欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2018, Vol. 44 ›› Issue (05): 750-761.doi: 10.3724/SP.J.1006.2018.00750

• 耕作栽培·生理生化 • 上一篇    下一篇

作物模型中单叶最大光合作用速率的温度响应修订

杨沈斌1(), 徐莎莎2, 江晓东1,3, 石春林3, 王应平4, 申双和1   

  1. 1南京信息工程大学气象灾害预报预警与评估协同创新中心 / 南京信息工程大学应用气象学院, 江苏南京 210044
    2扬州市气象局, 江苏扬州 225009
    3江苏省农业科学院, 江苏南京 210014
    4CSIRO Marine and Atmospheric Research, PMB # 1, Aspendale, Victoria 3195, Australia
  • 收稿日期:2017-11-15 接受日期:2018-03-15 出版日期:2018-05-20 网络出版日期:2018-03-16
  • 作者简介:

    第一作者联系方式: E-mail: jaasyang@163.com, Tel: 025-58731165

  • 基金资助:
    本研究由国家公益性行业(气象)科研专项(GYHY201306035, GYHY201306036)和国家“十二五”科技支撑计划项目(2011BAD32B01)资助

Correcting the Response of Maximum Leaf Photosynthetic Rate to Temperatures in Crop Models

Shen-Bin YANG1(), Sha-Sha XU2, Xiao-Dong JIANG1,3, Chun-Lin SHI3, Ying-Ping WANG4, Shuang-He SHEN1   

  1. 1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters / College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China;
    2 Yangzhou Meteorological Bureau, Yangzhou 225009, Jiangsu, China
    3 Jiangsu Academy of Agricultural Sciences, Nanjing 210014, Jiangsu, China
    4 CSIRO Marine and Atmospheric Research, PMB # 1, Aspendale, Victoria 3195, Australia
  • Received:2017-11-15 Accepted:2018-03-15 Published:2018-05-20 Published online:2018-03-16
  • Supported by:
    This study was supported by Special Fund for Meteorology-scientific Research in the Public Interest (GYHY201306035, GYHY201306036) and Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (2011BAD32B01)

RichHTML

4

PDF

942

摘要：

作物的光合作用对温度变化敏感, 其温度依存性随品种、生长环境的变化而改变。基于光效率模型的作物生长模型, 在应用中很少对光合作用的温度影响参数值进行订正, 且在全生育期使用相同的参数值, 难免会增加干物质模拟的误差。为此, 本文以ORYZA2000模型为例, 提出了一种修订光合作用温度影响参数值的方法。为确定方法的有效性, 结合2012年和2013年水稻品种两优培九的温度梯度控制实验, 首先利用抽穗开花期光合作用观测曲线提取了不同温度水平的光合作用参数值, 然后结合Arrhenius方程和Peaked方程建立了温度敏感性参数的温度影响方程。将这些方程代入机理性光合作用模型, 模拟了单叶最大光合作用速率与温度的曲线关系。最后, 以归一化后的曲线关系修订作物模型参数值, 并利用两年地上部分生物量(WAGT)观测值对其验证。结果显示, 两优培九单叶最大总光合作用速率随温度的变化关系不同于ORYZA2000的默认设置, 修订后的最适温度为38~40°C, 高于默认值。在10~20°C的低温段, 修订后的温度影响系数低于默认值。从WAGT模拟值的相对误差看, 修订后较修订前平均降低约3.3%。本研究为改进干物质模拟精度和分析不同品种光合作用的温度依存性提供了重要参考。

关键词: 温度依存性, 物候期, 干物质积累, 气候变暖, 参数值订正

Abstract:

Crop photosynthesis is sensitive to temperature variations, and the temperature dependence of photosynthesis is known to vary with growth environments and crop varieties. Crop models based on light use efficiency model, seldom correct parameter values related to the temperature dependence of photosynthesis for a specific crop, which unavoidably increases the simulation errors in dry biomass. In this paper, a scheme used to correct those parameter values was put forward with the rice crop model ORYZA2000 as an example to evaluate the scheme’s performance. The temperature-controlled experiments were conducted to observe photosynthesis at heading stage of rice variety Liangyoupeijiu in 2012 and 2013. The data were first analyzed to retrieve photosynthetic characteristics from light response curves and CO2 response curve. Based on their relationship with temperatures, temperature effect functions were established for all temperature sensitive photosynthetic parameters using Arrhenius and Peaked functions. A biochemical photosynthesis model was applied to simulate the changes of maximum leaf photosynthetic rate with temperatures, based on which temperature response curve for maximum leaf photosynthetic rate was produced and normalized to replace the default parameter values in ORYZA2000. The observations of above ground biomass (WAGT) of Liangyoupeijiu in two years were used to validate simulations before and after the correction. The normalized temperature response curve for maximum leaf photosynthetic rate of Liangyoupeijiu was different from the default response curve in ORYZA2000. From the corrected response curve, the optimal temperature for photosynthesis was between 38-40°C, higher than the default, and temperature effect coefficient was lower than the default between 10-20°C. Compared with the default parameter values, average relative error of the corrected parameter values was reduced by 3.3%. In conclusion, the method used in this paper can be an important reference for improving biomass simulation accuracy and analyzing temperature dependence of photosynthesis for different crop varieties.

