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Analyzing the risk related to climate change attributes and their impact, a step towards climate-smart village (CSV): a geospatial approach to bring geoponics sustainability in India

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  • Vindhyan Ecology and Natural History Foundation, Mirzapur,India

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The paper deals with various thematic parameters such as agriculture crop scenario (2000, 2010), water stress, precipitation trend and deficit, climate-induced risk towards crops, drought-prone area, suicide attributes of farmers, agro-ecological regions, prediction of future (2050) precipitation and temperature variation during kharif and rabi seasons of India and their spatial pattern were analyzed in GIS for better understanding of climate change. The analysis revealed about the need of synergic approach/strategies to address the impact of climate change. Few of the Climate-smart villages (CSVs) projects of India were discussed here based on their approach, achievement, and limitation. The CSV conceptual strategies are fully based on climate smart agriculture potentiality to achieve sustainability in food security, enhancing the livelihood, eradication of poverty and magnifying the farm household resilience. The climate-induced high and very high risk to the crops areas were found dominated in the arid and semi-arid regions which will be challenged in future due to water stress, inadequate irrigation facility, increasing trend of temperature and variation in precipitation pattern. The hotspot districts of farmer’s suicide were very significant in climate-induced very high risk zone and majority of them falls in the drought-prone areas/extremely high to high water-stressed areas which leads to crop failure. There is a need to formulate a concrete policy, legal, and institutional actions addressing the farmers problem significantly at country, state, district and village levels which will support investment/technology/guideline in and adoption of Climate-smart village (CSV) practices after seeing the socio-economic background (poverty/tribes/backward class) of them.
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1 23
Spatial Information Research
ISSN 2366-3286
Volume 27
Number 6
Spat. Inf. Res. (2019) 27:613-625
DOI 10.1007/s41324-019-00258-0
Analyzing the risk related to climate change
attributes and their impact, a step towards
climate-smart village (CSV): a geospatial
approach to bring geoponics sustainability
in India
Laxmi Goparaju & Firoz Ahmad
1 23
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Analyzing the risk related to climate change attributes and their
impact, a step towards climate-smart village (CSV): a geospatial
approach to bring geoponics sustainability in India
Laxmi Goparaju
1
Firoz Ahmad
1
Received: 10 January 2019 / Revised: 26 February 2019 / Accepted: 28 February 2019 / Published online: 9 March 2019
Korean Spatial Information Society 2019
Abstract The paper deals with various thematic parame-
ters such as agriculture crop scenario (2000, 2010), water
stress, precipitation trend and deficit, climate-induced risk
towards crops, drought-prone area, suicide attributes of
farmers, agro-ecological regions, prediction of future
(2050) precipitation and temperature variation during
kharif and rabi seasons of India and their spatial pattern
were analyzed in GIS for better understanding of climate
change. The analysis revealed about the need of synergic
approach/strategies to address the impact of climate
change. Few of the Climate-smart villages (CSVs) projects
of India were discussed here based on their approach,
achievement, and limitation. The CSV conceptual strate-
gies are fully based on climate smart agriculture poten-
tiality to achieve sustainability in food security, enhancing
the livelihood, eradication of poverty and magnifying the
farm household resilience. The climate-induced high and
very high risk to the crops areas were found dominated in
the arid and semi-arid regions which will be challenged in
future due to water stress, inadequate irrigation facility,
increasing trend of temperature and variation in precipita-
tion pattern. The hotspot districts of farmer’s suicide were
very significant in climate-induced very high risk zone and
majority of them falls in the drought-prone areas/extremely
high to high water-stressed areas which leads to crop
failure. There is a need to formulate a concrete policy,
legal, and institutional actions addressing the farmers
problem significantly at country, state, district and village
levels which will support investment/technology/guideline
in and adoption of Climate-smart village (CSV) practices
after seeing the socio-economic background (poverty/
tribes/backward class) of them.
Keywords Climate-smart village Climate-induced risk to
crops Farmer’s suicide Precipitation trend/deficit
Climate change scenario prediction
1 Introduction
Food production scenario in future will be adversely
affected due to climate change in term of temperatures
variation, rainfall fluctuation, frequent droughts and floods
[13]. The impacts of climate change on farm output and
associated environment landscapes of natural resources are
important and can be better perceived if local/re-
gional/communities issues are to be modified towards
addressing the influence of climate change by appropriate
logical/methodological/scientific approach [4]. Agricul-
tural production across the globe is expected to vary due to
climate change and create major challenges to the liveli-
hoods, food security and nutrition availability of a large
number of people [5]. Such challenges make more vul-
nerable to the socio-economic dependence of many poor
and marginalized people [6]. The future prediction of food
demand by the end of 2050 should be increased at least
70% to meet the demand of future population [7].
