Modelling the Impact of Climate Change on Rice Production: An Overview
M. Nasir Shamsudin,
In modern agricultural research, crop modelling has become
a critical tool which incorporates scientists
insights into the physiological and ecological processes by conducting crop
growth into mathematical equations. The crop simulation models have been shown
to be efficient in assessing the relationships between crop yield and environmental
factors. They are able to determine the response of crop plants to change in
weather. This study compares six different crop simulation models, namely RICEMOD,
CERES-Rice, MACROS, RICESYS, SIMRIW and ORYZA series which can be used to evaluate
the impacts of climate change on rice production. Comments are then made about
the application and usefulness of these models.
Received: May 05, 2013;
Accepted: September 02, 2013;
Published: January 25, 2014
Climate change can have important effect on crop growth, development and yield
by increasing carbon dioxide, temperature and uncertainty in rainfall. The relationship
between climatic change and agriculture is an important issue, since the worlds
food production resources are already under pressure due to a rapidly increasing
population. It also can affect the land use patterns and the productivity of
crops. Thus good understanding of the processes of changes in climate and changes
on the growth and development of crops is essential (Matthews
and Wassmann, 2003).
The impact of climate change on rice production is of particular interest due
to its importance as a food source in all over the world, especially in Asia.
Hossain (1998) estimated that about 60% increase in rice
yield would be likely needed by year 2020 due to population growth. Thus, attention
to climate change issue is urgency, as it poses a significant threat to food
supplies and security.
Crop modelling has become important tool in modern agricultural research. So
far different models and methods have been employed in attempts to assess the
impacts of climate change on rice production, such as crop production models,
yield prediction and quantities of water or fertilizer consumed. Mechanistic
crop production models are useful tools to study the impact of climate change
on c rop growth, development and yield in various agro-environments. The aim
of this study is to review the possible crop production models to estimate the
impacts of climate change on the rice production.
Every effort has been made to provide a comprehensive review and it is acknowledged
that there may be some research, in both published and unpublished form which
not explained in this review.
REVIEW OF CLIMATE CHANGE IMPACTS ON RICE PRODUCTION
Temperature effects: The major effect of temperature on crop growth
is to control the duration of the period when growth is possible in each year.
Furthermore, temperature may directly affect the other processes associated
with the accumulation of dry matter such as leaf area expansion, respiration
and photosynthesis (Olesen and Bindi, 2002). Increased
temperature accelerates plant phonological development; however it can decrease
the length of the grain filling period (Amthor, 2000;
Bachelet and Gay, 1993). Hence, temperature increase
may shorten the length of the growing period and thus reduce yield, if management
practise is not changed.
In different parts of the world, climate change may affect agriculture differently.
It depends on current climatic and soil conditions, the direction of change
and the availability of resources and infrastructure to face with change. Rosenzweig
et al. (1993) predicted that crop production are likely to decrease
in the low-latitudes, however, could increase in the mid-and high-latitude regions.
It is related to the current growing conditions. Since crops grow nearer their
temperature tolerance limitation in low-latitude areas, any warming exposes
them to higher stress. However, in many mid-and high-latitude regions, increased
warming would be beneficial to crops which currently limited by cold temperatures
and short growing seasons (Matthews et al., 1997).
For example global warming in Northern Europe will give more favourable conditions
for crop production and hence can increase productivity of European agricultural
system (Olesen and Bindi, 2002). Cure
and Acock (1986), predicted the higher rice yield variability in cooler
regions, particularly for rainfed rice. Jansen (1990)
argued that yields would rise if temperature increases were small and instead,
it would decrease if temperature increased more than 0.8°C per decade. In
fact, the fertility of spikelet in rice plant is very sensitive to temperatures
approximately 33°C and there is also a considerable variation between variability
in tolerance to high temperature (Satake and Yoshida, 1978).
The studies by Bachelet and Gay (1993) showed that potential
rice yield will be lower by increasing in mean daily temperature due to global
warming in many Asian countries. Nevertheless, it may also enable the northern
limits of rice growing regions to expand, especially in northern China and Japan.
The same study by Baker et al. (1992) and Vaghefi
et al. (2013) indicated a sharp decline in grain yield and then potential
negative effects on rice production in warmer regions if temperatures increase.
