Forest soil CO2 emission in Quercus robur level II monitoring site

Galić Zoran; Velisav Karaklić; Slobodan B. Marković; Alen Kiš; Miljan Samardžić

doi:10.1515/geo-2022-0723

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Forest soil CO₂ emission in Quercus robur level II monitoring site

Galić Zoran , Velisav Karaklić , Slobodan B. Marković , Alen Kiš and Miljan Samardžić

Published/Copyright: November 11, 2024

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From the journal Open Geosciences Volume 16 Issue 1

Abstract

In this study, the soil CO₂ emission was analysed at the level II ICP Forests monitoring plot in Serbia in the pedunculate oak forest. Two plots of pedunculate oak (Quercus robur L.) were selected for this study. The main question was to determine the differences in the impact of management (human impact) on CO₂ emission. Different time periods were compared to identify the main factors affecting soil CO₂ emission. Sampling was done by chambers. During the study period, climate indicators were quite different. A strong positive correlation between the soil temperature and soil CO₂ emission, as well as a strong negative correlation between the soil moisture and soil CO₂ emission, was found in the spring aspect (Plot). In other cases, a moderate to weak correlation was found. Multiple linear regressions showed that CO₂ emission from soil was primarily controlled by soil moisture. Increasing soil water content had a positive effect on soil respiration (except in spring). The effect of soil temperature appeared in the multiple regressions as a secondary factor during the period studied, and an increase in temperature resulted in a decrease in soil respiration (except in spring).

Keywords: soil CO2 emission; pedunculate oak; level II forest monitoring

1 Introduction

Carbon dioxide (CO₂) is recognised as one of the most important greenhouse gases (GHGs) [1,2,3,4]. On the other hand, forest ecosystems play a crucial role in the global carbon cycle and help reduce the negative impacts of ongoing climate change [5] with different forest plantations on global and regional levels [6,7,8]. Forest soils are part of the largest terrestrial ecosystem with a carbon pool [9], where the amount of soil organic carbon is estimated at an enormous amount of 3,000 Pg [10]. As the most important drivers of GHG emissions from soils, the soil temperature, soil water content, nutrients (C/N-ratios), soil pH value, land use, land cover, type and age of vegetation, local and regional climate, and hydrology were determined [11]. At the microsite level, soil temperature and soil water content are the most dominant factors that affect CO₂ emissions from soils, where one of these drivers can be more influential than another [12,13,14,15,16,17].

In the context of storage and emission of CO₂ from forests, forest store carbon and release CO₂ can be noted. Changes in the balance between these two processes can lead to changes in the sequestration or emission of CO₂ from forests to the atmosphere [18]. Management measures in forests can induce a change in the above-ground carbon stock and change in canopy coverage, which is reflected in changes in the soil temperature and moisture [19,20].

The International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) is the largest international forest monitoring system, established in 1985 under the United Nations Economic Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution (Air Convention, formerly CLRTAP). ICP Forests monitors forest conditions at two levels of monitoring: Level I monitoring is based on 5,624 observation plots, a systematic transnational grid of 16 km × 16 km across Europe and beyond, to provide insights into geographic and temporal variations in forest conditions. The level II intensive monitoring covers 561 plots (as of 2020) in selected forest ecosystems with the goal of clarifying cause–effect relationships with a review of the need for control and monitoring of air quality [21]. Although it is the largest forest monitoring system, it does not provide monitoring of CO₂ from soil (Figure 1).

Figure 1

Position of experimental plots.

Pedunculate oak (Quercus robur L.) forests in lowlands are hydrologically related forests. They usually occur as mixed forests with ash, hornbeam and Austrian oak, depending on the groundwater level. A typical association of pedunculate oak is Fraxino angustifoliae-Quercetum roboris Jov. et Tom. 1979, Genisto elatae-Quercetum roboris Horv. 1938, and Carpino-Fraxino-Quercetum roboris Miš. et Broz 1962. From previous studies, it appears that pedunculate oak tree dieback is related to medium-age, older, and old pedunculate oak stands and that the highest dieback intensity is related to forest communities of pedunculate oak trees in a lower stands position (with narrow-leaved ash) [22].

The aim of this study is to determine the soil CO₂ emission in pedunculate oak forests at the level II monitoring plot. The fundamental question posed in the study is how soil CO₂ emission is affected by climate conditions and some extremes; in addition, it compares the forest ecosystem with small human influence with the normally managed forest ecosystem. During the research period, we had the opportunity to conduct the study under very different climatic conditions. The actuality of this topic can be compared with the EU Horizon projects; on the one hand, the segment objective of the project is to harmonize the monitoring framework for estimating carbon and GHG fluxes (Holisoil project), and on the other hand, the aim is to report GHG emissions from forests (Pathfinder – An integrated forest monitoring and pathway assessment systems for the EU). In both cases, the forest as a system is required to monitor the progress towards targets and continuously adjust and adapt as new information becomes available. In this context, our study has several goals. The first goal is to obtain the first data for the pedunculate oak. The long-term goal is to establish monitoring of CO₂ emissions in forest ecosystems (Figure 2).