Kharif and rabi are the two major cropping seasons
which are adopted in many Asian monsoon countries
including India. Monsoon rainfall variation significantly
impacts the food grain production in India [8] will be
further challenged in future due to its abrupt behavior. In
India, the population is growing rapidly at the same time
the demand of more diversified foods are required to meet
&Laxmi Goparaju
goparajulaxmi@yahoo.com
1
Vindhyan Ecology and Natural History Foundation,
Mirzapur, Uttar Pradesh, India
123
Spat. Inf. Res. (2019) 27(6):613–625
https://doi.org/10.1007/s41324-019-00258-0
Author's personal copy
the demand of future population in the climate change
scenario. This issue needs to be addressed with new
adaptation and mitigation strategies [9]. The new scientific
approach in term of climate-smart village (CSV) was
established which can suitably be addressed the climate
change impact and improve the diversified farm output.
The climate-smart village (CSV) perception perspective is
based on the thoughts of a systematic participatory research
design based on collaborative efforts which suit the local
conditions [10]. Furthermore, it is designed based on Cli-
mate-Smart Agriculture (CSA) approach at local/re-
gional/national levels [10]. The word Climate-Smart
Agriculture (CSA) was first coined by the Food and
Agricultural Organization (FAO) in 2010, to address the
issue of food security, enhance livelihood and the impact of
climate change to maneuver the conventional farming into
a new reality [7]. CSA is a new design to transforming/
reorienting agricultural development under the new sce-
nario of climate change [11,12]. It has the ability to
increase agricultural productivity, adapting/building resi-
lience to climate change, strengthen the food security,
increasing stability in livelihood and also reducing GHG
emissions. Furthermore, CSA support which increases the
incomes, food security, livelihoods resilience which can
bring the change from the farm to national levels in a
sustainable manner. The potentiality of Climate-Smart
Villages was well demonstrated in Asia, Africa, and Latin
America with adopting the basic concept of climate-smart
agricultural technologies/practices/services which signifi-
cantly provides promising results in climatic risk-prone
areas [13].
The study is important because the climate induced risk
and their impact on farms and farmers are very significant
due to rising temperature, variation of rainfall pattern,
depletion of water resources, inadequate irrigation alter-
native and frequent drought which leads to crop failure/
diminishing livelihood/increasing financial burden to the
farmers.
The aim of this paper is to evaluate the various diver-
sified maps such as agriculture crop scenario (2000, 2010),
water stress, precipitation trend and deficit, climate-in-
duced risk towards crops, drought-prone area, suicide
attributes of farmers, agro-ecological regions, prediction of
future (2050) precipitation and temperature variation dur-
ing kharif and rabi seasons of India and examine their
spatial pattern and relationship in GIS domain for better
conceptualization of climate change. Furthermore, these
strong themes give a better understanding of a paradigm
approach of Climate-smart village (CSV) practices. Addi-
tionally, we have compiled few ongoing CSVs projects in
India with their approach, achievement, and limitation.
2 Materials and methods
In this study, we have utilized various diversified datasets
which are given below in Table 1.
In the present study, we have used various maps of India
and followed the basic step to bring different themes into
GIS domain. In the first step the non- projected images
were rectified with country shape file by the use of software
Erdas Imagine 9.1 and brought them into GIS environment.
The digitization was done by adequate zooming to bring
various segregated layers to polygon shape file and attri-
bute were assigned. The future climate change (tempera-
ture and precipitation) anomalies data were in point shape
file were brought into raster by the method of kriging
interpolation technique. The desired maps were created
with adequate cartographic representation as per objective
of our study. All thematic GIS layers along with ancillary
data were analyzed and their pattern/relationships were
examined with GIS (ArcGIS 10.1) based queries (sim-
ple/complex) which gives better comprehension about the
impact of climate change on farm/crops and farmers.
3 Result and discussion
The crop scenario maps for the year 2000 and 2010 are
presented in Fig. 1a, b respectively. In the year 2000,
35.2% of the total geographical area was under single crop.
The GIS analysis revealed approximately 24% of the single
crop was converted to double crop in the span of 10 years.
