Effects of Microbially Engineered Biochar Pellets on Net Ecosystem Carbon Balance, Greenhouse Gas Emissions, and Clubroot Disease in Organic Cabbage Cultivation

Abstract

Organic vegetable cultivation requires soil management strategies that improve carbon balance and suppress soilborne diseases. This study evaluated the efficacy of acidified microbial biochar pellets (ABPM) in enhancing net ecosystem carbon balance (NECB) and suppressing clubroot disease (Plasmodiophora brassicae) during organic Chinese cabbage (Brassica rapa ssp. pekinensis) cultivation. In a field-scale evaluation, three treatments were compared: guano fertilizer (control), ABPM 27 (inoculated with Pseudomonas fluorescens 22BCO027), and ABPM 86 (inoculated with Bacillus megaterium 22BCO086). Soil incorporation of ABPM 27 and ABPM 86 significantly increased soil carbon sequestration by 29.1% and 22.4%, respectively, while simultaneously reducing cumulative greenhouse gas emissions under the experimental conditions. This resulted in positive NECB values of 2.63 and 2.94 t CO₂-eq ha⁻¹, suggesting enhanced carbon retention potential within the studied cultivation system. Beyond its impact on carbon dynamics, ABPM 27 increased marketable yield by 8.6% (77.4 t ha⁻¹) and reduced clubroot incidence by 46.2%. Rhizosphere microbial analysis revealed that ABPM 27 promoted late-season microbial diversity and the persistence of beneficial Bacillus spp. and Pseudomonas spp. populations. These findings suggest the potential multifunctional role of microbially engineered biochar pellets in improving crop production, carbon retention, and pathogen suppression under organic cultivation conditions. However, these findings are based on a single-season field experiment and NECB-based carbon balance estimates, and therefore require validation across multiple growing seasons and cultivation environments.

1. Introduction

Cruciferous vegetables, including cabbage, cauliflower, Brussels sprouts, broccoli, and Chinese cabbage (Brassica rapa ssp. pekinensis), are globally important horticultural crops, with annual production exceeding 70 million tonnes [1]. These vegetables provide essential dietary components, including vitamins, dietary fiber, and glucosinolates [2], and are widely cultivated due to their relatively short growth cycles and adaptability to diverse environmental conditions. However, their productivity is frequently constrained by clubroot disease, caused by the soil-borne pathogen Plasmodiophora brassicae. This obligate biotrophic protist induces root gall formation, impairing water and nutrient uptake, leading to yield losses of 10–15% under moderate conditions and up to 100% in highly infested fields [3]. Conventional control measures such as liming, crop rotation, and the breeding of resistant cultivars often produce inconsistent results due to soil variability and pathogen diversity. Furthermore, many chemical strategies are incompatible with organic farming systems, which necessitate biologically based approaches for disease suppression [4].

While biochar is recognized for its capacity to influence soil carbon dynamics and improve physicochemical properties, most research has been confined to non-inoculated materials and short-term laboratory assessments. Field-scale evaluations quantifying the Net Ecosystem Carbon Balance (NECB) under practical agronomic conditions remain limited. Moreover, the integration of carbon management with biological disease suppression has rarely been addressed within a single soil management strategy, particularly in organic vegetable production systems where both soilborne diseases and greenhouse gas emissions (GHG) pose significant challenges [5]. Rather than functioning solely as a soil conditioner, biochar can influence nutrient retention, soil aggregation, and microbial habitat formation [6]. When combined with plant growth-promoting rhizobacteria (PGPR), including Pseudomonas fluorescens and Bacillus megaterium, biochar acts as a carrier matrix that supports microbial persistence and ecological interaction in soil environments. This integration establishes a multifunctional soil amendment system in which physical structure (pelletization), chemical modification (acidification), and biological functionality (PGPR inoculation) are combined within a single delivery platform.

Pelletized formulations further facilitate practical field application and support microbial viability under fluctuating field conditions [7]. However, studies simultaneously integrating acidified and pelletized biochar as a microbial carrier system for the combined evaluation of greenhouse gas mitigation, carbon sequestration, and disease suppression under field conditions remain limited. Acidification may also improve microbial colonization by reducing the highly alkaline surface conditions commonly associated with freshly produced biochar [8]. Recent advancements in biochar technology emphasize the critical role of post-production modification processes, such as acidification or nutrient enrichment, to improve microbial compatibility and functional integration within the rhizosphere [8]. Although acidification of biochar has been shown to modify its surface reactivity and nutrient release kinetics, its role in supporting inoculated PGPR populations under soilborne disease pressure remains insufficiently understood.

Beyond agronomic benefits, biochar application can influence soil carbon cycling and greenhouse gas dynamics. Its recalcitrant carbon structure contributes to long-term carbon storage, while interactions with soil microbial processes may affect N₂O and CH₄ emissions [9,10,11]. However, the magnitude of these effects varies depending on feedstock characteristics, pyrolysis conditions, and soil properties. Therefore, optimizing biochar formulations that integrate carbon management with microbial functionality remains an important research priority. Combining biochar with targeted microbial inoculants may provide a promising soil management approach that enhances soil ecological function while improving crop productivity.

This study aimed to evaluate the effects of soil-incorporated, microbially engineered biochar pellets on the net ecosystem carbon balance, crop productivity, and clubroot suppression in organic Chinese cabbage production. We hypothesized that this integrated biochar–microbial pellet system would simultaneously modulate rhizosphere microbial communities, enhance soil carbon retention, and suppress clubroot disease under field conditions in organic Chinese cabbage cultivation.