Key words: temperature dependence, phenology, dry biomass, climate warming, parameter value correction

图1

ORYZA2000模型中REDFTT默认参数取值"

图2

Am随温度的变化(a)和不同温度下AL随Ia的变化(b)曲线假设Ia=1000 W m-2 leaf, SN=0.7 g N m-2 leaf, CCO2=400 μmol mol-1。"

图3

修订方案"

图4

不同温度梯度下两优培九在抽穗开花期剑叶的光响应曲线(a)和CO2响应曲线(b)"

图5

从光响应曲线中提取的水稻光合作用特征参数值随温度处理的变化(a)为光饱和点; (b)为光补偿点; (c)为最大光合作用速率; (d)为表现量子效率; (e) Rd。图中柱状图数值为平均值, 竖线表示标准差。"

图6

从CO2响应曲线中提取的水稻光合作用特征参数值随叶温的变化"

表1

光合作用特征参数的温度影响方程参数值"

光合作用参数
Photosynthetic parameter		 方程参数
Function parameter		 参数值
Parameter value		 R2
Kc a1 406.3 μmol m-2 s-1 1.0
b1 79.48 kJ mol-1
Ko a1 277.2 mmol m-2 s-1 1.0
b1 36.31 kJ mol-1
Vcmax k25 115.0 μmol m-2 s-1 0.93
Ea 65.1 kJ mol-1
ΔS 607.4 J mol-1
Hd 200 kJ mol-1
Jmax k25 230 μmol m-2 s-1 0.89
Ea 35.44 kJ mol-1
ΔS 626.3 J mol-1
Hd 198.7 kJ mol-1
Rd k25 3.25 μmol m-2 s-1 0.92
Ea 39.35 kJ mol-1
ΔS 0.084 J mol-1
Hd 33.3 J mol-1

图7

An、Rd和An+Rd随TL的变化模拟设定光合有效辐射(PAR)为1400 μmol m-2 s-1, 胞间CO2浓度(Ci)为340 μmol mol-1。"

图8

不同Ci浓度下An、Rd和An+Rd随TL的变化模拟设定光合有效辐射(PAR)为1400 μmol m-2 s-1。"

图9

从观测中获取的叶温TL与空气温度Ta的关系曲线(a)和随空气温度Ta变化的温度影响系数曲线(b)"

图10

REDFTT参数值修改前后WAGT模拟值与实测值的相对误差(a) 2012年; (b) 2013年。竖线表示标准偏差。"

