Dominant Role of Irrigation Regime over Biochar in Controlling GHG Emissions from Paddy Fields

Abstract

Biochar is widely used in agriculture to enhance crop yield, improve soil fertility, and regulate greenhouse gas (GHG) emissions. Its effectiveness, however, depends not only on its properties but also on soil moisture conditions, making integrated water management essential for maximizing its benefits. The study reports the results of a laboratory incubation experiment using three biochar application rates (0, 20, and 40 t ha⁻¹) and two irrigation regimes—flooded irrigation and alternate wetting and drying (AWD)—to investigate the effects of biochar amendment and water management on soil greenhouse gas (GHG) emissions. The results indicated that there was no significant interaction between biochar and water regulation on GHG emissions, and changes in soil moisture and biochar application levels had no significant impact on carbon dioxide (CO₂) emissions. Compared to flooded irrigation, AWD effectively enhanced soil microbial activity, increasing nitrous oxide (N₂O) emissions by 62.50% to 88.35%, but significantly reducing methane (CH₄) emissions by 44.30% to 68.55%, thereby lowering the soil’s global warming potential (GWP). Additionally, biochar amendment significantly increased soil SOC and TN contents, enhanced soil enzyme activities, and significantly improved microbial carbon use efficiency (CUE), the C/N ratio, and the net nitrification rate (NNR). However, it had no significant effect on soil N₂O and CO₂ emissions, while significantly suppressed CH₄ emissions. Throughout the entire growth period, biochar application increased soil GWP overall. However, during the first water cycle, GWP increased with higher biochar application rates, whereas in the second water cycle, biochar application exhibited a suppressive effect on GWP. In conclusion, integrating biochar application with AWD irrigation can optimize soil CUE, enhance soil nutrient supply, and mitigate, to some extent, the potential increase in GHG emissions induced by biochar. This provides valuable insights for carbon management and sustainable agricultural development.

1. Introduction

Paddy fields have long been a focus of intensive research due to their role as a major anthropogenic source of GHG emissions resulting from rice cultivation. It is reported that emissions from paddy soils may account for up to 55% of total GHG outputs associated with agricultural soils [1]. Annually, rice paddies are responsible for approximately 20% to 30% of CH₄ emissions originating from global agriculture, contributing nearly 10% of total global CH₄ emissions [2]. As rice production continues to expand in response to growing food demands, associated GHG emissions from paddy systems are also expected to increase. This trend underscores the urgency of adopting climate-smart agricultural practices, especially regarding water management, given the high water intensity of conventional rice cultivation [3,4]. For instance, the overall mean irrigation water used in farmers’ paddies was approximately 841 mm; however, optimized irrigation practices have been shown to reduce water usage by 40% [5]. This trend underscores the urgency of adopting climate-smart agricultural practices, particularly in water use, since conventional rice cultivation is highly water-intensive. Soil management practices, such as the application of biochar and moisture regulation, have gained significant attention due to their potential to influence GHG emissions and improve soil health in agricultural systems [6,7,8]. The application of biochar, a stable form of carbon derived from organic materials, has been shown to enhance soil fertility, improve water retention, and influence microbial activity, all of which can contribute to the impact of GHG emissions [9]. Some studies considered that biochar could mitigate the greenhouse effect by decreasing soil N₂O emissions [10,11]. However, the impact of biochar on GHG emissions, particularly under different moisture conditions, remains interactive effect and complex [10,12]. Therefore, these findings suggest that the effectiveness of biochar in mitigating greenhouse gas emissions is strongly influenced by soil moisture conditions. However, its effectiveness in paddy fields, particularly when combined with different irrigation practices, is less understood.

In particular, paddy fields, which are characterized by their unique irrigation regimes and waterlogged conditions, offer both challenges and opportunities for carbon and nitrogen management [13,14]. In paddy fields, water management practices, such as submerged and AWD, significantly affect soil microbial communities and nutrient dynamics and raise the emission of N₂O, and CO₂ [8,15], which allows for alternating wet and dry periods, and may provide a more favorable environment for reducing emissions by promoting aerobic conditions [16,17]. Under submerged irrigation, the soil remains in a consistently anaerobic state, which promotes methanogenic activity and results in significantly increased CH₄ emissions. Meanwhile, aerobic processes such as nitrification are inhibited, leading to relatively low N₂O emissions [13,18]. However, since biochar can effectively modify soil pore size, oxygen content [19], and aggregate stability [20], it can interact with soil moisture regulation to influence the production of greenhouse gases in the soil. A systematic analysis of the interactive effects of water input and biochar application rate on soil GHG emissions is crucial for minimizing unnecessary resource consumption and protecting the environment.

