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https://www.tandfonline.com/doi/full/10.1080/19768354.2015.1069209?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Animal_Cells_and_Systems_TrendMD_0
Abstract
Standard protocols are required for the accurate measurement of global soil respiration rates. This research aimed to determine the appropriate sampling frequency for pine forest soil respiration rates by quantifying the optimal number of measurement chambers and Q10 values. We used an automatic open/closed chamber system with 16 chambers to obtain a high level of reliability about soil respiration rates. On average, 57 and 14 chambers were required to obtain 10% and 20% margins of error at the 95% confidence level, while 38 and 9 chambers were required for these 2 error margins at the 90% confidence level, respectively. Thus, our data were in the range of the 20% margin of error at the 95% confidence level. ANOVA statistical tests also separated the minimum required measurement interval into two groups using Q10 values based on actual measured temperatures: (1) short-term sampling intervals of 1 and 3 days and (2) long-term intervals of 7, 14, 20, and 30 days. Our results indicate that 3 days is the optimal interval for soil respiration measurement. The total amount of soil respiration was estimated as 9.35 t C ha−1 yr−1 in a pine forest, with soil respiration rates being positively related with temperature.
Keywords: AOCC system, spatial variation, Q10, coefficient of variation
Introduction
Soil respiration is a critical component of the carbon cycle in the earth's ecosystem. For instance, carbon dioxide emissions from the soil surface (Adachi et al. 2005) contribute to the circulation of nutrients, climate change, and the earth's carbon budget (Luo & Zhou 2006).
Forest soil both absorbs and emits carbon dioxide. This soil contains 1500 pg of accumulated carbon (Waring & Running 1998), which is the equivalent of 3 times that of vegetation and 2 times that of the atmosphere (Suh et al. 2005). Therefore, soil respiration is an important component of the carbon cycle. The importance of soil respiration in the carbon balance of the planet's ecosystem has led to more than 150 soil respiration studies being conducted annually in Europe and the USA since 2002 (Kang et al. 2010).
However, soil respiration values obtained from sampling points by different studies vary because of plant roots, soil organic matter, soil temperature, and soil water content. Therefore, spatial variation in the ecosystem has led to methodological issues, resulting in standardizing experimental chamber size and the number of chambers to measure. These issues require addressing in carbon balance studies to obtain representative values with high levels of confidence. Yet, studies focusing on spatial variation remain sparse, except for Yim et al. (2003), Adachi et al. (2005), Liang et al. (2004), and Davidson et al. (2002). One possibility to overcome existing issues is the use of Q10 values to compute the amount of annual soil respiration for data that cannot be measured. For instance, weather and economic issues may limit the ability to measure soil respiration in certain areas. Consequently, as the number of measurements decreases in a given area, the accuracy of the Q10 values drop, resulting in the over- or underestimation of annual soil respiration. In addition, it is difficult to estimate net ecosystem productivity (NEP) accurately. Yet, little is known about how Q10 values differ for soil respiration based on measurements using appropriate sampling frequencies, and how this variation influences the evaluation of annual soil respiration.
This study aimed to determine the appropriate sampling frequency for pine forest soil respiration rates by quantifying the optimal number of measurement chambers and Q10 values. First, we estimated the number of measuring points that are required to obtain a measured value within 10% and 20% margins of error at 95% and 90% confidence levels, respectively, with monthly ecological significance using an automatic open/closed chamber (AOCC) system. This system allows the collection of long-term continuous measurements. Second, we performed a comparative evaluation by computing Q10 values based on the appropriate sampling frequency of each measure, and then comparing Q10 estimates of annual soil respiration with data from actual long-term measurements obtained at 3-, 7-, 14-, 20-, and 30-day intervals based on soil respiration measured throughout the year.
