Let te r Ecology

Evaluating the influences of measurement time and frequencyon soil respiration in a semiarid temperate grassland

Bingwei Zhang • Zhiqiang Yang • Shiping Chen •

Liming Yan • Tingting Ren

Received: 17 November 2013 / Accepted: 30 December 2013 / Published online: 19 April 2014

� Science China Press and Springer-Verlag Berlin Heidelberg 2014

Abstract Soil respiration (Soil R) is one of the largest

CO2 fluxes from terrestrial ecosystems to the atmosphere.

The largely seasonal and daily patterns of Soil R in semi-

arid grassland ecosystems indicate that measurement time

and frequency would have significant influences on the

assessment of seasonal soil carbon release. Based on a

three-year continuous measurement of Soil R in a semiarid

grassland, we found that the Soil R value measured at

around 10:00 o’clock local time was the closest to its daily

mean, while the value at 14:00 o’clock was found to be the

highest daily rate. A measurement frequency higher than

every 10 days was necessary for estimating the seasonal

Soil R and its temperature sensitivity (Q10) reasonably. Our

study would be useful as guidelines for manual Soil

R measurements and model data selection in semiarid

temperate grasslands.

Keywords Soil respiration � Temperate grassland �Measurement time � Frequency � Q10

As one of the major pathways of carbon (C) loss from

terrestrial ecosystems to the atmosphere [1], soil respiration

(Soil R or soil CO2 efflux) has shown apparent diurnal [2]

and seasonal [3] dynamics, which largely depended on soil

temperature [4], soil water availability [5, 6], and substrate

supply [7–9]. However, Soil R has been measured by

various methods [10–13] with regular or irregular [5] fre-

quency at different times, which might cause an overesti-

mate or underestimate of the seasonal soil CO2 release. In

forest ecosystems, several researches indicated that Soil

R during 9:00–11:00 o’clock was representative of the

daily average [12–15], while Savage and Davidson [16]

showed that Soil R at this time was lower than the daily

mean by 13 %. Savage et al. [17] and Wang et al. [15] also

found that the frequency of weekly or biweekly measure-

ments was sufficient to estimate seasonal Soil R reason-

ably. In contrast, Parkin and Kaspar [18] suggested a

higher frequency of every 3 days. For semiarid and arid

grassland ecosystems with higher fluctuation in daily and

seasonal soil temperature and moisture, there is still limited

information about what measurement time and frequency

are suitable and recommended.

We used ‘‘soil respiration’’ or ‘‘soil CO2 efflux’’ and

‘‘temperate grassland’’ as keywords to search relative

studies in Web of Science during a recent 10 year period

(2001–2011) and found 93 published papers. Papers with-

out both measurement time and frequency information

were excluded. Totally, data from 68 experiments were

used in our analysis (Fig. 1). Daily variations of Soil

R were measured by continuously measurement systems or

manually in only 14.8 % experiments, while 19.1 %,

17.6 %, 27.9 %, and 7.4 % of experiments were measured

during daytime (measurements lasted more than 6 h),

around 10:00, 12:00, and 14:00 o’clock local time,

respectively (Fig. 1a). As to the measurement frequency,

there were only 14.7 % of the experiments measured once

or more than once weekly, 26.5 % and 39.7 % of them

B. Zhang � Z. Yang � S. Chen (&) � L. Yan � T. Ren

State Key Laboratory of Vegetation and Environmental Change,

Institute of Botany, Chinese Academy of Sciences,

Beijing 100093, China

e-mail: spchen@ibcas.ac.cn

B. Zhang � Z. Yang

University of Chinese Academy of Sciences,

Beijing 100049, China

L. Yan

School of Life Sciences, Fudan University,

Shanghai 200433, China

123

Chin. Sci. Bull. (2014) 59(22):2726–2730 csb.scichina.com

DOI 10.1007/s11434-014-0265-y www.springer.com/scp

used biweekly or monthly measurement frequencies,

respectively. There were also 2.9 % and 8.8 % of the

experiments measured beyond 1 month or irregularly,

respectively (Fig. 1b). These various measure strategies

made it hard to use these data equivalently. Our purpose

here was to provide some references on measurement times

and frequencies for manual Soil R researches.

Recent development of the Soil R continuous measure-

ment technique made it possible to assess the influences of

different measurement strategies on the estimation of sea-

sonal soil CO2 release. Our experiment was conducted in a

typical temperate steppe at the Xilin river basin, Xilingol,

Inner Mongolia, China (43�380N, 116�420E). The mean

annual temperature is 0.3 �C, and precipitation is 346 mm

with 298 mm occurring in the growing season (May–Oct.).

The vegetation of the study site is dominated by Leymus

chinensis and Stipa grandis [19].

