Passive adaptation to climate change among Indian farmers

Abstract

Effective adaptation is crucial for building climate resilience in agriculture. This study attempted to understand the perception of farmers about changing climate and its impact on agriculture, its consistency with observed trends. It further assessed the major adaptation strategies opted-in by the farmers along with the identification of the motivation that led to opt-in or opt-out. Multi-stage sampling was used to collect responses from farmers (n = 300) of eastern Uttar Pradesh, India. The validity of responses was verified through secondary data analysis. The findings revealed that 82% farmers perceived rise in temperature, 85% believed that the rainfall has altered, and 95% believed that the intensity of rainfall has changed. More than 60% of the farmers agreed that alterations in temperature and precipitation reduce the production as well as the revenue. A large fraction of farmers opted-in strategies like shifting of sowing dates (87%), change of variety (86%), and increase in irrigation (83%). While, resource saving strategies like conservation agriculture, water harvesting, were not considered (<25%). Interestingly, the motivation behind opting-in was not the knowledge but the monetary benefit generated by doing so i.e., passive adaptation. Among the non-adopters, a large fraction opted-out because they believed that 'It is not needed'. Constructive policies need to prioritize generation of awareness and sensitization of farmers for active adaptation preferably through participatory approach.

Full text

Ecological Indicators 154 (2023) 110637
1470-160X/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Passive adaptation to climate change among Indian farmers
Shubhi Patel
a
,
b
,
c
, R.K. Mall
b
,
*
, Abhiraj Chaturvedi
b
, Rakesh Singh
a
,
b
, Ramesh Chand
b
,
d
a
Department of Agricultural Economics, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India
b
DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
c
Department of Agricultural Economics, Narain College, Shikohabad, India
d
Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India
ARTICLE INFO
Keywords:
Perception
Climate change
Agriculture
Adaptation
Uttar Pradesh
ABSTRACT
Effective adaptation is crucial for building climate resilience in agriculture. This study attempted to understand
the perception of farmers about changing climate and its impact on agriculture, its consistency with observed
trends. It further assessed the major adaptation strategies opted-in by the farmers along with the identication of
the motivation that led to opt-in or opt-out. Multi-stage sampling was used to collect responses from farmers (n
=300) of eastern Uttar Pradesh, India. The validity of responses was veried through secondary data analysis.
The ndings revealed that 82% farmers perceived rise in temperature, 85% believed that the rainfall has altered,
and 95% believed that the intensity of rainfall has changed. More than 60% of the farmers agreed that alterations
in temperature and precipitation reduce the production as well as the revenue. A large fraction of farmers opted-
in strategies like shifting of sowing dates (87%), change of variety (86%), and increase in irrigation (83%).
While, resource saving strategies like conservation agriculture, water harvesting, were not considered (<25%).
Interestingly, the motivation behind opting-in was not the knowledge but the monetary benet generated by
doing so i.e., passive adaptation. Among the non-adopters, a large fraction opted-out because they believed that
‘It is not needed’. Constructive policies need to prioritize generation of awareness and sensitization of farmers for
active adaptation preferably through participatory approach.
1. Introduction
Adapting to climate change is irrevocably one of the last-mile solu-
tions for insulating agriculture against the impacts of climate change.
However, understanding and desired behaviour change are the major
prerequisites for an effective adaptation. Because, adaptation is a two-
step process, rst, the perception of climate change, followed by the
adaptation decisions taken to minimize the possible losses (Maddison,
2007; Deressa et al., 2009). There are pieces of evidence of adaptation
gaps i.e., the difference between actual adaptation and recommended
adaptation (IPCC, 2022). Possible reasons are a misinterpretation of
climate trends, and barriers like social, institutional, individual, tech-
nological, and economical at different levels (Mall et al., 2019; Singh,
2020; Mall et al., 2021). It may also be inuenced by external factors
that develop a biased belief (Myers et al., 2013). For example, a belief
that global warming is happening may inuence people to judge a rise in
temperature in their surroundings while same may not be felt by those
who do not believe so (Howe and Leiserowitz, 2013; Mall et al., 2018).
There is available literature that indicates that farmer’s perceptions are
not consistent with the climate records (Niles and Mueller, 2016) while,
in other regions, they mirror the trends in meteorological variables
(Ayanlade et al., 2017). Sometimes, farmers have the knowledge and,
adaptation take place at the farm level but, unknowingly i.e., passive
adaptation (Tripathi and Mishra, 2017). On the other hand, despite
accurate perception recommended adaptations may not take place
(Gandure et al., 2013).
There is a wide conundrum of factors that shape the adaptation de-
cisions ranging from perceptions of farmers, education, farming expe-
rience to infrastructural support available, as well as awareness
activities. At individual level for example, low income, small land-
holding, lack of education can limit the adaptive capacity (Singh, 2020).
Young generation of farmers who are risk-takers are more likely to adapt
as education, and accessibility to information increases the chances of
adaptation (Jha and Gupta, 2021). The adaptation strategies are often
costly and a lack of nance and, information of weather act as a barrier
(Pandey et al., 2018). A study by Swami and Parthasarathy (2020)
showed that challenges vary as per preference of the strategy as well as
the particular strategy itself. For example, the availability of kisan credit
* Corresponding author.