In this context, the study was based on two hypotheses: (a) There is a significant interaction between biochar application and water regime, which jointly regulates GHG emissions through changes in soil properties; (b) The strength and direction of this interaction vary with different biochar application rates. By examining the synergistic effects of biochar and soil moisture regulation, this study aims to provide a clearer understanding of their combined potential to mitigate greenhouse gas emissions in paddy field ecosystems.

2. Materials and Methods

2.1. Trial Design

A laboratory experiment was conducted in August 2024 at the Changjiang River Scientific Research Institute to simulate different irrigation regimes and biochar application rates in paddy fields using a constant-temperature incubation method. The basic physicochemical properties of the soil were as follows: the electrical conductivity (EC) was 102.8 μS cm⁻¹, and the pH was 7.95 (measured at a soil-to-water ratio of 1:2.5), indicating a slightly alkaline condition. The soil texture was dominated by silt, with clay, silt, and sand contents of 26.52%, 69.14%, and 4.34%, respectively. The concentrations of ammonium nitrogen (NH₄⁺-N) and nitrate nitrogen (NO₃⁻-N) were 2.47 mg kg⁻¹ and 6.69 mg kg⁻¹, respectively. The total nitrogen (TN) content was 0.46 g kg ⁻¹, and the soil organic carbon (SOC) content was 7.12 g kg⁻¹.

The experiment included two irrigation types (continuous flooding irrigation and intermittent irrigation) and three biochar application rates (0, 20, and 40 t ha⁻¹). Detailed treatment abbreviations are listed in

Table 1. Treatment abbreviations.

Abbreviation	Biochar Level (t ha−1)	Irrigation	WHC
W0B0	0	flooding	130%
W0B1	20	flooding	130%
W0B2	40	flooding	130%
W1B0	0	intermittent	100–70%
W1B1	20	intermittent	100–70%
W0B2	40	intermittent	100–70%

Note: Biochar type: rice straw-derived, produced at 500 °C.

. For each treatment, 75 g of air-dried soil was thoroughly mixed with the designated amount of biochar and evenly distributed into 250 mL incubation bottles. The corresponding biochar additions were 0.54 g for 20 t ha⁻¹ and 1.07 g for 40 t ha⁻¹ application rates. Deionized water was added to adjust the soil moisture content to 70% of the field capacity (WHC). WHC was determined using the gravimetric saturation method. Air-dried soil was saturated with deionized water and allowed to drain freely for 24 h at room temperature. The WHC was then calculated based on the difference between the saturated weight and the oven-dry weight of the soil sample and expressed as a percentage of dry weight. The bottles were covered with plastic wrap with 4–5 small perforations for ventilation and pre-incubated in the dark at 25 °C for 7 days to activate soil microorganisms.

After the pre-incubation period, the soil moisture content was adjusted to either flooded conditions (2 cm water layer) or 130% WHC (field capacity equivalent). The incubation bottles were then placed in an incubator at 25 °C in the dark and incubated for 30 days, with the incubation period divided into two time points (first and second), each spanning 15 days. Flooding irrigation treatments were weighed daily, and water was replenished to maintain a constant soil moisture level. AWD followed two wet–dry cycles, with each cycle lasting 15 days. During the flooding phase, the bottles remained open, and when the water evaporated to 70% WHC, they were covered with perforated plastic wrap. Water was added daily to maintain the soil moisture content at 70% WHC.

2.2. Gas and Soil Sampling

Gas sample: Gas sampling was conducted on days 1, 3, 5, 7, 11, 15, 16, 18, 20, 22, 26, and 30 during the incubation period. For each sampling point, three incubation bottles were randomly selected from each treatment, and gas samples were collected to analyze the concentrations of CO₂, N₂O, and CH₄. These gases were measured using gas chromatography with an Agilent GC-6820 system (Agilent Technologies Inc., Santa Clara, CA, USA). One day before gas collection, the gas inside the bottles was mixed by repeatedly drawing and releasing air five times using a syringe. The incubation bottles were then sealed and left for 24 h under airtight conditions. After 24 h of incubation, gas samples were extracted from the headspace of incubation bottles using a 20 mL gas-tight syringe equipped with a three-way stopcock. After mixing the headspace gas thoroughly, the syringe was inserted through the butyl rubber septum, and the sample was immediately transferred to pre-evacuated glass vials for storage and subsequent gas chromatography analysis. After sampling, all incubation bottles were opened and ventilated for 1 h to meet the respiratory needs of aerobic soil microorganisms. Subsequently, the bottles were covered with perforated plastic wrap and the incubation continued. The same gas collection procedure was repeated at each sampling point. The flux emission was calculated referred to Formula (1) [21]. The cumulative gas was determined as the sum of the daily concentrations, and the unmonitored values were estimated from the adjacent differences, referred to by Wei et al., 2019 [22].

