Projections
of the likely impacts of climate change in the future are based on complex
computer simulation models. A major emphasis in climate simulation modeling is
determining the likely impact of climate change on agriculture.
The
Africa Rice Center (AfricaRice) has long been involved with models that
simulate crop development and predict planting dates to avoid the worst of
seasonal stresses. In the mid- 1990s, AfricaRice researchers helped develop the
model ORYZA_S by combining two existing models: ORYZA1, a rice crop model
developed by Wageningen University and the International Rice Research
Institute (IRRI) for irrigated conditions in Asia, and RIDEV, a much simpler
model developed by AfricaRice simulating phenology of rice and sterility due to
heat or cold stress under irrigated conditions in the Sahel.
An
improved version, ORZYA2000, was released in 2001 by IRRI, followed by
progressive revisions until 2009, but it was based on ORYZA1 and therefore
fails to accurately simulate yield under Sahelian conditions.
“We
chose to proceed with the 2009 version of ORYZA2000,” says Pepijn van Oort,
crop modeler at AfricaRice, “because we hope that any improvement in the main
model will also be useful under different conditions from those we tested, such
as with water or nitrogen limitation, or in crop rotations. With ORYZA_S such
applications were not possible.”
This
meant that 20 years on, there was a need to take a fresh look at phenology and
cold and heat stress in the Sahel. Developing new subroutines and other refinements
to obtain a better predictive model for rice in the Sahel — in a changed
climate with respect to the 1990s — became the new challenge for AfricaRice.
Computer-based
models create simplified versions of reality and so should never be considered
perfect. “Perfect prediction is suspect, may be caused by over-parameterization
on a limited dataset, and runs a risk of adjusting parameter values without
sound eco-physiological justification,” says van Oort. “We have tried to avoid
this by using a large dataset, by making only modifications substantiated by
solid experimental research, and by keeping calibration to a minimum.”
The
large data set was obtained by Michiel de Vries, then AfricaRice irrigated-rice
agronomist, from monthly sowings of variety IR64 at two sites in the Senegal
River valley over 15 months in 2006–2007, a total of 29 treatments.
The
modifications made, chosen on the basis of previous research, comprised: (i)
so-called ‘cardinal’ temperatures for development; (ii) cardinal temperatures for
early leaf growth; (iii) spikelet-formation process; and (iv) heat- and
cold-induced sterility.
The
model was specifically calibrated only for developmental characters. Moreover,
to test the new heat- and cold-induced sterility subroutines, validation simulations
were run to predict yield, first using observed development and number of
spikelets, and second with simulated development and number of spikelets.
“The
first thing we needed to adjust for IR64 grown in the Sahel was the cardinal
temperatures,” says van Oort. “In particular, IR64 has a much higher base
temperature than the default setting in ORYZA2000 (14°C cf. 8°C), a slightly
higher optimum temperature (31°C cf. 30°C), and apparently experiences no delay
in development at temperatures above the optimum (i.e. there is no maximum
temperature, at least not under the conditions tested).” With these parameters corrected,
the model gave improved simulation of rice development and therefore yield.
“We
started with the situation in which ORYZA2000 over-estimated heat-induced
sterility and underestimated cold-induced sterility,” says van Oort. “The new heat
and cold subroutines give much better simulation of the two sterilities and,
consequently, final yield.” The keys to improving heat-induced sterility
simulation were transpirational cooling and flowering time, while the key to
improved cold-induced sterility was using minimum daily temperature rather than
average daily temperature.
“These
modifications are all logical if we think about where we’re working,” says van
Oort. “ORYZA2000 was developed in and for Southeast Asia which, for the most
part, is a humid tropical environment. In comparison, arid regions like the
Sahel experience much lower humidity and much greater ranges in daily
temperature.”
In
a dry environment, the relative humidity (RH) is much lower than in a humid
one. Thus, the ability of a plant to cool itself through transpiration is much greater
in the arid zone (just like humans can sweat to cool themselves in a dry
environment, while sweating in a humid environment just makes one wet!).
According
to the subroutines developed by van Oort, at 35°C and 30% RH (typical of the
Sahel), a plant can cool by 6°C relative to the air temperature via transpiration,
while at 30°C and 90% RH (typical of humid tropical Southeast Asia), there is
zero ability to cool via transpiration.
Flowering
earlier in the morning means the rice plants are exposed to a lower
temperature, which reduces the risk of heat sterility. In general, rice plants
flower earlier during the day in hotter environments, but this characteristic
is also genetically controlled and so varies with genotype. “Putting flowering
time into the model now allows us to simulate how much yield gain can be
obtained from breeding for earlier-flowering varieties,” says van Oort.
Arid
environments also have much larger temperature differences during the day. “On
one day in January, temperature increased from 8°C to 33°C. According to
ORYZA2000, the cold-sterility risk was small, because average temperature was
‘safe’, but it was clear that the minimum of 8°C caused severe cold sterility.
We therefore changed the subroutine to use minimum rather than average
temperature.”
“Model
calibration can be a tricky enterprise,” says van Oort. “At a certain point we
found that the model was overestimating biomass production and therefore also
yield. An effective trick to increase accuracy for yield was to modify the
parameter that determines that number of spikelets formed per unit of biomass.
But this led to unrealistic parameter values, because the real problem was that
the model was overestimating total biomass. So we kept focused on the real
causes of errors and played no artificial tricks with parameters.”
At
the end of the day, van Oort and the team were able to modify the ORYZA2000
model to better predict IR64 rice development and yield in the arid Sahel of the
Senegal River valley. Moreover, it did a better job of these predictions than
the benchmark ORYZA_S that was developed for the same environment and optimized
for IR64 in 1999.
“It
is important to remember that this work was not done in isolation,” says van
Oort. “It would not have been possible without the work done in the 1990s by Michael
Dingkuhn (formerly with AfricaRice) and his co-workers in developing ORYZA_S
and RIDEV.” In fact, the Sahel-adapted ORYZA2000 of van Oort and partners uses
several equations and parameters derived from ORYZA_S and RIDEV.
“Our
results indicate a need for further research into the components we identified,
and to re-assess the climate risk to rice in arid regions,” concludes van Oort.
“Our discoveries about the importance of cardinal temperatures, heat tolerance and
heat avoidance also provide a basis for variety selection, as these three
critical characteristics are genetically controlled and vary across cultivars.”
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