CO2 effects: The first primary effect of CO2 enrichment
on plant is increasing photosynthesis. The second one is to reduce stomatal
aperture and density which causes a reduction in stomatal conductance and transpiration.
The third primary effect is the reduction of dark respiration. The resulting
effects of these primary responses to increase atmospheric CO2 concentrations
are increasing resource use efficiencies for radiation, water and nitrogen and
thus increasing productivity of plant (Olesen and Bindi,
2002). In general, the direct effect of increased CO2 levels
is beneficial to vegetation (Baker et al., 1990;
Bowes, 1993; Farquhar, 1997).
Some previous studies indicated that elevated CO2 would increase
rice yield due to an increase in net assimilation rate and photosynthesis. However,
this would be offset by the effect of the expected rise in temperature as a
result of reduced length of the growing season and increased maintenance respiration
rates, such that the two factors cancelled each other out (Baker
et al., 1995; Horie et al., 1996; Kim
et al., 1996; Vaghefi et al., 2011;
Ziska et al., 1997).
Again, higher levels of CO2 accelerate the development rates of
rice plant. However, in rice growing under increased CO2 conditions,
at first, there is a large response and then over time, this response decreases
and go towards the rice growing under current CO2 levels (Rowland-Bamford
et al., 1991).
Rainfall effect: Climate change may modify precipitation, runoff, evaporation
and soil moisture strong. Changes in both total seasonal rainfall and its pattern
of variability are very important (Olesen and Bindi, 2002).
In a warmer climate, the demand for water for irrigation will be increased and
thus more water will be needed per unit area under drier conditions.
Agriculture is extremely influenced by the availability of water. Topography
and soil texture play an important role in defining the water availability of
plant (Gupta and O'Toole, 1986). Rainfall and soil water
availability can affect the duration of growth through effects on leaf area
duration and also may affect the photosynthetic efficiency through stomatal
closure (Olesen and Bindi, 2002).
Malabuyoc et al. (1993) found that, during
the reproductive stage, rainfall can explain 38-67% of upland rice yields variation
in the Philippines. In fact, under upland conditions, rice plant cannot maintain
high yield performance under low rainfall. Saito et
al. (2006) also found that rice yields are most associated with amount
of rainfall during vegetative and reproductive growth stage.
CROP SIMULATION MODELS
Crop simulation models are widely used to assess the impacts of climate change
on agricultural production which provide us with an opportunity of building
scenarios of agricultural output in changed climates. They are recognized as
useful tools in agricultural research. They can help to compare experimental
research findings across sites, extrapolate experimental field data to wider
environments, develop management recommendations and decision support systems,
explore effects of climate change and make yield predictions (Jones
et al., 2003). They are useful tools to assess the complex interactions
between weather, soil properties and management that affect crop performance
(Timsina and Humphreys, 2003).
In general, crop models do not include all factors which may affect yield in
a real situation and their input data requirements often are more than available
databases. Hence, simpler models may be more suitable in such situations. Normally,
the required input data for crop models are including the parameters of the
environmental conditions (weather characteristics and CO2 concentration),
the soil characteristics, the cultivar and the agrotechnological management
details (planting, fertilization, etc.) (Zalud and Dubrovsky,
Simulations can be an important step to specify the yield gap between farmers
yields and potential yields, assisting efforts to bridge the gap (Swain
et al., 2007). However, there is a significant mismatch between the
spatial and temporal scale of available data and the requirements of crop simulation
models (Xiong et al., 2008). One of the most
important practical limitations of using crop models in regional scale is the
spatial coverage and enough quality of the input data (Heuvelink,
1998). In fact, application of crop simulation models depends not only on
the availability of models and application software but also on the availability
of information to run models for specific scenarios and to define the models
accuracy for specific target regions as well (Hunt and Boote,
1998). Some models act better than others in specific contexts, for instance,
when they applied to particular crops, climates, cropping patterns, soil quality
indicators and management practices. Different crop growth simulation models
exist for rice but thorough validation and evaluation reports are inadequate.
OVERVIEW OF MODELS
The following section provides an explanation of the models for helping to
determine which model is most appropriate for certain objective. In this study,
six crop simulation models were selected based on their ability to simulate
the effect of climate change: RICEMOD (McMennamy and O'Toole,
1983), CERES-Rice (Godwin et al., 1990),
MACROS (Penning de Vries et al., 1989), RICESYS
(Graf et al., 1990), SIMRIW (Horie,
1987) and ORYZA series (Ten Berge and Kropff, 1995).