2. Materials and Methods

2.1. Preparation of the Acidified Biochar Pellets Inoculated with Microorganisms (ABPM)

Certified organic inputs, including rice hull biochar, guano, and citric acid, were used in accordance with national organic agricultural standards of the Republic of Korea. The rice hull biochar was produced via a top-to-bottom pyrolysis process [12], and guano was supplied by NOUSBO Co., Ltd. (Suwon, Republic of Korea). For acidification, biochar was treated with 0.3 M citric acid solution at a 1:1.7 (w/v; biochar to solution) ratio and air-dried at room temperature. The resulting acidified biochar had a final pH of 2.5 and was subsequently blended with guano at a 6:4 (w/w) ratio.

Antagonistic bacterial strains effective against Plasmodiophora brassicae were selected through preliminary pot bioassays. The strains Pseudomonas fluorescens 22BCO027 and Bacillus megaterium 22BCO086, originally isolated from agricultural soils under Chinese cabbage (Brassica rapa ssp. pekinensis) cultivation, were identified by 16S rRNA gene sequencing and confirmed through comparison with the NCBI GenBank database. Their antagonistic activity against P. brassicae was validated in previous in vitro and pot experiments (Figure 1).

For inoculum preparation, P. fluorescens 22BCO027 was cultured on Laked Blood Agar (LBA), and B. megaterium 22BCO086 was cultured on Tryptic Soy Agar (TSA). After incubation for 24 h at 25–28 °C, bacterial cells were harvested and adjusted to final concentrations of 6.3 × 10⁸ CFU mL⁻¹ (P. fluorescens 22BCO027) and 8.0 × 10⁷ CFU mL⁻¹ (B. megaterium 22BCO086), respectively, prior to incorporation into the biochar carrier matrix.

Acidified microbial biochar pellets (ABPM) were produced by incorporating the bacterial suspensions into the acidified biochar–guano carrier at a 3:1 (v/w) ratio, followed by mechanical mixing and pelletization (Figure 2). The pellets were then air-dried at room temperature and stored under ambient conditions prior to field application.

The physicochemical properties of the ABPM formulations are summarized in

Table 1. Physicochemical properties of acidified microbial biochar pellets (ABPM 27 and ABPM 86).

Biochar Pellets *	pH (1:20)	EC (dS m−1)	N	C	P2O5	K
			g kg−1		mg kg−1
Guano	7.45	6.00	135.2	163.2	33.2	0.9
Rice hull biochar	10.68	0.88	2.2	562.7	0.1	0.02
ABPM 27	7.54	6.00	43.5	192.6	0.6	0.2
ABPM 86	7.34	5.93	43.6	192.6	0.6	1.1

* ABPM 27 and ABPM 86 refer to acidified biochar pellets (rice hull biochar: guano = 6:4, w/w) inoculated with Pseudomonas fluorescens 22BCO027 and Bacillus megaterium 22BCO086, respectively.

. The pH values were 7.54 and 7.34 for ABPM 27 and ABPM 86, respectively, while both formulations contained 6.0% total nitrogen content. Potassium content in ABPM 86 was 5.5-fold higher than that of ABPM 27.

2.2. Cabbage Cultivation and Experimental Design

Chinese cabbage seedlings (cv. ‘Hyang-Gem Yellow’, a commercially cultivated Chinese cabbage cultivar for kimchi production) were transplanted during the autumn season of 2025 into the experimental field at the National Institute of Eco-friendly Environmental Microbiology (37.351895° N, 127.322729° E), Gyeonggi-do, Republic of Korea. To evaluate disease suppression efficacy, the field soil was artificially infested with Plasmodiophora brassicae prior to treatment application. Before plot establishment, the soil surface was homogenized using a tractor-mounted rotary tiller.

The experiment was arranged in a randomized complete block design (RCBD) with three replicates, and treatment allocation within each block was fully randomized. Each plot measured 12 m² (6 m × 2 m), and seedlings were transplanted at a spacing of 30 cm × 60 cm. The treatments included: (1) Control (guano fertilizer as the sole nutrient source); (2) ABPM 27 (acidified biochar pellets inoculated with Pseudomonas fluorescens 22BCO027); and (3) ABPM 86 (acidified biochar pellets inoculated with Bacillus megaterium 22BCO086). All treatments were applied at a nitrogen-equivalent rate of 320 kg N ha⁻¹, consistent with the national fertilization guidelines for cabbage production [13]. The guano and ABPM formulations were incorporated into the soil via rotary tillage prior to transplanting.

Meteorological conditions during the cropping period were representative of typical field weather patterns in the experimental region [14]. All plots were managed under uniform agronomic practices, and irrigation was applied according to crop water requirements using a standard drip irrigation system.

At harvest, plant growth parameters, including plant height, head width, head height, and marketable yield, were recorded. The baseline physicochemical properties of the experimental soil are summarized in

Table 2. Physicochemical properties of the experimental soil used in this study.

Soil Type	pH (1:10)	EC (mS m−1)	O.M (g kg−1)	NH4-N	NO3-N	P2O5	K
				mg kg−1
Silty loam	6.7	0.03	14	14.3	45.9	116.1	12.2

, including an initial P₂O₅ concentration above the recommended optimal range (350–450 mg kg⁻¹).

2.3. Soil Physicochemical Properties and Rhizosphere Microbial Community Analysis

Surface soil samples were collected weekly for physicochemical analyses from each plot throughout the growing season. The ABPM formulations, guano, and soil samples were air-dried and ground to pass through a 2-mm sieve prior to analysis. Soil pH and EC were measured in soil-to-water suspensions of 1:10 and 1:20 (w/v), respectively, using a pH meter (Orion 4 Star, Thermo Scientific, Waltham, MA, USA). Total nitrogen (TN) and total carbon (TC) contents were determined using a CHNS elemental analyzer (Vario Macro Cube, Elementar, Langenselbold, Germany).

For inorganic nitrogen analysis, dried samples were extracted with 2 M KCl, and the concentrations of NH₄⁺–N and NO₃⁻–N were quantified using a segmented-flow autoanalyzer (Seal Analytical Ltd., Mequon, WI, USA). Available phosphate (PO₄³⁻) and exchangeable potassium (K) were extracted using the Mehlich III method [15] and quantified with UV–visible spectrophotometric kits (C-Mac, Daejeon, Republic of Korea). The P₂O₅ concentrations were calculated by multiplying the PO₄ values by a conversion factor of 2.29.

