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8/14/2019 AGEE, Impact of CO2
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Impact of elevated CO2 and temperature on rice yield and methodsof adaptation as evaluated by crop simulation studies
P. Krishnan a,*, D.K. Swain b, S. Chandrabaskar b, S.K. Nayaka, R.N. Dash b
aDivision of Biochemistry, Plant Physiology and Environmental Sciences, Central Rice Research Institute, Cuttack 753006, India
bDivision of Soil Science and Microbiology, Central Rice Research Institute, Cuttack 753006, India
Received 7 February 2006; received in revised form 20 December 2006; accepted 19 January 2007
Abstract
Impact of elevated CO2 and temperature on rice yield in eastern India was simulated by using the ORYZA1 and the INFOCROP rice
models. The crop and weather data from 10 different sites, viz., Bhubaneswar, Chinsurah, Cuttack, Faizabad, Jabalpur, Jorhat, Kalyani, Pusa,
Raipur and Ranchi, which differed significantly in their geographical and climatological factors, were used in these two models. For every
1 8C increase in temperature, ORYZA1 and INFOCROP rice models predicted average yield changes of7.20 and 6.66%, respectively, atthe current level of CO2 (380 ppm). But increases in the CO2 concentration up to 700 ppm led to the average yield increases of about 30.73%
by ORYZA1 and 56.37% by INFOCROP rice. When temperature was increased by about +4 8C above the ambient level, the differences in the
responses by the two models became remarkably small. For the GDFL, GISS and UKMO scenarios, ORYZA1 predicted the yield changes of
7.63, 9.38 and 15.86%, respectively, while INFOCROP predicted changes of9.02, 11.30 and 21.35%. There were considerabledifferences in the yield predictions for individual sites, with declining trend for Cuttack and Bhubaneswar but an increasing trend for Jorhat.
These differences in yield predictions were mainly attributed to the sterility of rice spikelets at higher temperatures. Results suggest that the
limitations on rice yield imposed by high CO2 and temperature can be mitigated, at least in part, by altering the sowing time and the selection
of genotypes that possess higher fertility of spikelets at high temperatures.
# 2007 Published by Elsevier B.V.
Keywords: Climate change; CO2; INFOCROP; ORYZA; Oryza sativa L.; Simulation; Temperature; Yield
1. Introduction
The climatic variability and the predictedclimatic changes
are of major concern to the rice crop scientists because of
their potential threat to rice productivity and the associated
impact on the socioeconomic structure of many rice-growing
countries. Among the global atmospheric changes, the incre-
asing concentrations of greenhouse gases such as CO2 may
have significant effect on rice productivity due to increase in
both the average surface temperature and the amount of CO2
available for photosynthesis (Aggarwal,2003). In theabsenceof temperature increase, many studies have shown that the net
effect of doubling of CO2 was increase in the yield of rice
(Kimetal.,2003). It becomes necessaryto assessthe effects of
potential interactive changes of CO2 and temperature in order
to determine the future agricultural strategies that would
maintain higher rice productivity.
The simulations by different models and many field
experiments have shown the potential impact of climatic
change and the variability in rice productivity (Baker et al.,
www.elsevier.com/locate/ageeAgriculture, Ecosystems and Environment xxx (2007) xxxxxx
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Abbreviations: BVP, Basic Vegetative Phase; DLAI, Death Rate of
Leaf Area Index; FACE, Free Air Concentration Enrichment; GCMs,General Circulation Models; GFDL, General Fluid Dynamics Laboratory;
GFP, Grain Filling Phase; GISS, Goddard Institute of Space Studies; GLAI,
Leaf Area Growth Rate; IPCC, Intergovernmental Panel on Climate
Change; LAI, Leaf Area Index; NATP, National Agricultural Technology
Project; PFP, Panicle Formation Phase; PLTR, Net loss of LAI due to
transplanting; PSP, Photoperiod-Sensitive Phase; RLAI, Net Leaf Area
Growth Rate; RUE, Radiation Use Efficiency; RWLVG, Increment in Leaf
Weight; SLA, Specific Leaf Area; SUBLAI, LAI is simulated in the
subroutine SUBLAI; UKMO, United Kingdom Meteorological Office
* Corresponding author.
E-mail address: [email protected] (P. Krishnan).
