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Vulnerability of wheat production to climate change in India

Authors:
  • Borlaug Institute for South Asia. International Centre for Maize and Wheat Reserach (CIMMYT)

Abstract and Figures

The production of wheat, a crop sensitive to weather, may be influenced by climate change. The regional vulnerability of wheat production to climate change in India was assessed by quantifying the impacts and adaptation gains in a simulation analysis using the InfoCrop-WHEAT model. This study projects that climate change will reduce the wheat yield in India in the range of 6 to 23% by 2050 and 15 to 25% by 2080. Even though the magnitude of the projected impacts is variable, the direction is similar in the climate scenarios of both a global (GCM-MIROC3.2. HI) and a regional climate model (RCM-PRECIS). Negative impacts of climate change are projected to be less severe in low-emission scenarios than in high-emission scenarios. The magnitude of uncertainty varies spatially and increases with time. Differences in sowing time is one of the major reasons for variable impacts on yield. Late-sown areas are projected to suffer more than the timely-sown ones. Considerable spatial variation in impacts is projected. Warmer central and south-central regions of India may be more affected. Despite CO2 fertilization benefits in future climate, wheat yield is projected to be reduced in areas with mean seasonal maximum and minimum temperatures in excess of 27 and 13 degrees C, respectively. However, simple adaptation options, such as change in sowing times, and increased and efficient use of inputs, could not only offset yield reduction, but could also improve yields until the middle of the century. Converting late-sown areas into timely-sown regions could further significantly improve yield even with the existing varieties in the near future. However, some regions may still remain vulnerable despite the adaptation interventions considered. Therefore, this study emphasises the need for intensive, innovative and location-specific adaptations to improve wheat productivity in the future climate.
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CLIMATE RESEARCH
Clim Res
Vol. 59: 173–187, 2014
doi: 10.3354/cr01212 Published online April 16
1. INTRODUCTION
With the threat of climate change looming over
crop productivity, the most vulnerable regions of
the world are the tropics, particularly the semi-arid
regions (Parry et al. 2004, Easterling et al. 2007). The
21st century is projected to experience a rise in
surface air temperature between 1.8 to 4°C together
with frequent warm spells, heat waves, heavy rainfall
events and droughts (IPCC 2007a). These climate
change related events can affect agricultural pro -
duction with serious implications on food security
(Nelson et al. 2012). In fact, crop production needs to
be increased substantially in order to meet the rising
demand of a growing population and economy in
developing countries (FAO 2012).
© Inter-Research 2014 · www.int-res.com*Corresponding author: nareshkumar.soora@gmail.com
Vulnerability of wheat production to climate
change in India
S. Naresh Kumar1,*, P. K. Aggarwal1,2, D. N. Swaroopa Rani1, R. Saxena1,
N. Chauhan1, S. Jain1
1Environmental Sciences, Indian Agricultural Research Institute, PUSA, New Delhi 110012, India
2Present address: CGIAR Research Program on Climate Change, Agriculture and Food Security,
International Water Management Institute, NASC Complex, New Delhi 110012, India
ABSTRACT: The production of wheat, a crop sensitive to weather, may be influenced by climate
change. The regional vulnerability of wheat production to climate change in India was assessed
by quantifying the impacts and adaptation gains in a simulation analysis using the InfoCrop-
WHEAT model. This study projects that climate change will reduce the wheat yield in India in the
range of 6 to 23% by 2050 and 15 to 25% by 2080. Even though the magnitude of the projected
impacts is variable, the direction is similar in the climate scenarios of both a global (GCM-
MIROC3.2.HI) and a regional climate model (RCM-PRECIS). Negative impacts of climate change
are projected to be less severe in low-emission scenarios than in high-emission scenarios. The
magnitude of uncertainty varies spatially and increases with time. Differences in sowing time is
one of the major reasons for variable impacts on yield. Late-sown areas are projected to suffer
more than the timely-sown ones. Considerable spatial variation in impacts is projected. Warmer
central and south-central regions of India may be more affected. Despite CO2fertilization benefits
in future climate, wheat yield is projected to be reduced in areas with mean seasonal maximum
and minimum temperatures in excess of 27 and 13°C, respectively. However, simple adaptation
options, such as change in sowing times, and increased and efficient use of inputs, could not only
offset yield reduction, but could also improve yields until the middle of the century. Converting
late-sown areas into timely-sown regions could further significantly improve yield even with the
existing varieties in the near future. However, some regions may still remain vulnerable despite
the adaptation interventions considered. Therefore, this study emphasises the need for intensive,
innovative and location-specific adaptations to improve wheat productivity in the future climate.
KEY WORDS: Impacts · Adaptation · Irrigated system · Indo-Gangetic plains · Wheat · Adaptation ·
Agriculture · Climate change
Resale or republication not permitted without written consent of the publisher
Clim Res 59: 173–187, 2014
Cereals account for the major share of food grains,
and wheat is the most important cereal crop world-
wide. Among the 12 wheat mega-environments
proposed by the International Maize and Wheat
Improvement Center (CIMMYT), Mexico, the Indo-
Gangetic plains (IGP) and Central India are the
major wheat producing regions in South-Asia (Braun
et al. 1996). In India, wheat is the most important
staple crop along with rice. From an annual food
grain production of 241.6 Mt, wheat contributes
~36% (~85.7 Mt) of the total, covering 29.25 Mha at a
productivity of 2.93 t ha−1. It not only provides food
for consumers, but it is also a major source of liveli-
hood to millions of farmers. Wheat yield needs to be
increased from 2.6 to 3.5 t ha−1 within the next 25 yr
(Ortiz et al. 2008) to meet the projected increase in
demand. However, climate change is projected to
reduce crop production by 10 to 40 % in India
between 2080 and 2100 under current agricultural
management, according to studies on global climate
change (Rosenzweig & Parry 1994, Fischer et al.
2002, Parry et al. 2004, IPCC 2007b).
