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Mini-review Crop, Environment & Bioinformatics, Vol. 7, December 2010
Projection of Effects of Climate Change on Rice
Yield and Keys to Reduce its Uncertainties
Mayumi Yoshimoto 1 *, Masayuki Yokozawa 1 , Toshichika Iizumi 1,
Masashi Okada 2 , Motoki Nishimori 1 , Yoshimitsu Masaki 1 , Yasushi
Ishigooka1 , Tsuneo Kuwagata 1 , Motohiko Kondo 3 , Tsutomu
Ishimaru3 , Minehiko Fukuoka 1 and Toshihiro Hasegawa 1
1 National
Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
School of Life and Environmental Sciences, University of Tsukuba,
Tsukuba, Ibaraki, Japan
3 National Institute of Crop Science, National Agriculture and Food Research
Organization, Tsukuba, Ibaraki, Japan
2 Graduate
ABSTRACT
The
increase
in
atmospheric
CO2
concentration and accompanying global warming
should affect crop productivity. A number of
experiments and simulations have been
conducted to predict the impacts of climate
change on rice yield. When conducting large-scale
evaluation of rice yield, there are large
uncertainties, which resulted from a number of
sources, such as those in the greenhouse gas
(GHG) emission scenarios, global climate models
(GCMs) and its gaps between global and local
climates. In addition, the rice development
models themselves include uncertainties. In this
paper, we present our recent studies on
large-scale evaluation by crop models and trials to
elucidate and reduce uncertainties accompanied
with each aspect of evaluation. In modeling
technique aspect, statistical approach for model
parameters and the use of multi-scenarios and
multi-GCMs are reviewed. In field experiment
aspect, we present a field survey on spikelet
sterility in the hot summer of 2007 and some
insights from free-air CO2 enrichment (FACE)
experiment. They strongly suggest the necessity
for developing a process-based rice development
model including heat balance. The synthesized
process-based model study in tandem with FACE
experiments contributes not only for reducing the
evaluation uncertainties, but also for validating
the adapting or avoiding studies of heat stress or
negative influence on rice under projected climate
change.
Key words: Projection, Climate change, Rice
yield, Impact assessment, Prediction
uncertainty.
氣候變遷對水稻產量影響之預測及降
低不確定性之關鍵
Mayumi
Yoshimoto1*,
Masayuki
Yokozawa1, Toshichika Iizumi1, Masashi
Okada2, Motoki Nishimori1, Yoshimitsu
Masaki1, Yasushi Ishigooka1, Tsuneo
Kuwagata1, Motohiko Kondo3, Tsutomu
Ishimaru3, Minehiko Fukuoka1 and
Toshihiro Hasegawa1
1 日本、農業環境技術研究所
2 日本、筑波大學大學院生命環境科學研究所
* 通信作者, [email protected]
投 稿 日 期： 2010 年 7 月 20 日
接 受 日 期： 2010 年 9 月 14 日
作 物 、 環境 與生 物 資 訊 7:260-268 (2010)
Crop, Environment & Bioinformatics 7:260-268 (2010)
189 Chung-Cheng Rd., Wufeng, Taichung Hsien 41362,
Taiwan ROC
3
日本、農業‧食品產業技術總合研究機構作物研
究所
摘要
大氣中二氧化碳濃度的上升及伴隨而來
的全球暖化現象，勢將影響農作生產。已有
Effects of Climate Change on Rice Yield and Keys to Reduce its Uncertainties
許多試驗及模式模擬進行探討，期以預估氣
候變遷對水稻產量之影響，惟通常在進行大
面積水稻產量評估時，將可能會出現相當程
度之不確定性。這些不確定性來自不同的根
源，諸如設定之溫室氣體(GHGs)排放情境、
全球變遷模式(GCMs)、全球氣候與當地氣候
間之差距等，而稻株發育模式本身亦存在不
確定性。本文陳述了作者近年在大面積作物
模式及試驗研究上所發現的各種不確定性，
以及在進行評估時如何降低這些不確定性的
做法。針對模擬技巧方面，本文回顧了模式
參數及使用多種情境與多種全球變遷模式的
統計做法，在田間試驗方面，則綜合了 2007
年炎夏進行關於小穗花不稔率的田間調查方
法及關於田間開放空間高二氧化碳試驗
[free-air
CO2
enrichment
(FACE)
experiment]的部分內容。本文據此回顧提出
強烈建議，欲發展一套以程序為基礎的水稻
發育模式，應當將熱力(能量)平衡包括在內。
若能將田間開放空間高二氧化碳試驗加上以
程序為基礎的模式，將有助於減少評估時的
不確定性，以及在氣候變遷情境下研究調適
與避免熱逆境及負面效應時的驗證工作。
關鍵詞︰預測、氣候變遷、水稻產量、衝擊
評估、預估不確定性。
261
crop growth periods and increasing heat stress
risk and water use. These counteracting
influences determine the magnitude and even the
direction of the impacts of climate change.