The key attributes of these crop simulation models are presented in Table
RICEMOD: RICEMOD was developed at the International Rice Research Institute
(IRRI) by McMennamy (1980) as a rice crop growth and
yield simulation model to assess the completeness of knowledge about rice science.
The main objective of this model is to explain the complex biophysical and biochemical
systems interacting in a rice crop (Wu and Wilson, 1998).
RICEMOD is based ecophysiological model for irrigated rice production. It includes
a number of physical parameters which is designed to accommodate subroutines
dealing with soil, plant chemistry and physical processes of the atmospheric
environment. The components of this model include timings of plant growth initiation
and harvest, maximum leaf area index, Harvest Index (HI) and Radiation-use Efficiency
RICEMOD almost depends on soil, plant and atmospheric data derived from experiments
at the IRRI. Photosynthesis, growth and maintenance respiration, partitioning
of assimilates to different growth organs and the soil water balance are four
soil-plant-atmosphere processes in this model (Rao and
Rees, 1992). Daily weather data inputs for this model include maximum and
minimum temperatures, precipitation, pan evaporation, solar radiation and day
length which do not include the influence of CO2 (Bachelet
and Gay, 1993). In RICEMOD, photosynthesis is basically the process by which
atmospheric carbon dioxide is fixed by the plant (McMennamy
and O'Toole, 1983). It also assumes the best levels of nutrients and ignores
the potential effects of typhoons and pests. In this model, the effect of temperature
on photosynthesis is not considered and photosynthesis is a function of canopy
structure, solar radiation and the ratio of leaf nitrogen weight to area. Leaf
area is assumed to be consistent with leaf weight and leaf nitrogen content
is assumed to be consistent with plant age (Bachelet and
Growth respiration is proportional to the photosynthesis rate during daylight
hours and maintenance respiration is determined for the night hours of individual
plant parts. The partitioning of assimilates is done through a distribution
function approach by using partitioning coefficients which used to allocate
the photosynthesis to different plant organs at different stages of plant growth.
Based on the different rice variety and environmental conditions, the parameters
of the various growth relationships used in the model will be different (Rao
and Rees, 1992).
Calibration and validation of RICEMOD was done for high yielding rice variety
IR 36 by McMennamy and O'Toole, 1983 in the Philippines.
RICEMOD can be used to study the relative constraining effects of leaf blade
nitrogen content, respiration rate, radiation and assimilate partitioning on
rice plant growth. It would be also applicable to predict the future production
scenarios. It has been used to indicate leaf water stress and predict the growth
and yield component of different rice varieties in a number of rice-producing
countries (McMennamy, 1980; Rao
and Rees, 1992). In this model, more information is needed on respiration
and the environmental effects on nutrient uptake and distribution, leaf blade
thickness and plant growth control mechanisms, to predict growth precisely.
RICEMOD could be more applicable, if it was sensitive to water and nutrient
stresses (McMennamy and O'Toole, 1983).
CERES-rice: Crop Estimation through Resource and Environment Synthesis-Rice
(CERES-Rice) is a generic and dynamic simulation model which was developed under
the International Benchmark Sites for Agrotechnology Transfer (IBSNAT) project
(Ritchie et al., 1987).
It is part of the Decision Support System for Agrotechnology Transfer (DSSAT)
system. All the DSSAT models are continuously being refined, calibrated, validated
and applied by the scientists and their collaborators who developed the models.
CERES-Rice is a physiological-based and management-oriented model which can
simulate the growth and development of rice under optimal, nitrogen-limited
and water-limited conditions (Timsina and Humphreys, 2003).
CERES-Rice was designed to estimate yield as constrained for alternative technology
and new growing sites, by different characteristics, soil water and nitrogen.
It is able to reduce time and cost of agrotechnology transfer of new varieties
and management (Bachelet and Gay, 1993). The model is
able to compute the growth and development of rice plants in a homogeneous area
on a daily time step. The final crop yield can also be calculated on the date
of harvest (Xiong et al., 2008). It can estimate
the potential yield by combining the properties of crops, weather and soil (Wikarmpapraharn
and Kositsakulchai, 2010). CERES- Rice simulates detailed soil and water
N dynamics under changing hydrological conditions (Timsina
and Humphreys, 2003). The inputs required to run the model are weather variables
(daily solar radiation, maximum and minimum temperatures and precipitation),
management information (plant population, plant genetics, planting and harvesting
dates, row spacing and fertilizer application amounts and dates) and environmental
factors (soil type, saturated hydraulic conductivity, drained upper and lower
limits, etc.). This model does not consider the effect of typhoons and it is
assumed that the crop is well protected against insects, weeds and diseases.
