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

Zhang Hui; Gao Quan; Chang Shuting; Jin Chen; Liang Wanlu; Cai Fu

doi:10.11898/1001-7313.20230210

Vol.34, NO.2, 2023

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Article Navigation > Journal of Applied Meteorological Science > 2023 > 34(2): 246-256

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.

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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.

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

DOI: 10.11898/1001-7313.20230210

1.
Jinzhou Ecology and Agriculture Meteorological Center of Liaoning, Jinzhou 121011
2.
Jinzhou Meteorological Bureau of Liaoning, Jinzhou 121011
3.
Institute of Atmospheric Environment, CMA, Shenyang 110166

Received Date: 2022-11-02
Rev Recd Date: 2023-01-16
Publish Date: 2023-03-31

Abstract

Abstract

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.
- net ecosystem production;
- gross ecosystem production;
- ecosystem respiration;
- interannual variation;
- environmental and biotic controls

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Figures and Tables

Fig. 1 Interannual variations of net ecosystem production(NEP), gross ecosystem production(GEP) and ecosystem respiration(RE) from 2005 to 2020

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图 2 2005—2020年NEP，GEP和RE多年平均逐日变化

(黑线表示10 d滑动平均，绿色阴影表示逐日标准差，蓝色阴影表示玉米生长季)

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)

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图 3 2005—2020年光合有效辐射、二氧化碳浓度、空气温度、降水量、饱和水汽压差、土壤温度、土壤湿度、土壤有机碳、叶面积指数、气孔导度、蒸散、水分利用效率的年际变化

(黑线表示显著变化趋势)

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)

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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)

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图 5 NEP，GEP和RE的关系

(黑实线表示显著相关，灰色阴影表示达到0.05显著性水平，灰虚线表示预测边界)

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)

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图 6 碳通量与气象、土壤和生物因素的相关系数矩阵

(*表示达到0.05显著性水平，**表示达到0.01显著性水平)

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

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

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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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