As rice (Oryza sativa L.) is the staple food
especially in Asia, there are many climate change
impact assessment studies using various rice
development models (for example, Kropff et al.
1993, Horie et al. 1997, Tao et al. 2007). When
conducting the large-scale evaluation of rice
productivity, these predictions include large
uncertainties, which resulted from a number of
sources, such as those in the GHG emission
scenarios, global climate models (GCMs) and its
gaps between global and local climates. In
addition, the rice development models themselves
include uncertainties. The parameters of those
crop models were typically determined based on
small-scale experiments using environmental
controlled chambers or temperature gradient
chambers (TGCs). Extrapolating of these results to
the field or regional conditions under variable
climatic conditions creates another major source
of uncertainties in the predictions of the future
crop production. In this paper, we present our
recent studies on large-scale evaluation by crop
models and trials to elucidate and reduce
uncertainties accompanied with each aspect of
evaluation.
INTRODUCTION
LARGE SCALE CROP MODEL
FOR RICE YIELD IN JAPAN
The Intergovernmental Panel on Climate
Change (IPCC) concluded that global temperature
rise since the mid-20th century is most likely due
to the observed increase in anthropogenic
greenhouse gas (GHG) concentrations, and the
global warming is projected to have significant
impacts on agricultural productivity including
elevated carbon dioxide concentration ([CO2]),
precipitation change and the interaction of these
related elements (IPCC 2007).
There are generally projected both positive
and negative effects of climate change on
agricultural productivity. Elevated [CO2] will
have a positive influence on crop yield via
promoting photosynthesis and reducing the water
use due to stomatal closure. While, temperature
rise will have a negative influence via shortening
Paddy rice is grown throughout the country
in Japan; however, since Japan lies north and
south of the eastern coastal region of East Asia,
the meteorological influences on rice yield vary
by areas. Crop models for paddy rice have been
developed to explain the yield variance under
such varied climatic conditions. When conducting
the impact assessment on rice yield, the spatial
scale of those models is too small. Yokozawa et al.
(2009) developed a large-scale crop model, which
is ‘process-based model’ up-scaled from a
conventional field-scale model to meet the
intended spatial-scale of the high-resolution
climate model.
We briefly introduce the fundamental
components of the large-scale model.
The
original field-scale model is the Simulation Model
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Crop, Environment & Bioinformatics, Vol. 7, December 2010
for Rice-Weather Relationship (SIMRIW) (Horie et
al. 1995), which consisted of 3 sub-models, the
phenological development model, the dry matter
production model, and the yield formation model.
The phenological development is described by the
developmental index (DVI), which is zero, one
and two at transplanting, heading and maturity,
respectively. The DVI at day i after transplanting,
DVIi (day-1), is given by
j =i
DVIi =
∑ DVR
j =0
j
,
(1)
j = Maturity
∑ ΔW
j =0
h = min (hc, hh).
(6)
According to previous chamber experiments,
rice is susceptible to heat at flowering stage, and
heat-induced spikelet sterility (HISS) is the major
reason for the yield loss. Nakagawa et al. (2003)
propose the parameterization for the harvest
index relating to high temperature, hh, as follows:
hh = hm (1-0.95 γh),
where DVRj is the developmental rate at day j
(day-1), which is given by the daily mean
temperature and day length depending on the
rice growth stage.
Dry matter production, Wtotal (g m-2) is given
as a summation of the daily increment of crop dry
weight at day j, ΔWj by
Wtotal =
of h due to low-temperature stress, hc, and
high-temperature stress, hh:
j
,
ΔWj = Ss Cs,
(2)
(3)
Tc −T0
⎡
⎤
⎛ Tmax − Tb ⎞⎛ Tc − Tmax ⎞ T0 −Tb ⎥
⎢
⎟⎟
⎟⎟⎜⎜
γh = ⎢⎜⎜
⎥
⎝ T0 − Tb ⎠⎝ Tc − T0 ⎠
⎣⎢
⎦⎥
γh = 0
for Tmax
≤
Chot
for Tmax > T0,
(7)
T0,
where γh is the spikelet sterility due to high
temperature and Tmax is the daily maximum
temperature averaged over the flowering period.