Majority of the area falls in the state of Gujarat, Maha-
rashtra and Uttar Pradesh. Similarly, 8% of rainfed crop
area of the year 2000 was brought either to the single or
double crop during the same base period. The increase in
total agriculture crop area was found approximately 3% of
the total geographical area in the span of 10 years
(2000–2010). Such positive effort will reduce the yield gap
to meet the challenge for greater food supply [24].
Aridity is the function of precipitation/potential evapo-
transpiration/temperature which significantly reflected as
higher values for more humid condition and the lower
value for more xeric condition [25]. The crop growth sig-
nificantly varies in term of response due to aridity (http://
www.fao.org/docrep/t0122e/t0122e03.htm), soil moisture
interaction thus suitably manifested in term of climate-in-
duced risk [26]. Furthermore, such data can support studies
for bringing the geoponics sustainability as adaptation to
climate change [27,28]. Here we have produced climate-
induced risk map by classifying the annual aridity index
[18] to various risk categories overlaid by water stress layer
[19] is given in Fig. 2.
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614 L. Goparaju, F. Ahmad
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The analysis revealed the climate-induced risk to crops
are very high in some of the states such as Rajasthan,
Haryana, Gujarat, Maharashtra, Karnataka and Andhra
Pradesh due to the low aridity index value. Our high-risk
map to the crops (Fig. 2) was found very similar to the
findings of very high agriculture vulnerability area pro-
duced by Rama Rao et al. 2013 [29]. Furthermore, the
majority of climate-induced very high-risk area falls on
extreme high/high water-stressed areas which will influ-
ence farm resilience and the livelihood of farmers [30,31].
India is one the most water-stressed country among world
where the groundwater level is depleting with at an
alarming rate [32]. The high/extremely high water-stressed
area representing approximately 54% of India’s total area
[19] is a matter of serious concern. The two states viz.
Punjab and Haryana producing approximately 50% of the
national government’s rice supply and 85% of its wheat
crop are facing extremely high water stress (Fig. 2). Fur-
thermore, these states also face soil salinity problem [33]
due to intensive irrigation approach. Such adverse situa-
tions in term of risk/water stress will significantly reduce
crop production if not addressed in the present scenario.
Similar observations of agriculture crops sensitivity to
Table 1 Various data and their sources analyzed in this study
Data used Source/references
The agriculture crop scenario in term of single crop, double crop and multi-crop for the year 2000 and
2010
[14]
Long term precipitation pattern and decadal precipitation deficit during kharif and rabi seasons [15]
Agro-ecological region map of India [16]. (Cited by Ahmad et al. 2018a
[17])
Annual aridity index data [18]
Water stress area [19]
Major states of India showing farmers suicide scenario (2010–2013) [20]
Irrigation intensity percent map [21]
Drought prone area [22]
Future (2050) climate change (temperature and precipitation) anomalies (RCP-6) data for kharif and rabi
seasons
[23]
Fig. 1 The agriculture crop scenario in term of a single crop, double crop and multi-crop a: in the year 2000, b: in the year 2010
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Analyzing the risk related to climate change attributes and their impact, a step towards615
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climate change [34] water crisis [35] were widely observed
in India.
We have evaluated the long-term (1985–2014) precipi-
tation pattern and decadal (2005–2014) precipitation deficit
during major crop season such as kharif and rabi (Fig. 3a,
b). The present decadal (2005–2014) precipitation deficit
was produced by comparing it with the average long-term
(1985–2014) precipitation. The precipitation spatial pat-
terns during kharif season in India were significantly high
in the coast of Western Ghats and most of the North East
states whereas they were moderate in some of the states
such as Chhattisgarh and Orissa. Similarly, precipitation
spatial patterns during rabi season in India were more
pronounced in the coast of Kerala, Tamil Nadu and in the
northern side Jammu Kashmir, Himachal Pradesh, and
Uttarakhand. The decadal precipitation deficit during kharif
and rabi seasons were found more pronounced in Indo-
Gangetic plain is a matter of concern. The similar findings
of rainfall variation were reported in Indo-Gangetic plain
[36] and at the country level of India [3739].