Soil R, soil temperature (Soil T), and soil moisture (Soil

M) at 10 cm soil depth was measured continuously every

2 h during the growing season from 2010 to 2012. The

system included four dynamic chambers (Truwel Inc.,

Beijing, China) attached to an infrared gas analyzer (IRGA;

LI-840, LI-COR Inc., Lincoln, NE, USA), an air pump (LI-

COR Inc.), solenoid valves (Truwel, Inc.), and a power

supply system (Dahe Inc., Beijing, China). All the data

were recorded by a CR5000 data-logger (Campbell Sci-

entific Inc., Logan, IL, USA). We analyzed the influences

of measurement time on a daily dynamic and frequency on

a seasonal scale. All the graphics were performed by Sig-

maplot 12.5 (Systat Software, Inc).

The temperature sensitivity of Soil R (Q10) was calcu-

lated by van’t Hoff equation Eq. (1) [20]

R ¼ aebT ; ð1Þ

where R was soil daily respiration (Soil R), T was soil

temperature (Soil T) at 10 cm soil depth, a and b were

parameters of the exponential equation. Then, Q10 values

were calculated by the following Eq. (2)

Q10 ¼ e10b: ð2Þ

Precipitation of the three growing seasons was 295, 225,

and 434 mm, respectively (Fig. 2). Soil R showed

obviously inter-annual variations with total soil C release

Fig. 1 Measurement time (a) and frequency (b) of soil respiration collected from 68 published experiments in temperate grasslands from 2001 to

2011

Fig. 2 Seasonal dynamics of soil temperature (black line), soil

moisture (black dotted line), soil respiration (gray line), and

precipitation (gray rectangle) in the growing seasons of 2010 (a),

2011 (b), and 2012 (c), respectively

Chin. Sci. Bull. (2014) 59(22):2726–2730 2727

123

of 299, 319, and 416 g C/m2 during three growing seasons,

respectively. Seasonal dynamics showed that both Soil

R and Soil T reached their maximums during the middle of

growing season. Soil R was also affected by precipitation

and Soil M significantly (Fig. 2). The lowest soil carbon

release in 2010 was caused by the uneven seasonal

distribution of precipitation with a \40 % of the growing

season precipitation during the peak growing season (June

to August), which limited plant growth (much lower net

and gross ecosystem carbon exchange compared with the

other years, unpublished data) and microbial activity [21].

Rates of Soil R measured every two hours were com-

pared with daily mean values during the growing seasons

of 2010–2012 (Fig. 3). All data of rainy days were exclu-

ded in this analysis with 64 %, 56 %, and 54 % data

remained in growing seasons in 2010, 2011, and 2012,

respectively. The lowest Soil R rate occurred at 06:00

o’clock and the highest at 14:00 o’clock local time which

were equal to 88 % and 116 % of the daily mean,

respectively. Similar daily patterns of Soil R were also

found in a forest ecosystem [13]; however, the occurrence

time of minimum Soil R in our study was about 3 h earlier

than that study, which, probably because of the larger

diurnal fluctuation of soil temperature in the semiarid

grassland. Soil R rates measured at 10:00, 20:00, and 22:00

o’clock were the most approximate to the daily mean with

the ratios of 100.2 %, 102.3 %, and 98.4 %, respectively.

Considering the difficulties of working during nighttime,

10:00 o’clock local time would be the most suitable time to

measure Soil R in order to correctly represent its daily

mean in this semiarid grassland ecosystem. Similar results

were also reported by some forest studies [12–15].

Fig. 3 Correlations between hourly Soil R and their daily mean with function equations and coefficient values (R2). Data of days with rainfall

were all excluded from the correlations during the three growing seasons. There were 90, 82, and 92 pairs of data remained in the correlations in

2010, 2011, and 2012, respectively. The solid lines are fitted regression lines and the long dash lines are 1:1 lines. All the regression lines are

different from the 1:1 lines significantly (P \ 0.0001, n = 264)

2728 Chin. Sci. Bull. (2014) 59(22):2726–2730

123

Soil R values measured at 10:00 am in each growing

season were selected based on the frequencies of every day,

3, 7, 10, 14, and 30 days, respectively. For example, at the

frequency of every 3 days, datasets were selected from the

1st, 2nd, and 3rd day to the end of yearly measurements,

respectively, to get three datasets with the same measure-

ments. To simulate manual measurements as nearly as

possible, if the chosen data experienced rain events, it

would be replaced by the corresponding data from the next

no-rain day. This is an improvement from other jackknife

techniques [22] or completely random selection [17], for

the difficulty working and influence of rain pulse [23]

during the rainfall. The seasonal average and Q10 were

calculated from each dataset of different frequencies, and

the mean from the frequency of every day were used as

their reference values. Our results showed that the lower

measurement frequency, the larger deviation of Soil R from

its reference value (Fig. 4a–c). Measurements on a fre-

quency of every 3 days could ensure that the seasonal Soil

R averages changed within the deviation of 5 % from the

reference value. Using the frequency of every 30 days

resulted in more than 30 % and 10 % chances beyond a

20 % deviation in 2010 and 2011, respectively. Frequen-

cies on the scale of every 7–14 days could keep variations

of seasonal Soil R within 20 % deviation compared with

the reference Soil R.