Materials and methods
Site description
This study was conducted in a pine (Pinus densiflora) forest that is located in Uljin-gun, Gyeongsangbuk-do, South Korea. The study site covered an area of 40 × 40 m (36° 54′ 40″N, 129° 14′ 31″E; 560 m above sea level). The average annual temperature of the pine forest is 12.6°C and the average annual precipitation is 1135 mm, based on data assimilated over the last 30 years (1982–2012). Heavy rain occurs during the summer of each year, with over 100 mm of average monthly precipitation being recorded from June to September.
Measurement methods
The AOCC system was used in this research. This system consists of an infrared gas analyzer (IRGA) and 16 automatic open–closed chambers. Each chamber had a height of 30 cm and a diameter of 40 cm. Sealing circulation methods were used for the measurements. To estimate the optimal number of chambers, we measured changes in the CO2 concentration over a 225-s period, consecutively closing and sealing the 16 chambers with a cover. In this system, a chamber where measurements had been completed automatically opened, while the next chamber closed, allowing the collection of 16 measurements per hour. During the sampling period, CO2 was produced from the soil within a chamber, and was then transferred to the IRGA, where the amount of CO2 emission was analyzed. The analyzed CO2 was then circulated back to the inside of the chamber to maintain the pressure in the chamber.
The equation for the optimal number of chambers (n) is as follows (Petersen & Calvin 1986; Yim et al. 2003):
where denotes the degrees of freedom at α confidence level, s is the standard deviation, and D is the mean value.
where denotes the degrees of freedom at α confidence level, s is the standard deviation, and D is the mean value.
The equation used for the soil respiration rate (R) was:where represents the increased CO2 concentration (mg CO2 m3) in the chamber during the measurement period, represents measurement time (sec), V denotes the volume of the chamber (m3), A denotes the area of surface of soil covered by the chamber (m2), and h denotes the height of the chamber (m).
where represents the increased CO2 concentration (mg CO2 m3) in the chamber during the measurement period, represents measurement time (sec), V denotes the volume of the chamber (m3), A denotes the area of surface of soil covered by the chamber (m2), and h denotes the height of the chamber (m).
The equation used to correlate the soil respiration rate (SR) and temperature was:where a represents the soil respiration rate at a temperature of 0°C, b represents the coefficient related to the increase in soil respiration rate when the temperature increases by 10°C, and T represents the temperature.
where a represents the soil respiration rate at a temperature of 0°C, b represents the coefficient related to the increase in soil respiration rate when the temperature increases by 10°C, and T represents the temperature.
The equation used for the Q10 values was:
where b is a coefficient of increasing rate of soil respiration for every 10°C increase in temperature.
where b is a coefficient of increasing rate of soil respiration for every 10°C increase in temperature.
The experimental apparatus used in this study was an IRGA, an LI-820 (Li-Cor, Inc., Lincoln, NE, USA), a data logger (CR1000, Campbell Scientific Inc., USA), an air temperature sensor thermocouple (T-type, Oregon Sci. Co., USA), and a Hobo data logger (Onset Comp. Com, USA).
To compute Q10 values in relation to measured values and measuring periods with an ecologically significant confidence level (minimum 95% and 90%) and margin of error (10% and 20%), SPSS statistical software package was used for the statistical analysis (SPSS Inc., Chicago, IL, USA). One-way analysis of variance (ANOVA) was used to analyze statistical significance to examine any differences among the periods of measurement.