To, Tb, Tc and Chot are empirically determined
from field-scale experiments.
CROP MODEL PARAMETERS
where Ss is the amount of radiation absorbed by
the canopy, and Cs is the radiation use efficiency
(g dry matter MJ-1), which is influenced by [CO2]
(Horie et al. 1993) and parameterized by
Cs = C0
⎡ Rm (C a − 330) ⎤
⎢1 +
⎥,
⎣ C a − 330 + K c ⎦
(4)
where Rm and Kc are empirically determined from
field-scale experiment, and C0 is Cs at atmospheric
[CO2], Ca = 330 ppm. Ss and Cs, and thus ΔWj are
regulated by DVR through leaf area index and/or
its daily growth rate.
The large-scale actual grain yield, Ya (Mg
ha-1), is described as a multiplicative form of the
technical coefficient, τ, the harvest index, h, and
total dry matter, Wtotal, by
Ya = τ · h· Wtotal.
(5)
The influence of temperature stress are taken
into account by harvest index, h, as a lower values
General crop development models, including
SIMRIW, are originally developed for the
field-scale study, and the model parameters are
cultivar-specific or region-specific. It is not
realistic to apply all the parameters from each
field-scale process to large-scale evaluation in
Japan, since much information is needed on
varieties and cultivation management etc. to run
the models. While, mere up-scaling or
extrapolating of parameters may produce
uncertainties of yield estimation.
In order to reduce these uncertainties and
reflect the yearly variations and regional
differences of rice yield to large-scale evaluation,
Yokozawa et al. (2009) determined model
parameters as prefecture-specific, while some
parameters which seemed to be robust as rice
characteristics are fixed. They determined
parameters
by
the
Bayesian
approach
(Processed-based Regional-scale Rice Yield
Simulator with Bayesian Inference; PRYSBI) using
crop phenology and yield data in prefectures
Effects of Climate Change on Rice Yield and Keys to Reduce its Uncertainties
from governmental crop statistics provided by the
Ministry of Agriculture, Forestry, and Fisheries of
Japan (Iizumi et al. 2009).
PRYSBI statistically reflects the cultivar
characteristics and yearly yield variations in the
past, as well as takes into account of process of crop
responses to environment. The comparisons of
yield between statistic and estimation by PRYSBI
show that PRYSBI simulates the yearly yield
variations well (Fig. 1), whose errors are in 3 days
for heading date, and in 0.2 Mg ha-1 for yield.
CLIMATE CHANGE SCENARIOS
Climate change impacts assessment reported
in IPCC AR4 is based on climate change scenarios
by GCMs at a number of different modeling
groups in the world, which are summarized as
WCRP CMIP3 Multimodel Dataset (Meehl et al.
2007). Okada et al. (2009) provided the fundamental
dataset in Japan by their linear-interpolating.
Yokozawa et al. (2009) applied this dataset to
PRYSBI. The GHG emission scenarios used are
SRES A1B and A2, with 9 and 8 GCMs for each
scenario. By calculating ensemble averages and
their standard deviations of results from such
263
various multi-models and scenarios enables
scientists to consider the uncertainties of
projections.
Fig. 2 shows the projected relationship
between temperature rise in summer from current
level (1981-2000) and yields simulated by PRYSBI
applying to 34 cases of 2 periods (2046-2065 and
2081-2100) with 17 models. In northern Japan (NJ),
rice yield was projected to increase with
temperature rise but decrease in other areas. The
coefficient of variation (CV) of yield was projected
to increase with temperature rise in all areas,
especially in central Japan (CJ). One of the reasons
for this amplified CV is HISS sub-model
described in equations (7), where sterility, thus
harvest index also, decreases exponentially when
daily mean of air temperature exceeded a
threshold. Some synoptic scale climate models
predict that the high atmospheric pressure in the
Pacific Ocean get intense with global warming,
which should affect the high temperature in
summer especially in central Japan (CJ). As this
trend is already observed in recent years, which is
possible to decrease yield and amplify the yearly
variation of yield in central Japan (CJ).