$$F={\displaystyle \frac{{\mathrm{M}}_0\times 273\times {\mathrm{P}}_0\times \mathrm{V}}{22.4\times (237+\mathrm{T})\times \mathrm{P}\times \mathrm{M}}}$$

(1)

${\mathrm{M}}_0$ is the mass fraction of CO₂, N₂O, and CH₄. (g mol⁻¹), ${\mathrm{P}}_0$ is the standard atmospheric pressure (kPa), P represents actual pressure inside the sampling vial (kPa), $\mathrm{V}$ is the volume increment of CO₂, N₂O, and CH₄ in the sampling vial (mL), $\mathrm{M}$ is the quantity of the soil (kg), and T is the temperature of the bottle (°C).

GWP was calculated by the following Formula (2)

$$\mathrm{G}\mathrm{W}\mathrm{P}=265{\mathrm{C}}_{{\mathrm{N}}_2\mathrm{O}}+{\mathrm{C}}_{\mathrm{C}{\mathrm{O}}_2}+28{\mathrm{C}}_{\mathrm{C}{\mathrm{H}}_4}$$

(2)

${\mathrm{C}}_{{\mathrm{N}}_2\mathrm{O}}$ means cumulative soil N₂O emissions during the incubation period, ${\mathrm{C}}_{\mathrm{C}{\mathrm{O}}_2}$ means cumulative soil CO₂ emissions during the period of incubation, ${\mathrm{C}}_{\mathrm{C}{\mathrm{H}}_4}$ means cumulative soil CH₄ emissions during the period of incubation. These factors were selected from the Intergovernmental Panel on Climate Change (IPCC) report [23].

Soil properties: Every treatment had five replicates. Soil properties (pH, EC, ammonium nitrogen (NH₄⁺), nitrate (NO₃⁻) were measured at 3, 7, 11, 16, 22, and 30 d during the incubation. The SOC, TN and soil microbial biomass carbon (MBC) were measured at the end of incubation. Soil pH and EC were measured by by a multi-parameter tester (SG23, Mettler Toledo, Shanghai, China) and the NH₄⁺, NO₃⁻ and TN were measured by a continuous flowing analyzer (Alliance FUTURA, AMS, Frépillon, France). The detailed measurement methods can be found in Wei et al. [22]. The SOC was determined via the potassium dichromate volumetric method [24]. The MBC was measured by chloroform fumigation-incubation as previously described by Wu et al., 1990 [25].

Carbon Use Efficiency (CUE) was calculated by the following Formula (3) [26].

$$CUE={\displaystyle \frac{{∆}_{MBC}}{{∆}_{MBC}+{CO}_2}}$$

(3)

${∆}_{MBC}$ is the increase in MBC during the incubation period, and CO₂ is cumulative CO₂ emission during the incubation period.

The C/N ratio represents the proportion of SOC to TN, reflecting the balance between carbon and nitrogen availability. Net nitrification rate (NNR), refers to the rate at which NH₄⁺ is converted into ${\mathrm{N}\mathrm{O}}_3^{-}$ in the soil over a given period. To determine NNR, the concentration of ${\mathrm{N}\mathrm{O}}_3^{-}$ is plotted against time, and a linear regression is fitted to the data; the slope of the regression line represents the net nitrification rate [27].

Soil Enzymes: At the end of the incubation period, fresh soil samples were collected to analyze the activities of soil enzymes. Sucrase activity (S-S) was quantified using the 3,5-dinitrosalicylic acid (DNS) method, which measures the amount of reducing sugars released after incubation with sucrose at 37 °C for 1 h [28]. β-Glucosidase activity (S-β-G) was assessed following the protocol of Bell et al. (2013) [29], using a fluorescence-based microplate assay with 4-methylumbelliferyl-β-D-glucopyranoside as the substrate. Fluorescence intensity was recorded using a microplate reader at an excitation wavelength of 365 nm and an emission wavelength of 450 nm. Urease activity (S-U) was measured using urea as a substrate, and the amount of NH₄⁺ released was determined colorimetrically.