Results and discussion
Spatial variation in daily average soil respiration
When using 16 chambers with an AOCC system on 1 sunny day of each month, at the 95% confidence level, 32–97 chambers were required to obtain a 10% margin of error, with an average of 57 chambers. In comparison, 8–24 chambers were required to obtain a 20% margin of error, with an average of 14 chambers. At the 90% confidence level, 22–66 chambers were required to obtain a 10% margin of error, with an average of 38 chambers. In comparison, 5–16 chambers were required to obtain a 20% margin of error at an average of 9 chambers (Table 1). Thus, our data are in the range of the 20% margin of error at the 95% confidence level. From March to November, the pine forest soil respiration rates fell under a 20% margin of error at the 95% confidence level, while the rates from January, February, and December fell under a 20% margin of error at the 90% confidence level. In comparison, the coefficient of variation (CV) values representing the spatial variation ranged from 27% to 46%, with an average of 35%. This CV value was in the range of 10–150%, as reported by Rodeghiero and Cescatti (2008). The CV value obtained in the current study was also similar to the CV (30%) obtained during the growing period of a Ponderosa pine forest (Pinus ponderosa; Xu & Qi 2001b) and the CV (26–29%, average 28%) obtained by Yim et al. (2003) for a larch (Larix kaempferi) forest in Japan over 2 days during late August. In contrast to the current study and these preceding studies, comparatively high CV values (23–42%, average 40%) were obtained by Lee and Koizumi (2009) in a Japanese cedar plantation from 1100 to 1300, when the soil temperature was relatively high. These differences may be caused by differences in various environmental factors that influence soil respiration, such as root biomass, micro-organism biomass, the physiological and chemical characteristics of the soil (Xu & Qi 2001b), and soil temperature. In addition, the size of the chambers used by Lee and Koizumi was comparatively small (71.6 cm2).
Table 1. The number of sampling points required to estimate the mean soil respiration within ±10% and ±20% of its actual value at 95% and 90% confidence level (C.L.).
Correlation of Q10 values with soil respiration levels
In most studies, annual soil respiration rates are calculated from the Q10 values when data cannot be collected. In the current study, the Q10 values during the long-term measurement periods were 2.49, 2.75, 3.08, and 4.00 for air temperature and soil temperatures at 5 and 10-cm soil depth, respectively. Furthermore, for the long-term 3-, 7-, 14-, 20-, and 30-day intervals, the Q10 values of the 4 environmental factors ranged from 2.42 to 3.46, 2.40 to 3.23, 2.39 to 3.27, 2.40 to 3.27, and 2.41 to 3.27, respectively (Table 2). These values are below the Q10 range of 1.3–5.6 obtained for various ecosystems, as measured by Raich and Schlesinger (1992) and Hu et al. (2004). Moreover, the values obtained by the current study were similar to those obtained by Xu and Qi (2001a), who stated that Q10 values increase with increasing soil depth. This phenomenon is thought to arise because the water content of the soil increases with increasing soil depth. This interpretation is based on the research of Davidson et al. (1998), who found that trees with good drainage have higher Q10 values compared to trees with poor drainage in a hemlock spruce forest.
Table 2. The value of Q10 at each measurement interval.
Annual soil respiration emissions using a corrected formula based on soil respiration rates and air temperature indicated that carbon dioxide emissions tend to increase with increasing air temperature (Figure 1). However, after comparing these annual emissions with long-term measurements, it was clear that emissions were underestimated for January and from June to November and were overestimated from February to May (Figure 1). This is because the soil respiration rates for 3-, 7-, 14-, 20-, and 30-day intervals, which were estimated from temperature using Q10 values only, excluded the correlation with soil water content, despite its effect on soil respiration during periods with high temperatures (Inoue & Koizumi 2012). The soil respiration rates were 9.35, 8.33, 8.04, 7.94, 7.91, and 7.94 t C ha−1 for the long and short-term intervals of 1, 3, 7, 14, 20, and 30 days, respectively. Compared to the long-term measurements, the 3-, 7-, 14-, 20-, and 30-day intervals were underestimated by 10.91%, 14.01%, 15.08%, 15.4%, and 15.08%, respectively (Tables 3 and 4).
Figure 1. Annual change of soil respiration (t C/ha/yr) for each interval (1, 3, 7, 14, 20, and 30 days).
Table 3. Annual soil respiration at each measurement interval.
Table 4. Statistical analysis of ANOVA tests in each measurement interval on soil respirations in the sampling site (Tukey HSDa).
