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Article

Long-Term Effects of Biochar Application on Soil Heterotrophic Respiration in a Warm–Temperate Oak Forest

by
Shinpei Yoshitake
1,*,
Kakuya Enichi
2,
Yuki Tsukimori
2,
Toshiyuki Ohtsuka
3,
Hiroshi Koizumi
1 and
Mitsutoshi Tomotsune
4
1
Faculty of Education and Integrated Arts and Sciences, Waseda University, 2-2 Wakamatsucho, Shinjuku, Tokyo 162-0056, Japan
2
Faculty of Science and Engineering, Waseda University, 2-2 Wakamatsucho, Shinjuku, Tokyo 162-0056, Japan
3
Institute for Advanced Study, Gifu University, 1-1 Yanagito, Gifu 501-1193, Japan
4
College of Agriculture, Tamagawa University, 6-1-1 Tamagawagakuen, Machida, Tokyo 194-8610, Japan
*
Author to whom correspondence should be addressed.
Forests 2025, 16(3), 489; https://doi.org/10.3390/f16030489 (registering DOI)
Submission received: 6 February 2025 / Revised: 5 March 2025 / Accepted: 7 March 2025 / Published: 11 March 2025
(This article belongs to the Special Issue Functions of Biochar in Carbon Emission and Carbon Sequestration in Forest Ecosystems, 2nd Edition)
Browse Figures
Figure 1
<p>Temporal changes in (<b>a</b>) soil respiration rate (<span class="html-italic">R</span><sub>S</sub>) and (<b>b</b>) heterotrophic respiration rate (<span class="html-italic">R</span><sub>H</sub>) in the plots with or without biochar. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Values are means (<span class="html-italic">n</span> = 3–4).</p> "> Figure 2
<p>Temporal changes in (<b>a</b>) soil temperature and (<b>b</b>) soil moisture (volumetric soil water content) recorded during the respiration measurements in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Values are means (<span class="html-italic">n</span> = 3–4).</p> "> Figure 3
<p>Soil pH in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Bars and error bars indicate the mean ± SD (<span class="html-italic">n</span> = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, <span class="html-italic">p</span> &lt; 0.05).</p> "> Figure 4
<p>Soil microbial biomass carbon determined with the adenosine triphosphate method in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Bars and error bars represent the mean ± SD (<span class="html-italic">n</span> = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, <span class="html-italic">p</span> &lt; 0.05). n.d., not determined.</p> "> Figure 5
<p>(<b>a</b>) Annual soil respiration (SR) and (<b>b</b>) annual heterotrophic respiration (HR) in the plots with or without biochar from the second to the eighth year. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Bars and error bars represent the mean ± SD (<span class="html-italic">n</span> = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, <span class="html-italic">p</span> &lt; 0.05). n.d., not determined.</p> ">
Versions Notes

Abstract

:
Biochar application as a soil amendment is gaining attention as a stable, long-term carbon sequestration strategy for the mitigation of climate change. However, biochar applied to the soil may increase soil carbon efflux. This study aimed to determine the long-term (8 years) effects of biochar application to the forest floor on soil carbon effluxes (soil respiration [SR] and heterotrophic respiration [HR]) in a warm–temperate oak forest. Biochar was applied at the rate of 0, 5, or 10 Mg ha−1 to 20 m × 20 m plots (n = 4). The SR and HR rates were determined using the closed chamber method and the trenching method. The annual SR tended to increase over 8 years following biochar application, whereas a significant increase in the annual HR (+31%–37%) was observed in the short term (<3 years). The increased HR likely included CO2 emissions from the decomposition of the labile fraction of biochar carbon and from the microbial decomposition of the original soil organic matter stimulated through changes in the soil physicochemical environment, such as soil moisture and pH. The results suggest that a short-term increase in HR should be considered in the evaluation of carbon sequestration in response to biochar addition to forest ecosystems.