Figure 2

Soil respiration in a 2-year period (g m⁻² day⁻¹).

2 Methods

The study was conducted in the Bačka region, Autonomous Province of Vojvodina, Republic of Serbia (45°27′N, 19°10″E) during 2021 and 2022. The experimental plots were located within the forest monitoring ICP Forests Level II. This ICP Forests Level II site is a pedunculate oak stand in a non-flooded zone managed by the Public Enterprise “Vojvodinašume.” In the Republic of Serbia, the ICP Forests level II monitoring plots were established in five sites. This ICP Forests II level is an intensive monitoring site. The monitoring of soil CO₂ emission refers only to the sites where the Institute of Lowland Forestry and Environment in Novi Sad collects data.

Two plots of pedunculate oak (Quercus robur L.) were selected for this study (45°27´N and 19°10´E). The area of research on pedunculate oak stands is divided into two parts. The first part was not under management (Plot – the ICP Forests level II), while the other part of the stand was under management measures (Outside plot – outside ICP Forests level II). In both cases, tree dieback was observed. In the managed area (Outside plot), greater openness of the canopy was observed. The distance between the experimental plots was about 50 m. Since the plots were close to each other, the microclimatic conditions were similar. Soil temperature and soil water content values were measured during air sampling. During the air sampling, soil temperature was measured using a soil thermometer at a depth of 5 cm. Soil moisture content was determined by the gravimetric method. The soil samples were taken and put into aluminium tins. Afterwards, the samples were dried to constant weight in the oven at temperatures between 103 and 105°C.

The average monthly temperature and precipitation were obtained from the nearest weather station (Sombor) for the study period (https://www.hidmet.gov.rs/). A comparison of climatic conditions was performed using the FAI index [23]. According to the WRB classification [24], the soil is defined as mollic gleisoil (Figure 3).

Figure 3

Influence of soil temperature and moisture on CO₂ emission – summer months.

The methodology is described in detail in the study of Karaklić et al. [17]. This methodology is similar to that described in the study of Guo et al. [4]. In the methodology used, the following aspects were considered: contribution of autotrophic soil respiration to total soil respiration was considered as a function of season [17], period for stabilization of emissions after soil disturbance [25,26], and sampling intervals [17,27,28].

The collected samples were analysed using an Agilent 8890 gas chromatograph (Agilent Technologies, Santa Clara, CA, USA). CO₂ emission was calculated for each plot using the formula based on the linear increase in the concentration of gas in closed chambers during the sampling period [4,27]. Average values of CO₂ emission were obtained based on the emission values of five chambers in each plot. The obtained values of emission are expressed in g m⁻² per day [29].

Statistical analysis of the obtained data was performed using the Statistica 12 and Excel program package.

3 Results

3.1 Soil respiration

The climatic FAI indicator shows that the years 2021 and 2022 are very different (5.05 in 2021 versus 14.17 in 2022). This value indicates that in 2021, we have climatic conditions suitable for hornbeam-oak forests and in 2022 for forest-steppe (Figure 4).

Figure 4

Influence of soil temperature and moisture on CO₂ emission – 2-year period.

The soil respiration values in Plot and Outside plot ranged from 1.71 to 23.41 and from 0.70 to 26.49 g m⁻² day⁻¹, respectively. The CO₂ emissions in the 2 years in the two plots do not show significant differences in dynamics (Table 1).

Table 1

Soil temperature/CO₂ emission – correlation coefficient

Soil temperature/CO₂ emission	Plot	Outside plot
Two-year period	0.072999	−0.10153
Spring	0.900548	0.406491
Summer	−0.29639	−0.29664

3.2 Influence of soil temperature and soil moisture on soil respiration

The correlation coefficient for the relationship between the soil temperature and soil respiration in the 2-year period shows a very low correlation. During the same period, the correlation between soil moisture and soil respiration was moderate (Figure 5).

Figure 5

Correlation of soil temperature and moisture on CO₂ emission – 2-year period.