Statistical analysis of the measuring periods generated 2 groups [(a) and (b)]. The measuring periods for the long-term measurement and 3-day intervals [group (a)] had a daily mean of 0.1183, 0.1043 mg CO2 m−2 s−1 respectively, and were significantly similar. In comparison, the average values for the 7-, 14-, 20-, and 30-day intervals [group (b)] were 0.1003, 0.0990, 0.0986, and 0.0989 mg CO2 m−2 s−1, respectively, which were quite different from group (a). Therefore, it is difficult to estimate temporal patterns of annual soil respiration accurately from Q10 values for measurement periods exceeding 3 days. The cause of variation in soil respiration is strongly influenced by temperature and humidity (Luo & Zhou 2006). In addition, annual soil respiration may be underestimated, whereas NEP may be overestimated.
Hong (2013) analyzed the Q10 value of measured soil respiration from 10- and 20-day measurements in a Pinus koraiensis plantation. The author found that the long-term soil respiration measurement was 16% and 23% lower than the 10- and 20-day measurements, respectively.
The results of this study are similar to those obtained by Parkin and Kaspar (2004), who stated that the soil respiration of agricultural soils could be estimated accurately based on values measured at least once every 3 days. In addition, the values obtained in the current study were similar to those obtained by Savage et al. (2008), who obtained a value within 5% of the optimal measurement by measuring values twice within 7 days.
Long-term pine forest soil respiration rates
Seasonal variation in soil respiration was measured using a soil respiration measurement system with 16 chambers during January–December 2012, and is shown in Figure 2.
Figure 2. Seasonal trends of soil respiration rate, soil temperature and soil water content.
Clear seasonal variation was recorded. As the air temperature rose, soil respiration exponentially increased in our data-set. The observed trend supports several preceding studies (Raich & Tufeckcioglu 2000; Pyo et al. 2003; Tamai 2010; Oe et al. 2011). Epron et al. (1999) reported that there is the positive relationship between soil water content and soil respiration. In our data, despite a temperature increase during late July to early August, the decrease in soil respiration was attributed to extreme drought (Korea Meteorological Administration Service 2012), with low rainfall (1 mm) leading to a reduction in soil micro-activity.
The R2 values for air temperature, surface soil temperature, 5 cm-soil-depth temperature, and 10 cm-soil-depth temperature were 75%, 79%, 83%, and 91%, respectively (P < .001). Therefore, the highest and lowest correlations with soil respiration were obtained for 10 cm-depth soil and air temperature, respectively (Figure 3). The high correlation with the 10 cm-depth soil may be related to the fact that most fine roots and micro-organisms are found at this depth. Therefore, soil respiration is strongly related to soil temperature (Buyanovsky et al. 1986; Lessard et al. 1994; Lee et al. 2009).
Figure 3. Seasonal relationship between soil respiration rate and temperature factors (*p < .05, **p < .01, ***p < .001).
Following measurements of long-term soil respiration, the mean annual soil respiration rate was calculated to be 9.35 t C ha−1. This value fell within the range of 0.239–23.89 t C ha−1 obtained from a forest ecosystem in a temperate region (Singh & Gupta 1977). Furthermore, this value is similar to the soil respiration rate of 9.8 t C ha−1 yr−1 obtained for a 40-year-old Japanese cedar forest (Nakane 1995). Soil respiration rates in the pine forests of the Jinju Province and in a pine forest of the Chuncheon Province of Korea were 6.55 and 5.78 t C ha−1, respectively. These rates significantly differed from the annual soil respiration rate obtained in the current study. This discrepancy may be caused by differences in the sampling points, soil types, temperature regime (Ellert & Gregorich 1995; Moon 2004), and measurement methods. Furthermore, Moon (2004) and Jeong (2007) estimated the annual soil respiration rate from data that were measured once a month, rather than long-term measurements.
In conclusion, it is imperative that the sampling frequency and the optimal number of chambers needed are calculated to gain high confidence levels from future studies on soil respiration across diverse ecosystems.
Acknowledgements
This research was conducted by the Korea Forest Research Institute project.
Disclosure statement
No potential conflict of interest was reported by the authors.
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