As to the Q10, there was a much larger fluctuation than

Soil R with the decreasing measurement frequency

(Fig. 4d–f). On a frequency of every 30 days, more than

60 % of the Q10 values could not be calculated significantly

during the three growing seasons. The non-significant Q10

also occurred at any measurement frequency in 2010,

because of an abnormal precipitation pattern with most of

the rains falling at the beginning and end of the growing

season, which caused the non-synchronous dynamics of

Soil R and T. The frequency of every 10 days was a turning

point for the estimation of seasonal Q10, by there being

more than a 90 % chance of being within 20 % deviation

from the reference Q10 both in 2011 and 2012. Lower

frequencies such as every 14 days caused larger deviations

even beyond 60 % from the reference Q10, while higher

frequencies such as every 7 days induced less improve-

ment. Therefore, we suggest a frequency of every 10 days

as the lowest frequency necessary while estimating the

seasonal Soil R and Q10 in temperate grasslands. This

recommended frequency was higher than the biweekly

frequency reported in forest ecosystems [15, 17], but lower

than the every 3 days frequency [18] required in a no-till

corn/soybean field.

In conclusion, in order to estimate seasonal Soil R and

Q10 in temperate steppe reasonably, Soil R should be

measured around 10:00 o’clock local time with a frequency

Fig. 4 Deviations of seasonal mean Soil R (a–c) and Q10 values (d–f) from different measurement frequencies to their references during the

growing seasons in 2010–2012. Solid circles are deviations of average Soil R and Q10 from their references, open triangles are Q10 values from

the non-significantly exponential regressions between Soil R and soil temperature. The solid lines are y = 0, dotted lines are y = ±5 %

deviation, short dash lines are y = ±10 % deviation, long dash lines are y = ±20 % deviation

Chin. Sci. Bull. (2014) 59(22):2726–2730 2729

123

higher than every 10 days. Here, the local time we rec-

ommended should be adjusted by the time zone of research

station beyond its local time applied. Higher frequency

would improve the estimation of seasonal Soil R signifi-

cantly but less so with Q10, while lower frequency would

cause significantly larger deviation of seasonal Soil R and

Q10. These results would be useful as guidelines for manual

Soil R measurements and model data selections in tem-

perate semiarid grasslands.

Acknowledgments We thank Hanlin Zhao, Shan Li, and Fang

Wang for their helps on field measurements. We thank Nate Mikle

also for his help in our English writing. This work was supported in

part by the ‘‘Strategic Priority Research Program - Climate Change:

Carbon Budget and Relevant Issues’’ of the Chinese Academy of

Sciences (XDA05050402), the National Natural Science Foundation

of China (31170453) and a Selected Young Scientist Program sup-

ported by the State Key Laboratory of Vegetation and Environment

Change.

References

1. Raich JW, Schlesinger WH (1992) Tellus B Chem Phys Meteorol

44:81–99

2. Shi GX, Gen HL, Wang YL et al (2008) Acta Ecol Sin

28:3408–3416

3. Vargas R, Allen MF (2008) New Phytol 179:460–471

4. Zhou X, Wan S, Luo Y (2007) Glob Change Biol 13:761–775

5. Balogh J, Pinter K, Foti S et al (2011) Soil Biol Biochem

43:1006–1013

6. Chen S, Lin G, Huang J et al (2009) Glob Change Biol

15:2450–2461

7. Bahn M, Rodeghiero M, Anderson-Dunn M et al (2008) Eco-

systems 11:1352–1367

8. Hogberg P, Nordgren A, Buchmann N et al (2001) Nature

411:789–792

9. Yan L, Chen S, Huang J et al (2011) Glob Change Biol

17:1990–2001

10. Chen QS, Wang QB, Han XG et al (2010) Glob Biogeochem

Cycles 24:GB3021. doi:10.1029/2009GB003538

11. Dornbush ME, Raich JW (2006) Ecosystems 9:909–920

12. Harper CW, Blair JM, Fay PA et al (2005) Glob Change Biol

11:322–334

13. Xu M, Qi Y (2001) Glob Change Biol 7:667–677

14. Davidson EA, Belk E, Boone RD (1998) Glob Change Biol

4:217–227

15. Wang Y, Wang H, Wang Z-L et al (2012) Eur J Soil Biol

52:41–47

16. Savage KE, Davidson EA (2003) J Exp Bot 54:891–899

17. Savage K, Davidson EA, Richardson AD (2008) Funct Ecol

22:1000–1007

18. Parkin TB, Kaspar TC (2004) Soil Sci Soc Am J 68:1234–1241

19. Bai YF, Han XG, Wu JG et al (2004) Nature 431:181–184

20. Lloyd J, Taylor JA (1994) Funct Ecol 8:315–323

21. Liu WX, Zhang Z, Wan SQ (2009) Glob Change Biol

15:184–195

22. Efron B, Gong G (1983) Am Stat 37:36–48

23. Huxman TE, Snyder KA, Tissue D et al (2004) Oecologia

141:254–268

2730 Chin. Sci. Bull. (2014) 59(22):2726–2730

123