0167-8809/$ see front matter # 2007 Published by Elsevier B.V.
doi:10.1016/j.agee.2007.01.019
Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
mailto:[email protected]://dx.doi.org/10.1016/j.agee.2007.01.019http://dx.doi.org/10.1016/j.agee.2007.01.019http://dx.doi.org/10.1016/j.agee.2007.01.019http://dx.doi.org/10.1016/j.agee.2007.01.019mailto:[email protected]8/14/2019 AGEE, Impact of CO2
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1992; Peng et al., 2004; Kim et al., 2003). The modeling
studies from Bangladesh (Karim et al., 1994), Japan (Horie
et al., 2000), China (Bachelet et al., 1995) and India (Mall
and Aggarwal, 2002) reported the country-wise variations in
rice production, anticipated due to the climatic changes. The
simulated yields increased when temperature increases were
small, but declined when the decadal temperature increasewas more than 0.8 8C, with the greatest decline in crop
yields occurring between the latitudes of 108 and 358N.
Similar results were obtained by Penning de Vries (1993).
Many uncertainties exist in modeling studies, partly due
to the quality of the predictions by the models, from the use
of limited sites for which historical weather data are
available, due to the quality of the crop simulation models,
especially when applied under the rain-fed conditions
(Bachelet et al., 1995), and due to the quality of the climate
models used to predict future weather scenarios. These
uncertainties may be reduced only when a large number of
scenarios for different locations are compared and evaluated.
In order to overcome the predicted limitations for rice
production in the future, there is also a need to identify and
evaluate the suitable agronomic practices such as altered
sowing date and selection of improved varieties with
increased spikelet fertility at high temperature and other
useful traits.
Attempts have been made earlier to assess the general
effects of global environmental changes on rice yield using
simulation models, but little attention has been given to the
potential interactive effects between high temperature and
increasing CO2 levels. Lal et al. (1998) used the CERES
rice and predicted a 20% decline in rice yields in the
northwestern India due to elevated CO2 and temperature.Eastern India accounts for about 63% (26.5 million ha) of
the total rice-growing area in India. The rice ecosystems in
these regions show characteristic differences with respect to
the environmental factors as well as the cultural practices.
There is an urgent need to characterize the impact of future
climatic changes on rice yield in these regions for sustaining
the productivity. The present paper discusses the outcomes
of two rice growth simulation models, ORYZA and
INFOCROP rice, when applied to different rice-growing
sites in the eastern India for studying the potential interactive
effects between high temperature and increasing CO2 levels
and for the different thermal climate change scenarios.
2. Materials and methods
Two popular models of rice growth ORYZA1 (Kropff
et al., 1994) and INFOCROP rice (Aggarwal et al., 2006) are
used in this study. Prior to their use, both were evaluated
and compared (Fig. 1) and then these crop models were
calibrated for the indica variety IR 36 at all sites. In general,
the two models differ in the manner in which dry matter
production and partition, leaf area development, and pheno-
logical development are calculated. ORYZA1 calculates dry
matter production as a function of light, CO2 and tempe-
rature by considering photosynthetic processes at the leaf
level and integrating these over the canopy to obtain crop-
level values. Respiration is also modeled explicitly as a
function of temperature and partitioning of dry matter is
according to phenology-dependent functions. Thus, ORYZA
calculates dry matter production as a function of gross
canopy photosynthesis, depending on the detailed calcula-
tions of the distribution of light within the canopies,
the radiation absorbed by the canopy and photosynthesis
light response cure of leaves. Growth and maintenance
respiration are calculated as a function of tissue N content,
temperature and crop-specific coefficients. This methodol-
ogy, although yields very accurate results, poses practical
difficulties because of its requirement for detailed and
careful measurements (Kropff et al., 1994). The INFOCROPmodel, however, uses a simpler radiation use efficiency
(RUE) relationship between intercepted solar radiation and
growth, in which respiration is implicit. CO2 effects are
accounted for by using a curvilinear function relating RUE
to CO2 concentration. Temperature is assumed not to affect
RUE. More or less similar results can be obtained under
normal radiation situations by calculating the net dry matter
production as a function of RUE. Pre-determined values of
RUE were input in the model as a function of crop/cultivar
(RUEMAX), and RUE was further modified by the develop-
ment stage (Aggarwal et al., 2004).