1. Introduction

Biochar is charcoal produced through the pyrolysis of biomass (e.g., wood, leaves, rice husks, or manure) under oxygen-limited conditions [1,2]. Because it is carbon-enriched and more recalcitrant to microbial decomposition compared with the original organic matter, it can remain in the soil for 100–1000 years [3,4]. Therefore, biochar has been considered to be a useful material for carbon sequestration and thus the mitigation of climate change through the reduction of the atmospheric CO2 concentration [1]. Additionally, biochar has been used as a fertilizer to improve soil qualities and increase crop production for thousands of years [5] because it has many beneficial properties, such as a porous structure, alkalinity, and a rich mineral content [6]. As a result, the extent of biochar application to agricultural lands continues to increase, and considerable knowledge on the effects of biochar application on crops and agricultural soils has been accumulated [7,8,9]. In contrast, few studies have applied biochar to forest ecosystems and hence the response to biochar application is poorly understood. However, given that forests have a high productivity and occupy vast areas of terrestrial ecosystems, they may be an effective target for carbon sequestration using biochar application.
When biochar is used for carbon sequestration in natural ecosystems, it is important to know how biochar affects the decomposition of organic matter. If biochar accelerates the decomposition of the organic matter originally present at the site, it will cause additional CO2 emissions, which will reduce the expected carbon sequestration capacity. Forest ecosystems typically develop a layered soil structure with an organic soil layer (O or A0 horizon) uppermost and biochar is usually spread on the soil surface (not tilled) in forests [10,11,12] owing to operational constraints. This environment differs markedly from that of agroecosystems where biochar is usually mixed with the soil through tillage. Therefore, the results obtained in the previous studies conducted in agroecosystems cannot be simply applied to forests and data must be accumulated on the forest ecosystems. The impact of biochar application on organic matter decomposition may be greater in the forests, where the biochar directly enters the soil layer rich in organic carbon (O layer) and the soil layer structure is maintained.
Organic matter decomposition in a field setting is often evaluated as CO2 efflux from the soil surface, which is termed soil respiration (SR). Reported changes in SR in response to biochar application to the forest floor have been inconsistent, i.e., either a decrease [13], increase [14,15], or no change [16,17] in SR has been observed. Most such studies have been conducted in coniferous forests (boreal and subtropical forests) or bamboo forests, with few examples in temperate, deciduous, broad-leaved forests. Since the impact of biochar on the forest carbon cycle may vary by climatic zone and forest type, it is necessary to accumulate data on a variety of forest types. Additionally, SR includes not only heterotrophic respiration by microorganisms and soil fauna that decompose organic matter, but also autotrophic respiration by plant roots [18]. These respiration types respond differently to environmental factors [19], which complicates the elucidation of the effect of biochar application on organic matter decomposition [20]. Furthermore, the effects of biochar on SR may change over time. To date, information on the SR response after biochar application in forest ecosystems has mainly been obtained in short-term experiments (≤2 years) [11,15,17,21]. Therefore, there is considerable uncertainty regarding the long-term effects of biochar application on organic matter decomposition in forest ecosystems.
The broad objective of this study is to clarify the effect of biochar application on the soil CO2 efflux (especially HR) in a forest ecosystem, which requires a long-term field experiment with the separation of HR from SR. We hypothesized that biochar application would increase HR over time and aimed to determine: (1) how SR and HR change with biochar application; (2) the breakdown and mechanism of HR change; and (3) how these effects change over time. This study was conducted in a warm–temperate, deciduous, broad-leaved forest using a trenching method over an 8-year period.

2. Materials and Methods

2.1. Study Site

This study was conducted in a secondary deciduous broad-leaved forest in Honjo City, Saitama Prefecture, Japan (36°12′ N, 139°10′ E, ca. 100 m a.s.l.). The study area is classified as warm–temperate with an annual mean temperature of 15.0 °C and a total annual precipitation of 1286 mm (from 1981 to 2010). The forest was dominated by oak (Quercus serrata Thunb. ex Murray) of approximately 20 m in height with an understory of evergreen dwarf bamboo (Pleioblastus chino (Franch. et Sav.) Makino) less than 1 m in height (Figure S1a). The total basal area of this forest in 2015 was 25.2 m2 ha−1, of which Q. serrata comprised approximately 85% (Table S1). The tree density and aboveground biomass of this forest were 765 stems ha−1 and 167 Mg ha−1, respectively. A more detailed description of the forest structure is provided by Ohtsuka et al. [22]. The soil of this area was originally derived from alluvion volcanic ash and is classified as an Alic Hapludand according to US taxonomy (Figure S1b; Table S2) [23].

