留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

东北雨养玉米田碳交换年际变化及影响因素

张慧, 高全, 常姝婷, 等. 东北雨养玉米田碳交换年际变化及影响因素. 应用气象学报, 2023, 34(2): 246-256. DOI: 10.11898/1001-7313.20230210..
引用本文: 张慧, 高全, 常姝婷, 等. 东北雨养玉米田碳交换年际变化及影响因素. 应用气象学报, 2023, 34(2): 246-256. DOI: 10.11898/1001-7313.20230210.
Zhang Hui, Gao Quan, Chang Shuting, et al. Interannual carbon exchange variability of rain-fed maize fields in Northeast China and its influencing factors. J Appl Meteor Sci, 2023, 34(2): 246-256. DOI:  10.11898/1001-7313.20230210.
Citation: Zhang Hui, Gao Quan, Chang Shuting, et al. Interannual carbon exchange variability of rain-fed maize fields in Northeast China and its influencing factors. J Appl Meteor Sci, 2023, 34(2): 246-256. DOI:  10.11898/1001-7313.20230210.

东北雨养玉米田碳交换年际变化及影响因素

DOI: 10.11898/1001-7313.20230210
详细信息
    通信作者:

    蔡福,caifu@iaesy.cn

Interannual Carbon Exchange Variability of Rain-fed Maize Fields in Northeast China and Its Influencing Factors

  • 摘要: 在气候变化背景下,农田净生态系统生产力变化趋势和影响因素不确定性大,为有效评估农田生态系统的固碳潜力,利用2005—2020年东北雨养春玉米田涡动相关数据分析该区域碳通量年际变化趋势及其气象、土壤和生物影响因素。结果表明:东北雨养春玉米田净生态系统生产力为272±109 g·m-2·a-1,且无显著变化趋势;与生态系统呼吸相比,净生态系统生产力年际变化主要受总生态系统生产力影响。气象因素的降水量和生物因素的作物水分利用效率是净生态系统生产力年际变化的主要影响因素,影响权重分别为28.4%和31.4%;气象、土壤和生物因素对总生态系统生产力年际变化的影响权重分别为61.0%,43.8%和62.8%;土壤因素和生物因素是生态系统呼吸年际变化的主要影响因素,且土壤因素对生态系统呼吸年际变化的影响权重(39.3%)大于生物因素(29.2%)。在气候变暖背景下,东北雨养春玉米田对水分更为敏感,同时日照和温度通过影响饱和水汽压差和土壤湿度间接影响净生态系统生产力的年际变化。
  • [1] Dufranne D, Moureaux C, Vancutsem F, et al.Comparison of carbon fluxes, growth and productivity of a winter wheat crop in three contrasting growing seasons.Agric Ecosyst Environ, 2011, 141(1/2):133-142.
    [2] Wang Y, Zhou L, Jia Q, et al.Direct and indirect effects of environmental factors on daily CO2 exchange in a rainfed maize cropland-A SEM analysis with 10 year observations.Field Crop Res, 2019, 242:107591.
    [3] Baldocchi D, Chu H, Reichstein M.Inter-annual variability of net and gross ecosystem carbon fluxes:A review.Agric For Meteorol, 2018, 249:520-533.
    [4] Fu Z, Dong J W, Zhou Y K, et al.Long term trend and interannual variability of land carbon uptake the attribution and processes.Environ Res Lett, 2017, 12:014018.
    [5] Jensen R, Herbst M, Friborg T.Direct and indirect controls of the interannual variability in atmospheric CO2 exchange of three contrasting ecosystems in Denmark.Agric For Meteorol, 2017, 233:12-31.
    [6] Shao J, Zhou X, Luo Y, et al.Biotic and climatic controls on interannual variability in carbon fluxes across terrestrial ecosystems.Agric For Meteorol, 2015, 205:11-22.
    [7] 张玉书.东北粮食生产格局的气候变化响应与适应.沈阳:辽宁科学技术出版社, 2016.

    Zhang Y.Responses and Adapatation of Crop Production to Climate Change in Northeast China.Shenyang:Liaoning Science and Techinology Publishing, 2016.
    [8] 李蕊, 郭建平.东北春玉米非线性积温模型参数改进.应用气象学报, 2018, 29(2):154-164.