Studies conducted specifically on India also project
a decline in agricultural production due to climate
change, but at varying magnitudes (Aggarwal &
Sinha 1993, Lal et al. 1998, Saseendran et al. 2000,
Aggarwal & Mall 2002, Mall & Aggarwal 2002, Byjesh
et al. 2010, Srivastava et al. 2010, Naresh Kumar 2011,
Naresh Kumar et al. 2011, 2012, 2013). An increase in
temperature by 1°C is projected to reduce wheat pro -
duction in India by 4 to 5 Mt, even after taking CO2
fertilization into account (but not including benefits
from other potential adaptation measures) (Aggarwal
2008). Furthermore, a re duction of ~19 and ~27.5 Mt of
wheat is projected following a rise in temperature of 3
and 5°C, respectively (Aggarwal & Swaroopa Rani
2009). Wheat contributes ~21% of the world’s total
food grains, and ~81% of wheat consumed in devel-
oping countries is produced and utilized within the
same country (CIMMYT 2005). Hence, it is essential
to assess the gains due to possible adaptation strate-
gies in addition to quantifying the impacts. Such
analysis is aimed to provide information on the vul-
nerability of wheat-growing areas and to help prepare
against the adverse impacts of climate change.
Climate change affects crops mainly through ele-
vated CO2, temperature increase and change in rain-
fall. In India, wheat is grown during the winter sea-
son. The sowing starts in November, and crop is
harvested by the early half of April. Since more than
85% of the wheat land area is irrigated, the influence
of rainfall is not significant. But elevated CO2levels
increase grain yield due to an increase in leaf area
duration, straw yield, number of ears per m2and
kernel weight (Rawson 1995, Pleijel et al. 2000). The
reported gain in yield has ranged from 17 to 19 % at
550 μmol CO2mol−1 to ~31% at 700 μmol CO2mol−1
(Amthor 2001, Tubiello et al. 2007, Chakrabarti et
al. 2012).
Heat stress is considered to be the major climatic
factor affecting wheat yield in the IGP region of India
(Ortiz et al. 2008). A substantial area is under late-
and very late-sown conditions (until the third week of
December), exposing the crop to heat stress. This
results in considerable yield reduction in central and
eastern India. The crop is sensitive to high tempera-
ture (Rawson & Bagga 1979, Rawson 1992, Porter &
Gawith 1999, Ortiz et al. 2008), which affects photo-
synthesis (Blum et al. 1994, Pushpalatha et al. 2008),
growth and development (Porter & Gawith 1999),
number of grains (Rawson & Bagga 1979) and grain
yield (Asseng et al. 2011). Wheat crops exposed to
temperatures >34°C have significantly low yields be -
cause of accelerated senescence (Asseng et al. 2011,
Lobell et al. 2012). The optimum temperature range
is 17 to 23°C during the entire growth period, with
maximum temperatures not exceeding 37°C (Porter
& Gawith 1999). Temperature optima are ~22°C for
vegetative development and 21°C for reproductive
development, while ~35.4°C is the maximum limit for
grain filling (Porter & Gawith 1999). Temperatures
>31°C just before anthesis induce pollen sterility and
reduced grain number and yield (Ferris et al. 1998).
In March 2004, high temperatures in the IGP has-
tened crop maturity, reducing wheat production by
4 Mt (Samra & Singh 2004). Lobell et al. (2012) re -
ported wheat yield reductions of up to 20% in certain
pockets of the IGP, due to a 2°C increase in seasonal
temperature. On the other hand, low temperatures
can be problematic for seed set. Projected increases
in temperatures and frequency of weather extremes
(IPCC 2007a) could therefore significantly constrain
wheat production in a future climate.
Considering the importance of wheat to India’s
food security, it is imperative to understand the spa-
tial and temporal magnitudes of climate change im -
pacts on the crop at a regional level. Several low-cost
technologies can reduce the negative impacts of
climate change (Easterling et al. 2007). These adap-
tation strategies include improved varieties (Braun et
al. 1996, Chapman et al. 2012) and im proved or
altered agronomy (Easterling et al. 2003, Ingram et
al. 2008) including efficient input use. A recent ana -
lysis on irrigated and rainfed-rice in India showed
that such adaptation can significantly reduce the
negative impacts of climate change (Naresh Kumar
174
Naresh Kumar et al.: Climate change vulnerability of Indian wheat
et al. 2013). There is no such assessment available for
wheat. The present study was carried out to provide
this information in order to help plan adaptation
strategies. The aims were (1) to quantify the impacts
on wheat yields at a regional level, and (2) to quantify
the adaptation gains and identify the vulnerable
regions for wheat production in a future climate.
2. MATERIALS AND METHODS
2.1. Simulation analysis using InfoCrop
In this study, irrigated wheat for timely-, late- and
very late-sown conditions were considered. To carry
out the analysis, InfoCrop-WHEAT model was used
due to its suitability for simulating the growth, devel-
opment and yield of wheat in sub-tropical and tropi-
cal conditions such as in India. InfoCrop is a generic
crop growth model that can simulate the effects of
weather, soil, agronomic managements (planting,
nitrogen, residue and irrigation), and major pests on
crop growth and yield (Aggarwal et al. 2006). The
model dynamically simulates different growth and
development processes of a crop. The total crop
growth period in the model is divided into 3 phases:
(1) sowing to seedling emergence, (2) seedling emer-
gence to anthesis and (3) the storage organ filling
phase. The model requires various coefficients such
as thermal time for phenological stages, potential
grain weight, specific leaf area, maximum relative
growth rate and maximum radiation use efficiency.
Crop management inputs include time of sowing,
application schedule, and the amount and type of
fertilizer and irrigation. Soil input data include pH,
texture, layer-wise thickness, bulk density, saturated
hydraulic conductivity, organic carbon, slope, water
holding capacity and permanent wilting point.
Location-specific daily weather data (solar radiation,
maximum and minimum temperatures, rainfall, wind
speed and vapour pressure) are also required to
simulate the crop performance. The details on the
simulation framework of temperature, CO2, and rain-
fall effects on crop growth and development have
been described earlier (Aggarwal et al. 2006, Srivas-
tava et al. 2010, Naresh Kumar et al. 2011).