2.3. Statistical Analysis

All data were tested for normality using the Shapiro–Wilk test and for homogeneity of variances using Levene’s test prior to performing parametric analyses. Outliers were visually inspected using boxplots, and no extreme values were removed from the dataset. A two-way analysis of variance (ANOVA) was conducted using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) to evaluate the effects of biochar application rates and irrigation regimes on the measured variables. Differences between treatment means were assessed using the least significant difference (LSD) test at the 0.05 significance level. The term “average” refers to the arithmetic mean throughout the study. To assess the relationships between biochar application levels and soil GHG emissions (CO₂, CH₄, and N₂O), Mantel tests were performed using the “vegan” package in R, based on Euclidean distance matrices. The data used for Mantel analysis were calculated based on mean values across the incubation period.

3. Results

3.1. GHG Emission

As shown in Figure 1a, all treatments exhibited a similar pattern of CH₄ emissions, with a peak occurring during the first wet–dry cycle, followed by a gradual decline. The CH₄ emission peaks for W0B0, W0B1, W1B0, and W1B1 were observed on day 5 of incubation, reaching 1.60, 2.61, 0.43, and 1.64 mg kg⁻¹ d⁻¹, respectively. Meanwhile, the peak emissions for W0B2 and W1B2 occurred on day 7, with values of 2.64 and 2.92 mg kg⁻¹ d⁻¹, respectively. Further analysis of the effects of moisture and biochar on soil CH₄ emissions (

Table 2. Statistical significance analysis for soil greenhouse gas cumulative emissions.

Factors	First Period			Second Period			Whole Period
Gas	CH4	N2O	CO2	CH4	N2O	CO2	CH4	N2O	CO2
Ir	24.36 **	66.21 **	1.11 n.s	47.85 **	5.55 *	118.93 ***	29.84 *	57.16 **	3.23 *
Bi	6.62 *	1.99 n.s	0.94 n.s	9.49 *	3.01 *	1.53 n.s	1.11 n.s	0.47 n.s	0.59 n.s
Ir&Bi	0.05 n.s	0.026 n.s	1.08 n.s	4.51 *	2.52 n.s	3.95 *	0.19 n.s	0.09 n.s	2.00 n.s

Note: The values represent the F-values from the two-way ANOVA; Each treatment had three replicates (n = 3); Ir means irrigation type; Bi means biochar; n.s means p > 0.05; * means p < 0.05; ** means p < 0.01; *** means p < 0.001.

) revealed that both biochar application and irrigation significantly influenced cumulative CH₄ emissions during the first period, with no significant interaction between the two factors. In contrast, a clear interaction effect between biochar and irrigation was observed during the second stage. In the first period, AWD irrigation effectively reduced cumulative CH₄ emissions (p < 0.05) (Figure 1b), and CH₄ emissions decreased with increasing biochar application rates (p < 0.05). In the second period, under the same biochar treatment, CH₄ emissions in the W0 (continuous flooding) treatment were higher than in the W1 (AWD) treatment. Under the same irrigation regime, biochar application effectively reduced CH₄ emissions, with the lowest cumulative CH₄ emissions observed in the W1B2 treatment (Figure 1b).

As shown in Figure 1a, CO₂ emissions in the W1 treatment followed a similar pattern, with a peak occurring during the first wet–dry cycle, after which emissions gradually stabilized. The peak CO₂ emissions for W1B0, W1B1, and W1B2 were 16.91, 17.74, and 19.60 mg·kg⁻¹·d⁻¹, respectively. In contrast, CO₂ emissions in the W0 treatment showed a continuous increase over the incubation period, stabilizing around day 15, with peak values of 13.08, 15.13, and 16.22 mg·kg⁻¹·d⁻¹ for W0B0, W0B1, and W0B2, respectively. Further analysis of the effects of moisture and biochar on soil CO₂ emissions (Table 2) indicates that biochar application had no significant effect (p > 0.05), while moisture treatment was the dominant influencing factor (p < 0.05). As shown in Figure 1b, W1 significantly reduced cumulative CO₂ emissions, with W1 showing a 2.56% to 23.58% decrease compared to W0. The impact of moisture on cumulative soil CO₂ emissions was particularly evident during the second wet–dry cycle. During the first cycle, W1 resulted in a 6.42% to 36.27% increase in cumulative CO₂ emissions compared to W0, but this difference was not statistically significant (p > 0.05). However, during the second cycle, cumulative CO₂ emissions in W1 were significantly higher than in W0, with an increase ranging from 37.20% to 77.56% (p < 0.05).