    Li R, Guo J P.Improving parameters of nonlinear accumulated temperature model for spring maize in Northeast China.J Appl Meteor Sci, 2018, 29(2):154-164.
    [9] Baldocchi D, Penuelas J.The physics and ecology of mining carbon dioxide from the atmosphere by ecosystems.Glob Change Biol, 2019, 25(4):1191-1197.
    [10] Knox S H, Matthes J H, Sturtevant C, et al.Biophysical controls on interannual variability in ecosystem-scale CO2 and CH4 exchange in a California rice paddy.J Geophys Res Biogeo, 2016, 121(3):978-1001.
    [11] Suyker A E, Verma S B.Gross primary production and ecosystem respiration of irrigated and rainfed maize-soybean cropping systems over 8 years.Agric For Meteorol, 2012, 165:12-24.
    [12] Baldocchi D D.How eddy covariance flux measurements have contributed to our understanding of global change biology.Glob Change Biol, 2019, 26(1):242-260.
    [13] Froelich N, Croft H, Chen J M, et al.Trends of carbon fluxes and climate over a mixed temperate-boreal transition forest in southern Ontario, Canada.Agric For Meteorol, 2015, 211/212:72-84.
    [14] Pilegaard K, Ibrom A, Courtney M S, et al.Increasing net CO2 uptake by a Danish beech forest during the period from 1996 to 2009.Agric For Meteorol, 2011, 151(7):934-946.
    [15] Euskirchen E S, Bret-Harte M S, Shaver G R, et al.Long-term release of carbon dioxide from Arctic tundra ecosystems in Alaska.Ecosystems, 2017, 20(5):960-974.
    [16] Bajgain R, Xiao X M, Basara J, et al.Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma.Sci Total Environ, 2018, 644:1511-1524.
    [17] Li G, Han H, Du Y, et al.Effects of warming and increased precipitation on net ecosystem productivity:A long-term manipulative experiment in a semiarid grassland.Agric For Meteorol, 2017, 232:359-366.
    [18] Wilkinson M, Eaton E L, Broadmeadow M S J, et al.Inter-annual variation of carbon uptake by a plantation oak woodland in south-eastern England.Biogeosciences, 2012, 9(12):5373-5389.
    [19] Verduzco V S, Vivoni E R, Yépez E A, et al.Climate change impacts on net ecosystem productivity in a subtropical shrubland of northwestern México.Biogeosciences, 2018, 123(2):688-711.
    [20] Zhang H, Zhao T, Lyu S, et al.Interannual variability in net ecosystem carbon production in a rain-fed maize ecosystem and its climatic and biotic controls during 2005-2018.Plos One, 2021, 16(5):e0237684.
    [21] Chi J, Waldo S, Pressley S N, et al.Effects of climatic conditions and management practices on agricultural carbon and water budgets in the inland pacific northwest USA.J Geophys Res Biogeo, 2017, 122(12):3142-3160.
    [22] Guo Q, Hu Z, Li S, et al.Contrasting responses of gross primary productivity to precipitation events in a water-limited and a temperature-limited grassland ecosystem.Agric For Meteorol, 2015, 214/215:169-177.
    [23] Quan Q, Tian D, Luo Y, et al.Water scaling of ecosystem carbon cycle feedback to climate warming.Sci Adv, 2019, 5(8).DOI: 10.1126/sciadv.aav1131.
    [24] Dold C, Büyükcangaz H, Rondinelli W, et al.Long-term carbon uptake of agro-ecosystems in the Midwest.Agric For Meteorol, 2017, 232:128-140.
    [25] Wagle P, Gowda P H, Northup B K, et al.Variability in carbon dioxide fluxes among six winter wheat paddocks managed under different tillage and grazing practices.Atmos Environ, 2018, 185:100-108.
    [26] Guo H, Li S, Kang S, et al.Annual ecosystem respiration of maize was primarily driven by crop growth and soil water conditions.Agric Ecosyst Environ, 2019, 272:254-265.
    [27] Chen C, Li D, Gao Z Q, et al.Seasonal and interannual variations of carbon exchange over a rice-wheat rotation system on the north China plain.Adv Atmos Sci, 2015, 32:1365-1380.
    [28] Zhou L, Wang Y, Jia Q, et al.Increasing temperature shortened the carbon uptake period and decreased the cumulative net ecosystem productivity in a maize cropland in Northeast China.Field Crop Res, 2021, 267:108150.
    [29] 陈雨烨, 王培娟, 张源达, 等.基于3种遥感指数的东北春玉米干旱识别对比.应用气象学报, 2022, 33(4):466-476.

    Chen Y Y, Wang P J, Zhang Y D, et al.Comparison of drought recognition of spring maize in Northeast China based on 3 remote sensing indices.J Appl Meteor Sci, 2022, 33(4):466-476.
    [30] 蔡福, 米娜, 明惠青, 等.WOFOST模型蒸散过程改进对玉米干旱模拟影响.应用气象学报, 2021, 32(1):52-64.

    Cai F, Mi N, Ming H Q, et al.Effects of improving evapotranspiration parameterization scheme on WOFOST model performance in simulating maize drought stress prodess.J Appl Meteor Sci, 2021, 32(1):52-64.
    [31] 初征, 郭建平.未来气候变化对东北玉米品种布局的影响.应用气象学报, 2018, 29(2):165-176.