2.2. Experimental Plots

A total of 12 experimental plots (20 m × 20 m) were established on the same slope in the forest in November 2015. The plots were randomly assigned to the three treatments (C0, C5, and C10), to which a different amount of biochar was applied (0 [control], 5, and 10 Mg ha−1, respectively) with four replicates (n = 4). Commercial biochar (particle size < 5 mm), which was produced by pyrolyzing broad-leaved and coniferous wood chips at 600–700 °C (Shiratori Super MOKUTAN, grade C; Shiratori Mokuzai Kakoh Cooperative Society, Gifu, Japan), was spread manually on the surface of the organic soil layer on the forest floor in late November 2015. When spreading the biochar, each plot was divided into smaller areas for convenience, and the biochar corresponding to that area was applied carefully, taking as much care as possible to ensure uniformity. The volatile carbon, fixed carbon, and ash contents of the biochar accounted for approximately 30%, 59%, and 11%, respectively. In addition, the carbon and nitrogen contents were 71% and 0.78%, respectively.

2.3. Measurement of Soil Respiration

The SR rate (RS) was measured each month in principle from April 2016 to December 2023 using the closed chamber method [24]. In some years, the frequency of measurements during the winter season was reduced because the biological activity was expected to be lower. In addition, measurements were skipped during March–May 2020 because the field survey could not be conducted due to the spread of the coronavirus infection in Japan. Initially, 16 soil collars (21 cm in diameter, 7 cm in height) were installed in each plot but the number was reduced to 8 from May 2019. As described below, one of these collars was assigned to the trenching subplot. At each measurement, each soil collar was covered by a chamber (21 cm in diameter, 7 cm in height) equipped with a portable infrared gas analyzer (GMP343, VAISALA, Helsinki, Finland) and changes in CO2 concentrations within the chamber were recorded at 30-second intervals for 5 min. The RS (mg CO2 m−2 h−1) was calculated using the portion of the data that showed a linear increase in the CO2 concentration. For convenience, the data were categorized from November to October in each of the study years (years 1, 2, 3, … 8).

2.4. Separation of HR from SR

The HR rate (RH) was estimated using the trenching method [24]. One soil collar that represented the average RS within a plot was chosen for the trenching in each plot (n = 4 for each treatment). A trenched subplot (area 1.0 m × 1.0 m, depth 0.6 m) was established around the selected soil collar in June, August, and September 2017. The CO2 efflux rate from the soil surface in the trenched subplots (Rtre; mg CO2 m−2 h−1) was measured from October 2019 onwards in the same manner as the RS measurement. The RH (mg CO2 m−2 h−1) was calculated using Equation (1) [24]:
R H = R t r e − R D
where RD is the CO2 emission rate from the decomposition of residual roots in the trenched subplots and is estimated from the decomposition rate of the dead roots using the root bag technique [24]. The average initial root mass (RM0; g dry weight [dw] m−2) was determined by excavating the roots from three quadrats (1 m × 1 m, 60 cm deep) and weighing them in the study area at the start of the trench treatment. Nine root bags (0.1 m × 0.1 m, 1 mm mesh) containing 10 g of air-dried roots were buried at a soil depth of 0.1–0.2 m in one plot of each treatment in June and September 2017 (54 bags in total). After burial for 3, 6, and 9 months, three bags per plot were collected, the oven-dried weight of the roots was measured, and the ratio of the mass remaining to the initial dry weight was calculated. The change in the residual root mass in the trenched subplot (RMt; g dw m−2) with time (t) was estimated using the exponential Equation (2) [25].
R M t = R M 0 × e − k t
where k is a fitting parameter and indicates the rate of mass loss (Table S3). To determine the rate of decrease in RMt at a measurement time point, Equation (2) was differentiated with respect to t (dRMt/dt). This value was then multiplied by 0.64, as the mineralization ratio [26,27], because the rate of the decrease in the root mass involves both mineralization and fragmentation. Finally, we estimated RD by expressing the value as that per milligrams of CO2.

