Soil CO₂ Efflux Measurements by Alkali Absorption and Infrared Gas Analyzer in the Brazilian Semiarid Region

ABSTRACT

The CO₂ emission from the soil surface, commonly referred to as soil CO₂ efflux (ECO₂) or soil respiration, is the sum of processes that include root respiration and microbial activity. Measuring this evolution is important to establish sustainable land use models and to estimate global fluxes of carbon, which affect climate change. Despite its importance, few measurements have been made in areas of the semiarid Brazilian Northeast region, and most of them were made using the alkali absorption method (AA), which can underestimate ECO₂. Measurements using AA were compared to measurements using the infrared gas analyzer method (IRGA) over ten months (in rainy and dry seasons), during the day and night, in areas of Caatinga (xeric shrubland and thorn forest) and pasture in the Agreste region of the state of Pernambuco. The ECO₂ measurements from AA varied little from night to day and throughout the year or in the rainy and dry seasons. However, those obtained from IRGA were higher in the rainy than in the dry season, but also without significant differences from day to night. The values of both methods were similar in the dry season, but in the rainy season they were higher with the IRGA. Therefore, AA seems to have little sensitivity to seasonal variations, in contrast with measurements from the IRGA, and it may underestimate soil ECO₂ when it attains higher values. This result indicates that some of the soil ECO₂ values determined in areas of the Brazilian semiarid region, and consequently annual C losses, may have been underestimated.

Keywords
CO₂ emission; rainy season; dry season; Caatinga; pasture

INTRODUCTION

The carbon cycle has received increased attention due to escalating concentrations of atmospheric carbon gas and its relationship with global warming and climatic changes (IPCC, 2014). Soil CO₂ efflux (ECO₂), also referred to as soil respiration, is an important component of the global carbon balance, returning about 80 × 10⁹ tons of C to the atmosphere each year (Raich et al., 2002). Therefore, ECO₂ can have a large influence on global atmospheric CO₂ (Ryan and Law, 2005; Zheng et al., 2014) and the increase of ECO₂ may promote disturbing climatic changes (Pinto-Junior et al., 2009; Panosso et al., 2012).

Several studies have quantified ECO₂ in specific areas in Brazil (Valentini et al., 2008; Panosso et al., 2009; Souto et al., 2009; Araujo et al., 2011; Ivo and Salcedo, 2012; Correia et al., 2015; Holanda et al., 2015) and in other countries (Deng et al., 2012; Chen et al., 2014; Zheng et al., 2014). These studies used different methodologies to quantify ECO₂, the most common ones being CO₂ absorption in an alkaline solution (AA), in general 0.5 or 1.0 mol L⁻¹ KOH or NaOH (Souto et al., 2009; Araujo et al., 2011; Correia et al., 2015). Other studies adopt dynamic measurement made with an infrared gas analyzer (Valentini et al., 2008; Panosso et al., 2009). These methodologies differ in precision, applicability, and spatial and temporal resolution (Janssens et al., 2000; Yim et al., 2002).

The infrared gas analyzer (IRGA) measurement has been considered to be more precise but depends on the availability of relatively expensive equipment and, in general, is conducted over short periods of time (Rochette and Eller, 1991; Haynes and Gower, 1995). The AA methodology is cheaper since it requires only one simple piece of equipment (pH meter) and can integrate CO₂ evolution for many hours, which facilitates nocturnal measurements. These differences are probably the reason why AA has been the most commonly used methodology for ECO₂ measurement in the Northeast region of Brazil (Souto et al., 2009; Araujo et al., 2011; Ivo and Salcedo, 2012; Correia et al., 2015; Holanda et al., 2015). However, some reports point to possible overestimation of low ECO₂ fluxes and underestimation of high fluxes by the AA method (Janssens et al., 2000), although it can be reliably used in the intermediate flux range (Davidson et al., 2002).

Considering that both IRGA and AA methods have been used world-widely, our hypothesis is that they provide similar results when measuring ECO₂ under Caatinga or pasture and in different seasons in the Brazilian Northeastern semiarid region. Therefore, we aimed to compare ECO₂ measurements using the AA and IRGA methodologies applied to soils under Caatinga (xeric shrubland and thorn forest) and pasture vegetation in the Agreste region of the state of Pernambuco, Brazil.