[1] Medlyn B E, Dreyer E, Ellsworth D, Forstreuter M, Harley P C, Kirschbaum M U F, Le Roux X, Montpied P, Strassemeyer J, Walcroft A, Wang K, Loustau D. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data.Plant Cell Environ, 2002, 25: 1167-1179
doi: 10.1046/j.1365-3040.2002.00891.x
[2] 阿里穆斯, 于贵瑞. 植物光合作用模型参数的温度依存性研究进展. 应用生态学报, 2013, 24: 3588-3594
Almaz B J, Yu G R.Temperature dependence of parameters of plant photosynthesis models: a review.Chin J Appl Ecol, 2013, 24: 3588-3594 (in Chinese with English abstract)
[3] Farquhar G D, Caemmerer S, Berry J A.A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species.Planta, 1980, 149: 78-90
doi: 10.1007/BF00386231
[4] Warren C R, Dreyer E.Temperature response of photosynthesis and internal conductance to CO2: results from two independent approaches.J Exp Bot, 2006, 57: 3057-3067
doi: 10.1093/jxb/erl067 pmid: 16882645
[5] Thornley J H M, France F. Mathematical Models in Agriculture: Quantitative Methods for the Plant, Animal and Ecological Sciences, 2nd edn. Wallingford: CABI Publishing, 2007
[6] Leuning R.Temperature dependence of two parameters in a photosynthesis model.Plant Cell Environ, 2002, 25: 1205-1210
doi: 10.1046/j.1365-3040.2002.00898.x
[7] Borjigidai A, Hikosaka K, Hirose T, Hasegawa T, Okada M, Kobayashi K.Seasonal changes in temperature dependence of photosynthetic rate in rice under a free-air CO2 enrichment.Ann Bot, 2006, 97: 549-557
doi: 10.1093/aob/mcl001 pmid: 2803663
[8] 莫兴国, 刘苏峡, 林忠辉. 基于SVAT模型的冬小麦光合作用和蒸散过程研究. 应用生态学报, 2002, 13: 1394-1398
Mo X G, Liu S X, Lin Z H.Simulating photosynthesis and evapotranspiration of winter wheat with a SVAT model.Chin J Appl Ecol, 2002, 13: 1394-1398 (in Chinese with English abstract)
[9] Steduto P, Hsiao T C, Raes D, Fereres E.AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles.Agron J, 2008, 101: 426-437
[10] Timsina J, Humphreys E.Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems: a review.Agric Syst, 2006, 90: 5-31
doi: 10.1016/j.agsy.2005.11.007
[11] Boogaard L H, Van Diepen C A, Rotter R P, Cabrera J C M A, Van Laar H H. User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. Wageningen: DLO Winand Staring Centre, 1998
[12] Holzworth D P, Huth N I, deVoil P G, Zurcher E J, Herrmann N I, McLean G, Chenu K, Van Oosterom E J, Snow V, Murphy C, Moore A D, Brown H, Whish J P M, Verrall S, Fainges J, Bell L W, Peake A S, Poulton P L, Keating B A. APSIM—Evolution towards a new generation of agricultural systems simulation.Environ Modell Softw, 2014, 62: 327-350
doi: 10.1016/j.envsoft.2014.07.009
[13] Bouman B A M, Van Laar H H. Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions.Agric Syst, 2006, 87: 249-273
doi: 10.1016/j.agsy.2004.09.011
[14] 钱莲文, 张新时, 杨智杰, 韩志刚. 几种光合作用光响应典型模型的比较研究. 武汉植物学研究, 2009, 27: 197-203
doi: 10.3969/j.issn.2095-0837.2009.02.013
Qian L W, Zhang X S, Yang Z J, Han Z G.Comparison of different light response models for photosynthesis.J Wuhan Bot Res, 2009, 27: 197-203 (in Chinese with English abstract)
doi: 10.3969/j.issn.2095-0837.2009.02.013
[15] 何亮, 侯英雨, 赵刚, 邬定荣, 于强. 基于全局敏感性分析和贝叶斯方法的WOFOST作物模型参数优化. 农业工程学报, 2016, 32(2): 169-179
He L, Hou Y Y, Zhao G, Wu D R, Yu Q.Parameters optimization of WOFOST model by integration of global sensitivity analysis and Bayesian calibration method.Trans CSAE, 2016, 32(2): 169-179 (in Chinese with English abstract)
[16] 刘建栋, 周秀骥, 于强. FAO生产潜力模型中基本参数的修正. 自然资源学报, 2001, 16: 240-247
Liu J D, Zhou X J, Yu Q.Modification of the basic parameter in FAO productivity model.J Nat Resour, 2001, 16: 240-247 (in Chinese with English abstract)