The various agro-ecological regions of India [16] which
was cited by Ahmad et al. 2018a [17], and all regions
boundaries were brought in GIS environment. The decadal
precipitation deficit (2005–2014) categories of kharif and
rabi seasons were overlaid over agro-ecological region map
were given in Fig. 4. The analysis revealed the major
categories such as ‘‘Northern Plain and Central High-
lands’’, ‘‘Northern Plain’’, ‘‘Eastern Plain Bengal and
Assam plains’’, ‘‘Eastern Himalayas’’ and ‘‘North Eastern
Hills (Purvanchal)’’ highlighted the precipitation deficit
during kharif season whereas the majority of the same
agro-ecological regions were also showed the precipitation
deficit during the rabi season (Fig. 4). These highly fertile
lands area of the river Ganga and Brahmaputra basin har-
bor a large number of small and marginalized farmers
provides livelihood to millions of poor people cultivating
crops such as rice/maize during kharif season and
wheat/mustard/pulse during rabi season were in threat of
climate change [40] /precipitation deficit [41].
Fig. 2 Climate-induced risk
map to crops of India produced
based on annual aridity index
[18] overlaid with water-
stressed areas [19]
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We have created a map (Fig. 5) to visualize cropland
area, drought-prone area and irrigation intensity percent
map for a better understanding of various themes and their
spatial distribution pattern. The GIS analysis revealed
approximately 52% of land area of India in the year 2000 is
under crop cultivation (excluding the rain-fed area). The
similar percentage of cropland area of India was also
reported by Ahmad et al. 2018a [17]. The states such as
Punjab, Haryana and few parts of Bihar manifested very
high irrigation intensity ([60%). The majority of the
cropland in the drought-prone area have low irrigation
intensity % (\15%) largely falls in the state of central
Maharashtra, western Gujarat, northern Karnataka, south-
ern Telangana and western Andhra Pradesh (Fig. 5).
Temperature extreme effect the plant growth due to soil
moisture deficit [42] and impact adversely the kharif and
rabi crop production [43]. The majority of farmers of India
heavily rely on growing season temperature and precipi-
tation for agriculture production [44,45]. The increase in
temperature not only leads to the reduction of agriculture
output/crop failure but also leads to suicide events in
farmers [46]. We have analyzed the 4 years (2010–2013)
data of farmer’s suicide at the state level. The major states
where the farmer’s suicides were found highest are
Maharashtra, Andhra Pradesh, Karnataka, Madhya Pra-
desh, Kerala, Uttar Pradesh, Tamil Nadu, Gujarat, Rajas-
than, Assam, and Haryana (Fig. 6).
The major farmer’s suicide hotspot districts are given in
the Table 2. Some significantly high number of farmer’s
suicides hotspot districts were found are Anantapur, Cud-
dapah, Kurnool, West Godavari, Mahbubnagar and Nal-
gonda, Kolar, Tiruvannamalai and Vellore in south Indian
states whereas Sirsa, Sonepat, Alwar, Ganganagar and
Hanumangarh in north Indian states. The GIS analysis
revealed the majority of these hotspot districts falls in
climate-induced very high risk to the crop area (represented
by red colour) with high to the extremely high water-
stressed area (Fig. 2). Furthermore, these hotspot districts
will give a tough challenge in the future to the policy
makers/administrators due to the increasing trend of tem-
perature and variation in precipitation pattern (Figs. 7and
8). The important reason for suicides are cost of cultivation
(chemicals/seeds/Agricultural equipment), the inadequate
market facility (for selling their agriculture output at rea-
sonable price), overburden of loan due to weak socio-
economic condition, water/moisture deficiency in agricul-
ture field and climate change impact (drought/flood/cy-
clones/delayed rainfall/rainfall deficit/temperature
increase) which leads to crop failure [47]. Some important
policies such as crop insurance policy [48] time to time
farmer’s loan waiver policy are good in a short run but not
significant in a long run. Furthermore, the agriculture
sector needs new approaches/strategies with synergic
policies which will adequately address the farmer’s diver-
sified problems at local/regional/communities levels for
Fig. 3 Precipitation pattern (average of 30 years: 1985–2014) overlaid with the decadal (2005–2014) precipitation deficit (same season)
compared with 30 years of average of India a: kharif season, b: rabi season
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Analyzing the risk related to climate change attributes and their impact, a step towards617
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achieving the long-term goal. These efforts will certainly
provide benefits in term of resilience in agriculture/liveli-
hood to the farmers and will reduce significantly the sui-
cide incidence among them in the long run.
