofele, and 58.3 per cent of the respondents in Hagere Selam have experienced
negative impacts of climate changes. Further, 40 per cent of the respondents in
Hagere Selam have experienced reduced crop or animal yield because of shortage
of rainfall and also an increase in diseases and pest on crop and livestock.
2.6.2 Impact of short term climate changeWHO (2002) states that in year 2000, climate changes was estimated to be
responsible for approximately 2.4 per cent of worldwide diarrhea, and 6 per cent of
malaria in some middle income countries. Patil (2008) argued that Orissa from
1955 to 2008 experienced 28 years of flood, 19 years of drought and 7 devastating
cyclones, along with the “super cyclone” in 1999. The majority of these events are
concentrated within the last 18 years (from 1990 to 2008). In this Period Orissa
experienced 12 years of flood, 5 years of drought, one “super cyclone” and many
smaller depressions and cyclones. The study suggests that in Orissa natural
extreme events are multiplying in frequency, which is probably due to climate
change. TERI (2008) reported that in the arid regions of Andhra Pradesh, the yields
of all the major crops like, rice, groundnut, and jowar are expected to decline,
although groundnut is expected to fare better than others due to its resistance to
prolonged dry spells.
SAGUN (2009) mentioned that scientific communities believe changes in
temperature and rainfall are creating favorable environments for pests, diseases and
invasive species to emerge, spread and encroach on agriculture and forestlands.
Thornton et al. (2009) observed climatic impacts that included reduced
productivity of animal feed, higher disease prevalence, and reduced fresh water
availability. This was due to the negative effects of lower rainfall and more
37
droughts on crops and on pasture growth, and of the direct effects of high
temperature and solar radiation on animals.
Sharma (2010) reported that according to farmers incidences of untimely
rainfall have increased (85%) and attack of insect pest has increased resulting in
the reduction of crop yield (81%). Patwal (2010) inferred that villagers are facing a
continuous failure of winter crops for the last 2 years. This is happening due to
absolute lack of rains during critical winter period. This is a recent phenomenon
seen in the village forcing people to look for other options for their survival.
Pande and Akermann (2010) argued that there are important reductions in
crop yields due to changes in rainfall patterns. Crop losses due to untimely rains
have multiplied and low yields due to insufficient monsoon rains are becoming the
rule rather than the exception. In the winter season higher temperatures reduce the
length of the growing period for winter crops, especially wheat. Farmers are
constrained to sow the crops later, which also results in decreased yields.
According to farmers of Maharashtra, impacts of pests and pathogens are felt
heavily in recent years, which also negatively affected crop yields. On the contrary,
Hassan (2010) studied on potential impacts of climate change on agriculture in 11
African countries and concluded that warming was harmful to crop production but
beneficial to crop production under irrigation.
Krishna et al. (2011) reported that there were drastically decreased Millet,
Black gram and Mustard production over the last 4 years in the Mid-mountain and
Siwalik region. This may affect agriculture production, and subsequently food
security.
2.7 Coping mechanism/adaptation in response to climate changeEllis (2000) reported that rural people in developing areas accrue specific
responses to cope with short-term shock events. However, these are often
responsive rather than planned actions, with capacity to regenerate and initiate
planned livelihoods adaptations limited by poverty and livelihood shocks.
According to O’Brien et al. (2004), areas with better infrastructure are
expected to have a higher capacity to adapt to climate change. Bradshaw et al.
38
(2004) stated that important adaptation options in the agricultural sector include:
crop diversification, mixed crop, livestock farming systems, using different crop
varieties, changing planting and harvesting dates, and mixing less productive,
drought-resistant varieties and high-yield water sensitive crops. Bhushal et al.
(2009) reported that most of the coping activities were found to be event specific
based on local knowledge and innovations, because most of the respondents were
not aware about actual impacts of climate changes.
FAO (2009) considered climate change adaptation as spontaneous or
organised processes whereby human beings and society adjust to changes in
climate, by making changes in the operation of land and natural resource used
systems and other forms of social and economic organisation in order to reduce
vulnerability to changing climatic conditions.
Pande and Akermann (2010) mentioned that according to interviewed
respondents, traditional seed varieties were much more resistant to dry spells, high
temperatures and other detrimental weather influences. Three decades ago, most of
the farmers were using indigenous seed varieties. Now this ratio is reduced to 50
per cent. Around half of the farmers are completely dependent on the market for
their seed supply. The farmers of Uttarakhand explained that they grow more than
one variety of rice because different varieties are suitable for different types of
fields. Some of the traditional rice varieties require relatively little water compared
to new high yielding varieties, it is being in certain cases the traditional varieties
are being promoted by the government.
Dhaka et al. (2010) revealed that an integrated farming system was
considered to be one of the most important adaptations in response to climatic
vagaries. Adjusting the cropping sequence, including changing the timing of
sowing, planting, spraying and harvesting, to take advantage of the changing
duration of growing seasons and associated heat and moisture levels was another
option. According to Sharma (2010), a majority of respondents had perceived that
use of fertilizers and pesticides have increased due to climate change. They also
perceived that the area under fruit crops and cereal crops had increased.
Akponikpe et al. (2010) reported that as an adaptation to late rain onset the
majority of farmer delay sowing dates in the Sahelian areas of Burkina and Niger
39
compared to the Guinean and Sudanian zones (Benin, Togo and Ghana). The
majority of farmers did not change crop density but there is a tendency to decrease
or increase it by some farmers. Changing from late to early crop cultivars have
been cited / adopted by a significant proportion of farmers to deal with the rainy
season shortening (Benin, Togo and Burkina Faso). But the majority of farmer in
Ghana and Niger / or the Sahel made no change in their cultivar use.
Sorhang and Kristiansen (2011) reported that 50 per cent of the respondents
in Hagere Selam and 56 per cent of the respondents in Kofele planted trees to
prevent flood and to mitigate impacts from climate changes.
2.8 Crop diversification in response to climate changeAdger et al. (2003) and Orindi and Eriksen (2005) indicated that crop
diversification can serve as insurance against rainfall variability as different crops
are affected differently by climate events. Kurukulasuriya and Mendelsohn (2006)
used multinomial logit models to analyze crop and livestock choice as adaptation
options, respectively. The study on crop choice showed that crop choice is climate
sensitive and farmers adapt to changes in climate by switching crops.
Nhemachena and Hassan (2007) reported that mixed farming systems are
better able to cope with changes to climatic conditions through undertaking various
changes in management practices. Cooper et al. (2008) inferred that diversification
is identified as a coping strategy that has evolved to deal with both expected
rainfall uncertainty and evolving within season fluctuations in rainfall. Deressa et
al. (2009) stated that crop diversification is the most commonly used method to
overcome climate changes in Ethiopia.
Bhushal et al. (2009) reported that majority of the local farmers were
practicing vegetable farming instead of cereal crops as crop diversification as well
as to earn more income than cereal crops. Optimum utilization of marginal lands
by planting fodder trees, fruit trees, and other grasses also observed. Pande and
Akermann (2010) argued according to group discussion with farmers that the need
for diversifying agricultural activities is increasingly recognized by farmers in the
Wardha District of Maharashtra. Despite farmers preference for cotton and
40
soybean cultivation in the area, a shift to alternative, less water-intensive crops,
such as sorghum and pulses, grown in combination with fruit trees, is happening.
Kemausuar et al. (2011) revealed that over the past 10 years majority of
farmers (97.3%) changed their farming operations in response to numerous farm
risks. Out of this total, 98 per cent was in response to changes in climate. Sorhang
and Kristiansen (2011) reported that as response to climate change 98 per cent of
the respondents in Kofele use crop diversification; they sow several crop varieties
in one season to reduce risk. As response to climate 23.3 per cent of the
respondents in Hagere Selam said they had tried new varieties of seeds the last few
years. 34 per cent of the respondents in Kofele said they had used new seed
varieties the last few years.
2.9 Relationship between dependent and independent variables
Maddison (2007) reported that the coefficient on the farmer experience is
negatively signed and statistically significant at the 1 per cent level. Experienced
farmers are significantly less likely to perceive no change in the climate. Dhaka et
al. (2010) revealed that the age, farming experience, innovativeness, environmental
consciousness and exposures to mass media had a positive and significant
relationship with farmer perceptions to climate change.
Pande and Akermann (2010) reported that altered climate change, noticed
climate change frequency of droughts, age and sex all had no significance effect on
adaptation. Sorhang and Kristiansen (2011) reported that there seems to be a
positive relationship between education and how active the farmers are in
adaptation strategies, and those households where the head of household has 10
years or more in school are most likely to adopt more numbers of adaptation
strategies.
2.10 Factors affecting adaptationDoss and Morris (2001) suggest that gender affects adoption rates
indirectly through access to complementary inputs. Adger et al. (2003) revealed
that the adaptive capacity is influenced by factors such as knowledge about climate
41
change, assets, access to appropriate technology, institutions, policies and
perceptions inter alia.
Tenge and Hella (2004) argued that having a female head of household
may have a negative effect on the adoption of soil and water conservation
activities, because women may have limited access to information, land, and other
resources due to traditional social barriers. Asfaw and Admassie (2004) said that
male headed households are preferable, because male-headed households are more
likely to get information about new technologies and undertake risky businesses
than female-headed households.
Archer (2005) reported that failure to implement adaptation options and
poor agricultural performances by many African farmers has been blamed on lack
of information and resources. Gbetibouo (2006) concluded from the study that
household size, farming experience, wealth, access to credit and water, tenure
rights, off-farm activities, and access to extension were the main factors that
enabled farmers to adapt to climate change.
Nhemachena and Hassan (2007) study showed contrary results, arguing
that female-headed households are more likely to take up climate change
adaptation methods. IPCC (2007a) reported that the extent of sustainable
adaptation depends on the adaptive capacity, knowledge, skills, robustness of
livelihoods and alternatives, resources and institutions accessible to enable
undertaking effective adaptation. Bryan et al. (2009) stated that factors influencing
Ethiopian farmers‟ decision to adapt include wealth, and access to extension,
credit, and climate information.
2.11 Constraints in adaptationSalehyan (2005) reported that poor adaptive capacity, unresponsive
governments, and weak policy mechanisms might be barriers to adaptation.
Nhemachena and Hassan (2007) indicated that lack of credit and information
concerning climate change forecasting and information concerning adaptation
options and other agricultural production activities; rationing of inputs, and lack of
seed inputs are important constraints for most farmers. Ishaya and Abaje (2008)
reported that constraining factors to the adoption of modern techniques of
combating climate changes in the area were observed to include lack of improved
42
seeds, lack of access to water for irrigation, lack of current knowledge of modern
adaptation strategies, lack of capital, lack of awareness and knowledge of climate
change.
Bryan et al. (2009) and Deressa et al. (2009) stated that some of the
greatest barriers are financial constraints, and also poor potential for irrigation,
shortage of land and labour, and lack of information on adaptation methods. Pande
and Akermann (2010) revealed that as the growing seasons get shorter, farmers are
constraint to adapt their cropping patterns. Where no irrigation is available, farmers
have stopped growing paddy as the crop failed too often because of deficient
rainfall. Pearl- and Finger-Millet as well as short duration pulses (red gram) are
planted instead.
Nzeadibe et al. (2011) found that the major constraints to adapting to
climate change by farmers in the Niger Delta included lack of information, low
awareness level, irregularities of extension services, poor government attention to
climate problems, inability to access available information, lack of access to
improved crop varieties. However, other constraining factors were ineffectiveness
of indigenous methods, no subsidies on planting materials, limited knowledge on
adaptation measures, low institutional capacity and absence of government policy
on climate change.
2.12 SuggestionsAccording to Pande and Akermann (2010) farmers suggested need of
agricultural insurance (74%), weather alert (71%) to help for effective adaptation.
Also, effective meteorological facilities in keeping adequate records of weather
forecast are provided. Need extension agents to educate more on zero tillage,
organic agriculture, and better land management techniques.
Pettengell (2010) suggested that technologies for adaptation should be
targeted at the needs of the poorest and most vulnerable people, including women,
favoring small-scale technologies that can be taken up and adapted locally.
Adapting rural livelihoods will require a range of investments, policies, planning,
and information.
43
Materials and Methods
CHAPER – III
RESEARCH METHODOLOGYThis chapter deals with the description of the procedure followed to carry
out the investigation. The location of the study and sampling technique for
investigation and devices used for analysis of the data are also explained in this
chapter under the following sub-headings.
3.1 Location of the study area
3.2 Sample and sampling procedure
3.3 Variables of the study
3.4 Operationalization of independent variables and their measurement
3.5 Operationalization of dependent variables and their measurement
3.6 Coping mechanism/adaptation in response to climate change
3.7 Relationship between dependent and independent variables
3.8 Constraints faced by farmers in coping mechanism/adaptation
3.9 Suggestions given by farmers to overcome the constraints
3.10 Type of data
3.11 Developing the interview schedule
3.12 Method of data collection
3.13 Statistical analysis
3.1 Location of the study areaThe present study was carried out in Plains of Chhattisgarh state during the
years 2013-14 and 2014-15. Chhattisgarh state is divided in to 27 districts and 3
agro climatic zones namely Bastar Plateau, Chhattisgarh Plains and Northern Hills
in which four districts of Chhattisgarh Plains were selected for present study.
3.2 Sample and Sampling Procedure3.2.1 Selection of districts
The present investigation was carried out in four randomly selected districts
out of the total 15 districts of Chhattisgarh Plains namely Raipur, Durg,
Balodabazar-Bhatapara and Bemetara.
44
Fig. 3.1: Location map of the study area
45
3.2.2 Selection of blocksTwo blocks from each selected district were selected for the selection of
villages. In this way a total of 8 Blocks (Total 4 X 2 = 8) were selected randomly.3.2.3 Selection of villages
From each selected block, 3 villages (Total 3 X 8 = 24) were selected randomly for the selection of respondents.3.2.4 Selection of respondents
From each selected village, 10 farmers were selected randomly, who had more than 15 years of farming experience. In this way, a total of 240 farmers (Total 24 X 10 = 240) were considered as respondent for the present study. These selections were done by using simple random sampling method for the purpose of the study. Table 3.1: List of selected blocks, villages and number of respondents in different
districts of Chhattisgarh Plains
Selected district Selected blocks
Selected villages in the blocks
No. of respondents
1. Raipur
1. Aarang 1. Palaud 102. Godhi 103. Todgaon 10
2. Dharsiva1. Kapasda 102. Chikhali 103. Murethi 10
2. Durg
1. Durg1. Purai 102. Pauwara 103. Mahmara 10
2. Dhamdha1. Oteband 102. Malpuri 103. Godhi 10
3. Bemetara
1. Saza1. Matara 102. Pharasbod 103. Khapri 10
2. Berla1. Ufara 102. Gudheli 103. Gadamor 10
4. Balodabazar/Bhatapara
1. Bhatapara
1. Gudeliya 102. Tikuliya 103. Pendri 10
2. Simga1. Marrakona 102. Sanjari Navagaon 103. Rohara 10
Total 08 24 240
46
Table 3.2: Scales used for measuring the variables
S. No.
Variables Empirical Measurement
I. Independent VariablesA. Socio-personal characteristics1 Age Procedure followed by Usha Rani (1999) with
suitable modifications2 Educational status Procedure followed by Markad (1996) with
suitable modifications3 Size of family Procedure followed by Thoke (1999)4 Farming experience Procedure followed by Thoke (1999) with
some modifications5 Social participation Procedure followed by Hardikar (1998) with
slight modificationsB. Socio – economic characteristics1 Occupation Procedure followed by Chandramouli (2005)
with slight modifications2 Land holding Criteria adopted by Ministry of Rural
Development, GOI3 Irrigation Structured schedule 4 Access to credit Structured schedule 5 Annual income Structured schedule 6 Annual expenditure Structured schedule 7 Distance to market Structured schedule 8 Socio-economic status Scale developed by Trivedi (1963)9 Crop insurance Structured scheduleC. Communicational characteristics1 Sources of information Structured schedule2 Exposure to mass media Procedure suggested by Trivedi (1963)3 Contact with extension
personnelProcedure followed by Byrareddy (1971) with slight modifications
4 Access to weather forecasts Structured schedule5 Cosmopoliteness Procedure as followed by Shashidhar (2004)D. Psychological
characteristics1 Awareness Structured schedule2 Decision making pattern Structured schedule3 Innovativeness Scale developed by Moulik (1965) 4 Risk orientation Scale developed by Supe (1969)5 Scientific orientation Scale developed by Sakharkar (1995) was
used with some modificationsII. Dependent Variables
1 Perception of farmers about climate change
Structured schedule
2 Impact of climate change on agriculture and allied activities
Structured schedule
47
3.3 Variables of the study 3.3.1 Independent variables
(A) Socio-personal characteristics 1. Age2. Educational status3. Size of family4. Farming experience5. Social participation
(B) Socio – economic characteristics1. Occupation 2. Land holding3. Irrigation4. Access to credit 5. Annual income6. Annual expenditure7. Distance to market8. Socio-economic status9. Crop insurance
(C) Communicational characteristics1. Sources of information 2. Exposure to mass media 3. Contact with extension personnel4. Access to weather forecasts5. Cosmopoliteness
(D) Psychological characteristics1. Awareness 2. Scientific orientation3. Decision making pattern4. Innovativeness5. Risk orientation
3.3.2 Dependent variables
3.3.2.1 Perception of farmers about climate change
3.3.2.2 Impact of climate change on agriculture and allied activities
3.4 Operationalization of independent variables and their measurement
3.4.1 Socio-personal characteristics3.4.1.1 Age
It refers to the chronological age of the respondent in completed years at the time of interview. Categorization of age was done on the basis of procedure as followed by Usha Rani (1999) with some slight modifications and categorized as follows:
Categories Score
Young (Between 30 - 45 years) 1 Middle (Between 46 - 60 years) 2 Old (Above 60 years) 3
48
Further frequency and percentage were calculated to obtain respondents
under various categories and actual ages of the respondents were considered for the
analysis of data.
3.4.1.2 Educational status
Educational status may influence the level of perception and awareness of
the farmers. It refers to the formal schooling of an individual from school to the
university degree. Number of classes completed by the respondents was considered
as his educational score. The procedure followed by Markad (1996) was used here
with suitable modifications and respondents were categorized in following five
categories for analysis of data.
Categories Score
Illiterate 1
Up to primary school 2
Up to middle school 3
Up to high & higher secondary 4
Up to degree and above 5
3.4.1.3 Caste
A system in which an individual is ranked on the basis of accompanying
right and obligations and described on the basis of birth in to particular groups is
defined caste. In this study the caste of the respondents were categorized and
scores given in following manner:
Categories Score
Scheduled caste (SC) 1
Scheduled tribe (ST) 2
Other backward class (OBC) 3
General 4
49
Fig. 3.2: Conceptual Model of Study
a. Perception of farmers about climate changeb. Impact of climate change on agriculture and allied
activities
(B) Socio – economic characteristics• Occupation
• Land holding• Irrigation
• Access to credit • Annual income
• Annual expenditure• Distance to market
• Socio-economic status• Crop insurance
(A) Socio-personal characteristics
• Age
• Educational status
• Size of family
• Farming experience
• Social participation
(C) Communicational characteristics
• Sources of information
• Exposure to mass media
• Contact with extension personnel
• Access to weather forecasts
• Cosmopoliteness
(D) Psychological characteristics
• Awareness
• Scientific orientation
• Decision making pattern
• Innovativeness
• Risk orientation
50
3.4.1.4 Type of family
Family type refers to two-way classification of family as nuclear and joint.
The basic grouping of mates and their children is called nuclear family. On the
other hand more members living together than one nuclear family on the basis of
close blood ties and common residence are called joint family. In the present
research respondents were categorized under joint family and nuclear family as
procedure followed by Jadhav (2000). Data was analyzed by using frequency and
percentage.
3.4.1.5 Size of family
It was measured as the absolute number of members in the household
sharing the same economic unit and common kitchen. Respondents’ families were
classified into three categories. Procedure followed by Thoke (1999) was used in
this study.
Categories Score
Small family (Less than 5 members) 1
Medium family (5 - 8 members) 2
Large family (More than 8 members) 3
The results were expressed on frequency and percentage for each category.
3.4.1.6 Farming experience
This is a period from which farmer is actually cultivating land with his own
experience. It was recorded in complete years as reported by the respondents. The
respondents were categorized based on procedure followed by Thoke (1999) with
some modifications. Actual year of experiences of farmers were considered for
analysis of data.
Categories Score
Low (Up to 20 years) 1
Medium (21 - 40 years) 2
High (Above 40 years) 3
51
3.4.1.7 Social participation
Social participation is the degree of involvement of the respondents in
formal organizations. Social participation of respondents was studied by
considering their membership and extent of participation. Respondents were
grouped under three categories such as ‘member’, ‘non-member’ and office
bearers. While, extent of participation was measured on three point continuum i.e.,
regular, occasional and never. The frequency and percentage were used to analyse
the data. Procedure followed by Hardikar (1998) was used here with slight
modifications. The scoring was done in following manner for analysis of data.
Categories Score
No participation 0
Member of one organisation 1
Member of two or more organisation 2
Office bearer 3
3.4.2 Socio–economic characteristics3.4.2.1 Occupation
Occupation is the main source of earning for their livelihood and fulfills
necessary requirements. The occupation practiced by respondents such as
agriculture, agriculture along with labour, agriculture along with services etc. were
included in this study. Various kinds of occupation practiced by the farmers were
categorized by using procedure followed by Chandramouli (2005) with slight
modification as follows:
CategoriesAgriculture
Agriculture + LabourAgriculture + Service
Agriculture + Service + LabourAgriculture + Business + Service + Labour
Agriculture + Business + Service + Labour/Others
52
On the basis of various kind of occupation practiced by the farmers scores
were provided for analysis as follows:
Categories Score
Only one occupation 1
Two occupation 2
Three occupation 3
Four occupation 4
More than five occupation 5
3.4.2.2 Land holding
The land holding was operationalised by considering the size of land owned
by the respondent. The number of standard acres of land owned and cultivated by
each respondent family was considered in determination of their size of land
holding. Depending upon the farm size, the respondents were grouped in to five
categories using the criterion adopted by Ministry of Rural Development, GOI,
circular No. 280-12/16/19-RD-III, (vol., II), dated 15th November 1991
(Anonymous, 1992).
Categories Score
Marginal farmer (Up to 1.0 ha) 1
Small farmer (1.1 to 2.0 ha) 2
Medium farmer (2. 1 to 4 ha) 3
Big farmer (More than 4 ha) 4
Total land (in ha) acquired by each respondents were considered for the
purpose analysis.
53
3.4.2.3 Irrigation
Farmers were asked about their sources of irrigation with percent irrigated
area by various sources for different crops grown by them in kharif, rabi and zaiad
season. Further, they were categorized according to availability of irrigation by
assigning scores as follows:
Categories Score
Not irrigated 0
Only kharif 1
Kharif and Rabi 2
Round the year 3
3.4.2.4 Annual income
Annual income earned by the respondent from all available resources was
assessed considering the following items
Main income
It was conceived as the income derived from farming during the previous
year.
Subsidiary income
The income obtained by the respondents from source other than farming
during the previous year. Respondents were grouped in to five categories based on
their annual income as follows:
Categories Score
Very low (Up to Rs.75000/-) 1
Low (Rs.75001 - 150000/-) 2
Medium (Rs.150001 - 300000/-) 3
High (Rs.300001 – 450000/-) 4
Very high (More than Rs.450000/-) 5
54
Data was analysed by using actual income (in terms thousands rupees)
earned by respondents during the previous year.
3.4.2.5 Expenditure pattern
Expenditure pattern is determined by considering the expenditure in rupees
on items such as food, agriculture, dairying, clothing, religious functions and
marriages, medical, education and personal expenditure collected from the
respondents. Further, to categorize the expenditure on various items, percent share
of each item from total expenditure was obtained.
3.4.2.6 Access to credit
Easily access to credit helps the farmers to purchase the required inputs that
may influence the extent of adoption of the farmers and adaptation towards adverse
effect of climate change. Sources of credit were identified like (cooperative
society, nationalized banks, moneylenders, friends, neighbour relatives, etc) from
where they can borrow loans. The acquisition of credit was measured on a 2 point
scale as follow:
3.4.2.7 Distance to market
Market is the place where, farmers can buy inputs for agriculture and also
sell their produces. Distance to market is positively correlated with easily and
timely availability of farm inputs, lesser the distance means easier and timely they
get inputs. Farmers were asked questions about distance of market to purchase
farm inputs and on the basis of responses recorded they were assigned scores and
categorized as below for analysis purpose.
Categories Score
Not acquired 0
Acquired 1
55
Distance to Market Score
Within village (0 km) 5
Up to 2 km 4
3 km to 5 km 3
6 km to 8 km 2
More than 8 km 1
3.4.2.8 Availability of farm implements
Farm implements are prime necessity for timely operation of agriculture
practices in current scenario of climate change. Availability of farm implement
may help farmers to change their farm practices according to short term climatic
variability. Respondents were asked for availability of various farm implements
and further they were categorized on the basis of number of implements they
possessed by assigning corresponding scores as under:
3.4.2.9 Crop insurance
Crop insurance helps farmers to recover more quickly from the losses
administered due to adverse effect of extreme climatic events and weather induces
risks.
For the purpose of analysis respondents are characterised into four
categories on the basis of crop insurance facility availed by them from various
institution in following manner:
Categories Score
Not available 0
1-4 Implements 1
5-8 Implements 2
> 8 Implements 3
56
3.4.2.10 Socio-economic status
The position of the respondents in the society is termed as socio-economic
status, which is determined by various social and economic variables, viz,
education, caste, type of family, social participation, land holding, possession of
farm implement, income, occupation and irrigation availability. Socio-economic
status of the respondents was measured by using scale developed by Trivedi (1963)
with slight modifications. Scale was developed by considering all the variables of
socio-economic characteristics accept age, farming experience and distant to
market. The socio-economic status score of each respondent was obtained by
adding all the scores of individual items of socio-economic variables. The different
categories of socio-economic status were made according to scores obtained by the
respondents as follows:
Categories Score
Nil 0
From government institution 1
From private institution 2
From government and private institutio 3
Categories Score
Lower class (Up to 9 score) 1
Lower middle class (10 - 18 score) 2
Medium class (19 - 27 score) 3
Upper middle class (28 - 36 score) 4
Upper class (More than 36 score) 5
57
3.4.3 Communicational characteristics 3.4.3.1 Sources of information
Information regarding weather forecast are supposed to directly associate
with the measures taken by the respondents to combat with the adverse effect of
instant climatic variability. These information sources provide regular and timely
information to the respondents regarding favorableness and un-favorableness of
weather for agriculture and allied activities. To determining the extent of
utilization of various information sources, different 7 communication mass media
were selected and respondents were asked about their frequency of use and utility
of information. Furthermore, the respondents were categorized by assigning the
scores as follows:
Categories Score
Nil 0
Low (Up to 12 score) 1
Medium (13 - 24 score) 2
High (More than 24 score) 3
Total number of information sources used by each respondent for collecting
weather related information was considered for analysis of the data.
Utilization pattern of information sources for weather forecast
The utilization pattern of information sources for seeking weather related
information by respondents were determined by finding the credibility of
information sources, extent of use of information sources and extent of utility of
information sources. For determining utilization pattern of each information
sources by respondents, the three indexes were worked out as follows:
(i) Credibility Index
Respondents were asked about the credibility of information sources being
utilized by them on four point continuum scale viz. fully credible, medium
58
credible, partial credible and not credible by assigning scores 3, 2, 1 & 0,
respectively. Further, an index was worked out as below:
Where,
= Credibility index of ith information source
= Sum of credibility score obtained by respondents for ith information
source
= Maximum obtainable credibility score
(ii) Usage Index
Respondents were asked about frequency of use of various information
sources for getting weather information on three point scale viz. regular, occasional
and never and index was worked out as follows:
Where,= Usage index of ith information source= Sum of usage score obtained by respondents for ith information
source= Maximum obtainable usage score
(iii) Utility IndexRespondents were asked about the utility of information sources being
utilized by them on four point continuum scale viz. fully, medium, partial and nilby assigning scores 3, 2, 1 & 0, respectively. Further, an index was worked out as below:
Where,= Utility index of ith information source
= Sum of utility score obtained by respondents for ith information source= Maximum obtainable utility score
3.4.3.2 Access to weather forecastAccess to accurate weather forecast is directly or indirectly linked with the
success of farmers in their cultivation practices because erratic rainfall patterns and
59
changing seasons are upsetting farming cycles in many parts of the world. Aweather forecast is important for the farmers to be able to plan what to do on the field. Each respondents were asked about whether they acquired weather forecast or not and measured on two point scale providing scores as follows for analysis of data.
Categories Score
Not acquired 0
Acquired 1
3.4.3.3 Contact with extension personnel
Extension contact refers to the frequency with which farmer comes in
contact with the extension agency/workers. It was measured by using the
procedure followed by Byrareddy (1971) with slight modifications. This variable
was measured on three point scale viz. regular, occasional and never. Moreover,
extent of contact of the respondents with extension personnel was determined and
scores were assigned for analysis as follows:
Categories Score
No contact 0
Low (Up to 4 score) 1
Medium (5 - 8 score) 2
High (More than 8 score) 3
Number of extension personnel contacted by each respondent was
considered for the purpose of analysis of data.