The InfoCrop-WHEAT model was calibrated and
verified for Indian varieties (Aggarwal & Kalra 1994,
Aggarwal 2003, Aggarwal et al. 2006, Aggrawal &
Swaroopa Rani 2009). The model was able to capture
the year-to-year variation in dry matter (mean ±
RMSE: 9.9 ± 0.55 t ha−1) and grain yield (4.7 ± 0.21 t
ha−1) of the experiments (Aggarwal et al. 2006). The
model performance indicators, such as RMSE, model
efficiency, agreement index and bias (Wallach et al.
2006), indicate that the model could adequately sim-
ulate the phenology and grain yield (Fig. 1a−c) of dif-
ferent varieties sown in timely-, late- and very late-
conditions, as well as for different locations (Table 1).
This calibrated and verified model was used for sim-
ulating the yield during the baseline period (1969−
1990) and for assessing the future climate impact on
(1) timely-sown (2) late-sown and (3) very-late sown
irrigated crops with and without-adaptation.
2.2. Processing of input data
2.2.1. Weather
The Indian Meteorological Department (IMD) sup-
plied daily gridded (1° × 1°) data on rainfall, minimum
and maximum temperatures. Based on the availability
of observed weather data for all grids across India, we
used the 1969 to 1990 period data coinciding with the
baseline period (1960 to 1990) of climate models.
These data were converted to InfoCrop weather file
format using custom made software. Files for 22 yr
(1969 to 1990) for each grid were prepared and served
as the observed data for the baseline period. Solar
radiation was calculated based on the Hargreaves
method (Hargreaves 1994), which is reported to be
the best suited for Indian conditions (Bandyopadhyay
et al. 2008). The potential evapotranspiration was
calculated by the Priestley−Taylor method.
2.2.2. Soil data.
Data on soil parameters such as texture, water
holding characteristics, bulk density, soil pH, and
depth of 3 soil layers were adopted from the soil data-
base of the National Bureau of Soil Science and Land
Use Planning (NBSSLUP) and Harmonized World
Soil Database (HWSD) v1.1 (FAO/IIASA/World Soil
Information−ISRIC/ISSCAS/JRC 2009). The HWSD
v1.1 is a 30’ raster database with more than 15 000
different soil mapping units containing information
within the 1:5 000 000 scale world soil map. The
NBSSLUP data base is at a 1:250 000 scale providing
soil series information for 60 agro-ecological sub-
regions of India. The characteristic data of major soil
type in a grid (1° × 1°) were extracted using GIS tools
and entered into the model. The pedo-transfer func-
tions were used to derive the hydraulic characteristic
coefficients.
175
Clim Res 59: 173–187, 2014
2.2.3. Varietal coefficients
The coefficients of dominant wheat varieties in dif-
ferent regions of India were taken from the published
literature (Aggarwal & Kalra 1994, Aggarwal 2003,
Aggarwal et al. 2006, AICW&BIP 2012). Grids cover-
ing a region with similar type of dominant cultivars
had similar varietal coefficients. The performance of
short-, medium- and long-duration varieties sown in
timely-, late- and very late-conditions, respectively,
was simulated and the combination that gave the
highest grain yield was taken for the baseline and
impact assessment.
2.2.4. Management.
In order to mimic the situation in farmers’ field con-
ditions, the crop was provided with variable doses of
fertilizers for timely (120 kg N ha−1) and late (100 kg
N ha−1) sown conditions. Half of the nitrogen was
applied as urea at the time of sowing and the remain-
ing half at crown root initiation (CRI; 20 to 25 d after
sowing) stage. In addition to a pre-sowing irrigation,
5 irrigations (50 mm each) were provided at the CRI,
jointing, flowering, milk and late grain-filling stages
of the crop. It was assumed that the crop was main-
tained free of pest and disease infestation.
2.3. Estimating impact of climate change
2.3.1. Estimating baseline yields
Simulations were run for each of the sowing times
for 21 yr (sowings in 1969 to 1989 and harvests in
1970 to 1990) using the IMD gridded data, resulting
in 21 yr averaged yields per grid. District-wise yield
was obtained as a sum of the weighted yield from
each grid fraction in the district. This was the base-
line yield of a district for the respective sowing condi-
tion. State-level yield was calculated separately for
timely-, late- and very late-sown crops for the respec-
tive state based on simulated yield of its districts.
About half of the wheat area was considered to be
under late- and very late-sown condition in north-
176
3.5
4.0
4.5
5.0
5.5
6.0
6.5
3.5 4.0 4.5 5.0 5.5 6.0 6.5
Observed grain yield (Mg ha–1)
Simulated
g
rain yield (M
g
ha–1)
Grain yield
60
70
80
90
100
110
120
60 70 80 90 100 110 120
Days after sowing (observed)
Days after sowing (simulated)
Days to 50% anthesis
90
100
110
120
130
140
150
160
170
90 100 110 120 130 140 150 160 170
Days after sowing (observed)
Days after sowing (simulated)
Days to physiological maturity
1:1 line
1:1 line
1:1 line
a
b
c
r² = 0.88
RMSE = 3.68
ME = 0.99
AI = 0.99
Bias = –2.18
r² = 0.85
RMSE = 5.33
ME = 0.99
AI = 0.99
Bias = –0.58
r² = 0.88
RMSE = 3.21
ME = 0.89
AI = 0.99
Bias = –0.24
Fig. 1. Verification results of InfoCrop-WHEAT model for (a)
days to 50% anthesis, (b) days to physiological maturity and
(c) grain yield. ME: model efficiency, AI: agreement index.
See Table 1 for details of data source
Naresh Kumar et al.: Climate change vulnerability of Indian wheat
west and central IGP, compared to
~18% of the area in eastern IGP and
~40% in Central India. State-level
yield, obtained from total production
and area, were then up-scaled to
national level to obtain the national-
level yield for timely-, late- and very
late-sown conditions. Additionally, the
consolidated yield at the national level
was calculated by taking state-level
weighted yield under each sowing
condition.
2.3.2. Simulating yields in future
scenarios
To simulate the impact of climate
change on wheat yield, the climate
outputs of a global climate model
(GCM; MIROC3.2.HI, Atmosphere and
Ocean Research Institute, Japan; Na -
tional Institute for Environmental
Studies, Japan; Frontier Research
Centre for Global Change, Japan) and
a regional climate model (RCM; PRE-
CIS: Providing Regional Climates for
Impact Studies, which included the
Hadley Centre Climate Model [Had -
CM3] as the GCM) were used. They
are found to suitably simulate Indian
climatic conditions (Rupa Kumar et al.