Rhizosphere soil samples were collected at 3 and 30 days after transplanting (DAT) and at harvest from the Control, ABPM 27, and ABPM 86 treatments. Total genomic DNA was extracted from 0.25 g of homogenized soil using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The V3–V4 region of the bacterial 16S rRNA gene was amplified using the primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′). Amplicons were purified and sequenced using paired-end (2 × 300 bp) Illumina MiSeq technology (Illumina, San Diego, CA, USA).

The raw sequence reads were quality-filtered and clustered into operational taxonomic units (OTUs) at a 97% sequence similarity threshold. Taxonomic classification was performed against the SILVA 138 reference database. Microbial community composition and relative abundance were compared among treatments at the phylum and genus levels to evaluate the impact of ABPM application.

2.4. Carbon Budget

Carbon sequestration for each treatment was estimated from the difference in soil carbon stocks between pre-transplanting and post-harvest sampling, using a modified equation [12]:

$$\mathrm{CS}=[{\sum }_{i=1}^{n}T\left(Li-Ii\right)-C(Li-Ii)]\times \mathrm{SW}$$

(1)

where CS (kg ha⁻¹) is the soil carbon sequestration attributable to the treatment; T and C denote the treatment and control soils, respectively; i represents each sampling date during the cultivation period; $L_i$ and $I_i$ are the carbon contents of the soil at sampling date $i$ and at the initial sampling, respectively (g kg⁻¹); and SW is the soil weight (kg ha⁻¹), calculated from the soil bulk density (1.3 g cm⁻³) and plow layer depth (10 cm).

Equivalent CO₂ emissions (CO₂-eq) were calculated as follows:

CO₂-equiv. = CS × CF

(2)

where CO₂-eq is the carbon dioxide equivalent (t CO₂-eq ha⁻¹), CS was the carbon sequestration (t ha⁻¹), and CF was the stoichiometric conversion factor of CO₂ emission from soil carbon (1 kg C = 3.664 kg CO₂-eq). Thus, 1 kg of sequestered carbon corresponds to 3.664 kg CO₂-eq.

2.5. Global Warming Potential (GWP)

To quantify greenhouse gas (GHG) emissions, N₂O and CH₄ were monitored weekly during cultivation period using closed static chambers installed within the experimental field (Supplementary Figure S1). Each chamber consisted of a permanently installed base frame (inserted 10 cm into the soil) and a removable upper chamber deployed only during gas sampling. The chambers were constructed from transparent acrylic to minimize shading and prevent interference with plant growth. To capture rhizosphere-associated emissions, a single cabbage seedling was transplanted at the center of each base frame.

During each sampling event, the upper chamber was sealed onto the base frame between 10:00 and 11:00 a.m., a period commonly used to minimize temporal variation among sampling events. The chambers were removed immediately after sampling to mitigate internal temperature increases and pressure disturbances. Gas samples were collected from the chamber headspace using 50-mL gas-tight syringes. The chamber system was designed to quantify greenhouse gas fluxes originating from the plant–soil interface, including emissions associated with microbial decomposition, root activity, and rhizosphere nutrient transformations.

Concentrations of methane (CH₄) and nitrous oxide (N₂O) were quantified using a gas chromatograph (Agilent 7890B, Santa Clara, CA, USA) equipped with a flame ionization detector (FID) and an electron capture detector (ECD), respectively. The hourly fluxes of CH₄ and N₂O emissions were calculated based on the linear change in gas concentration over time, using the following equation [16].

$$\mathrm{F}=p{\displaystyle \frac{V}{A}}\times {\displaystyle \frac{\Delta \mathrm{C}}{\Delta \mathrm{t}}}\times {\displaystyle \frac{273}{(T+273)}}$$

(3)

where F was the gas flux (µg N₂O m⁻² h⁻¹) or mg CH₄ m⁻² h⁻¹), p was the gas density at standard temperature and pressure (0.714 kg m⁻³ for CH₄ and 1.96 kg m⁻³ for N₂O), V is the chamber headspace volume (m³), A was the chamber surface area (m²), ΔC/Δt was the rate of change in gas concentration over time, T was the mean air temperature inside the chamber (°C), and 273 is the conversion constant from Celsius to Kelvin.

The cumulative GHG emission (CGE) was calculated by linear interpolation between sampling dates as:

$$\mathrm{CGE}={\displaystyle {\sum }_{i=1}^n}(Fi\times 24\times NSPi)$$

(4)

where CGE was the cumulative CH₄ or N₂O emission (mg m⁻²), F_i was the flux of CH₄ or N₂O during the ith sampling interval, and NSP_i was the number of days between two consecutive sampling dates. Finally, these values were converted to CO₂-equivalent (CO₂-eq) units to determine the total Global Warming Potential (GWP) on a 100-year time horizon:

GWP (t CO₂-eq ha⁻¹) = CH₄ (t ha⁻¹) × GWP_CH4 + N₂O (t ha⁻¹) × GWP_N2O

(5)

where the 100-year GWP values used were 27.3 for CH₄ and 273 for N₂O [17]. These conversions allowed direct comparison of total greenhouse gas impacts among treatments. These GWP estimates were subsequently incorporated into the net ecosystem carbon balance (NECB) calculations.

2.6. Estimation of Net Ecosystem Carbon Balance (NECB)

The Net Ecosystem Carbon Balance (NECB) was estimated to evaluate the potential climatic impact of the different soil management strategies within the defined experimental boundary. The system boundary was defined as the Chinese cabbage cultivation field, focusing on the carbon dynamics within the soil–plant–atmosphere interface. To maintain methodological transparency, NECB calculations were specifically limited to on-field processes, excluding indirect CO₂-equivalent (CO₂-eq) emissions associated with external inputs such as production, transportation, and mechanical application of ABPM. A full life-cycle assessment (LCA) was not conducted in this study.