In different studies, CERES-Rice has been used to simulate the effects of weather,
soil properties, plant genetics and management practices on the yield, growth
and development of rice plants (Kumar et al., 2010;
Mahmood, 1998; Saseendran et
al., 2000; Xiong et al., 2008). This
model has been widely used to assess the impact of climate change on rice production
worldwide (Amien et al., 1999; Basak
et al., 2010; Felkner et al., 2009;
Mall and Aggarwal, 2002; Van Oort
et al., 2011).
MACROS: MACROS (Modules of an Annual CROp Simulator) was developed in
Wageningen in the Netherlands and has been applied for educational purposed,
specifically in developing countries (Bachelet and Gay,
1993). It was developed as a part of the Simulation and Systems Analysis
for Rice Production (SARP) project for crops in the semi-humid tropics. One
of the objectives of this project was transferring the technology of simulation
and system analysis to multi-disciplinary teams of scientists in Southeast Asia.
MACROS supported these objectives in two ways. First, as a training instrument
to transfer the agrotechnology and system analysis and second, as a tool to
apply the models in the cropping system, nutrients, water
and roots, potential production and diseases, pests
and weeds research areas (Bouman et al., 1996).
In fact, studies on the climate change impacts on rice production began at IRRI
in the late 1980s, by using MACROS crop simulation model (Penning
de Vries et al., 1990). The model simulated the potential rice production
by determining the daily rates of photosynthesis, transpiration, respiration
and phonological development, by considering the effects of temperature, air
humidity, wind speed and radiation as well. It was modified to consider the
response to changes in temperature and CO2 level, on the basis of
a number of crops summarised by Kimball (1983), Cure
and Acock (1986) and Matthews and Wassmann (2003).
MACROS is a generic crop growth model based on comprehensive physiological
processes which consist of parameter sets for crop. It can simulate crop growth
and development under conditions of water limitation and potential production.
The model is well adapted to the study of response to the environmental changes
and has been widely used in crop growth simulations for rice (Bachelet
et al., 1993; Jansen, 1990), soybean (Eitzinger
et al., 1996) and wheat. It focuses on the biochemical aspect of
plant physiology on the basis of growth limitation factors. This model is included
a set of equation which can explain the relations between the main physiological
processes and the environment. Main physiological processes refer to respiration,
photosynthesis, biomass accumulation and partitioning, phenological and leaf
area development and the structure of the canopy. The environment also refers
to radiation, temperature and CO2 concentration which radiation and
CO2 affect photosynthesis and temperature affects photosynthesis,
respiration and the rate of phenological development (Penning
de Vries, 1993). The daily weather inputs in MACROS model are as follows:
Maximum and minimum temperature, precipitation, solar radiation, air humidity,
wind speed, daylength and vapour pressure.
The model includes series of basic modules for potential and water limited
crop growth and for the water balance of soils as well. There are two different
modules for water balance of soils which the first one is for free draining
soils (SAHEL) and the second one is for soils with impeded drainage (SAWAH).
MACROS provides a development tool to apply models for various application,
such as management of water, nutrients and pests (Jones
et al., 2001). In fact, this model can be run in one of three different
situations where (1) Nutrients and water are in optimum conditions and pests,
weeds and disease are absent, (2) Water stress may be occur because of limiting
water availability, (3) Plant production may be restricted by water and nitrogen
during part of the year. However, the carbon fraction in dry matter and the
biochemical composition are fixed in all situations (Bachelet
and Gay, 1993).