As shown in Figure 1a, all treatments exhibited a similar N₂O emission pattern, with a peak occurring during the first wet–dry cycle, followed by a gradual decline. The peak N₂O emissions for W1B0, W1B1, and W1B2 occurred on day 5, reaching 152.06, 127.16, and 126.66 μg kg⁻¹ d⁻¹, respectively. For the W0 treatments, the peak emissions for W0B0 and W0B1 were also observed on day 5, with values of 10.57 and 6.32 μg kg⁻¹ d⁻¹, while W0B2 showed a delayed peak on day 9, reaching 19.98 μg kg⁻¹ d⁻¹. Further analysis of the effects of moisture and biochar on soil N₂O emissions (Table 2) indicates that moisture treatment was the primary influencing factor (p < 0.05). W1 significantly increased soil N₂O emissions, with an increase of 62.50% to 88.35% compared to W0. As shown in Table 2, although biochar application had no significant effect on soil N₂O emissions over the entire incubation period (p > 0.05), it significantly increased N₂O emissions during the second stage of incubation (p < 0.05). However, among the biochar treatments, the B1 treatment resulted in the lowest cumulative N₂O emissions, with values of 47.82 μg kg⁻¹ d⁻¹ under W0 and 410.68 μg kg⁻¹ d⁻¹ under W1.

3.2. Soil Carbon and Nitrogen Pools

As shown in Figure 2, AWD irrigation significantly increased soil TN content compared to flooded irrigation (p < 0.05). Specifically, TN in the W1B0 treatment was 7.02% higher than in W0B0, and W1B2 was 6.56% higher than in W0B2, with both differences being statistically significant (p < 0.05). In addition, biochar application led to a slight increase in TN content. Under the W0 regime, TN increased by 7.17% and 7.82% in the W0B1 and W0B2 treatments, respectively, compared to W0B0 (p < 0.05). Under the W1 regime, TN increased by 0.31% and 5.79% in W1B1 and W1B2, respectively, compared to W1B0; however, these differences were not statistically significant (p > 0.05).

Analysis of soil MBC showed that the irrigation regime had a significant effect. As illustrated in Figure 2, W1 treatments resulted in a 24.85% to 33.11% increase in MBC compared to W0 treatments (p < 0.05). Moreover, MBC exhibited a trend of increasing and then decreasing with rising biochar application rates. Under W0 conditions, no significant differences in MBC were observed among biochar treatments. In contrast, under W1 conditions, the W1B1 treatment significantly increased MBC by 12.77% and 9.18% compared to W1B0 and W1B2, respectively (p < 0.05).

Regarding soil SOC, the irrigation regime had no significant effect (p > 0.05), while biochar was the primary influencing factor (p < 0.05). As shown in Figure 2, SOC content increased with increasing biochar application. The W0B2 treatment significantly increased SOC by 53.86% and 22.96% compared to W0B0 and W0B1, respectively, while W1B2 increased SOC by 53.18% and 14.85% compared to W1B0 and W1B1 (p < 0.05).

3.3. Soil Enzymes Activity

For S-U activity, the experiment found that W1 consistently increased S-U activity (p < 0.05), with W1 increasing by 45.08–92.79% compared to W0 (Figure 3). Additionally, under W0 conditions, biochar had no significant effect on S-U activity (p > 0.05), whereas under W1 conditions, higher biochar (B2) significantly increased S-U activity (p < 0.05). Specifically, S-U activity in the W1B2 treatment increased by 22.56% and 63.64% compared to W1B1 and W1B0, respectively (p < 0.05).

As shown in Figure 3, under the W0 treatment, biochar application had no significant effect on S-β-G activity (p > 0.05). However, under the W1 treatment, S-β-G activity decreased progressively with increasing biochar application. Specifically, S-β-G activity in W1B1 and W1B2 was significantly reduced by 20.33% and 19.34%, respectively, compared to W1B0 (p < 0.05), indicating a potential inhibitory effect of biochar on S-β-G activity under AWD conditions.

In contrast, for S-S activity, biochar application under the same irrigation regime showed no significant effect (p >0.05), as also illustrated in Figure 3. When comparing different irrigation regimes at the same biochar level, AWD tended to increase S-S activity relative to flooded irrigation. Notably, S-S activity in the B1 treatment under W1 was significantly higher than that under W0, with an increase of 58.85% (p < 0.05).