To facilitate a direct comparison, both soil carbon sequestration and greenhouse gas (GHG) emissions (CH₄ and N₂O) were expressed in a common functional unit (t CO₂-eq ha⁻¹). NECB was calculated as the net balance between carbon sequestration and total GHG emissions during the cultivation period according to Equation (6):

NECB = Carbon Sequestration (CO₂-eq) − Total GHG Emissions (CO₂-eq)

(6)

where all components are expressed as t CO₂-eq ha⁻¹. Soil carbon sequestration was converted to CO₂-equivalent using the stoichiometric conversion factor of 44/12 (3.664). The global warming potential (GWP) values used for converting CH₄ and N₂O to CO₂-equivalents were 27.3 and 273, respectively [17]. Regarding CO₂ fluxes, soil CO₂ emissions were not directly included in the NECB calculation, as short-term cropland systems are often assumed to exhibit near-balanced CO₂ exchange over a single growing season when accounting for both photosynthetic uptake and total respiration. Consequently, NECB estimates in this study were based on changes in soil carbon storage and non-CO₂ greenhouse gas emissions (CH₄ and N₂O). Under this framework, positive NECB values indicate net carbon retention within the cultivation system, whereas negative values indicate net carbon loss. Therefore, NECB serves as a field-scale indicator of the carbon stabilization potential of microbially engineered biochar under the experimental conditions.

2.7. Clubroot Assessment

To quantify these visual observations (Figure 3), the Clubroot Severity Index (%) was calculated according to the Method of Townsend and Heuberger [18] using Equation (7):

$$\mathrm{Clubroot}\mathrm{severity}(\%)={\sum }_{i=1}^n{\displaystyle \frac{\left(\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\times \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{a}\mathrm{t}\mathrm{t}\mathrm{h}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\right)}{\left(\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{d}\times 5\right)}}\times 100$$

(7)

where disease grades ranged from 0 to 5, with 0 indicating no visible symptoms and 5 indicating the highest level of disease severity.

The control efficacy was calculated using the formula:

$$\mathrm{Control}\mathrm{efficacy}(\%)=(1-{\displaystyle \frac{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\mathrm{o}\mathrm{f}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}})\times 100$$

(8)

where the disease indices of treatment and control represent the mean disease severity in ABPM-treated plots and guano-only control plots, respectively.

2.8. Statistical Analysis

All statistical analyses were conducted using one-way analysis of variance (ANOVA) followed by Duncan’s multiple range test in SAS software (version 12.0; SAS Institute, Cary, NC, USA). The parameters analyzed included carbon sequestration, greenhouse gas (GHG) emissions, plant growth responses, and clubroot suppression rates. Differences among treatments were considered statistically significant at p < 0.05.

Data are presented as means ± standard deviation (SD).

For derived emission estimates (Table 5), uncertainties were quantified using error propagation based on the variability of input parameters and are reported as ± SD or 95% confidence intervals, as appropriate.

For rhizosphere microbial community analysis, taxonomic abundance data were log10-transformed (log₁₀ ng µL⁻¹) prior to statistical analysis to improve normality and enable comparison across sampling periods.

During manuscript preparation, generative artificial intelligence (AI) tools were used solely for language editing and formatting assistance. All AI-assisted content was critically reviewed and revised by the authors, who take full responsibility for the integrity of the manuscript. AI tools were not used for experimental design, data analysis, data interpretation, or scientific decision-making.

3. Results and Discussion

3.1. Estimating Carbon Sequestration

Carbon sequestration values estimated using Equations (1) and (2) are presented in

Table 3. Assessment of soil carbon sequestration and CO₂-equivalent (CO₂-eq) potential in the organic Chinese cabbage field under ABPM treatments.

Treatments *	Carbon Sequestration (t ha−1)	CO2-eq (t ha−1)
Control	1.34 a	4.91
ABPM 27	1.73 b	6.34
ABPM 86	1.64 b	6.01
F	3.0	-
p-values	<0.0001	-

* Legend: CO₂-equivalent (CO₂-eq) values were calculated using a conversion factor of 1 kg C = 3.664 kg CO₂-eq. Means followed by different letters indicate significant differences among treatments according to one-way ANOVA followed by Duncan’s multiple range test (p < 0.001).

. ABPM 27 exhibited the highest sequestration efficiency at 1.73 t ha⁻¹ (6.34 t CO₂-eq ha⁻¹), followed by ABPM 86 at 1.64 t ha⁻¹ (6.01 t CO₂-eq ha⁻¹). These values represent significant increases of 29.1% and 22.4%, respectively, compared to the control (1.34 t ha⁻¹; 4.91 t CO₂-eq ha⁻¹) (p < 0.001).

The enhanced carbon sequestration observed in ABPM treatments may be associated with interactions between the biochar matrix and the inoculated microbial strains. One possible explanation is that biochar provides porous microhabitats that support microbial colonization, while associated rhizobacteria may promote extracellular polymeric substance (EPS) production and soil aggregate formation. These findings align with the established role of biochar as a recalcitrant carbon material whose condensed aromatic structure confers resistance to microbial decomposition and promotes long-term soil carbon stabilization [9,10,11]. Overall, the results indicate that microbially engineered biochar pellets can contribute to improved soil carbon retention under the experimental conditions.

Previous meta-analyses have reported significant increases in soil organic carbon stocks following biochar application relative to untreated control [9]. Recent studies further demonstrate that pelletized biochar formulations can enhance soil carbon accumulation across diverse cropping systems [9,11]. Pelletization may improve the spatial distribution of biochar within the soil profile and enable gradual, sustained interactions with soil microbial communities, further facilitating stabilization processes. Beyond direct carbon input, ABPM may indirectly influence carbon dynamics through improved nutrient retention and modified microbial metabolic activities [9].