MATERIALS AND METHODS

The experiment was conducted in two areas, one cultivated with the grass Brachiaria decumbens Stapf. and the other a neighboring Caatinga area, both located on the Riacho do Papagaio farm (8° 48’ 34.2” S; 36° 24’ 29.3” W and 702 m altitude) in the municipality of São João in the southern portion of the Agreste mesoregion of Pernambuco. Average annual rainfall is 782 mm, concentrated mainly from May to August (Silva et al., 2014). The climate is predominantly hot and humid, As’ according to the Köppen classification system (Alvares et al., 2014), with dry summers and average temperatures ranging from 18.8 to 22.6 °C (Borges Júnior et al., 2012). The native vegetation is classified as hypo xerophilic Caatinga (Santos et al., 2012), and the soil in the experimental area is a sandy Neossolo Regolítico Eutrófico típico (Santos et al., 2012) or Entisol Arent (Soil Survey Staff, 2014).

The experimental area was deforested in the 1950s. Part of the area was not cropped, and the native Caatinga vegetation has been spontaneously regenerating ever since. Another part of the area was planted with Guatemala grass, followed by cassava and beans (1974-1980), corn (1981-1999), and Brachiaria grass (since 2000) (Almeida et al., 2015). In the pasture area, 24 points were marked for soil ECO₂ determination along two perpendicular transects in a cross shape (one with 14 and the other with 10 points); each point was 15 m distant from the next. In the Caatinga area, 12 points were marked along two winding trails.

The experiment followed a completely randomized design with two treatments (the AA and IRGA methods) and 24 replicates in the pasture area and 12 in the Caatinga area.

At each point, ECO₂ measurements were made with IRGA between 6:00 and 11:00 a.m. and between 6:00 and 11:00 p.m. once a month from April 2012 to January 2013. The period from April to August 2012 corresponded to the rainy season (total rainfall of 160 mm) and from September 2012 to January 2013 to the dry season (39 mm rainfall). It is important to note that 2012 was a drought year; therefore, rains during the rainy season were below average.

The IRGA apparatus (Licor LI-6400-09) has a gas retention chamber of 991 cm³, covering a soil surface area of 71.6 cm², an infrared irradiator, and a measurement chamber, also described as an optical path and filter plus a detector. The infrared signal traverses the measurement chamber, which is filled with the sampled gas, and it is measured by the detector. The CO₂ emission is calculated by the linear regression of the increase in CO₂ concentration inside the chamber along the measurement period (Davidson et al., 2002). Before beginning measurement, the CO₂ concentration near the soil surface was registered (about 350 μmol mol⁻¹), and this value was introduced in the software system of the apparatus to function as a reference value. At the beginning of each measurement, part of the gas chamber was inserted into the soil, and the CO₂ concentration inside the chamber was reduced to 10 μmol mol⁻¹, flushing the gas through a mixture of calcium oxide and sodium hydroxide. After this reduction, the subsequent CO₂ concentration increases inside the chamber, due to soil emission, were measured every 2.5 seconds, for a total period of 90 seconds, at which time the concentration inside the chamber was approximately 10 μmol mol⁻¹ above the reference CO₂ concentration value. After this period, the LI-6400-09 software system calculated the linear regression between the CO₂ emission and its concentration inside the chamber, and the emission was assumed to have the value registered when the concentration inside the chamber was equal to the concentration of the reference value.

On the same days and in the periods when the IRGA measurements were made, AA measurements of soil ECO₂ were made at points close to those marked for the IRGA measurements. The CO₂ evolved from an area of 390.57 cm² was absorbed by a 0.5 mol L⁻¹ KOH solution placed in a 100-mL beaker with a 4.2 cm diameter opening (13.9 cm²) under a cylindrical bucket with a 22.3 cm diameter opening and 21 cm height, which was inserted 2 to 3 cm inside the soil to avoid direct gas exchange with the atmosphere. Measurements were made separately during the day (from 6:00 a.m. to 6:00 p.m.) and during the night (from 6:00 p.m. to 6:00 a.m.), integrating the emissions for each 12-hour period. During each measurement, three control measurements were made, placing beakers with 10 mL KOH inside buckets that were hermetically sealed. After each measurement period, the KOH solution was transferred to hermetically sealed flasks to avoid exchange with the atmosphere, and the flasks were taken to the laboratory, where CO₂ was determined by titration with 0.1 mol L⁻¹ HCl using 1 % phenolphthalein and methyl orange as pH indicators. The ECO₂ value (mg CO₂ m^-2 h⁻¹) was converted to μmol CO₂ m^-2 s⁻¹ through multiplication by 0.0063.