3.4.3.4 Exposure to mass media
It refers to the extent to which the farmer is exposed to different mass
media of communication such as newspaper, radio, farm magazines and television.
The procedure suggested by Trivedi (1963) with little modification was followed
60
for measuring exposure to mass media of the respondents. The respondents were
asked about their frequency of use of different mass media sources by assigning
scores 2, 1 & 0 for regular, occasional and not used, respectively. Further, the
respondents were categorized as follows:
Categories Score
Nil 0
Low (Up to 3 score) 1
Medium (4 - 6 score) 2
High (More than 6 score) 3
Number of mass media sources utilised by each respondents was used for
analysis of data.
3.4.3.5 Extension participation
It referred to the awareness of respondents about various extension
activities and their extent of participation in those activities. This variable was
quantified by following the procedure of Hardikar (1998). Selected extension
activities like training, demonstration, field day, field visit, group discussion,
exhibition, kisan mela etc. were listed and the respondents were asked to indicate
their extent of participation in each of them. The scoring was done for analysis as
below.
Categories Score
No participation 0
Low (Up to 4 score) 1
Medium (5 - 8 score) 2
High (9 - 12 score) 3
Very high (More than 12 score) 4
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3.4.3.6 Cosmopoliteness
It is the degree to which an individual is oriented outside to his immediate
social system. The cosmopolite farmer is likely to be a unique individual, in that he
is motivated to look beyond this environment when most others are content to
maintain a localistic frame of reference. Two dimensions of the variable
considered in this case were;
1. The frequency of visit to the nearest town
2. The purpose of visit to the town
Above two dimensions indicated extent of cosmopoliteness of the
respondents were quantified by using frequencies and percentage as the procedure
followed by Shashidhar (2004). The respondents were categorized in three
categories as follows:
3.4.4 Psychological characteristics3.4.4.1 Innovativeness
It refers to the behaviour pattern of an individual who has interest and
desire to seek changes in farming techniques and to introduce such changes into his
operation when practicable and feasible and also it refers to the degree to which a
farmer is eager to adopt the innovations in their cultural operations which may
helps better adaptation against adverse effect of climate change. Forced choice
method of self rating scale developed by Moulik (1965) and followed by Reddy
(2005) with slight modification was administered for quantification of the degree
of farmers self evaluation with regard to their innovation proneness. This scale
consisted set of nine statements comprising 6 positive and 3 negative statements.
Farmers responses were taken in 5 point scale with weights of 5, 4, 3, 2, and 1
Categories Score
Low (Up to 4 score) 1
Medium (5 – 8 score) 2
High (More than 8 score) 3
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indicating “strongly agree”, “agree”, can’t decided, “disagree” and strongly
disagree and vice versa for negative statements. The total scores ranged from 5 to
45. Based on the scores obtained, the respondents were grouped into following
three categories:
Categories Score
Low (Up to 15 score ) 1
Medium (16 – 30 score) 2
High (More than 30 score) 3
Total score obtained by each respondent was considered for analysis
purpose.
3.4.4.2 Scientific orientation
This refers to the degree to which a respondent is oriented towards the use
of scientific methods. The scale developed by Sakharkar (1995) was used with
some modifications. The scale consisted of six statements with two response
categories as completely agree, agree, undecided, disagree and completely
disagree. For each statement a score of 5, 4, 3, 2 and 1 was assigned for positive
statements and in its reverse order for negative statements, respectively. The
summation of scores obtained by respondent for all the six statements indicated his
level of scientific orientation. Based on the score obtained the respondents were
categorized into three categories as follows:
Categories Score
Low (Up to 10 score) 1
Medium (11 – 20 score) 2
High (More than 20 score) 3
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3.4.4.3 Risk orientation
It was operationalised as the degree to which the respondent was oriented
towards risk and uncertainty in adopting new ideas or technologies in farming.
Risk orientation scale of Supe (1969) was used in this study. The scale consists of
one positive item and five negative items. The responses for positive items were
scored as 5, 4, 3, 2, and 1, while, for negative items the scores were reversed in the
order of magnitude, respectively. The scores obtained for each statement were
summed up to get individual respondents risk orientation score. Further, the
respondents were grouped into three categories as below:
Categories Score
Low (Up to 10 score) 1
Medium (11 – 20 score) 2
High (More than 20 score) 3
3.4.4.4 Decision making pattern
As Byarle et al. (1987) pointed out it is farmers, not judges, that make the
decisions and therefore special thrust will have to be given to both socio-economic
environment as well as decision making process taking place within the household.
Decision making process was operationalised as the nature of the decision making
(individual, joint or collective) that the farm family has resorted to, while
performing farming activities. Decision making related to farming and land
development was considered in the study. To determine decision making pattern of
respondents 10 items were considered and measured on three point continuum. The
score of 1, 2, 3 were given to self, joint and collective style of decision making,
respectively. Higher the score indicate more the collective decisions and lesser the
score indicate the more the individual decisions. The respondents were later
categorized as follows:
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Categories Score
Low (Up to 10 score) 1
Medium Between (11 – 20 score) 2
High (More than 20 score) 3
3.4.4.5 Awareness about climate change
To determine level of awareness of respondents regarding climate change 9
statements were considered and responses were recorded in three point continuum
scale as scores were given 0, 1, & 2 for No, Partial and Complete awareness of
respondents, respectively. Further, respondents were categorized into four
categories according to scores obtained by them out of total score 18 as follows:
Categories Score
Nil 0
Low (Up to 6 score) 1
Medium (7 – 12 score) 2
High (More than 12 score) 3
Scores obtained by each respondents (out of total obtainable score 18) was
taken for analysis of data.
3.4.4.6 Vulnerability
The degree to which a system is susceptible to, or unable to cope with,
adverse effects of climate change, including climate variability and extremes.
Vulnerability is a function of the character, magnitude, and rate of climate
variation to which a system is exposed, its sensitivity, and its adaptive capacity”
(McCarthy et al., 2001). The IPCC Fourth Assessment Report (AR4), which
reports recent advances in our understanding of climate change, contains a
vulnerability definition consistent with that of the TAR (IPCC 2007c). Under this
framework, a highly vulnerable system would be one that is very sensitive to
65
modest changes in climate, where the sensitivity includes the potential for
substantial harmful effects, and for which the ability to adapt is severely
constrained. In the present study, the extent of vulnerability was ascertain in terms
of vulnerability index based on socio-economic status, food and fodder availability
and various disasters (flooding, erratic rainfall, drought, storm/typhoon, disease
and pest outbreak, epidemic, theft/grazing and environmental pollution) faced by
respondents during last 15 years. The responses of respondents regarding type of
losses from disasters and its coping mechanism were recorded on two point
continuum 0 & 1 for no & yes, respectively and vulnerability index was developed
for each respondents by using the following formula:
Where,
VIi = Vulnerability index of ith respondent.
Oi = Total score obtained by ith respondent
S = Total obtainable score
On the basis of vulnerability index (VI) respondents were categorized into
the following five categories:
Categories Score
Very Low (Up to 20%) 1
Low (Between 21 - 40%) 2
Medium (Between 41 - 60%) 3
High (Between 61 - 80%) 4
Very High (More than 80%) 5
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Fig. 3.3: Empirical Model of Vulnerability
VULNERABILITY
IMPACTS
SENSITIVITY
CharacteristicsHarmfull effect on economic structures, assets and
human capitals
EXPOSURE
Characteristics Frequency of
occurance, magnitude and
duration of disasters
ADAPTATION & MITIGATION
ADAPTIVE CAPACITY
Determinants Coping
mechanisms, Mitigation measures
67
3.5 Operationalization of dependent variables and their measurement
3.5.1 Perception of farmers about climate change
Perception is the process by which we receive information or stimuli from
our environment and transform it into psychological awareness. To ascertain level
of perception regarding climatic events/changes, respondents were asked about 09
selected events/changes occurred in each rainy season, winter season & summer
season and 03 other events. Responses of respondents were collected on two point
continuum scale viz. change (Increase or Decrease) and no change on climatic
phenomena providing score 1 and 0, respectively. Further, respondents were
categorized in to three categories as follows for analysis:
Categories Score
Low (Up to 10 score) 1
Medium (11 - 20 score) 2
High (More than 20 score) 3
Out of total obtainable score, actual score obtained by each respondent was
considered for analysis of data.
3.5.2 Impact of climate change on agriculture and allied activities
3.5.2.1 Impact of long term climate change
Climate change has both long term and short term impact on agriculture
and other events. To ascertain the impact of long term climate change on various
events, respondents were asked for their general perceptions on climate change
impacts using 11 statements regarding agricultural incidences as well as 11
statements regarding other incidences; whether they have expressed their
agreement or disagreement on impacts of climate change. Responses were
recorded on 3 point continuum scale viz. agree, can’t decided and disagree by
assigning scores 3, 2 & 1, respectively. Further, the overall impact of long term
climate change was determined and respondents were categorized on following
three categories as below:
68
Categories Score
Low (Up to 22 Score) 1
Medium (23 - 44 Score) 2
High (More than 44 Score) 3
Out of total obtainable score from both the categories (Agriculture and
allied activities), actual score obtained by each respondent was considered for
analysis of data.
3.5.2.2 Impact of short term climate change
To determine the impact of short term climate change paddy crop was
considered as this was the major crop of study area. Respondents were asked about
changes they performed in cultural operations of paddy according to arrival of
monsoon. Also responses were taken from them about impact of amount of
precipitation on some selected major rabi crops of study area. Further, percentage
change was calculated for each operation by comparing the happenings in
abnormalities (early/late arrival of monsoon or deficit/surplus precipitation) with
normal situation as follows:
3.6 Coping mechanism/adaptation in response to climate changeAdaptation or coping mechanism refers to the measures employed by the
farmers to curb the immediate-term and long-term negative effects of climate
change on agriculture and allied activities. Understanding the existing coping and
adaptive strategies of farmers in specific crop context is a first step toward the
identification of appropriate options to increase the potential for adaptation of
vulnerable section of farmer. In this regard farmers were interviewed for their
69
responses towards adaptation measures taken by them in case of deficit and excess
rainfall during paddy cultivation, as paddy was the main crop of study area.
Afterwards, for presentation of data frequency and percentage were calculated for
each adaptation measures.
3.7 Relationship between dependent and independent variablesThe relationship between dependent variable and selected independent
variable were ascertained by calculating correlation coefficient (‘r’ value) and
multiple regression analysis, which was further used to find out the relative
importance of different components (independent variables) of dependent variable.
Multiple Linear Regression (MLR) analysis is generally considered as an efficient
and powerful hypothesis–testing and inference making technique. Since correlation
analysis only gives the nature of relationship between dependent and independent
variables.
Pearson’s Product Moment Correlation Coefficient (r)
Pearson’s correlation coefficient (r) was computed in order to know the
nature of relationship between the dependent and selected independent variables.
The values of the correlation coefficients were then tested for statistical
significance. The coefficient (r) was calculated by using following formulae:
Where,
r = Co-efficient of correlation between X and Y
= Sum of scores of variable X
= Sum of scores of variable Y
= Sum of product of X and Y variable
= Sum of the squares of X variable
= Sum of the squares of Y variable
n = Size of the sample
70
Multiple regression analysis
Multiple linear regression analysis was calculated to find out the extent of
relationship between dependent and selected independent variables and to know
the influence of independent variables on perception of farmers about climate
change and its impact on agriculture and allied activities. Further, the computed ‘b’
values (regression coefficients) were tested with ‘t’ test for its significance.
Y = a + b1X1 + b2X2 + B3X3 + ... + BtXt + e
Where,
Y = the variable that we are trying to predict
Xi = the variable that we are using to predict Y
a = the intercept
b = the slope (regression coefficient)
e = the regression residual.
The Coefficient of Determination ( )
This represents the proportion of the total sample variability in Y that is
explained by a linear relationship between X and Y. R2 is always less than unit and
expressed in percentage. It means the extent of variation in dependent variable (Y)
which can be explained by the independent variables (Xi) together. R-Squared
measures how well the model fits the data. Values of R2 to 1 fit well. Values of R2
close to 0 fit badly. Coefficient of multiple regression (R2) was calculated by
Where,
= Coefficient of multiple regression
= Sum of squares of dependent variable (Y)
= Sum of squires due to deviation from regression
71
3.8 Constraints faced by the farmers in coping mechanism /adaptation
As adaptation has the potential to significantly contribute to reductions in
negative impacts from changes in climatic conditions, therefore, an analysis of
constraints faced by farmers in adaptation is very imperative for its rectification.
The constraint refers to the hurdles or obstacles faced by farmers in adaptation to
climate change. The open-ended questions were used to collect responses on
constraints faced by them in adaptation. Furthermore, frequency and percentage
were calculated and accordingly ranks were assigned for presentation of data.
3.9 Suggestions given by the farmers to overcome the constraintsSuggestions of the farmers about climate related issues may play significant
role in policy making to mitigate negative effect of climate change. To overcome
the constraints in adaptation to climate change, respondents were asked to indicate
possible suggestions by using open-ended questions. Frequency and percentage
were calculated for each suggestion and ranks were provided accordingly.
3.10 Type of dataThe data pertaining to selected characteristics about socio-personal, socio-
economic, psychological, communicational, constraints perceived in terms of
adaptation to climate change and suggestions given by them to overcome
constraints were collected as per objectives of the study as primary data. The
official information’s and records were also consulted from the concerning
departments as secondary data.
3.11 Developing the interview schedule The interview schedule was designed on the basis of objectives and
independent and dependent variables considered for present investigation. To
facilitate the respondents, the interview schedule was framed in “Hindi”. Each
question was thoroughly examined and discussed with the experts before finalizing
the interview schedule. Adequate precautions and care were taken into
consideration to formulate the questions in a manner that they were well
understood by the respondents and would find it easier to respond.
72
Before using prepared interview schedule for collection of data it was pre-
tested by 20 non-sample respondents and also checked its reliability and validity.
On the basis of experience gained in pre-testing, the necessary modifications and
suggestions were incorporated before giving a final touch to interview schedule.
The final interview schedule is given in appendix.
3.11.1 Validity
Validity refers to “the degree to which the data collection instrument
measures what it is supposed to measure rather than something else”. In other
words it is best available approximation to the truth or falsity of a given inference,
proposition or conclusion. The validity of interview schedule used for this study
was maximized by taking following steps:
1. The interview schedule was thoroughly discussed with the concerned
scientists and member of advisory committee and their suggestions were
incorporated.
2. Pre-testing of interview schedule provided an additional check for
improving the instrument.
3. The relevancy of each question in terms of objectives of study, their logical
order and wordings of each question was checked carefully.
3.11.2 Reliability
Reliability of an interview schedule refers to the extent to which a
questionnaire, test, observation or any measurement procedure produces the same
results on repeated trials. In short, it is the stability or consistency of scores
obtained from the respondents over time.
The reliability of interview schedule used in present investigation was
tested by using test-retest method of estimating reliability. A total of twenty non-
respondent farmers of the study area were randomly selected and interviewed and
again they were re-interviewed after 2 to 3 weeks by schedule used at the time of
first interview. Since same responses were observed, the reliability of the interview
schedule was ensured.
73
3.12 Method of data collectionIn initial face of the study secondary data were collected from Directorate of
Agriculture and Agriculture Department of selected districts for basic information
about study area. Weather related information of previous years were also
collected from Indian Metrological Department Raipur center, Meteorology
department, IGKV, Raipur, Internet and reports of climate related studies to find
out the trend of climate change and for scientific affirmation of present study. For
collection of primary data respondents were personally interviewed by investigator
through personal interview. Prior to interview, respondents were taken in to
confidence by revealing the actual purpose of the study and also full care was
taken to develop good rapport with them to secure full co-operation for collecting
data. They were assured that the information given by them would be kept
confidential. The interview was conducted in the most formal and friendly
atmosphere without any complications. In addition to personal interview, group
discussions were conducted among the farmers in each selected village to affirm
the response in group about climate change.
3.13 Statistical analysis The data collected from the selected respondents during the course of
investigation was entered and tabulated in the excel worksheet and then
appropriate analysis of data was made according to objectives formulated for the
study. Further, the statistical techniques were applied to analyse tabulated data and
interpreted it to reach up to the findings.
74
Data Collection from the Farmers of Various Selected Villages
75
Group Discussions among Farmers of Various Selected Villages
76
Results and Discussion
CHAPTER – IV
RESULT AND DISCUSSION
It is evidenced that climate change has a strong impact in the areas of
agriculture, land use, energy, biodiversity, health and water resources. To know the
perception of farmers of Chhattisgarh about climate change impacts, the present
study was conducted among 240 respondents. The data collected from respondents
through personal interview and group discussion were coded, tabulated and
subjected to statistical analysis in accordance with the objectives of the study. The
results so obtained from analysis of data supported with appropriate justification
have been presented in this chapter under the following heads:
4.1 Independent Variables
4.1.1 Socio-personal characteristics
4.1.2 Socio-economic characteristics
4.1.3 Communicational characteristics
4.1.4 Psychological characteristics
4.2 Dependent variables
4.2.1 Perception of farmers about climate change
4.2.2 Impact of climate change on agriculture and allied activities
4.2.2.1 Impact of long term climate change
4.2.2.2 Impact of short term climate change
4.3 Coping mechanism/adaptation in response to climate change
4.4 Relationship between dependent and independent variables
4. 5 Constraints faced by farmers in adaptation to climate change and their
suggestions to minimize the constraints
4.1 Independent VariablesIndependent variables are the variables used to model or to predict the
dependent variable, and are often referred to as explanatory variables. This section
consists of socio-personal, socio-economic, communicational and psychological
variables undertaken in the study. The findings of these variables are given as
follows:
77
4.1.1 Socio-personal characteristics
This section includes the socio-personal characteristics of the respondents
which is associated with dependent variables and may influence their perceptions.
However, some socio-personal characteristics of respondents were identified
namely age, caste, education, type of family, size of family, farming experience
and social participation. The findings on socio-personal characteristics are
presented in Table 4.1.
4.1.1.1 Age
Age of the head of household can be used to capture farming experience
that might often mean better perception, access to information and knowledge. The
data regarding age of the respondents are presented in Table 4.1. It shows that
majority of the respondents (47.50%) belonged to middle age group (46-60 years),
whereas, 33.75 and 18.75 per cent of them belonged to young age (35-45 years)
and old age (more than 60 years), respectively.
It can be concluded from data that most of the interviewed respondents
were middle aged, while, around one third of them were belonged to young age
followed by old aged. This reflected that respondents in study area were much
experienced, which may help them to better adaptation against climate change. The
findings are in line with results of Shiferaw and Holden (1998), Kumar and
Narayana Gowda (1999), Maddison (2006), Nhemachena and Hassan (2007),
Deressa et al. (2009) and More (2000).
4.1.1.2 Caste
The data presented in Table 4.1 shows the distribution of respondents
according to their caste. It indicates that most of the respondents (85.83%)
belonged to other backward class, followed by 6.67, 4.17 and 3.33 per cent of them
belonged to general, scheduled caste and scheduled tribe category, respectively. On
the basis of above findings it can be revealed that study area is dominated with
other backward class. The findings are in line with the findings of Rathi (2004) and
Kulshrestha et al. (2010).
78
Table 4.1: Distribution of respondents according to their socio-personal characteristics
Characteristics Frequency Percentage
Age Young (30-45 years) 81 33.75 Middle (46-60 years) 114 47.50 Old (More than 60 years) 45 18.75
Caste Schedule Caste (SC) 10 4.17 Schedule Tribe (ST) 8 3.33 Other Backward Class (OBC) 206 85.83 General 16 6.67
Education Illiterate 13 5.42 Up to Primary 57 23.75 Up to Middle 65 27.08 Up to High & Higher Secondary 85 35.42 Up to Degree & Above 20 8.33
Type of Family Nuclear 104 43.33 Joint 136 56.67
Size of Family Small Family (< 5 Members) 40 16.67 Medium family (5-8 Members) 116 48.33 Large family (> 8 Members) 84 35.00
Farming Experience Up to 20 Years 65 27.08 21 to 40 Years 140 58.33 More Than 40 Years 35 14.58
Social Participation No participation 04 1.67 Member of one organization 37 15.42 Member of two organizations 139 57.91 Member of more than two organisations 28 11.67 Office bearer 32 13.33
79
4.1.1.3 Education
Higher level of education is believed to be associated with access to
information on improved technologies and higher productivity. Farmers with
higher levels of education are more likely to adapt better to climate changes. The
data presented in Table 4.1. reveals that among respondents, 35.42 per cent were
educated up to high & higher secondary level, whereas, 27.08 and 23.75 per cent
were educated up to middle and primary level, respectively. Fewer respondents
(8.33%) were reported they educated up to degree & above and only 5.42 per cent
of the respondents were illiterate.
The findings revealed that most of the respondents of study area were high
& higher secondary passed followed by middle and primary school passed. The
above results are broadly supported by Norris and Batie (1987), Smith and Lenhart
(1996), Igoden et al. (1990), Maddison (2006), Deressa et al. (2009) and
Akponikpe et al. (2010).
4.1.1.4 Type of family
A family may be classified in two categories nuclear and joint. Nuclear
family is the social institution consisting of married man and woman with their
children living together under same roof and sharing a common hearth. Joint
family is the social institution consisting of several related individual families,
especially those of a man and his sons residing in a single large dwelling. The data
regarding type of family (Table 4.1) indicate that about 57 per cent of the
respondents were residing as joint family, whereas, 43 per cent of them as nuclear
family. It indicates that respondents in study area were residing in the villages in
nuclear as well as joint families. Above findings is in line with the results of Ingle
et al. (1999).
4.1.1.5 Size of Family
It is assumption that large family size is normally associated with a higher
labour endowment, which would enable a household to accomplish various
agricultural tasks.
The data in Table 4.1 reveals that majority of the respondents (48.33%)
were having 5 to 8 members in their family, whereas, 35.00 and 16.67 per cent of
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the respondents reported they had more than 8 members and less than 5 members
in their family. Similar findings were reported by Karjagi (2006) and Yirga (2007).
4.1.1.6 Farming Experience
Experience is directly associated with the chronological age of
respondents, old aged may have more experiences than younger one. Experience
helps an individual to think in a better way and makes a person more mature to
take right decision. Respondents who had 15 or more than 15 years of farming
experience were selected for present study.
It can be observed from Table 4.1 that about 58 per cent of the
respondents had 21 to 40 years of farming experience followed by about 27 and 15
per cent of them reported that they had up to 20 years and more than 40 years of
farming experience. From the above findings it can be said that respondents were
enough experienced in agriculture which might be helpful for the present study.
Similar findings were also reported by Kebede et al. (1990), Sumathi and
Annamalai (1993) and Maddison (2006).
4.1.1.7 Social Participation
As human beings are known as social elements they can’t survive unless
being a part any social organisation prevailing in the village. Countries with well
developed social organisations are considered to have greater adaptive capacity
than those with less effective organisational arrangements.
It can be depicted from the Table 4.1 that majority of the respondents
(57.91%) were member of two organisation, whereas, 15.42 and 11.67 per cent
respondents were member of one organisation and more than two organisations,
respectively. Among the respondents about 13 per cent were office bearers in
various organisations. Fewer respondents (1.67%) had no participation in any of
the social organisation.
Participation of respondents in various organisations given in Fig. 4.1
revealed that 63.8, 44.6, 22.9 and 14.6 per cent of respondents participated
regularly in organisations Cooperative society, gram sabha, village panchayat and
school, respectively. Whereas, 48.8, 32.1 and 7.9 per cent of the respondents
81
Fig.
4.1
:Dis
trib
utio
n of
res
pond
ents
acc
ordi
ng to
thei
r pa
rtic
ipat
ion
in so
cial
org
anis
atio
n
82
participated occasionally in organisations gram sabha, cooperative society and
cultural groups, respectively. About 92, 90, 81, 79 and 74 per cent of respondents
had never participated in SHGs, youth clubs, cultural groups, school and village
panchayat, respectively. Above findings is in line with the Khan et al. (1997),
Kumar (2001), Jasudkar (2000) and Gaikwad (2000).
Thus, from the data presented in Table 4.1 and Fig. 4.1 and above
discussions, it can be concluded that respondents belonged to middle age group
high and higher level of education, belonged to other backward class category,
residing in joint family between 5 to 8 family members having farming experience
of 21 to 40 years, member of two organisations with regular participation in
cooperative society and gram sabha.
4.1.2 Socio-economic characteristics
Socio-economic characteristics of the respondents are very important to
determine the impact of climate change on agriculture, thus, important variables
those may influence the perceptions of the respondents and are directly associated
with impact of climate change are considered in this section. Selected variables are
sequentially arranged and presented under following subheads:
4.1.2.1 Land holding
The number of standard acres/hectares of land owned and cultivated by
each respondent family was considered in determination of their size of land
holding. The economic and social position of respondents in the society depends
upon the size and fertility of the land in his/her possession. The data in Table 4.2
clearly indicates that 37.92 per cent of the respondents were possessing 1.1 to 2 ha
of land and belonged to small farmers category, while, 28.33 and 23.75 per cent of
the respondents belonged to medium farmers (2.1 to 4 ha) and marginal farmers
(up to 1.0 ha) category. Only 10 per cent of the respondents were having more than
4 ha (big farmers) of land.
83
Table 4.2: Distribution of respondents according to their land holding
Category of Farmers Frequency Percentage
Marginal farmer (Up to 1.0 ha) 57 23.75
Small farmer (1.1 to 2.0 ha) 91 37.92
Medium farmer (2. 1 to 4 ha) 68 28.33
Big farmer (More than 4 ha) 24 10.00
Thus, it can be stated that more than 60 per cent of the respondents
belonged under marginal and small farmers’ category occupying 1 to 2 ha of land.
The above finding is in concurrence with the findings of Suresh (2004) and Karjagi
(2006).
4.1.2.2 Irrigation
The economic activity of the respondents in Chhattisgarh is closely
linked to the natural resource base and they are dependent on agriculture for their
livelihood. In Chhattisgarh, only 30 per cent of total cultivable area is irrigated in
kharif season, out of which only 27 per cent area is irrigated by tube well and rest
of the area is irrigated by canal or other seasonal sources. It clearly indicates that
most of the agriculture in Chhattisgarh is dependent on monsoon rainfall, therefore
it is highly sensitive to changes in climatic conditions, especially in the absence of
irrigation facilities. Irrigation potential was selected because of the assumption that
places with more potentially irrigable land are more adaptable to adverse climatic
conditions and access to water for irrigation increases the resilience of farmers to
climate variability.
4.1.2.2.1 Availability of Irrigation
Availability of irrigation is directly related with the production and
productivity of crops grown by the respondents. Also area coverage in rabi season
is linked with availability of irrigation.
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Table 4.3 indicates that about 76 per cent of the respondents were having
irrigation facilities, out of which about 40 per cent of them had irrigation
availability for kharif season only followed by 28.42 and 31.69 per cent of them
were having irrigation availability for both kharif & rabi and round the year,
respectively. As for as per cent irrigated area is concerned, about 36 per cent of the
respondents said that only 25 to 50 per cent of their total land was irrigated,
whereas, 30.42 and 20.00 per cent of them mentioned that irrigation was available
for more than 75 per cent and 0 to 25 per cent of their total land.
It can be concluded that majority of the respondents were having irrigation
facility only for kharif season and 0 to 50 per cent of their total land was irrigated.
Similar findings were also reported by Khan (2001).
Table 4.3: Distribution of respondents according to availability of irrigation
Particulars Frequency Percentage
Availability of irrigation
Not available 57 23.75
Available 183 76.25
Only Kharif 73 39.89
Kharif and Rabi 52 28.42
Round the year 58 31.69
Irrigated area
0 to 25% 48 20.00
25 to 50% 86 35.83
50 to 75% 33 13.75
More than 75% 73 30.42
85
4.1.2.2.2 Source wise irrigation availability
Table 4.4 shows source wise irrigation availability of the respondents. Out
of the total 583.70 ha land of the respondents only 49.88 per cent was irrigated by
different sources in kharif season. However, out of the total irrigated land (291.15
ha) of the respondents about 40 per cent land was irrigated by tube well only
followed by about 34 and 17 per cent by tube well & canal and canal only,
respectively.
Table 4.4: Distribution of respondents according to availability of irrigation and source wise irrigated area among the respondents
Season/SourcesFrequency Area (ha)
N % Area %
Kharif
Unirrigated 57 23.75 292.55 50.12
Irrigated 183 76.25 291.15 49.88
Tubewell only 69 37.70 116.84 40.13
Tubewell and canal 38 20.77 97.98 33.65
canal only 47 25.68 50.04 17.19
Tubewell & Tank 3 1.64 5.54 1.90
Canal & Nala 4 2.19 3.02 1.04
Tubewell & Nala 7 3.83 4.91 1.69
Nala only 10 5.46 6.78 2.33
Tank only 5 2.73 6.03 2.07
Total 240 100 583.70 100
86
4.1.2.2.3 Season wise crops grown
Paddy is the principle crop of Chhattisgarh and contribute major share in
national paddy production that’s why State is popularly known as “bowl of paddy”.
Most of the agriculture is dependent on monsoon rainfall, which is vulnerable to
changing climatic conditions and variability. Due to lacking of assured irrigation in
rabi area under second crop is only one third of total cultivated land.