    Chu Z, Guo J P.Effects of climatic change on maize varieties distribution in the future of Northeast China.J Appl Meteor Sci, 2018, 29(2):165-176.
    [32] 郭建平, 栾青, 王婧瑄, 等.玉米冠层对降水的截留模型构建.应用气象学报, 2020, 31(4):397-404.

    Guo J P, Luan Q, Wang J X, et al.Model construction of rainfall interception by maize canopy.J Appl Meteor Sci, 2020, 31(4):397-404.
    [33] 朱梦媛, 张慧, 李爽, 等.播种期水分胁迫及补水对玉米出苗率的影响.气象与环境学报, 2019, 35(1):101-107.

    Zhu M Y, Zhang H, Li S, et al.Effects of water stress and supplement after sowing on emergence rates of maize.J Meteor Environ, 2019, 35(1):101-107.
    [34] Zhang H, Wen X.Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China.J Meteorol Res Prc, 2015, 29:654-666.
    [35] Hu Z, Yu G, Zhou Y, et al.Partitioning of evapotranspiration and its controls in four grassland ecosystems:Application of a two-source model.Agric For Meteorol, 2009, 149(9):1410-1420.
    [36] Ball J T, Woodrow I E, Berry J A.A Model Predicting Stomatal Conductance and Its Contribution to the Control of Photosynthesis Under Different Environmental Conditions.Dordrecht:Springer, 1987.
    [37] 于贵瑞, 王秋凤.植物光合、蒸腾与水分利用的生理生态学.北京:科学出版社, 2010.

    Yu G R, Wang Q F.Ecophysiology of Plant Photosynthesis, Transpitation, and Water Use.Beijing:Science Press, 2010.
    [38] 于贵瑞, 孙晓敏.陆地生态系统通量观测的原理与方法(第二版).北京:高等教育出版社, 2017.

    Yu G R, Sun X M.Principles of Flux Measurement in Terrestrial Ecosystems(Second Edition).Beijing:Higher Education Press, 2017.
    [39] Burba G.Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications.Lincoln:LI-COR Biosciences, 2022.
    [40] Reichstein M, Falge E, Baldocchi D, et al.On the separation of net ecosystem exchange into assimilation and ecosystem respiration:Review and improved algorithm.Glob Change Biol, 2005, 11(9):1424-1439.
    [41] Lloyd J, Taylor J.On the temperature dependence of soil respiration.Funct Ecol, 1994, 8:315-323.
    [42] Magney T S, Bowling D R, Logan B A, et al.Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence.PNAS, 2019, 116(24):11640.
    [43] 刘维, 宋迎波.基于气象要素的逐日玉米产量气象影响指数.应用气象学报, 2022, 33(3):364-374.

    Liu W, Song Y B.A daily meteorological impact index of maize yield based on weather elements.J Appl Meteor Sci, 2022, 33(3):364-374.
    [44] 冯晓钰, 周广胜.碳四植物光合生化机理模型的叶片含水量修正.应用气象学报, 2022, 33(3):375-384.

    Feng X Y, Zhou G S.Modification of leaf water content for the photosynthetic and biochemical mechanism model of C4 plant.J Appl Meteor Sci, 2022, 33(3):375-384.
    [45] Niu S, Fu Z, Luo Y, et al.Interannual variability of ecosystem carbon exchange:From observation to prediction.Global Ecol Biogeogr, 2017, 26(11):1225-1237.
    [46] 霍治国, 张海燕, 李春晖, 等.中国玉米高温热害研究进展.应用气象学报, 2023, 34(1):1-14.

    Huo Z G, Zhang H Y, Li C H, et al.Review on high temperature heat damage of maize in China.J Appl Meteor Sci, 2023, 34(1):1-14.
    [47] 宋艳玲.全球干旱指数研究进展.应用气象学报, 2022, 33(5):513-526.

    Song Y L.Global research progress of drought indices.J Appl Meteor Sci, 2022, 33(5):513-526.
    [48] 米前川, 高西宁, 李玥, 等.深度学习方法在干旱预测中的应用.应用气象学报, 2022, 33(1):104-114.

    Mi Q C, Gao X N, Li Y, et al.Application of deep learning method to drought prediction.J Appl Meteor Sci, 2022, 33(1):104-114.
    [49] Goulden M L, Bales R C.California forest die-off linked to multi-year deep soil drying in 2012-2015 drought.Nat Geosci, 2019, 12:632-637.
    [50] Yuan W, Zheng Y, Piao S, et al.Increased atmospheric vapor pressure deficit reduces global vegetation growth.Sci Adv, 2019, 5(8).DOI: 10.1126/sciadv.aax1396.
  • 加载中
计量
  • 摘要浏览量:  43
  • HTML全文浏览量:  3
  • PDF下载量:  10
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-11-02
  • 修回日期:  2023-01-16
  • 网络出版日期:  2023-03-02

目录

    /

    返回文章
    返回