In addition to the ECO₂ measurements, ten soil samples were also collected in each area (pasture and Caatinga) from the 0.00-0.20 m surface layer to determine some soil properties: total organic carbon (TOC), texture, soil bulk density (SD), total porosity (TP), and macro- and micro-porosity. These determinations were made following the methodologies described by Donagema et al. (2011). The data were subjected to analysis of variance (F-test), and the averages compared by the Tukey test at the 5 % probability level. The identity test proposed by Leite and Oliveira (2002) was used to test if ECO₂ data obtained by the AA and IRGA methodologies were similar. Since the aim of the study was to compare the two methodologies, data from pasture were not compared to Caatinga, but only data from one methodology were compared to data from the other methodology in each area separately. These comparisons were made considering the whole period (April 2012 to January 2013), the rainy (April to August 2012) and the dry (August 2012 to January 2013) periods, and the diurnal and nocturnal periods.

RESULTS AND DISCUSSION

Soils from the two areas were not significantly different regarding their particle size fractions, both were classified as sandy. They were also similar regarding soil bulk density (SD), total porosity (TP), macroporosity (Ma), and microporosity (Mi). However, the Caatinga soil had a higher total organic carbon (TOC) content than the pasture soil (Table 1). Both contents were within the range usually found in sandy soils of semiarid regions (Santos et al., 2012; Medeiros et al., 2015) and the higher Caatinga TOC was not enough to cause a different pore size distribution compared to that from pasture soil. As expected for sandy soils (Santos et al., 2012; Silva et al., 2014; Almeida et al., 2015), macropores predominated over micropores. Texture can influence soil CO₂ movement because it alters porosity (Bouma and Bryla, 2000); more macropores favor soil respiration due to greater penetration of air.

Table 1
Soil physical properties and total organic carbon in areas of Caatinga and pasture, in the municipality of São João, Pernambuco, Brazil

	Sand	Silt	Clay	SD	TP	Ma	Mi	TOC
	––––––––––––––––––––––g kg−1––––––––––––––––––––––			Mg m-3	––––––––––––––––––––––m3 m-3––––––––––––––––––––––			g kg−1
Pasture	888a	72a	41a	1.51a	0.431a	0.315a	0.116a	5.1b
Caatinga	909a	51a	40a	1.50a	0.433a	0.304a	0.129a	8.3a

• SD: soil bulk density; TP: total porosity; Ma: macro-porosity; Mi: micro-porosity; TOC: total organic carbon. Sand, silt, clay, SD, TP, Ma, Mi, and TOC were determined following the methodologies described by Donagema et al. (2011). Averages followed by the same letter in the column are not significantly different by the Tukey test at the 5 % probability level.

Monthly variations in soil ECO₂ values obtained from the AA methodology were small, both for diurnal and nocturnal measurements and for Caatinga and pasture, with average values around 1.05 μmol CO₂ m^-2 s¹ (Figure 1). Values obtained from the IRGA methodology were also similar for the diurnal and nocturnal periods and Caatinga and pasture areas, but they differed significantly between the rainy and the dry seasons, both in the Caatinga and pasture areas (Figure 1 and Table 2).

Figure 1
Average soil ECO₂ values, in diurnal and nocturnal periods, measured by infrared gas analyzer and alkali absorption methodologies, in areas of Caatinga and pasture, in the municipality of São João, Pernambuco, Brazil

Table 2
Summary of statistical analysis, according to the methodology proposed by Leite and Oliveira (2002), comparing infrared gas analyzer (IRGA) and alkali absorption (AA) methodologies for ECO₂ measurements in areas of Caatinga and pasture, in the municipality of São João, Pernambuco, Brazil

ECO2 average			Statistical tests
μmol m-2 s−1
Period/ Data	IRGA	AA	F(Ho)	tē	RYjY1≥ (1-|ē|)	Conclusion
Caatinga diurnal
All	2.22±1.34	1.03±0.10	9237.9*	7.256*	No	Yj ≠ Y1
Rainy	3.16±1.29	1.02±0.09	11158*	15.701*	Yes	Yj ≠ Y1
Dry	1.28±0.40	1.05±0.10	233.8*	1.895ns	No	Yj≠ Y1
Caatinga nocturnal
All	2.32±1.47	1.06±0.12	8096.9*	4.535*	No	Yj ≠ Y1
Rainy	3.47±1.21	1.10±0.14	5922.2*	15.605*	Yes	Yj ≠ Y1
Dry	1.18±0.44	1.02±0.09	299.5*	0.126ns	No	Yj ≠ Y1
Pasture diurnal
All	1.80±1.41	1.04±0.18	2142.1*	1.133ns	No	Yj ≠ Y1
Rainy	2.69±1.48	1.09±0.14	1663.1*	10.443*	No	Yj ≠ Y1
Dry	1.03±0.40	0.91±0.09	97.3*	2.662*	No	Yj ≠ Y1
Pasture nocturnal
All	1.77±1.16	1.06±0.13	4944.4*	2.112ns	No	Yj ≠ Y1
Rainy	2.55±1.15	1.14±0.15	3453.4*	6.041*	No	Yj ≠ Y1
Dry	1.06±0.44	0.98±0.10	465.6*	1.660ns	No	Yj ≠ Y1

• ^ns
  not significant;
• ^*
  significant at the 5 % probability level; Yj ≠ Y1: the AA methodology differs from the IRGA methodology.