Table 4.5: Season wise crops grown by respondents with average area and productivity
Season/Crop
No. of Farmers Area (ha)
% in Total cropped area
Productivity(qha-1)
I* UI* I UI I UI I UI
Kharif
Paddy 183 146 277.61 275.99 47.56 47.28 43.85 37.18
Pigeon pea 07 19 3.36 6.73 0.58 1.15 6.25 5.00
Soybean 08 11 5.68 7.40 0.97 1.27 13.45 7.20
Vegetables 08 03 2.72 0.86 0.47 0.15 - -
Others 05 07 1.78 1.57 0.30 0.27 - -
RabiSummer Paddy 68 00 66.60 0.00 11.41 0.00 51.23 0.00
Wheat 59 26 46.40 1.60 7.95 0.27 14.93 13.75
Gram 86 26 71.86 27.06 12.31 4.64 8.90 7.30
Lathyrus 18 64 11.33 33.47 1.94 5.73 6.03 5.10
Vegetable 27 06 13.00 1.74 2.23 0.30 - -
Others 19 08 9.82 2.51 1.68 0.43 6.05 4.18
ZaidMoong 06 00 4.00 0.00 0.69 0.00 5.50 2.50
Urd 02 00 0.80 0.00 0.14 0.00 5.00 2.50
Til 01 00 0.80 0.00 0.14 0.00 5.00 -
Vegetable 08 00 2.86 0.00 0.49 0.00 - -
* Based on multiple responses (Total cropped area = 583.70 ha)
87
Season wise crops grown by respondents are given in Table 4.5. It can be
observed that based on the multiple responses, out of total 183 respondents were
growing paddy in irrigated condition and 146 of them were growing paddy in un-
irrigated condition. In rabi season, 86, 68 and 59 respondents were growing gram,
summer paddy and wheat in irrigated condition, whereas, each 26 respondents
were growing gram and wheat in un-irrigated condition, respectively. Lathyrus was
grown by 64 respondents in un-irrigated condition.
With regards to crop wise irrigation availability (Table 4.5), out of total
cultivated area (583.70 ha) paddy was cultivated in 47.56 per cent of area in
irrigated condition and 47.28 per cent of area in un-irrigated condition. In rabi
season 12.31, 11.41 and 7.95 per cent of total area were cultivated under gram,
summer paddy and wheat in irrigated condition, respectively. While, 5.73 and 4.64
per cent of area were cultivated by lathyrus and gram in un-irrigated condition,
respectively.
As for productivity of crops, it is obvious from the data given in Table 4.5
that respondents produced 43.85 qha-1 of paddy in irrigated condition and 37.18
qha-1 of paddy in un-irrigated condition. However, in rabi, respondents produced
14.93, 8.90 and 6.03 qha-1 of wheat, gram and lathyrus in irrigated condition,
respectively. Productivity of crops wheat, gram and lathyrus was 13.75, 7.30 and
5.10 qha-1 in case of un-irrigated condition, respectively.
Thus, it can be concluded from the above findings that almost all the
respondents grow paddy in kharif season in irrigated as well in un-irrigated
condition. Area of other crops like pigeon pea, soybean, vegetables etc. is nominal
in kharif season. In rabi season major crops are grown in irrigated condition accept
lathyrus.
4.1.2.3 Occupation
Occupation of the respondents is the main source of earning for their
livelihood and fulfills necessary requirements. In the study area almost every
respondent depend on agriculture for their livelihoods. In is an assumption that
who are having more than one occupation in addition to agriculture more capable
to adjust themselves against adverse effect of climate change.
88
Table 4.6: Distribution of respondents according to their occupation
Occupation Frequency Percentage
Agriculture 58 24.16
Agriculture + Labour 82 34.17
Agriculture + Service 23 9.58
Agriculture + Service + Labour 42 17.50
Agriculture + Business + Service + Labour 19 7.92
Agriculture + Business + Service +
Labour/Others 16 6.67
The respondents with irrigation for only kharif season and possessing small
size of land holding were employing themselves in other activities in addition to
agriculture for their occupation. The data presented in Table 4.6 clearly shows that
agriculture was the main occupation of the respondents and 24.16 per cent of them
were engaged in agriculture alone. About 34 per cent of them were doing
agriculture along with labour, whereas, 17.50 per cent were engaged in agriculture
along with service and labour. Nearly 10 per cent of the respondents were
practicing agriculture along with service as their main occupation followed by
about 8 per cent of them were engaged in agriculture along with business, service
& labour. Similar findings were also reported by Patange et al. (2001) and
Jhamtani et al. (2003).
4.1.2.4 Annual Income
The sum of total earnings from all the sources in particular year is
termed as annual income. As agriculture is main source of income of the
respondents and the implementation of new agricultural technologies requires
sufficient financial wellbeing. With income and resource limitations, farmers fail
to meet transaction costs necessary to acquire adaptation measures and at times
farmers cannot make beneficial use of the available information they might have.
89
The data pertaining to annual income of the respondents is given in Table
4.7. Majority of the respondents were having low annual income between Rs.
75001 to 150000/-, whereas, 22.50 and 18.33 per cent were having medium and
very low annual income. Only about 6 per cent of the respondents belonged to very
high (more than Rs. 450000/-) annual income group category. The findings are
supported with the findings of Karjagi (2006), Knowler and Bradshaw (2007) and
Binkadakatti (2008).
Table 4.7: Distribution of respondents according to their annual income
Income Frequency Percentage
Very low (Up to Rs. 75000) 44 18.33
Low (Rs. 75001 to 150000) 96 40.00
Medium (Rs. 150001 to 300000) 54 22.50
High (Rs. 300001 to 450000) 32 13.33
Very high (More than Rs. 450000) 14 5.83
Income and expenditure Pattern
The income of the respondents from various sources and its average
contribution are presented in Fig. 4.2. Agriculture was main source of income and
the average annual income of respondents was Rs. 87534.62/-. Out of the total
income respondents earned 68 per cent from agriculture only in which contribution
of kharif crop income was 46 per cent, while, rabi and zaid crop were contributing
20 and 2 per cent of total income, respectively. Labour work, agriculture labour
and service were next important source of earning contributing 9, 8 and 7 per cent
in total income, respectively. Only 2 per cent of the total income was contributed
by livestock. Similar findings were reported by Khan (2001).
With regards to expenditure pattern of the respondents the data presented in
Fig. 4.2 reveals that 31 per cent of their total income was spent for food materials
and 26 per cent expenditure from total income was incurred for agriculture
purpose, whereas, expenditure on festival/social functions, disaster management
and medicines was 8, 6 and 5 per cent, respectively. Only 6 per cent of the total
90
Average Income Rs. 87534.62
Fig. 4.2: Income and expenditure patterns of respondents
91
income was saved by the respondents. On entertainment, livestock and fuel 2 per
cent expenditure was incurred for each. Very less amount (1%) was spent for crop
insurance by the respondents. Similar results were reported by Khan (2001).
4.1.2.5 Access to Credit
Acquisition of credit is main practice of the respondents to recuperate from
adverse circumstances due to uneven climatic conditions. Access to affordable
credit increases their ability and flexibility to change production strategies in
response to the forecasted climate conditions. Availability of credit eases the cash
constraints and allows them to buy inputs such as fertilizer, improved crop
varieties, and irrigation facilities. Easily access to credit helps the farmers to
purchase the required inputs that may influence the extent of adoption of the
farmers and adaptation towards adverse effect of climate change.
The credit acquisition patterns of the respondents are given in Table 4.8.
The data reveals that majority of the respondents (87.08%) had acquired credit, out
of which 72.73 per cent had obtained credit from cooperative society, whereas,
15.31 per cent of them had obtained credit from bank as well as cooperative
society.
Almost equal number of respondents about 29, 28 and 27 per cent were
obtained credit in range of Rs. 25001- 50000/-, up to 25000/- and Rs. 50001 to
75000/-, respectively. Only 16.27 per cent of the respondents had acquired credit
more than Rs. 75000/-.
The respondents obtained credit for various purposes, in which majority of
the respondents (82.77%) obtained credit as crop loan followed by 10.53 and 6.70
per cent of them were obtained credit for farm implements and other purposes,
respectively.
Majority of the respondents (66.51%) had repaid their credit in kind while
selling their produce like paddy in cooperative society, whereas, 18.18 and 15.31
per cent of them had repaid their credit as cash and cash as well as kind,
respectively.
92
Table 4.8: Credit acquisition pattern of the respondents
Particulars Frequency Percentage
Credit acquired (n = 240)
No 31 12.92
Yes 209 87.08
Source of credit (n = 209)
Co-operative society 152 72.73
Bank + Co-operative 32 15.31
Bank 11 5.26
Other sources 14 6.70
Amount of credit (n = 209)
Up to Rs. 25000 58 27.75
Rs. 25001 – Rs. 50000 61 29.19
Rs. 50001 – Rs. 75000 56 26.79
More than Rs. 75000 34 16.27
Purpose of credit (n = 209)
Crop loan 173 82.77
Farm implement 22 10.53
Other purpose 14 6.70
Mode of repayment (n = 209)
Cash 139 66.51
Kind 38 18.18
Cash + Kind 32 15.31
93
4.1.2.6 Availability of farm implements
With reducing labour forces, use of implements in agriculture is only
solution for timely operation of cultivation practices. Availability of farm
implement may help farmers to change their farm practices according to short term
climatic variability. The data availability of farm implements is presented in Table
4.9. It indicated that most of the respondents (82.92%) were having between 1 to 4
implements for their cultural operations, while, 13.75 per cent of them were having
between 5 to 8 implements. About 2 per cent of the respondents had possessed
more than 8 implements, whereas, 1.25 per cent of the respondents reported that
they did not have any of the farm implement.
Also respondents were asked for item wise availability of farm
implements as shown in Fig. 4.3. Among the respondents only 17.5 per cent were
having their own tractor and 66.7 per cent of them used tractor in hire basis for
their form operations. Only 3 per cent of the respondents possessed seed drill,
whereas, about 61 per cent said that they harvested their paddy crop using
harvester in hire basis. About 38 per cent of the respondents were having their own
diesel pump and sprayer/duster was possessed by majority of the respondents
(74.6%). Equal numbers of respondents (2.5%) were having rotovator and reaper,
while, very few respondents (1.3%) each said that they had drip system and power
tiller.
Thus, it can be concluded from above discussions that among
respondents the availability of farm implement is very less and they are dependent
in hired implements for their farm operations.
Table 4.9: Distribution of respondents according to their availability of farm implements
Availability of farm implements Frequency Percentage
Not available 03 1.25
1-4 Implements 199 82.92
5-8 Implements 33 13.75
> 8 Implements 05 2.08
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Fig.
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4.1.2.7 Distance to market
Market is the place where buying of agriculture inputs and selling of
agriculture produces are taken place among large number of buyers and sellers.
Distance to market is taken as variable in accordance with availability of
agriculture inputs. Distance to market is directly correlated with availability of
agriculture inputs, lesser the distance means easier they get.
Table 4.10 shows the distribution of respondents according to their
distance to market for seasonal farm inputs. It can be observed that 36.25 per cent
of the respondents were getting farm inputs from the market within 3 to 5 km of
distance followed by 30 per cent of them were getting inputs within the village.
Nearly 14 per cent of the respondents had input markets within 2 km, whereas,
about 9 per cent of them said that they used to travel more than 8 km for
agriculture inputs.
The availability of farm inputs shown in Fig. 4.4 indicates, 43.3, 42.9
and 27.5 per cent of respondents said that manure & fertilizer, improved seed and
insecticide/pesticide/weedicide were easily available for them, respectively.
Insecticide/pesticide/weedicide, manure & fertilizer, improved seeds and small
farm implements were available with difficulty for 67.5, 53.3, 46.3 and 40.0 per
cent of respondents, respectively. Similar findings were reported by Maddison
(2006) and Arya (2010).
Table 4.10: Distribution of respondents according to their distance to market for seasonal farm inputs
Distance to market for seasonal agriculture inputs Frequency Percentage
Within village (0 km) 72 30.00
Up to 2 km 34 14.17
3 km to 5 km 87 36.25
6 km to 8 km 26 10.83
More than 8 km 21 8.75
96
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4.1.2.8 Crop insurance
Crop insurance provides security to the farmers from unexpected loss of
crops due to natural calamities. Farmers compelled for compulsory insurance while
purchasing inputs from cooperative society but they are not getting remunerative
benefits from it.
Table 4.11: Distribution of respondents according to their crop insuranceinstitution
Crop insurance Frequency Percentage
Nil 52 21.66
From private institution 04 1.67
From government institution (Compulsory
insurance) 178 74.17
From government & private institution 06 2.50
The data in Table 4.11 shows that majority of the respondents (74.17%)
insured their crop from cooperative society (government institution) as compulsory
insurance. Nearly 22 per cent of the respondents didn’t take crop insurance policy
from any of the institution, while, only 2.50 per cent of them insured their crop
from both government & private institution.
4.1.2.9 Socio-economic Status
Socio-economic status refers to the position of an individual and his family
occupies with reference to the prevailing social standard. With better socio-
economic status other resources farmers are able to change their management
practices in response to changing climatic condition. Table 4.12 shows the
distribution of respondents according to their socio-economic status. Majority of
the respondents (40.83%) belonged to lower class group followed by 36.25 per
cent of them belonged to lower middle class group. Whereas, 14.59 per cent of the
respondents were coming under medium class group and only 3.33 per cent of
them belonged to upper class group.
98
Thus, it can be conclusively say that socio-economic status of most of the
respondents belonged within lower to lower middle class. Similar findings were
also reported by Oloruntoba and Fakoya (2000) and Rao and Rupkumar (2005)
Table 4.12: Distribution of respondents according to their socio-economic status
Socio-economic status Frequency Percentage
Lower class (Up to 9 score) 98 40.83
Lower middle class (10-18 score) 87 36.25
Medium class (19-27 score) 35 14.59
Upper middle class (28-36 score) 12 5.00
Upper class (More than 36 score) 08 3.33
4.1.3 Communicational characteristics
Communication is the process by which farmer can get information
regarding improved agriculture technologies and weather forecast. Weather related
information may help them in changing their farm operations according to climatic
conditions. Under this section various communicational characteristics of
respondents which were taken as variables are discussed.
4.1.3.1 Contact with extension personnel
With regards to contact of respondents with extension personnel, the
respondents were asked about their contact with six enlisted personnel (RAEO,
ADO, SMS, NGOs, Scientist and Input dealers) and compiled results are given in
Table 4.13. Majority of the respondents had low level of contact with extension
personnel, whereas, 37.08 and 6.25 per cent of them had medium and high level of
extension contact, respectively. Only 2.50 per cent of the respondents never
contacted with extension personnel.
99
Fig.
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Table 4.13: Extent of contact of the respondents with extension personnel
Extent of contact Frequency Percentage
No contact 06 2.50
Low (Up to 4 score) 130 54.17
Medium (5-8 score) 89 37.08
High (More than 8 score) 15 6.25
Regarding contact with extension personnel (Fig. 4.5), 47.08 per cent of
the respondents contacted occasionally with Rural Agriculture Extension Officer
(RAEO) and 45.42 per cent had contacted regularly. About 68 per cent of the
respondents had never contacted with scientist, while, 29.58 and 2.50 per cent had
occasional and regular contact with them. Majority of the respondents (49.58%)
had regular contact with input dealers and 46.25 per cent of them contacted
occasionally. Almost all the respondents (93%) never contacted with Non
Government Organisation (NGO) functionaries. Above findings are in line with the
findings of Markad (1996), Dixit and Bhople (2001) and Rathod (2001).
4.1.3.2 Participation in extension activities
As for participation of respondents in extension activities a total of seven
extension activities were enlisted and asked about their participation in those
activities. The recorded data were compiled and presented in Table 4.14 and Fig.
4.6.
Table 4.14: Extent of participation of respondents in extension activities
Extent of participation Frequency Percentage
No participation 45 18.75
Low (1-4 score) 73 30.42
Medium (5-8 score) 83 34.58
High (9-12 score) 26 10.83
Very high (More than 12 score) 13 5.42
101
Fig.
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:Dis
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The finding indicates that majority of the respondents (34.58%) were
having medium level of participation in extension activities followed by 30.42 per
cent had low level of extension participation. Nearly 19 per cent of the respondents
never participated in any of the extension activities. Only 10.83 and 5.42 per cent
of the respondents had high and very high level of participation in extension
activities.
The data in Fig. 4.6 reveals that about 53 and 51 per cent of respondents
participated occasionally in training programmes and demonstration, whereas,
20.42 and 12.08 per cent of them participated regularly in those programmes.
Regular participation in field visit (5.00%) was more as compare to field day
(4.58%). Only 7.92 per cent of the respondents regularly participated in kisan
mela, while 50.42 per cent had participated occasionally. Similar findings were
reported by Gupta (1999), Angadi (1999), Kumar (2004) and Anitha (2004).
4.1.3.3 Exposure to mass media
The respondents were asked about the mass media sources used by them
and compiled results are presented in Fig. 4.7. Majority of the respondents
(52.92%) regularly watched television, whereas, 42.92 per cent came under
occasional users of television. About 43 per cent of the respondents were regular
readers of news paper and more than 31 per cent of them were occasional readers.
Among the respondents regular listeners of radio were very less (3.75%), while,
nearly 9 per cent were occasionally listened radio. Hardly 12.08 per cent of the
respondents read agriculture articles in agricultural magazine, whereas, about 24
per cent of them read occasionally.
The overall extents of use of mass media sources of respondents was
determined and given in Table 4.15. Almost half of respondents were having low
level of use of mass media sources, whereas, 46.25 per cent had medium level of
use. Nearly 2 per cent were having high level of use, while, about 3 per cent of
them had never used any king of mass media. Shashidhar (2003), Kumar (2004)
and Nirban (2006) were also reported similar findings.
103
Fig.
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Table 4.15: Distribution of respondents according to their extent of use of massmedia
Extent of use of mass media Frequency Percentage
Nil 07 2.92
Low (1-3 score) 117 48.75
Medium (4-6 score) 111 46.25
High (More than 6 score) 05 2.08
4.1.3.4 Access to weather forecast
Timely and accurate information related to weather forecast is very
important for the farmers in present scenario of changing climatic conditions. It is
needful that the weather forecasts often should correct, so that the farmers can
make agricultural decisions based on the weather forecasts. The respondents were
asked about sources utilized by them for gathering information on weather forecast
and results are depicted in Table 4.16 and Fig. 4.8.
The summation of scores obtained by respondents for frequency of use
and utility of information sources were considered to determine the extent of
utilization of information sources. It is apparent from Table 4.16 that majority
(55.83 %) of the respondents were having low level of utilization of information
sources for collecting weather information followed by 33.75 per cent of them had
reported medium level of utilization. The information sources were utilized highly
by very little (2.50%) number of respondents to collect weather information.
Table 4.16 : Extent of utilization of information sources for weather forecast
Extent of utilization Frequency Percentage
Nil 19 7.92
Low (1-12 score) 134 55.83
Medium (13-24 score) 81 33.75
High (More than 24 score) 06 2.50
105
4.1.3.5 Utilization pattern of information sources for weather forecast
Various sources of information are being utilized by the respondents but
it cannot be necessarily say that they are getting relevant information timely.
Weather related information are gathering by most of the farmers by different
sources but how much of them are applying it in actual practice is a question. For
the purpose utilization pattern of respondents for gathering information related to
weather forecast was worked out and presented in Fig. 4.8. This can be discussed
on following heads:
Credibility of information sources
Information related to weather forecast were being collected by
respondents using different information sources. To know the credibility of those
sources data were recorded from the respondents and credibility index were
worked out (Fig. 4.8). The results revealed that among the respondents
friends/relatives/etc., newspaper, mobile and national TV channel were most
credible sources of information to collect weather related information with
credibility index of 76.54, 69.24, 65.48 and 60.54 per cent, respectively. The
credibility of other sources like radio, regional TV channel and extension
functionaries were 57.36, 51.52 and 47.52 per cent, respectively. The overall
credibility index value of all the information sources was 61.18 per cent.
Extent of use of information sources
Data regarding frequency of use of information sources for collecting
weather related information were recorded and worked out an index. The results
presented in Fig. 4.8 indicate that national TV channel, friends/relatives/etc.,
newspaper and regional TV channel were most frequently used information
sources for gathering weather related information with the extent of 61.46, 52.50,
49.17 and 28.13 per cent, respectively. Whereas, extension functionaries (11.67%),
radio (5.63%) and mobile (0.83%) were the information sources used with less
extent. The overall extent of use information sources was 29.91 per cent.
Extent of utility of information sources
All the information gathered from various information sources were not
utilized by the respondents. The data on utility of information provided by
information sources were recorded and the results are presented in Fig. 4.8. It
indicates that according to respondents the utility of information related to weather
106
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:Util
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107
forecast were 39.30, 35.55 and 31.80 per cent for national TV channel,
friends/relatives/etc. and news paper, respectively. The utility of information were
19.30 and 8.47 per cent in case of regional TV channel and extension
functionaries. However, 19.80 per cent utility of information was reported by the
respondents with regards to overall utility.
Hence, it can conclusively say that friends/relatives/etc. and newspaper
were more credible among the respondents. More than half of them were using
those sources for gathering information but most of the respondents were not
applying it in their actual practice due to irrelevancy of weather related
information. Therefore, relevant and timely weather forecast is needed to build
trust among the respondents so that they can change their cultivation practices
according to changing climatic condition. Similar finding were also reported by
Athimuthu (1982), Jyothi (2000) and Luni et al. (2012).
4.1.3.6 Cosmopoliteness
Cosmopoliteness is the degree to which an individual is oriented outside
to his immediate social system. It provides outside exposure to the farmers that
may be beneficial for them to gather agricultural information. The data recording
cosmopoliteness of the respondents are given in Table 4.17 and Fig. 4.9. It can
inferred that majority of the respondents (37.91%) were having low level of
cosmopoliteness followed by 36.67 and 25.42 per cent of them were having
medium and high level of cosmopoliteness.
Further, Fig. 4.9 elucidates the frequency and purpose of visit of the
respondents at various places. It indicates that 29.58 per cent of the respondents
visited outside often of their social system for domestic/personal purpose followed
by 27.92 per cent visited often for agriculture purpose. Moreover, majority of the
respondents (32.50%) visited outside sometimes for agriculture purpose, whereas
32.08 and 31.25 per cent visited sometimes for entertainment and other purpose.
Thus, it can be concluded that nearly three fourth of the respondents
were having low to medium level of cosmopoliteness and nearly 60 per cent of
them visited often to sometimes for agriculture purpose. Similar results were also
reported by Shashidhar (2003) and Kumar (2004).
108
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Table 4.17: Distribution of respondents according to their cosmopoliteness
Cosmopoliteness Frequency Percentage
Low (Up to 4 score) 91 37.91
Medium (5 – 8 score) 88 36.67
High (More than 8 score) 61 25.42
4.1.4 Psychological characteristics
Based on the previous research studies and with the consultation of experts,
the variables which were found directly or indirectly related with the farmer’s
perception and adaptation of climate change were identified for the study. Further,
the data regarding various psychological traits were collected from the respondents
and compiled. The results are presented in Table 4.18.
4.1.4.1 Risk orientation
Risk orientation in the present study referred to the degree to which a
respondent is oriented towards risks and uncertainty due to changing climatic
conditions and has the courage to face various risks involved in farming and other
activities. The results compiled in the Table 4.18 clearly reveal that majority
(56.67%) of the respondents were having medium level of risk orientation,
whereas, 30.00 per cent and 13.33 per cent belonged to low and high category. The
findings are supported by the results reported by Ravishankar (1995), Sawant
(1999) and Bhagyalaxmi et al. (2003).
4.1.4.2 Innovativeness
Innovativeness is the socio-psychological orientation of an individual to get
linked or closely associated with change, adopting innovative ideas and practices
to minimize the adverse effect of climate change. It can be inferred from the Table
4.18 that 49.17 per cent of study area farmers were in the medium innovative
proneness category, while, 35 and 15.83 per cent of them were in low and high
innovativeness category, respectively. Raghupathi (1994) and Bhagyalaxmi et al.
(2003) were also reported similar findings in their study.
110
Table 4.18: Distribution of respondents according to their psychological characteristics
Particulars Frequency Percentage
Risk orientation
Low (1 – 10 score) 72 30.00
Medium (11 – 20 score) 136 56.67
High (More than 20 score) 32 13.33
Innovativeness
Low (1 – 15 score ) 84 35.00
Medium (16 – 30 score) 118 49.17
High (More than 30 score) 38 15.83
Scientific orientation
Low (1 – 10 score) 79 32.92
Medium (11 – 20 score) 124 51.67
High (More than 20 score) 37 15.41
Decision making pattern
Low (1 – 10 score) 145 60.42
Medium Between (11 – 20 score) 37 15.41
High (More than 20 score) 58 24.17
111
4.1.4.3 Scientific orientation
Scientific orientation refers to the degree to which a respondent is oriented
towards the use of scientific methods. The data in Table 4.18 shows that 51.67 per
cent of the respondents belonged to the medium scientific orientation category,
while, 32.92 and 15.41 per cent of them were found to have medium and high
scientific orientation, respectively. The findings are in partial accordance with the
findings reported by Sakharkar (1995) and Karpagam (2000).
4.1.4.4 Decision making pattern
The decision making pattern of a farmer is operationally defined as the
degree of weighing the available alternatives in terms of their desirability and their
likelihoods and choosing the most appropriate one for achieving maximum profit
on his farming. The results in Table 4.18 reveal that 60.42 per cent of the
respondents belonged to low decision making category followed by 24.17 and
15.41 per cent of respondents belonged to high and medium decision making
categories.
Decision making patterns of the respondents was operationalised according
to nature of the decision making (individual, joint or collective) that the farm
family has resorted to, while performing farming activities. As per the assumptions
collective decisions to have considered as better way of decision making to cope
with negative effect of climate change. The results in Fig. 4.10 indicate that almost
65 per cent of the respondents in each case had taken self decision for choice of
crop/its varieties and choice of cropping pattern/sowing method, whereas, nearly
29 per cent each had taken joint decision along with his wife for selling of
produces and determination of time for agriculture activities. About 34 per cent of
the respondents were indulge in taking collective decisions for application of
insecticide and pesticides followed by 30.42 and 27.08 per cent of them were
taking collective decision in case of determination of time for agriculture activities
and application of manure and fertilizers.
4.1.4.5 Awareness
As the understanding on global climate and its change is pre requisite to
take appropriate initiatives to combat climate change. Climate change with
expected long-term changes in rainfall patterns and shifting temperature zones are
112
Fig.
4.1
0:D
istr
ibut
ion
of r
espo
nden
ts a
ccor
ding
to th
eir
deci
sion
mak
ing
patte
rn
113
expected to have significant negative effects on agriculture, food security and
livelihood of the farmers. Most of the farming communities cannot classify the
term climate change but are well capable of describing changes in weather.
Table 4.19: Distribution of respondents according to their awareness about climatic variability
Particulars
Fully aware (%)
Somewhat aware (%)
Not aware at all (%)
Overall awareness
(%)Rank
Climate is getting warmer 54.58 40.42 5.00 74.79 IV
Weather has become unpredictable
52.50 42.08 5.42 73.54 V
Duration of seasons is changing
26.67 49.17 24.17 51.25 IX
Occurrence of extreme weather conditions
42.92 48.75 8.33 67.29 VI
Risk of crop failure has increased
70.00 25.83 4.17 82.92 I
Pollution is increasing in the atmosphere
65.00 30.00 5.00 80.00 II
Occurrence of natural disasters are increasing
55.42 41.67 2.92 76.25 III
Rainfall pattern has beenchanging
40.83 52.08 7.08 66.88 VII
Human and animal health problems are increasing
39.17 52.92 7.92 65.63 VIII
It can be observed from the Table 4.19 that majority of the farmers
(70.00%) were fully aware that risk of crop failure has increased due to climate
change, whereas, 65, 55.42, and 54.58 per cent of the farmers were fully aware
114
about pollution is increasing in the atmosphere, occurrence of natural disasters are
increasing and climate is getting warmer, respectively. While, somewhat
awareness belonged to about 52.92, 52.08, 49.17 and 48.75 per cent of the
respondents for the phenomena viz. human and animal health problems are
increasing, rainfall pattern has been changing, duration of season is changing and
occurrence of extreme weather condition, respectively.
With regards to overall awareness for each phenomena, respondents
were more aware about risk of crop failure has increased (82.92%), pollution is
increasing in the atmosphere (80.00%) and occurrence of natural disasters are
increasing (76.25%) with the rank of I, II and III, respectively.
The level of awareness of the respondents about climate change is
presented in Table 4.20. It reveal that about 55 per cent of them were moderately
aware, whereas, 32.08 and 9.58 per cent farmers belonged to highly aware and
somewhat awareness category. Very few farmers (3.33%) were not aware about
phenomena due to climate change. Similar findings were also reported by Dietz et
al. (2007), Kotei et al. (2007), Aggarwal (2009) and Sharma (2010).