2.5. Measurement of Physiological and Biological Soil Parameters

Soil temperature (°C) at 5 cm depth and volumetric soil water content (%) at 0–5 cm depth were measured at three points around the soil collar during the measurements of RS using a portable thermo sensor (TT508, TANITA, Tokyo, Japan) and portable moisture sensor (Theta Probe, model ML2 and ML3, Delta-T Devices, Cambridge, UK), respectively. In addition, the hourly soil temperature at 5 cm depth was measured using a HOBO TidbiT v2 temperature data logger (UTBI-001, Onset Computer Corp., Bourne, MA, USA) continuously at 1-h intervals in each plot over the entire study period to evaluate the annual respiration described below.
Soil samples for the pH measurements were collected in February 2016, November 2016, November 2017, and November 2018. Four soil core samples (0–5 cm depth in the mineral layer) were collected from each plot using a stainless-steel soil core (5 cm in diameter, 5 cm in height) and combined into one sample. The soil pH (H2O; air-dried soil-to-water ratio 1:5, v/w) was measured using a pH meter fitted with a glass electrode (HM-30R; TOA Corp., Kobe, Japan).
Soil samples for the microbial biomass measurement were collected in the same manner as samples for the pH measurement in October 2016, October 2018, and October 2019. These samples were freeze-dried and sieved (<2 mm) before the analysis. The soil microbial biomass was estimated using the modified adenosine triphosphate method [28].

2.6. Data Processing

We used an exponential equation to describe the relationship between respiration and soil temperature (Equation (3)):
R = α e − β T
where R (mg CO2 m−2 h−1) is the respiration rate (RS or RH), T is the soil temperature at 5 cm depth (°C), and α and β are fitted parameters. This step was not performed for the first year of RS, and the first and second years of RH owing to missing values during those years. To determine the respiration characteristics, R20 (defined as the respiration rate at 20 °C) was calculated using Equation (3) and Q10 (the parameter sensitivity to temperature) was calculated using Equation (4):
Q 10 = e 10 β
Annual SR and HR values were calculated by summing the hourly respiration rates estimated using the hourly soil temperature and Equation (3). The annual HR value for the second year was complemented by multiplying the annual SR value of the second year and the HR proportion of the SR obtained based on the data for the third year.

2.7. Statistical Analysis

A two-way analysis of variance (ANOVA) followed by Tukey’s test was conducted to examine the effects of treatment (biochar application) and season (measurement time point) on RS, RH, soil temperature, and volumetric soil water content in each year. A one-way ANOVA followed by Tukey’s test was conducted to examine the effect of treatment on Q10, R20, annual SR, HR, soil pH, and microbial biomass in each year. All the statistical analyses were conducted using the statistical computing language R (version 4.3.0) [29].

3. Results

3.1. Temporal Variation of RS and RH

Both RS and RH showed a clear seasonal variation with high values observed in the summer in each year in all the plots (Figure 1). In most years, interactive effects between treatment (biochar application) and season (measurement timing) on RS and RH were not significant (two-way ANOVA, p > 0.05; Table S4). The RS and RH values tended to be higher in the plots with biochar application compared with the control plots at each measurement time point (Figure 1). The main effect of treatment on both RS and RH was significant in all years (Table S4). However, the post hoc Tukey’s test did not detect any significant differences among the treatments.

3.2. Soil Environmental Factors

Soil temperature and moisture (volumetric soil water content) showed a clear seasonal variation in each year (Figure 2). Interactive effects between treatment and season and the main effects of treatment were not significant (Table S4). Although soil moisture tended to be higher in the plots with biochar application, the post hoc Tukey’s tests did not detect significant differences among the treatments.
The soil pH tended to be higher in the plots with biochar application than in the control plot for 3 years after biochar application (Figure 3). The pH in C10 was significantly higher than the pH in C0 in the first and third years, although no significant difference was detected in the second and fourth years.
The soil microbial biomass was not significantly different among the treatments in all the years measured (Figure 4).