Differences between CO₂ evolution during the day and the night may be caused by lower night temperatures, which would result in higher relative humidity and, consequently, higher soil microbial activity (Souto et al., 2009; Holanda et al., 2015). However, no differences were found between day and night in either of the two methods (Table 2). It is possible that, in this region, even at night, soil temperatures remain high enough not to cause effects different from those observed during the day. In addition, soil in the areas has a sandy texture, and its high macroporosity (Table 1) may lead to high diffusion and aeration.

These results confirm those of Correia et al. (2015) who measured ECO₂ using the AA method in Caatinga and pasture areas in the semiarid region of the state of Paraíba under climatic and soil conditions similar to those of the present study. Several authors (Souto et al., 2009; Correia et al., 2015; Holanda et al., 2015) report that high soil temperatures limit microbial activity in Caatinga soils because they reduce microbial populations and, consequently, reduce the intensity of organic residue decomposition and soil ECO₂.

Soil ECO₂ values determined by IRGA were two to four times higher than those by AA when measured during the rainy season, from April to August 2012, whereas in the dry season (September 2012 to January 2013), values obtained from both methods were similar (Table 2). Considering that ECO₂ varies as a function of climate and soil variables, such as rainfall, air temperature, and soil moisture and temperature (Davidson et al., 2000; Deng et al., 2012; Chen et al., 2014), it was expected that ECO₂ values measured by AA would change significantly from the wet to the dry season, as they did when measured by IRGA (Figure 1 and Table 2). However, the average values measured by AA were only 10 % higher in the rainy than in the dry season, whereas those measured by IRGA were more than 260 % higher in the rainy season. Therefore, the IRGA methodology is more sensitive to seasonal variations than the AA methodology. Martins et al. (2010) stated that higher water availability during the rainy season should result in higher biological activity. Therefore, higher ECO₂ should also have been registered during the rainy season in this area in the Agreste region of the state of Pernambuco, which was only observed using the IRGA methodology (Figure 1 and Table 2).

Measurements performed in other Caatinga areas (Souto et al., 2009; Correia et al., 2015; Holanda et al., 2015), all of them using the AA methodology, registered small (0.28 μmol CO₂ m^-2 s⁻¹) and high amplitudes (0.75 μmol CO₂ m^-2 s⁻¹) between maximum (1.00 to 1.32 μmol CO₂ m^-2 s⁻¹) and minimum values (0.57 to 0.72 μmol CO₂ m^-2 s⁻¹). It is not possible to know if the similarity of ECO₂ values throughout the year, reported by some of these articles, was due to absence of environmental variables that substantially altered root respiration and soil microbial activity, or if it was caused by a limitation of the methodology. Nonetheless, in this study, it is more likely that rainfall variation was enough to explain the high variations in ECO₂ obtained from the IRGA methodology.

Considering the extreme difference in water availability usually observed between the rainy and the dry season in the Northeastern semi-arid region, and the effect that this availability has on root and soil microbial activities (Souto et al., 2009), large differences in ECO₂ are expected. Therefore, a doubt arises as to whether AA measurements during the rainy season of the semiarid region were not underestimated. In other Brazilian regions where measurements were also made with the AA methodology (Assis Júnior et al., 2003; Valentini et al., 2015), including forest and Cerrado (Brazilian tropical savanna) areas, there are no cases of low amplitude in ECO₂ values. The highest values were at least 50 % higher than the lowest, but were as much as eight times higher.

Since no ECO₂ measurements made with IRGA in the Northeast region were found in the literature beyond those of the present study, comparisons are not possible. However, measurements in forest, Cerrado, and agricultural areas in other Brazilian regions (Sotta et al., 2004; Valentini et al., 2008; Panosso et al., 2009; Pinto-Junior et al., 2009; Zanchi et al., 2012) report higher amplitudes (2.51 to 6.16 μmol CO₂ m^-2 s⁻¹) between maximum (3.96 to 10.51 μmol CO₂ m^-2 s⁻¹) and minimum (1.45 to 4.35 μmol CO₂ m^-2 s⁻¹) values than those measured with the AA methodology. The smallest difference (about 50 %) occurred in a sugarcane field in Mato Grosso do Sul (Moitinho et al., 2013); all other maximum values were two to five times higher than the minimum values. These findings suggest that the IRGA methodology has higher sensitivity in detecting ECO₂ differences than the AA methodology. However, these are comparisons derived from few studies and under different climate and soil conditions, and it cannot be confirmed that the AA methodology was not adequate under the conditions in which it was used.

