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Interannual Carbon Exchange Variability of Rain-fed Maize Fields in Northeast China and Its Influencing Factors
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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。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.
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图 1 2005—2020年NEP，GEP和RE的年际变化
Fig. 1 Interannual variations of net ecosystem production(NEP), gross ecosystem production(GEP) and ecosystem respiration(RE) from 2005 to 2020
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)
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)
图 4 2005—2020年光合有效辐射、二氧化碳浓度、空气温度(灰点)、土壤温度(褐点)、饱和水汽压差、土壤湿度(灰点)、降水量(红柱)、叶面积指数(灰点)和气孔导度(褐点)的逐日变化(绿色和褐色阴影表示逐日标准差，蓝色阴影表示平均生长季)
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)
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)
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)
表 1 基于冗余分析确定气象、土壤和生物因素对碳通量年际间变化的影响权重
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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