Table 4.20: Distribution of respondents according to their level of awareness about climate change
Level of awareness about climate change Frequency Percentage
Nil 08 3.33
Low (1 – 6 score) 23 9.58
Medium (7 – 12 score) 132 55.00
High (More than 12 score) 77 32.09
4.1.4.6 Vulnerability
Vulnerability to climate change is the extent to which a natural or social
system is susceptible to sustaining damage from climate change. Vulnerability is a
function of the sensitivity of a system to changes in climate, adaptive capacity and
the degree of exposure of the system to climatic hazards. While, it is increasingly
115
accepted that the vulnerability of farmers due to climatic conditions cannot be
solely understood through the quantification of biophysical impacts, very few
studies in climate change indicates the social aspects of vulnerability to climate
change with an in depth examination of the underlying socio-economic and
institutional factors that determine how farmers respond to and cope with climate
hazards. This was a little effort to quantify farmer’s vulnerability due to climate
change and their coping mechanisms to mitigate its adverse effect.
The data on Table 4.21 explains the natural disasters faced by
respondents during last 15 years along with their coping mechanism. It indicate
that majority of the respondents (90.00%) had faced drought during last 15 years,
whereas, 89.58, 70.00 and 36.25 per cent had faced erratic rainfall, flooding and
storm/typhoon as disasters, respectively. Most of the farmers pointed that their
income and crop yield were reduced due to disasters faced by them with first rank.
Further, other major losses incurred due to disaster could be ordered sequentially
as house damaged, livestock lost, loss of business/services, loss of water sources,
family members harmed and rabi crop area reduced, respectively.
Whatsoever, various coping mechanism had been practicing by the
respondents to minimize losses caused by disasters. Among the respondents use of
savings, borrowing loan from various sources, government relief and aid, selling of
assets, selling of land and reduce consumption were the coping mechanisms used
by them to minimize losses by the disasters.
Table 4.22 demonstrates other disasters faced by the respondents. It reveals
that majority of the respondents (80.00%) had faced disease and pest out break
followed by 60.00, 59.58 and 55.00 per cent who had faced epidemic,
environmental pollution and theft/grazing, respectively. Types of losses incurred
due to disaster were income and yield reduced, family members harmed, livestock
lost, health problems and business/service lost accordingly ranked by the
respondents. Nevertheless, use of saving, borrowing loan, selling of assets, selling
of land, leased out land, government relief and aid and getting medical treatment
were common coping mechanisms to minimize losses caused by disasters.
116
Tabl
e 4.
21: N
atur
al d
isas
ters
face
d by
resp
onde
nts d
urin
g la
st 1
5 ye
ars a
long
with
thei
r cop
ing
mec
hani
sms
Typ
e of
dis
aste
rR
espo
nden
ts w
ho
face
d di
sast
erT
ype
of d
amag
e/lo
ss%
*C
opin
g m
echa
nism
to m
inim
ize
loss
es
from
dis
aste
rFl
oodi
ng
168
Loss
of b
usin
ess/
serv
ice
16.6
7
Use
savi
ngs
(70.
00%
)In
com
e re
duce
d63
.69
Se
lling
of a
sset
sFa
mily
mem
bers
har
med
4.17
Lo
an/C
redi
tH
ouse
dam
aged
22.0
2
Red
uce
cons
umpt
ion
Yie
ld re
duce
d58
.33
G
over
nmen
t rel
ief a
nd a
idLi
vest
ock
loss
17.2
6
Mig
ratio
nEr
ratic
rain
fall
215
Loss
of b
usin
ess/
serv
ice
4.19
U
se sa
ving
s(8
9.58
%)
Inco
me
redu
ced
98.6
0
Selli
ng o
f ass
ets
Hou
se d
amag
ed9.
77
Loan
/Cre
dit
Yie
ld re
duce
d98
.60
R
educ
e co
nsum
ptio
nLi
vest
ock
loss
3.26
G
over
nmen
t rel
ief a
nd a
idLo
ss o
f wat
er so
urce
s 19
.53
M
igra
tion
Dro
ught
216
Loss
of b
usin
ess/
serv
ice
21.3
0
Use
savi
ngs
(90.
00%
)In
com
e re
duce
d99
.54
La
nd le
ase/
mor
tgag
eFa
mily
mem
bers
har
med
7.87
Se
lling
of l
and
Loss
of w
ater
sour
ces
28.7
0
Selli
ng o
f liv
esto
ckY
ield
redu
ced
98.1
5
Selli
ng o
f ass
ets
Live
stoc
k lo
ss44
.44
Lo
an/C
redi
tRa
bicr
op a
rea
redu
ced
22.2
2
Red
uce
cons
umpt
ion
G
over
nmen
t rel
ief a
nd a
id
Mig
ratio
nSt
orm
/Typ
hoon
87In
com
e re
duce
d83
.91
U
se sa
ving
s(3
6.25
%)
Fam
ily m
embe
rs h
arm
ed17
.24
Se
lling
of a
sset
sH
ouse
dam
aged
75.8
6
Loan
/Cre
dit
Yie
ld re
duce
d82
.76
G
over
nmen
t rel
ief a
nd a
idLi
vest
ock
loss
10.3
4*P
erce
ntag
e is
cal
cula
ted
from
the
resp
onde
nts a
ffec
ted
by p
artic
ular
dis
aste
r and
dat
a ar
e ba
sed
on m
ultip
le re
spon
ses
117
Tabl
e 4.
22: O
ther
dis
aste
rs fa
ced
by re
spon
dent
s dur
ing
last
15
year
s alo
ng w
ith th
eir c
opin
g m
echa
nism
s
Typ
e of
dis
aste
rR
espo
nden
ts w
ho
face
d di
sast
erT
ype
of d
amag
e/lo
ss%
*C
opin
g m
echa
nism
to m
inim
ize
loss
es fr
om d
isas
ter
Dis
ease
and
pes
t ou
t bre
ak19
2In
com
e re
duce
d10
0.00
U
se sa
ving
s(8
0.00
%)
Fam
ily m
embe
rs h
arm
ed9.
37
Loan
/Cre
dit
Yie
ld re
duce
d97
.92
G
over
nmen
t rel
ief a
nd a
idLi
vest
ock
loss
5.48
Epi
dem
ic14
4In
com
e re
duce
d2.
08
Use
savi
ngs
(60.
00%
)Fa
mily
mem
bers
har
med
52.7
7
Selli
ng o
f lan
dY
ield
redu
ced
3.47
Se
lling
of l
ives
tock
Live
stoc
k lo
ss60
.42
Se
lling
of a
sset
s
Loan
/Cre
dit
The
ft/gr
azin
g13
2Lo
ss o
f bus
ines
s/se
rvic
e 6.
06
Use
savi
ngs
(55.
00%
)In
com
e re
duce
d92
.42
Se
lling
of a
sset
sY
ield
redu
ced
90.9
0
Loan
/Cre
dit
Env
ironm
enta
l po
llutio
n14
3In
com
e re
duce
d86
.71
U
se sa
ving
s(5
9.58
%)
Hea
lth p
robl
ems
35.6
6
Land
leas
e/m
ortg
age
Hou
se d
amag
ed12
.59
Se
lling
of l
and
Yie
ld re
duce
d83
.91
Fi
lterin
g of
drin
king
wat
erLi
vest
ock
loss
23.7
8
Mor
e pl
anta
tion
Con
tam
inat
ion
of ir
rigat
ion
and
drin
king
w
ater
30.0
6
Get
ting
med
ical
trea
tmen
t
*Per
cent
age
is c
alcu
late
d fr
om th
e re
spon
dent
s aff
ecte
d by
par
ticul
ar d
isas
ter a
nd d
ata
are
base
d on
mul
tiple
resp
onse
s
118
The distribution of respondents according to extent of losses faced by
respondents due to the disasters is presented in Table 4.23. It illustrates that
majority of the respondents (68.5%) among those who faced disaster reported that
the loss caused by drought was to a great extent followed by 28.0, 26.5 and 25.2
per cent of them who faced disaster said that the losses caused by flooding, erratic
rainfall and storm/typhoon were to a great extent, respectively. Moreover, a
considerable per cent of respondents (59.4%), (59.0%) and (45.8%) had faced
losses with moderate extent from disasters viz. disease and pest outbreak, erratic
rainfall and epidemic, respectively. Furthermore, 83.3, 69.2 and 58.3 per cent had
faced losses with small extent from the disasters theft/grazing, environmental
pollution and flooding, respectively.
Table 4.23: Disasters faced by respondents along with extent of losses during last15 years
Type of disaster
FrequencyExtent of loss
Great Extent
Moderate Extent
Small Extent Nil
Flooding 168(70.0%) 47(28.0%) 23(13.7%) 98(58.3%) 72(30.0%)
Erratic rainfall 215(89.5%) 57(26.5%) 127(59.0%) 31(14.4%) 25(10.4%)
Drought 216(90.0%) 148(68.5%) 61(28.2%) 07(3.2%) 24(10.0%)
Storm/Typhoon 87(36.3%) 22(25.2%) 34(39.0%) 31(35.6%) 153(63.7%)
Disease and pest outbreak
192(80.0%) 25(13.0%) 114(59.4%) 53(27.6%) 48(20.0%)
Epidemic 144(60.0%) 34(23.6%) 66(45.8%) 44(30.5%) 96(40.0%)
Theft/grazing 132(55.0%) 06(4.5%) 16(12.1%) 110(83.3%) 108(45.0%)
Environmental pollution
143(59.6%) 25(17.5%) 19(13.3%) 99(69.2%) 97(40.4%)
*percentage is calculated from the respondents who faced disaster
119
Table 4.24 explains the distribution of respondents according to their
extent of vulnerability due to disasters. It illustrates that majority of the
respondents (43.34%) fell under the category of low vulnerability, whereas, 37.08,
12.92 and 5.83 per cent of them were belonged to category of medium, very low
and high vulnerability. Only 0.83 per cent of the respondents were coming under
the category of very high vulnerability.
Table 4.24: Distribution of respondents according to their extent of vulnerability
Extent of vulnerability Frequency Percentage
Very Low (Up to 20%) 31 12.92
Low (21-40%) 104 43.34
Medium (41-60%) 89 37.08
High (61-80%) 14 5.83
Very High (More than 80%) 02 0.83
Hence, conclusively it can be say that most of the farmers are vulnerable to
climate change as they had faced various disasters during last 15 years. However,
they were able to cope with and to minimize losses caused by it by using several
coping mechanism. The above results are in line with the findings of Allen (2003),
Fischer et al. (2005), Thomas and Twyman (2005), Desalegn et al. (2006) and
Morton (2007).
4.2 Dependent variables
4.2.1 Perception of farmers about climate change
People’s perceptions are very much useful to establish the fact that the
particular region is facing direct or indirect problems in agriculture and other
activities due to climate change. Consequently, understanding the perception of
climate change by farmers is important as perception can shape the preparedness of
these actors to adapt and change their practices. The adoption and successful
implementation of new technology by farmers in their ecosystems depend on their
tendency to perceive and
120
Table 4.25: Distribution of respondents according to their perception aboutclimatic variability
Climatic variabilityIncreased Decreased No change
F P F P F P
A. Rainy Season Timing of rain onset 179 74.58 32 13.34 29 12.08 Timing of rain cessation/offset 42 17.50 168 70.00 30 12.50 Season duration 14 5.83 178 74.17 48 20.00 Dry spell frequency 175 72.92 41 17.08 24 10.00 Rainy days frequency 20 8.33 201 83.75 19 7.92 Uneven distribution of rainfall 147 61.25 17 7.08 76 31.67 Total amount of precipitation 11 4.58 88 36.67 141 58.75 Cloudy weathers/cloudy days 123 51.25 26 10.83 91 37.92 Sunshine hours 25 10.42 123 51.25 92 38.33
B. Winter Season Starting of winter 165 68.75 09 3.75 66 27.50 Ending of winter 18 7.50 166 69.17 56 23.33 Intensity of cold 24 10.00 163 67.92 53 22.08 Minimum temperature in winter 184 76.67 45 18.75 11 4.58 Maximum temperature in winter 165 68.75 31 12.92 44 18.33 Winter duration 08 3.33 162 67.50 70 29.17 Number of cool days 03 1.25 181 75.42 56 23.33 Frequency of heavy fogged days 17 7.08 173 72.08 50 20.84 Winter rainy days 129 53.75 68 28.33 43 17.92
C. Summer Season Minimum temperature in summer 169 70.42 41 17.08 30 12.50 Maximum temperature in summer 182 75.83 31 12.92 27 11.25 Starting of summer 07 2.92 184 76.67 49 20.41 Ending of summer 141 58.75 22 9.17 77 32.08 Duration of season 179 74.58 19 7.92 42 17.50 Number of hot days 146 60.83 23 9.59 71 29.58 Intensity of loo 88 36.67 82 34.17 70 29.17 Prickly-heat during summers 154 64.17 38 15.83 48 20.00 Summer rainy days 40 16.67 141 58.75 59 24.58
D. Other Occurrences Air pollution 224 93.33 04 1.67 12 5.00 Occurrence/frequency of storm 126 52.50 73 30.42 41 17.08 Thunderbolt/thunderstorm 06 2.50 02 0.83 232 96.67
121
react favorably towards changes in climate and environment. This study also tries
to quantify the people's perception on various seasonal climatic variability.
Findings on farmer’s perception regarding change in climate are presented
in Table 4.25. The results indicated that most of the respondents (74.58%)
perceived that the timing of rain onset has increased, whereas, about 73 per cent
were responded that dry spell frequency has increased in rainy season over the past
15 years. Additionally, more than 83 per cent of the respondents were reported that
rainy days frequency has decreased followed by season duration (74.17%) and
timing of rain cessation/offset. Furthermore, they have been experiencing no
change (58.75%) in total amount of precipitation over the past 15 years.
The results for winter season (Table 4.25) show a similar uniformity of
opinion across the sample. The majority of farmers (76.67%) believed that the
minimum temperature in winter season had increased followed by maximum
temperature in winter (68.75%) and starting of winter (68.75%). Decreasing trend
in number of cool days, frequency of heavy fogged days and ending of winter were
reported by 75.42, 72.08 and 69.17 per cent of the respondents, respectively.
Furthermore, about 76 per cent of the respondents said that maximum
temperature in summer has increased, while, nearly 75 per cent of them were
responded that duration of summer season has increased. In addition, majority of
the respondents (76.67%) observed that starting of summer has decreased, while,
58.75 per cent were reported summer rainy days has decreased. Almost all the
respondents (93.33%) were facing problem of air pollution. More than half of the
respondents looked increasing trend in occurrence of storm.
Table 4.26 elucidates extent of perception of the respondents about climatic
variability. It shows that majority of the respondents (61.25%) in study area
perceived high changes in climatic condition in rainy season due to changing
rainfall patterns like shifting of timing of rain onset & withdrawal, increasing trend
in dry spell frequency and decreasing trend in rainy days frequency.
122
Table 4.26: Distribution of respondents according to their extent of perception about climatic variability
Extent of perception Frequency Percentage
Rainy season
Low (Up to 3 score) 19 7.92
Medium (4-6 score) 74 30.83
High (More than 6 score) 147 61.25
Winter season 0.00
Low (Up to 3 score) 24 10.00
Medium (4-6 score) 65 27.08
High (More than 6 score) 151 62.92
Summer season 0.00
Low (Up to 3 score) 21 8.75
Medium (4-6 score) 58 24.17
High (More than 6 score) 161 67.08
Other occurrences 0.00
Low (Up to 1 score) 46 19.16
Medium (1-2 score) 181 75.42
High (More than 2 score) 13 5.42
Overall perception
Low (Up to 10 score) 23 9.58
Medium (11-20 score) 75 31.25
High (More than 20 score) 142 59.17
Moreover, about 63 per cent of the respondents perceived high level of
changes in climatic condition in winter season because they felt that minimum &
maximum temperature in winter has increased and number of cool & heavy fog
days has decreased. Nearly 67 per cent of the respondents reported that high level
of changes occurred in summer season due to increasing trend in minimum &
maximum temperature, duration of season and number of hot days. Further,
medium level of changes perceived by majority of the respondents (75.42%) in
123
other occurrences like air pollution, occurrence/frequency of storm and
thunderbolt/thunderstorm. With regards to overall perception of climate change
59.17 per cent of the respondents reported high level of overall change in climatic
condition, whereas, 31.25 and 9.58 per cent of them perceived medium to low level
of overall change in climatic condition.
In general, most people’s understanding of the underlying issues and causes
of climate change varies a lot, with some taking a more scientific approach and
others a more religious one. Some of the perceptions are unscientific, mainly
because many subsistence farmers, who are by definition often poorly educated,
resort to superstition to explain natural events because that is their only source of
‘information’. Similar findings were also reported by Bhushal et al. (2009),
Akponikpe et al. (2010), McSweeney et al. (2010), Johnsen and Aune (2011) and
Krishna et al. (2011).
4.2.2 Impact of climate change on agriculture and allied activitiesClimate change impacts and associated vulnerability are of particular
concern to developing countries, where large parts of the population depend on
climate sensitive sectors like agriculture for livelihood. Agriculture plays a
prominent role in the Indian economy. India is a land of small cultivators and about
80 per cent of its farmers owning less than 2 ha of land. In other words, the land
provides livelihood security for more than 50 per cent of the people. Scientific
evidence about the seriousness of the climate threat to agriculture and allied
activities is now unambiguous, although the exact magnitude is uncertain because
of the complex interactions and feedback processes in the ecosystem and in the
economy. So farmers’ perspectives are equally important to quantify the climate
change impacts on agriculture and allied activities. Impact of climate change at the
local level is difficult to assess due to lack of data and poor understanding of
microclimate. Most of the farming communities cannot classify the term climate
change but are well capable of describing changes in weather and its impact.
Farmers were asked about changes occurred in agriculture and allied activities
according to their past experiences as impact of long term climate change and
sudden changes performed by them in agriculture operations due climatic
variability as impact of short term climate change.
124
4.2.2.1 Impact of long term climate change
Long term climate change has been not only affecting the growth and
quality of various crops but also its effects could be clearly seen in the activities of
the farmers other than agriculture. From a food security perspective, India as whole
and state like Chhattisgarh particularly is arguably the most vulnerable region to
many adverse effects of climate change due to a very high reliance on rainfed
agriculture for basic food security and economic growth, and entrenched poverty.
Climate change is certain to amplify these vulnerabilities given projections of
warming temperatures. The present study was an attempt to document the farmers’
perception on impact of long term climate change on agriculture and allied
activities as per their farming experiences of 15 years or more.
4.2.2.1.1 Impact of long term climate change on agriculture
Table 2.27 reveals the impact of long term climate change on agriculture.
As per the past experiences, majority of the respondents (86.25%) agreed that due
to climate change, investment in agriculture has increased. This is mainly due to
more infestation of insects & diseases on crops and more expenses on irrigation
water. Moreover, 82.92, 82.08 and 79.59 per cent of them said that cropping
pattern has changed, use of traditional crop varieties decreased and climate change
has reduced traditional irrigation sources like pond, respectively. It might be due to
fluctuations in rain onset, heat stress, longer dry seasons, uncertain rainfall and
changing patterns of rainfall. A total of 75.00 per cent key informant believed that
there was drastically conditions getting favorable to flourish weeds/
insects/diseases, whereas, 71.67 per cent agreed that population of rodent like rat
has increased in recent past years due to climatic conditions have been supportive
to its growth and 67.91 per cent of the farmers said that new species of seasonal
weeds seen in recent years due to climate change.
Almost half of the respondents believed that due to climate change area of
some crops like minor millets, sesame, pigeon pea, maize, jowar etc. in kharif and
linseed, lathyrus, lentil etc. in rabi has decreased, on the other hand 33.75 per cent
of them were disagreed with that. The results of the impact of long term climate
change observed in the present study are similar to the earlier studies of Pearce et
al. (1996), Kinuthia (1997), FAO (2005) and Bhushal et al. (2009).
125
Table 4.27: Perception of respondents about impact of long term climate change on agriculture
Statement Agree (%)
Can’t say (%)
Disagree (%)
Area of some crops (like minor millets, sesame, pigeon pea, maize, jowar etc. in kharif and linseed, lathyrus, lentil etc. in rabi) has decreased
48.33 17.92 33.75
Use of traditional crop varieties decreased 82.08 12.50 5.42
Changes occurred in flowering and fruiting time of crops
48.33 32.50 19.17
Cropping pattern has changed 82.92 15.83 1.25
Population of rodent like rat has increased 71.67 22.50 5.83
Some insects have extinct and some have been getting adapted with changing climatic condition
67.09 29.16 3.75
New species of seasonal weeds seen in recent years
67.91 27.50 4.59
Conditions getting favorable to flourish weeds/insects/diseases
75.00 20.00 5.00
Investment in agriculture has increased 86.25 12.08 1.67
Traditional irrigation sources like pond has reduced
79.59 14.16 6.25
Level of ground water has decreased 63.75 29.16 7.09
4.2.2.1.2 Impact of long term climate change on allied activities
The results of analysis examining the impact of long term climate change
on allied activities depicted in Table 4.28. The results revealed that majority of
respondents (86.30%) agreed, over the past 15 years migration of birds and
animals has increased due to climate change, while, 82.92 per cent believed that
climate change has increased drudgery of farmers/farm women. A significant
majority of respondents (82.51%) agreed that drinking water availability decreased
due to climate change. Though drinking water has decreased in summer due to
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changing pattern of rainfall resulting more runoff, local people said that they were
facing more drought periods resulting decrease in ground water level. It was also
perceived by a substantial percentage of respondents that the change in climate has
resulted in scarcity of fodder in the area, increased human health problems and air
pollution.
Table 4.28: Perception of respondents about impact of long term climate change on allied activities
Statement Agree (%)
Can’t say (%)
Disagree (%)
Species of some animal and bird has extinct 67.97 21.33 10.70
Scarcity of fodder in the area 80.42 17.91 1.67
Behavioral changes and adverse effect on health of livestock 60.00 29.17 10.83
New fish species found and old species have extinct in rivers 60.42 38.33 1.25
Investment on physical facilities increased 68.75 12.92 18.33
Human health problems are increasing 72.92 25.41 1.67
Migration of birds and animals has increased 86.30 9.58 4.12
Drinking water availability decreased 82.51 10.41 7.08
Air pollution are increasing 71.67 28.33 0.00
Water pollution are increasing 51.66 47.92 0.42
Drudgery of farmers/farm women has increased 82.92 7.50 9.58
However, it was surprising to note that 68.75 per cent of the respondents perceived that due to climate change investment on physical facilities increased, there life style have improved. Those who agreed that extinct of some animal and bird species has resulted due to climate change and new fish species found and old
127
species have extinct in rivers due to climate were 67.97 and 60.42 per cent, respectively.
Similar findings were also reported by Rischkowsky et al. (2004), Arya (2010), Bhushal et al. (2009), Pettengell (2010) and Owusu-Sekyere et al. (2011).
Furthermore, overall impact of long term climate change was determined by summed up the scores of farmers’ perception on impact of long term climate change on agriculture and allied activities. Findings of farmers’ perceptionregarding overall impact of long term climate change are presented in Table 4.29.The results indicated that nearly 37 per cent of the respondents perceived mediumlevel of overall impact of long term climate change, while, 34.17 and 29.17 per cent of the respondents reported high to low level of overall impact of long term climate change on agriculture and allied activities.
Table 4.29: Perception of respondents about overall impact of long term climatechange
Impact of long term climate change Frequency Percentage
Low (Up to 22 score) 70 29.17
Medium (23-44 score) 88 36.67
High (More than 44 score) 82 34.17
4.2.2.1.3 Impact on various crops grown by respondents
Farmers are the best judge of their own concerns and they alter their farm
operations to get adapted with changing climatic conditions by new technological
interventions. Selection of crops and its varieties are dependent on prevailing
climatic conditions and recourses available with them.
This study further assessed farmers' perception on impact of long term
climate change on various crops grown by the farmers. Table 4.30 shows that
paddy was dominant crop of study area which was grown by 100 per cent of the
respondents 15 years back and unchanged at present only per cent area covered
might be differ little bit. With regards to various varieties grown by respondents
there was drastic change in 15 years, local varieties like Gurmatia, Mundaria,
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Kanthbhulaw, Nankeshar, Bhejri, Asamchudi etc. were grown by 86.25 per cent of
the respondents 15 year back which has confined to only 1.67 per cent of the
respondents with varieties like Gurmatia, Nankeshar, Asamchudi etc. at present.
As for improved varieties of paddy, Safari was most preferred variety 15 years
back which was grown by about 89 per cent of respondents followed by Kranti
(36.25%), IR-36 (30.42%) and Culture (17.50%), at present time, area of these
varieties has replaced by Swarna (88.33%), MTU-1010 (70.00) and Mahamaya
(55.83). Swarna has been preferably growing by farmers in low land, whereas,
MTU-1010 and Mahamaya has been growing in mid land and up land as well with
some extent at present time. Replacement of paddy varieties was mainly due to
technological advancement with changing climatic conditions.
In kharif season other than paddy, crops like kodo (minor millet), pigeon
pea, sesame and moong/urd were grown by 28.33, 17.50, 16.25 and 13.75 per cent
of the respondents 15 years back which has reduced at present by 0.00, 10.83, 1.67
and 2.08 per cent, respectively. Only soybean growers were in increasing trend
during previous 15 years. The decrement in number of growers of above
mentioned crops was mainly because most of the farmers converted their suitable
lands into paddy fields by making big bunds to store run off waters in fear of
insufficient rainfall and to make efficient use and take full advantage of the
prevailing water and temperature conditions in this changing scenario of climate.
In rabi season drastic change was occurred in number of lathyrus grower
farmers which was reduced from 55.00 to 34.17 per cent during last 15 years.
Lathyrus is cultivated by farmers as relay crop in matured paddy fields in rainfed
condition. At present declining moisture content at the time of harvesting of paddy
is main reason behind the decrement of lathyrus growers. These findings are in
partial accordance with those reported by Bhushal et al. (2009), Pande and
Akermann (2010), Kemausuar et al. (2011) and Sorhang and Kristiansen (2011).
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Table 4.30: Impact of long term climate change on various crops grown by respondents
Season/Crops
15 years back Season/Crops
At present time Change
DirectionRespondents (%)
Respondents (%)
Kharif Kharif Paddy 100.00 Paddy 100.00
Local varieties (Gurmatia, Mundaria, Kanthbhulaw, Nankeshar, Bhejri, Asamchudi etc. )
86.25
Local varieties(Gurmatia, Nankeshar, Asamchudi etc.) 1.67 -
Scented varieties (Dubraj, Rani kajar, Luchai, Lohandi, Tulsimala etc. )
20.00
Scented varieties (Dubraj, Vishnu bhog, Badsah bhog, Tulsimala etc.)
5.00 -
Improved varieties Improved varieties Swarna 9.58 Swarna 88.33 + Safari 89.17 MTU-1010 70.00 Mahamaya 2.08 Mahamaya 55.83 + Culture 17.50 Hybrid 17.08 IR-64 12.08 IR-64 5.83 - IR-36 30.42 IR-36 13.33 - Kranti 36.25 HMT 13.33
Others (Falguna, Shyamla, Masuri etc.)
4.12
Others (BPT, Karma masuri, Bamleshwari, Samleshwari, 1001 etc.)
5.24
Kodo (Minor millets) 28.33 Kodo (Minor millets) 0.00 -
Pigeon pea 17.50 Pigeon pea 10.83 - Sesame 16.25 Sesame 1.67 - Soybean 4.17 Soybean 7.92 + Moong/Urd 13.75 Moong/Urd 2.08 - Rabi Rabi Lathyrus 55.00 Lathyrus 34.17 - Wheat 22.50 Wheat 26.25 + Gram 41.17 Gram 46.67 + Summer paddy 0.00 Summer paddy 20.00 + Linseed 30.00 Linseed 3.75 - Lentil 14.17 Lentil 7.50 -
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4.2.2.2 Impact of short term climate changeIndia’s agriculture is more dependent on monsoon from the ancient periods.
Any change in monsoon trend drastically affects agriculture. To study about the
impact of short term climate change on various cultural operations of paddy and
area covered under different kharif & rabi crops, the time of arrival of monsoon
and the amount of precipitation in kharif were undertaken, further the data were
collected from the farmers accordingly.
4.2.2.2.1 Impact on area under various varieties of paddy
For paddy, the cultivation period is the basic condition for planning its
production, which is decided by the climate conditions and the rice variety. Among
several agricultural climate conditions, temperature and arrival of monsoon are the
critical factor in deciding the rice cultivation period and selection of its variety. In
general, rice is a kharif season crop and when the monsoon arrives early or late, the
area available with farmers for cultivating different rice varieties changes for
adapting to the changing climatic conditions.
The data regarding area covered under various varsities of paddy according
to arrival of monsoon is given in Table 4.31. It clearly indicates that in case of
timely (15 June) arrival of monsoon, long duration variety Swarna (145 days) was
grown in about 174.44 ha of land out of 553.70 ha of total land of farmers in study
area that was increased up to 210.32 ha when monsoon arrived early and decreased
up to 145.48 ha when it arrived late with change per cent of (+) 20.57 and (-) 16.60
in comparison to normal, respectively. Area of medium duration (120-125 days)
varieties MTU-1010 and Mahamaya were 136.35 ha and 83.54 ha that were
decreased by 9.01 & 9.83 per cent and increased by 8.32 & 10.99 per cent with
respect to early and late arrival of monsoon, respectively. While, short duration
(90-95 days) varieties like Purnima and Annapurna were grown by farmers in
about 5.28 ha and 3.13 ha of their land which were decreased by 43.18 & 100.00
per cent in case of early arrival of monsoon and increased by 70.27 & 51.44 per
cent in case of late arrival of monsoon, respectively.