RICESYS: RICESYS is an ecosystem model for predicting the inter-species
and herbivores competition between rice and weeds which provides an applicable
tool for integrated pest management studies. In other word, this is a demographic
model for rice growth and development as affected by temperature and solar radiation
and does not include CO2 effects. The demographic component of this
model consists of bookkeeping device for births, deaths, growth, ageing of mass
and numbers of plants subunits. This structure prepares a base for the linkage
of insect pest and weed model. Furthermore, to simulate the competition with
weeds, the model should be included the dynamics of nutrients (Graf
et al., 1990). In RICESYS, all nutrients, except nitrogen and water,
in the irrigated paddies, are assumed to be non-limiting. The model also ignores
the effects of typhoons and assumes there are no other pests except herbivorous
Graf et al. (1990) in their study used (Frazer
and Gilbert, 1976) functional response model to predict photosynthesis.
This approach permits the simulation of energy acquisition at different nutrition
level which energy acquisition is a function of resource availability, demand
and the search rate. Therefore, photosynthesis is a function of temperature,
solar radiation, Leaf Area Index (LAI) and total demand of plant for carbohydrate.
The product of photosynthesis was used first for respiration and then for reproduction,
growth and reserves, respectively.
RICESYS is a useful model to explain the dynamics of rice growth and development
and to represent the effect of delays in transplanting date and planting density
on growth and yield of rice plant. The model was designed to simulate growth
and development of the rice variety Makalioka 34 from Madagascar under irrigated
condition but other rice varieties in other location can also be simulated via
simple parameter changes. The model is structurally a well documented program
and can be easily modified.
SIMRIW: SIMRIW (Simulation Model for Rice and Weather- relationship)
is a simplified process model for rice which developed by Kyoto University to
estimate the potential rice growth and yield from climate and weather variation.
It can predict the growth and yield of rice under the best managements of pests,
diseases, nutrients and good condition of an irrigated paddy field. This model
was developed by a reasonable simplification of the underlying physiological
and physical process of the rice growth; hence, it needs just a limited number
of crop parameters which can be obtained from field experiments. Because of
this, SIMRIW is useable for a wide range of environment. It can be executed
as a Web application by displaying the optimal transplanting date, the potential
for cultivation and the maximum yield on a map. The original SIMRIW model was
developed for researchers; however, the Web application can be used as a decision
support system tool for policy makers and farmers (Tanaka
et al., 2010). The actual farmers yield at a given location
can be obtained by multiplying the simulated potential yield by a technological
coefficient which describes the current level of rice cultivation technology,
such as fertilizer applications, pest management, soil, water, etc. (Horie
et al., 1995).
Previous studies show that the model has adequate capability to explain the
locational variability and special distribution of rice yield based on the respective
climate (Horie, 1987; Tanaka et
al., 2010). It can also acceptably explain the yearly variations in
yield at different regions based on the weather (Horie et
al., 1992). In spite of its good applicability, the model has limited
capability in climate acclimatization.
SIMRIW program consists of a subroutine to input weather data and one main
program to calculate the equations. It also needs two external files. One of
them is CROPARAM.DAT, for specification of cultivar specific crop parameters
and the other one is a weather data file which includes daily weather data.
The required weather data for this model is temperature and solar radiation
which can be easily obtained and does not need any regional parameters (Horie
et al., 1995). Although solar radiation data are a little difficult
to obtain than precipitation and air temperature data but a sunshine hours solar
radiation conversion model is available in SIMRIW to support the requirement
of solar radiation data. The model determines the maturity or heading as a crop
growth stage by using Developmental Index (DVI) which integrates the Developmental
Rate (DVR). Since SIMRIW just simulates potential growth in irrigated and fertilized
paddy field, so the effect of water stress and nitrogen stress cannot be simulated
by this model (Tanaka et al., 2010).
A sensitivity analysis of SIMRIW can be done by testing responses of simulated
yield to daily mean temperature, CO2 concentration and solar radiation,
under constant environmental conditions and over the whole growth season.
ORYZA series: International Rice Research Institute (IRRI) in cooperation
with Wageningen University developed the ORYZA model series to simulate tropical
lowland rice growth, development and water balance under conditions of potential
production, water limitations and nitrogen limitations. They have been developed
from MACROS model and SUCROS (Simple and Universal CROp growth Simulator) model
(Spitters et al., 1989) to serve specific application.
ORYZA1 (Kropff et al., 1994) was the first model
for potential production which derived largely from the MACROS model, followed
by ORYZA-W (Wopereis et al., 1996) for water-limited
production and by ORYZA-N (Drenth et al., 1994)
and ORYZA1N (Aggarwal et al., 1997) which was
partly based on ORYZA-N, for nitrogen-limited production (Bouman
and Van Laar, 2006).