The predicted annual temperature and precipitation
anomalies data [23] the RCP 6 simulation models [49,50]
for the year 2050 were used to produce the map [51] for
kharif and rabi seasons of India. The temperature (C)
increase in the year 2050 during kharif and rabi seasons
were found to be in the range of (0.84–1.81) and
(0.75–1.97) respectively. In one of the recent study by
Zhao et al. 2017 [43] significantly manifested the decrease
of yields on average of rice, maize, wheat and soybean with
the range of 3.1–7.4% [52]. The maximum temperature
increase was witnessed in the state of Himachal Pradesh,
Uttrakhand and some part of the northeastern state of India
(Fig. 7). Similarly, rainfall deficit during kharif season in
the year 2050 was found significant in the state of Gujarat,
Himachal Pradesh and Punjab (Fig. 8). The rainfall deficit
during rabi season in the same base year was found notably
Fig. 4 Broad ecosystem of India representing various agro-ecological regions (1 to 20) overlaid with decadal (2005–2014) precipitation deficit
obtained by comparing with long-term (1985–2014) precipitation during kharif and rabi seasons
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618 L. Goparaju, F. Ahmad
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in the state of Tamil Nadu, Kerala and some part of the
northeastern state of India. Ahmad and Goparaju 2018 [53]
also used the future climate data and analyzed their spatial
pattern in a similar way. Finally based on the climate data
prediction map for the year 2050 we can conclude the
climate change impact during kharif and rabi seasons
should be significant over India whereas it is more crucial
for some of the states such as Himachal Pradesh, Haryana,
Punjab, Gujarat and some part of northeastern states of
India.
In the whole globe, the CSVs have been implemented at
several places as an experimental basis [10] whereas few
projects are also running in some of the states of India
marked in Fig. 5. The CSVs pilot project was implemented
in 27 villages in the district of Karnal in the state of Har-
yana by international efforts [54]. The objective was to
strengthen the adaptive capacity farming communities
largely paddy/wheat growing areas to climate change.
Seeing the success in term of managing water, weather,
nutrient, carbon, energy and local knowledge it has been
further extended in several other districts (Yamunanagar,
Ambala, Kurukshetra, Karnal, Jind, Kaithal, Panipat,
Sonepat, Sirsa and Fatehabad) of Haryana [54]. The work
includes protecting the village’s paddy and other crops
with adverse climate change. Furthermore, in the model
village, the concept of the ambassador was introduced to
involve young generation youths and adequate women
participation with technological improvement such as laser
land leveler, zero tillage machines etc. which magnify the
food production in a sustainable manner. Furthermore, the
initiation of subsidy to farmers for the conservation of
water/increasing productivity and stress has been given
eradicating the practice of stubble burning [55].
In Punjab, the Climate-Smart Village was in Patiala
districts. The village farm owner/participant agreed cli-
mate-smart agricultural practices being undertaken in this
CSVs are some of the best action/use for making farm/
agriculture resilience and sustainable in the Punjab that
Fig. 5 India’s map showing climate smart village projects, cropland area, drought-prone area, and irrigation intensity %
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Analyzing the risk related to climate change attributes and their impact, a step towards619
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includes direct-seeded rice (DSR), precision nutrient and
water management, efficient weed management, nitrogen
management and spraying techniques for weed control
[56]. The farmers in Climate Smart Villages are using a
variety of advanced technology and institutional interven-
tions related to weather forecasting, index-based insurance
schemes, effective irrigation management strategies,
adapted cultivars, and a range of other low greenhouse gas
emitting options a paradigm approach of Climate-Smart
Agriculture [57]. Some of the limitations also described by
Groot et al. 2018 [58] such as lack of market intelligence in
Punjab negatively affects the profitability of the product.