3.2. Dynamics of GHG Emissions (CH₄ and N₂O) Under ABPM Treatments

The temporal dynamics of CH₄ fluxes and cumulative emissions, calculated according to Equations (3)–(5), are presented in Figure 4. Across all treatments, CH₄ emissions exhibited a distinct temporal pattern, characterized by a rapid increase to a peak at 15 days after transplanting (DAT), followed by a gradual decline by day 36 and subsequent stabilization.

ABPM 27 exhibited the highest peak flux (1.22 mg m⁻² day⁻¹), whereas ABPM 86 maintained a considerably lower peak flux (0.49 mg m⁻² day⁻¹). Cumulative CH₄ emissions reflected these trends: ABPM 27 reached 1.67 kg ha⁻¹, representing a 16.0% increase relative to the control, while ABPM 86 significantly reduced cumulative emissions to 0.89 kg ha⁻¹, corresponding to a 38.2% reduction.

These results suggest that the reduced emissions observed in the ABPM 86 treatment during the early growth stage may be associated with changes in soil aeration and microsite conditions that are less favorable for methane production [19]. Biochar pellet incorporation can enhance soil porosity and gas diffusivity, reducing the prevalence of anaerobic microsites associated with methane production.

In contrast, the elevated emissions under ABPM 27 indicate that the influence of biochar on CH₄ dynamics is highly context-dependent, being influenced by specific microbial compositions and the availability of labile carbon substrates within the “charosphere” [9,19,20]. Such labile carbon fractions may temporarily stimulate microbial processes associated with methane production within localized anaerobic microsites. These results emphasize that biochar–microbe interactions play an important role in regulating methane dynamics in intensive vegetable production systems [20].

The temporal dynamics of N₂O fluxes and cumulative emissions, calculated according to Equation (3) and (4), are presented in Figure 5. Across all treatments, N₂O emissions peaked within the first 15 days after transplanting (DAT) and subsequently stabilized. The control exhibited the highest instantaneous flux (19.35 mg m⁻² day⁻¹) at 15 DAT, whereas ABPM 86 showed an earlier and significantly lower peak (10.81 mg m⁻² day⁻¹) at 8 DAT. Cumulative N₂O emissions followed a similar pattern, increasing sharply until 15 DAT and plateauing thereafter. ABPM 86 recorded the lowest cumulative emission (11.14 kg ha⁻¹), followed by ABPM 27 (13.43 kg ha⁻¹), while the control reached 22.76 kg ha⁻¹. These values correspond to substantial reductions of 51.1% and 41.0% for ABPM 86 and ABPM 27, respectively, relative to the control. Total cumulative N₂O losses represented approximately 3.5–7.1% of the applied nitrogen (320 kg N ha⁻¹). These results suggest that the observed mitigation may be associated with enhanced NH₄⁺ retention by the acidified biochar and reduced substrate availability for nitrification and denitrification processes [4]. Biochar possesses a high specific surface area and cation exchange capacity (CEC) that can adsorb ammonium, thereby slowing its conversion to nitrate and limiting the substrates available for denitrification. Furthermore, synergistic interactions between the biochar matrix and the inoculated microbial strains (Pseudomonas and Bacillus) may facilitate more efficient nitrogen utilization and influence soil microsite conditions. These inoculated rhizobacteria may influence nitrogen transformation pathways and enhance plant–microbe competition for available nitrogen, thereby limiting nitrate accumulation prior to N₂O production.

These mechanisms are consistent with previous reports of reduced N₂O emissions following biochar application [21,22]. Moreover, biochar-amended soils may favor microbial communities associated with more complete denitrification (i.e., conversion of N₂O to N₂), potentially lowering net emissions. The integration of these biological and physicochemical processes appears to play a role in regulating nitrification and denitrification pathways, contributing to the observed mitigation effect.

3.3. Global Warming Potential (GWP) and Mitigation Efficiency

The net global warming potential (GWP), expressed as CO₂-equivalent (CO₂-eq), was calculated according to Equation (5). The lowest GWP was observed in the ABPM 86 treatment (3.07 t CO₂-eq ha⁻¹), representing a significant 50.9% reduction compared to the control (6.25 t CO₂-eq ha⁻¹). The ABPM 27 treatment also effectively reduced total emissions to 3.71 t CO₂-eq ha⁻¹, corresponding to a 40.6% decrease relative to the control (

Table 4. Global warming potential (GWP) and greenhouse gas mitigation under ABPM treatments during Chinese cabbage cultivation.

Treatments	CH4 Emissions (kg ha−1)	N2O Emissions (kg ha−1)	GWP (CO2-eq, t ha−1)
Control	1.44 b	22.76 b	6.25
ABPM 27	1.67 a	13.43 a	3.71
ABPM 86	0.89 b	11.14 a	3.07
F	2.7	10.22	-
p-values	<0.00002	<0.0004	-

CO₂ equivalence factors; CH₄, 27.3; N₂O, 273. Mean values followed by different letters indicate significant differences among treatments (p < 0.001) with CH₄ and N₂O emissions. (ANOVA and subsequent Duncan multiple range test).

).

Agricultural greenhouse gases, specifically CH₄ and N₂O, are primary contributors to the climatic footprint of cropping systems, with 100-year global warming potentials approximately 27.3 and 273 times greater than that of CO₂, respectively [17]. Given the substantially higher radiative forcing potential of N₂O, the reductions in N₂O emissions achieved in this study were the primary driver of the lower GWP values observed in the ABPM treatments. Notably, these reductions were achieved under a relatively high nitrogen application rate (320 kg N ha⁻¹), a condition generally associated with increased risks of N₂O emissions.