The rice-growing regions included for the present study
lie in eastern India, and these sites (Bhubaneswar, Chin-
surah, Cuttack, Faizabad, Jabalpur, Jorhat, Kalyani, Pusa,
Raipur and Ranchi), which are geographically apart at varied
altitudes ranging from 7.8 to 86.56 m above mean sea level,
show characteristic features with respect to the weather and
crop factors. The crop parameters included in the study were
obtained from the field experiments conducted at these sites
under National Agricultural Technology Project (NATP)
RRPS 25 during 20012004 (Table 1).
The input parameters used for the two models are given in
Table 2a (parameters such as varietal data, soil data and
weather data), Table 2b (physical and chemical properties of
P. Krishnan et al. / Agriculture, Ecosystems and Environment xxx (2007) xxxxxx2
AGEE 3025 110
Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
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Fig. 1. Comparison of yield simulated by ORYZA1 and INFOCROP rice.
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change scenarios were specified as +1 to +5 8C for
temperatures and the percent changes in precipitation.
The inbuilt weather generator of that model generated the
modified daily weather data accordingly, and these data were
used as the daily weather inputs. The climate change
scenarios under different levels of CO2 were applied by
changing the ambient CO2 concentration parameter in these
two models.
Besides, the outputs from the climate models were used
in combination with these crop models. The coarse grid from
each GCM was interpolated using a four point inverse-
distance-squared algorithm to a 0.58 latitude 0.58 long-itude grid using a raster-based Geographical Information
Systems (GIS) software package. Scenarios were produced
by applying ratios of precipitation or differences in tempe-
rature predicted for the 2 CO2 and 1 CO2 simulation tothe baseline present climate data set for different sites
(Mathews and Wassmann, 2003). The important features of
three General Circulation Models (GCMs) used such as
the General Fluid Dynamics Laboratory (GFDL) Model,
Goddard Institute of Space Studies (GISS) model and the
United Kingdom Meteorological Office (UKMO) model are
provided in Table 3.
The GCM scenarios were produced by applying the ratios
of precipitation or differences in temperature predicted forthe 1 CO2 (380 ppm) and 2 CO2 (760 ppm) simulationsto the baseline daily weather data set. The changes between
1 CO2 and 2 CO2 conditions were representative of thedifferences between the present and the future climate
scenarios following an equivalent doubling of CO2.
2.2. Simulation
For the simulation analysis, only runs that terminated
normally or by crop deaths as a result of temperature were
included. The relative yield changes under the scenarios,
referred to yields predicted for the current climate, were
used in the analysis rather than absolute yields. To provide
an estimate of the overall effect of climate change on rice
production under the three GCMs scenarios, the average
relative increase predicted for each site was weighed by its
current production observed in the field trials of an earlier
study (Annual Report of NATP RRPS 25, 20022003). The
differences in the production capacities among these sites
were evaluated.
3. Results
3.1. Effect of temperature and CO2 levels at fixed
increments on yields
At all the CO2 levels tested (380, 400, 500, 600 and
700 ppm), both the models predicted the declining yields of
rice due to an increase in temperature. On the contrary, an
increase in CO2 level at any particular temperature increased
the rice yields (Table 4). At the current level of CO2(considered at 380 ppm), ORYZA predicted a mean change
of7.20% in yields for every 1 8C increase in temperature,while INFOCROP predicted 6.66%. But increasing CO2concentration (700 ppm) resulted in increases of 30.37 and
56.37% in yield by ORYZA and INFOCROP, respectively.
However, with temperature increase of +4 8C above
ambient, the differences in the yield predictions by the
two models became remarkably small (Table 4).
3.2. Effect of predicted GCM scenarios on rice yields
The predicted changes in overall production for each
site under different climate scenarios using the two crop
models are provided in Tables 5a and 5b. In general, the
ORYZA model suggested the decreases of 7.63, 9.38and 15.86% in yield for the GDFL, GISS and UKMOscenarios, respectively. For the corresponding scenarios,
INFOCROP indicated larger reductions at 9.02, 11.30and 21.35%, respectively. When each site was analysedindividually under three GISS and UKMO scenarios, almost
all the sites except one showed the declining trend in yields.