It can be concluded from above discussions that area under long duration
varieties of paddy increases with early arrival of monsoon and decreases with late
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arrival of monsoon. On the other hand area under medium and short duration
varieties decreases with early arrival of monsoon and increases with late arrival of
monsoon.
Table 4.31: Impact of short term climate change on area under various varieties of paddy
Varieties N
Arrival of Monsoon
Normal (ha)
Early(ha)
Change (%)
Late(ha)
Change (%)
Swarna 212 174.44 210.32 + 20.57 145.48 - 16.60
MTU-1010 168 136.35 124.07 - 9.01 147.69 + 8.32
Mahamaya 134 83.54 75.33 - 9.83 92.72 + 10.99
Hybrid 41 26.52 21.69 - 18.21 26.52 0.00
HMT 32 17.58 18.38 + 4.55 16.91 - 3.81
IR-36 32 54.58 51.79 - 5.11 58.50 + 7.18
Purnima 10 5.28 3.00 - 43.18 8.99 + 70.27
Annapurna 05 3.13 0.00 - 100.00 4.74 + 51.44
Others 43 52.18 49.02 - 6.06 52.05 - 0.25
Total cropped area = 553.70 ha
4.2.2.2.2 Impact on area under various crops
Farmers change their farming operations in response to numerous farm
risks and uncertainty due to dependency of farming on climatic conditions. Various
studies inferred that diversification is identified as a best coping strategy that has
evolved to deal with both expected rainfall uncertainty and evolving within season
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fluctuations in rainfall. The need for diversifying agricultural activities is
increasingly recognized by farmers.
Table 4.32 presents data regarding impact of short term climate change on
area under various crops grown by respondents in kharif and rabi season. The data
recorded were based on past experiences of respondents in case of normal, deficit
or surplus rainfall in monsoon. It can be revealed from data that all the farmers
were growing paddy in almost all of their cultivable land in kharif, a little portion
of area (about 4-6%) was covered under other crops like pigeon pea, soybean,
vegetable, etc. Farmers were growing paddy in 553.60 ha out of 583.70 ha of total
cultivable land which was decreased up to 550.81 ha and increased up to 556.23 ha
with change of -0.50 and +0.48 per cent in case of deficit and surplus precipitation
in kharif. This is mainly due to most of the up land farmers divert for low water
requiring crops like pigeon pea, soybean, moong, urd, til etc. in rainfed condition.
A fluctuation in total amount of precipitation in kharif mainly affected the
area covered under different crops in rabi season. Lathyrus was major effected
crop in rabi season which was grown as relay crop in matured paddy field to utilize
excess moisture in rainfed condition. In case of deficit rainfall in kharif, area
covered under Lathyrus crop was decreased by 49.51 per cent and increased by
55.63 per cent when amount of precipitation was surplus.
Furthermore, data reveal that gram was most liking crop of the study area
in rabi season and because of low water requirement comparing to other crops, its
area increased by 18.46 per cent and decreased by 29.15 per cent with deficit and
surplus amount of precipitation, respectively. This is mainly because farmers divert
for wheat and vegetable in irrigated condition and for lathyrus crops in rainfed
condition in case of surplus precipitation. Due to high water requiring attribute of
the crops like wheat and vegetables, area under cultivation was decreased with
deficit amount of precipitation and increased with surplus amount of precipitation
in kharif season. Similar findings were reported by Adger et al. (2003), Orindi and
Eriksen (2005), Nhemachena and Hassan (2007) and Cooper et al. (2008).
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Table 4.32: Impact of short term climate change on area under various crops
Season/CropN
Amount of Precipitation
Normal(ha)
Deficit(ha)
Change (%)
Surplus(ha)
Change (%)
Kharif
Paddy 240 553.60 550.81 -0.50 556.23 0.48
Pigeon pea 26 10.09 11.15 10.51 9.13 -9.51
Soybean 19 13.08 14.24 8.87 11.76 -10.09
Vegetables 11 3.58 3.12 -12.85 3.92 9.50
Others 12 3.35 4.38 30.75 2.66 -20.60
Total cropped area 583.70 583.70 00.00 583.70 00.00
Rabi
Summer paddy 48 41.60 37.42 -10.05 45.26 8.80
Lathyrus 82 44.80 22.62 -49.51 69.72 55.63
Gram 132 123.92 146.80 18.46 87.80 -29.15
Wheat 85 48.00 27.50 -42.71 68.90 43.54
Vegetables 33 14.74 13.24 -10.18 18.86 27.95
Others 27 12.33 11.17 -9.41 14.56 18.09
Total cropped area 285.39 258.75 -9.33 305.10 6.91
4.2.2.2.3 Impact on infestation of weeds, insects and diseases in paddy crop Incidence of weeds, insects and diseases is most severe in study area due to
favorable climatic conditions, multiple cropping and availability of alternate host throughout the year. Its development is strongly dependent upon the temperature, humidity and rainfall fluctuations. Any change in them, depending upon their base value, they can significantly alter the scenario, which ultimately may result in yield loss. Any small change in temperature and rainfall in region can result in changed
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virulence as well as appearance of new insects and diseases. Likewise, crop-weed competition may be affected, depending upon their crop behaviour.
The present study also recorded the responses of farmers as per their past experiences regarding infestation of weeds, insects and diseases on paddy crop according to arrival of monsoon and presented in Table 4.33. It can be revealed from the data that Echinicloa colonum was reported as major weed of paddy by 110 out of 240 respondents of study area in case of normal arrival of monsoon,which get more favorable conditions for its infestation with early arrival as reported by 226 respondents with change of +105.45 per cent. Its infestation getsdecreased with late arrival as only 41 respondents faced it was major weed of paddy. Infestation of weed like Cyperus spp. was drastically increase with late arrival of monsoon as reported by 83 respondents against 16 and 9 respondents in case of normal and late arrival. Severity of weed like Ischeamum rugosum and Agropyron repens was increased with early arrival and decreased with late arrival of monsoon. According to respondents they observed more infestation of some of the weeds like Fimbristylis mileaceae and Commelina benghalensis in both the cases of early and late arrival but it get more favorable conditions in case of late arrival.
With regards to insects, BPH/GLH was reported as major insect of paddy by 106 respondents when monsoon arrived in its normal time as against 170 and 67 respondents in case of late and early arrival with change of (+) 60.38 and (-) 36.79 per cent, respectively. Majority of the respondents (205) said that paddy crop gotmore infested with insect like leaf folder in case of late arrival of monsoon and its infestation was negligible when monsoon arrives earlier. No change of infestation of stem borer was occurred with respect to arrival of monsoon as almost similar number of respondents reported that stem borer was major insect of paddy in all three conditions. Respondents perceived that population of insects like dragon fly and army worm had decreased with early arrival and decreased with late arrival of monsoon.
As for infestation of disease in paddy crop, it was reported by respondents that disease like blast and leaf blight was major problem in paddy for 138 and 125respondents in case of late arrival as against 58 and 13 respondents in case of early arrival of monsoon, respectively. No change in infestation of false smut was reported by respondents with time of arrival of monsoon. Above findings are in
135
line with the findings of Food and Agriculture Organisation (FAO) in (2007),Jianchu et al. (2007), SAGUN (2009) and Sharma (2010).
Table 4.33: Impact of short term climate change on infestation of weeds, insects and diseases in paddy crop
Particulars
Arrival of Monsoon
Normal EarlyChange
(%) LateChange
(%)
Weeds
Echinicloa colonum 110 226 + 105.45 41 - 62.73
Alternenthra triendra 103 52 - 49.51 102 - 0.97
Ischeamum rugosum 70 81 + 15.71 66 - 5.71
Fimbristylis mileaceae 8 21 + 162.50 36 + 350.00
Cyperus spp. 16 9 - 43.75 83 + 418.75
Commelina benghalensis 21 32 + 52.38 48 + 128.57
Agropyron repens 45 63 + 40.00 38 -15.56
Others (Cynodon, Portulaca etc.)
31 31 0.00 28 - 9.68
Insects
Stem borer 65 61 - 6.15 67 + 3.08
BPH/GLH 106 67 - 36.79 170 + 60.38
Leaf folder 27 5 - 81.48 205 + 659.26
Gundhi bug 21 21 0.00 15 - 28.57
Dragon fly 12 7 - 41.67 73 + 508.33
Army worm 18 12 - 33.33 43 + 138.89 Disease
Blast 67 58 - 13.43 138 + 105.97
Leaf blight 45 13 - 71.11 125 + 177.78
False smut 44 46 + 4.55 45 + 2.27
Sheath blight 14 11 - 21.43 17 + 21.43
Neck blast 10 10 0.00 12 + 20.00
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4.2.2.2.4 Impact on other selected cultural operations of paddy
Most of the cultural practices of paddy in rainfed condition depend upon
the arrival of monsoon and farmers alter their practices accordingly. Further,
farmers were asked about the changes they perform in other selected cultural
operations of paddy according to time of arrival of monsoon and presented in
Table 4.34.
It elucidates that out of 240 respondents of study area 127 respondents
performed more than three ploughing if monsoon arrived in its normal time, which
was decreased up to 92 and 67 respondents with change of (-) 27.56 and (-) 47.24
per cent in case of early and late arrival of monsoon, respectively. Majority of the
respondents shifted from more than three ploughing to less than 3 ploughing to 3
ploughing in both the cases of early and late arrival of monsoon. When monsoon
arrived earlier than normal, farmers had tendency to reduce ploughing to take
advantage of early moisture and in case of late arrival they were compelled to
reduce ploughing to save time.
As for method of sowing was concerned variation were found in number of
farmers according to arrival of monsoon. With early arrival of monsoon number of
farmers of lehi method increased by (+) 177.36 per cent and farmers of line sowing
method decreased by (-) 66.67 per cent. On the contrast with late arrival number of
farmers of transplanting method decreased by (-) 13.64 per cent, whereas, lehi
method and line sowing method farmers increased by (+) 20.75 and (+) 13.33 per
cent, respectively.
Data regarding area covered under different method of sowing as per
arrival of monsoon is given in Table 4.34. It indicate that in case of early monsoon
area under broadcasting/biasi method decreased from 332.00 ha to 276.77 ha and
area under line sowing method decreased from 18.57 ha to 5.23 ha with change per
cent of (-) 16.64 and (-) 71.84, this decrement of area mainly shifted in lehi method
and transplanting method with change per cent of (+) 311.52 and (+) 8.87,
respectively. In case of late monsoon area under transplanting method decreased
by (-) 19.05 per cent, which was mainly shifted in lehi method, line sowing method
137
and broadcasting/biasi method with change per cent of (+) 55.35, (+) 46.15 and
(+) 4.81, respectively. Lehi method of sowing was in increasing trend in both the
cases but it was most preferred practice in case of early arrival of monsoon with
more per cent in number and area.
Farmers also change quantity of seed required for sowing in per unit area
(1 ha) with fluctuations in arrival of monsoon. Out of total 229 broadcasting
farmers 198 farmers applied seed in recommended quantity which was reduced by
174 and 103 farmers with change per cent of (-) 12.12 and (-) 47.98 with early and
late arrival of monsoon, respectively. Among the respondents a total of 27 farmers
applied seed more than recommended which was increased with change per cent of
(+) 88.89 and 351.85 in case of early and late arrival of monsoon, respectively.
A total of 176 transplanting farmers, 158 applied seed in recommended
dose which decreased up to 132 and 79 in case of early and late arrival of
monsoon, this decrement was mainly shifted in group of farmers who were
applying seed with increased rate. In short it can be say that majority of farmers
increase seed rate with late arrival of monsoon in both the method of sowing.
Biasi is main practice in broadcasting method of sowing. Out of 229 biasi
farmers 183 performed biasi in proper time when monsoon arrives timely, but in
case of early arrival it increased up to 209 farmers and which was decreased up to
158 farmers with late arrival of monsoon. Among the respondents 42 farmers
performed biasi delayed which was decreased with early arrival and increased with
late arrival by (-) 52.38 and (+) 52.38 per cent, respectively. With regards to weed
control hand weeding increased (+ 11.93%) and chemical weeding deceased
(-20.18%) with early arrival of monsoon. On the contrary chemical weeding
decreased with early arrival and increased with late arrival of monsoon.
It can be concluded from the above discussions more than three ploughing
is common practice of farmers which is reduced in both the cases. Lehi method of
sowing is mostly preferred by farmers in case of early arrival of monsoon and they
apply more seed rate in case of late arrival of monsoon.
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Table 4.34: Impact of short term climate change on other selected cultural operations of paddy
Cultural practices
Arrival of Monsoon
Normal Early Change (%) Late Change
(%)
Land preparation (n = 240 ) < 3 ploughing 16 31 + 93.75 82 + 412.50 3 ploughing 97 117 + 20.62 91 - 6.19 > 3 ploughing 127 92 - 27.56 67 - 47.24
Method of sowing (n = 240 ) Broadcasting/Biasi method 229 225 - 1.75 232 + 1.31 Transplanting method 176 186 + 5.68 152 -13.64 Lehi method 53 147 + 177.36 64 + 20.75 Line sowing method 30 10 - 66.67 34 + 13.33 SRI method 13 12 - 7.69 14 + 7.69
Method of sowing (Area in ha ) Broadcasting/Biasi method 332.00 276.77 - 16.64 347.96 + 4.81 Transplanting method 180.72 196.75 + 8.87 146.31 - 19.05 Lehi method 16.93 69.67 + 311.52 26.30 + 55.35 Line sowing method 18.57 5.23 - 71.84 27.14 + 46.15 SRI method 5.38 5.18 - 3.72 5.90 + 9.67
Seed rateBroadcasting (n = 229 ) Recommended 198 174 - 12.12 103 - 47.98 Reduced 04 04 00.00 04 0.00 Increased 27 51 + 88.89 122 + 351.85
Transplanting (n = 176) Recommended 158 132 - 16.46 79 - 50.00 Reduced 04 06 + 50.00 03 - 25.00 Increased 14 38 + 171.43 94 + 571.43
Biasi (n = 229) In proper time 183 209 + 14.21 158 - 13.66 Earlier 04 00 - 100.00 07 + 75.00 Delayed 42 20 - 52.38 64 + 52.38
Weed control Hand weeding 109 122 + 11.93 87 - 20.18 Chemical weeding 12 06 - 50.00 25 + 108.33 Hand & Chemical weeding 119 112 - 5.88 128 + 7.56
139
140
4.3 Coping mechanism/adaptation to climate change
Coping mechanism/adaptation is the ability of farmers to respond and
adjust against actual or potential impacts of changing climate conditions on crop in
ways that cause moderate harm or takes advantage of any positive opportunities
that the climate may afford. It includes policies and measures to reduce expected
harmful impacts of climate variability and extremes, and the strengthening of
adaptive capacity. They should include local actions taken by the farmers
themselves in response to changing market or environmental conditions. The
process of adaptation includes learning about risks, evaluating response options,
creating the conditions that enable adaptation, mobilizing resources, implementing
adaptations, and revising choices with new learning.
Most studies assessing the potential effects of climate change on agriculture
are regional or national and yet adaptation is place-based and needs the use of
place-specific strategies. This study therefore examined how rural smallholder
farmers in different selected study area perceive the effects of changes in climatic
variables, and how they have adjusted their farming practices to cope with the
changes in climate. The research revealed that, the coping strategies (adaptation
options) adopted by farmers to sustain adverse effect imposed on paddy production
by climate change can be categorized into crop management strategy, soil fertility
strategy, land preparation strategy and farm size strategy or diversification of crop.
Further, we collected data from the farmers and investigated actual farm-level
coping strategies and documented how paddy farmers cop with extreme conditions
generated by excess or deficit/no rainfall during various stages of crop. The
specific methods embedded in each of these strategies are elaborated below.
4.3.1 Coping mechanism in paddy against excess rainfall
Table 4.35 illustrates farmers coping mechanism in paddy, as an adaptation
to excess rainfall the majority of respondents delayed sowing dates. This change in
sowing date was adopted by 68.33 per cent of the farmers in study area. The
majority of farmers (67.91%) were opted late harvesting in case of excess rainfall
at the time of maturity of crop.
141
Table 4.35: Distribution of respondents according to their coping mechanism
against excess rainfall
Coping mechanism Frequency Percentage
Late sowing 164 68.33
Double sowing 53 22.08
Use of short duration varieties 149 62.08
Sowing by lehi method 132 55.00
Increase broadcasting method of sowing 09 3.75
Prepare more seedlings than required 23 9.58
Sowing without ploughing 8 3.33
Increase seed rate 28 11.67
Purchasing of seedlings 12 5.00
Transplanting by thinning dense field 08 3.33
Gap filling 46 19.17
Prepare channels inside the field to drain excess water 38 15.83
Application of potash 06 2.50
Late harvesting 163 67.91
Put harvested paddy (Karpa) on bunds for drying 128 53.33
Turn harvested paddy (Karpa Palatna) several timesfor drying
99 41.25
Keep harvested paddy (Karpa) on big size of stubbles 28 11.67
Trailing of harvested paddy (Karpa katar) to save from excess water
18 7.50
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According to data in Table 4.35, majority of the informants (62.08%) believed that use of short duration varieties might be beneficial if there was excessrainfall at the time of sowing of paddy. Sowing by lehi method, put harvested paddy (Karpa) on bunds for drying, turn harvested paddy (Karpa palatna) several times for drying, double sowing and gap filling were cited by 55.00, 53.33, 41.25, 22.08 and 19.17 per cent of the respondents as a core strategy to deal with excessrainfall during various stages of crop period, respectively.
Other coping mechanisms were mentioned by some of the farmers to deal with excess rainfall which may be profitable to the whole farming community if successful. A quite number of farmers believed in preparation of channels inside the field to drain excess water, keeping harvested paddy (Karpa) on big size of stubbles and prepare more seedlings than required.
The disparity of adoption of these strategies clearly indicates the need to test their effectiveness of their efforts and available resource with them to cop against these circumstances.4.3.2 Coping mechanism in paddy against deficit rainfall
No rainfall during sowing of crop land becomes dry and difficult to plough, and lack of precipitation hinders seed cultivation and germination of cultivated seeds. Even weeks delay in the onset of rain and long dry spells in between the various stages of crop cultivation was found to have significant difference on the harvest and has deprivation of households’ livelihood due low productivity of crop.
Table 4.36 presents coping mechanisms actually adopted by the respondents against deficit rainfall during various stages of paddy cultivation. As an adaptation to deficit rainfall at the time of sowing majority of the farmers(70.41%) delayed sowing dates, whereas, 54.58, 49.17 and 24.17 per cent of the respondents cited that they increase seed rate, use short duration varieties and use different varieties for sowing, respectively. Use dry seeding method (30.41%), crop diversification (15.83%), increase broadcasting method of sowing (12.92%), use of line sowing method (10.83%), and transplanting of young aged seedlings(3.75%) were the main coping strategies used by the respondents in study area to reduce the risk of crop failure.
Soil water management and arrangement of irrigation is very crucial in case of deficit rainfall. Furthermore in Table 4.36, it was mentioned by 42.50 per cent of the farmers that they were dependent on canal for irrigation, while, 31.25 per
143
Table 4.36: Distribution respondents according to their coping mechanism against deficit rainfall
Coping mechanism Frequency Percentage
Late sowing 169 70.41
Sowing of different varieties 58 24.17
Use dry seeding method 73 30.41
Increase seed rate 131 54.58
Transplanting of young aged seedlings 09 3.75
Use of short duration varieties 118 49.17
Increase broadcasting method of sowing 31 12.92
Use of line sowing method 26 10.83
Crop diversification 38 15.83
Change the dose of N & P 07 2.92
Application of FYM to increase water holding capacity
13 5.42
Purchasing of water for irrigation 08 3.33
Dependent on canal for irrigation 102 42.50
Irrigation from storage water tank by diesel pump
75 31.25
Weeding without biasi/Delayed biasi 45 18.75
Use crop insurance 11 4.58
Late harvesting 08 3.33
144
cent of them arranged irrigation water from storage water tank by using diesel pump. However, 18.75 per cent of the respondents used to weeding without biasi or delayed biasi, 5.42 per cent believed in application of FYM to increase water holding capacity, while, equal number of farmers (2.92%) change dose of Nitrogenous (N) and Phosphoric (P) fertilizers to accelerate vegetative growth of crop and to increase water holding capacity of soil, respectively.
4.4 Relationship between dependent and independent variables
Determination of relationship between dependent and independent
variables might help the identification of explanatory variables to describe in better
way the farmer’s perception about climate change and its impact on agriculture and
allied activities. Correlation and linear regression analysis were worked out to find
out the nature and extent of relationship between dependent and selected
independent variables. Accordingly the results are presented and discussed under
following categories.
4.4.1 Correlation analysis among independent and dependent variables
To determine the degree and nature of relationship and direction of
association among independent and dependent variables a correlation analysis were
worked out and presented in the form of correlation matrix in Table 4.37. Out of
twenty four independent variables taken in the study, eighteen variables like Age
(X1), Educational status (X2), Farming experience (X4), Social participation (X5),
Land holding (X7), Irrigation (X8), Annual income (X10), Annual expenditure
(X11), Distance to market (X12), Socio-economic status (X13), Crop insurance
(X14), Sources of information (X15), Contact with extension personnel (X17),
Cosmopoliteness (X19), Awareness (X20), Innovativeness (X22), Scientific
orientation (X23), Risk orientation (X24) were highly and positively significantly
correlated with perception of farmers about climate change (Y1) at 0.05 level of
probability.
In case of impact of climate change twenty variables like Age (X1),
Educational status (X2), Farming experience (X4), Social participation (X5), Land
holding (X7), Irrigation (X8), Annual income (X10), Annual expenditure (X11),
Distance to market (X12), Socio-economic status (X13), Crop insurance (X14),
145
Tabl
e 4.
37:C
orre
latio
n m
atrix
of s
elec
ted
inde
pend
ent a
nd d
epen
dent
var
iabl
es
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
X12
X13
X14
X15
X16
X17
X18
X19
X20
X21
X22
X23
X24
Y1
Y2
X1
1.00
-0.2
3*0.
25*
0.91
*-0
.15*
0.12
0.13
*-0
.05
-0.1
10.
080.
09-0
.03
-0.0
90.
00-0
.18*
-0.2
2*-0
.12
-0.0
3-0
.10
0.12
0.14
*-0
.18*
-0.2
2*-0
.21*
0.33
*0.
36*
X2
1.00
-0.0
5-0
.24*
0.31
*-0
.24*
0.32
*0.
23*
0.15
*0.
34*
0.32
*0.
36*
0.45
*0.
40*
0.55
*0.
60*
0.37
*0.
27*
0.37
*0.
54*
0.00
0.52
*0.
57*
0.55
*0.
25*
0.35
*X
31.
000.
19*
-0.0
10.
21*
0.04
0.02
0.01
0.17
*0.
20*
0.10
0.16
*0.
06-0
.14*
-0.1
20.
04-0
.04
0.06
-0.0
20.
22*
-0.0
5-0
.05
-0.0
40.
05-0
.01
X4
1.00
-0.1
8*0.
080.
16*
-0.0
6-0
.05
0.11
0.12
0.04
-0.1
20.
04-0
.16*
-0.2
2*-0
.09
-0.0
3-0
.08
0.11
0.16
*-0
.16*
-0.2
2*-0
.19*
0.30
*0.
33*
X5
1.00
-0.0
40.
36*
0.16
*0.
000.
29*
0.31
*0.
33*
0.79
*0.
25*
0.41
*0.
39*
0.35
*0.
22*
0.38
*0.
35*
-0.1
5*0.
35*
0.42
*0.
31*
0.19
*0.
31*
X6
1.00
-0.1
8*-0
.04
-0.1
6*-0
.16*
-0.1
4*-0
.25*
0.09
-0.1
8*-0
.24*
-0.2
5*-0
.23*
-0.0
8-0
.16*
-0.1
8*0.
15*
-0.2
4*-0
.23*
-0.2
6*-0
.09
-0.1
6*X
71.
000.
090.
13*
0.77
*0.
78*
0.50
*0.
43*
0.55
*0.
38*
0.38
*0.
41*
0.12
0.50
*0.
44*
-0.0
40.
55*
0.49
*0.
43*
0.26
*0.
42*
X8
1.00
0.18
*0.
13*
0.14
*0.
15*
0.39
*0.
22*
0.25
*0.
29*
0.11
0.13
*0.
18*
0.15
*0.
000.
13*
0.20
*0.
14*
0.18
*0.
19*
X9
1.00
0.11
0.11
0.21
*0.
13*
0.30
*0.
17*
0.23
*0.
15*
0.12
0.20
*0.
090.
020.
18*
0.19
*0.
22*
0.05
0.03
X10
1.00
0.98
*0.
48*
0.38
*0.
46*
0.38
*0.
38*
0.47
*0.
13*
0.54
*0.
44*
0.04
0.60
*0.
53*
0.50
*0.
34*
0.43
*X
111.
000.
49*
0.40
*0.
46*
0.39
*0.
38*
0.46
*0.
14*
0.55
*0.
45*
0.05
0.60
*0.
53*
0.49
*0.
33*
0.43
*X
121.
000.
34*
0.42
*0.
45*
0.44
*0.
49*
0.23
*0.
56*
0.46
*0.
010.
53*
0.51
*0.
50*
0.23
*0.
42*
X13
1.00
0.33
*0.
43*
0.44
*0.
37*
0.21
*0.
40*
0.42
*-0
.06
0.43
*0.
51*
0.38
*0.
27*
0.38
*X
141.
000.
36*
0.40
*0.
38*
0.16
*0.
41*
0.45
*0.
000.
47*
0.45
*0.
44*
0.22
*0.
37*
X15
1.00
0.73
*0.
57*
0.63
*0.
55*
0.58
*-0
.06
0.56
*0.
58*
0.56
*0.
26*
0.38
*X
161.
000.
45*
0.56
*0.
50*
0.53
*-0
.05
0.57
*0.
57*
0.53
*0.
110.
35*
X17
1.00
0.24
*0.
66*
0.55
*-0
.12
0.65
*0.
62*
0.63
*0.
31*
0.42
*X
181.
000.
27*
0.33
*0.
000.
21*
0.25
*0.
20*
0.09
0.21
*X
191.
000.
53*
0.03
0.67
*0.
65*
0.66
*0.
27*
0.43
*X
201.
00-0
.11
0.67
*0.
69*
0.63
*0.
59*
0.69
*X
211.
00-0
.14*
-0.1
0-0
.10
-0.1
1-0
.06
X22
1.00
0.86
*0.
83*
0.39
*0.
50*
X23
1.00
0.84
*0.
35*
0.50
*X
241.
000.
33*
0.43
*
* Si
gnifi
cant
at 0
.05
leve
l of p
roba
bilit
y
146
Sources of information (X15), Exposure to mass media (X16), Contact with
extension personnel (X17), Access to weather forecasts (X18), Cosmopoliteness
(X19), Awareness (X20), Innovativeness (X22), Scientific orientation (X23), Risk
orientation (X24) were highly and positively significantly correlated with
perception of farmers about impact of climate change on agriculture and allied
activities (Y2) at 0.05 level of probability, while, only one variable i.e. occupation
(X6) was highly and negatively significantly with perception of farmers about
impact of climate change on agriculture and allied activities (Y2) at 0.05 level of
probability.
Independent variables like size of family (X3), access to credit (X9) and
decision making pattern (X21) were showing non-significant correlation with both
the dependent variables Y1 and Y2. Most of the independent variables were
positively significantly correlated with one another, while some of the variables
were showing negative and significant correlation with one another. Only one
variable i.e. decision making pattern was significantly correlated with a little
number of independent variables.
4.4.2 Multiple regression analysis
To determine the strength of the relationship between both the dependent
variables and independent variables considered under study regression analysis
were worked out separately. The analysis consisted of choosing and fitting an
appropriate model, done by the method of step down regression analysis, with a
view to exploiting the relationship between the variables to help estimate the
expected response for a given value of the independent variable.
4.4.2.1 Multiple regression analysis of independent variables with perception
of farmers’ about climate change
To find out the best predictor and appropriate fit model for predicting
perception of farmers’ about climate change a step down multiple regression
analysis was worked out. In each step of analysis one variable was dropped that
showing more than 10 and maximum value of variable inflation factor (VIF). In
this way best fit model was found by dropping the variable i.e., expenditure (X11)
and presented in Table 4.38.
147
Out of twenty three variables considered in the model, seven variables like
age (X1), land holding (X7), irrigation (X8), annual income (X10), exposure to
mass media (X16), awareness (X20) and innovativeness (X22) showed significant
contribution on predicting perception of farmers’ about climate change (Y1) at
0.05 level of probability. The model revealed that 52.50 per cent of the variation in
perception of farmers about climate change (Y1) can be explained by considering
twenty three independent variables and one dependent variable (Y1). The model is
significant in predicting dependent variable (Y1) with 10.391 ‘F’ value at 0.05
level of probability.