Bihar state has the site of several CSVs run by the
government and international organizations. In the initial
stage, it was spread in 31 CSVs as a pilot project in blocks
of Rajapaker, Hajipur, Mahua and Pusa blocks of Vaishali
and Samastipur districts of Bihar state [59]. Seeing the
success of CSV pilot project the Bihar government is going
to extend it in 100 new villages in eight districts of Bihar,
which have been divided into four groups (each group has
MAHAR
ASHTR
A
ANDHR
A
PRADE
SH
KARNA
TAKA
MADHY
A
PRADE
SH
KERALA
UTTAR
PRADE
SH
TAMIL
NADU
GUJAR
AT
RAJAST
HAN ASSAM HARYA
NA
2010 3141 2525 2585 1237 895 548 541 523 390 369 297
2011 3337 2206 2100 1326 830 645 623 578 268 312 384
2012 3786 2572 1875 1172 1081 745 499 564 270 344 276
2013 3146 2014 1403 1090 972 750 105 582 292 305 374
0
500
1000
1500
2000
2500
3000
3500
4000
Number of farmers suicide
Farmers suicide scenario (2010-2013) in Indian states
Fig. 6 The major states of India showing farmers suicide scenario (2010–2013)
Table 2 Major farmer’s suicide hotspot district in India
The major farmers suicide
states
Farmers suicidal hotspot districts of India
Maharashtra Akola, Bid, Buldana, Parbhani, Yavatmal, Nanded, Pune and Washim
Andhra Pradesh Anantapur, Cuddapah, Kurnool and West Godavari(Andhra Pradesh)
Mahbubnagar and Nalgonda (Telangana new state created in the year 2014 by bifurcating from Andhra
Pradesh)
Karnataka Kolar, Bagalkot, Bijapur, Chitradurga, Davanagere and Gulbarga
Madhya Pradesh Ashoknagar, Datia, Guna, Gwalior, Morena, Rajgarh, Sheopur and Shivpuri
Kerala Ernakulam, Idukki and Kottayam
Uttar Pradesh Agra, Muzaffarnagar, Baghpat, Bulandshahr, Hathras and Mathura
Tamil Nadu Dharmapuri, Namakkal, Salem, Tiruvannamalai and Vellore
Gujarat Navsari, Surat and Valsad
Rajasthan Alwar, Ganganagar, Hanumangarh, Jaipur, Jhunjhunun and Sikar
Assam Cachar, Hailakandi and Karimganj
Haryana Jhajjar, Rewari, Rohtak, Sirsa and Sonepat
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620 L. Goparaju, F. Ahmad
Author's personal copy
25 villages)—Purnea–Katihar, Darbhanga–Samastipur,
Patna–Nalanda, and Munger–Sheikhpura [60]. The main
aim is to train 3000 farmers from these villages in resource
conservation crop intensification and other technology
which will significantly support to reduce the adverse
impact of climate change on agriculture landscape which
will fulfill the goal of making climate-smart villages [60].
Karnataka state has the site of four CSV in the district of
Fig. 7 The prediction of temperature variation for the year 2050 a: kharif season, b: rabi season
Fig. 8 The prediction of precipitation variation for the year 2050 a: kharif season, b: rabi season
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Analyzing the risk related to climate change attributes and their impact, a step towards621
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Bellari because of it is the hotspot of water shortage, land
degradation, and poverty which resulted into low crop
yields have impacted agriculture negatively, resulting in
food insecurity and poor nutrition of humans and cattle in
this region [61]. This was managed by International Crops
Research Institute for the Semi-Arid Tropics (ICRISAT)
along with the State Department of Agriculture, and
Watershed Development Department, few NGOs and the
local population. The automatic weather station, hydro-
logical gauging station, groundwater level monitoring, and
soil/water conservation practice has been adopted which
significantly increased crop production including maize
and groundnuts [61]. Now few CSV has been also in
existence as an experimental basis in Betul district of
Madhya Pradesh state which is dominated by the Sched-
uled Tribes with integrating the gender [62]. The result
highlights the gender inclusive interventions in a CSV
provides significant success when supported by village
climate management committee (VCMC). The similar
types of assessment of CSVs have been done in other parts
of the country in the world [13,63,64]. The analysis of
CSV approach in the various village of India significantly
highlighted the success towards climate change. Some of
the highest poverty states (https://www.mapsofindia.com/
maps/india/poverty.html) such as Chhattisgarh, Assam,
Jharkhand, Bihar, Orissa and northeast states of India [65]
need special attention (policy intervention) for priorities as
far as climate change is concern because these states keep a
significantly high population of ethnic tribes/backward
class which retain weak adaptive capacity towards climate
change and losing significantly their livelihood
opportunity.
4 Conclusion
In the present study, we have used diversified sets of data
of India which directly or indirectly related to the attribute
to climate change which impact the farming system and
farmers.