E-mail address: rkmall@bhu.ac.in (R.K. Mall).
Contents lists available at ScienceDirect
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https://doi.org/10.1016/j.ecolind.2023.110637
Received 4 January 2023; Received in revised form 5 July 2023; Accepted 6 July 2023

Ecological Indicators 154 (2023) 110637
2
cards, soil health cards, crop insurance among the institutional factors
are the major challenges for adoption of strategies which are more
preferred by the farmers. It is noteworthy that, institutions have a
crucial role to play in build-up of perceptions and decisions of adapta-
tions by farmers, they make the adaptation process easier and seamless
through provisions of facilities that support adaptation (Niles and
Mueller, 2016). For example, technical and nancial assistance promote
the adaptation ability of the farmers (Chen et al., 2014). Awareness
about the cost-benets of adaptation, and use of extension activities, is
also crucial (Singh et al., 2018). It is not only the government but private
sector also that can help increase the adaptive capacity. For example,
public private partnership has been identied to be useful for enhancing
adaptation for drought (Zhang et al., 2018). In case of markets, reduced
production due to climate change combined with the existence of mar-
ket imperfections at domestic and international trade intensies the
income losses for farmers (Mendelsohn 2006). While sometimes the
facilities also inuence the perceptions viz., availability of irrigation has
the potential to direct the perceptions of farmers in a different direction
altogether (Niles and Mueller, 2016; Patel et al., 2022). This is where
mainstreaming i.e., integration of adaption with other policies comes
into play, emphasising that one solution ts all or running only specic
policies will not bring the solution but an integrated approach will
(Singh et al., 2018). Recently participatory action research has emerged
as an option to improve the development of localised adaptations
(Hochman et al., 2017). Growing body of literature has cited different
barriers to adaptation in different fragments. However, current litera-
ture does not address the thought process behind the barriers as faced by
the farmers to practice a given adaptation strategy. This study attempts
to ll the research gap by assessing the motives behind opting for an
adaptation strategy, what are the reasons for opting out, the consistency
of perceived climate change with the observed records and how the
external factors inuenced the adaptation decisions to be intentional or
unintentional in Uttar Pradesh state of India. Since this state is endowed
being the leading producer of foodgrains while 89% of the farmers are
small and marginal farmers. The outcome of the study will help shape
the policy framework for effective adaptation at local levels, overcome
the barriers stated by the farmers and, transfer knowledge for supporting
active adaptation.
2. Material and methods
2.1. Study area
The study was undertaken in three districts of Uttar Pradesh state.
Uttar Pradesh, one of the largest states in India, is located between 23◦
50
′
– 30◦45
′
N latitude and 77◦04
′
– 84◦38
′
E longitudes. It covers an
area of 2,43,286 sq. km. which is 7.4% of the total geographical area of
India. The state is endowed with ample alluvial soil along with a diverse
agro-climatic prole which can support the cultivation of a variety of
crops. Due to this reason, it is distinguished as the” agricultural state” of
the country. It is a leading producer of wheat and food grains. The state
is the second most populous state with a dominating share of the
farmers. The average size of holdings in UP is around 0.76 ha and the per
capita land area is 0.14 ha, less than half of the national average of 0.32
ha. 92.81% of the farming population is small and marginal farmers
(Agriculture Census 2015–16). The economy of Uttar Pradesh is
Fig. 1. Representation of the study area depicting the three districts of Uttar Pradesh on the Map. The numbers represent the survey sites which are the blocks
(district subdivision) from where farmers of different villages were interviewed. The land use/land cover shows the distinction of each district. Source of map:
Author’s preparation. Source of land use/land cover data – ESRI (2022).
S. Patel et al.

Ecological Indicators 154 (2023) 110637
3
predominantly agricultural with heavy dependence on Monsoon. This
sector employs about two-thirds of the workforce and contributes about
one-third of the State’s income. Dependence on agriculture combined
with population pressure and low-income makes this region vulnerable
to climate change. There have been few studies in the Indian region and
also Uttar Pradesh.
These districts were Varanasi, Chandauli, and Mirzapur. All the
districts have distinct farming practices and cropping patterns as well as
topography. Varanasi falls in Eastern Plain Zone, and Chandauli and
Mirzapur fall in the Vindhyan zone (Fig. 1).
Varanasi, considered the oldest city, is situated on the banks of the
river Ganga. It holds a 1535 sq. km. area in Uttar Pradesh and part of the
great Ganga River basin. Due to this reason, most of the area is exhibited
under at topography with a gentle slope and fertile soil. Active & Older
ood plain of river Ganga and its tributaries and Older Alluvial Plains
are the three morphological classications of this district. It is a major
pilgrim and an attraction for tourists as well as it is well-known as a
knowledge, cultural and educational center of India. The cropping
pattern of Varanasi is dominated by vegetables with Raja Talab being
the biggest vegetable producer. This city has also beneted from the
Indian Institute of Vegetable Research. Varanasi is divided into 8 blocks.