3.4. Soil Inorganic Nitrogen

All treatments exhibited a similar pattern of soil nitrogen content variation. As shown in Figure 4, soil NO₃⁻ concentrations increased over time, while soil NH₄⁺ concentrations gradually decreased. Additionally, soil NO₂⁻ showed a peak between days 20 and 25. Analysis of the impact of biochar on soil nitrogen content suggests that biochar effectively enhances soil nitrogen levels. As shown in Figure 4, the W0B2 and W1B2 treatments had higher NH₄⁺ and NO₃⁻ concentrations compared to other treatments, and their NO₂⁻ peak values were also numerically higher. This is primarily due to the higher biochar content, which can effectively promote soil nitrogen utilization, accelerate organic nitrogen mineralization, and enhance nitrogen transformation processes.

3.5. Influencing Factors

As shown in Figure 5, the changes in soil environmental factors differ between the W0 and W1 treatments. Under the W0 treatment, the variation in soil NO₃⁻ is negatively correlated with other soil factors, whereas under the W1 treatment, the changes in NH₄⁺ and NO₂⁻ are negatively correlated with their environmental factors. Further analysis of the effects of biochar application on soil environment and GHG emissions under different irrigation treatments revealed that, under the W0 treatment, the changes in biochar amount significantly correlate with N₂O emissions, particularly with EC, MBC, NO₂⁻, and SOC. However, CH₄ and CO₂ emissions show no significant correlation with soil environmental factors (Figure 5a). In contrast, under the W1 treatment, only CH₄ emissions are significantly correlated with TN, while CO₂ and N₂O emissions show no significant relationship with soil environmental factors (Figure 5b). This could be because, under the submerged conditions, the anoxic environment in the soil may cause significant microbial activity changes. The increase in biochar application likely enhances microbial activity by improving soil physical and chemical properties (such as aeration, porosity, and nutrient retention capacity), thereby significantly influencing N₂O emissions. However, under intermittent irrigation, the large fluctuations in soil moisture may reduce the impact of biochar, leading to minimal effects of environmental factor changes on N₂O emissions.

As shown in

Table 3. Accumulated temperature effect and soil carbon and nitrogen transformation indicators.

Label	First Period					Second Period
	CUE	C/N	NNR	GWP	CUE	C/N	NNR	GWP
W0B0	7.32 ± 0.11 c	11.32 ± 0.14 b	3.16 ± 0.12 c	526.83 ± 38.94 b	5.58 ± 0.46 b	14.27 ± 0.98 c	1.05 ± 0.07 c	199.45 ± 8.68 a
W0B1	11.22 ± 1.23 a	14.21 ± 1.21 a	2.19 ± 0.21 c	771.54 ± 49.05 a	4.81 ± 0.17 c	16.80 ± 0.91 b	0.13 ± 0.02 d	206.53 ± 23.15 a
W0B2	12.11 ± 0.43 a	16.39 ± 0.27 a	2.08 ± 0.04 a	754.31 ± 125.25 a	4.34 ± 0.22 c	20.61 ± 1.42 c	2.27 ± 0.72 b	135.17 ± 23.07 d
W1B0	9.02 ± 2.11 b	10.53 ± 1.21 b	10.51 ± 1.11 b	442.85 ± 82.67 c	7.56 ± 0.87 a	13.23 ± 1.08 c	2.91 ± 0.01 b	171.45 ± 8.62 b
W1B1	13.44 ± 0.16 a	15.23 ± 0.92 a	9.64 ± 0.54 b	603.88 ± 129.58 b	5.62 ± 0.10 b	18.23 ± 0.31 ab	1.71 ± 0.04 c	159.83 ± 38.48 bc
W1B2	12.91 ± 0.18 a	15.91 ± 0.72 a	16.2 ± 0.81 a	640.34 ± 193.72 b	5.34 ± 0.08 b	19.77 ± 0.47 a	6.73 ± 0.74 a	157.73 ± 16.86 c
Ir	*	n.s	***	**	*	n.s	***	*
Bi	*	*	***	*	**	**	***	*
Ir&Bi	n.s	n.s	***	n.s	*	n.s	***	n.s

Note: CUE means carbon use efficiency; C/N means carbon to nitrogen ratio; NNR means net nitrification rate (mg kg⁻¹ d⁻¹); GWP means global warming potential (mg kg⁻¹); Ir means irrigation type; Bi means biochar; n.s means p > 0.05; * means p < 0.05; ** means p < 0.01; *** means p < 0.001. Different lowercase letters indicate significant differences among the treatments (p < 0.05)