The study showed that the total crop area in the year
2000 was 52% of the total geographical area and an
increase of 3% was witnessed in the span of 10 years
(2000–2010). The 24% of the single crop was converted to
double crop in the same base period. The climate-induced
risk map to crops is very high in some of the states which
fall in arid and semi-arid regions of India. Furthermore, the
majority of climate-induced very high-risk area falls on
extreme high/high water-stressed areas which will influ-
ence farm resilience and the livelihood of farmers. The
decadal (2005–2014) precipitation deficit during kharif and
rabi seasons are more pronounced in some of the agro-
ecological regions of India and majority of them fall in the
river Ganga and Brahmaputra basin are a matter of concern
because it provides livelihood to millions of peo-
ples/farmers. The majorities of suicidal hotspot districts fall
in climate-induced very high risk to the crop area and fall
in the high/extremely high water-stressed areas. Further-
more, few of these districts fall in the drought prone area
with low irrigation intensity. The predicted climate map for
the year 2050 during kharif and rabi seasons showed the
significant increase in temperature and variation in pre-
cipitation pattern which will impact adversely to the agri-
culture output. The diversified analysis and their
relationships highlight an innovative collective approach
with new strategies to address the farmer’s problem in the
new reality of climate change. Climate-smart villages
(CSVs) approach is fully based on climate smart agricul-
ture (CSA) principles which have the capacity to achieve
sustainability in agriculture, livelihood generation, poverty
eradication and can bring farm household resilience. In this
article, we have discussed the few CSVs which were
implemented in various states of India with their objec-
tives/achievements/limitations. There is a need to extend
CSVs in various states of India urgently where the climate
change impacts are more severe. The areas also need
special attention/prioritization where the people have the
weak socio-economic condition because they have weak
adaptive capacity towards climate change.
4.1 Limitation of this research
The study used diversified dataset therefore the results are
showing the circa estimates and can be used with
precaution.
Acknowledgements The authors are grateful to all websites/litera-
tures from where we have used the data sets/maps for evaluation.
Authors’ contributions FA proposed the idea and analyzed the
satellite and ancillary data in GIS domain, LG supervised the analysis,
and FA & LG drafted the manuscript. All authors read and approved
the final manuscript.
Funding information No funding in any form has been received by
any of the author for current work.
Compliance with ethical standards
Conflict of interest The authors declare that they have no competing
interests.
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... Country-level (large-scale) and high-resolution (small-scale) localized data can be used to explore the utility of both frameworks since India experiences a high frequency of droughts that decrease crop yields. Goparaju and Ahmad [72] included a map of India showing the decadal (2005 to 2014) precipitation deficit, suggesting that the entire country experiences droughts that will impact crop growth and yields. The extensive areal coverage of droughts in India indicates the importance of having a framework that allows crops to be selected based on the diversity of soil and climatic conditions found at the site level. ...
... In some situations, with interest in developing sustainable agricultural practices, alternative approaches that ameliorate the soil organic matter levels or interplanting trees/shrubs with crop plants will need to be explored [77][78][79]. This would approach agriculture from the angle of remediation of the edaphic environment to increase its retention or water-holding capacity when climate change results in decreased precipitation levels, as shown by Goparaju and Ahmad [72] for India's major grain production areas. They called for a diversified approach to address climate change impacts and better-diversified farm output [80]. ...
... Still, we would suggest that there needs to be a better approach to selecting plants to grow in different parts of India (and other places around the world), considering that drought frequencies are high. A high proportion of India's agriculture experiences droughts as shown by Goparaju and Ahmad [72], with 54% of India's total land area experiencing high or extremely high-water stress. ...
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... In view of the foregoing challenges imposed by climate change on agricultural systems, the climate-smart village (CSV) concept was designed to promote the adoption of strategies that help farmers in vulnerable areas to cope with and adapt to adverse climate change impacts. The Consultative Group for International Agricultural Research (CGIAR) programme on Climate Change, Agriculture and Food Security (CCAFS) introduced climate-smart agricultural (CSA) practices in selected CSVs to help improve farmers' welfare (Aggarwal et al., 2018;Aryal et al., 2016;Barbon et al., 2017;Goparaju & Ahmad, 2019). Currently, 36 CSVs are in operation in Africa, Latin America and Asia. ...
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The climate-smart village (CSV) concept is aimed at helping to improve household welfare through better climate change adaptation. This study used data from small ruminant producers in CSV and non-CSV communities of the Lawra and Jirapa municipalities in the Upper West Region of Ghana. First, we assessed how the project related to adoption of climate change coping and adaptation strategies. Next, we examined how the adoption influenced goat producers’ welfare through its effect on herd size. The results showed that farmers located in CSVs adopted about 0.57 more strategy per season for coping with and adapting to climate change than those in non-CSVs. Nevertheless, this higher adoption rate did not translate into any substantial difference in herd size, even though for every additional strategy adopted farmers’ welfare increased by about 0.21%, all else equal. These findings indicate that the CSV concept can help reduce farmers’ vulnerabilities to climate change and foster sustainable livestock production through better adoption of strategies and enhanced welfare. Thus, strengthening the capacities of existing CSVs and scaling up the project to cover new communities would improve the social welfare function for livestock farmers in Ghana and elsewhere. Nevertheless, future research should investigate reasons why higher adoption did not directly increase herd size and advise project implementers accordingly.