The decreases in the yield of rice, when the mean of the
corresponding values in Tables 5a and 5b were considered,
P. Krishnan et al. / Agriculture, Ecosystems and Environment xxx (2007) xxxxxx4
AGEE 3025 110
Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
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Table 2c
Model inputs of geographical information of different locations
Soil properties Bhubaneswar Kalyani Faizabad Ranchi Pusa Raipur Jabalpur Jorhat Cuttack
Latitude 208140N 228570N 228870N 238170N 22850N 258590N 238090N 26880N 208300N
Longitude 858520E 888210E 88840E 858190E 868E 85840E 798580E 95850 E 868E
Altitude (m) 25.9 7.8 8.62 625 23 51.84 411 86.56 23
Table 3
The major features of the General Circulation Models used in this study
GFDL GISS UKMO
Source laboratory Geophysical Fluid Dynamics
Laboratory
Goddard Institute for
Space Studies
United Kingdom
Meteorological Office
Base 1 CO2 (ppm) 380 380 380Change in global temperature (8C) +4.0 +4.2 +5.2
Change in global precipitation (%) 8 11 15
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P. Krishnan et al. / Agriculture, Ecosystems and Environment xxx (2007) xxxxxx 5
AGEE 3025 110
Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
Table 4
Mean predicted changea (%) in the potential yield under the fixed temperature and CO2 scenarios
CO2 concentration Temperature increments (8C)b
0 +1 +2 +3 +4 +5 Average
INFOCROP
380 0 2.68 18.04 28.15 36.36 42.49 20.39400 5.23
2.90
12.03
23.21
33.05
40.32
18.12
500 25.92 13.90 3.60 12.32 25.08 35.11 4.85600 40.61 29.39 16.74 1.04 15.80 29.17 7.14700 56.37 41.21 27.96 9.85 7.67 23.15 17.43Average 25.14 16.86 3.65 10.56 23.59 34.05 3.76
ORYZA
380 0 4.27 15.32 26.78 29.45 38.96 19.13400 2.78 0.93 8.44 19.74 23.67 33.65 13.22500 18.57 11.73 0.24 11.75 15.43 26.57 3.87600 26.8 18.54 5.03 6.86 10.87 21.84 1.80700 30.73 23.77 8.67 3.68 7.32 19.04 5.52Average 16.27 10.14 1.96 13.76 17.35 28.01 5.78
a Changes are averaged across all sites and at all the available years.b Temperature increments are above the current temperatures at each site.
Table 5a
Estimated changes in rice yield predicteda by the INFOCROP rice model for each observation site in the eastern India under the three GCM scenarios
Sites Rice yield
(t/ha)
GFDL GISS UKMO
Predicted
change (%)
Predicted
yield (t/ha)
Predicted
change (%)
Predicted
yield (t/ha)
Predicted
change (%)
Predicted
yield (t/ha)
Bhubaneswar 4.46 23.87 3.40 27.45 3.24 37.22 2.80Chinsurah 5.18 7.03 4.82 7.38 4.80 8.11 4.76Cuttack 4.93 25.44 3.68 27.67 3.57 40.87 2.92Faizabad 4.72 13.55 4.08 17.65 3.89 28.34 3.38Jabalpur 7.54 10.7 6.73 14.04 6.48 25.66 5.61Jorhat 3.83 13.51 4.35 12.32 4.30 7.55 4.12
Kalyani 3.55 8.73 3.24 11.65 3.14 22.38 2.76Pusa 3.82 3.74 3.68 4.35 3.65 5.26 3.62Raipur 3.75 1.71 3.69 5.11 3.56 18.01 3.07Ranchi 4.5 8.89 4.10 12.01 3.96 35.15 2.92Average change (%) 4.63 9.02 4.18 11.50 4.06 21.35 3.60
a Predicted rice yield is adjusted by the simulated changes in the experimental rice yield obtained.
