渭河平原农田冬小麦土壤呼吸及其影响因素

基于对半湿润易旱区的渭河平原农田2013—2014年冬小麦生长期土壤呼吸(SR)及环境因子和生物因子的观测,研究了冬小麦土壤呼吸日变化、季节变化特征,综合分析了温度(T)、土壤含水量(W)、总初级生产力(GPP)和叶面积指数(LAI)对土壤呼吸的影响.结果表明: 冬小麦土壤呼吸日变化呈单峰型,呼吸速率变化范围为1.5～6.94 μmol CO₂·m^-2·s^-1,最大值出现在12:00—14:00;温度是影响土壤呼吸日变化的驱动因子,其中地表温度(T_s)能解释土壤呼吸时间变异的80.9%;土壤呼吸速率与温度的昼夜变化对应关系呈顺时针近椭圆曲线.冬小麦土壤呼吸速率从出苗后到冬季呈下降趋势,在冬季时保持较低水平,进入返青期后迅速增加,在抽穗期和灌浆期达到最大,成熟期后有所下降,变化范围为0.65～4.85 μmol CO₂·m^-2·s^-1;土壤呼吸季节变化与温度、土壤含水量、GPP、LAI均呈显著(P<0.01)正相关关系;土壤温度和水分是影响土壤呼吸季节变化的关键因素,使用复合模型SR=e^{(a+bT_{5 cm}}+cW10 cm+dW_{10 cm²}),可以解释土壤呼吸时间变异的82.6%,比单因子模型(不超过65.7%)的解释能力显著提高.经模型计算,该区域2013—2014年冬小麦生长期平均土壤呼吸速率为1.67 μmol CO₂·m^-2·s^-1.

Soil respiration rates and its affecting factors in winter wheat land in the Weihe Plain,Northwest China.

Based on measurement on winter wheat soil respiration (SR), environmental and biotic factors from 2013 to 2014 in semi-humid and prone-to-drought area of Weihe Plain, this paper analyzed the characteristics of diurnal and seasonal variation of SR and influences of temperature (T), soil water content (W), gross primary productivity (GPP) and leaf area index (LAI). The results showed that the SR diurnal variation appeared as a single-peak curve ranging between 1.5 and 6.94 μmol CO₂·m^-2·s^-1, with the peak presented during 12:00-14:00. Temperature was the driving factor for the SR diurnal variation, and land surface temperature (T_s) could explain 80.9% of the temporal variation for SR. The corresponding relationship between SR and temperature showed a clockwise nearly elliptic curve. From seedling stage to winter, the SR rate of wheat showed a descending trend, and stayed at a relatively low level until regreening stage. Then the rate of SR increased rapidly and reached peak in heading stage and filling stage, but there was a descending trend in mature stage. The SR values in growth period were between 0.65 and 4.85 μmol CO₂·m^-2·s^-1. T, W, GPP and LAI were positively related to SR (P<0.01). Soil temperature and soil water content were the dominant factors affecting the SR seasonal variation, and they could explain 82.6% of its variation by a compound model SR=e^{(a+bT_{5 cm}}+cW10 cm+dW_{10 cm²}), which was much better than monovariant model (below 65.7%). Based on the compound model results, the average rate of wheat SR during growth period in 2013-2014 was 1.67 μmol CO₂·m^-2·s^-1.