2006, Das et al. 2012), and PRECIS is
extensively used in climate change
studies in India (INCCA 2010, NAT-
COM 2012). Climate data of the
MIROC3.2.HI model for A1b and B1
emission scenarios, and those of the
PRECIS model for A1b, A2 and B2
emission scenarios for 2050 and 2080
were used. The spatial resolution of
MIROC3.2.HI is 1.125° × 1.125° and
that of PRECIS is 0.44° × 0.44°. Since
the observed weather is in 1° × 1° reso-
lution, the GCM and RCM outputs
were rescaled to 1° × 1° resolution for
comparison and up-scaling of the crop
model outputs. Climate scenarios were
derived using the climate model pro-
jected changes in monthly tempera-
tures (minimum and maximum) and
rainfall for 2050 and 2080 following the
formulae given below.
177
Location Latitude Longitude Altitude Crop season Soil type Experiment, Wheat variety Number of treatments
(°N) (°E) (m a.s.l.) and year treatment used for comparing
with simulated values
Ludhiana 30.9 75.5 242 Nov−Dec Sandy loam IVT, TS HD-2687, K-9107, HUW-468 9
2000, 2002, 2003
Karnal 29.7 76.8 235 Nov−Dec Sandy loam IVT, TS HD-2687, K-9107, HUW-468 9
2000, 2002, 2003
Karnal 29.7 76.8 235 December Sandy clay IVT, LS,VLS PBW-435, UP-2425, HD- 2643, 18
2000, 2001 loam HP-1744, DL-788-2, WR- 251,
WR- 544, Raj- 3765, PBW-373
Delhi 29 77.5 228 Nov−Dec
2000, 2002, 2003 Sandy Loam IVT, TS, LS HD-2687, PB-W373 9
HD-2733, HUW-468,
Delhi 29 77.5 228 Oct−Jan 2005, Sandy loam IVT, TS,LS,VLS HD-2851, PBW-343, HDR-77, 35
2006 HD-2936, HI-8498
Hisar 29 75.7 200 Nov−Dec
2000, 2002, 2003 Sandy loam IVT, TS HD-2687, K-9107, HUW-468 6
Pantnagar 29 79.5 344 Nov−Dec Sandy loam IVT, TS HD-2687, K-9107, HUW-468 6
2000, 2002, 2003
Faizabad 26.8 82.1 1200 Nov−Dec Sandy loam IVT, TS HD-2687 1
2003, 2004
Varanasi 25.2 82.3 81 Nov−Dec Sandy loam IVT, TS HD-2687 6
2000, 2002, 2003
Ranchi 23.3 85.3 654 Nov−Dec Sandy loam IVT, TS HD-2687 6
2000, 2002, 2003
Jabalpur 23.1 79.9 411 Nov−Dec Clay loam IVT, TS, LS HI-8498, GW-347 3
2000, 2002, 2003
Indore 22.7 75.8 553 Nov−Dec Loamy clay IVT, TS HI-8498, GW-347 3
2000, 2002, 2003
Table 1. Details of the data base used for the calibration and validation of InfoCrop-WHEAT. Locations: given represent >90 % of wheat area in India. a.s.l.: above mean
sea level. IVT: initial varietal trial; TS: timely sown; LS: late sown; VLS: very late sown. Data source: AICW&BIP (2012) and Chakrabarti et al. (2011)
Clim Res 59: 173–187, 2014
For temperature,
TS= TOB + TD (1)
where TS= scenario temperature, TOB = IMD gridded
daily temperature, TD= daily change in temperature.
Monthly change in temperature (TDdmi) is linearly
interpolated to get the TD. TDmi = monthly tem -
perature in scenario minus monthly temperature in
baseline.
For rainfall,
RS= ROB × (1 + RD) (2)
RD= (RSmi −R
Bmi) ÷ RBmi (3)
where RS= scenario rainfall, ROB = IMD gridded daily
rainfall, RD= relative change in rainfall, RSmi =
monthly rainfall in scenario, RBmi = monthly rainfall in
baseline.
A major advantage of this method is that it over-
comes the bias of the climate model for baseline
weather. To coincide with the climate model baseline
period (1960 to 1990), we used observed data for
1969 to 1990. The carbon dioxide level for each sce-
nario (522, 523, 482 and 473 μmol mol−1 for year 2050
and 639, 682, 530 and 552 μmol mol−1 for year 2080,
for scenarios A1b, A2, B1 and B2, respectively) was
also included in the crop model for simulations. All
other simulation conditions were maintained as
explained earlier. Based on the simulated yield for
the future scenarios, district yield was calculated as
in the case of baseline yield assuming that the crop
land area in each district remains the same in the
future.
The impact of climate change on yield was calcu-
lated using the following formula:
(4)
where Yd= yield deviation in a climate scenario, Ys=
mean simulated yield in a climate scenario, Yc=
mean observed yield for 2000−2007, Yb= mean simu-
lated baseline yield.
The observed yield for the period 2000 to 2007
was used for expressing the yield deviation as
shown in Eq. (4). Grid values were used for
mapping the im pacts in the study region in the
GIS platform. Yield deviation in future climate
scenarios (2050 and 2080) were also plotted
against the current seasonal mean minimum and
maximum temperatures for the wheat growing
period.
2.4. Simulating adaptation gains in future scenarios
Several low-cost and easy-to-adopt adaptation
options were tested independently or in combination
to assess the adaptive capacity of the wheat crop
to climate change. These strategies included (1) the
use of improved varieties: short-, medium- and long-
duration varieties with high temperature stress tol -
erance; (2) change in sowing time: advanced or
delayed by 1 wk for late- and very late- sowing win-
dow, advanced or delayed by 10 d for current optimal
sowing window; (3) rescheduling the time of irriga-
tion to suit the phenological stages in future climates,
and extra split application of nitrogen (i.e. 50% as
basal, 25% at CRI stage and 25% at jointing period,
45 to 60 d after sowing); and (4) without 25% addi-
tional nitrogen. The combination that gave the high-
est yield in each grid or scenario was taken as the
best suitable adaptation option. The yield deviation
from mean baseline yield was expressed as in Eq. (4).