Table 5b
Estimated changes in rice yield predicteda by the ORYZA1 model for each observation site in the eastern India under the three GCM scenarios
Sites Rice yield
(t/ha)
GFDL GISS UKMO
Predictedchange (%)
Predictedyield (t/ha)
Predictedchange (%)
Predictedyield (t/ha)
Predictedchange (%)
Predictedyield (t/ha)
Bhubaneswar 4.46 17.33 3.69 20.36 3.55 27.53 3.23Chinsurah 5.18 8.03 4.76 8.72 4.73 9.59 4.68Cuttack 4.93 19.67 3.96 20.32 3.93 30.75 3.41Faizabad 4.72 9.02 4.29 11.27 4.19 18.82 3.83Jabalpur 7.54 11.05 6.71 14.08 6.48 21.05 5.95Jorhat 3.83 12.13 4.29 12.64 4.31 8.31 4.15
Kalyani 3.55 7.75 3.27 9.76 3.20 16.51 2.96Pusa 3.82 4.93 3.63 6.31 3.58 6.58 3.57Raipur 3.75 2.79 3.65 5.22 3.55 10.09 3.37Ranchi 4.50 7.87 4.15 10.35 4.03 25.98 3.33Average change (%) 4.63 7.63 4.24 9.38 4.16 15.86 3.85
a Predicted rice yield is adjusted by the simulated changes in the experimental rice yield obtained.
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to describe the response of spikelet fertility to temperature
is
d 100
1 e0:853TmaxTmp
where d is the fertility percentage, Tmax the average daily
maximum temperature (8C) during the flowering period and
Tmp the average daily maximum temperature (8C) at which
50% of the spikelets are fertile. For the indica variety, Tmphad a value of 36.5. To simulate the possible effect of an
increase in tolerance of spikelet to high temperatures, it was
assumed that this response was shifted by 2 8C by increasing
the value of Tmp to 38.5 8C. This adaptation in the spikelet
trait was examined in Cuttack site. With the availableweather data for this site, and with a constant sowing date
of June 15, a comparative study using the ORYZA1 model
was made for the current climate and other GCM scenarios
as obtained by the GFDL, GISS and UKMO (Fig. 5). Under
the GCMs scenarios, temperature at the time of flowering for
the main season was already high. Without any temperature
tolerance of the variety by not adjusting the value of Tmp,
large decreases in yield due to spikelet sterility were pre-
dicted. But with the adaptation of variety by improved
temperature tolerance of the spikelet, the yield increased
higher than that of the current scenario level, at about +10.7,
+13.6 and 8.4, respectively, under the GFDL, GISS andUKMO model scenarios (Fig. 5).
4. Discussion
Both the crop simulation models predicted that any
increase in temperature at all the CO2 levels tested would
cause declines in yields but an increase in CO2 level at
each temperature increment would increase yields. Theseresults corroborated with that of Bachelet et al. (1995).
Summarizing the data from several experimental studies on
different agricultural crops, Kimbal et al. (2002) found a
30% increase in growth rate with a doubling of CO2 levels,
which was midway between the predicted values of the two
models in the present study. Nevertheless, the experimental
findings from the growth chamber studies (Baker et al., 1990
a,b) showed a 32% increase in rice grain yield due to
doubling of the CO2 concentration from 330 to 660 mmol -
CO2 mol1 air (ppm). The increased growth response with
increasing CO2 concentration was attributed to greater
tillering and more grain-bearing panicles. The net assimila-
tion rate and canopy net photosynthesis also increased with
increasing CO2 concentration. The elevated CO2 concentra-
tion was found to accelerate the development but shorten the
total growth duration of rice.
There are many indirect influences of elevated CO2on rice growth and development. When photosynthesis is
enhanced by increased CO2, the C/N ratio also increases in
the plants, which can reduce the nutritional quality of leaves
and increase feeding by the herbivorous insects (Johnson
and Lincon, 1990). There can be considerable changes in the
nutrient-cycling processes in soils also (Strain, 1985). Since
the current crop growth simulation models have not taken
P. Krishnan et al. / Agriculture, Ecosystems and Environment xxx (2007) xxxxxx 7
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Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
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Fig. 3. Changes in yield (%) under the GCMs scenarios of the rice variety
IR 36 under different sowing dates grown during the kharif season at
Cuttack.
Fig. 4. Changes in yield (%) under the GCMs scenarios of the rice variety
IR 36 under different sowing dates grown during the kharifseason at Jorhat.
Fig. 5. Changes in yield (%) under the GCMs scenarios of the rice variety
IR 36 and IR 36 with improved temperature tolerance grown during the
kharif season at Cuttack.
