大气CO2和O3浓度升高对水稻'汕优63'叶片光合作用的影响

大气CO2浓度升高使水稻光合作用增强,而地表O3浓度增加则相反,但人们对大气CO2和O3浓度同时升高情景下水稻光合作用的响应和适应知之甚少。本文利用新型的自然光气体熏蒸平台,以杂交籼稻‘汕优63’为供试材料,设置室内对照(CK,大气本底浓度,实时模拟室外环境)、高浓度CO2(CO2本底浓度+200μmol·mol-1)、高浓度O3(O3本底浓度的1.6倍)、高浓度CO2+O3 4个处理,于拔节期、抽穗期和灌浆期测定稻叶的主要光合参数。整个布气期间,CO2和O3浓度平均的控制目标完成比(TAR)分别为1.04和1.00。与CK相比,CO2处理使拔节、抽穗和灌浆期净光合速率(Pn)分别增加15%、11%和28%,O3处理使对应生育期Pn分别降低32%、32%和88%,CO2+O3处理对拔节期和抽穗期Pn无显著影响,但成熟期Pn平均下降48%。CO2处理使拔节和抽穗期叶片气孔导度(Gs)和蒸腾速率(Tr)显著下降,但灌浆期无显著变化;O3处理对各期Gs和Tr的影响明显大于CO2处理,且以灌浆期的降幅最大;CO2+O3处理叶片Gs和Tr的降幅总体上明显低于单独的O3处理。CO2处理或CO2+O3处理叶片胞间CO2浓度(Ci)明显增加,而O3处理叶片Ci的变化相对较小。CO2处理使各期水分利用效率(WUE)增加,而O3处理则呈相反趋势,特别是生长后期。CO2+O3处理叶片拔节期和抽穗期WUE平均增加约15%,但灌浆期因O3的累积伤害,WUE不升反降。以上结果表明,大气CO2浓度升高将使杂交稻‘汕优63’叶片光合能力增强,但地表同步升高的O3浓度则使光合能力削弱并表现出明显的累积伤害,大气CO2和O3浓度同时升高可缓解O3胁迫对‘汕优63’光合作用的负效应。

Impact of elevated atmospheric carbon dioxide and ozone concentrations on leaf photosynthesis of 'Shanyou 63' hybrid rice

Starch is a major photosynthate and quality index for winter wheat. Planting density influences the growth and development of winter wheat through factors, such as, thermal, light, temperature, etc. This in turn influences the generation, development and transportation of photosynthate to wheat grains which eventually determine wheat yield and quality. Chlorophyll density is strongly related with spectral parameters and accumulated starch. Thus, chlorophyll density was used to serve as a link between canopy spectra and starch accumulation in this study. The aim of the study was to explore suitable density for forecasting accumulated starch content for the purpose of building a model for the accurate forecasting of starch accumulation via spectral remote sensing. In this study, "Jing 9549" winter wheat cultivar was cultivated in 2009 and the "Jing 9549", "Le 639" and "Chang 4738" cultivars cultivated in 2010 at planting densities of 3.0×10⁶ plant·hm^-2, 4.5×10⁶ plant·hm^-2, 6.0×10⁶ plant·hm^-2, 7.5×10⁶ plant·hm^-2, 9.0×10⁶ plant·hm^-2. In the field experiments, canopy spectral, chlorophyll density and starch accumulation of winter wheat were measured in the five different planting densities. The accuracy of the monitoring model with NDVI (1 200 nm, 670 nm) was highest (0.920 6) at 7.50×10⁶ plant·hm^-2 wheat planting density. The model was verified with data for the cultivation period of 2009 to 2010. The result showed a strong agreement with a correlation coefficient of 0.954 2. The 7.5×10⁶ plant·hm^-2 density was the most reasonable planting density for monitoring starch accumulation in winter wheat. Also the data for the five densities were integrated to construct a multi-density simulation model. The multi-density model accuracy was 0.883 1 and its relative error (RE) was also the lowest (0.905 4). Thus to some extent, the multi-density simulation model was widely applicable and practically significant. The spectral remote sensing monitoring model for observed optimum density and accumulated starch at different wheat planting densities gave the theoretical basis and guidance for large-scale monitoring of wheat quality from space.