Although the underlying mechanisms were not directly investigated in the present study, previous studies have reported that biochar amendments may enhance nitrogen retention and modify soil environmental conditions, thereby potentially influencing N₂O production pathways [21,22]. In addition, plant growth-promoting rhizobacteria such as Pseudomonas and Bacillus have been reported to improve nutrient utilization and plant–soil interactions. Therefore, these mechanisms are presented only as possible explanations based on previous literature and should not be considered direct evidence from the current study.

It should be noted that the GWP values reported in this study were calculated from cumulative CH₄ and N₂O emissions measured during the 80-day cultivation period using IPCC GWP100 conversion factors. Therefore, the reported values represent the climate impact associated with greenhouse gas emissions during the experimental period and should not be interpreted as direct measurements, annualized estimates, or projections of long-term greenhouse gas dynamics.

Overall, these findings indicate that microbially engineered biochar pellets can contribute to reducing the climate impact of intensive vegetable production within the experimental boundary conditions. This approach may represent a promising strategy for integrated carbon–nitrogen management in sustainable agricultural systems [7].

However, soil physicochemical properties and plant nutrient uptake were not measured in the present study. Therefore, the proposed mechanisms should be regarded as potential explanations derived from previous studies rather than experimentally verified processes. Future research incorporating soil property characterization and plant nutrient analyses is needed to elucidate the mechanisms responsible for the observed reductions in greenhouse gas emissions.

3.4. Assessing Net Ecosystem Carbon Balance (NECB) and Its Role in Climate Change Mitigation

The Net Ecosystem Carbon Balance (NECB) of the organic Chinese cabbage production system was determined by integrating stable carbon sequestration with non-CO₂ greenhouse gas emissions (CH₄ and N₂O) according to Equation (6). As defined in our methodology, direct soil–atmosphere CO₂ exchange was excluded from the system boundary, as cropland systems are often assumed in field-scale carbon balance studies to exhibit near-balanced seasonal CO₂ exchange over a single growing period. By focusing on persistent carbon pools and non-CO₂ GHG pathways, our assessment identifies the primary factors governing the immediate climatic footprint of these agricultural soils, though it remains a field-level evaluation.

As summarized in

Table 5. Net ecosystem carbon balance (NECB) in the organic Chinese cabbage field under ABPM treatments.

Parameters *	Units	Equivalence Factors	Treatments
			Control	ABPM 27	ABPM 86
CH4 emissions (a)	kg ha−1	27.3	1.44	1.67	0.89
N2O emissions (b)	kg ha−1	273	22.76	13.43	11.14
CO2-eq of GHG emissions (a + b)	t ha−1	-	6.25	3.71	3.07
Carbon sequestration	t ha−1	-	1.34	1.73	1.64
CO2-eq of carbon sequestration (c)	t ha−1	3.664	4.91	6.34	6.01
NECB [c − (a + b)]	t ha−1	-	−1.34	2.63	2.94

* Values are means of three replicates. ABPM 27 and 86: acidified biochar–microbial pellets. (a), (b): CH₄ and N₂O converted to CO₂-eq using GWP₁₀₀ factors of 27.3 and 273 [16]. (c): Carbon sequestration (t C ha⁻¹) converted to CO₂-eq by a factor of 3.664 (44/12). Net ecosystem carbon balance: Calculated as [c − (a + b)]; positive and negative values indicate net carbon sink and source, respectively. GHG: Greenhouse gas; CO₂-eq: Carbon dioxide equivalent.

, the control exhibited a negative NECB (−1.34 t CO₂-eq ha⁻¹), indicating that the system functioned as a net GHG source under conventional guano amendment. In contrast, both ABPM treatments substantially improved the carbon balance, yielding positive NECB values of 2.63 and 2.94 t CO₂-eq ha⁻¹ for ABPM 27 and ABPM 86, respectively. These results indicate a transition of the production system from a net greenhouse gas source to a net carbon sink under ABPM treatments within the defined system boundary.

This shift is attributed to the combined effects of enhanced carbon stabilization and reduced non-CO₂ greenhouse gas emissions (CH₄ and N₂O). The porous structure of biochar may promote soil aggregation and provide physical protection for organic carbon within the soil matrix, while interactions with the inoculated microbial communities improve nitrogen cycling efficiency and reduce N₂O formation. While a full life-cycle assessment—incorporating production and transport emissions—would be required to confirm absolute carbon neutrality, these findings indicate that microbially engineered biochar pellets may improve short-term carbon balance and reduce field-level greenhouse gas emissions under the experimental conditions.

Recent assessments suggest that biochar application holds considerable potential to contribute to negative emission strategies in agricultural landscapes [23]. Furthermore, the high aromaticity and structural stability of biochar support its capacity for long-term carbon persistence in soils [11,24]. These characteristics, combined with targeted microbial inoculation, suggest that ABPM may represent a promising strategy for improving soil carbon management in intensive vegetable production systems.

3.5. Assessing Plant Growth and Clubroot Suppression

Biochar-mediated microbial activity significantly influences rhizosphere processes, including nutrient cycling and microbial diversity [11]. Consistent with these mechanisms, the growth and yield of Chinese cabbage varied significantly among treatments (

Table 6. Growth parameters and marketable yield of Chinese cabbage under different acidified biochar–microbial pellet (ABPM) treatments.

Treatments	Plant Height (cm plant−1)	Head Height (cm plant−1)	Head Width (cm plant−1)	Yield (tonnes ha−1)
Control	33.1 ± 1.2	35.8 a	19.8 a	71.3 b
ABPM 27	35.8 ± 0.8	36.1 a	19.0 ab	77.4 a
ABPM 86	28.9 ± 2.0	34.9 a	15.8 c	70.2 bc
F	-	4.9	10.4	12.5
p-values	-	<0.004	<0.00002	<0.001

Mean values followed by different letters indicate significant differences (p < 0.001) among treatments for head height, head width, and yield (ANOVA and subsequent Duncan multiple range test).