Several studies point to advantages in using the AA methodology: Assis Júnior et al. (2003) and Souto et al. (2009) highlighted that it is a simple, low cost, and sensitive method that can be applied simultaneously at many points and that can integrate CO₂ evolutions over many hours (even 24 hours), therefore reducing spatial and temporal variability. If it is not possible to use an IRGA, which requires equipment that is expensive, has high maintenance costs, and demands a specially trained operator, use of the AA methodology is justified. However, many authors, especially those working in other countries, have claimed that the AA methodology can underestimate ECO₂ values (Haynes and Gower, 1995; Jensen et al., 1996; Yim et al., 2003).

There are several explanations for these underestimations. The underestimations might result from the measuring chamber remaining in the same spot for a long time, which could disturb the micro-climate, demands the daily temperature amplitude (Jensen et al., 1996). However, Hendry et al. (2001) performed measurements for periods longer than 24 hours and reported values similar to those obtained from other methodologies. Absorption of CO₂ by the alkaline solution could also decrease over the measurement period due to low diffusion (Freijer and Bouten, 1991), the CO₂ concentration inside the chamber decreasing the diffusion gradient between the soil and air inside the chamber, reducing the flux absorbed by the alkaline solution. Under laboratory conditions, Freijer and Bouten (1991) observed that static absorption was not sufficient to react with all the respired CO₂, due to lack of contact between the solution and the entire CO₂ volume inside the chamber. One or more of these problems probably occurred in the present study since the ECO₂ values obtained by the AA methodology were underestimated and did not reflect the changes that happened from the dry to the rainy season. However, it is not possible to know for sure if those problems occurred during the present study. Considering that the methodology was applied in a way similar to other studies using AA, similar problems could have occurred in these other studies, which were not detected due to the absence of comparisons with other methodologies.

The IRGA methodology has been criticized because it does not integrate CO₂ fluxes over long time periods, as the AA methodology does, since measurements last only a few minutes (Davidson et al., 2002). To extend these periods, it would be necessary to perform a large number of measurements (Panosso et al., 2012). According to Perez-Quezada et al. (2016), this is possible, and despite the high cost of the equipment, the IRGA methodology has a better cost/benefit ratio than others.

CONCLUSIONS

Soil ECO₂ measurements with the infrared gas analyzer (IRGA) methodology were more sensitive to seasonal variations than those with the alkaline absorption (AA) methodology. Soil ECO₂ values measured with the IRGA were significantly higher during the rainy than the dry season, differing from those obtained from AA, which had little or no differences between seasons.

In sandy soils, under semiarid conditions, the IRGA methodology is recommended, since the AA methodology underestimates ECO₂ during periods of high CO₂ evolution.

ACKNOWLEDGMENTS

The authors of this paper thank the CNPq and Facepe for scholarships to students and research scientists and also for financial support through the following research grants: Ondacbc - National Observatory of Water and Carbon Dynamics in the Caatinga Biome (INCT-MCTI/CNPq/Capes/FAPs No.16/2014, Proc. 465764/2014-2); "Consolidation of the Research Center on Water and Carbon Dynamics in Ecosystems in the State of Pernambuco" (Edital 08/2014 Facepe Pronem, Proc. APQ-0532-5.01/14); "Consolidation of a research network on carbon stocks and flows in soils and vegetation in the Northeast Region of Brazil (Caatinga, Mata Atlântica, and Cerrado Biomes) and modeling of impacts associated with climate change and coverage and use" (Edital MCTI/CNPq/ANA No. 23/2015, Proc. 446137 / 2015-4); "Land use changes and effects in the carbon fluxes and evapotranspiration in watersheds of the state of Pernambuco" (Edital MCTI/CNPq/Universal 14/2014, Proc. 448504/2014-46); Water, CO₂, and energy fluxes in areas of Caatinga and grasslands in the Pernambuco semiarid region (Edital MCTI/CNPq/Universal 14/2014, Proc. 458227/2014-5), and also for the project "Data generation and modeling to support policies for adaptation to climatic variability in agricultural systems in the Northeast region" (CNPq Edital 37/2013 - Climatic Changes, Proc. 403129/2013-3).