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these factors into account, there are still limitations on their
predictive value.
The average yield changes of 8.23 and 7.31% byORYZA and INFOCROP, respectively, due to the effect
of temperature when simulated on per degree Celsius
basis, were comparable with that of10% measured in the
controlled environment experiments (Baker and Allen,1993). The low responses at 400 ppm and 1 8C in both the
models ORYZA (0.93%) and INFOCROP (2.90%) clearlyshow the positive effects of temperature increase, simulated
by step-wise 1 8C increase with corresponding rise in CO2 to
400 ppm from the present ambient condition. However, the
CO2 concentration of 700 ppm resulted in increases of about
30.73 and 56.37% by ORYZA and INFOCROP, respectively.
In the light of recent experimental evidence (Kim et al.,
2003), these values appeared to be very high, probably
because the simulation models predicted the crop yield
mathematically from either RUE or net photosynthesis. In
ORYZA1, the hyperbolic relationship between the max-
imum rate of leaf photosynthesis at 1 g N/m2, and the
external CO2 concentration during rice growth has been
used. The rate of photosynthesis increased from 34 kg
CO2 ha1 h1 at the 350 ppm CO2 concentration to 47 kg
CO2 ha1 h1 at the 700 ppm CO2 concentration. The CO2
fertilization factor is applied in INFOCROP to reflect the
direct physiological stimulation by elevated CO2 concen-
tration. When compared with the results from the FACE
experiments (Kim et al., 2003), the fertilization effects used
in these two models are probably overestimated.
When simulated for the climate change scenarios, the
ORYZA model predicted changes of 7.63, 9.38 and
15.86% for the GDFL, GISS and UKMO scenarios,respectively, and INFOCROP predicted changes of 9.02,11.30 and 21.35%, respectively (Tables 5b and 5a). Themain cause for the differences in the predictions of the two
crop models was the way in which the leaf area development
and crop growth rate were calculated, and in the routines
describing phenological events in the crop. In ORYZA1, the
leaf area is calculated from leaf dry matter using the Specific
Leaf Area (SLA). LAI is simulated in the subroutine
SUBLAI. For a closed canopy, the LAI is calculated from
the leaf dry weight using SLA. When the canopy is not
closed, the plants grow exponentially as a function of the
temperature sum. The temperature sum is calculated using
the same procedure used to calculate the heat units for the
phenological development. The relative death rate of leaves
is applied to the leaf weight to calculate the weight loss
of leaves. The reduction in leaf area is calculated from the
loss of leaf weight using SLA (Kropff et al., 1994). In
INFOCROP, the Leaf Area Index changes proportionally
with Leaf Area Growth Rate (GLAI); its value is obtained by
multiplying the Increment in Leaf Weight (RWLVG) by
the SLA. The Net Leaf Area Growth Rate (RLAI) was
calculated based on the Initial Leaf Area Index (LAII),
GLAI, death rate of LAI (DLAI) and net loss of LAI due to
transplanting (PLTR) (Aggarwal et al., 2004).
In both the models, the phenological phases are charac-
terized by the thermal time and day length. In the ORYZA1
model, the phenological development of the rice crop is
divided into four main phases, namely Basic Vegetative
Phase (BVP), Photoperiod-Sensitive Phase (PSP), Panicle
Formation Phase (PFP) and Grain Filling Phase (GFP)
(Kropff et al., 1994). In INFOCROP model, the phenologicaldevelopment is divided into three main phases, namely
sowing to seedling emergence, seedling emergence to
anthesis and storage organ filling phase. The seedling
emergence to anthesis phase is further subdivided into three
major sub-phases depending on the environmental factors
affecting them and the organs formed, namely basic juvenile
phase, PSP and storage organ formation phase (Aggarwal
et al., 2004, 2006).
For each crop model, the GFDL scenario was the most
benign and the UKMO the most severe, corresponding to the
severity of temperature increases predicted by each GCM.