). ABPM 27 produced the highest marketable yield (77.4 t ha⁻¹), representing an 8.6% increase compared to the control (71.3 t ha⁻¹). This improvement is likely associated with the interactions between the acidified biochar matrix and the inoculated microbial consortia, which enhanced nutrient bioavailability and rhizosphere functionality. Beneficial rhizobacteria (PGPR) colonized on biochar surfaces can promote plant growth through mechanisms such as phosphorus solubilization, phytohormone production, and the competitive exclusion of soil-borne pathogens.

The incorporation of biochar pellets also modifies soil physicochemical properties, including pH and nutrient retention capacity, which directly influence the development of clubroot disease caused by Plasmodiophora brassicae. In this study, ABPM treatments substantially reduced clubroot severity (

Table 7. Disease severity index and control efficacy against clubroot (Plasmodiophora brassicae) under different acidified biochar–microbial pellet (ABPM) treatments.

Treatments	Disease Severities (%)	Control Efficiency (%)
Control	61.9 ± 24.3	-
ABPM 27	33.3 ± 8.3	46.2 a
ABPM 86	52.4 ± 8.3	15.4 b

Mean values followed by different letters indicate significant differences (p < 0.001) among treatments in control efficiency (Duncan multiple range test). Disease severity was assessed using a 0–5 scale, where 0 = no symptoms and 5 = severe clubroot symptoms.

). The control exhibited a disease severity of 61.9%, whereas ABPM 27 significantly reduced it to 33.3%, corresponding to a control efficacy of 46.2% (p < 0.001). In contrast, ABPM 86 resulted in lower plant height (28.9 cm) and yield (70.2 t ha⁻¹), and showed relatively limited disease suppression (15.4%). This suggests that formulation-specific properties and biochar–microbe interactions may play a more important role than biochar input alone under field conditions. This is consistent with global meta-analyses reporting that while moderate biochar application typically increases yields by 8–10%, high application can induce immobilization in certain soil systems [7,19].

Beyond direct growth promotion, the combined effects of acidified biochar and PGPR inoculation may contribute to shifts in rhizosphere microbial communities and suppression of pathogen activity in Brassica crops [25,26,27]. The increased soil pH (locally modulated by biochar) and enhanced microbial competition in the rhizosphere may contribute to reduced proliferation of P. brassicae and lower infection pressure on host roots. These findings demonstrate that microbially engineered biochar pellets, particularly the ABPM 27 formulation, represent a multifunctional strategy for simultaneously enhancing crop productivity and biological disease suppression in sustainable organic production systems.

3.6. Dynamics of Microbial Community Shifts and Succession

The diversity and relative abundance of Bacillus spp. in ABPM-amended soils are summarized in

Table 8. Heatmap of the taxonomic composition and relative abundance (log-transformed values) of Bacillus species in rhizosphere soils under different acidified biochar–microbial pellet (ABPM) treatments during Chinese cabbage cultivation.

Sampling Periods	Bacillus Strains	Control	ABPM 27	ABPM 86		Scale Bar
3 DAT	Bacillus					>3
	Other					2.5
	Bacillus_smithii					2
	Bacillus_subtilis					1.5
30 DAT	Bacillus					1
	Other					0.5
	Bacillus_amyloliquefaciens					0
	Bacillus_andreraoultii
	Bacillus_benzoevorans
	Bacillus_infernus
	Bacillus_renqingensis
	Bacillus_smithii
	Bacillus_thuringiensis
Post-harvest	Bacillus
	Other
	Bacillus_amyloliquefaciens
	Bacillus_andreraoultii
	Bacillus_benzoevorans
	Bacillus_borbori
	Bacillus_cheonanensis
	Bacillus_infernus
	Bacillus_marasmi
	Bacillus_niameyensis
	Bacillus_salipaludis
	Bacillus_smithii
	Bacillus_stratosphericus

DAT, days after transplanting. Heatmap colors indicate relative abundance based on log-transformed read counts; darker colors represent higher abundance.

. At 3 DAT, B. smithii, B. subtilis, and several other taxa were present at comparable levels across all treatments. In ABPM 86 (inoculated with B. megaterium 22BCO086), specialized taxa—including B. andreraoultii, B. benzoevorans, B. infernus, B. renqingensis, and B. thuringiensis—were detected at concentrations of 0.48–1.18 log units. By 30 DAT, B. smithii (1.18 log units) was the only species consistently detected in both the control and ABPM 27 soils, indicating a mid-season decline in Bacillus diversity. Notably, at the post-harvest stage, ABPM 27 exhibited higher apparent taxonomic richness; species such as B. benzoevorans, B. borbori, B. cheonanensis, B. salipaludis, and B. stratosphericus (0.60–3.48 log units) were detected only in this treatment, while these taxa were not observed in ABPM 86.

Our temporal analysis suggests distinct patterns of microbial succession between the two formations: ABPM 86 promoted higher early-stage Bacillus abundance, whereas ABPM 27 was associated with greater post-harvest diversity. This pattern may reflect differences in microenvironmental conditions, including nutrient availability and habitat heterogeneity, associated with the two biochar formulations. The porous structure and high specific surface area of biochar create protected ecological niches that facilitate microbial colonization and persistence, thereby influencing observed successional dynamics over the cropping period.

These observations align with prior studies demonstrating that biochar amendments enhance PGPR persistence by improving root–microbe interactions and nutrient retention [6]. Biochar surfaces act as microbial refugia, protecting beneficial bacteria from environmental stressors while enabling close association with the host rhizosphere. Furthermore, the co-application of biochar and beneficial microbes has been reported to suppress soil-borne pathogens, including P. brassicae, potentially through shifts in microbial community structure and antagonistic interactions [26,27]. Such shifts may contribute to competitive interactions within the rhizosphere that limit pathogen establishment.