The vulnerability in a specific scenario was ob -
tained using the following formula:
Vulnerability (yield reduction from baseline
even after adaptation) = Impact (yield reduction
due to climate change) − Adaptation gain (5)
In instances where impact on yield is positive, sim-
ulations were run for similar adaptation strategies to
quantify additional benefits to represent net prefer-
able impacts maximized with additional adaptation
measures. In all, about 5.15 million simulations (21 yr
× [8 scenarios + baseline] × 220 grids × 3 sowings × 8
varieties × 5 rescheduled sowing dates, plus 0.71 mil-
lion simulations for rescheduling of nitrogen and irri-
gation, and for 25% additional nitrogen) were car-
ried out for this entire analysis.
3. RESULTS AND DISCUSSION
3.1. Quantification of impacts
The yield of timely-sown wheat is projected to
reduce by ~6 and 15% by 2050 and 2080, respec-
tively. However, in late- and very late-sown condi-
tions, yield is projected to decrease ~28 and ~45%,
respectively, in 2050, and by ~35 and 52%, respec-
tively, by 2080. The magnitude of projected impacts
is slightly higher in GCM-derived climate scenarios
than in those derived using RCM scenarios (Fig. 2).
The projected increase in minimum and maximum
temperatures by 2080 for Indian regions is higher by
0.4 and 1°C, respectively, in MIROC3.2.HI projec-
Y
YY
YYY
Y
YY
Y
d
sc
bbc
b
bc
b
=
×
100
178
Naresh Kumar et al.: Climate change vulnerability of Indian wheat
tions than those by PRECIS, regardless of emission
scenarios. On an all-India scale, in the consolidated
impacts, considering timely-, late- and very late-sown
conditions, the projected yield reduction is ~23% by
2050 and ~25% by 2080.
3.2. Regional impacts and uncertainty assessment
Among the wheat-growing regions (Fig. 3a), the
impact of climate change on yield is projected to vary
spatially, and with climate and emission scenario
(Fig. 3b,c). By 2050, wheat yield in north-western IGP
(NWIGP), consisting of the states of Punjab and
Haryana, is projected to decrease 8 to 22 %, with a
greater reduction in Haryana. The initial gains in pro-
ductivity due to climate change in this region (Fig. 4)
may taper at a later period of this century. In the cen-
tral IGP (CIGP) region, yield in Uttar Pradesh (UP) is
projected to be reduced by ~24%. A similar yield
reduction is projected for West Bengal in eastern IGP
(EIGP). In the IGP region, climate change impact on
wheat yield is projected to be more in Haryana, Uttar
Pradesh, Bihar and West Bengal. In the warm central
zone (CZ), yield reduction is projected to be ~25% in
Rajasthan and Madhya Pradesh, which are the major
wheat producing states in this zone. In the warmest
south-central zone (SCZ), where wheat area is much
less, yield is projected to reduce ~42% in Maha rash -
tra and Andhra Pradesh. For 2080, the projected yield
reduction in these regions is even higher, especially
in the above mentioned states in each region
(Fig. 3b). Among all major zones of wheat cultivation,
higher yield reduction is projected for central and
south-central zones (Fig. 3c & 4). Wheat yield is pro-
jected to reduce the most in scenario A2, followed by
A1b, B1 and B2 emission scenarios; though only a sin-
gle GCM is considered for each emission scenario.
The uncertainty of impacts, in general, is higher
towards the end of the century, with spatial variation
in all zones. Generally, the uncertainty of projected
impacts on wheat yield is about 10%, but it is signifi-
cantly higher in central and south-central zones
(Fig. 3c) and least in CIGP.
3.3. Adaptation to climate change
Adjusting the time of sowing within the timely-,
late- and very late- sowing windows is projected to
minimize yield reduction from ~23% (impact, with-
out adaptation) to ~17% in 2050 even with existing
varieties under improved nutrient and irrigation
management and with higher dose of nitrogen fertil-
izer (25% higher than the dose currently applied by
farmers) (Fig. 5a). In addition, by growing im proved
varieties, projected yield reduction may be mini-
mized to ~9% in 2050 and to ~13% in 2080. In order
to sustain the yield in future, timely sowing of im -
proved wheat varieties across India along with better
management (nutrients and irrigation) and applica-
tion of higher dose of nitrogen fertilizer is essential.
By doing so, the impacts can be offset (~2% increase)
(Fig. 5b) up to 2050. Even if all the above mentioned
strategies are em ployed together, the wheat pro -
duction in India by 2080 is projected to still reduce
by ~5%.
3.4. Vulnerability of the wheat crop to
climate change
The magnitude of impact, adaptation gains and
therefore that of vulnerability are projected to vary
with emission scenario, adaptation option and region
in future climates (Figs. 6 & 7). Climate change is
projected to cause 3 basic types of impacts (Fig. 7).
Category (1) includes regions that are projected to be
179
–70
–60
–50
–40
–30
–20
–10
0
Consolidated
Timely sown Late sown
Very late
sown Consolidated Timely sown Late sown
Very late
sown
Mid-century (2020-2050) End century (2070-2100)
Yield deviation (%)
GCM (MIROC)
RCM (PRECIS)
Fig. 2. Impact of climate change on
wheat yield (percent deviation from
mean yield between 2000 and 2007) in
India for mid- (up to 2050) and end of
century (2080) climate scenarios of a
global (MIROC 3.2.HI) and a regional cli-
mate model (PRECIS). Bars: 21 yr mean
impact on all wheat growing areas in
India for A1b, A2, B1 and B2 emission
sce narios. Consolidated: weighted im -
pacts on timely-, late- and very late-
sown wheat. Timely-sown: as per the
recommended date of sowing in differ-
ent regions; late sown: 15 d delay (as
compared to the recommended date);
very late sown: 1 mo delayed
Clim Res 59: 173–187, 2014
adversely affected by climate change,
but can gain yield (over current yield) in
future climate with adaptation as men-
tioned above. Most of the wheat areas in
IGP fall in this category (Fig. 6). Cate-
gory (2) consists of regions such as cen-
tral and south-central zones that are to
be adversely affected and remain vul-
nerable despite adaptation gains (Figs. 6
& 7). Category (3) consists of areas
where climate change may increase
yield in the near future, but may de -
crease in the later part of the century
(Figs. 6 & 7). Adaptation in these areas
can increase the positive effects. Parts of
Punjab and Haryana fall in this category.