The predictions across both crop models and the three
GCM scenarios indicated a 12.45% decline in the overallregional rice yield. Averaged across all three GCM scena-
rios, the mean change in yield predicted by INFOCROP to
be 13.95% and by ORYZA to be 10.96%. Nevertheless,these values were lesser than the average values for the
scenarios in which temperature and CO2 were varied at the
fixed increments, independently or in combination, above
the current temperature for each site. It is likely that the
GCM scenarios have appropriate temperature corrections
associated with the elevated CO2 concentration, resulting in
a better predictive value compared to that of the scenarios
with arbitrary combinations of elevated CO2 and tempera-
tures.Among the different sites tested, both the models pre-
dicted the maximum loss in yield at Cuttack (27.45%),while the maximum gain in yield was at Jorhat (+11.08%).
These differences in yield predictions were mainly due to the
rice spikelet sterility at high temperature. The temperature at
the time of flowering affects the spikelet fertility and hence
the yield (Krishnan and Surya Rao, 2005). The rice-growing
sites such as Cuttack and Bhubaneswar (hot, moist sub-
humid climate type) had high maximum temperature of
about 34 8C and minimum temperature of 25 8C during the
flowering period. Other sites such as Jabalpur, Faizabad and
Ranchi (hot dry and moist subhumid type) had a high
maximum temperature of about 31 8C and the minimum
temperature of about 21 8C, which were lower than that of
Bhubaneswar and Cuttack. The low minimum temperature
probably helps to reduce the respiration at night. Likewise,
the rice-growing sites such as Kalyani, Pusa, Raipur and
Chinsurah (hot subhumid type) had a maximum temperature
of 30 8C and a minimum temperature of 20 8C during the
flowering period. But Jorhat (warm moist perhumid type)
had the maximum temperature of about 28 8C a n d a
minimum temperature of 19 8C only, which probably contri-
buted to the benefits from the predicted effects of climate
change scenarios. The predicted declines in the overall rice
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Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
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yield by both cop models for the GFDL, GISS and UKMO
scenarios showed the need to increase the rice production
than what was achieved at present. The differences in the
yield predicted due to different scenarios were largely due to
the differences in the temperature at the time of flowering
(Fig. 2).
Adjustment of management practices may help to offsetany detrimental effects of climate change on rice production.
Probably the easiest adaptation is to adjust the sowing
dates. Adjustment of sowing dates options was explored to
investigate a suitable agronomic option for adaptation under
the future climate change scenarios. In this way, for the
Cuttack site, the average yield changes of +6.6, +4.1 and
9.8% were predicted during July 15 sowing for the GFDL,GISS and UKMO scenarios, respectively, considerably
higher than those of19.67,20.32 and30.75% observedduring June 15 sowing for the GFDL, GISS and UKMO
scenarios, respectively. Likewise, the Jorhat site showed
+27.1, +24.3 and +13.4% changes for the sowing on July
1 under the GFDL, GISS and UKMO model scenarios,
respectively; these changes were considerably higher than
those of +12.13, +12.64 and +8.31% for the June 15 sowing
for the corresponding scenarios. Further postponement in
sowing did not improve the grain yield, probably due to low
incident solar radiation and temperature.
There were striking differences in the predicted yield
changes among the three scenarios. The large differences
were mainly due to the sensitivity of spikelet sterility to
temperature. Even a small difference of just 1 8C could
result in a large yield decrease due to lower number of grains
being formed (Sheehy et al., 2006). This was illustrated
in the two examples used in the present study (Table 6).This was further justified by the current changes in the
temperature at different sites during the main cropping
season (Fig. 2). The imposed climate change scenarios
further enhanced this temperature effect. The increased
spikelet sterility at Cuttack was mainly due to the predicted
increment in temperature above the already high daily
maximum temperature (34 8C) (Prasad et al., 2006; Krish-
nan and Surya Rao, 2005) under the climate change
scenarios reaching levels ($38 8C) where spikelet damagewas considerable. Although the predicted temperature
increments were similar to Cuttack, the lower average
temperature (28 8C) at Jorhat was well below the level
(30 8C) at which spikelet fertility is affected. At both the
locations, the number of spikelets formed was greater under
the changed climates. This could be due to the enhanced
growth rate of the crop between panicle initiation and
flowering as the consequences of fertilizing effect of higher
CO2 level.