3.3. Relationship Between Soil Temperature and Respiration

Both RS and RH showed a strong exponential correlation with soil temperature in all plots (Table S5). The characteristics of the correlation curves, R20 and Q10, are summarized in Table 1. The Q10 did not differ significantly among the treatments with the exception of the fourth year of RS. In contrast, the R20 for RS was significantly higher in the plots only in the third year and no significant difference was detected in the other years.

3.4. Annual SR and HR

The annual SR tended to increase with the increment in the amount of biochar applied throughout the experimental period and was significantly higher in C10 than in C0 in the second, third, and seventh years (Figure 5a). The annual HR in C5 tended to be higher than that in C0 throughout the experimental period but the difference was not statistically significant except in the seventh year (Figure 5b). Although the annual HR in C10 was significantly higher than that in C0 in the second and third years (+31 and +37%, respectively), no significant differences were detected after the fourth year. Excess carbon emission in C5 (difference in HR compared with that of the control) during the second–eighth years was 7.7 Mg C ha−1. In contrast, excess carbon emission in C10 in the second and third years, and during the fourth–eighth years was 1.7, 1.8, and 0.4 Mg C ha−1, respectively.

4. Discussion

4.1. Long-Term Temporal Variation of SR and HR

Biochar application to the surface of the forest floor in a warm–temperate, deciduous, broad-leaved forest tended to increase SR for 8 years (Figure 5a). In the plots treated with the lower amount of biochar (C5), the HR tended to be higher than in the control throughout the experimental period, which would be partly responsible for the increase in SR. In contrast, a significant increase in HR was only observed up to the third year after biochar application in the C10 treatment. The reason for this difference in the response of HR between C5 and C10 is unclear. In the plots to which less biochar was applied (C5), it was difficult to apply the biochar evenly and, as far as we could visually ascertain, there were areas where the thickness of the biochar layer was uneven (i.e., there was heterogeneity in the biochar layer). Additionally, pine dieback was widespread in this area and large pine trees (>35 cm in diameter) died in two of the four C5 plots, which indicates that the litter supply may have increased temporarily in these plots; thus, it cannot be ruled out that the increase in HR in C5 plots was caused by such an incidental influence other than biochar application. In any case, no statistically significant difference in HR was observed after the fourth year and it could not be concluded that biochar promoted organic matter decomposition in the long term. Focusing on the C10 plots, SR tended to be high without an increase in HR after the fourth year (Figure 5a), indicating that biochar application would have increased root respiration in the long term. Previous studies have reported that biochar application increases plant root respiration activity, root biomass, and turnover [21,30,31,32], which results in a long-term increase in root respiration. Although short-term studies (≤2 years) have reported an increase in SR or HR in response to biochar application [33,34], the long-term effects of biochar on SR and HR are not well understood, as noted in many previous studies [11]. In this context, the present study is the only example of monitoring SR and HR over an 8-year period following biochar application to the forest floor.

4.2. Excess Carbon Emission from the Plots with Biochar Application

The excess carbon emissions in the C10 plots during the 8-year experimental period and in the first 3 years after biochar application were estimated to be approximately 5.8 and 5.3 Mg C ha−1, respectively, with the assumption that the degree of the increase in HR in the first year was the same as in the second and third years. Previous studies have reported that the labile carbon in applied biochar causes the high HR rate [35,36], although it is almost completely decomposed within one year in many cases [37,38,39]. Although the exact amount of labile carbon in the biochar used in the present study was not known, its volatile carbon fraction was approximately 30%. If this fraction was relatively labile and mineralized within a short period, treatment with 10 Mg ha−1 of biochar with a 71% carbon concentration would release approximately 2.1 Mg C ha−1 as CO2. This value corresponds to approximately 40% of the excess carbon emissions observed in the C10 plots. Minamino et al. [40], who examined the effect of biochar application on the litter layer in the same experimental plots, reported that litter decomposition was enhanced by biochar application through maintaining a higher moisture content in the litter, especially under drought, and estimated the amount of excess carbon emission from the L layer through the acceleration of litter decomposition to be 0.3 Mg C ha−1 during 932 days (equivalent to 0.12 Mg C ha−1 y−1). This value is less than 10% of the aforementioned excess carbon emissions from the plots treated with biochar. Thus, the remaining 50% of the excess carbon emissions can be attributed to the increased decomposition of native organic matter in soil layers other than the L layer.