REFERENCES

• Almeida AVL, Corrêa MM, Lima JRS, Souza ES, Santoro KR, Antonino ACD. Atributos físicos, macro e micromorfológicos de Neossolos Regolíticos no agreste meridional de Pernambuco. Rev Bras Cienc Solo. 2015;39:1235-46. https://doi.org/10.1590/01000683rbcs20140757
  » https://doi.org/10.1590/01000683rbcs20140757
• Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G. Köppen's climate classification map for Brazil. Meteorologische Zeitschrift. 2014;22:711-28. https://doi.org/10.1127/0941-2948/2013/0507
  » https://doi.org/10.1127/0941-2948/2013/0507
• Araujo KD, Dantas RT, Andrade AP, Parente HN. Cinética de evolução de dióxido de carbono em área de caatinga em São João do Cariri-PB. Rev Arvore. 2011;35:1099-106. https://doi.org/10.1590/S0100-67622011000600016
  » https://doi.org/10.1590/S0100-67622011000600016
• Assis Júnior SL, Zanuncio JC, Kasuya MCM, Couto L, Melido RCN. Atividade microbiana do solo em sistemas agroflorestais, monoculturas, mata natural e área desmatada. Rev Arvore. 2003;27:35-41. https://doi.org/10.1590/S0100-67622003000100005
  » https://doi.org/10.1590/S0100-67622003000100005
• Borges Júnior JCF, Anjos RJ, Silva TJA, Lima JRS, Andrade CLT. Métodos de estimativa da evapotranspiração de referência diária para a microrregião de Garanhuns, PE. Rev Bras Eng Agr Amb. 2012;16:380-90. https://doi.org/10.1590/S1415-43662012000400008
  » https://doi.org/10.1590/S1415-43662012000400008
• Bouma TJ, Bryla DR. On the assessment of root and soil respiration for soils of different textures: interactions with soil moisture contents and soil CO₂ concentrations. Plant Soil. 2000;227:215-21. https://doi.org/10.1023/A:1026502414977
  » https://doi.org/10.1023/A:1026502414977
• Chen S, Zou J, Hu Z, Chen H, Lu Y Global annual soil respiration in relation to climate, soil properties and vegetation characteristics: summary of available data. Agr Meteorol. 2014;198-199:335-46. https://doi.org/10.1016/j.agrformet.2014.08.020
  » https://doi.org/10.1016/j.agrformet.2014.08.020
• Correia KG, Araújo Filho RN, Menezes RSC, Souto JS, Fernandes PD. Atividade microbiana e matéria orgânica leve em áreas de caatinga de diferentes estágios sucessionais no semiárido paraibano. Rev Caatinga. 2015;28:196-202.
• Davidson EA, Savage K, Verchot LV, Navarro R. Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agr Meteorol. 2002;113:21-37. https://doi.org/10.1016/S0168-1923(02)00100-4
  » https://doi.org/10.1016/S0168-1923(02)00100-4
• Davidson EA, Verchot LV, Cattânio JH, Ackerman IL, Carvalho JEM. Effects of soil water content on soil respiration in forests and cattle pastures of eastern Amazonia. Biogeochemistry. 2000;48:53-69. https://doi.org/10.1023/A:1006204113917
  » https://doi.org/10.1023/A:1006204113917
• Deng Q, Hui D, Zhang D, Zhou G, Liu J, Liu S, Chu G, Li J. Effects of precipitation increase on soil respiration: a three-year field experiment in subtropical forests in China. PLoS ONE. 2012;7:e41493. https://doi.org/10.1371/journal.pone.0041493
  » https://doi.org/10.1371/journal.pone.0041493
• Donagema GK, Campos DVB, Calderano SB, Teixeira WG, Viana JHM, organizadores. Manual de métodos de análise do solo. 2a ed rev. Rio de Janeiro: Embrapa Solos; 2011.
• Freijer JI, Bouten WA. A comparison of field methods for measuring soil carbon dioxide evolution: experiments and simulation. Plant Soil. 1991;135:133-42. https://doi.org/10.1007/BF00014786
  » https://doi.org/10.1007/BF00014786
• Haynes BE, Gower ST. Belowground carbon allocation in unfertilized and fertilized red pine plantations in northern Wisconsin. Tree Physiol. 1995;15:317-25. https://doi.org/10.1093/treephys/15.5317
  » https://doi.org/10.1093/treephys/15.5317
