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施肥对高粱地土壤呼吸及其温度敏感性的影响

本站小编 Free考研考试/2021-12-31

中文关键词培肥模式环境因子土壤呼吸温度敏感性高粱作物 英文关键词fertilization treatmentenvironmental factorssoil respirationtemperature sensitivitysorghum
作者单位E-mail
严俊霞山西大学黄土高原研究所, 太原 030006yjx422@sxu.edu.cn
张媛山西大学黄土高原研究所, 太原 030006
焦晓燕山西省农业科学院农业环境与资源研究所, 太原 030031xiaoyan_jiao@126.com
中文摘要 基于对高粱地进行的5种培肥模式的长期定位试验,研究了不同培肥模式下土壤呼吸速率(Rs)及其温度敏感性(Q10)与环境因子以及光谱特征参数的关系.试验共设5个处理:不施肥(CK)、无机肥(INF)、无机肥+有机肥(INF+M)、无机肥+有机肥+秸秆(INF+M+S)和有机肥+秸秆(M+S).结果表明,施肥处理没有改变Rs的时间动态变化趋势.INF与CK的Rs没有明显差异,测定期间的平均值分别为3.68 μmol·(m2·s)-1和3.51 μmol·(m2·s)-1.与INF或CK相比,INF+M、M+S和INF+M+S的Rs分别增加了28.2%~39.1%、47.9%~76.0%和46.2%~50.8%,有机肥和秸秆还田处理后Rs增加.土壤温度和土壤水分分别能解释Rs季节变化的14%~96%和6%~37%,施肥处理显著提高了土壤温度的解释能力,而土壤水分的解释能力没有明显差异;Rs与差值植被指数、比值植被指数、增强植被指数的相关系数高于归一化植被指数;与红边斜率和红边面积的相关系数高于红边位置.有机肥和秸秆的施用降低了Rs与光谱特征参数的相关性.以光谱特征参数、T10Ws为自变量的3因子模型的决定系数R2都高于双因子和单因子模型.与CK相比,INF、INF+M、INF+M+S和M+S的Q10分别提高了26%、39%、21%和37%,表明施肥可以提高土壤呼吸的温度敏感性.造成不同处理Rs/Q10/R10差异的主要因子分别为Shannon多样性指数/容重/土壤有机质,可以解释其97.6%/78.2%/92.8%的变异. 英文摘要 The characteristics of soil respiration under the condition of fertilization have not been fully understood,especially for a long-term fertilization condition. In this study we measured both soil respiration using an LI-COR-6400-09 soil chamber attached to LI-COR-6400 portable photosynthesis system, and the vegetation spectrum using an ASD FieldSpec HandHeld2, in five different fertilization treatment fields. The soil respiration (Rs) and vegetation spectrum were simultaneously measured with two samples per month in the growing season in 2016 and 2017. The soil temperature at 10 cm depth (T10) and moisture (Ws) for the surface of 10 cm were also measured simultaneously. The five different fertilization treatments included no fertilization (CK), inorganic fertilizer (INF), inorganic fertilizer+organic fertilizer (INF+M), inorganic fertilizer+organic fertilizer+straw turnover (INF+M+S) and organic fertilizer+straw turnover (M+S), and all treatments had been conducted since 2011. Based on those observation data, we made an analysis of Rs and its temperature sensitivity (Q10) in the five different fertilization treatments. The results showed that no significant temporal change in Rs among the five treatments was found. No significant difference was found in Rs between the CK and INF treatments. Compared with the values of Rs in CK and INF, the Rs values in INF+M, M+S, and INF+M+S treatments increased by 28.2%-39.1%, 47.9%-76.0%, and 46.2%-50.8%, respectively. This indicated that use of organic fertilization and straw application increased Rs. Both the Ts and Ws showed 14%-96% and 6%-37% in Rs seasonal variations, respectively. Among the treatments, the correlation coefficient of the fitted equations between Rsand Ts was higher in the INF+M, INF+M+S and M+S treatment than in CK and INF, but was not between Rsand Ws. For the relationship between Rs and vegetation indexes we found that the correlation coefficients between Rs and the difference vegetation index (DVI), ratio vegetation index (RVI), and enhanced vegetation index (EVI), respectively, were higher than that of Rs and the normalized differential vegetation index (NDVI); and that the correlation coefficients between Rsand the red edge slope (Dred) and red edge area (Sred) were higher than between Rs and the red edge position (λred). This indicated that the treatments in INF+M+S increased the correlation coefficient between Rs and the spectrum characteristics index. The determination coefficient of the fitted equations including the feature spectral parameters, T10, and Wsvariables was higher than that of the equations only including both T10 and Ws variables, or a single variable of T10 or Ws. Compared with CK, the Q10 value increased by 26%, 39%, 21%, and 37% for the INF, INF+M, INF+M+S, and M+S treatments, respectively. This indicated that temperature sensitivity Q10 increased under the condition of fertilization treatments. The Shannon diversity index, bulk density, and soil organic matter were the main factors causing the difference in Rs, Q10, and R10, i.e., Rs at a temperature of 10℃, in the different treatments, which could explain the 97.6%, 78.2%, and 92.8% variations in Rs, Q10, and R10, respectively.

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