4.3. Factors That Stimulate Microbial Organic Matter Decomposition After Biochar Application

In general, soil physiological environmental factors, such as soil temperature and moisture content, greatly affect the soil microbial activity [18,23]. Previous agricultural field studies have often reported that biochar addition increases the soil temperature and moisture content through the improvement of soil physiological properties owing to the high albedo and porosity of biochar [41,42,43], which would stimulate microbial activity. In the current study, however, the soil temperature in the mineral soil layer did not differ significantly among the treatments (Figure 2; Table S4). In forest ecosystems, sunlight is largely intercepted by the tree canopy and does not reach the ground surface directly, resulting in a smaller temporal variation in the soil temperature than in agricultural ecosystems. In contrast, the moisture content in the mineral soil tended to be higher in the plots with biochar application, although the difference was not statistically significant (Figure 2), which may have contributed to the increase in RH. Furthermore, the soil moisture content may have remained high in the FH layer, which is located above the mineral soil layer and where greater amounts of organic matter accumulate, which would accelerate decomposition and release more CO2. In the present study, the biochar was spread on the forest floor (not mixed into the mineral soil), so the effects of biochar may have been stronger in the upper layers (L and FH). Future research should consider the layered structure characteristic of forest soils and isolate the effects of biochar application on each layer.
Changes in microbial biomass and community structure can also affect the variation in RH [6,44]. Biochar addition has been reported to increase microbial biomass [45]. In the present study, however, no significant difference in microbial biomass was observed between the plots with or without biochar (Figure 4). Additionally, the Q10 value, which is used as an index of the microbial community structure, was not affected by biochar application (Table 1). Therefore, it is likely that no significant changes occurred in the functional attributes of the microbial community structure and thus the increase in HR was unlikely to reflect a shift in the microbial community structure. However, the R20 (respiration rate at 20 °C) of RH was distinctly higher in the plots treated with biochar (Table 1). An increase in microbial respiration without major changes in biomass or community structure would simply mean an increase in the respiration rate per microbial biomass, i.e., an increase in the microbial physiological activity.
An increase in the soil pH after alkaline biochar application is widely observed [45,46] and is often cited as an important factor stimulating soil microbial communities [6,47]. Similarly, in the present study, the pH of the mineral soil layer tended to increase until the third year after biochar application (Figure 3), which would affect the soil microbial respiration activity (respiration per biomass, or R20) and, in turn, RH. In addition to the increase in the soil pH, the positive priming effect of biochar-derived labile carbon has been a research focus as a mechanism to increase microbial respiration after biochar application [35,36]. While biochar is strongly resistant to microbial degradation, it also contains small amounts of labile carbon, which is considered to affect the decomposition of native organic matter [48,49]. Although the biochar used in the present study (pyrolyzed at 600–700 °C) would contain less labile carbon because the amount in biochar generally decreases with the increase in the pyrolysis temperature (300–700 °C) [50], the carbon emission attributable to the priming effect would contribute to the increase in HR to some extent.

4.4. Effectiveness of Carbon Sequestration Using Biochar in Forests and Future Directions

In this study, carbon loss resulting from biochar addition over 8 years (5.8 Mg C ha−1 in the C10 plots) was equivalent to more than two-thirds of the carbon applied as biochar (7.1 Mg C ha−1). Therefore, when considering carbon sequestration using biochar in forest ecosystems, careful attention should be paid to the non-negligible enhancement of organic matter decomposition caused by biochar application. Alternatively, it may be worthwhile to explore measures to control the increase in HR after biochar application. Previous studies have applied diverse types of biochar that differed considerably in volume, quality (feedstock material or pyrolysis temperature), and application method (mixed with the soil or applied to the soil surface), and the responses suggest that these factors often affect the decomposition of soil organic matter [49]. Therefore, further studies using several types of biochar or various application regimens (e.g., amount applied, timing of application, single or continuous application, and mixing with soil or surface application) might clarify whether effective carbon sequestration can be achieved without inducing an increase in HR in forest ecosystems. Additionally, the stimulation of soil organic matter decomposition indicates the strong influence of the dynamics of nutrient contents and plant nutrient fixation, which are responsible for the assimilation of atmospheric carbon [5]. Therefore, to discuss the long-term carbon sequestration of a whole ecosystem, the carbon balance of both soil organic matter decomposition and the net primary production of vegetation in the field should be examined.

