Abstract
BackgroundStudying the ecosystem carbon cycle requires analysis of interrelationships between soil respiration (Rs) and the environment to evaluate the balance. Various methods and instruments have been used to measure Rs. The closed chamber method, which is currently widely used to determine Rs, creates a closed space on the soil surface, measures CO2 concentration in the inner space, and calculates Rs from the increase. Accordingly, the method is divided into automatic or manual chamber methods (ACM and MCM, respectively). However, errors of these methods and differences in instruments are unclear. Therefore, we evaluated the characteristics and difference of Rs values calculated using both methods with actual data.ResultsBoth methods determined seasonal variation patterns of Rs, reflecting overall changes in soil temperature (Ts). ACM clearly showed detailed changes in Rs, but MCM did not, because such small changes are unknown as Rs values are collected monthly. Additionally, Rs measured using MCM was higher than that using ACM and differed depending on measured plots, but showed similar tendencies with all measurement times and plots. Contrastingly, MCM Rs values in August for plot 4 were very high compared with ACM Rs values because of soil disturbances that easily occur during MCM measurements. Comparing Rs values calculated using monthly means with those calculated using MCM, the ACM calculated values for monthly averages were higher or lower than those of similar measurement times using the MCM. The difference between the ACM and MCM was attributed to greater or lesser differences. These Rs values estimated the carbon released into the atmosphere during measurement periods to be approximately 57% higher with MCM than with ACM, at 5.1 and 7.9 C ton ha−1, respectively.ConclusionACM calculated average values based on various Rs values as high and low for measurement periods, but the MCM produced only specific values for measurement times as representative values. Therefore, MCM may exhibit large errors in selection differences during Rs measurements. Therefore, to reduce this error using MCM, the time and frequency of measurement should be set to obtain Rs under various environmental conditions. Contrastingly, the MCM measurement is obtained during CO2 evaluation in the soil owing to soil disturbance caused by measuring equipment, so close attention should be paid to measurements. This is because the measurement process is disturbed by high CO2 soil concentration, and even small soil disturbances could release high levels into the chamber, causing large Rs errors. Therefore, the MCM should be adequately mastered before using the device to measure Rs.
Highlights
Studying the ecosystem carbon cycle requires analysis of interrelationships between soil respiration (Rs) and the environment to evaluate the balance
In plot 4, the automatic chamber method (ACM) and manual chamber method (MCM) values were comparable, but in July, the ACM value was 933.7 mg CO2 m−2 h−1 compared to 1882.1 mg CO2 m−2 h−1 with the MCM, which was approximately 50% lower. We investigated if these values were affected by certain factors, and the result showed that data collection using the MCM required careful handling
In this study, we compared the characteristics of ACM and MCM used to evaluate Rs
Summary
Studying the ecosystem carbon cycle requires analysis of interrelationships between soil respiration (Rs) and the environment to evaluate the balance. The method is divided into automatic or manual chamber methods (ACM and MCM, respectively). Errors of these methods and differences in instruments are unclear. Soils contain more carbon than that in the atmosphere and vegetation. These soil carbon movements are directly linked to the atmospheric CO2 concentration and affect the global carbon cycle (Oikawa 1991; Lee et al 2017). Since the Rs is very highly correlated to environmental factors such as soil temperature (Ts) (Wu et al 2006; Wang et al 2006; Eom et al 2018; Jeong et al 2017a; Jeong et al 2017b), soil moisture content (SMC), and organic matter content, many studies have measured such environmental factors to analyze their relationship (Aerts 1997; Lee et al 2008; Lee et al 2010; Jeong et al 2017a, b; Eom et al 2018)
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