ORYZA1 is an ecophysiological model for irrigated rice production which was
modified from different models such as SUCROS, LID module of MACROS, INTERCOM
(Kropff and van Laar, 1993) and GUMCAS (Matthews
and Hunt, 1994). The required environmental and crop management data of
the model are included daily weather data (such as minimum and maximum temperature
and solar radiation), geographical latitude, plant density, crop emergency date,
transplanting date and parameter values which explain the morpho-physiological
characteristics of rice. The model estimates daily growth rates for dry matter
production of plant organs, phonological development and leaf area. Daily canopy
CO2 assimilation is estimated based on leaf area index and the climate
variables such as temperature and solar radiation. This model assumes that nitrogen
and water are non-limiting factors (Olszyka et al.,
1999). ORYZA1 includes a carbon balance check, to make sure that the total
net assimilated carbon equals the carbon fixed in dry matter and the carbon
lost due to maintenance respiration and growth. The model has been successfully
calibrated and evaluated by using data from previous experiments carried out
at IRRI (Kropff et al., 1995). The results of
Olszyka et al. (1999) show that under high temperature
and normal CO2 scenarios, ORYZA1 over predicted by about 70%. However,
under normal temperature and high CO2 level, the over prediction
was about 7%. It shows that simulations based on the ORYZA1 model may overestimate
rice yield and it is not able to predict the absolute values. These results
are consistent with the findings of Matthews and Wassmann
(2003). Hence, the model needs some improvements and integration of modelling
to provide more accurate prediction.
In 2001, ORYZA2000 was released as a product of the modelling school
of De Wit (Bouman et al., 1996) that improved
and integrated all previous versions into one model which was a new version
in the ORYZA model series (Bouman et al., 2001).
Hence, ORYZA2000 can simulate growth and development of lowland rice in situations
of potential production, water limitations and nitrogen limitations. The model
assumes that, in all these situations, there are complete control of growth
factors such as pests, weeds, disease and other management variables and that
no decreases in production take place. From Bouman and Van
Laar (2006) evaluation, they concluded that ORYZA2000 was adequately accurate
in the simulation of yield, LAI and biomass of crop organs over time for irrigated
rice. The ORYZA2000 model also simulates the increases of daily Dry Matter (DM)
in phonological development progress and plants organs (Artacho
et al., 2011).
Matthews and Wassmann (2003), in their article reviewed
the characteristics of three crop simulation models, ORYZA1, SIMRIW and CERES-Rice
which can be applied to simulate the potential rice production. They found that
SIMRIW needed fewer crop parameters than ORYZA1 which all of them can be obtained
easily from well defined field experiments; because SIMRIW was based on underlying
physiological processes involved in the growth of the rice crop. In addition,
CERES-Rice model has routines describing the main crop components involved in
CH4 dynamics, i.e., organic matter decomposition, root growth and
death and root exudation, along with routines describing the relevant crop management
options such as water management and applications of organic and inorganic fertilisers.
It can be then noted that SIMRIW is simpler than CERES-Rice and ORYZA model.
In fact, in global studies, it is required to choose a rice simulation model
which does not need regional parameters, because it is not feasible to do the
cultivation experiment to predict the rice cultivation possibility throughout
the world. SIMRIW fulfils this requirement, because it already obtained good
result in a simulation of large area without any regional parameters. It does
not use regional parameters and just uses temperature and solar radiation as
meteorological data. It is beneficial that small amount of meteorological data
is required to run the model. These characteristics make it suitable to predict
the possibility of rice cultivation in areas all over the world.
Bachelet and Gay (1993) compared the performances of
four rice crop simulation models namely MACROS, CERES-Rice, RICEMOD and RICESYS,
about their ability to simulate the effects of climate change on rice growth
and productivity. They found that the first two were the most suitable for climate
change studies, because they simulated more realistic responses to temperature
and CO2 than the others. On the other hand, RICEMOD and RICESYS did
not simulate the effect of CO2 level. RICESYS is able to assess the
effect of climate change on the rice plant, its weeds and herbivores, that is
to say, at the system level of the plant. This model consists of competition
between rice and its natural herbivores and weeds, under condition of increased
temperature only. Hence, it can simulate more realistic yield than CERES-Rice
which simulates pest-free yield and MACROS which estimates potential yield.