, the CUE in the first stage was higher than that in the second stage In the first period biochar significantly increased CUE (p < 0.05), whereas in the second period, it led to a notable decrease in CUE. Interaction analysis revealed that there was no significant interaction between irrigation and biochar during the first period (p > 0.05); however, a significant interaction was observed in the second stage (p < 0.05). Simple effect analysis further showed that under the same biochar treatment, the reduction in CUE was more pronounced under W1 than under W0 in the second stage (p < 0.05). For instance, CUE in W1B1 decreased by 25.67% compared to W1B0, while W0B1 showed a 13.78% decrease relative to W0B0. In addition, the C/N ratio was primarily influenced by the biochar application rate (p < 0.05), showing an increasing trend with higher biochar levels, while the irrigation regime had no significant effect on this parameter (p > 0.05).

Analyzing the effects of biochar application and irrigation methods on soil NNR, as presented in Table 3, it was found that NNR was significantly influenced by both factors, with a clear interaction effect observed in both the first and second periods (p < 0.05). In the first period, biochar application reduced NNR under the W0 irrigation regime, with W0B2 showing a 34.18% decrease compared to W0B0 (p < 0.05). In contrast, under the W1 regime, biochar significantly increased NNR, with W1B2 exhibiting a 54.14% increase relative to W1B0 (p < 0.05). In the second period, With increasing biochar application rates, NNR initially decreased and then increased, showing significant differences among biochar treatments under the same irrigation regime (p < 0.05). Under both W0 and W1 conditions, the lowest NNR values were observed in the B1 treatment. High biochar application (B2) significantly enhanced NNR under both irrigation regimes (p < 0.05). Specifically, NNR in the W0B2 treatment increased by 116.19% compared to W0B0 (p < 0.05), and in the W1B2 treatment, it increased by 131.27% relative to W1B0 (p < 0.05).

Significance testing showed that there was no significant interaction between the irrigation regime and biochar application on GWP in either period (p > 0.05; Table 3). Additionally, the GWP in the second period was lower than that in the first period, as shown in Table 3. During the first period, under the same biochar application rate, the W1 treatment significantly reduced GWP compared to W0 (p < 0.05). Under the same irrigation regime, biochar addition significantly increased GWP (p < 0.05); however, there was no significant difference between the B1 and B2 treatments (p > 0.05). In contrast, during the second period, GWP under W1 was significantly lower than under W0 at the same biochar level (p < 0.05). Under the same irrigation regime, biochar application significantly reduced GWP (p < 0.05). Specifically, the W0B2 treatment showed a 32.22% reduction compared to W0B0 (p < 0.05), while W1B2 showed a 13.73% reduction compared to W1B0 (p < 0.05).

4. Discussion

4.1. Effect of Biochar on GHG Emissions

Numerous studies have demonstrated that biochar has the potential to increase soil CO₂ emissions [30,31,32,33]. The present trial showed a similar trend, with cumulative CO₂ emissions increasing during the incubation period; however, the differences were not statistically significant. This may be attributed to the relatively short duration of the experiment. Biochar can enhance SOC content (Figure 2) and stimulate microbial activity, thereby promoting soil respiration [32]. In addition, previous studies have reported that biochar can enhance the activity of methane-oxidizing bacteria, thereby suppressing methanogenesis and reducing soil CH₄ emissions to some extent [34]. In the present study, CH₄ uptake was observed during the second stage under the AWD treatment with biochar application. This suggests that biochar improved soil aeration and oxygen availability [35], particularly under the alternating wet and dry conditions of AWD, which favored the activity of methanotrophs. The intermittent drying phases likely disrupted the anaerobic environment required by methanogens, thus inhibiting CH₄ production. Moreover, the porous structure and surface functional groups of biochar may have provided suitable microhabitats for methanotrophs and enhanced their retention in the soil. However, biochar also may inhibit soil methanotrophic bacteria, potentially leading to increased CH₄ emissions [36]. This effect is largely influenced by the type of biochar used and the conditions of disturbed soil in experimental settings. Faloye et al., in 2024 [37], found that biochar from mango twigs and branches has a greater impact on soil water retention and hydraulic properties than biochar from mango branches. In this experiment, the use of disturbed soil helped ensure consistency in soil aggregate structure across all treatments.