The analysis also indicates that adap-
tation gains with timely sowing may not
be uniform in all wheat regions, because
of differential impacts and relative area
under late- and very late-sown condi-
tions (Fig. 8). Combining timely sowing
of wheat with other adaptation measures
that have been mentioned in this study is
projected to improve yield up to ~18% in
different states by 2050 (Fig. 8a). How-
ever, these projected benefits may re -
duce to ~15% in 2080 (Fig. 8b). Regions
with projected yield reduction of ~20 to
30% may gain substantially by adopting
timely sowing of wheat.
3.5. Seasonal mean minimum and
maximum temperatures in relation to
wheat yield
Yield reduction is projected to be less
in areas with current mean seasonal
minimum temperatures of 10 to 12°C
than those having >12°C, such as in
parts of EIGP, central and south-central
India (Fig. 9a). In various emission sce-
narios, projected increase in mean sea-
sonal minimum temperatures in these
regions is by ~1.5−2°C in 2020, ~2.5−4°C
in 2050 and 4−6.5°C in 2080. Even
though a similar or slightly higher
increase in temperature is projected for
the NWIGP region, the projected
impacts are less, due to current lower
mean seasonal minimum temperatures
of ~7 to 10°C (Fig. 9a). Seasonal mean
180
0
–10
–20
–30
–40
–50
–60
–70
–80
–90
0
–10
–20
–30
–40
–50
–60
–70
–80
–90
Punjab
Haryana
Uttar Pradesh
West Bengal
Bihar+Jharkhand
Assam
Pajasthan
Madhya Pradesh
Gujrat
Chattisgarh
Orissa
Maharastra
Andhra Pradesh
Karnataka
NW IGP C IGP E IGP
NW IGP C IGP E IGP
NE CSC
Yield deviation (%) Yield deviation (%)
Mean 2050 Mean 2080
20802050 20802050 2080 2050 20802050 20802050
CZ S CZ
b
c
North-western IGP (NWIGP)
Central IGP (CIGP)
Eastern IGP (EIGP)
Central Zone (CZ)
South-central zone (SCZ)
a
65°E 70° 75° 80° 85° 90° 95° 100°
40°
35°
30°
25°
20°
15°
10°
N
Arun achal Pr adesh
Arun achal Pr adesh
Arun achal Pr adesh
Himachal Pradesh
Himac hal Pradesh
Himachal Pradesh
Utta r Pradesh
Utta r Pradesh
Utta r Pradesh
Madya Pradesh
Madya P radesh
Madya Pradesh
Orissa
Oriss a
Orissa
West Bengal
West Ben gal
West Bengal
Jharkhand
Jhark hand
Jharkhand
Chhattisgarh
Chhat tisg arh
Chhattisgarh
Andra Pradesh
Andr a Prade sh
Andra Pradesh
Tamil Nadu
Tamil Nad u
Tamil Nadu
Karnataka
Karna taka
Karnataka
Maharashtra
Mahar asht ra
Maharashtra
Kerala
Kerala
Kerala
Jammu & Kashmir
Jammu & K ashmir
Jammu & Kashmir
Assam
Assam
Assam
Meghalaya
Megha laya
Meghalaya
Sikkim
Sikki m
Sikkim
Nagaland
Nagal and
Nagaland
Manipur
Manip ur
Manipur
Tri pur a
Tripura
Tri pur a
Mizoram
Mizor am
Mizoram
Andama n and
Nicobar Islands
Bihar
Bihar
Bihar
Gujarat
Gujar at
Gujarat
Rajasthan
Rajas than
Rajasthan
Punjab
Punja b
Punjab
Haryana
Hary ana
Haryana
Uttaranchal
Utta ranch al
Uttaranchal
Goa
Goa
Goa
Fig. 3. (a) Wheat-growing-regions. (b) Climate change impact on wheat
yield (percent deviation from mean yield between 2000 and 2007) in differ-
ent regions of India (covering several states). Mean impact for 2050 and
2080 in different states of a re gion. (c) Extent of uncertainty among differ-
ent emission scenarios (A1b, A2, B1 and B2) in global MIROC3.2.HI and
regional PRECIS climate models for 2050 and 2080 in different regions of
India. Horizontal line within a box: median value; error bars: standard error.
IGP: Indo-Gangetic Plain; C: central. CZ: central zone
Naresh Kumar et al.: Climate change vulnerability of Indian wheat
maximum tem peratures in wheat growing areas in
India also vary significantly from 23−25°C in the
NWIGP to 29−30°C in the EIGP region (Fig. 9b). The
central and south-central regions are even warmer at
28 to 31°C. In fact, yield levels are significantly neg-
atively correlated with the current seasonal mean
minimum temperatures in the range of 12 to 18°C
(Fig. 9c) and with mean maximum temperatures of 21
to 31°C (Fig. 9d). The projected increase in mean sea-
sonal maximum temperatures varies 1−1.5°C in the
IGP to ~1.75°C in central India. However, less warm-
ing of up to 1°C by 2020 is projected for the north-
eastern states. In 2050, the projected warming in the
IGP region is ~3 to 4.5°C for seasonal minimum tem-
peratures and ~1.75 to 4°C for seasonal maximum
temperatures, with a relatively higher increase in the
NWIGP. A rise of 2−3.5°C and 3.5−4°C in seasonal
maximum and minimum temperatures, respectively,
is projected for central and south-central India. Such
projected warming, beyond the upper limit of opti-
mal temperatures, may constrain wheat productivity.