Mirzapur is the south-eastern district of Uttar Pradesh. The extension
of the Mirzapur district is in the mountainous region of the Vindhyan
ranges. It is situated at the junction of the Vindhya and Middle Ganga
plain basins. Mountains, plateaus, and plains are characteristic of the
district and it reects in the land use of the area. It covers an area of
about 4521 sq. km. Mirzapur has rich alluvial soil in the part of the
Ganga plain basin and major crops are rice–wheat but other major crops
are also grown. Soil of Vindhyan ranges, is degraded with shallow depth
for cultivation. Therefore, farmers are practicing rain-fed/ dry crops,
and vegetables. Mirzapur is divided into 12 development blocks.
Chandauli is a south-eastern district in Uttar Pradesh, which is sit-
uated on the UP and Bihar border. It is geographically divided into three
major parts- Chakia Plateau, Chandauli Plain, and Ganga Khadar. The
district is well drained in the Chakia plateau part but there is certainly
water logging, and water erosion in the rainy season in Chandauli plain
and Ganga Khadar. Chandauli plain and Ganga Khadar get new soil
every year with oods which are highly fertile for paddy and wheat.
Chandauli district is rich in paddy cultivation so it is called the paddy
bowl in eastern Uttar Pradesh. Chandauli covers 2485 sq. km. area in
Uttar Pradesh and is constituted by 9 blocks.
2.2. Data
The study is based on both primary and secondary data. The primary
data was collected using a pre-designed survey schedule. The secondary
data on climate and crop production statistics was used to verify the
perceptions of farmers.
2.2.1. Survey design and data collection
The awareness and perception along with adaptation options were
collected at three stages through a pre-designed survey schedule. In the
rst stage, the questions were framed based on the available literature.
In the second stage, the preliminary survey schedule was tested by doing
pilot interviews and focused group discussions with two climate experts,
two experts working in farmer’s advisory services, 5 researchers, and 7
farmers. In the third stage, the schedule was modied and nalized as
per the response of the participants, incorporating the required ques-
tions and removing the unnecessary parts. This was again tested on 10
laborers working in the university agriculture research farm. A repre-
sentative, multi-stage sampling was done. Firstly, for the selection of
farmers a list of farmers from each block of the district was obtained,
then, villages were selected from each block of the district to cover the
whole area. The farmers were interviewed from the selected villages on
a random basis. Finally, a total of 310 farmers were interviewed in 2020
out of which 300 responses (100 each in Chandauli, Varanasi and
Mirzapur) were used for the analysis. The survey schedule is provided in
the supplementary data (Table S1).
A team of trained researchers was assigned the task of interviewing
the farmers. The survey schedule began with a general description of the
farmer and his farming practice which act as a confounding factor for
awareness and perception build-up. Farmers were asked about their
knowledge on weather and climate. Then based on their long-term
experience with the weather variables for 10–15 years the questions
were asked. The perception towards climate change and its likely im-
pacts was assessed using a three-point Likert scale. The last section of the
questionnaire was the innovative where the novelty of this study lies.
First, the farmers were asked whether they have taken some adaptive
measures against the changing climate. Then they were asked about
each possible adaptive practice suitable for their region along with the
reason behind doing so and if not, why? This helped to identify the
different constraints related to non-adoption at the farmer’s level. Each
interview lasted for about 40 min. The conceptual diagram of associa-
tions, inuences between perception and adaptation is presented in
Fig. 2.
2.3. Data analysis
The socio-economic characteristics and perceptions of farmers and
adaptation practices responses were quantied using simple descriptive
statistics. The awareness of farmers was also displayed using the same.
2.3.1. Climate data analysis
The agreement between farmers’ perceptions and observed climate
trends was assessed to check whether the perceptions are drawn from
experience or general knowledge and awareness. The long term
observed climate data of the three districts was obtained from IMD Pune
for a period of 1951 to 2021. This period of 69 years was taken to cover
the range of farming experience of the respondent farmers. The weather
variables included Maximum temperature (T
max
), minimum tempera-
ture (T
min
), and precipitation.
The trend in Tmax, Tmin, and precipitation was analysed using or-
dinary least squares. Modied Mann-Kendall trend (MMKT) analysis
was used to determine the change in trends at 95% condence interval i.
e., p <0.05. MMKT was preferred over Mann Kendall (MK); it takes into
account the problem of autocorrelation, and thus, the tau and slope
values are free of autocorrelation and data normalization reducing the
type I error (Hamed and Rao, 1998). MMKT performs better in the case
of non-Gaussian datasets. This test maintains distribution free charac-
teristics with the normalisation pre-treatment of datasets (Hu et al.,
2020). So, this test is widely used for long term climate data. For the
transformation of observational data into the normal Gaussian datasets
the following equation has been used:
Zi =∅−1(Ri
(N+1))(1)
Here, R
i
is the rank of normal distribution series x
i
, N is the length of
the series, and −1 is inverse function of standard normal distribution.
Now, both of the series are similar because the series Z is created based
on the ranks of the series X. As the MK test is essentially a test for the
rank sequence, X and Z must have the same signicance for the statistic S
in the MK test. As a result, it is suggested that Z replace X in the MK test.