Table 4.38: Multiple regression analysis of best fit model among selected independent variables with perception of farmers’ about climate change
Variables Regression coefficient‘b’ value ‘t’ value
X1 Age 0.146* 2.337X2 Educational status 0.473 1.244X3 Size of family -0.380 -0.817X4 Farming experience -0.002 -0.030X5 Social participation 0.098 0.686X6 Occupation -0.236 -0.697X7 Land holding -0.254* -2.011X8 Irrigation 0.800* 2.571X9 Access to credit 1.186 1.316X10 Annual income 0.020* 2.578X12 Distance to market -0.178 -0.381X13 Socio-economic status 0.309 0.384X14 Crop insurance -0.634 -0.946X15 Sources of information 0.324 0.916X16 Exposure to mass media -2.304* -4.626X17 Contact with extension personnel 0.067 0.238X18 Access to weather forecasts 0.030 0.067X19 Cosmopoliteness -0.305 -0.548X20 Awareness 0.568* 6.490X21 Decision making pattern -0.033 -1.314X22 Innovativeness 0.213* 2.266X23 Scientific orientation -0.219 -1.283X24 Risk orientation 0.012 0.105* Significant at 0.05 level of probability
Multiple R2 = 0.525, Intercept = 6.956,‘F’ Value = 10.391 at 23, 216 df
148
Among above discussed twenty three independent variables various models
were developed and tested for finding their predicting ability for variation in the
perception of farmers’ about climate change (Y1). The best model was picked out
and presented in Table 4.39. It was found that the model developed by considering
variables (X1, X7, X8, X10, X16, X20 and X22) showing significant relationship
with dependent variable (Y1) explained highest variation (49.40%) in predicting
perception of farmers’ about climate change with significant ‘F’ value (32.344) at
5 per cent level of probability.
Table 4.39: Multiple regression analysis of selected model among independent variables with perception of farmers’ about climate change
VariablesRegression coefficient
‘b’ value ‘t’ value
X1 Age 0.120* 4.386
X7 Land holding -0.205 -1.874
X8 Irrigation 0.881* 3.316
X10 Annual income 0.015* 2.024
X16 Exposure to mass media -1.785* -4.677
X20 Awareness 0.599* 8.028
X22 Innovativeness 0.156* 2.326
* Significant at 0.05 level of probability
Multiple R2 = 0.494, Intercept = 4.719,
‘F’ Value = 32.344 at 07, 232 df
These findings are in partial accordance with those reported by Shiferaw
and Holden (1998) that age of the head of household can be used to capture
farming experience, Nhemachena and Hassan (2007) argued that higher age with
149
highly experienced farmers are likely to have more information and knowledge on
changes in climatic conditions and crop and livestock management practices. They
also discovered that higher income farmers might however be less risk-averse and
have enough access to information and access to extension services with mass
media exposure was one of the important determinants of farmers perception on
climate change and farm-level adaptation. Total size of farm area also had positive
effect on climate change perceptions but the likelihood of farmers’ adaptation to
climate change varied.
4.4.2.2 Multiple regression analysis of independent variables with perception
of farmers’ about impact of climate change on agriculture and allied
activities
To find out the best predictor and appropriate fit model for predicting
perception of farmers’ about impact of climate change on agriculture and allied
activities a step down multiple regression analysis was worked out. In each step of
analysis one variable was dropped that showing more than 10 and maximum value
of variable inflation factor (VIF). In this way best fit model was found by dropping
the variable i.e., expenditure (X11) and presented in Table 4.40.
Out of twenty three variables considered in the model, seven variables like
age (X1), size of family (X3), irrigation (X8), distance to market (X12), sources of
information (X15), awareness (X20) and innovativeness (X22) showed significant
contribution on predicting perception of farmers’ about impact of climate change
on agriculture and allied activities (Y2) at 0.05 level of probability. The model
revealed that 63.20 per cent of the variation in perception of farmers about impact
of climate change on agriculture and allied activities (Y2) can be explained by
considering twenty three independent variables and one dependent variable (Y2).
The model is significant in predicting dependent variable (Y2) with 16.106 ‘F’
value at 0.05 level of probability.
150
Table 4.40: Multiple regression analysis of best fit model among selected independent variables with perception of farmers’ about impact of climate change
on agriculture and allied activities
Variables Regression coefficient
‘b’ value ‘t’ value
X1 Age 0.422* 3.522
X2 Educational status 0.577 0.789
X3 Size of family -2.327* -2.602
X4 Farming experience 0.024 0.204
X5 Social participation 0.182 0.662
X6 Occupation -0.738 -1.132
X7 Land holding -0.278 -1.144
X8 Irrigation 0.901* 2.015
X9 Access to credit -1.883 -1.086
X10 Annual income 0.017 1.133
X12 Distance to market 1.518* 2.120
X13 Socio-economic status 1.362 0.879
X14 Crop insurance 0.801 0.621
X15 Sources of information -0.844* -2.142
X16 Exposure to mass media -0.131 -0.136
X17 Contact with extension personnel 0.504 0.934
X18 Access to weather forecasts 0.240 0.277
X19 Cosmopoliteness 0.664 0.621
X20 Awareness 0.912* 5.412
X21 Decision making pattern -0.009 -0.187
X22 Innovativeness 0.211* 2.106
X23 Scientific orientation 0.231 0.705
X24 Risk orientation -0.125 -0.564
* Significant at 0.05 level of probability
Multiple R2 = 0.632, Intercept = 49.078,
‘F’ Value = 16.106 at 15, 216 df
151
Among above discussed twenty three independent variables various models
were developed and tested for finding their predicting ability for variation in the
perception of farmers’ about impact of climate change on agriculture and allied
activities (Y2). The best model was picked out and presented in Table 4.41. It was
found that the model developed by considering variables (X1, X3, X8, X12, X15,
X20 and X22) showing significant relationship with dependent variable (Y2)
explained highest variation (60.80%) in predicting perception of farmers’ about
climate change with significant ‘F’ value (51.384) at 5 per cent level of probability.
Table 4.41: Multiple regression analysis of selected model among independent variable with perception of farmers’ about impact of climate change on agriculture
and allied activities
Model wise VariablesRegression coefficient
‘b’ value ‘t’ value
X1 Age 0.404* 7.809
X3 Size of family -1.741* -2.255
X8 Irrigation 1.302* 2.585
X12 Distance to market 1.997* 2.487
X15 Sources of information -0.290 -0.597
X20 Awareness 1.069* 7.188
X22 Innovativeness 0.365* 3.120
* Significant at 0.05 level of probability
Multiple R2 = 0.608, Intercept = 48.318,
‘F’ Value = 51.384 at 06, 232 df
These findings are supported by the results reported by Mandleni (2011)
that access to extension services was positively related to climate change impacts.
Among the exogenous variables, it was the only variable that had the highest
weighting coefficient. Nhemachena and Hassan (2007) stated that raising
awareness of changes in climatic conditions among farmers would have greater
impact in increasing adaptation to changes in climatic conditions. Households that
152
had large sizes were therefore expected to have enough labour to take up
adaptation measures in response to climate change impacts (Hassan and
Nhemachena, 2008). Zhang and Flick (2001) however, found that long distances to
input markets decreased the likelihood of adaptation against climate change
impacts. Innovative farmers perceive more about climate change impacts.
4.5 Constraints faced by farmers in adaptation to climate change and theirsuggestions to minimize the constraints
4.5.1 Constraints in coping/adaptation to climate change
The present study also assessed farmers’ perception on constraints
experienced by them in using various coping mechanisms to mitigate adverse
effect of climate change on agriculture and allied activities. The information
received by the farmers on constraints was further ranked as per maximum number
of responses obtained and presented in Table 4.42.
Analysis of the data collected from respondent's shows that the major
constraints to coping to climate change faced by farmers in the study area included
lack of information about accurate weather forecast (68.33%), irregularity of
extension services (66.25%) and lack of knowledge about need based improved
agriculture technologies (64.58%) with rank of I, II and III, respectively.
Moreover, findings of the study indicate that other major constraints reported by
respondents were lack of information about climate change (61.67%), lack of
resources (48.33%), unavailability of inputs on proper time (46.67%), lack of
believe on current weather forecast system (43.75%) and inadequate supply of
irrigation water in canal (42.50%).
The result further showed that lacking of training programmes on disaster
management, less/no subsidies on desired agricultural inputs, irregularity in
electricity supply and lack of government policies to combat against natural
calamities were other constraining factors reported by respondents for their better
coping to climate change. These findings are similar with findings reported by
153
Nhemachena and Hassan (2007), Ishaya and Abaje (2008), Bryan et al. (2009),
Deressa et al. (2009), Pande and Akermann (2010) and Nzeadibe et al. (2011).
Table 4.42: Distribution of respondents according to constraints faced by them in coping to climate change
4.5.2 Suggestions given by farmers to overcome the constraintsThe farmers of the study area were also asked about their suggestions to
overcome constrains faced by them in coping to climate change and presented in
Table 4.43. Majority (65.83%) of the respondents suggested that weather forecast
should be more accurate and timely, whereas, 63.33 and 57.91 per cent of them
said that effective extension services should be available to the farmers and proper
information should be provided about climate change which might be enable them
to adapt against climate change.
Constraints F P Rank
Lack of information about accurate weather forecast 164 68.33 I
Lack of information about climate change 148 61.67 IV
Lack of knowledge about need based improved agriculture technologies 155 64.58 III
Lack of resources 116 48.33 V
Unavailability of inputs on proper time 112 46.67 VI
Irregularity of extension services 159 66.25 II
Less/no subsidies on desired agricultural inputs 82 34.17 X
Lack of government policies to combat against natural calamities 27 11.25 XII
Lack of believe on current weather forecast system 105 43.75 VII
Irregularity in electricity supply 49 20.41 XI
Lacking of training programmes on disaster management 94 39.17 IX
Inadequate supply of irrigation water in canal 102 42.50 VIII
154
Table 4.43: Distribution of respondents according to their suggestions to minimizethe constraints in coping to climate change
Suggestions F P Rank
Weather forecast should be more accurate and timely 158 65.83 I
Proper information should be provided about climate change 139 57.91 III
Regular training programme should be organised on disaster management
102 42.50 V
Good quality of agricultural inputs should be available on subsidized rate in proper time
88 36.67 X
Availability of agricultural inputs at village level on time 97 40.41 VI
Efforts should be made to create awareness among the people about the effect of climate change and its consequences
128 53.33 IV
Effective extension services should be available to the
farmers152 63.33 II
Need based water supply in canal should be ensured 91 37.91 VIII
Government policies should be made to support the farmers during natural calamities 95 39.58 VII
Location specific water storage structure should be developed for effective utilization of rainwater
76 31.67 IX
Electricity supply should be proper 18 7.50 XI
The other suggestions given by respondents were efforts should be made to
create awareness among the people about the effect of climate change and its
consequences (53.33%), training should be imparted to build the capacity for better
adaptation (42.50%) and government policies should be made to support the
farmers during natural calamities. Furthermore, farmers suggested that availability
of agricultural inputs at village level on time should be ensured, need based water
supply in canal should be ensured and good quality of agricultural inputs should be
available on subsidized rate in proper time. According to the farmers of study area
above arrangements may help them to overcome constraints in coping against
155
climate change. Above findings are in line with the findings of Pande and
Akermann (2010) and Pettengell (2010).
4.5.3 Recommendations
As per the present study conducted among the farmers, it is evidenced that
farmers are experiencing change in climate and they have already devised a means
to survive. But farmers are facing some constraints in adaptation to climate change,
therefore, it is advised that policy of reliable and effective measures of adaptation
need to be implemented and must be accessible to the end users. Looking at the
issue of climate change adaptation, the role of agricultural extension in this regard
is significant to raise both the consciousness of the need to climate change
adaptation and possible methods of mitigation to both the end users and policy
makers.
The farmers of study area are facing problem of water availability, reduced
farm productivity due to drought and erratic & untimely rainfall. Soil fertility loss,
soil erosion problem and intensified agriculture practices lead to an overall
decrease in income of marginal and small farmers of study area. Moreover, mono –
cropping, practiced in many places adds to their problems. At the same time, these
farmers at most of the times do not wait for external interventions and develop
their own adaptation strategies. In many cases, there is a good understanding of the
challenges and problems faced by the farmers, they also know which strategies to
adopt in order to tackle those problems. However, in many cases they lacked the
capacity to implement the necessary changes. There is a lack of financial ability
and sometimes technical knowledge, which impedes the implementation of
adaptive capacities. According to the farmers’ perception on climate change, its
impact on agriculture, constraints faced in adaptation and suggestions given by
them, the recommendations proposed as an outcome of the study in this section
and further elaborated in Fig. 4.13.
In the study area farmers were unaware and lacking of information about
climate change. This study therefore, recommends dissemination of information to
be a critical element because farmers were not informed about climate change in
the study area. The information on changing climatic conditions and its impact on
agriculture and allied activities must be made available to those farmers that are far
from weather stations. Extension officers who are already agents of information
156
can be assigned to convey messages about the climate change related weather
forecasts to farmers. It further suggests farmers need accurate weather forecasts
and agro-advisory services, to take vital decisions regarding farming practices.
However, until date the Indian Meteorological Department extends its agro-
advisory services only up to the district level. The information often does not reach
the end users. Establishing an efficient service delivery system down to the village
level is a daunting task. However, external agencies can play an important role in
supporting these services by including them in their area of their operations.
Management strategies must include adaptation activities that minimize the
impact of drought, flood and erratic & uneven rainfall on agriculture production
systems. The role of drought and flood in agriculture needs to be better understood
and appropriate adaptation measures must be implemented. National policies need
to support research and development programmes that prepare appropriate location
specific technologies to help farmers adapt to changing climatic conditions.
Climate change also is seen and predicted to have worse impacts in the future. It is
therefore crucial to develop early warning systems that can be used to reduce
disasters that can be caused by drought and flood. Capacity building at community
level in order to enable the farmers to implement adaptation strategies must be one
of the top priorities for decision makers.
Most of the farming communities are unaware of government schemes and
programmes related to drought mitigation, agriculture and rural development.
Raising awareness is crucial in order to enable the farming communities to take
advantage from the various existing schemes. The livelihoods of farmers need to
have a broad base and should not be restricted solely to the income out of farming
activities. There is an urgent need for sensitization of the rural communities about
the various schemes of the government for which the extension services need to
have more interaction with rural masses. While the Govt. of India provides enough
subsidies for inorganic fertilizers and pesticides, not much effort is being given to
encourage environmentally sound farming practices. There should be a mechanism
of direct subsidies to farmers, who are practicing environmental friendly practices.
Compulsory insurance scheme is provided to the farmers by cooperative
societies at the time of seasonal loan which is not actually benefitting them. At the
same time existing insurance schemes are covering only certain cereal, pulses and
157
cash crops. Besides, there are certain technical issues also, for example, until date
insurance is provided against total rainfall in the growing season. Under climate
change scenarios, the total rainfall may not vary much but its distribution might be
grossly affected. This change in rainfall distribution pattern seriously affects crop
production. However, this important aspect is not yet taken into consideration
while designing crop insurance schemes. For this, close collaboration between
Agriculture Research Institutes and Insurance Companies with Government
mediation is needed. Designing and putting into practice economically viable crop
insurance schemes would substantially improve the situation of farmers.
Basic infrastructure needs to be improved in all the sectors for making
agriculture sustainable. Better road connectivity for greater market access as well
as increasing the storage capacity of both food and fodder at local level is need of
farmers. Village level water harvesting structures may be another important
intervention. Strengthening of agricultural extension services through the existing
Krishi Vigyan Kendras (KVKs), by focusing on low input agricultural practices
and locally adapted cropping patterns is critical and urgently needed. Farming
communities should get support in learning about market mechanisms and
merchandising their products. They should have easy access to markets and
information about global market prices.
158
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159
Summary and Conclusions
CHAPTER –V
SUMMARY AND CONCLUSIONAgriculture is the most important sector in Indian economy that provides
food and livelihood security to majority of its population. Agriculture places heavy burden on the environment in the process of providing humanity with food and fiber, while climate is the primary determinant of agricultural productivity. Given the fundamental role of agriculture in human welfare, concern has been expressed by federal agencies and others regarding the potential effects of climate change on agricultural productivity. In India, climate change has been putting additional stress on ecological and socioeconomic systems that already facing tremendous pressures due to rapid urbanization, industrialization and economic development.
Climate change is predicted by scientists to have the main impact on agriculture, economy and livelihood of the populations of developing countries and India is one of them, where large parts of the population depend on climate sensitive sectors like agriculture and forestry for livelihood. In order to understand how farmers would respond to climate change, it is essential to study farmers’perceptions on climate change and its impact on agriculture. As the understanding on global climate and its change is pre requisite to take appropriate initiatives to combat climate change. The only solution for these huge populations seems to be adequate and relevant adaptation strategies.
Hence, the investigation entitled “Farmers’ perception about climate
change and its impact on agriculture and allied activities in Chhattisgarh
plains” was carried out in plain zone of Chhattisgarh state during the year 2013-14
and 2014-15 with the following specific objectives:
1. To study the profile of the farmers,
2. To determine the awareness and perception about climate change among the
farmers,
3. To assess the farmers vulnerability due to climatic variability,
4. To find out the impacts of climate change on various agriculture and allied
activities,
5. To find out adaptation/mitigation measures being taken by farmers in response
to climate change,
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6. To ascertain the association between perception of farmers about climate change
and impact of climate change with selected independent variables, and
7. To find out the constraints faced by farmers in various adaptation activities in
response to climate change and obtained suggestions from them to minimize the
constraints.
The study was conducted in four randomly selected districts of Plain Zone
of Chhattisgarh State during the year 2013-14 and 2014-15. From each selected
district 2 blocks were selected, where three villages from each block were
considered to obtain a sample of 240 farmers as respondents who had 15 or more
years of farming experience. The data collected from respondents through personal
interview and group discussion were coded, tabulated and subjected to statistical
analysis in accordance with the objectives of the study.
This study was carried out to measure perception of farmers about climate
change and impact of climate change on agriculture and allied activities
considering as dependent variables. These tactical observations were influenced by
a number of socio-personal, socio-economic, communicational and socio-
psychological factors as independent variables. Efforts were also made to find out
farmers vulnerability due to climate change, their coping mechanisms to mitigate
the adverse effect and constraints faced by them to adapt with climate change.
Major findings of the study are summarized in this section under following heads:
5.1 Independent Variables5.1.1 Socio-personal characteristics
This section included the socio-personal characteristics of the respondents
which were associated with dependent variables and might influence their
perceptions. Majority of the respondents were belonged to middle age group
ranging from 46-60 years of age and more than two third of them belonged to other
backward class. Among respondents more than half of them educated up to middle
to higher secondary level. Most of the respondents were residing as joint family
with 5 to 8 members in their family. A little more than half of them had 21 to 40
years of farming experience with membership of two social organisations and
participated regularly in organisation like cooperative society.
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5.1.2 Socio-economic characteristics
Important socio-economic characteristics that might be influence the
perceptions of the respondents and directly associated with impact of climate
change were considered in the study. Majority of the respondents were possessing
1.1 to 2 ha of land and belonged to small farmers’ category. About three fourth of
them were having irrigation facilities, out of which about 40 per cent of them had
irrigation availability for kharif season only. Paddy was the principal crop of study
area, based on the multiple responses most of the respondents were growing paddy
in irrigated as well as un-irrigated condition. In rabi season more than half of the
respondents were growing Lathyrus in un-irrigated condition. Gram, summer
paddy and wheat were other major crops grown by the respondents in rabi season.
Agriculture along with labour was main occupation of the respondents followed by
agriculture alone and agriculture along with service and labour. Majority of the
respondents were having low annual income between Rs. 75000 to 150000/- per
annum. Agriculture was main source of income and the average annual income of
respondents was Rs. 87534.62/-. About one third of their total income was spent
for food materials and nearly one forth expenditure from total income was incurred
for agriculture purpose.
More than three forth of the respondents were acquiring credit mainly
from cooperative society as crop loan which was repaid in kind while selling their
produce like paddy. Most of the respondents were having between 1 to 4
implements for their cultural operations, while, 13.75 per cent of them were having
between 5 to 8 implements. About 2 per cent of the respondents had possessed
more than 8 implements, whereas, 1.25 per cent of the respondents reported that
they did not have any of the farm implement but majority of them were using
tractor in hire basis for their form operations. More than one third of the
respondents were getting farm inputs from the market within 3 to 5 km of distance
and nearly half of the respondents said that manure & fertilizer, improved seed and
insecticide/pesticide/weedicide were easily available for them. Nearly three forth
were insuring their crop from cooperative society as compulsory insurance.
162
Majority of the respondents (40.83%) belonged to lower class group followed by
36.25 per cent of them belonged to lower middle class group.
5.1.3 Communicational characteristics
Communicational characteristics of respondents were also studied and it
was found that majority of them had low level of contact with extension personnel.
A little less than half of the respondents had occasional contact with Rural
Agriculture Extension Officer (RAEO) and more than three fifth of them had never
contacted with scientist, while, almost all the respondents (93%) never contacted
with Non Government Organisation (NGO) functionaries. One third of the
respondents were having medium level of participation in extension activities and
little more than fifty per cent of them participated occasionally in training
programmes and demonstrations. Majority of the respondents (52.92%) regularly
watched television and almost half of respondents were having low level of use of
mass media sources.
Various sources of information were being utilized by the respondents to
collect weather related information. Among the respondents friends/relatives/etc.,
newspaper, mobile and national TV channel were most credible and frequently
used sources for collecting weather information. All the information gathered from
various information sources were not utilized by the respondents. According to
respondents the utility of information related to weather forecast were 39.30, 35.55
and 31.80 per cent for national TV channel, friends/relatives/etc. and news paper,
respectively. However, 19.80 per cent utility of information was reported by the
respondents with regards to overall utility. Nearly three fourth of the respondents
were having low to medium level of cosmopoliteness and nearly three fifth of them
visited often to sometimes for agriculture purpose.
5.1.4 Psychological characteristics
Under this section the variables which were found directly or indirectly
related with the farmer’s perception and adaptation of climate change were
identified for the study. About half of the respondents were having medium level
of risk orientation, innovative proneness and scientific orientation. A little more
than three fifth of the respondents belonged to low decision making ability
category and almost 65 per cent of the respondents in each case had taken self
163
decision for choice of crop/its varieties and choice of cropping patter/sowing
method.
Majority of the farmers (70.00%) were fully aware about risk of crop
failure has increased due to climate change. With regards to overall awareness for
each phenomena, respondents were more aware about risk of crop failure has
increased, pollution is increasing in the atmosphere and occurrence of natural
disasters are increasing with the rank of I, II and III, respectively. Majority of the
respondents (90.00%) had faced drought during last 15 years, whereas, 89.58,
70.00 and 36.25 per cent had faced erratic rainfall, flooding and storm/typhoon as
disasters, respectively. Most of the farmers pointed that their income and crop
yield were reduced due to disasters faced by them with first rank. A little more
than two third of the respondents among those who faced disaster reported that the
loss caused by drought was to a great extent. It was found that majority of the
respondents fell under the category of low vulnerability.
5.2 Dependent variables 5.2.1 Perception of farmers about climate change
Findings on farmer’s perception regarding change in climate indicated that
almost three forth of the respondents perceived the timing of rain onset has
increased and more than 83 per cent of the respondents were reported that rainy
days frequency has decreased. They have been experiencing no change (58.75%)
in total amount of precipitation over the past 15 years. The majority of farmers
(76.67%) believed that the minimum temperature in winter season had increased.
Decreasing trend in number of cool days was reported by 75.42 per cent of the
respondents. Furthermore, about 76 per cent of the respondents said that maximum
temperature in summer is increasing, while, nearly three forth of them were
responded that duration of summer season has increased. Majority of the
respondents (61.25%) in study area perceived high changes in climatic condition in
rainy season due to changing rainfall patterns like shifting of timing of rain onset
& withdrawal, increasing trend in dry spell frequency and decreasing trend in rainy
days frequency. Moreover, about 63 per cent of the respondents perceived high
level of changes in climatic condition in winter season because they felt that
minimum & maximum temperature in winter has increased and number of cool &
164
heavy fog days has decreased. Nearly 67 per cent of the respondents reported that
high level of changes occurred in summer season due to increasing trend in
minimum & maximum temperature, duration of season and number of hot days.
5.2.2 Impact of climate change on agriculture and allied
activities
5.2.2.1 Impact of long term climate change
As per the past experiences majority of the respondents (86.25%) agreed
that due to climate change, investment in agriculture has increased. This is mainly
due to more infestation of insects & diseases on crop and more expenses on
irrigation water. About 82.92 per cent of them said that cropping pattern has
changed and almost half of the respondents believed that due to climate change
area of some crops like minor millets, sesame, pigeon pea, maize, joar etc. in kharif
and linseed, lathyrus, lentil etc. in rabi has decreased, on the other hand 33.75 per
cent of them were disagreed with that. The results revealed that majority of
respondents (86.30%) agreed, over the past 15 years migration of birds and
animals has increased due to climate change, while, 82.92 per cent believed that
climate change has increased drudgery of farmers/farm women. It was also
perceived by a substantial percentage of respondents that the change in climate has
resulted in scarcity of fodder in the area, increased human health problems and air
pollution. The results indicated that majority (36.67%) of the respondents
perceived medium level of overall impact of long term climate change.
With regards to various varieties grown by respondents there was drastic
change in 15 years, local varieties like Gurmatia, Mundaria, Kanthbhulaw,
Nankeshar, Bhejri, Asamchudi etc. were grown by 86.25 per cent of the
respondents 15 year back which has confined to only 1.67 per cent of the
respondents with varieties like Gurmatia, Nankeshar, Asamchudi etc. at present.
As for improved variety of paddy, Safari was most preferred variety 15 years back.
In kharif season other than paddy, crops like kodo (minor millet), pigeon pea,
sesame and moong/urd were grown by the considerable number of respondents 15
years back which has reduced at present. Only soybean growers were in increasing
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trend during previous 15 years. In rabi season drastic change occurred in number
of lathyrus grower farmers which was reduced from 55.00 to 34.17 per cent during
last 15 years.
5.2.2.2 Impact of short term climate change
In case of timely (15 June) arrival of monsoon, long duration variety Swarna
(145 days) was grown in about 174.44 ha of land out of 553.70 ha of total land of
farmers in study area that was increased by (+) 20.57 and (-) 16.60 per cent in case
of early and late arrival of monsoon. Area of medium duration (120-125 days)
varieties MTU-1010 and Mahamaya were 136.35 ha and 83.54 ha that were
decreased by 9.01 & 9.83 per cent and increased by 8.32 & 10.99 per cent with
respect to early and late arrival of monsoon, respectively.
Farmers were grown paddy in 553.60 ha out of 583.70 ha of total cultivable
land which was changed by -0.50 and +0.48 per cent in case of deficit and surplus
precipitation in kharif. Lathyrus was major effected crop in rabi season which was
grown as relay crop in matured paddy field to utilize excess moisture in renfaid
condition. In case of deficit rainfall in kharif, area covered under Lathyrus crop
was decreased by 49.51 per cent and increased by 55.63 per cent when amount of
precipitation was surplus. In rabi season area of gram was increased by 18.46 per
cent and decreased by 29.15 per cent with deficit and surplus amount of
precipitation.
It was found in study that Echinicloa colonum was reported as major weed
of paddy by 110 out of 240 respondents of study area in case of normal arrival of
monsoon, which get more favorable conditions for its infestation with early arrival
as reported by 226 respondents with change of +105.45 per cent. Paddy weds like
Ischeamum rugosum and Agropyron repens was increased with early arrival and
decreased with late arrival of monsoon. With regards to insects, BPH/GLH was
reported as major insect of paddy by 106 respondents when monsoon arrived in its
normal time as against 170 and 67 respondents in case of late and early arrival.
Majority of the respondents (205) said that paddy crop got more infested with
insect like leaf folder in case of late arrival of monsoon and its infestation was
166
negligible when monsoon arrives earlier. It was reported by respondents that
disease like blast and leaf blight was major problem in paddy for 138 and 125
respondents in case of late arrival as against 58 and 13 respondents in case of early
arrival of monsoon, respectively.
Majority of the respondents shifted from more than three ploughing to less
than 3 ploughing to 3 ploughing in both the cases of early and late arrival of
monsoon. With early arrival of monsoon number of farmers of Lehi Method
increased by (+) 177.36 per cent and farmers of line sowing method decreased by
(-) 66.67 per cent. In case of late monsoon area under transplanting method
decreased by (-) 19.05 per cent, which was mainly shifted in lehi method, line
sowing method and broadcasting/biasi method. Out of total 229 broadcasting
farmers 198 farmers applied seed in recommended quantity which was reduced by
174 and 103 farmers with change per cent of (-) 12.12 and (-) 47.98 with early and
late arrival of monsoon, respectively.
A total of 176 transplanting farmers, 158 applied seed in recommended
dose which decreased up to 132 and 79 in case of early and late arrival of
monsoon, this decrement was mainly shifted in group of farmers who were
applying seed with increased rate. In short it can be say that majority of farmers
increase seed rate with late arrival of monsoon in both the method of sowing. Biasi
is main practice in broadcasting method of sowing. Out of 229 biasi farmers 183
performed biasi in proper time when monsoon arrives timely, but in case of early
arrival it increased and decreased with late arrival of monsoon.