These results indicate that microbially engineered biochar may modulate microbial succession patterns and contribute to soil functional resilience in intensive cabbage production. The observed increase in taxonomic richness in ABPM 27 post-harvest further suggests that optimized biochar–microbe formulations may support more stable and functionally diverse microbial assemblages under field conditions.

The Pseudomonas community composition exhibited distinct successional trajectories across treatments and sampling stages (

Table 9. Headmap of taxonomic compositions and relative abundance (log units) of Pseudomonas species in rhizosphere soils under different acidified biochar–microbial pellet (ABPM) treatments during Chinese cabbage cultivation.

Treatments	Pseudomonas Strains	Control	APM 27	APM 86		Scale Bar
3 DAT	Pseudomonas					>3.5
	Pseudomonas_chlororaphisd					3
	Pseudomonas_flexibilis					2.5
	Pseudomonas_fragi					2
	Pseudomonas_oryzae					1.5
	Pseudomonas_putida					1
	Pseudomonas_tolaasii					0.5
30 DAT	Pseudomonas					0
	Other
	Pseudomonas_chlororaphis
	Pseudomonas_fluorescens
	Pseudomonas_frederiksbergensis
	Pseudomonas_laurylsulfativorans
	Pseudomonas_mandelii
	Pseudomonas_marginalis
	Pseudomonas_migulae
	Pseudomonas_monteilii
	Pseudomonas_putida
	Pseudomonas_tolaasii
Post-harvest	Pseudomonas
	Other
	Pseudomonas_brassicacearum
	Pseudomonas_chlororaphis
	Pseudomonas_frederiksbergensis
	Pseudomonas_laurylsulfativorans
	Pseudomonas_marginalis
	Pseudomonas_migulae
	Pseudomonas_monteilii
	Pseudomonas_oleovorans
	Pseudomonas_putida

DAT, days after transplanting. Heatmap colors indicate relative abundance based on log-transformed read counts; darker colors represent higher abundance.

). At 3 DAT, the total abundance of Pseudomonas was comparable among treatments, with P. chlororaphis consistently identified in all plots. Compared to the control, P. flexibilis and P. fragi were exclusively detected in ABPM 86 at this early stage, suggesting an early treatment-associated response to pellet application. This initial phase likely reflects rapid colonization of rhizosphere niches facilitated by the microbial inoculum and the protective microhabitat provided by the biochar matrix.

By 30 DAT, community divergence became more pronounced. Several mid-season taxa—including P. mandelii, P. migulae, P. monteilii, and P. putida (1.08–2.08 log units)—were identified exclusively in ABPM 27, whereas these species were absent or present only at negligible levels in the control. Many species within the genus Pseudomonas are recognized as plant growth-promoting rhizobacteria (PGPR), capable of nutrient solubilization, siderophore production, and the synthesis of antimicrobial compounds. At the post-harvest stage, P. migulae remained dominant in ABPM 86 (1.86 log units), indicating that treatment-specific successional trajectories over the cropping period.

These temporal patterns suggest that microbial inoculation influenced community assembly dynamics rather than merely altering initial abundance. In particular, the broader mid-season representation of Pseudomonas taxa in ABPM 27 reflects greater niche differentiation within the biochar-amended rhizosphere. Biochar-derived microsites provide spatially heterogeneous habitats that support coexistence of multiple rhizobacterial taxa. Biochar has been shown to offer both physical refugia and labile carbon fractions that facilitate rhizobacterial establishment and metabolic activity [10].

The enrichment of multiple Pseudomonas taxa in ABPM 27 indicates potential functional diversification within the rhizosphere community. Such treatment-specific succession patterns are consistent with reports that biochar–microbe co-application can modify rhizosphere network structure and strengthen biological control capacity [6,23].

Enhanced populations of beneficial Pseudomonas spp. likely contribute to the suppression of soil-borne pathogens through antibiosis, siderophore-mediated iron competition, and possible induction of plant defense responses. Overall, ABPM 27 showed a more diversified and temporally stable Pseudomonas assemblage, suggesting improved functional potential of the rhizosphere microbiome under this formulation.

4. Conclusions

This study demonstrates that the application of acidified, microbially engineered biochar pellets (ABPM) can enhance carbon sequestration, greenhouse gas (GHG) mitigation, and agronomic performance in organic Chinese cabbage (Brassica rapa ssp. pekinensis) cultivation. Compared to the control, ABPM 27 and ABPM 86 increased soil carbon sequestration by 29.1% and 22.4%, respectively. ABPM 86 achieved the most substantial reduction in global warming potential (GWP), decreasing cumulative CH₄ and N₂O emissions by 38.2% and 51.1%, respectively. Consequently, the net ecosystem carbon balance (NECB) reached 2.63 and 2.94 t CO₂-eq ha⁻¹ for ABPM 27 and ABPM 86. These positive NECB values indicate a shift from a net greenhouse gas source toward a net carbon sink within the field-scale boundary defined in this study.

Agronomic productivity and plant health were concurrently improved. ABPM 27 exhibited the highest agronomic performance, achieving the highest marketable yield (+8.6%) and the most effective suppression of clubroot disease (46.2%), while ABPM 86 provided a lower level of disease suppression (15.4%). Rhizosphere microbial analyses confirmed that these improvements were associated with treatment-specific shifts in community succession, including the enrichment of beneficial Pseudomonas and Bacillus taxa. Integrating targeted microbial inoculation with engineered biochar pellets provides a multifunctional framework to simultaneously strengthen carbon sink capacity, mitigate GHG emissions, and ensure crop productivity. These findings demonstrate the potential of microbially engineered biochar systems as a climate-smart strategy for sustainable vegetable production. However, to bridge the gap between field-level observations and global climate goals, future research should focus on long-term field monitoring and comprehensive life-cycle assessments (LCA) to further evaluate the ecological persistence and scalability of biochar–microbe technologies in diverse agricultural systems. These subsequent evaluations will provide a more comprehensive understanding of the technology’s scalability and its long-term impact on soil health.