Instead of using X, apply H estimation and trend detection to Z. As an
overview, the MMKT data scaling process is as follows:
•First, calculate the Mann Kendall statistics (S).
•Second, Get the similar standard normal series Z with the use of
equation (1).
•Third, Hurst coefcient or scaling coefcient (H) has been estimated
with the equation (1).
•After it, obtain variance of S of given N and H.
S. Patel et al.

Ecological Indicators 154 (2023) 110637
4
V(S) =
η
N(N−1)(2N+5)
18
η
=1+2
N(N−1)(N−2)X∑n−1
i=1(N−i)(N−i−2)Ri
•Lastly, to calculate the statistic Z (Z
s
) with this equation and also
compare with Z
tab
on certain signicance level (
α
). The Z
s
is calcu-
lated as follows:
Zs =
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
S−1
√V(S)S>0
0S=0
S+1
√V(S)S<0
R software was used for these calculations. According to the positive
and negative value of Z
s
, depict the positive and negative trend for the
parameters respectively. The null hypothesis H
0
was assumed that there
is no signicant trend. Details of the MMKT method can be found in
Hamed and Rao, 1998.
2.3.2. Crop production analysis
To understand the extent of changes in cropping patterns as an
adaptation towards climate change, the yearly data of the area, pro-
duction, and yield of major crops were collected for the period of
1980–2018 from dacnet.gov. The decadal average was calculated for the
area under crops, and production and yield of crops were calculated for
two decades i.e., 1980–2008 and 2009–2018. The comparison of the
change in these aspects was then done to identify the extent of adap-
tation as said by the farmers and statistical records.
3. Results
3.1. General prole of the respondents
Perception determines the extent of adaptation. The extent of
adaptation is however also determined by the socio-economic charac-
teristics of the respondents. The socioeconomic characteristics of the
farmers are given in Table 1. The majority of the farmers are of age
41–60 years and are educated. The landholding of a majority of farmers
is <1 ha but they have access to credit. The farmers have a considerable
experience of 15–30 years in farming.
3.2. Farmers’ perception of climate change
It was observed that 82% of the farmers believed that they have
Fig. 2. Conceptual description of the perceptions and adaptations by farmers and their associations with each other. The diagram shows that there is downward
movement i.e., adaptation is followed by perception. The solid lines represent the greater association and the dotted lines represent the possibilities of such asso-
ciations. The bold arrows represent the driving force behind such decisions.
Table 1
General characteristics of respondents.
Variables Descriptions Percentage (%)
Age 19–40 years 35
41–60 years 44
>60 years 21
Education Upto high school 18
Higher secondary 26
UG and above 56
Land holding Marginal 31
Small 29
Semi-medium 21
Medium 15
Large 3
Farming experience <15 years 27
15–30 years 46
>30 years 27
S. Patel et al.

Ecological Indicators 154 (2023) 110637
5
experienced an increase in temperature (Fig. 3). While for precipitation,
85% and 95% agreed upon altered timings and variation in the intensity
respectively. Pest & diseases, occurrence in crops is inuenced by
climate change (Luck et al., 2011; Ghini et al., 2011). In our study it was
found that, 93% of the farmers agreed that incidence of pest & diseases
has increased over the years. They have also experienced rise in tem-
perature, change in rainfall and this negatively impacts production as
well as returns.
Majority of farmers believed that the results of climate change have
an adverse impact on production and revenue generation (Fig. 4). More
than 60% of the farmers agreed that temperature changes, precipitation
reduce the crop yield as well as the revenue while 13–17% were neutral
about this fact.
3.3. Observed climate trends
The analysis of the weather variables of the region for the period of
1951–2021 showed that there was an increase in maximum temperature
and the rainfall has shown a decrease in intensity (Fig. 5). The maximum
temperature has shown an increasing over the period. Thus, the
perception of farmers about the temperature rise is based on their
experience. Additionally, if we see the trend of minimum temperature, it
has shown a negative trend over the years. The rainfall trend analysis
showed that there has been a negative trend in the study region. How-
ever, this was not signicant.
3.4. Adaptation to changing climate
The ndings of this study showed that farmers have opted for few
strategies but their driving force is monetary benets and not just their
knowledge (Table 2). Among the 15 adaptation strategies, change of
variety (86%), shifting of sowing dates (87%), and increased irrigation
(83%) was the most adopted ones. Literature has also shown that the
majority of researchers suggest these strategies to reduce losses and
escape heat stress (Tooley et al., 2021; Gardi et al., 2022; Gunawat et al.,
2022). What is more striking is that the reasons for opting for new va-
riety (46%) and shifting of sowing dates (46%) are primarily due to
monetary benet. However, the farmers have the knowledge of climate
change also. While for increased irrigation, farmers had knowledge that
when heat increases, more irrigation helps to bring the cooling effect
and thus reduce the losses. Change in the crop, shift to non-agriculture,
soil testing, and mixed farming was the other strategies opted by around
50% of the farmers. And, the reason they did so was that same, i.e.,
monetary benet. For obvious reasons, crop change, and mixed farming
increases the income from the eld while the centrally sponsored Govt
schemes like Soil Health cards provided an opportunity to use balanced
fertilizer and reduce the input cost of fertilizer and hence increasing the
net income. Opting for non-agricultural activities (51%) serve as a
reliable source of income for the farmers. This shows evidence of passive
adaptation by the farmers as the motive behind adoption is not their
knowledge on changing climate but to generate more income. Studies
have shown that passive adaptation is common among farmers (Apata
et al., 2009 ). The fact that farmers have gone for a change of crops,
change of crop area, and also variety change in the study area was
established with the secondary data analysis. The secondary data on
area, production, and productivity showed that there is change in the
crop area of many crops specially mustard, wheat, rice and masoor in
recent decade of 2009–18 as compared to 1998–2008 (Supplementary
Table S2). While in general there is a rise in production of the crops in
2009–18 despite area reduction. This is because the use of high yielding
varieties that compensated the reduction in area and increased the
production (Supplementary Fig. S1). Very few farmers have opted for
conservation agriculture, water harvesting, and even crop insurance.