By 2080, large areas under wheat cultivation are
projected to have mean seasonal minimum and
maximum temperatures that are higher by 4−6°C
and 3−6°C, respectively. A greater warming in the
NWIGP and in central India is projected. Increases in
tem perature are projected to cause yield reduction at
an accelerated pace towards 2080 (Fig. 9e,f). There-
fore, for wheat cultivation, future temperatures may
play a major limiting role for higher productivity par-
ticularly in areas with current high seasonal mean
maximum temperatures, such as Central India.
Wheat yields are projected to decrease in areas with
mean seasonal maximum and minimum tempera-
tures in excess of 27 and 13°C, respectively, in spite
of CO2fertilization benefits.
181
Fig. 4. Spatial variation in impact of climate change on timely-sown wheat yield in India for 2050 and 2080. Ensemble average
of emission scenarios. Dots: 10 000 ha of wheat area
–35
–30
–25
–20
–15
–10
–5
0
5
No
adaptation
TS, CV,
IM
TS, CV,
IM, AF
TS, IM TS, IV,
IM
TS, IV,
IM, AF
Mean 2050 Mean 2080
–35
–30
–25
–20
–15
–10
–5
0
No
adaptation
CST, CV CST, CV,
IM, AF
CS T, I V CS T, I V,
IM
CST, IV,
IM, AF
Yield deviation (%)Yield deviation (%)
Mean 2050 Mean 2080
a
b
Fig. 5. Overall impact with and without adaptation on wheat
yield (a) adjusting the sowing time within timely-, late- and
very late-sown conditions and (b) for adjusted timely sowing of
wheat in all regions in India for 2050 and 2080. Negative adap-
tation gain values: vulnerability. Impacts in both cases are as
per the current ratio of area under timely-, late- and very late-
sown conditions. Error bars: standard error; CST: change in
sowing time; CV: current variety; IM: improved management;
AF: additional fertilizer; IV: improved variety; TS: timely sown
Clim Res 59: 173–187, 2014
The projected differential impacts on wheat yield
in different emission scenarios are mainly due to
variations in concentrations of atmospheric CO2and
rise in temperatures. While a rise in atmospheric CO2
is projected to benefit the wheat crop, the magnitude
of the benefit may depend on the trade-off with
reduction due to a rise in temperature. The uncer-
tainty in the magnitude of impacts on wheat yield is
significantly high in central and south-central zones,
while least in CIGP. This may be due to (1) less vari-
ation in projected temperature rises among the emis-
sion scenarios for CIGP, and (2) wide variations in the
current and projected growing season temperatures
in the central zone spread between 20−28° N and
65−86° E, covering several states, the largest among
the zones that have been considered. In addition,
change in rainfall amount, intensity and distribution
may influence wheat crop in limited irrigation farms.
Current winter season rainfall in wheat growing
regions is up to 100 mm. The PRECIS and MIROC
3.2.HI scenarios project a 5 to 10% increase in winter
rainfall in parts of north eastern states and central
India by 2050.
In near the future, agronomical management can
help in overcoming the negative impacts of climate
change. However, developing suitable varieties and
efficient crop husbandry becomes essential for im -
proving the productivity in the mid- and latter parts
of the century. Results also indicate that timely sown
wheat is less vulnerable to climate change, and pro-
jected negative impacts can be overcome by adap-
tation. On the other hand, late- and very late-sown
wheat yields are projected to decline further in
future potential climates. Therefore, there is an
urgent need to impress upon farmers the need to
undertake timely sowing by adjusting the preceding
rice crop transplantation or by adjusting the crop
calendar and cropping pattern. Adjusting time of
transplantation is one of the suggested adaptation
options for rice cultivation in a future climate sce-
nario (Naresh Kumar et al. 2013). Regions that are
vulnerable may require more intensive, specific and
innovative research and development interventions
beyond those tested in this study. Short-duration,
heat tolerant, high yielding varieties may be needed
for areas with high end season temperatures and
water scarcity. Even though the CO2response of
recent wheat cultivars is relatively less than that of
older cultivars, agronomic management can maxi-
mize the individual plant performance (Ziska et al.
2004). Availability of nitrogen can significantly
influence the response of wheat to high CO2con-
centrations (Cardoso-Vilhena & Barnes 2001) and
nitrogen concentration in tissue and grain (Cardoso-
Vilhena & Barnes 2001, Kimball et al. 2001). In the
present study, the application of 25% more nitrogen
led to higher harvests. Additional quantities of
nitrogen and other nutrients may be required to
reap the benefits of CO2fertilization in poorly fertil-
ized fields or soils with limited fertility. This also
helps to maintain the crop C:N ratio and grain pro-
tein concentration.
182
Yield deviation (%)
56 to 60
51 to 55
46 to 50
41 to 45
36 to 40
31 to 35
26 to 30
21 to 25
16 to 20
11 to 15
5.1 to 10
0.01 to 5
4 to 0
–9 to –5
–14 to –10
–19 to –15
24 to –20
29 to –25
34 to –30
39 to –35
44 to –40
49 to –45
54 to –50
60 to –55
2050 2080
Fig. 6. Spatial variation in vulnerability (after adaptation) of timely-sown wheat yield to climate change in India for 2050 and
2080. Areas in red: vulnerable even after adaptation. Ensemble average of emis sion scenarios. Dots: 10 000 ha of wheat area
Naresh Kumar et al.: Climate change vulnerability of Indian wheat 183
All timely sown
All timely sown
40
30
20
10
0
–10
–20
–30
–40
–50
–60
–70
–80
–90
40
30
20
10
0
–10
–20
–30
–40
–50
–60
–70
–80
–90
40
30
20
10
0
–10
–20
–30
–40
–50
–60
–70
–80
–90
IV, CM
CV, IM
IV, IM
CV, IM, 25% N
IV, IM, 25% N
40
30
20
10
0
–10
–20
–30
–40
–50
–60
–70
–80
–90
–90 80 –70 60 –50 – 40 – 30 –20 –10 0 10 20 –90 80 –70 60 –50 – 40 – 30 –20 –10 0 10 20
–90 80 –70 60 –50 – 40 – 30 –20 –10 0 10 20 –90 80 –70 60 –50 – 40 – 30 –20 –10 0 10 20
Yield deviation with adaptation (%)Yield deviation with adaptation (%)
Yield deviation with adaptation (%)Yield deviation with adaptation (%)
All sowing combinations
Impact
(yield deviation, %)
Impact
(yield deviation, %)
All sowing combinations
2050
2080
Fig. 7. Vulnerability of wheat yield with variable adaptation gains in regions differentially impacted by climate change in India.