... Their effects include tea plants becoming stressed by rising temperatures and excessive rainfall, consequently lowering their output, while sufficient rainfall can have a favorable impact on yields. Ahmed et al. [53] opined that climate change and variability modifies humidity which has obstructive impacts on tea yield, quality, and regional suitability, rendering tea systems and the related economies vulnerable. According to Lou et al. [43], rising temperatures shorten plucking periods, thereby reducing yields and quality. ...
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Climate change (CC) is a global issue, with effects felt across nations, including India. The influences of CC, such as rising temperatures, irregular rainfall, and extreme weather events, have a direct impact on agricultural productivity, thereby affecting food security, income, livelihoods, and overall population health. This study aims to identify trends, patterns, and common themes in research on Climate Change and Resilience, Adaptation, and Sustainability of Agriculture in India (CCRASAI). It also seeks to illuminate potential future research directions to guide subsequent research and policy initiatives. The adverse impacts of CC could push farmers into poverty and undernourishment, underscoring the imperative to focus on the resilience, adaptation, and sustainability of agriculture in India. A bibliometric review was conducted using Biblioshiny and VoSviewer software to analyze 572 articles focused on CCRASAI from the Scopus and Web of Science databases, published between 1994 and 2022. There was an evident upward trend in CCRASAI publications during this period, with steady growth appearing after 2007. Among the States and Union Territories, Delhi, Tamil Nadu, West Bengal, Andhra Pradesh, and Karnataka have the highest number of published research articles. Research on CCRASAI is most concentrated in the southern plateau, the trans-Gangetic and middle Gangetic plains, and the Himalayan regions. The frequently used terms—'climate change impacts,’ ‘adaptation strategies,’ and ‘sustainable agriculture'—in CCRASAI research emphasize the focus on analyzing the effects of CC, creating adaptation strategies, and promoting sustainable agricultural practices.
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... The terminology of "smart" came to stay, including for the cities [171]. The concept of climate-smart village and the associated practices are particularly relevant in countries where there are serious problems of food security, such as the India context [172]. ...
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As a step towards building climate resilience, a USAID funded project aimed at scaling out climate-smart agriculture through the CSV approach in Madhya Pradesh in India, is providing an enabling platform to women farmers to manage and lead climate change adaptation and mitigation interventions in their villages. This info note presents preliminary information on each of the major steps of a CSV approach and its components and linking them with gender and CSA. It focuses on the initial findings of a baseline assessment, the process of CSV formation and the initial output of CSA implementation (for Rabi/monsoon season) with due consideration of gender dynamics in agriculture.
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Agroforestry system has the enormous capacity to achieve social, economic, and environmental goals by optimizing land productivity. The aim of the present study was to evaluate the land potentiality in India for agroforestry based on FAO land suitability criteria utilizing various land, soil, climate, and topographic themes. This was achieved in GIS Domain by integrating various thematic layers scientifically. The analysis of land potentiality in India for agroforestry suitability reveals 32.8% as highly suitable (S1), 40.4% moderately suitable (S2), 11.7% marginally suitable (S3), and 9.1% not suitable (NS). About 52% of land of India is under the cropland category. In addition, it revealed that the 46% of these cropland areas fall into high agroforestry suitable category “S1,” and provide huge opportunity for harnessing agroforestry practices. Furthermore, agroforestry suitability mapping in broad ecosystem and in different agroecological regions will assist various projects in India at the regional level. Such results will also boost the various objectives of the National Agroforestry Policy (2014, http://www.cafri.res.in/NAF_Policy.pdf) and policymakers of India where they need to extend it. The potential of geospatial technology can be exploited in the field of agroforestry for the benefit of rural poor people/farmers by ensuring food and ecological security, resilience in livelihoods, and can sustain extreme weather events such as droughts and climate change impact. Such type of research can be replicated in India at village level (local level) to state level (regional level) utilizing the significant themes which affect the agroforestry suitability. This will certainly fetch better results on ground and will significantly assist the management programs. Full article can be read using the given below link: https://rdcu.be/NHw8
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