5. Conclusions

To our knowledge, the current study is the only study in which biochar was applied to the forest floor in a warm–temperate, deciduous, broad-leaved forest and the impact of biochar application on HR, which was separated from SR using the trenching method, was monitored in the long term (8 years). Biochar application increased the SR rate over 8 years. Whereas, contrary to our initial expectations, the increase in HR due to biochar application was not sustained over time (≤3 years). This short-term stimulation in HR and the relatively long-term increase in root respiration both contributed to the increase in SR. The increase in HR would include the CO2 emission from the decomposition of labile organic carbon in the biochar applied and the CO2 emission from the soil microbial communities that were stimulated by the supply of labile organic carbon from the biochar (i.e., the priming effect) and by changes in the soil physicochemical environment, such as soil moisture content and pH. These results emphasize the need to pay attention to increased HR when applying biochar to forest ecosystems for carbon sequestration. In our study, the annual HR increased by approximately 31% to 37% during the first 3 years after biochar application. However, the response of HR may vary with soil type, forest type, and biochar type, so similar monitoring should be conducted in other forests to accumulate data. In addition, further research on the mechanism by which biochar increases HR would also be needed. For example, it would be useful to determine the effects of biochar application on the community composition and functionality (e.g., extracellular enzyme activity) of the soil microbial communities in each soil layer. Furthermore, in considering the long-term effects of biochar application, attention should be paid to the aspect that biochar initially applied to the surface layer of forest soils will migrate downward over time.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16030489/s1, Figure S1: Overview and soil cross section image of the study site; Table S1: Name, basal area, density, and biomass of woody species in this study site in 2015; Table S2: Soil characteristics of the study site; Table S3: Decomposition constant (k in Equation (2), indicating the rate of mass loss) of dead roots determined by the trenching method in different seasons based on root bag experiments; Table S4: Results of two-way analysis of variance showing the effects of measurement season and biochar treatment on soil CO2 fluxes (soil respiration and heterotrophic respiration rates) and environmental factors (soil temperature and moisture content); Table S5: Constants (α and β) in soil temperature–respiration curves (generated with Equation (3)) for each plot in each year.

Author Contributions

Conceptualization, S.Y., H.K. and M.T.; investigation, K.E. and Y.T.; data curation, S.Y. and M.T.; formal analysis, K.E., Y.T. and M.T.; writing—original draft preparation, S.Y., K.E. and M.T.; editing, S.Y. and T.O.; project administration, S.Y., T.O. and H.K.; funding acquisition, S.Y. and H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partly supported by the Japan Society for the Promotion of Science KAKENHI to H.K. (15H01730) and to S.Y. (19H04237), and by the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency provided by Ministry of the Environment of Japan to S.Y. (JPMEERF20221C07).

Data Availability Statement

Data sharing is not applicable to this article.

