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260 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 262 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). 264 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. 266 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. 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