Data points: one state. Negative values in y-axis: vulnerability of region in spite of adaptation. Positive values: adaptation bene-
fits exceeding negative impacts and providing improvement in yield over mean yield of 2000−2007. IV: improved variety; CM:
current management; CV: current variety; IM: improved management; 25 %N: 25% additional nitrogen
IV, CM
CV, IM
IV, IM
CV, IM, 25% N
IV, IM, 25% N
–2
0
2
4
6
8
10
12
14
16
18
20
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
Yield gain (%)
Impact (yield deviation, %) Impact (yield deviation, %)
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
Yield gain (%)
2050 2080
–2
0
2
4
6
8
10
12
14
16
18
20
Fig. 8. Additional adaptation gains by timely sowing of wheat as against the adaptation gains by adjusting the sowing time
within the normal-, late- and very late-sowing windows in 2050 and 2080. Data points: one state in India. y-axis: gain in yield
because of timely sowing of wheat (with all other adaptation options inclusive) over the adaptation gains by adjusting the sow-
ing time within the normal-, late- and very late-sowing windows. Change in yield: % deviation from 2000−2007 mean yield
Clim Res 59: 173–187, 2014
184
y
y = –0.15 x2 + 0.20x + 57
R2 = 0.59
y2050 = –0.72 x2
+ 10.1x – 41.1
R2 = 0.64
y2080 = –0.25 x2
– 3.86x – 42.5
R2 = 0.87
y2050 = –0.9 x2
+ 39.1x – 428.9
R2 = 0.69
y2080 = –0.86 x2
+ 35.3x – 366.1
R2 = 0.78
y = –0.07x2 – 0.59 x + 103
R2 = 0.82
0
10
20
30
40
50
60
70
80
6 8 10 12 14 16 18 20
Simulated grain yield (kg × 100 ha–1)
–100
–80
–60
–40
–20
0
20
–100
–80
–60
–40
–20
0
20
6 8 10 12 14 16 18 20
Yield deviation (%)
Min temperature (°C) Max temperature (°C)
Min temperature (°C) Max temperature (°C)
0
10
20
30
40
50
60
70
80
18 20 22 24 26 28 30 32
18 20 22 24 26 28 30 32
y2050
y2080
2050
2080
2050
y2080
c d
e f
35° –
30° –
25° –
20° –
15° –
10° –
70°E 75° 80° 85° 90° 95°
N
35° –
30° –
25° –
20° –
15° –
10° –
70°E 75° 80° 85° 90° 95°
N
a b
Fig. 9. (a,b) Iso-thermal lines for current mean seasonal (a) minimum and (b) maximum temperatures for the wheat growing
period (November to mid-April) in India. (c,d) Simulated grain yield in relation to current growing season (c) mean minimum
and (d) mean maximum temperatures. (e,f) Impact of climate change on wheat yield (as percent reduction from mean yield
of 2000−2007 at each respective region) in relation to current growing season (e) mean minimum and (f) mean maximum tem-
peratures in 2050 and 2080. Data points: 21 yr yield mean from ensemble of emission scenarios for each grid point (1° × 1°)
Naresh Kumar et al.: Climate change vulnerability of Indian wheat
4. CONCLUSIONS
The study projects a progressive reduction in
wheat yield towards the end of the century due to cli-
mate change. Projected impacts are more for late-
sown wheat. However, the spatio-temporal variations
exist for a wide range of potential of impacts.
Even though the magnitude of impacts vary, the
direction of impact is similar in RCM and GCM based
assessments. Negative impacts are less severe in the
B1 and B2 emission scenarios as compared to the A2
and A1b scenarios. The magnitude of uncertainty has
spatial variation and increases with time period.
Adaptation to climate change can reduce the nega-
tive impacts. Timely sowing of wheat crop, adoption
of improved and heat-tolerant varieties under in -
creased input amount and efficiency regimes can not
only offset yield reduction but also result in an
increase in yield up to the mid-century. The reduc-
tion or complete conversion of areas under late- and
very late-sown conditions to timely-sown conditions
can significantly improve yield even with current
varieties in the near future.
The 3 basic types of influence that are projected as
a result of climate change include (1) regions that will
be adversely affected by climate change and can
gain yield (over current yield) through adaptation
strategies; (2) regions that are currently adversely
affected, and remain vulnerable despite the adapta-
tion strategies considered in this study; and (3)
regions that are projected to gain yield in the near
future, for which adaptation can enhance the positive
effects. Regions falling in the vulnerable category,
even after adopting suggested climate-change adap-
tation strategies, require more intensive, specific and
innovative adaptation options.
Wheat yields are projected to decrease in areas
with mean seasonal maximum and minimum temper-
atures in excess of 27 and 13°C, respectively, in spite
of CO2fertilization benefits.
Acknowledgements. Authors thank the team of Indian Trop-
ical Meteorological Institute, Pune, for providing the climate
scenarios data.
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Editorial responsibility: Gerrit Hoogenboom,
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Proofs received from author(s): March 13, 2014
... 18.26% and 45.10% respectively by the end of this century (Zhang et al. 2016). Naresh Kumar et al. (2014) project a 6-23% and 15-25% reduction in the wheat yield in India during the 2050s and 2080s respectively under the mainstream projected climate change scenarios. The loss of coral and the acidification of the seas are predicted to reduce fishery productivity by more than half (Rogers et al. 2017). ...
... Comparing the impacts of seasonal precipitation ( Figure 9) and seasonal temperature (Figure 7), we find that seasonal precipitation has an adverse impact on wheat yield in a lesser number of states than seasonal temperature. As per Naresh Kumar et al. (2014), .85% of the wheat harvested area is irrigated in India; therefore, the impact of precipitation is not so significant. The adverse effects of low precipitation can be partly mitigated by artificial irrigation facilities (Vogel et al. 2019), but it is relatively challenging to address the impact of rising temperatures. ...
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