• Hendry MJ, Mendoza CA, Kirkland R, Lawrence JR. An assessment of a mesocosm approach to the study of microbial respiration in a sandy unsaturated zone. Ground Water. 2001;39:391-400. https://doi.org/10.1111/j.1745-6584.2001.tb02323.x
  » https://doi.org/10.1111/j.1745-6584.2001.tb02323.x
• Holanda AC, Feliciano ALP, Marangon LC, Freire FJ, Holanda EM. Decomposição da serapilheira foliar e respiração edáfica em um remanescente de Caatinga na Paraíba. Rev Arvore. 2015;39:245-54. https://doi.org/10.1590/0100-67622015000200004
  » https://doi.org/10.1590/0100-67622015000200004
• Intergovernmental Panel on Climate Change - IPCC. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment Report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press; 2014.
• Ivo WMPM, Salcedo IH. Soil CO₂ flux: a method comparison of closed static chambers in a sugarcane field. Rev Bras Cienc Solo. 2012;36:421-6. https://doi.org/10.1590/S0100-06832012000200011
  » https://doi.org/10.1590/S0100-06832012000200011
• Janssens IA, Kowalski AS, Longdoz B, Ceulemans R. Assessing forest soil CO2 efflux: an in situ comparison of four techniques. Tree Physiol. 2000;20:23-32. https://doi.org/10.1093/treephys/20.L23
  » https://doi.org/10.1093/treephys/20.L23
• Jensen LS, Mueller T, Tate KR, Ross DJ, Magid J, Nielsen NE. Soil surface CO₂ flux as an index of soil respiration in situ. A comparison of two chamber methods. Soil Biol Biochem. 1996;28:1297-306. https://doi.org/10.1016/S0038-0717(96)00136-8
  » https://doi.org/10.1016/S0038-0717(96)00136-8
• Leite HG, Oliveira FHT. Statistical procedure to test identity between analytical methods. Commun. Soil Sci Plant Anal. 2002;33:1105-18. https://doi.org/10.1081/CSS-120003875
  » https://doi.org/10.1081/CSS-120003875
• Martins cm, Galindo ICL, Souza ER, Poroca HA. Atributos químicos e microbianos do solo de áreas em processo de desertificação no semiárido de Pernambuco. Rev Bras Cienc Solo. 2010;34:1883-90. https://doi.org/10.1590/S0100-06832010000600012
  » https://doi.org/10.1590/S0100-06832010000600012
• Medeiros EV, Notaro KA, Barros JA, Moraes WS, Silva AO, Moreira KA. Absolute and specific enzymatic activities of sandy entisol from tropical dry forest, monoculture and intercropping areas. Soil Till Res. 2015;145:208-15. https://doi.org/10.1016/j.still.2014.09.013
  » https://doi.org/10.1016/j.still.2014.09.013
• Moitinho MR, Padovan MP, Panosso AR, La Scala Junior N. Efeito do preparo do solo e resíduo da colheita de cana-de-açúcar sobre a emissão de CO₂ Rev Bras Cienc Solo. 2013;37:1720-8. https://doi.org/10.1590/S0100-06832013000600028
  » https://doi.org/10.1590/S0100-06832013000600028
• Panosso AR, Marques Junior J, Milori DMBP, Ferraudo AS, Barbieri DM, Pereira GT, La Scala Junior N. Soil CO₂ emission and its relation to soil properties in sugarcane areas under Slash-and-burn and Green harvest. Soil Till Res. 2012;111:190-6. https://doi.org/10.1016/j.still.2010.10.002
  » https://doi.org/10.1016/j.still.2010.10.002
• Panosso AR, Marques Junior J, Pereira GT, La Scala Junior N. Spatial and temporal variability of soil CO₂ emission in a sugarcane area under green and slash-and-burn managements. Soil Till Res. 2009;105:275-82. https://doi.org/10.1016/j.stiM.2009.09.008
  » https://doi.org/10.1016/j.stiM.2009.09.008
• Perez-Quezada JF, Brito CE, Cabezas J, Galleguillos M, Fuentes JP, Bown HE, Franck N. How many measurements are needed to estimate accurate daily and annual soil respiration fluxes? Analysis using data from a temperate rainforest. Biogeosciences. 2016;13:6599-609. https://doi.org/10.5194/bg-13-6599-2016
  » https://doi.org/10.5194/bg-13-6599-2016
• Pinto-Junior OB, Sanches L, Dalmolin AC, Nogueira JS. Efluxo de CO₂ do solo em floresta de transição Amazônia Cerrado e em área de pastagem. Acta Amazon. 2009;39:813-22. https://doi.org/10.1590/S0044-59672009000400009