5.3 Coping mechanism/adaptation
As an adaptation to excess rainfall the majority of respondents delayed
sowing dates. This change in sowing date was adopted by 68.33 per cent of the
farmers in study area. Majority of the informants (62.08%) believed that use of
short duration varieties might be beneficial if there was excess rainfall at the time
of sowing of paddy. Sowing by lehi method, put harvested paddy (Karpa) on
bunds for drying, turn harvested paddy (Karpa palatna) several times for drying,
double sowing and gap filling were cited by the respondents as a core strategy to
167
deal with excess rainfall during various stages of crop period. A quite number of
farmers believed in preparation of channels inside the field to drain excess water,
keeping harvested paddy (Karpa) on big size of stubbles and prepare more
seedlings than required.
As an adaptation to deficit rainfall at the time of sowing, the majority of the
farmers (70.41%) delayed sowing dates. Increase seed rate (54.58%), use short
duration varieties (49.17%) use dry seeding method (30.41%), use different
varieties (24.17%) and crop diversification were other main coping strategies used
by the respondents in study area to reduce the risk of crop failure. It was mentioned
by 42.50 per cent of the farmers that they are dependent on canal for irrigation,
while, 31.25 per cent of them arrange irrigation water from storage water tank by
using diesel pump.
5.4. Constraints in adaptation
The major constraints to coping to climate change faced by farmers in the
study area included lack of information about accurate weather forecast (68.33%),
irregularity of extension services (66.25%) and lack of knowledge about need
based improved agriculture technologies (64.58%) with rank of I, II and III,
respectively. Moreover, findings of the study indicate that other major constraints
reported by respondents were lack of information about climate change (61.67%),
lack of resources (48.33%), unavailability of inputs on proper time (46.67%), lack
of believe on current weather forecast system (43.75%) and inadequate supply of
irrigation water in canal (42.50%).
5.5 Suggestion
To overcome the above constraints, the majority (65.83%) of the
respondents suggested that weather forecast should be more accurate and timely,
whereas, 63.33 and 57.91 per cent of them said that effective extension services
should be available to the farmers and proper information should be provided
about climate change which might be enable them to adapt against climate change.
The other suggestions given by respondents were efforts should be made to create
awareness among the people about the effect of climate change and its
168
consequences (53.33%), training should be imparted to build the capacity for better
adaptation (42.50%) and government policies should be made to support the
farmers during natural calamities.
5.6 Conclusion
It was found in the investigation that farmers’ in the study area were able to
recognize that temperatures have increased, intensity of winter decreased and there
has been a fluctuation in the rainfall pattern. So the present study disproved the
hypothesis that climate change is merely a hoax as most of the sample population
has experienced some changes in relation to different climatic phenomenon over
the last few years. There was limited awareness, knowledge and capacity at local
level to understand climate change scenarios, address issues, and conduct long-
term planning. Coping strategies and adaptation mechanism were limited at the
study site. So, to solve the problem of climate change at first we have to create
awareness among the farmers by using mass media followed by individual contact
method through trained extension agents. In addition, empowerment is crucial in
enhancing farmers’ awareness. This is vital for adaptation decision making and
planning. Combining access to extension and credit ensures that farmers have the
information for decision making and the means to take up relevant adaptation
measures. Farming experience and access to education were found to promote
adaptation. Agriculture was the main source of livelihood of the farmers in study
area and that was most vulnerable section due to climate change because majority
of the farmers in the study area were relied on rainfed agriculture, while
considering risky, mono-cropping practicing under dry land. Government policies
should therefore ensure that farmers have access to affordable credit to increase
their ability and flexibility to change production strategies in response to the
forecasted climate conditions. Because access to water for irrigation increases the
resilience of farmers to climate variability, irrigation investment needs should be
reconsidered to allow farmers increased water control to counteract adverse
impacts from climate variability and change. Furthermore, government should
improve off-farm income-earning opportunities. There is urgent need to undertake
the steps towards awareness increasing programs regarding future unavoidable
169
impacts of climate change and strategies to cope with its adverse effect on
agriculture and allied activities.
5.7 Suggestions for future research work
Based on the findings obtained and experience gained from the present
investigation, the following suggestions can be drawn to improve the further
studies on climate change.
1. The present investigation was confined to eight blocks and twenty four
villages of four districts. The study needs to be replicated in large sample
covering all the major potential areas in Chhattisgarh, so that the inference
drawn can be generalized to a greater extent.
2. The present study concentrated on impact of climate change on agriculture
especially paddy crop in kharif and some selected major crops in rabi
season. So similar studies may be conducted for other crops and allied
sectors like horticulture, fisheries, animal husbandry etc.
3. The study was conducted with some selected independent variables which
may be limited to determine the farmer’s perception on climate change and
its impact on agriculture and allied activities. Thus, the future study may be
made more comprehensive by incorporating some additional attributes.
4. A location wise action-research must be conducted to identify and
document climate change impacts and adaptation strategy. Because the
local observations may provide a clear direction for future strategies and for
development planning and adaptation management programs in different
ecological regions.
170
Fa
rmer
s’ p
erce
ptio
n ab
out c
limat
e ch
ange
and
its i
mpa
ct o
n ag
ricu
lture
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ed a
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eptio
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farm
ers a
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ate
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n ag
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171
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Appendices
APPENDIX – A
NNfRrlx<+ ds eSnkuh {¨= ds d`"kdksa dk tyok;q IkfjorZu ij jk; rFkk bldk d`f"k ,oa vU; fdz;kv¨a ij izHkko¨a dk v/;;Uk
ekxZn'kZd MkW ,e ,y 'kekZ
izkË;kid
'k¨ËkdrkZ dk uke v¨eizdk'k ijxfugk ih- ,+- Mh- LdkYkj
d`f"k foLrkj foHkkx] ba--xk--d`--fo--fo--] jk;iqj ¼N--x--½
1-- Ñ"kd dk uke ------------------------------------------------------- e®‐ua‐----------------------------------------xzke -------------------
---------------------- fo-[k- ---------------------------------------------------------------------- ftyk ---------------------------------
2-- Ñ"kd dh vk;q ¼o"kkZs esa½ ------------------------------ 3- tkfr -----------------------------------------------------------------
4- eqf[k;k dk fyax % L=h@iq:"k 5-- f'k{kk dk Lrj ------------------------------------------------
7-- ifjokj ds lnL;ksa dh la[;k % iq:"k ------------------------------L=h ------------------------------dqy --------------
8-- ifjokj dk izdkj % la;qDr@,dkdh 6-- Ñf"k dk;Z djus dk vUkqHko ¼o"kkZs esa½ ------
9- lkekftd Hkkxhnkjh ¼v½ D;k vki fdlh laLFkk@laxBu esa lnL;rk j[krs gS & gk¡@ugha ¼c½ ;fn gk¡ rks fuEufyf[kr tkudkjh nhft,A
Ø-- laLFkk dk uke gkW@ugha lnL;rk Hkkxhnkjh dk Lrj lnL; inkf/kdkjh ges'kk dHkh&dHkh dHkh
uugha 1 xzke iapk;r 2 xzke lHkk 3 lgdkjh laLFkk 4 Ldwy 5 Lo- lgk;rk leqg 6 lkaLÑfrd leqg 7 ;qod Dyc 8 vU;
10- viuh Hkwfe lEcfU/k fuEu fooj.k nhft,A
Ø-- Hkwfe dk fooj.k flafpr vflafpr dqy
1 Loa; dh Hkwfe 2 yht ds :i esa nh xbZ 3 yht ds :i esa yh xbZ 4 dqy Hkwfe ¼d`f"k ;¨X;½
191
111- Ñi;k flapkbZ ds L=ksr ,oa flapkbZ ds Lrj ds ckjs esa tkudkjh nhft,A
Ø-- ekSle flafpr {¨=Qy ¼¼,dM+ esa½
¼%½½ flapkbZ dk LL=ksr*
¼%½½
1-- [kjhQ 1- /kkUk
2- vjgj 3- eDdk 4- ------------
2 jcch 1- xsag¡w
2- puk 3- ------------
3 tk;n 1- Ewakx
2- mM+n 3- ------------
*flapkbZ ds L=ksr % 1- uydqi 2- dw¡vk 3- ugj 4- Rkkykc 5- vU;
12- fiNys o"kZ mxkbZ QlYk¨a ds ckjs esa fuEu tkudkjh nhft, A
Ø-- Qly {¨=Qy ¼¼,dM+½
mRiknDrk ¼¼fDo-@@,-½½
dqy mmRiknu ¼fDo--½
miHk¨x dh xbZ ek=k ¼¼fDo-½½
csaph xbZ eek=k ¼fDo--½
vk; ¼¼:-½
1-- [kjhQ fla-- vfla-- fla-- vfla-- 1- /kkUk
2- vjgj 3- eDdk 4- ------------
2 Jcch 1- xsag¡w
2- puk 3- ------------ 4- ------------
3 tk;n 1- Ewakx
2- mM+n 3- ------------ 4- ------------
192
113- viuh vk; ls lacaf/kr fuEu Tkkudkjh nhft,A
ØØ-
fdz;k;sa
Ifjokj ds ffdrus lnL; lkfey gSa
ekg es afdrus ffnu dk;Z djrs
gSa
o"kZ es afdrus ekg dk;Z djrs gS
nj izfRkfnu ¼:-½
dqy [[kPkZ ¼:--½
'kq) vvk; ¼:--½
efgyk iq:"k efgyk iq:"k efgyk iq:"k efgyk iq:"k 1 Ñf"k Ektnwjh 2 i'kqikyu 3 ukSdjh 4 /ka/kk@O;olk; 5 Ektnwjh 6 fuekZ.k dk;Z 7 vU; 8 9
14- fofHkUu enksa ij fd;s tkus okys okf"kZd O;; dk fooj.k nsaA
Ø-- Ekn O;; ¼:--½ O;; dk iizfr'kr 1 [kkn~; lkexzh Pkk¡oy Xksag¡w nky rsy 'kCth elkys Qy vU; 2 Ñf"k 3 i'kqikyu 4 f'k{kk 5 fpfdRlk 6 bZa/ku 7 vkink fu;a=.k 8 R;kSgkj@ lkekftd dk;Z 9 Ekuksjatu 10 Qly chek gsrq 11 'kjkc 12 rackdq@fcfM+@xqV[kk 13 vU; dqy
193
115- Ñf"k ;a=ksa dh miYkC/krk ds ckjs esa fuEu tkudkjh nhft,A
Ø-- Ñf"k ;a= dk uke gk¡@ugha ekfydkUkk gd
dqy [kpZ 'kq) vk; Loa; dk fdjk;s ls yh xbZ
1 VsªDVj 2 lhM Mªhy 3 IykÅ 4 gkjosLVj 5 Lizhadyj lsV 6 Mªhi fLkLVe 7 Mhty iai 8 Lizs;j@MLVj 9 10
16- D;k vki us d`f"k@vU; fdz;kv¨a ds fy;s _.k fYk;k gS ;fn gk¡ rks fuEu tkudkjh nhft,A
Ø-- _.k dk L=ksr _.k dh jkf'k ¼:-½
_.k ysus dk dkj.k*
_.k dk lgh ddk;Z esa mi;ksx
¼%½½
_.k vvknk;xh**
1 cSad 2 lgdkjh laLFkk,a 3 nqdkunkj@VsªMj 4 Lkkgqdkj 5 v'kkldh;@xSj
ljdkjh laLFkk,a
6 fe=@nksLr@fjLrsnkj 7 vU;
* 1- d`f"k ;a= gsrq 2- d`f"k vknku gsrq 3- tehUk [kjhnU¨ gsrq 4- fuekZ.k dk;Z gsrq 5- fookg gsrq 6- bZYkkt gsrq 7- lkekftd dk;Z gsrq
** 1. uxn 2- oLrq 3- n¨Uk a
17- D;k vki orZeku esa py jgs ljdkjh ;kstukvksa ls ykHkkfUor gks jgs gS ;fn gk¡ rkss fuEu tkudkjh nhft,A
Ø-- ljdkjh ;kstuk dk uke fdl izdkj ykHkkfUor gks jgs gSA 1 2 3 4
194
118- cktkj miYkC/krk ds ckjs esa fuEu tkudkjh nhft,A ¼v½ cktkj dh xkao ls nwjh tgk¡ ls vki Ñf"k vknku izkIr djrs gS --------------fdeh- ¼c½ Ñf"k vknku@;a=ksa dh miyC/krk ds ckjs esa fuEu tkudkjh iznku dhft,A
ØØ-
vvknku@;a=
ggk¡@ugh
cktkj ttgk¡ ls izkIr
ddjrs gSA
xk¡o ls nwjh ¼fdeh-½
le; ij miyC/krk dk Lrj vklkuh
ls miyC/k
dqN iijs'kkuh ds
lkFk
vf/kd ddfBukbZ
ls 1 mUur cht 2 [kkn ,oa moZjd 3 dhVuk'kh]
jksxuk'kh ,oa [kjirokjuk'kh
4 y?kq Ñf"k ;a= 5 vU;
19- Qlyksa dk chek djkrs gS ;fn gk¡ rks fUkEu tkudkjh iznku dhft,A
ØØ- Qly dk Ukke {ks=Qy ¼,dM+½ {ks= ¼%½½ 1 /kku 2 yk[kM+h 3 Pkuk 4 xsagw¡ 5 vU;
20- ffofHkUu LFkkuksa ij Hkze.k dh foLr`r tkudkjh nhft,A
Ø-- Hkze.k dk vfHkizk; Hkze.k dk LFkku Hkze.k dh vkofr lnSo lkekU;r% dHkh&dHkh
1 Ñf"k 2 O;fDrxr@?kjsyq 3 Ekuksjatu 4 lkekU;@vU;
21- izlkj dk;ZdrkZvks ls vius laidZ ds ckjs esa tkudkjh nhft,A
Ø-- izlkj dk;ZdrkZ laidZ dk Lrj dHkh ugha dHkh&dHkh ges'kk
1 xzkeh.k Ñf"k foLrkj vf/kdkjh 2 Ñf"k fodkl vf/kdkjh 3 fo"k; oLrq fo'ks"kK 4 Ñf"k oSKkfud 5 xSj ljdkjh laxBu 6 Ñf"k vknku foØsrk
195
222- fofHkUu ekl fefM;k L=ksrksa ds mi;ksx dh tkudkjh iznku djsaaA
Ø-- ekl fefM;k LL=ksr
gk¡@ugh mi;ksx dk mn~ns'; * mi;ksx dk Lrj lnSo dHkh&dHkh dHkh ugha
1 Vsyhfot+u 2 jsfM;ks 3 lekpkj i= 4 Ñf"k if=dk;sa
* 1- Ñf"k laca/kh dk;ZØe 2- lekpkj 3- euksjatu 4- foKkiu 5- Ñf"k laca/kh ys[k
23- D;k vki ekSle iwokZuqeku dh tkudkjh izkIr djrs gS & gk¡@ugh ;fn gk¡ rks fuEu tkudkjh iznku dhft,A
Ø-- lwpuk dk L=ksr lwpuk dh izkfIr lwpuk dh mi;ksfxrk
ges'kk dHkh&dHkh dHkh ugha iw.kZr% e/;e vkaf'kd Ukgha 1 jsfM;ksa 2 jk"Vªh; Vh-Ogh-
pSuyksa ls
3 LFkkuh; Vh- Ogh- pSuyks ls
4 lekpkj i= 5 izlkj dk;ZdrkZ 6 nksLr@fjLrsnkj 7 vU;
24- Ñi;k vki Ñf"k izlkj dk;ZØeksa ds ckjs esa viuh tkudkjh rFkk mlesa Hkkxhnkjh ds ckjs esa mRrj iznku djsaA
Ø-- izlkj dk;Z Tkkudkjh ¼¼gk¡@ ugha½
Hkkxhnkjh dk Lrj ges'kk dHkh&dHkh dHkh ugh
1 izf'k{k.k dk;ZØe 2 izn'kZu 3 iz{ks= fnol 4 iz{ks= Hkze.k 5 lkeqfgd ifjppkZ 6 izn'kZuh 7 fdlku esyk 8 vU; 9
196
225- fuEufyf[kr oDrO;ksa ds ckjs esa vki D;k lksprs gSA ¼tksf[ke ogu {kerk½
ØØ-
oodrO;
lgefr dk Lrj iw.kZr% llger
lger r; uugha
vlger iw.kZr% vvlger
1 fdlkuksa dks de tksf[ke okys de ykHk iznku djus okys dk;ksZ dh rqyuk esa vf/kd tksf[ke ijUrq vf/kd ykHk okys voljksa ds ckjs esa lkspuk rFkk dk;Z djuk pkfg,A
2 Ñ"kd tks vf/kd tksf[ke ysus ds fy, vxzlj jgrs gS os lkekUr% vU; Ñ"kdksa dh rqyuk esa vkfFkZd :i ls vf/kd lger jgrs gSA
3 Ñ"kd rHkh tksf[ke ysrs gS] ;fn fdlh dk;Z esa lQyrk ds volj vf/kd gSA
4 fdlh mUUkr i)fr dks Ñ"kd }kjk u viukuk mfpr gS ;fn og vU; Ñ"kdksa }kjk lQyrk iwoZd u viuk;k x;k gSaA
5 Ñf"k lacaf/k u;h i)fr dks viukuk tksf[ke Hkjk dke gS exj Qk;nsean gSA
6 vfuf'pr tyok;q ds bl nkSj esa tksf[ke ls cpus ds fy, Ñ"kdksa dks ,d ;k nks Qlyksa dh rqyuk esqa vf/kd Qly fdLe yxkuk pkfg,A
26- fuEu oDrO;ksa ds ckjs esa viuh lgefr ;k vlgefr n'kkZ,A ¼ufoÑr Ñ"kd½
ØØ-
oodrO;
lgefr dk Lrj iw.kZr% llger
lger r; uugha
vlger iw.kZr% vvlger
1 eSa Ñf"k lacaf/kr mUUkr rduhd ds ckjs esa ges'kk tkx:d jgrk gw¡ exj bldk eryc ;s ugha gS fd gj i)fr miuk;k tk;A
2 vkt dy cgqr lh mUur Ñf"k i)fr;ksa dh ckr gksrh gS] exj dkSu tkurk gS fd ;g iqjkuh i)fr;ksa ls T;knk ykHknk;d gSA
3 fdlh ubZ i)fr ds ckjs esa lqudj eSa rc rd 'kkUr ugha cSBrk tc rd mls viuk uk ywaA
4 le;≤ ij eSSaus cgqrlkjh mUur i)fr;ksa ds ckjs esa tkuk gS rFkk fiNys dqN o"kkZs esa muesa ls cgqrksa dks viuk;k Hkh gSA
5 mUur i)fr;ksa dks viukus ls iwoZ eSa vius iM+kslh Ñ"kdksa ls mldk ifj.kke tkuus dk bPNqd jgrk gw¡A
6 dqn gn rd eSa fo'okl djrk gw¡ iqjkuh Ñf"k i)fr;ka csgrj gSA
7 ubZ Ñf"k i)fr;ksa dks viukus ds izfr eSa ges'kk
197
ltx jgrk gw¡A 8 gekjs iwoZt Ñf"k ds izfr T;knk bekunkj Fks vr%
eq>s dksbZ dkj.k ugha yxrk fd bu iqjkuh Ñf"k i)fr;ksa dks cnyk tk;A
9 ges'kk ubZ i)fr;ka lQy@Qk;nsean ugha gksrh gS ;fn mfpr gS rks fcYdqy eS mls miukrk gw¡A
227- ffuEu oDrO;ksa ds ckjs esa vki D;k jk; j[krs gSA ¼Scintific Orientation½
ØØ-
ooDrO;
lgefr dk Lrj iw.kZr% llger
lger r; uugha
vlger iw.kZr% vvlger
1 Ñf"k dh mUur ,oa u;h i)fr;ka iqjkuh i)fr;ksa dh rqyuk eas vPNs ifj.kke nsrs gSA
2 gekjs iwoZt tks Ñf"k i)fr viukrs Fks os vkt Hkh cgqr vPNs rFkk egRoiw.kZ gSaA
3 Ñ"kdksa ds ikl cgqr vPNs Ñf"k vuqHko gksus ds ckn Hkh ifjorZuksa ds vuq:i ubZ i)fr;ksa dks viukrs jguk pkfg,A
4 lEHkor% Ñ"kdksa ds fy, ubZ i)fr;kas dks lh[kuk rFkk vius [ksrksa esa mi;ksx djuk eqf'dy Hkjk dk;Z gS ijUrq ;s Qk;nseUn rFkk mi;ksxh gSA
5 vPNs Ñ"kd ubZ i)fr;ksa dks vius [ksrksa esa iz;ksx djrs jgrs gSA
6 Ñ"kdksa dks vius thou Lrj esa lq/kkj ds fy, Ñf"k laca/kh iqjkuh rduhd ds LFkku ij ubZ i)fr;ka viukuh pkfg,A
28- Ñf"k fØ;kvksa laca/kh fofHkUu fu.kZ; dkSu ysrk gSA
dz-- Ñf"k fØ;kdyki Lo;a iRuh n¨u¨ iwjk
iifjokj ifjokj@
nn¨Lr@fjLrsnkj 1 Qly ,oa iztkfr dk p;u 2 Qly i)fr laca/kh fu.kZ; 3 Hkwfe dh rS;kjh 4 [kkn~; ,oa moZjd dk mi;ksx 5 jksx ,oa dhVuk'kd ds iz;ksx laca/kh 6 Ñf"k vkStkj [kjhnus ds laca/k esa 7 mRikn cspus ds laca/k esa 8 Ñf"k dk;Z gsrq yksu ysus ds laca/k esa 9 Ñf"k dk;kZs gsrq le; fu/kkZj.k ds laca/k
esa
10 i'kqikyu ds laca/k esa
198
229- D;k vkius yEcs le; ls tyok;q esa fdlh izdkj dk ifjorZu vuqHko fd;k gSA gk¡@ugha ;fn gk¡ rks fuEu tkudkjh iznku djsaA
¼v½ D;k vkidks tyok;q ifjorZu ds ckjs esa Kku gS\ gk¡@ugha ¼c½ D;k vkius 'kCn Xykscy okfeZax dk uke lquk gS\ gk¡@ugha ¼l½ fuEu ?kVukvksa ls lacaf/kr viuh tkx:drk ds Lrj ds ckjs esa tkudkjh iznku djsaA
Ø-- Ekn gk¡@uugha
tkx:drk dk Lrj iw.kZr% vkaf'kd Ukgha
1 ekSle xeZ gksrh tk jgh gSA 2 tyok;q esa vfuf'Pkrrk c<+ xbZ gSA 3 fofHkUu ekSleksa dh vof/k ifjofrZr gks jgh gSA 4 vizR;kflr@vuqfpr tyok;qoh; ?kVukvksa dh vko`fr
c< jgha gSA
5 [kjkc ekSle dh otg ls Qlyksa ds [kjkc gksus dh ?kVuk;sa c<+ jgh gSA
6 okrkoj.k es iznq"k.k dk Lrj c<+ jgk gSA 7 BaMs izns'k¨a esa cQZ ds pV~Vku rhoz xfr ls fi?ky jgh
gSA
8 leqnz¨ esa pØokr dh vko`fr c<+ jgh gSA 9 leqnz esa ikuh dk Lrj c<+ jgk gSA 10 izkÑfrd vkink;sa fnu c fnu c<+ jgk gSA 11 tyok;q ifjofrZr ls euq";ksa esa LokLF; lacaf/k leL;k;sa
c<+ jgh gSA
12 tyok;q ifjofrZr ls i'kqvksa esa LokLF; lacaf/k leL;k;sa c<+ jgh gSA
30- viuh vuqHkoks ds vk/kkj ij ekSle vuqlkj fofHkUu ekSleh@tyok;qoh; ?kVukvksa@ cnykvksa ds ckjs esa fuEu tkudkjh iznku djsaA
Ø-- ekSleh ?kVuk;sa@cnyko ugha ddg ldrs
dksbZ iifjorZu ugha
deh ggqbZ@iwoZ
c<+ xxbZ@ nsjh ls
A oo"kkZ _rq 1 ekulqu dk vkxeu 2 ekulqu dk izLFkku 3 ekSle dh vof/k 4 fcuk o"kkZ okys fnuks dh la[;k 5 lq[ks iM+us dh vko`fr 6 o"kkZ okys fnuks dh vko`fr 7 ckjh'k dk fo"ke foHkktu 8 vfuf'Pkr o"kkZ
199
9 ckjh'k dh ek=k 10 cnyh okys fnuksa dh la[;k 11 /kqi okys ?kaVks dh la[;k 12 [ksrksa esa ck<+@[ksrh okys tehuksa esa
tyHkjko
13 vknzrk dh ek=k B ''khr _rq 1 'khr _rq vkjaHk dk le; 2 'khr _rq izLFkku dk le; 3 tkM+s dh rhozrk 4 'khr _rq esa U;qure rkieku 5 'khr _rq esa vf/kdre rkieku 6 'khr _rq dh vof/k 7 tkM+s okys fnuks fd la[;k 8 rhoz tkM+s okys eghus 9 rhoz /kqi okys fnuks dh la[;k 10 rhoz dksgjs okys fnuks dh vko`fr 11 'khr _rq esa o"kkZ 12 BaMh ok;q izokg@rjax C xxzh"e _rq 1 xzh"e _rq esa U;qure rkieku 2 xzh"e _rq esa vf/kdre rkieku 3 xzh"e _rq dh lqjokr@vkjaHk 4 xzh"e _rq dk izLFkku 5 ekSle dh vof/k 6 Rkhoz xehZ okys nhuksa dh la[;k 7 yq dh rhozrk 8 'kjhj esa fpiphikiu@ilhus dk vkuk 9 rhoz xehZ okys eghus 10 xehZ ls 'kjhj esa pqHku 11 xzh"e _rq esa ckjh'k
D vvU; ?kVuk;sa 1 ok;q iznq"k.k 2 vka/kh rqQku dh vko`fr 3 4 5 6
200