Fig. 1. Comparisons of yearly variations of area-averaged yield between estimated and statistic
values. NJ: Hokkaido and Tohoku regions, EJ: Kanto and Koshinetsu regions, CJ: Tokai,
Chubu and Kinki regions, and WJ: Chugoku, Shikoku and Kyushu regions. Adapted from
Yokozawa et al. (2009).
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Crop, Environment & Bioinformatics, Vol. 7, December 2010
Fig. 2. Changes of area-averaged yield and its coefficient of variation due to air temperature rise
in cultivation period (May to October). Adapted from Yokozawa et al. (2009).
HEAT STRESS MODELING
As shown above, heat stress modeling is one
of the most important components for climate
change impacts assessment. However, as they are
mostly modeled by experimental results using
closed chambers or TGCs, the model itself is
possible to create uncertainties to apply to the
actual fields and larger-scale evaluation.
The extreme hot summer in 2007 in Japan
gave us a lesson. During the summer of 2007,
many areas in the Kanto and Tokai regions of
Japan experienced extreme heat; for example, an
unprecedented 40.9℃ being recorded in
mid-August in Kumagaya in Saitama prefecture
and Tajimi in Gifu prefecture. We collected
panicle samples from 132 paddy fields in five
prefectures (Gunma, Saitama, Ibaraki, Gifu and
Aichi) where heading and flowering occurred
between late July and late August to examine the
occurrence of HISS (Hasegawa et al. 2008).
Estimation of data recorded at the
government
meteorological
stations
and
AMeDAS (Automated Meteorological Data
Acquisition System) points near the studied
paddy fields revealed that more than 40% of the
paddy fields experienced daily maximum
temperatures of over 35 ℃ around flowering
period. About 20% of the paddy fields
investigated showed sterility rates of over 10%
(Fig. 3), which is larger rate of sterility than that of
usual year (less than 5%), but much less than
expected by previous HISS sub-models by
chamber experiments [for example, equations (7)].
One possible explanation for the fact is that
the temperature of the panicle that is the sensitive
organ differed from air temperature. In fact,
spatial distribution of the panicle temperatures
estimated using a heat balance model (Yoshimoto
et al. 2005b) during the hours of rice flowering
(10:00-12:00) does not necessarily match daily
maximum temperature distribution (Fig. 4). This
is because, in addition to the air temperature
during flowering hours being lower than daily
maximum temperature, other meteorological
factors such as solar radiation, wind speed and
humidity also affect panicle temperature. The
correlation between panicle temperature and
sterility was higher than that between daily
maximum temperature and sterility. The lesson
from this survey and analyses on extreme heat
event in 2007 is that air temperature per se is not
sufficient to predict the occurrence of HISS, but
factors influencing the heat budget of the panicles
are needed to account for the crop damages under
open field conditions.
Effects of Climate Change on Rice Yield and Keys to Reduce its Uncertainties
265
Fig. 3. Frequency distributions of maximum temperature during the 5-d period around flowering
in study fields and percentage of sterile spikelets. Adapted from Hasegawa et al. (2008).
Fig. 4. Distributions of daily maximum air temperature (left) and panicle temperature at
flowering time (10:00-12:00) (right) on the extreme hot day, August 16 2007. Panicle
temperature is estimated by heat balance model (Yoshimoto et al. 2005b).
We developed the model-coupled agrometeorological
database,
called
MeteoCrop
(Kuwagata et al. 2008). The MeteoCrop database
contains daily meteorological data since 1980
obtained from AMeDAS stations (about 850 sites)
and for 1961 to 2007 from surface meteorological
stations (156 sites). Currently, a Japanese version
of the Web site is available at http://
meteocrop.dc.affrc.go.jp/. In MeteoCrop database, a micro-meteorological model of crop
canopy and a simple rice growth model are
coupled with the meteorological data. By
applying these models, we can evaluate the
diurnal variation of rice panicle temperature
during the flowering period, the growth stage
(heading date) and evolution of leaf area index as
well as general daily data of air temperature and
solar radiation. We believe that the use of such
database coupled with field surveys of heat stress
in farmers’ field contributes to improve the
precision of HISS models for large-scale
evaluation of rice yield.
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Crop, Environment & Bioinformatics, Vol. 7, December 2010
ELEVATED CO2 EFFECTS
High CO2 response of crop had been also mostly
modeled empirically by experimental results using
closed chambers or TGCs [for example, equation (4)].