Most studies have reported that biochar application tends to reduce soil N₂O emissions in paddy systems, which contrasts with the results of the present study [6,10,11,33]. The suppressive effect of biochar on soil N₂O emissions is primarily attributed to its ability to improve soil redox conditions and its potential toxicity, which can inhibit nitrifying and denitrifying bacteria to some extent [38]. However, this effect may vary depending on the type of biochar used. For example, some studies have shown that low-temperature biochar significantly reduces soil N₂O emissions, whereas high-temperature biochar may increase them [39]. Additionally, the short duration of this experiment may have contributed to the lack of an observable reduction. Previous research has found that biochar application in paddy fields can enhance soil nitrification processes in the short term, while long-term application tends to suppress denitrification, ultimately leading to reduced N₂O emissions [40].

When evaluating the GHG mitigation potential of biochar, the research focus should shift from simply assessing “whether it is effective” to understanding “under what conditions it is effective”. It is essential to explore the interactions between key influencing factors—such as soil moisture status, soil type, application depth, and method—and the underlying mechanisms of biochar’s mitigation effects, in order to refine its regulatory framework. The fact that biochar did not significantly reduce GHG emissions in this study does not negate its potential value. Rather, it highlights the context-dependent nature of its effectiveness and the limitations of its applicability. This study was based on a short-term laboratory incubation experiment, which may not fully represent the complexity of field conditions. Therefore, the practical implications of the results should be further evaluated under long-term field conditions to ensure their applicability in real agricultural systems.

4.2. Interactive Effect of Soil Water Regulation and Biochar on GHG Emissions

Soil moisture regulation has long been recognized as a primary strategy for mitigating GHG emissions, with numerous studies confirming the effectiveness of AWD in reducing GHG emissions from paddy fields [4,8,13,15]. The findings of this study are consistent with those reports. Moisture regulation directly alters the soil redox environment, effectively inhibiting CH₄ production and release, making it a key environmental factor in controlling GHG emissions from paddy fields [3,15]. It also enhances soil microbial activity and promotes soil respiration [41]. Existing research has shown that AWD irrigation can promote nitrification in paddy soils, increase the abundance of the amo gene, and thereby enhance N₂O emissions [42,43,44].

With the addition of biochar, both W₀ and W₁ treatments showed similarities as well as differences. A common observation was that increasing biochar application led to a significant increase in GWP under both W₀ and W₁ conditions. This may be due to the increased availability of carbon and nitrogen in the soil, which enhances their utilization efficiency [45,46]. The experiment also found that biochar addition significantly increased TN and SOC contents. During the first period of incubation, different biochar application rates resulted in similar carbon and nitrogen use efficiency; however, in the second period, high biochar application significantly increased NNR, thereby promoting N₂O emissions.

A key difference emerged in the effect of biochar on NNR under different irrigation regimes: under W₁ conditions, biochar consistently caused a significantly greater increase in NNR compared to W₀, in both periods of incubation. This suggests that biochar can enhance the redox regulation effect of AWD irrigation, effectively promoting nitrogen transformation and increasing N₂O emissions. Further analysis of the influencing factors under different irrigation regimes revealed that, under W₀, biochar primarily influenced N₂O emissions. This is likely because CH₄ emissions under strongly reduced conditions are primarily limited by oxygen availability rather than nitrogen availability; thus, biochar has a limited impact on CH₄ but can affect N₂O emissions by modulating intermediate steps in the denitrification process. In contrast, under AWD conditions (W₁), biochar mainly influenced CH₄ emissions by regulating TN content. Due to the dynamic redox environment under AWD, nitrogen becomes one of the regulatory factors in the balance between methane production and oxidation. Increased TN availability, facilitated by biochar, can enhance the function of methane-oxidizing microbes, thereby reducing CH₄ emissions.

5. Conclusions

Biochar addition could enhance soil carbon and nitrogen contents, stimulate microbial activity, and significantly promote microbial CUE and NNR. Importantly, the impact of biochar on the greenhouse effect varied with incubation time: while it initially increased GWP, a significant mitigation effect was observed in the later periods. However, it also led to a significant increase in soil GWP. Therefore, before applying biochar in field settings, it is essential to comprehensively evaluate the combined effects of local soil properties, hydrological conditions, climate, and management practices, rather than adopting a “one-size-fits-all” strategy.

Water management had a more pronounced impact on soil GHG emissions than biochar application. Under AWD irrigation, although N₂O emissions significantly increased, the substantial reduction in CH₄ emissions led to a markedly lower GWP compared to continuous flooding. Moreover, GHG emission trends under AWD appeared less sensitive to the rate of biochar applied. Furthermore, no significant interaction was observed between the irrigation regime and biochar application in affecting GHG emissions. Optimizing water management may be more effective than biochar application alone in mitigating the climate impact of paddy fields.