Acknowledgments

We express our deepest gratitude to the members of the Laboratory for Environmental Ecology, Waseda University, for their assistance with the field research. We thank Yasuo Iimura, University of Shiga Prefecture, for providing data on the volatile carbon fraction of the biochar used in this study. We thank Nobuhide Fujitake, Kobe University, for providing photographs and data on soil cross sections in the study area.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 16 00489 g001
Figure 1. Temporal changes in (a) soil respiration rate (RS) and (b) heterotrophic respiration rate (RH) in the plots with or without biochar. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Values are means (n = 3–4).
Figure 1. Temporal changes in (a) soil respiration rate (RS) and (b) heterotrophic respiration rate (RH) in the plots with or without biochar. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Values are means (n = 3–4).
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Figure 2. Temporal changes in (a) soil temperature and (b) soil moisture (volumetric soil water content) recorded during the respiration measurements in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Values are means (n = 3–4).
Figure 2. Temporal changes in (a) soil temperature and (b) soil moisture (volumetric soil water content) recorded during the respiration measurements in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Values are means (n = 3–4).
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Figure 3. Soil pH in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Bars and error bars indicate the mean ± SD (n = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, p < 0.05).
Figure 3. Soil pH in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Bars and error bars indicate the mean ± SD (n = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, p < 0.05).
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Figure 4. Soil microbial biomass carbon determined with the adenosine triphosphate method in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Bars and error bars represent the mean ± SD (n = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, p < 0.05). n.d., not determined.
Figure 4. Soil microbial biomass carbon determined with the adenosine triphosphate method in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Bars and error bars represent the mean ± SD (n = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, p < 0.05). n.d., not determined.
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Figure 5. (a) Annual soil respiration (SR) and (b) annual heterotrophic respiration (HR) in the plots with or without biochar from the second to the eighth year. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Bars and error bars represent the mean ± SD (n = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, p < 0.05). n.d., not determined.
Figure 5. (a) Annual soil respiration (SR) and (b) annual heterotrophic respiration (HR) in the plots with or without biochar from the second to the eighth year. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha−1 biochar application, respectively. Bars and error bars represent the mean ± SD (n = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, p < 0.05). n.d., not determined.
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Table 1. R20 and Q10 values estimated from soil temperature–respiration curves generated with Equation (3) (n = 4). Different letters identify significant difference among the treatments. n.d., not determined.
Table 1. R20 and Q10 values estimated from soil temperature–respiration curves generated with Equation (3) (n = 4). Different letters identify significant difference among the treatments. n.d., not determined.
Year1st2nd3rd4th5th6th7th8th
R20Q10R20Q10R20Q10R20Q10R20Q10R20Q10R20Q10R20Q10
Soil respiration rate (RS)
C0n.d.n.d.432 a3.12 a349 a3.10 a421 a3.57 a380 a2.75 a444 a4.01 a409 a3.72 a376 a2.49 a
C5n.d.n.d.461 ab2.91 a388 ab2.91 a453 a3.30 ab439 b2.79 a480 a3.39 a454 ab3.67 a413 a2.26 a
C10n.d.n.d.500 b2.95 a421 b3.09 a457 a2.95 b425 ab2.96 a466 a3.40 a476 b3.47 a410 a2.26 a
Heterotrophic respiration (RH)
C0n.d.n.d.n.d.n.d.266 a8.55 a363 a4.52 a346 a2.94 a363 a3.79 a346 a4.14 a308 a2.38 a
C5n.d.n.d.n.d.n.d.331 ab5.22 a405 a3.88 a410 a2.80 a419 a3.54 a438 b3.86 a349 a2.39 a
C10n.d.n.d.n.d.n.d.358 b7.77 a350 a4.30 a343 a2.65 a384 a3.93 a362 ab3.84 a302 a2.30 a
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Yoshitake, S.; Enichi, K.; Tsukimori, Y.; Ohtsuka, T.; Koizumi, H.; Tomotsune, M. Long-Term Effects of Biochar Application on Soil Heterotrophic Respiration in a Warm–Temperate Oak Forest. Forests 2025, 16, 489. https://doi.org/10.3390/f16030489

AMA Style

Yoshitake S, Enichi K, Tsukimori Y, Ohtsuka T, Koizumi H, Tomotsune M. Long-Term Effects of Biochar Application on Soil Heterotrophic Respiration in a Warm–Temperate Oak Forest. Forests. 2025; 16(3):489. https://doi.org/10.3390/f16030489

Chicago/Turabian Style

Yoshitake, Shinpei, Kakuya Enichi, Yuki Tsukimori, Toshiyuki Ohtsuka, Hiroshi Koizumi, and Mitsutoshi Tomotsune. 2025. "Long-Term Effects of Biochar Application on Soil Heterotrophic Respiration in a Warm–Temperate Oak Forest" Forests 16, no. 3: 489. https://doi.org/10.3390/f16030489

APA Style

Yoshitake, S., Enichi, K., Tsukimori, Y., Ohtsuka, T., Koizumi, H., & Tomotsune, M. (2025). Long-Term Effects of Biochar Application on Soil Heterotrophic Respiration in a Warm–Temperate Oak Forest. Forests, 16(3), 489. https://doi.org/10.3390/f16030489

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