However, the open-field experiment of the respective
and interactive effects of environments on the
atmosphere-plant-soil system is needed for reduction of
uncertainties to predict rice production under climate
change. The free-air CO2 enrichment (FACE)
experiment for rice started in Shizukuishi, Iwate
prefecture in Japan in 1998 and was moved to
Tsukubamirai, Ibaraki prefecture in 2010. The same
experiment system has been imported into Rice-wheat
FACE in China since 2001.
Those FACE experiments for rice showed
that elevated [CO2] increased the rice yield via
photosynthetic enhancement and biomass
increase. However, this enhancement due to
elevated [CO2] often decreases as the plants age,
as confirmed by both of rice FACE experiments in
Japan and China (Seneweera et al. 2002, Chen et al.
2005). Similarly, biomass enhancement in these
studies was initially high (30-40%) but decreased
to about 13% at harvest (Hasegawa et al. 2007). A
yield enhancement averaged about 14% was
recorded and was largely attributed to the
increase in panicle number, the yield component
that was determined in the relatively early growth
stages. This enhancement rate of rice yield was
smaller than the results from closed chamber
experiments, which means that previous
predictions of rice yield enhancement due to
higher [CO2] were over-estimated.
As the elevated [CO2] decreases plant
transpiration via stomatal closure, the water use
efficiency (WUE) increases. This enhancement of
WUE due to elevated [CO2] was initially high but
decreased as the plants age. The enhancement of
final WUE through a whole growing period due
to elevated [CO2] was 19% for Akitakomachi
cultivar in 2000 (Yoshimoto et al. 2005a). However,
the enhancement rate differed among sites,
climate of years and cultivars; between 15 and
37% in rice FACE experiments. It is suggested that
WUE is not constant and vary depending on
environment, which should be taken into account
crop yield modeling based on WUE.
Plant transpiration plays an important role in
thermal environment in rice paddy as well as in
water use. Stomatal closure due to elevated [CO2]
increases canopy and panicle temperatures. In the
China FACE study, panicle temperature was 0.5 1℃ higher under elevated [CO2] compared to
ambient [CO2] (Yoshimoto et al. 2005b); this could
exacerbate HISS.
The information from those open-field
experiments strongly suggests the necessity for
developing a process-based rice development
model responding as a rice paddy ecosystem
including energy balance. Such modeling study in
tandem with FACE experiments should be
capable not only of reducing the evaluation
uncertainties, but also of validating the adapting
or avoiding studies of heat stress or negative
influence on rice under projected climate change
CONCLUDING REMARKS
The latest research results were reviewed on
effects of global warming on rice production.
When conducting large-scale evaluation of rice
yield, there are large uncertainties, which resulted
from a number of sources, such as those in the
greenhouse gas emission scenarios, global climate
models (GCMs) and their gaps between global and
local climates. In addition, the rice development
models themselves include uncertainties. There are
various measures to overcome those uncertainties.
Accepting and adopting uncertainties of
model parameters by statistical approach is one of
valid measures, which utilize the uncertainties
themselves, issued from multi-scenarios and
multi-GCMs. While in field experiment aspect, a
field survey and FACE experiment are still
needed for reducing uncertainties concerning the
response of crop and the ecosystem. Those field
surveys/experiments strongly suggest the
necessity for developing a process-based rice
development model including heat balance. The
synthesized process-based model study in
tandem with FACE experiments contributes not
only for reducing the uncertainties in crop yield
evaluation, but also for validating the studies on
adapting or avoiding of heat stress or negative
influence on rice under projected climate change.
REFERENCES
Chen GY, Z Yong, Y Liao, DY Zhang, Y Chen, HB
Zhang, J Chen, JG Zhu, DQ Xu (2005)
Photosynthetic acclimation in rice leaves to
Effects of Climate Change on Rice Yield and Keys to Reduce its Uncertainties
free-air CO2 enrichment related to both
ribulose-1,5-bisphosphate carboxylation limitation and ribulose-1,5-bisphosphate regeneration
limitation. Plant Cell Physiol. 46:1036-1045.
Hasegawa T, M Yoshimoto, T Kuwagata, Y
Ishigooka, Y Kondo, T Ishimaru (2008) The
impact of global warming on rice production.