In spring, we found a very strong positive correlation between the soil temperature and soil respiration in the Plot case. At the same time, a very negative relationship was found between soil moisture and soil respiration. In the case of the Outside plot, we found a moderate correlation between temperature and soil moisture. Analyzing the differences, we found that the highest difference in soil respiration was observed at the beginning of the year (data collection in spring), and the rate ranged from 6.77 to 10.95 g m⁻² day ⁻¹. The main factor was temperature, and two cases were identified. In the first case, the temperature was low, and significantly higher soil respiration was detected in the Outside plot. In the opposite case, when the temperature was very high, greater soil respiration was detected in the Plot. In summer, a moderate influence of soil respiration and soil moisture on soil respiration was detected in both cases (Plot and Outside plot) (Table 2).

Table 2

Soil moisture %mass/CO₂ emission – correlation coefficient

Soil moisture %mass/CO₂ emission	Plot	Outside plot
Two-year period	0.403279	0.459066
Spring	−0.90908	−0.54777
Summer	0.534687	0.56897

A simple linear regression in spring on the site plot shows that increasing soil temperature (Y = −1.967 + 1.039St) and decreasing soil moisture (Y = 91.097 − 3.518Sw) had the largest effects on CO₂ respiration. In the analysis of other simple linear regressions, such a high value of the coefficient of determination was not found. The coefficient of determination value for other cases ranged from R ² = 0.010 to 0.32.

Multiple linear regression models as a function of soil temperature and moisture show that the best multiple linear regression model was found for the Plot in spring (R ² = 0.898; Y = 49.851 + 0.544St − 2.013Sw). The coefficient of determination values for the other cases ranged from R ² = 0.248 to 0.324 (Table 3).

Table 3

Simple linear regression – soil temperature and moisture

		Plot		Outside plot
			R ²		R ²
Spring	St	Y = −1.967 + 1.039St	0.811	Y = −6.412 + 0.479St	0.165
Spring	Sw	Y = 91.097 − 3.518Sw	0.826	Y = 61.387 − 2.166Sw	0.301
Summer	St	Y = −13.893 − 0.347St	0.088	Y = 18.543 − 0.547St	0.088
Summer	Sw	Y = 2.323 + 0.299Sw	0.285	Y = −0.045 + 0.501Sw	0.323
Two-year period	St	Y = 6.381 + 0.717St	0.005	Y = 9.623 − 0.1154St	0.010
Two-year period	Sw	Y = 2.372 + 0.351Sw	0.163	Y = 0.269 + 0.463Sw	0.211

St – soil temperature; Sw – soil water content.

Multiple linear regression showed that CO₂ emission from soil was primarily controlled by soil moisture. Increasing soil water content had a positive effect on soil respiration (except in spring). The effect of soil temperature appeared in the multiple regressions as a secondary factor during the period studied. An increase in the soil temperature resulted in decreased soil respiration (except in spring) (Table 4).

Table 4

Multiple linear regression – soil temperature and moisture

	Plot		Outside plot
		R ²		R ²
Spring	Y = 49.851 + 0.544St − 2.013Sw	0.898	Y = 73.858 − 0.165St − 2.621Sw	0.306
Summer	Y = 3.079 − 0.029St + 0.292Sw	0.286	Y = 0.0025 − 0.00186St + 0.501Sw	0.324
Two-year period	Y = −9.555 + 0.439St + 0.573Sw	0.298	Y = −7.012 + 0.268St + 0.598Sw	0.248

4 Discussion

During the study period, climate indicators were quite variable, so the discussion is divided into two parts. In the first part, the discussion refers to the spring months, and in the second part, to the summer months. Proximal drivers that influence soil emissions in the direct environment (e.g., local climate) were discussed [11,30]. Also, in the discussion, we used the most important drivers of the FAI index as well as the drivers with the greatest importance for the CO₂ emission from soil.

4.1 Climatic conditions

Prior to the analysis by time period, the analysis of the 2-year period first defines the conditions under which the research was conducted. According to the FAI index, hornbeam-oak forests develop that are wetter than forest steppes. According to the FAI index [23], we determined the climatic conditions for the development of hornbeam-oak forests in 2021 and forest steppe in 2022. This means that climatic conditions in 2021 were wetter (more humid).

4.2 Soil respiration

During the 2-year research period, no correlation between the soil temperature and CO₂ emission from soil was observed in either of the experimental plots. In this study, a very weak positive correlation for Plot and a very weak negative correlation for Outside plot were found. However, the correlation for the influence of soil moisture on CO₂ emission is moderately positive in both cases (Plot and Outside plot). A multiple linear regression over a 2-year period shows that both temperature and soil moisture have a positive influence on CO₂ emission (Plot: Y = −9.555 + 0.439St + 0.573Sw; Outside plot: Y = −7.012 + 0.268St + 0.598Sw), although different climatic factors were determined.