The positive sign here is that although only 10% of the farmers opted-in
water harvesting the motive was driven by their knowledge (74%).
3.5. Barriers for non-adoption
A deeper insight is also important to identify the farmer’s point of
view for opting-out or not adopting a given strategy. The analysis shows
that the one of biggest reasons for not adopting a given strategy is that
the farmers feel that it is not required (Table 3). Majority (10 out of 15)
of the strategies were opted-out because farmers responded that it was
not needed in their elds. Other reasons were also reported like, when
farmers were asked about mixed farming, opting for dairy or poultry at a
commercial level, they were unable to do it alone. Other activities like
crop insurance faced the issue that their crop was not notied under the
scheme followed by it being faulty.
3.6. Is it just the farmers or other factors also involved?
The study has clearly reported passive adaptation among farmers.
But it is not only the perception of farmers but other external factors that
shape adaptation decisions and beliefs. Identication of these factors
help the research community and policy makers develop solutions for
inducing active adaptation among farmers. Deriving the concept from
IPCC 2007 where the adaptation strategies can be divided on the basis of
- type of action, by actor, by spatial scale, by baseline income, by sector
or a combination of these and other categories for understanding pur-
pose (Adger et al., 2007). Giving an explicit focus on the type of action
taken by the farmers we divided the strategies into four major categories
i.e., Physical, technological, risk transferring and diversication. Phys-
ical strategies comprised of shifting in sowing dates, increase in irriga-
tion, change in crop area, change of crop and change of variety. These
are changes which can be done in short term and have immediate re-
sults. Technological strategies comprised of conservation agriculture,
soil testing, water harvesting and use of new technology. These are the
options that support resource conservation like water, soil and nutrients
Fig. 3. Farmers’ perception of climate change in (%).
S. Patel et al.

Ecological Indicators 154 (2023) 110637
6
and also show results in the long run and also capital intensive. Opting
these is easier when the farmer is educated and know the importance of
resource conservation. The risk transferring strategies included contract
farming and crop insurance. Crop insurance is a risk transferring tech-
nique in case of crop failure and contract farming ensures assured price
during price volatility. The last category diversication strategy
comprised of agro-forestry, mixed farming, shift to non-agriculture and
opting livestock/poultry. These options diversify the sources of income
and hence stabilize the farmers income. Descriptive approach was used
to identify the external factors that might have relation with the opting-
in decision of the farmers. The prevailing government policies, market
infrastructure, have a role in altering the adaptation beliefs for example
if the farmer is unaware of the ways and need to adapt in changing
climate his farm level decisions will be driven by income generation
motive and resource conservation or other strategies will not be opted
until his farm requirements are being fullled through infrastructural
facilities.
From Table 2 it was revealed that risk transferring strategies were the
least adopted ones. Farmers stated that it was not required but also,
there is a possible reason that, contract farming is not popular in the
area. While, it is noteworthy that, to avail the indemnity in crop insur-
ance the crop grown by the farmer must be notied by the insurance
company for the given area. The unavailability barrier shows that the
insurance was not available for the crops grown by those farmers and
faulty facility shows that despite crop losses, indemnity was not paid.
While, most widely adopted strategies were the physical strategies
(~80%). These strategies require credit for purchasing seeds and fer-
tilizers and increase irrigation. It was found that 97% of the farmers had
access to irrigation, 78% had access to credit thus easing the opting-in
option (Table 4). The technological strategies also had a low adoption
Fig. 4. Farmers’ perception on negative impacts of climate change on production, revenue, pest & diseases (%).
Fig. 5. Trend of (a) maximum temperature and minimum temperature (
◦C/year) and (b) rainfall (mm/year) in Chandauli, Mirzapur and Varanasi for a period of
1951–2021 at 95% condence interval.
Table 2
Adaptation strategies opted by respondents and reasons of adoption (values in
%).