The limitation on yields imposed by the increased
spikelet sterility can be largely overcome by the selection of
genotypes that possess a higher potential of spikelet fertility
at high temperatures. The fertilizing effect of increased
atmospheric CO2 level is then likely to offset the changes in
crop development rate brought about by the increased
temperatures, so that significant yield increases may also be
obtained (Horie et al., 2000). Thus, the sensitivity of spikelet
sterility to temperature is a factor that must be taken into
account while evaluating the model predictions about the
effect of climate change on rice production. Further studies
on the adjustments to the management practices may help to
offset any detrimental effects of climate change on riceproduction.
Some considerations are necessary, when interpreting
results from the scenarios predicted by the GCMs. The most
significant limitations are their poor resolution, inadequate
coupling of atmospheric and oceanic processes, poor simu-
lation of cloud processes and inadequate representation of
the biosphere and its feedbacks. The poor resolution is likely
to be significant in northeastern parts of India where the
relief is varied and local climate may be quite different
from the average across the area used by a GCM. Most
GCMs have difficulty in even describing the current climate
adequately (Bachelet et al., 1995). The current GCMs are
able to predict neither the changes in the variability of the
weather nor the frequency of catastrophic events such as
hurricanes, floods or even the intensity of monsoons, all of
which can be important in determining crop yields as the
average climatic data. It seems, therefore, that GCMs can at
best be used to suggest the likelydirection and rate of change
of future climates.
According to Long et al. (2005), fertilization effect of
[CO2] has probably been overestimated, while omission of
[O3] effects from most models could have led to a 20%
overestimation of crop production in the Northern Hemi-
sphere. Database of chamber studies are the mechanistic
basis for crop yield models. Hence, these models over-estimate the yield gain due to elevated [CO2] compared to
those observed under fully open-air condition (FACE)
experiments in the field. The current FACE experiments are,
however, not adequate enough to reparameterize the existing
models (Long et al., 2005). In a recent study, Bannyayan
et al. (2005) evaluated ORYZA 2000 (Bouman and Van
Laar, 2006) against the observed growth and yield of rice in a
3-year field experiment in Japan where rice plants were
subjected to the elevated CO2 with FACE under varying N
fertilizer rates. The simulation results showed that the model
overestimated the increases in green leaf area indices due to
the elevated CO2
concentration but the enhancement of total
biomass was only a minor overestimation. While the model
was successful in simulating the increase in rice yield due to
the CO2 enrichment, it failed to reproduce the observe
interaction with N in the rice yield response to elevated CO2concentration. Thus, the lack of complete understanding
of the effects and the potential interactions of environment
variables on plant processes preludes the definitive pre-
dictions of the effects of global climate change.
Despite the limitations imposed by the assumptions made
in both the GCM and the crop simulation models, the current
study provides significant progress in our understanding of
how future climates are likely to affect rice production in the
P. Krishnan et al. / Agriculture, Ecosystems and Environment xxx (2007) xxxxxx 9
AGEE 3025 110
Please cite this article in press as: Krishnan, P. et al., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as
evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
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eastern India. The use of simulation models to predict the
likely effects of climate change on crop production is an
evolving process. Our study in view of the findings of the
recent FACE studies clearly shows the need for modification
of the existing models. Other levels of production such as the
influences of water, nutrients and pests, and diseases due to
climate change are to be included in the refined models.Some of these limitations in the use of present models can be
addressed so that increasingly more accurate predictions can
be made in future.
Uncited references
IPCC (2001), Penning de Vries et al. (1989) and Schneider
(2001).
Acknowledgement
Our acknowledgements are to the collaborating members
from Orissa University of Agriculture & Technology
(OUAT), Bhubaneswar; Rice research Station (RRS),
Chinsurah; Narendra Deva University of Agriculture &
Technology (NDUAT), Faizabad; Jawaharlal Nehru Krishi
Vishwa Vidyalaaya (JNKVV), Jabalpur; Assam Agricultural
University (AAU), Jorhat; Bidan Chandra Krishi Viswa
Vidyalaya (BCKV), Kalyani, Rajendra Agricultural Uni-
versity (RAU), Pusa; Indira Gandhi Agricultural University
(IGAU), Raipur; Birsa Agricultural University (BAU),
Ranchi; for providing the crop parameter and weather data
under the NATP RRPS 25 and Indian MeteorologicalDepartment (IMD), Pune, for weather data.
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evaluated by crop simulation studies, Agric. Ecosyst. Environ. (2007), doi:10.1016/j.agee.2007.01.019
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