  » https://doi.org/10.1590/S0044-59672009000400009
• Raich JW, Potter CS, Bhagawati D. Interannual variability in global soil respiration, 1980-94. Global Change Biol. 2002;8:800-12. https://doi.org/10.1046/j.1365-2486.2002.00511.x
  » https://doi.org/10.1046/j.1365-2486.2002.00511.x
• Rochette P, Ellert B. Description of a dynamic closed respiration chamber of measuring soil respiration and its comparison with other techniques. Can J Soil Sci. 1991;77:195-203. https://doi.org/10.4141/S96-110
  » https://doi.org/10.4141/S96-110
• Ryan MG, Law BE. Interpreting, measuring, and modeling soil respiration. Biogeochemistry. 2005;73:3-27. https://doi.org/10.1007/s10533-004-5167-7
  » https://doi.org/10.1007/s10533-004-5167-7
• Santos JCB, Souza Júnior VS, Corrêa MM, Ribeiro MR, Almeida MC, Borges LEP. Caracterização de Neossolos Regolíticos da região semiárida do estado de Pernambuco. Rev Bras Cienc Solo. 2012;36:683-96. https://doi.org/10.1590/S0100-06832012000300001
  » https://doi.org/10.1590/S0100-06832012000300001
• Silva RAB, Lima JRS, Antonino ACD, Gondim PSS, Souza ES, Barros Júnior G. Balanço hídrico em Neossolo Regolítico cultivado com braquiária (Brachiaria decumbens Stapf). Rev Bras Cienc Solo. 2014;38:147-57. https://doi.org/10.1590/S0100-06832014000100014
  » https://doi.org/10.1590/S0100-06832014000100014
• Soil Survey Staff. Keys to soil taxonomy. 12th ed. Washington, DC: United States Department of Agriculture, Natural Resources Conservation Service; 2014.
• Sotta ED, Meier P, Malhi Y, Nobre AD, Hodnett M, Grace J. Soil CO₂ efflux in a tropical forest in central Amazon. Global Change Biol. 2004;10:601-17. https://doi.org/10.1111/j.1529-8817.2003.00761.x
  » https://doi.org/10.1111/j.1529-8817.2003.00761.x
• Souto PC, Bakke IA, Souto JS, Oliveira VM. Cinética da respiração edáfica em dois ambientes distintos no semi-árido da Paraíba, Brasil. Rev Caatinga. 2009;22:52-8.
• Valentini CMA, Abreu JG, Faria RAPG. Respiração do solo como bioindicador em áreas degradadas. Rev Int Cienc. 2015;5:127-43. https://doi.org/10.12957/ric.2015.19581
  » https://doi.org/10.12957/ric.2015.19581
• Valentini CMA, Sanches L, Paula SR, Vourlitis GL, Nogueira JS, Pinto Junior OB, Lobo FA. Soil respiration and aboveground litter dynamics of a tropical transitional forest in northwest Mato Grosso, Brazil. J Geophys Res. 2008;113:G00B10. https://doi.org/10.1029/2007JG000619
  » https://doi.org/10.1029/2007JG000619
• Yim MH, Joo SJ, Nakane K. Comparison of field methods for measuring soil respiration: a static alkali absorption method and two dynamic closed chamber methods. For Ecol Manage. 2002;170:189-97. https://doi.org/10.1016/S0378-1127(01)00773-3
  » https://doi.org/10.1016/S0378-1127(01)00773-3
• Yim MH, Joo SJ, Shutou K, Nakane K. Spatial variability of soil respiration in a larch plantation: estimation of the number of sampling points required. Forest Ecol Manag. 2003;175:585-8. https://doi.org/10.1016/S0378-1127(02)00222-0
  » https://doi.org/10.1016/S0378-1127(02)00222-0
• Zanchi FB, Waterloo MJ, Kruijt B, Kesselmeier J, Luizão FJ, Manzi AO, Dolman AJ. Soil CO₂ efflux in central Amazonia: environmental and methodological effects. Acta Amazon. 2012;42:173-84. https://doi.org/10.1590/S0044-59672012000200001
  » https://doi.org/10.1590/S0044-59672012000200001
• Zheng X, Zhao C, Peng S, Jian S, Liang B, Wang X, Yang S, Wang C, Peng H, Wang Y Soil CO₂ efflux along an elevation gradient in Qinghai spruce forests in the upper reaches of the Heihe River, northwest China. Environ Earth Sci. 2014;71:2065-76. https://doi.org/10.1007/s12665-013-2608-4
  » https://doi.org/10.1007/s12665-013-2608-4

Publication Dates

• Publication in this collection
  2018

History

• Received
  12 Dec 2016
• Accepted
  14 June 2017