Adaptation strategy Adopted Reasons for adoption
Knowledge Monetary benet Both
Shifting of sowing dates 87 28 46 26
Increase irrigation 83 46 43 11
Mixed farming 47 35 53 12
Contract farming 12 3 77 20
Shift to non-agriculture 51 8 87 5
Conservation agriculture 24 37 59 4
Opted dairy/poultry 31 34 60 7
Change in crop area 38 36 55 10
Variety change 86 37 46 17
Soil testing 49 30 59 11
Crop insurance 37 23 71 5
Water harvesting 10 74 19 6
Agro-forestry 37 36 59 5
New technology 29 25 57 17
Crop change 56 43 45 12
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Ecological Indicators 154 (2023) 110637
7
rate (<40%) despite the fact that 75% of the farmers have experienced a
reduction in surface water sources. Pointing out two issues rst farmers
are not aware of the need to saving water and second, how water har-
vesting and agro-forestry play a crucial role in water cycle. Conservation
agriculture in one way promotes integrated pest management and hence
reduce the use of pesticides but, it was not adopted by the farmers. The
secondary data showed that the pesticide consumption in the study area
has increased since last two decades thus deteriorating soil and ground
water (Supplementary Fig. S2). Again, indicating the lack of extension
activities or specically the programmes focusing on resource conser-
vation in the long run.
4. Discussion
4.1. Farmers’ perception and climate trends
Perception analysis showed that the more than 80% of the farmers
agreed that they have experienced changes in temperature and rainfall
patterns. Notably, the farmers perceived that a temperature rise reduced
production as well as revenue. The farmers have experienced losses in
yields, shrivelling of grains, death of owers due to high temperatures at
critical stages, and drop of fruit. The rainfall intensity and change in
timing also had a similar impact on the production and revenue gener-
ated. It delayed the sowing of crop and destroys the standing crops,
washes away the pollen, and owers. The trend analysis showed that
there has been a rise in maximum temperature over the years and a
reduction in rainfall. Farmers’ perception in the region is consistent with
the trends of observed meteorological variables, indicating that, their
perception is built upon their own experience.
4.2. Adaptation, motives and barriers
The adoption of several adaptation strategies motivated by monetary
benet emphasized that they are unaware of what they can do from their
end to cope with it. This showed that passive adaptation was observed in
the region i.e., the farmers know the climate is changing but they are
unaware of the strategies they can practice for minimizing the losses or
what they are doing is adaptation.
Interestingly, we found that among those who had not adopted the
given strategy, the biggest barrier was their belief that it was not
required. This response is driven by the perception of farmers that “we
cannot do anything” about the changes in weather variables. For
example, more than 80% farmers have experienced rise in temperature
and more than 60% agree that this causes losses in production and
revenue but, <40% of them could identify that the adaptation strategy
opted by them was due to their climate knowledge. Again, showing
passive adaptation and elucidating harmony between perception and
active adaptation is challenging. Similar ndings of passive adaptation
have been documented by and Tripathi and Mishra, 2017; Apata et al.,
2009.
While studies have emphasised that farmers with institutional
connection are better in adaptation (Islam & Nursey-Bray, 2017), the
role of government is important for ante-adaptation (Mendelsohn, 2000)
and the need of active adaptation (Bradshaw et al., 2004). In our study
also, the declaration by farmers that ‘it is not needed’ and ‘it is for more
income’ demonstrates that at present institutional involvement in
generating knowledge about sincere steps that can be taken is lacking.
Accessibility to credit and irrigation has made it easier to adopt new
varieties, change the crop, increase the irrigation but strategies that are
more inuenced by knowledge were not opted despite the fact farmers
are members of Kisan clubs, NGOs and have access to consultancy.
Institutions, market, community have a role in adaptation decisions
as seen. Policies like MGNREGA that guarantee 100 days’ work to rural
has contributed to the reduction in agricultural labour (Agarwal et al.,
2015). There is migration of rural youth from agriculture to other in-
come sources majorly because of the poor income generation from
farming (Chandrasekhar and Sharma 2014). This leads to preference of
less labour-intensive farming practices. To address the importance of
weather information, Agromet Advisory services were started to ensure
a weekly weather forecast sent to farmers individually. While for credit
facilities, Kisan Credit Card was introduced in 1998 that provides credit
to farmers to meet credit need with interest subvention option. At pre-
sent Prime Minister Crop Insurance Scheme, Soil Health Card scheme
are active for insuring income and reducing input costs. Recently Kisan
drones have been introduced in the budget 2022 for crop assessment,
digitization of land records, plant protection measures etc. Also, Na-
tional Innovations on Climate Resilient Agriculture, a network project
was launched by the Indian Council of Agricultural Research to promote
Table 3
Barriers stated by respondents who didn’t adopt the adaptation strategy (values in %).
Don’t know
about it
It is not
needed
Cannot do
alone
Faulty
facility
No
awareness
Schemes not
known
Not
available
Lack of
credit
Costly
measure
Neel
gai
Shifting of sowing
dates
13 73 8 8 0 0 0 0 0 0
Increase irrigation 8 71 15 2 4 0 0 0 0 0
Mixed farming 2 34 39 6 1 3 1 1 11 1
Contract farming 6 35 6 28 8 0 0 14 1 2
Shift to non-
agriculture
7 49 19 5 3 5 10 1 0 0
Conservation
agriculture
15 26 7 22 4 1 9 2 0 14
Opted dairy/poultry 0 38 38 4 1 6 9 2 0 1
Change in crop area 2 51 6 2 0 3 32 1 3 0
Variety change 0 51 5 17 2 5 0 5 2 12
Soil testing 3 11 10 34 5 3 11 2 0 22
Crop insurance 2 15 1 32 6 2 40 3 0 0
Water harvesting 15 45 6 22 2 4 4 1 0 0
Agro-forestry 8 46 12 10 3 0 4 1 5 11
New technology 5 15 4 2 4 10 20 2 38 0
Crop change 3 46 23 5 1 2 3 0 16 2
Table 4
Availability of various institutional facilities in the study area.