The most important difference is noted in the soil temperature in the first part of the growing season. Sudden increases in spring temperature in a more humid year (May 2021) resulted in higher rates of CO₂ emission from the soil and generally affected soil respiration by altering soil microbial activities [4,31,32]. During the studied period, higher CO₂ emissions from soil were suddenly observed in the Plot in spring, which can be explained by a denser canopy compared to the Outside plot. The statistical simple linear regression in spring on the site plot (Y = −1.967 + 1.039St) supports the previous statement. Another case for the Outside plot does not show strong statistical dependence (R ² 0.165). Soil moisture as the second driver of CO₂ emission in spring on the Plot shows a strong statistical dependence, indicating that decreasing soil moisture (Y = 91.097 − 3.518Sw) has the greatest impact on CO₂ respiration.

In the summer period, the most important factor was higher soil moisture. In both cases (Plot and Outside plot), it was found that the correlation between soil CO₂ emission and soil moisture content was moderate. The correlation between soil temperature and soil CO₂ emission during the summer months was weakly negative. We assume that the main cause for these results is the great climatic differences during the growing season between 2 years [31,33]. In summer, multiple regression in both cases shows that an increase in temperature leads to a decrease in CO₂ emission from the soil, while an increase in soil moisture leads to an increase in CO₂ emission from the soil [13]. In other words, we observed a positive correlation between CO₂ emissions from soil due to rainfall events and soil drought constraints [34].

If we consider climate change into account (heavy rains, large fluctuations in air temperature), we should expect higher temperatures and less precipitation in the future. In this case, the results show trends that can be expected in the future. Increased soil temperatures in a managed forest will have in future less effect on the increase of soil CO₂ emission in spring than in an unmanaged forest. If the forest dieback continues, the forest stands will open more and more. This can lead to a further loss of soil moisture and thus affect the soil CO₂ emission in this pedunculate oak forest. In the second case, extremes (low temperatures in May and very high at the beginning of June) can lead to high activity of soil microorganisms and, thus, high CO₂ emissions from the soil in a short period of time. In summer, the mentioned differences could be lost due to increasingly pronounced dry periods with heavy rainfall. Finally, the Birch effect could decrease with higher frequencies of wet–dry cycles [35].

5 Conclusions

In this study, we examined the soil CO₂ emissions in different periods in a pedunculate oak forest and investigated their connections with the proximal environmental variables (soil temperature and soil moisture). Our results during the 2-year period indicate that various soil CO₂ emissions were determined. The differences in soil CO₂ emission were statistically significant in only one period – spring in the Plot site. Other research periods indicate moderate and weak statistical dependence. Sudden increases in the spring temperature in a more humid year resulted in higher rates of CO₂ emission from the soil. The correlation between soil temperature and soil CO₂ emission during the summer months was weakly negative. We assume that the main cause for these results is the significant climatic differences during the growing season between 2 years.

Heavy rains and large fluctuations in air temperature in a short period of time are visible consequences of climate change. As such, they affect the soil CO₂ emission, and a part of the observed differences is described in this article.

As mentioned Section 1, the data are presented for continuous adjustment to new monitoring data of CO₂ emission. Data are related to only one ICP Forests level II monitoring plot, and it should be extended to all points of this monitoring in Serbia. In this way, data would also be obtained for other tree species represented by this monitoring. Subsequently, research should be carried out to study the impact of various management measures on CO₂ emissions in forests. In this way, it would be possible to observe GHG emissions in forests in Serbia.

Two years of research is not enough to define trends only to describe the need for further research. According to this study, for pedunculate oak, it is necessary that the forest stands to be as less open as possible. Possible canopy openings can lead to a further loss of soil moisture and thus affect the soil CO₂ emission in this pedunculate oak forest. On the other hand, extremes in spring can lead to high activity of soil microorganisms and, thus high CO₂ emissions from the soil in a short time period.

baca.markovic@gmail.com

Acknowledgements

This study was supported by the Ministry of Science, Technological Development and Innovation Project No. 451-03-66/2024-03/200197. We acknowledge the PE Vojvodinasume for conducting research in the area where they manage forests.

Author contributions: Z.G. and M.S. conceived and designed the experiments; Z.G. and V.K. performed the field experiments; and Z.G. analysed the data; Z.G., M.S., S.M., A.K. and V.K. wrote the paper. All authors have read and agreed to the published version of the manuscript.
Conflict of interest: The authors declare no conflict of interest.
Data availability statement: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2024-03-22

Revised: 2024-09-03

Accepted: 2024-09-25

Published Online: 2024-11-11

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0723

Keywords for this article

soil CO2 emission; pedunculate oak; level II forest monitoring

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