331- fiNys 15 lky ds vuqHkoksa ls vki vius thou ;kiu ds ckjs D;k vuqHko djrs gS A 1- csgrj gqbZ gS A 2- dksbZ ifjorZu ughA 3- cnrj gqbZ gSA
32- vkius foxr 15 o"kksZ esa dksbZ vkink,a@foifÙk;k eglql fd;k gSA gk¡@ugh ;fn gk¡ rks fdu vkinkvksa dk lkeuk fd;k gSA
vkinkvkas dk izdkj uqd'kku @ gkfu dk izdkj
uqd'kku dk Lrj vkinkvkas ls cpus ds mik;
eghuksa esa vvkinkvkas dh
vof/k vf/kd e/;e de
ck<+
vfuf'pr ckfj’k
lq[kk
dhV @fcekjh;ksa dk izdksi
vksyk @ikyk
vka/kh @ rqQku
[kkn~; iznkFkksZ @pkjk dh deh
fcekjh
Pkksjh
var%@varj leqnkf;d dyg
okrkoj.kh; iznq"k.k
------- ------
uqd'kku dk izdkj% 1- ukSdjh@O;olk; [kksuk 2- vk; es deh 3- ifjokj ds lnL;ks dks uqd'kku@[kks nsuk 4- ?kj dks uqdlku 5- ikuh ds L=ksar dks uqdlku 6- Qlyks dks uqd'kku 7- i'kqv a dks uqd'kku
uqd'kku dk Lrj % 1- vf/kd 2- e/;e 3- de
vkinkvkas ls cpus ds mik;% 1- cpr 2- iÍs@fxjoh es tehu j[kuk 3- tehu cspuk 4- i'kq/ku cspuk 5- laifÙk cspuk 6- dtZ 7- miHkksx de djuk 8- ljdkjh lg;ksx@vuqnku 9- iyk;u
201
333- foxr iwoZ o"kksZa eas tyok;q esa ifjorZu ds dkj.k vkiusa fofHkUu Ñf"k fØ;kvksa esa D;k ifjorZu vuqHko fd;k gSA
Ø-- odrO; iw.kZr% llger
Lkger r; uugha dj lldrs
vlger iw.kZr% vvlger
1 tyok;q ifjorZu ds dkj.k fofHkUu Qlyksa dk mRiknu de gqvk gSA
2 ijEijkxr Qly iztkfr;ksa dk mi;ksx de gqvk gSA
3 Qlyksa ds Qwy ,oa Qy yxus ds le; esa ifjorZu gqvk gSA
4 Qlyksa ds dVkbZ dk le; ifjorZu gqvk gSA
5 Qyksa@Qlyksa ds idus dk le; ifjorZu gqvk gSA
6 [kk| Qlyksa dh xq.k@DokfyVh esa fxjkoV vkbZ gSA
7 Qly i)fr ifjorZu gqbZ gSA 8 tyok;q ifjorZu dh otg ls flapkbZ ds
fy, ty dh miyC/krk de gqbZ gSA
9 tyok;q ifjorZu ds dkj.k Hkwfexr ty L=ksrksa ,oa Lrj esa deh vkbZ gSA
10 tyok;q ifjorZu ds dkj.k [ksrksa esa ty Hkjko dh ?kVuk c<+h gSA
11 rkts Qyksa ,oa lfCt;ksa dh miyC/krk de gqbZ gSA
12 tyok;q esa ifjorZu ds dkj.k Qlyksa esa dhVksa ,oa chekjh;ksa dk izdksi c<+k gSA
13 dhVksa@fcekjh;ksa ¼Qlyksa esa½ dh fofHkUu ubZ fdLeksa dk inkiZ.k gqvk gSA
14 [kjirokjksa dh fofHkUu ubZ fdLe fn[kkbZ nsus yxs gSA
15 ekSleh [kjirojksa dk izdksi Qlyksa esa c<+k gSA
16 [kjirkokjksa@dhVksa @chekjh;ksa ds c<+okj ds fy, ekSle mi;qDr gksrh tk jgh gSA
17 tyok;q ifjorZu ds dkj.k fofHkUu Qlyksa ,oa tkuojksa dh iztkfr;ka yqIr gksrh tk jgh gSaA
18 oukPNkfnr {ks=Qy de gksrk tk jgk gSA 19 tyok;q ifjorZu ds dkj.k fuf'pr LFkku
202
ij ik;s tkus okys taxyh isM+ks ,oa i'kqvksa dh iztkfr ifjofrZr gks xbZ gSA
20 tyok;q ifjorZu taxyksa ds mTkkM+ dk ,d dkj.k gSA
21 tyok;q ifjorZu ds dkj.k e`nk vijnu c<+ jgk gSA
22 tyok;q ifjorZu ds dkj.k [ksrh esa [kpZ c<+rk tk jgk gSA
23 tyok;q ifjorZu fofHkUu {ks=ksa esa pkjk dh deh dk dkj.k gSA
24 tyok;q ifjorZu ds dkj.k i'kq/ku ds O;ogkj esa ifjorZu gksrs tk jgk gSa
25 tyok;q ifjorZu i'kq/ku ds LokLFk esa foijhr izHkko Mky jgk gSA
26 ufn;ksa esa eNfy;ksa dh iqjkuh iztkfr;ka yqIr gks jgh gS rFkk u;h itkfr;ka ns[kh tk jgh gSA
334- tyok;q ifjorZu dk vki fuEu ?kVukvksa@enksa@phtksa ij D;k izHkko ns[krs gS\ Ø-- odrO;@en iw.kZr%
llger lger dg
uugha ldrs
vlger iw.kZr% vvlger
1 tyok;q ifjorZu ds dkj.k HkkSfrd lk/kuksa esa fuos'k c<+ jgk gSA
2 tyok;q ifjorZu ds dkj.k yksxksa dk thou Lrj izHkkfor gksrk tk jgk gSA
3 [kk| inkFkksZ dh deh dk ,d otg tyok;q ifjorZu gSA
4 tyok;q ifjorZu ds dkj.k yksx xaHkhj chekjh;ksa ls xzflr gks jgs gSA
5 tyok;q ifjorZu ds dkj.k euq";ksa dk iyk;u c<+ jgk gSA
6 tyok;q ifjorZu ds dkj.k i'kq&if{k;ksa dk iyk;u c<+ jgk gSA
7 tyok;q esa ifjorZu ds dkj.k fofHkUu ty L=ksr lw[k jgs gSA
8 ihus dh ikuh dh miyC/krk de gks jgh gSA 9 ok;q iznq"k.k tyok;q ifjorZu ds dkj.k c<+
jgk gSA
10 ikuh dk iznq"k.k tyok;q esa ifjorZu ds dkj.k c<+ jgk gSA
11 tyok;q ifjorZu ds dkj.k vkidh dk;Z djus dh {kerk izHkkfor gqbZ gSaA
203
335- vYidkyhu ekSle ifjorZu ds lkFk vki d`f"k fØ;kvksa esa fdl izdkj ifjorZu djrs gS ;k dkSu lh d`f"k fØ;k, viukrs gSaA
1. /kku cqokbZ dh fof/k dkSu lh viukrs gSA
ccqokbZ dh fof/kgk¡@ugha ekulwu dk vkxeu
le; ls iwoZ (% {{ks«k)
le; ij (% {{ks«k)
nsjh ls (% {{ks«k)
a. fNVdko@C;klh i)fr
b. j¨ik i)fr
c. ysgh i)fr
d. drkj i)fr ls cokbZ
e. SRI i)fr ls cqokbZ
2. //kku dh fdl izdkj dh fdLe viukrs gS ,oa D;k fo'¨"krk gSi fuPkYkh Hkwfe ¼1½ ¼2½ ¼3½ ¼4½
ii lkekU; Hkwfe ¼1½ ¼2½ ¼3½ ¼4½
iii mPPk Hkwfe ¼1½ ¼2½ ¼3½
3. [[ksr dh rS;kjh ds llaca/k esa tkudkjh nhft,i fuPkYkh Hkwfe ii lkekU; Hkwfe iii mPPk Hkwfe 4. //kku dh cqokbZ fdl le; djrs gSAi fuPkYkh Hkwfe ii lkekU; Hkwfe iii mPPk Hkwfe
204
5. ££¢Rk dh rS;kjh g¢rq d`f"k ;a=ksa dk mi;ksxAi fuPkYkh Hkwfe ii lkekU; Hkwfe iii mPPk Hkwfe 6. ccht dh ek=k*i fuPkYkh Hkwfe ii lkekU; Hkwfe iii mPPk Hkwfe *ccht dh ek=k : a fu/kkZfjr cht nj ls de b fu/kkZfjr cht nj ds cjkcj c fu/kkZfjr cht nj ls vf/kd
7. [[kjirokj, dhV ,oa jksxksa d¢ izdksi d¢ laca/k esa tkudkjh nhft,A
a. [kjirokj dk izdksi ekulwu dk vkxeu
le; ls iwoZ** le; ij* nsjh ls**1 2 3 1 2 3 1 2 3
1
2
3
4
5
b. ddhM+ks dk izdksi 1
2
3
4
5
c. jjksxksa dk izdksi 1
2
3
4
5
* A=vf/kd B=lkekU; C=de D=ugha ** a= vf/kd b= lkekU;c de =d= ugha ¼ le; ij ekulqu vkU¨ dh rqyuk esa ½
205
8. C;;klh dj ikus ds laca/k esa tkudkjh nhft,A
a. ugha dj ikrs gS
b. le; ij djrs gSA
c. le; ds iqoZ djrs gSa
d. nsjh ls djrs gSA
99. ffunkabZ xqM+kbZ ds laca/k es tkudkjh nhft,A
a. ugha dj ikrs gSA
b. ;kf=dh rjhds ls djrs gSA
i. le; ij dj ikrs gSA
ii. djus es foyac gks tkrk gSA
c. jklk;fud rjhds ls djrs gSA
i. le; ij dj ikrs gSA
ii. djus es foyac gks tkrk gSA
110. [[kkn ,oa moZjdksa dk iz;ksx
a. fu/kkZfjr le; ijA
b. fu/kkZfjr le; ls iqoZ
c. fu/kkZfjr le; ds cknA
d. fu/kkZfjr ek=k ds cjkcj
e. fu/kkZfjr ek=k ls de
f. fu/kkZfjr ek=k ls T;knk
111. QQly dVkbZ ds laca/k es tkudkjh nhft,A
a. le; ij dj ikrs gSAb. dVkbZ djus esa foyac gks
tkrk gSAc. ;kaf=dh fof/k ls dVkbZ djrs
gSAd. gkjosLVj ds iz;ksx ls dVkbZ
djrs gSA112. mmRiknu ds laca/k es tkudkjh iznku djsaA mRiku ¼fDo-@,dM+½ HHkwfe dk izdkj : 11-- fuPkYkh 2- lkekU; 3- mPPk
206
336- /kku dh fofHkUUk voLFkkv¨ esa lq[kk iM+us dh fLFkfr es fUkEu iz'u® dk mRRkj iznku djsaA
dd`f"k fdz;k;sa
lq[kk iM+us dk le;
cqokbZ ds igys vadqj.k ds le; okLifrd o`f) dds le;
Qwy@Qy yxrs lle;
cqokbZ dh fof/k /kku dh fdLe eq[; dhV eq[; fcekjh eq[; [kjirokj mit esa deh ¼fDo-½
jch QlYk dk jdck ¼,dM+½
37- jch esa yxk;s tkus okyh Qlyksa ds jdck ¼,dM+½ d¢ ckjs esa tkudkjh iznku djsa
A. eekulqu dk vkxeu le; ls iwoZ QQly@iztkfr
[kjhQ esa v©lr o"kkZ dh ek«kk dee lkekU; vf/kd
a. jjch Qlyksa dk dqy jdck
b. yk[kM+h
c. puk
d. xsagw¡
e. ckM+h esa 'kCth
B. eekulqu dk vkxeu le; ij a. jjch Qlyksa dk dqy jdck
b. yk[kM+h
c. puk
d. xsagw¡
e. ckM+h esa 'kCth
C. eekulqu dk vkxeu nsjh ls a. jjch Qlyksa dk dqy jdck
b. yk[kM+h
c. puk
d. xsagw¡
e. ckM+h esa 'kCth
207
338- jch Qlyksa ds mRiknu ¼fDo-@,dM+½ ds ckjs esa tkudkjh iznku djsa
A. eekulqu dk vkxeu le; ls iwoZ QQly dk uke
[kjhQ esa v©lr oo"kkZ dh ek«kk dee lkekU; vf/kd
a. yk[kM+h
b. puk
c. xsagw¡
d. ckM+h esa 'kCth
B. eekulqu dk vkxeu le; ij a. yk[kM+h
b. puk
c. xsagw¡
d. ckM+h esa 'kCth
C. eekulqu dk vkxeu nsjh ls a. yk[kM+h
b. puk
c. xsagw¡
d. ckM+h esa 'kCth
39- foXkr 15 o"kksZ esa vkius yxk;s tkus okys Qlyksa] mRikndrk rFkk fdLe es D;k ifjorZu vuqHko fd;k gS d`i;k crk,A
Øa-- 15 o"kZ iwoZ orZeku Qly@@fdLe {kS=
¼¼%½½ mRikndrk ¼¼fDo-@@,-++½
Qly@@fdLe {kS= ¼%½½ mRikndrk ¼fDo--@,--+½
[kjhQ 1 /kku 1-- /kku
a. a. b. b.
c. c. 2 3 jcch 1 2 3 tk;n 1 2
208
440- fofHkUUk e©leh ÄVUkkv¨a dh iwoZ 15 o"kksZ esa iqujko`fRRk] g¨us okys uqd'kku rFkk cpko ds ckjs es foLr`r tkudkjh nhft, A
Ø-- e©leh ÄVUkk;sa iqujko`fRRk mit esa ddeh ¼fDo--@gS--½
cpko ds mik;
1 vfRk o`f"V
A cqokbZ ds igys
B cqokbZ ds le;
C cqokbZ ds ckn
D dVkbZ ds igys
E dVkbZ ds le;
F dVkbZ ds ckn
2 vYi o`f"V
A cqokbZ ds igys
B cqokbZ ds le;
C cqokbZ ds ckn
D dVkbZ ds igys
E dVkbZ ds le;
F dVkbZ ds ckn
3 vfUk;fer o`f"V
A cqokbZ ds igys
B cqokbZ ds le;
C cqokbZ ds ckn
D dVkbZ ds igys
E dVkbZ ds le;
F dVkbZ ds ckn
4 cnyh okys ekSle
A cqokbZ ds igys
B cqokbZ ds le;
C cqokbZ ds ckn
D dVkbZ ds igys
E dVkbZ ds le;
F dVkbZ ds ckn
209
441- vkids vuqlkj ,sls dkSu ls dkjd gS tks tyok;q ifjorZu ds vuq:i viu¢ vkid¨ <kYku¢ esa lgk;d gSaA
Ø-- Ekn gk¡@ugha iw.kZr% e/;e vkaf'kd 1 Ñ"kd dh vk;q vf/kd e/;e de 2 ?kj dk eqf[k;k efgyk iw#"k 3 f'k{kk dk Lrj vf/kd de 4 Ñf"k dk;Z dk vuqHko vf/kd de 5 ifjokj dk vkdkj N¨Vk cM+k 6 QkeZ dk vkdkj N¨Vk cM+k 7 vk; dk Lrj vf/kd de 8 Ñf"k ;a=ksa dh miyC/krk 9 _.k dh izkfIr 10 flapkbZ lka/kuksa dh miyC/krk 11 eq¶r izlkj lsokv a dh miyC/krk 12 Ñf"k vknkuksa dh miyC/krk 13 ?kj esa fctyh@bysDVªhflVh dh miyC/rk 14 Ñf"k lwpukvksa dh izkfIr 15 ekSle iwokZuqeku dh tkudkjh
210
442- tyok;q ifjorZu ds vuqlkj vuqdwyu esa vki dkSu&dkSu lh ck/kkvksa dk lkeuk djrs gSA
Ø- Ekn gk¡ Ukgha 1 ekSle ds iwokZuqeku dk vHkko 2 tyok;q ifjorZu ds ckjs esa tkudkjh dk vHkko 3 Ñf"k dh vuqlaflr i)fr;ksa ds ckjs esa tkudkjh dk vHkko 4 lalk/ku® dh deh 5 flapkbZ lqfo/kkvksa dh deh 6 Ñf"k vknkuksa dh vuqIyC/krk 7 vuqlaflr Qly iztkfr;ksa dh deh@vuqIyC/krk 8 lwpuk dk vHkko 9 jk"Vªh; j.kuhfr;ka izHkkoiw.kZ u gksukA 10 izlkj lsokvksa dk fu;fer u gksukA 11 Ñf"k vknkuksa esa ljdkj }kjk lHkh d`"kd¨a d¨ NqV u fn;k tkuk 12 mfpr ty izca/ku rduhd dk vHkko 13 tyok;q ifjorZu ls fuiVus gsrq ljdkjh ;kstukvksa dk vHkko 14 miyC/k tkudkjh;ksa dks izkIr djus dh vleFkZrk 15 miyC/k vuqdwyu fof/k;ksa dk fdQk;rh u gksukA 16
43- tyok;q ifjorZu ds foijhr izHkkoksa ls fuiVus@de djus gsrq vki D;k lq>ko nsuk pkgsxsaA
1- taxyksa dks cpkus ds fy, izHkko'kkyh ,oa okLrfod ;kstuk cukdj ;kstukvksa dk fØ;kUo;u djuk pkfg,A
2- leqnk; Lrj ij ikS/kjksi.k dk dk;Z izkjaHk djuk pkfg,A 3- vks|ksfxd {ks=ksa ds vklikl iznq"k.k dks fu;af=r djus rFkk tSo ra= dks lajf{kr djus ds fy,
vko';d dne mBk;k tkuk pkfg,A 4- lkS;Z mTkkZ dk leqfpr iz;ksx] lkS;Z ykbZV] lkS;Z pqYgk ,oa lkS;Z ghVj ds :i esa djuk pkfg,A 5- Ñf"k vknku] HkaMkj.k ,oa cktkj dh miyC/krk c<+kuk pkfg,A 6- fofHkUu ekl fefM;k lk/kuksa Vh-Ogh-] jsMh;ks] fQYe ,ao lekpkj i=ksa ds ek/;e ls yksxksa esa
tyok;q ifjorZu rFkk blds izHkkoksa ds izfr tkx:drk c<+k;k tkuk pkfg,A 7- taxyksa eas ikS/kjksi.k] mlds ckn cpko ,oa izca/ku dk dk;Z iapk;rkas dks ns nsuk pkfg,A 8- tyok;q ifjorZu ds dkj.kksa ,oa izHkkoksa ds ckjs esa yksxksa dks tkx:d djus ds fy, lkekftd
,oa 'kkldh; Lrj ij izpkj&izlkj fd;k tkuk pkfg,A 9- Ldwy ,oa dkWyst Lrj ij tyok;q ifuorZu dks ,d vko';d fo"k; ds :i esa lfEefyr
fd;k tkuk pkfg,A 10- ljdkj }kjk ty laj{k.k ds fy, mfpr mik; fd;k tkuk pkfg,A 11- e`nk vijnu jksdus ds fy, ty L=ksrksa ds pkjksa vksj vf/kd ls vf/kd ikS/kksa dk jksi.k fd;k
tkuk pkfg,A
211
APP
EN
DIX
–B
Mon
thly
ave
rage
max
imum
& m
inim
um te
mpe
ratu
re, r
ainf
all a
nd su
nshi
ne h
our
of R
aipu
r di
stri
ct o
f Chh
attis
garh
Pla
in
I.M
onth
ly M
axim
um T
empe
ratu
re
Mon
th/Y
ear
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Jan
28.9
28.3
24.1
2826
.125
.926
.928
.828
.127
.528
.126
.827
.528
.728
.728
30.3
26.6
27.4
25.7
Feb
31.2
29.1
29.1
30.3
30.1
29.6
31.4
28.4
32.6
30.5
30.2
28.9
30.4
33.4
30.2
28.1
33.5
30.7
30.2
30.7
Mar
33.9
37.5
32.5
36.7
35.6
32.5
36.2
34.7
35.2
35.8
34.2
36.6
35.2
33.2
34.4
34.7
36.7
37.7
35.6
35.6
Apr
40.1
37.7
38.8
39.4
36.8
38.6
41.9
41.1
38.5
40.9
40.3
39.9
39.1
38.6
39.5
3940
.642
.636
.639
.5
May
43.2
42.6
38.9
43.6
41.6
4240
.640
.942
.843
.443
.241
.141
.740
.241
.742
.542
.942
.341
.442
.9
Jun
37.1
3340
.238
.637
.837
.735
.834
.833
.137
39.4
3639
.837
.737
.134
.941
.338
.735
.338
.3
Jul
31.6
2931
.332
.731
.331
.431
30.7
29.4
34.3
31.5
31.8
30.5
30.1
30.9
31.6
30.6
31.4
31.8
29.9
Aug
29.8
28.8
30.8
29.8
30.2
31.1
29.5
30.9
30.6
29.6
29.9
29.1
29.5
29.3
30.2
30.2
31.2
31.2
30.1
29.2
Sep
30.3
30.1
3132
.131
31.4
29.2
31.4
32.8
3129
.532
.231
.131
.330
.731
.232
.331
.130
.231
.1
Oct
31.2
30.3
30.5
30.6
30.8
30.6
30.7
33.7
31.9
3229
.930
.830
.332
.231
3231
.531
.132
.231
.4
Nav
29.4
2829
.229
.730
.428
.129
.631
.930
.630
.130
30.2
29.2
29.8
29.5
30.3
28.9
30.2
3129
Dec
26.5
26.8
28.9
27.1
25.3
27.2
27.4
28.6
28.5
29.7
26.8
28.1
26.8
28.8
28.3
29.7
27.5
26.6
28.6
28.5
Ave
rage
32.7
731
.77
32.1
133
.22
32.2
532
.18
32.5
232
.99
32.8
433
.48
32.7
532
.63
32.5
932
.78
32.6
832
.68
33.9
433
.35
32.5
332
.65
212
II.
Mon
thly
Min
imum
Tem
pera
ture
Mon
th/Y
ear
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Jan
10.8
11.5
10.3
13.8
1014
.28.
410
.310
.711
.110
.511
.911
.910
.111
12.6
13.3
10.2
10.5
13.4
Feb
11.5
13.2
13.2
14.1
10.5
14.9
15.7
14.8
13.4
14.5
15.7
12.3
15.4
13.7
15.1
12.9
15.7
14.8
14.9
14.3
Mar
16.6
1718
.318
.616
.716
.717
.415
.819
.817
.817
.317
.718
.918
.418
.319
.119
20.7
18.8
17.1
Apr
22.7
2222
22.6
20.3
22.8
21.7
22.5
22.5
24.2
23.9
23.4
21.9
22.6
23.3
22.7
2324
.922
.424
.3
May
26.4
25.5
25.3
27.1
24.8
26.4
26.3
26.3
27.5
27.6
26.7
27.9
2626
.527
26.8
27.9
27.8
27.1
27.3
Jun
25.2
24.4
26.9
27.2
25.5
2725
.425
24.7
26.1
27.7
25.9
28.6
26.9
26.6
25.5
28.3
28.0
25.9
27.4
Jul
23.5
22.6
24.2
24.5
24.2
24.7
24.3
24.3
24.5
25.8
24.8
24.3
24.5
24.8
24.9
24.7
25.2
25.1
25.1
24.5
Aug
23.4
22.7
24.5
23.9
24.3
24.6
23.9
24.7
24.9
24.1
24.5
24.4
24.5
24.3
2524
.825
.425
.524
.824
.8
Sep
22.8
22.5
24.3
24.3
23.9
24.6
23.7
23.8
24.4
23.8
2424
.424
.624
.224
.423
.925
.224
.924
.224
.8
Oct
20.7
20.4
20.5
20.5
20.4
22.7
21.8
20.2
21.6
20.6
21.6
19.3
2221
.419
.920
.220
.322
.520
.619
.9
Nav
12.4
14.8
14.4
14.1
18.2
16.9
13.9
1416
13.8
15.7
13.8
12.6
17.1
14.7
16.1
16.9
19.6
15.4
16.1
Dec
8.2
9.6
12.4
8.9
15.7
8.7
9.9
8.6
10.5
12.4
11.5
10.9
10.4
12.7
12.2
11.9
13.1
13.4
11.6
12.7
Ave
rage
18.6
818
.85
19.6
919
.97
19.5
420
.35
19.3
719
.19
20.0
420
.15
20.3
319
.68
20.1
120
.23
20.2
020
.10
21.1
121
.46
20.1
120
.55
213
III.
Mon
thly
Rai
nfal
l
Mon
th/Y
ear
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Jan
06.
650
.95.
212
.626
00
11.2
13.8
026
104.
40
07.
60
15.4
057
.5
Feb
10.2
1921
.80.
80
13.8
1139
.40
5.2
29.2
3131
022
.411
.60
8.4
12.2
2.2
Mar
18.5
050
.310
.219
.236
.60
020
.65.
717
.711
.61
45.2
2.5
24.8
2.2
0.8
0.4
0
Apr
14.2
20.2
5.1
13.6
6412
.40
012
.43
18.4
4.2
251
.63.
25.
215
4.8
83.4
15.8
May
32.8
35.8
22.6
1.8
6.4
37.6
7018
.612
.814
.13.
48.
922
.462
.212
.20.
639
20.0
43.6
0
Jun
135.
444
6.8
126.
288
.412
0.4
202.
698
.619
9.1
281
195.
689
.825
2.2
127.
894
.971
3.4
203.
641
.310
0.6
146.
323
4.2
Jul
499.
849
5.2
414
263.
144
8.4
226
135.
933
0.2
273.
771
.836
9.1
379.
855
0.6
363.
127
3.9
125.
171
5.5
480.
635
4.6
639.
4
Aug
405.
236
6.2
352.
647
3.9
301.
818
4.6
421
141.
521
0.7
344.
655
3.4
156.
636
0.1
409.
621
822
9.3
264.
416
0.2
421.
851
6.3
Sep
166.
520
6.6
108.
619
813
6.6
182.
226
3.7
56.1
104.
490
.228
7.3
136.
427
6.8
232.
322
5.3
219.
899
.432
7.4
381
208.
6
oct
45.3
39.2
28.4
6830
.299
.421
096
.423
.211
1.2
20.2
121.
85.
929
12.6
51.6
64.6
24.8
9.2
Nav
02.
68.
20
966
.80
00
014
.40
01.
67.
40
49.5
7.2
032
.9
Dec
00
00
72.6
00
00
017
.10
00
00
19.3
57.2
00.
2
Tot
al13
27.9
016
38.2
1188
.711
23.0
1221
.210
88.0
1021
.278
4.9
1023
.276
7.15
11.0
1026
.915
97.9
1266
.415
07.3
840.
212
97.2
1247
.214
68.0
1716
.3
214
IV.
BSS
(Sun
shin
e H
ours
)
Mon
th/Y
ear
1993
19
94
1995
19
96
1997
19
98
1999
20
00
2001
20
02
2003
20
04
2005
20
06
2007
20
08
2009
20
10
2011
20
12
Jan
9.4
8.2
7 7.
2 7.
9 5.
2 8.
9 8.
9 8.
6 8
7.8
8.1
6.1
8.9
8.1
7.2
7.6
7.7
9.1
4.9
Feb
9.4
8.3
9 8.
9 10
.1
8.9
8.3
6.9
9.7
8.6
8.3
8.8
8.9
9.4
8.7
7.7
8.6
8.5
8.0
8.5
Mar
8.
9 10
8.
6 9.
6 9.
3 8.
9 9.
3 9.
2 8.
7 9
8.6
9 8.
4 7.
8 8.
9 7.
1 7.
4 9.
1 9.
7 8.
5
Apr
9.
9 8.
2 9.
5 8.
9 8.
7 9.
6 9.
6 8.
6 8.
7 9.
2 9
9.3
8 8.
5 8.
2 8.
8 9
10.1
9.
0 8.
6
May
9.
8 9.
6 6.
6 9.
9 9.
9 9.
3 7.
2 8.
8 8.
8 7.
9 8
7.9
9 6.
9 7.
3 7.
3 8.
4 8.
8 9.
5 8.
8
Jun
5.2
2.9
6.2
5.8
6.5
4.5
4.1
4.2
2.9
4.6
6.3
5.7
4.2
4.7
5.2
2.7
6.6
6.3
5.4
5.6
Jul
4.3
1.7
2.8
2.8
1.8
3.5
2 3.
7 1.
2 4.
1 3.
7 3.
3 2.
3 2
2.5
2.8
2.4
3.5
4.2
2.1
Aug
2.
8 2.
1 3.
7 2.
4 2.
7 3.
5 1.
9 3.
6 3.
6 2.
2 2.
4 2.
5 2.
5 2.
8 3
2.8
4 4.
4 3.
0 2.
6
Sep
5.1
5.2
6.1
6.9
4.8
4.9
2.9
5.2
6.5
6.3
3.2
6.3
5.2
6.4
4.2
5.9
5.9
5.5
4.0
4.8
oct
8.3
7.4
8.2
8 7.
6 6.
4 7.
5 8.
8 7.
5 8.
9 6.
4 8.
7 6.
1 8.
2 8
7.7
7.1
9.4
8.5
7.8
Nav
9.
2 7.
4 8.
8 8.
5 7.
5 6.
3 9.
1 9.
5 7.
6 9.
1 9.
1 8.
8 9.
1 6.
9 8.
4 6.
8 6.
3 7.
0 8.
4 7.
2
Dec
8.
1 8.
4 8.
7 8.
3 3.
6 8.
9 8.
4 9.
7 7.
6 8.
4 7.
8 7.
2 6.
8 8
8.2
7 7.
2 7.
3 7.
6 7.
8
Ave
rage
7.
53
6.62
7.
10
7.27
6.
70
6.66
6.
60
7.26
6.
78
7.19
6.
72
7.13
6.
38
6.71
6.
73
6.15
6.
71
7.29
7.
20
6.43
Socu
re :
Dep
artm
ent o
f Agr
omet
erol
ogy,
IGK
V R
aipu
r
215
APPENDIX – CAnnual rainfall trends in different districts of Chhattisgarh Plain
y = -1.651x + 1327.R² = 0.035
0200400600800
100012001400160018002000220024002600
1901 1911 1921 1931 1941 1951 1961 1971 1981 1991
Rai
nfal
l (m
m)
Years
Average annual rainfall 1327.8 mm
Trend line
y = -4.691x + 1517.R² = 0.170
0200400600800
100012001400160018002000220024002600
1901 1911 1921 1931 1941 1951 1961 1971 1981 1991
Rai
nfal
l (m
m)
Years
Trend line
Average annual rainfall 1436.4 mm
Dhamtari
y = -2.374x + 1317.R² = 0.054
0200400600800
100012001400160018002000220024002600
1901 1911 1921 1931 1941 1951 1961 1971 1981 1991
Rai
nfal
l (m
m)
Years
Trend line
Average annual rainfall 1276.9 mm
Durg
216
y = -10.69x + 1815.R² = 0.4
0200400600800
10001200140016001800200022002400
1906 1916 1926 1936 1946 1956 1966 1978 1988 1998
Rai
nfal
l (m
m)
Years
Average annual rainfall 1549.5 mm
Trend line
y = -1.139x + 1116.R² = 0.018
0200400600800
100012001400160018002000220024002600
1902 1912 1922 1932 1942 1952 1963 1976 1986 1996
Rai
nfal
l(mm
)
Years
Average annual rainfall 1108 mm
Trend line
y = -1.176x + 1462.R² = 0.006
0200400600800
100012001400160018002000220024002600
1901 1911 1921 1931 1941 1951 1961 1997
Rai
nfal
l(mm
)
Years
Average annual rainfall 1478.6 mm
Trend line
Source: Department Meteorology, IGKV, Raipur
217
y = -1.529x + 1381.R² = 0.021
0200400600800
100012001400160018002000220024002600
1901 1911 1921 1931 1941 1951 1961 1971 1981 1991
Rai
nfal
l(mm
)
Years
Average annual rainfall 1358.8 mm
Trend line
Raipur
y = -0.796x + 1380.R² = 0.003
0200400600800
10001200140016001800200022002400260028003000
1902 1912 1922 1932 1942 1952 1962 1973 1983 1993
Rai
nfal
l (m
m)
Years
Average annual rainfall 1332 mm
Trend line
Rajnandgaon
y = -5.264x + 1770.R² = 0.173
0200400600800
10001200140016001800200022002400260028003000
1901 1911 1921 1931 1941 1951 1961 1971 1981 1991
Rai
nfal
l (m
m)
Years
Average annual rainfall 1626.9 mm
Trend line
218
APP
EN
DIX
–D
Pape
r cu
ttin
g of
clim
ate
chan
ge r
elat
ed n
ews
219
Vita