Attribute Yes/No Percentage of farmers
Access to credit Yes 78
Access to irrigation Yes 97
AGROMET advisory Yes 70
Weather information Yes 88
Source of information Mobile 49
Reduced surface water sources Yes 75
Associated with kisan club/ NGO etc. Yes 76
S. Patel et al.

Ecological Indicators 154 (2023) 110637
8
climate resilience through research & development, extension and ca-
pacity building.
There are several studies which have examined the perceptions of
farmers and the adaptation strategies opted by them in Indian region
(Table 5). The body of literature showed that across India effects of
climate change have been experienced by the farmers in form of
elevated temperatures, irregular rainfall and increased incidence of
climate extremes. Regional variations were observed in the adaptation
strategies opted by the farmers. However, increased irrigation and
change in crop varieties were the most common while migration was
majorly seen in Himalayan regions.
5. Policy implications
The ndings of the study provide an approximate roadmap for
framing and implementing policy to stimulate active adaptation. With
immediate effect the following policy initiatives can be taken-.
•Constructive initiatives like utilizing folklores, proverbs, riddles that
are intricately interwoven in the traditional-religious beliefs or rural
community. Use of folklores for generating awareness and cope up
with climate change has been highly benecial (Paulraj and And-
haria, 2015; Gupta and Singh, 2011).
•Utilizing the kisan clubs, NGOs existing in the villages for extension
of knowledge, gender sensitization to improve awareness and
adaptation rate.
•Connect all the farmers through mobile phones and disseminate the
AGROMET advisory, with more precise suggestions and possibly in
regional languages also.
•Start customised extension activities, awareness programmes for
knowledge of resistant varieities, harnessing the recent event of In-
ternational Year of Millets that is giving boost to millet production
and use, as this crop is climate resilient and highly nutritious.
Long run-.
•Updating of the crop insurance facility by use of real time data about
the cropping pattern of the district, now use of kisan drones can be
done for digital data collection about the crops grown and losses
occurred. This will ease the insurance claim process and bring
transparency, thus remove the existing lacunae.
•There is need to study the projections for the climate trend in the area
and frame policy for introducing new cropping pattern, new crops
Table 5
Studies on perception and adaptation towards climate change in India.
Study Region Findings
Perception Adaptation
Vedwan & Rhoades
2001
Kullu valley, Himachal Pradesh Reduction in snowfall, no change in rainfall, increase
and shift in temperatures and increased extreme
events like cloud bursts
–
Dhaka et al., 2010 Bundi, Rajasthan Increase and shift in temperatures, decrease in rainfall Integrated farming system, crop rotation, intercropping,
change in time of farm operation
Basannagari et al.,
2013)
Kinnaur, Himachal Pradesh Increase in temperature, decrease in snowfall. Experienced change in land use practices, replacement of apple
withcoarse cereals.
Sahu and Mishra
2013
Kendrapara, Odisha Almost 98% of farmers believed that there is change in
temperature and rainfall patterns.
Only 59% of the farmers have opted for adaptation strategies
Banerjee 2015 Nasik, Maharashtra and
Guntur, Andhra Pradesh
Increase in summer temperature, uctuations in
winter temperatures, late rainfall onset and increased
pest & disease incidence.
Diversication, shift to commercial crops, use of drip irrigation,
creation of surface pond.
Dhanya &
Ramachandran
2016
Kancheepuram, Tamil Nadu Increase in temperature, decrease in rainfall, increased
dry spell
Short duration crops like pulses, vegetables, perennial crops
like coconut, banana.
Hussain et al., 2016 Eastern Brahmaputra
(Hindukush Himalaya region)
Increased occurrence of oods, landslides, drought,
livestock diseases and crop pest.
New off-farm activities, out migration, changes in farming
practices.
Panda, 2016 Balangir and Nuapada, Odisha Changes in temperature and rainfall pattern –
Shukla et al., 2016 Kanchandzonga biosphere
reserve, Sikkim
Increasing temperature and unpredictable pattern of
rainfall, warmer and stronger winds
Use of locally available materials as mulches, agroforestry,
intercropping
Dubey et al., 2017 Sunderban, West Bengal Changes in temperature, rainfall, and also tropical
cyclones & sea level rise.
Adopted short term coping measures.
Negi et al., 2017 Uttarakhand (Western
Himalaya region)
Increase in temperature, decrease in snowfall,
irregular rainfall, change in agrobiodiversity
Change in cropping pattern, livestock integration, protected
cultivation, high yielding varieties, migration
Tripathi and Mishra
2017
Faizabad, Uttar Pradesh There is change in temperature, rainfall. Passive adaptation through timing changes of sowings and
harvesting, short-duration varieties, inter-cropping, changed
cropping pattern, agroforestry and irrigation.