Making a choice between MACROS and CERES-Rice depends on which the scientist
was familiar with (Matthews and Wassmann, 2003). Based
on Bachelet and Gay (1993) findings, RICEMODE simulated
the smallest decrease in yield among these models. CERES-Rice also predicted
a lower impact of temperature (18% from 25-30°C) than MACROS (62% from 25-30°C).
However, CERES-Rice predicted a higher increase in yield due to a doubling of
CO2 (without temperature increase) than MACROS model (Bachelet
and Gay, 1993).
MACROS and CERES-Rice are both physiologically-based models and both of them
are based on the concept of a generic crop growth model. MACROS needs a greater
number of climatic inputs compared to CERES-Rice, however its code is structurally
easier to modify and well documented than CERES-Rice. MACROS can be used as
training tool because of its obvious modular structure which allows scientist
to select and combine suitable crop growth and water balanced modules for addressing
their particular production situations and research questions.
Actually, the most commonly used rice models are ORYZA2000 and CERES-Rice models
which are very similar in terms of processes included (Van
Oort et al., 2011). However, these two models have some limitations.
These models, like any other crop simulation models, are built on specific assumptions.
It assumed that weeds, insects and diseases are entirely controlled. It is also
assumed that there are no nutrient insufficiencies, except for nitrogen. In
addition, crop losses which may increase due to extreme events such as droughts,
floods, frosts and heat waves are not taken into account in the model. The damaging
effects of catastrophic weather events and deteriorated soils are not considered
CERES-Rice and ORYZA1N are two popular rice growth models for the same input
conditions. The main objective of CERES-Rice model is to evaluate how the weather
and genetic characteristics can affect the rice production yield at a given
location under a certain management scheme (Lal et al.,
1998). This model is able to simulate the effects of CO2 on photosynthesis
and water use. But, it cannot compute gross photosynthesis and respiration separately
as it can be done in ORYZA1N. CERES-Rice can estimate net photosynthesis (CARBO)
based on a constant radiation use efficiency (RUE), Leaf Area Index (LAI), extinction
coefficient (k) and light absorption (IPAR) by the canopy (Mall
and Aggarwal, 2002). Hence, photosynthesis sensitivity in CERES-Rice is
greater than ORYZA1N which could probably be due to separate estimation of gross
photosynthesis and maintenance respiration in the ORYZA1N model.
ORYZA1 which is a derivative of MACROS model and SIMRIW have been successfully
tested and compared in Matthews et al. (1995).
It was concluded that both models were suitable to predict changes in rice yield
caused by changes in temperature and atmospheric CO2 concentration.
However, ORYZA1 is poor at predicting CH4 emissions (Olszyka
et al., 1999). On the other hand, CERES-Rice model which already
included soil organic matter decomposition routing and routines describing the
relevant crop management, is capable to evaluate the effects of any changes
of these on both rice yields and CH4 emissions.
Although all of these models have been well tested for the model validity but
there are some uncertainties with the results of simulations which could be
due to many assumptions that built into the model used. For example, in most
of them the influences of water, nutrients, pests and diseases are not included
in the models. In addition, most of the relationships about the effect of temperature
and CO2 on rice plant processes obtain from experiments which its
environment was changed for just part of the season. Thus, crops
acclimation to changes in its environment is not taken account in the model.
Based on physiological and physical perception, RICEMOD was quite simple and
did not received extensive attention. For assessing the effect of temperature
and CO2 changes on rice production, CERES-Rice and MACROS should
be preferred over RICEMOD and RICESYS. For climate change impacts studies that
just examine the effect of temperature without changes in CO2 level,
the more realistic model between these models is RICESYS, because it considers
the effect of climate change at plant system level as well. For large area and
global rice yield prediction, SIMRIW model is more preferable. At the regional
level, ORYZA2000 and CERES-Rice are the most commonly used models, especially
in Asian countries. However, CERES-Rice received more attention than ORYZA2000,
because it has been well tested in a range of environments and could simulate
the growth and development of rice crop under both upland and lowland conditions.
The rice simulation model should be simple; however, it should be comprehensive
enough to predict the growth of various varieties under different agroclimatic
conditions. The use of crop simulation models to predict the likely impact of
climate change on rice production is a developing science and knowledge on the
limitations of each crop model can help to make a better prediction.
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