Lessons from spikelet sterility observed under
the record hot summer of 2007. NIAES Annual
Report 2008, p.23-25. http://www.niaes.affrc.go.
jp/annual/r2008/index.html
Hasegawa T, H Shimono, LX Yang, HY Kim, T
Kobayashi, H Sakai, M Yoshimoto, M Liffering,
K Ishiguro, YL Wang, JG Zhu, K Kobayashi, M
Okada (2007) Response of rice to increasing
CO2 and temperature: recent findings from
large-scale free-air CO2 enrichment (FACE)
experiments. p.439-447. In: Science, Technology,
and Trade for Peace and Prosperity, IRRI. Los
Baños, Philippines.
Horie T (1993) Predicting the effects of climatic
variation and elevated CO2 on rice yield in
Japan. J. Agric. Meteorol. 48:567-574.
Horie T, HGS Centeno, H Nakagawa, T Matsui
(1997) Effect of elevated carbon dioxide and
climate change on rice production in East and
Southeast Asia. p.49-58. In: Proceedings of the
International Scientific Symposium on Asian
Paddy Fields. Y Oshima (ed.) University of
Saskatchewan, Saskatchewan, Canada.
267
Penning de Vries (1993) Predicting the impact
of CO2 and temperature on rice production.
IRRI Seminar Series on Climate Change and
Rice, International Rice Research Institute. Los
Baños, Philippines.
Kuwagata T, M Yoshimoto, Y Ishigooka, T
Hasegawa (2008) Model-coupled agrometeorological
database for estimating the effects of global warming
and climate variability on rice production. NIAES
Annual Report 2008, p.16-17. http://www.niaes.
affrc.go.jp/annual/r2008/index.html
Meehl GA, C Convey, T Delworth, M Latif, B
McAvaney, JFB Mitchell, RJ Stouffer, KE Tailor
(2007) The WCRP CMIP3 Multimodel Dataset:
A new era in climate change research. Bullet.
Amer. Meteorol. Soc. 88:1383-1394.
Nakagawa H, T Horie, T Matsui (2003) Effects of
climate change on rice production and
adaptive technologies. p.635-658. In: Rice
Science; Innovations and Impact for Livelihood.
TW Mew et al. (eds.) Proceedings of the
International Rice Research Conference. 16-19
September 2002. Beijing, China.
Okada M, T Iizumi, M Nishimori, M Yokozawa
(2009) Mesh climate change data of Japan Ver.2
for climate change impact assessments under
IPCC SRES A1B and A2. J. Agric. Meteorol.
65:97-109.
Horie T, H Nakagawa, HGS Centeno, M Kropff
(1995) The rice crop simulation model SIMRIW
and its testing. p.51-66. In: Modeling the
Impact of Climate Change on Rice in Asia. RB
Matthews et al. (eds.) CAB International, Oxon.
Seneweera SP, JP Conroy, K Ishimaru, O
Ghannoum, M Okada, M Liffering, HY Kim, K
Kobayashi (2002) Changes in source-sink
relations during development influence
photosynthetic acclimation of rice to free-air
CO2 enrichment (FACE). Func. Plant Biol. 29:
945-953.
Iizumi T, M Yokozawa, M Nishimori (2009)
Parameter estimation and uncertainty analysis
of a large-scale crop model for paddy rice:
Application of a Bayesian approach. Agric. For.
Meteorol. 149:333-348.
Tao F, Y Hayashi, Z Zhang, T Sakamoto, M
Yokozawa (2008) Global warming, rice
production, and water use in China:
Developing a probabilistic assessment. Agric.
For. Meteorol. 148:94-110.
IPCC (2007) Climate Change 2007: The physical
science basis. Contribution of working group I
to the fourth assessment report of the
Intergovernmental Panel on Climate Change.
Cambridge University Press, UK.
Yokozawa M, T Iizumi, M Okada (2009) Large
scale projection of climate change impacts on
variability in rice yield in Japan. (in Japanese
with English abstract) Global Environ. Res.
14:199-206.
Kropff MJ, G Centeno, D Bachelet, MH Lee, S
Mohan Dass, T Horie, S De Feng, S Singh, FWT
Yoshimoto M, H Oue, K Kobayashi (2005a)
Responses of energy balance, evapotrans-
268
Crop, Environment & Bioinformatics, Vol. 7, December 2010
piration and water use efficiency in rice
canopies to free-air CO2 enrichment. Agric. For.
Meteorol. 133:226-246.
Yoshimoto M, H Oue, H Takahashi, K Kobayashi
－編輯：楊純明
(2005b) The effects of FACE (Free-Air CO2
Enrichment) on temperatures and transpiration
of rice panicles at flowering stage. J. Agric.
Meteorol. 60:597-600.