Dey et al., 2018 Chilapata reserve forest, West
Bengal (Eastern Himalaya
region)
increasing temperature, decrease in winter spell, low
amount of rainfall, uneven distribution of rainfall,
intensication of drought and ood
Pre-monsoon seeding, agroforestry, crop rotation, short
duration crop varieties and use of organic/inorganic products
Shukla et al., 2019 Dehradun, Almora, Uttrakhand
(Indian western Himalaya
region)
83% farmers not know the term ‘climate change’.
Decrease in summer and winter rainfall. Increase in
summer temperature.
–
Singh, 2020 Bundelkhand region, Central
India
Changes in temperature and rainfall leading to food
insecurity.
Early maturing seed varieties and less water consuming crop
varieties
Baruah et al., 2021 Assam Increased temperature, no change in rainfall intensity Use of resistant varieties, application of fertilizers and
pesticides, crop diversication, changes in sowing dates,
irrigation, soil conservation, changes in cropping pattern.
Jha and Gupta 2021 Bihar Increase in number of hot days, decrease in number of
cold days, changes in rainfall
Irrigation, migration to urban area, change in crop area, change
in crop variety, crop insurance, vegetable cultivation
Kundu & Mondal
2022
Maldah, West Bengal Increase in annual summer temperature while
decrease in winter temperature, decrease in rainfall
Change in cropping pattern, change in sowing dates, organic
farming, crop diversication, conversion of cropland into
orchard and crops and livestock
Datta and Behera
2022
Coochbehar, West Bengal (Sub
Himalayan region)
Increase in temperature and decrease in rainfall. Irrigation, high value crops and high yielding varieties,
mechanisation
S. Patel et al.

Ecological Indicators 154 (2023) 110637
9
suitable for the elevated temperatures, introduction of resistant va-
rieties in the region.
•Mainstreaming of policies like water management, dams, road plans,
drainage plans, market infrastructure that can withstand weather
extremes like oods in the long run.
•Promotion of research and development through institutional sup-
port, budget allocation to identify vulnerable regions and develop
customised plans for upliftment of the affected communities in the
vulnerable regions preferably through participatory approach.
6. Limitations
The research ndings always inherit a conundrum of some limita-
tions. For example. the ndings from this study are based on only two
Agro-climatic Zones (ACZs) of Uttar Pradesh. More detailed research can
be conducted to assess responses from different ACZs addressing the
heterogeneity among local climate, cropping system, socio-economic
status of farmers, institutional support, and culture. Second, female
farmers can also be included and a gender-based study can be done to
identify variability if any. It is noteworthy that females have a greater
role as agricultural laborers on the farms and handle both agricultural
and domestic responsibilities. It is noteworthy that perceptions are
inuenced by multiple factors like education, awareness, and local or
short-term experiences. A more detailed understanding of the responses
with a larger sample size, careful framing of questions and scale, and
consideration of the complex division of society can contribute to better
response collection. Hence, there lies the scope for future research on
farmers’ perception and adaptation strategies, their adoption & barriers.
7. Conclusion
Adaptation to climate change is crucial for income security of the
stakeholders and food as well as nutrition security of the nation. This
study attempted to ll the research gap in literature for addressing the
driving force of adaptation and non-adoption in context with the
response of farmers directly. The wide spectrum of literature has iden-
tied various factors affecting adaptation, barriers to adaptation but few
studies have reported evidences of passive adaptation. Present study
used a combination of primary and secondary data to demonstrate the
rst step of adaptation i.e., perception and then reported the psycho-
logical factor impeding the behaviour change. It was revealed that
despite knowledge of climate change the monetary aspect drives the
adaptation decisions and farmers still believe that they don’t need to
adapt as it is beyond their capacity. This evidence of passive adaptation
is important for researchers and policymakers to develop awareness or
sensitization programmes that educate farmers about active adaptation.
Uttar Pradesh is vulnerable to climate change, and the ndings of this
study can be instrumental in uplifting the development of agriculture in
the upcoming future.
CRediT authorship contribution statement
Shubhi Patel: Methodology, Investigation, Formal analysis, Data
curation, Validation, Visualization, Writing – original draft. R.K. Mall:
Conceptualization, Supervision, Methodology, Resources, Funding
acquisition, Data curation, Project administration, Visualization,
Writing – review & editing. Abhiraj Chaturvedi: Visualization, Data
curation, Formal analysis, Investigation. Rakesh Singh: Supervision,
Writing – review & editing. Ramesh Chand: Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
Authors wish to thank the Climate Change Programme, Department
of Science and Technology, New Delhi for providing nancial support
(Award no.: DST/CCP/CoE/ 80/2017(G)). The authors wish to thank the
India Meteorology Department (IMD) for making available the obser-
vation dataset.
Funding
Authors thank the Climate Change Programme, Department of Sci-
ence and Technology, New Delhi, for nancial support (DST/CCP/CoE/
80/2017(G)).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecolind.2023.110637.
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