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.

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

DOI: 10.11898/1001-7313.20230210
  • Received Date: 2022-11-02
  • Rev Recd Date: 2023-01-16
  • Publish Date: 2023-03-31
  • Interannual variation in net ecosystem carbon production (NEP) plays an important role in the carbon cycle processes. An agricultural ecosystem may fluctuate between carbon net source and carbon sink, or it may remain neutral. Thus, the long-term trends in NEP and the relevant meteorological, soil and biotic control of interannual variation in NEP remain unclear in farmland agroecosystems. To effectively assess the carbon sequestration potential of the farmland ecosystem, the eddy covariance dataset of rain-fed spring maize in Northeast China from 2005 to 2020 are used to investigate the interannual variations in NEP and the relevant meteorological, soil and biotic influences. NEP is partitioned into gross ecosystem productivity (GEP) and ecosystem respiration (RE) to explain the interannual variations of NEP and its influencing factors. The average annual NEP, GEP and RE are 272±109, 1086±177, 820±130 g·m-2·a-1, respectively, with no significant changes. The day-to-day dynamics of NEP, GEP and RE show single peak curves. NEP and GEP reach the maximums at the very time of maize tasseling, and the maximum value of RE occurs 13 days after NEP and GEP. Compared with RE, NEP variations are mainly caused by GEP. The redundancy analysis shows the interannual variations in NEP are mainly affected by precipitation as the meteorological factor and water use efficiency as the biotic factor, and the influence weights of the meteorological and biotic factors are 28.4% and 31.4%. Meanwhile, the influence weights of the meteorological factors (photosynthetically active radiation, carbon dioxide and precipitation), soil (soil volumetric water content and soil organic carbon) and biotic factors (leaf area index and water use efficiency) are 61.0%, 43.8% and 62.8% for the interannual variations in GEP. The interannual variations in RE are mainly affected by the soil (soil volumetric water content and soil organic carbon) and the biotic factors (leaf area index), and the influence weight of the soil factors (39.3%) is larger than that of the biotic factor (29.2%). The results indicate that, under the background of climate warming, interannual variations in NEP in rain-fed spring maize agroecosystems are likely to be more sensitive to changes in moisture, while radiation and temperature will contribute to interannual NEP variations by affecting vapor pressure difference and soil water content.
  • Fig. 1  Interannual variations of net ecosystem production(NEP), gross ecosystem production(GEP) and ecosystem respiration(RE) from 2005 to 2020

    Fig. 2  Daily variations of net ecosystem production(NEP), gross ecosystem production(GEP) and ecosystem respiration(RE) from 2005 to 2020

    (the black line denotes 10-day moving average, the green shaded denotes standard deviation, the blue shaded denotes growing season)

    Fig. 3  Interannual variations of photosynthetically active radiation, carbon dioxide, air temperature, precipitation, vapor pressure difference, soil temperature, soil volumetric water content, soil organic carbon, leaf area index, canopy stomatal conductance, evapotranspiration and water use efficiency from 2005 to 2020

    (the black line denotes significant trend)

    Fig. 4  Daily photosynthetically active radiation, carbon dioxide, air temperature(gray dots), soil temperature (brown dots), vapor pressure difference, soil volumetric water content(gray dots), precipitation(red bars), leaf area index(gray dots) and canopy stomatal conductance(brown dots) from 2005 to 2020 (the green and brown shaded denote standard deviations, the blue shaded denotes average growing season)

    Fig. 5  Correlations among net ecosystem production(NEP), gross ecosystem productivity(GEP) and ecosystem respiration(RE)

    (the black line denotes significant correlation, the grey shaded denotes 0.05 level, grey dashed lines denote prediction bounds)

    Fig. 6  Correlation coefficient matrix among carbon flux, meteorological, soil and biotic variables

    (* denotes 0.05 level,** denotes 0.01 level)

    Table  1  Weighing for interannual variability of carbon flux accounted by meteorological, soil and biotic factors using redundancy analysis

    影响因素 NEP GEP RE
    气象因素 28.4%* 61.0%**
    土壤因素 43.8%* 39.3%*
    生物因素 31.4%* 62.8%** 29.2%*
    气象与土壤因素 7.2%
    气象与生物因素 16.8% 16.8%
    土壤与生物因素 7.4% 25.2%
    气象、土壤与生物因素 27.7%
    注: *表示达到0.05显著性水平,**表示达到0.01显著性水平。
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  • [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. doi:  10.1016/j.fcr.2019.107591
    [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. doi:  10.1016/j.agrformet.2017.05.015
    [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. doi:  10.1088/1748-9326/aa5685
    [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. doi:  10.1016/j.agrformet.2016.10.023
    [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. doi:  10.1016/j.agrformet.2015.02.007
    [7]
    Zhang Y. Responses and Adapatation of Crop Production to Climate Change in Northeast China. Shenyang: Liaoning Science and Technology Publishing, 2016.
    [8]
    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. doi:  10.11898/1001-7313.20180203
    [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. doi:  10.1111/gcb.14559
    [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. doi:  10.1002/2015JG003247
    [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. doi:  10.1016/j.agrformet.2012.05.021
    [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. doi:  10.1016/j.agrformet.2015.05.009
    [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. doi:  10.1016/j.agrformet.2011.02.013
    [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. doi:  10.1007/s10021-016-0085-9
    [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. doi:  10.1016/j.scitotenv.2018.07.010
    [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. doi:  10.1016/j.agrformet.2016.09.004
    [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. doi:  10.5194/bg-9-5373-2012
    [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. doi:  10.1002/2017JG004361
    [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. doi:  10.1371/journal.pone.0237684
    [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. doi:  10.1002/2017JG004148
    [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. doi:  10.1016/j.agrformet.2015.08.251
    [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. doi:  10.1016/j.agrformet.2016.07.012
    [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. doi:  10.1016/j.atmosenv.2018.05.003
    [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. doi:  10.1016/j.agee.2018.11.026
    [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. doi:  10.1007/s00376-015-4253-1
    [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. doi:  10.1016/j.fcr.2021.108150
    [29]
    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. doi:  10.11898/1001-7313.20220407
    [30]
    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. doi:  10.11898/1001-7313.20210105
    [31]
    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. doi:  10.11898/1001-7313.20180204
    [32]
    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. doi:  10.11898/1001-7313.20200402
    [33]
    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. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201901014.htm
    [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]
    Yu G R, Wang Q F. Ecophysiology of Plant Photosynthesis, Transpitation, and Water Use. Beijing: Science Press, 2010.
    [38]
    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]
    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. doi:  10.11898/1001-7313.20220310
    [44]
    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. doi:  10.11898/1001-7313.20220311
    [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]
    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. doi:  10.11898/1001-7313.20230101
    [47]
    Song Y L. Global research progress of drought indices. J Appl Meteor Sci, 2022, 33(5): 513-526. doi:  10.11898/1001-7313.20220501
    [48]
    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. doi:  10.11898/1001-7313.20220109
    [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.
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    • Received : 2022-11-02
    • Accepted : 2023-01-16
    • Published : 2023-03-31

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