[17] Lobell D B, Gourdji S M.The influence of climate change on global crop productivity.Plant Physiol, 2012, 160: 1686-1697
doi: 10.1104/pp.112.208298
[18] 杨沈斌, 申双和, 赵小艳, 赵艳霞, 许吟隆, 王主玉, 刘娟, 张玮玮. 气候变化对长江中下游稻区水稻产量的影响. 作物学报, 2010, 36: 1519-1528
doi: 10.3724/SP.J.1006.2010.01519
Yang S B, Shen S H, Zhao X Y, Zhao Y X, Xu Y L, Wang Z Y, Liu J, Zhang W W.Impact of climate change on rice production in the middle and lower researches of the Yangtze River.Acta Agron Sin, 2010, 36: 1519-1528 (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2010.01519
[19] Yadav S, Li T, Humphreys E, Gill G, Kukal S S.Evaluation and application of ORYZA2000 for irrigation scheduling of puddled transplanted rice in north west India.Field Crops Res, 2011, 122: 104-117
doi: 10.1016/j.fcr.2011.03.004
[20] Li T, Angeles O, Marcaida III M, Manalo E, Manalili M P, Padanielson A, Mohanty S. From ORYZA2000 to ORYZA (v3): an improved simulation model for rice in drought and nitrogen-deficient environments. Agric For Meteorol, 2017, 237/238: 246-256
doi: 10.1016/j.agrformet.2017.02.025 pmid: 28469286
[21] 杨沈斌, 江晓东, 王应平, 申双和, 石春林, 王萌萌, 陈斐. 基于Richards扩展方程提取水稻灌浆结实光温特性参数. 作物学报, 2014, 40: 1792-1802
Yang S B, Jiang X D, Wang Y P, Shen S H, Shi C L, Wang M M, Chen F.Characterizing light and temperature effects on rice grain filling using extended Richards equation.Acta Agron Sin, 2014, 40: 1792-1802 (in Chinese with English abstract)
[22] 王萌萌, 杨沈斌, 江晓东, 王应平, 陈德, 黄维, 于庚康, 石春林. 光温要素对水稻群体茎蘖增长动态影响的分析及模拟. 作物学报, 2016, 42: 82-92
Wang M M, Yang S B, Jiang X D, Wang Y P, Chen D, Huang W, Yu G K, Shi C L.Analysis and simulation of impact of light and temperature on rice tillering.Acta Agron Sin, 2016, 42: 82-92 (in Chinese with English abstract)
[23] Long S P, Bernacchi C J.Gas exchange measurements, what can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error.J Exp Bot, 2003, 54: 2393-2401
doi: 10.1093/jxb/erg262 pmid: 14512377
[24] Jones H G.Plants and Microclimate, 2nd edn. Cambridge: Cambridge University Press, 1992
[25] Amthor J S.The role of maintenance respiration in plant growth.Plant Cell Environ, 1984, 7: 561-569
doi: 10.1111/1365-3040.ep11591833
[1] 闫晓宇, 郭文君, 秦都林, 王双磊, 聂军军, 赵娜, 祁杰, 宋宪亮, 毛丽丽, 孙学振. 滨海盐碱地棉花秸秆还田和深松对棉花干物质积累、养分吸收及产量的影响[J]. 作物学报, 2022, 48(5): 1235-1247.
[2] 阮俊梅, 张俊, 刘猷红, 董文军, 孟英, 邓艾兴, 杨万深, 宋振伟, 张卫建. 田间开放式增温对东北水稻氮素利用的影响[J]. 作物学报, 2022, 48(1): 193-202.
[3] 韦还和, 张徐彬, 葛佳琳, 孟天瑶, 陆钰, 李心月, 陶源, 丁恩浩, 陈英龙, 戴其根. 甬优籼粳杂交稻栽后地上部干物质积累动态与特征分析[J]. 作物学报, 2021, 47(3): 546-555.
[4] 柳燕兰, 郭贤仕, 张绪成, 马明生, 王宏康. 密度和施肥对旱地马铃薯干物质积累、产量和水肥利用的影响[J]. 作物学报, 2021, 47(2): 320-331.
[5] 王飞, 郭彬彬, 孙增光, 尹飞, 刘领, 焦念元, 付国占. 增温增CO2浓度对玉米||花生体系玉米生长发育及产量的影响[J]. 作物学报, 2021, 47(11): 2220-2231.
[6] 于宁宁,张吉旺,任佰朝,赵斌,刘鹏. 综合农艺管理对夏玉米叶片生长发育及内源激素含量的影响[J]. 作物学报, 2020, 46(6): 960-967.
[7] 凌霄霞,张作林,翟景秋,叶树春,黄见良. 气候变化对中国水稻生产的影响研究进展[J]. 作物学报, 2019, 45(3): 323-334.
[8] 陈四龙,程增书,宋亚辉,王瑾,刘义杰,张朋娟,李玉荣. 高产高油花生品种的光合与物质生产特征[J]. 作物学报, 2019, 45(2): 276-288.
[9] 柏延文,杨永红,朱亚利,李红杰,薛吉全,张仁和. 种植密度对不同株型玉米冠层光能截获和产量的影响[J]. 作物学报, 2019, 45(12): 1868-1879.
[10] 孟繁圆,冯利平,张丰瑶,张祎,伍露,王春雷,闫锦涛,彭明喜,莫志鸿,余卫东. 北部冬麦区冬小麦越冬冻害时空变化特征[J]. 作物学报, 2019, 45(10): 1576-1585.
[11] 赵财,王巧梅,郭瑶,殷文,樊志龙,胡发龙,于爱忠,柴强. 水氮耦合对地膜玉米免耕轮作小麦干物质积累及产量的影响[J]. 作物学报, 2018, 44(11): 1694-1703.
[12] 徐田军, 吕天放, 赵久然, 王荣焕, 陈传永, 刘月娥, 刘秀芝, 王元东, 刘春阁. 玉米生产上3个主推品种光合特性、干物质积累转运及灌浆特性[J]. 作物学报, 2018, 44(03): 414-422.
[13] 林祥,王东*. 不同底墒条件下补灌对冬小麦耗水特性、产量和水分利用效率的影响[J]. 作物学报, 2017, 43(09): 1357-1369.
[14] 沈杰,蔡艳,何玉亭,李启权,杜宣延,王昌全,罗定棋. 基于归一化法的烤烟干物质积累建模与特征分析[J]. 作物学报, 2017, 43(03): 442-453.
[15] 吴晓丽,汤永禄,李朝苏,吴春,黄钢. 秋季玉米秸秆覆盖对丘陵旱地小麦生理特性及水分利用效率的影响[J]. 作物学报, 2015, 41(06): 929-937.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2