Ferrate-Modified Biochar for Greenhouse Gas Mitigation: First-Principles Calculation and Paddy Field Trails

Abstract

Modified biochars have attracted attention for reducing greenhouse gas emissions in paddy fields. However, material screening difficulties and lengthy effect validation periods have restricted their development. We proposed a theoretical calculation method to guide short-term field trials in this study. Utilizing first-principles calculations, we determined that sodium ferrate-modified biochar (Fe@C) would limit methane (CH₄) and nitrous oxide (N₂O) emissions. Field trials confirmed that Fe@C increased rice yields and active organic carbon content in soil and reduced methane emissions and the global warming potential. However, applying sodium ferrate alone significantly reduced N₂O emissions. Correlation analysis showed that methane was significantly negatively correlated with yield and the soil carbon pool labile index. N₂O was significantly negatively correlated with urease activity, and significantly positively correlated with the soil carbon pool management index. Therefore, Fe@C provides a high-yielding management measure that enhances soil labile organic carbon. Additionally, its effects were controlled by the proportion of sodium ferrate. Our work provides a new strategy to guide the design of paddy field experiments via theoretical calculations, greatly shortening research time and providing solutions for carbon sequestration and emissions reduction.

1. Introduction

According to the latest assessment from the Intergovernmental Panel on Climate Change (IPCC), climate change and food security are the most serious challenges to human survival in history [1]. CH₄ and N₂O, the second and third most abundant greenhouse gases, respectively, have 34 and 298 times the warming potential of CO₂ [2]. Rice fields, which emit large amounts of CH₄ and N₂O, account for 58% and 51% of the emissions from global agricultural land, respectively [3,4,5]. China is a major consumer of rice, and its domestic agricultural production supplies more than 95% of its food and other important agricultural products [6]. Therefore, China should reduce greenhouse gas emissions from rice paddies if it intends to achieve its carbon neutrality target by 2060 [7]. Reducing greenhouse gas emissions from paddy while ensuring food production security is a vital strategy for combating climate change. The application of non-polluting abatement materials on agricultural fields can influence the carbon and nitrogen cycles by altering soil physicochemical and biological properties. Therefore, developing new materials is crucial for efficient carbon sequestration and emissions reduction.

Biochar has attracted attention for carbon sequestration and emissions reduction in rice fields because of its abundant specific surface area, excellent ion replacement capacity, plentiful organic carbon content, suitable chemical properties, and high thermal stability [8,9]. Prior studies have demonstrated that biochar can significantly reduce CH₄ emissions and enhance soil carbon sinks for paddy ecosystems, which are mainly attributed to changes in CH₄ production, oxidation, and transport [10,11,12,13,14]. These changes are caused by the physicochemical properties of biochar, which can change the physicochemical properties and resident microorganisms of paddy, and inhibit CH₄ emissions [15,16]. However, other studies have noted that biochar can increase CH₄ emissions [17]. Therefore, the effects of biochar input on CH₄ and N₂O in rice fields are inconsistent [18,19]. Owing to the complexity of raw materials, diverse preparation processes, and varied application environments, many results are disparate. Thus, biochar has not been convincingly promoted to the public for reducing emissions in agricultural fields. Biochar is known to have a significant effect on CH₄ and N₂O emissions. The causes of this condition from altering soil permeability (increasing porosity) [20], modifying soil moisture [21], improving pH [22], and changing soil texture (temperature and humidity; clay, silt, and sand grains) [23], modifying microbial communities associated with CH₄ and N₂O [24], and directly adsorbing NH₄⁺ [25,26]. However, the negative charges of biochar have exhibited poor adsorption ability for the negative ions in the paddy (NO₃⁻, PO₄⁻, etc.). Soil enzymes are released by soil microorganisms, plant roots, and decomposing plant and animal residues. Enzymes are the most bioactive substances in the ecosystem and are involved in soil metabolism. Urease reflects the decomposition of soil urea, whereas peroxidase is indicative of soil toxicity [27,28]. The effect of biochar on soil enzyme activity was multifactorial [29]. Increased soil enzymatic activity may result from the promotion of enzymatic reactions via the adsorption of reaction substrates by biochar [30].

Due to the different opinions on effects of applied biochar on greenhouse gases. The modification of biochar with various materials (such as metal monomers and their oxides) has been promoted to reduce the emission of both CH₄ and N₂O. Iron oxides are commonly used as remediation agents for rice field soils and inhibit greenhouse gas emissions [31,32]. CH₄ emissions from rice fields are influenced by the oxidation-reduction potential (Eh) of the soil; when the soil Eh is −150 to −100 mV, methanogens produce large amounts of CH₄ [33]. Fe-modified biochar can maintain a low soil Eh and high levels of root-surface Fe films, in addition to improving the stability of the biochar itself [34]. Trivalent iron in the root-surface Fe film acts as an electron acceptor that accelerates organic carbon mineralization, then effectively slowing greenhouse gas emissions from rice soils [35]. Fe reduction can decrease the steady-state concentration of H₂ and acetic acid in the soil, which effectively inhibits CH₄ production and the competitive consumption of electrons. Thus, CH₄-generating processes in the soil are strongly inhibited [36]. Sodium ferrate is a hexavalent iron salt with strong oxidizing properties. It can release large quantities of atomic oxygen and Fe(OH)₃ after dissolving in water, which increases the oxygen in the soil and creates a good environment for aerobic soil bacteria. Moreover, sodium ferrate dissolves in water and quickly forms trivalent iron, which rapidly adheres to the surfaces of biochar. The resulting iron-rich biochar surface significantly enhances the negatively charged surface area of the char. This provides more sites for dipole–dipole interactions with greenhouse gases. Thus, it can effectively suppress greenhouse gas emissions. However, the reduction effects and mechanism of sodium ferrate-modified biochar for greenhouse gases are not clear.

Therefore, this study adopted combination methods of theoretical calculation predictions and field experimental verification. Theoretical calculations directly reflected the effect of carbon sequestration and emission reduction in sodium ferrate-modified biochar. This study specifically aimed to predict the abatement effect and mechanism of sodium ferrate-modified biochar using theoretical calculations and verify this prediction via relevant indicators during a field experiment. It provided a novel material research strategy for greenhouse gas reduction in rice fields.

2. Materials and Methods

2.1. The Theoretical Calculation

The first-principles calculations were executed via Vienna Ab initio Simulation Package (VASP) based on the projector augmented wave (PAW) method [37,38,39]. The graphene (C) and its ferric hydroxide modification (Fe@C) are used as the substrate for research. Exchange–correction interactions and van der Waals interactions were considered by Perdew, Burke, and Ernzerhof (PBE) type, and DFT-D3, respectively [40]. The energy cutoff for all calculations was set to 500 eV, and the energy and force of convergence criteria were set to 10⁻⁵ eV and 0.02 eV Å⁻¹ per atom. The 15 Å vacuum layer was employed to prevent the influence from adjacent periodic unit cells. The k-points parameter of geometry optimization and DOS calculation was set to 3 × 3 × 1 and 11 × 11 × 1 with the Monkhorst–Pack method. The DFT+U formalism described the localized d electrons, and the U value of Fe was set to 4 eV [41]. The adsorption energy (Ea) formula was defined as

E_a = E_gas + E_substrate − E_{(gas-substrate)},

(1)

where E_gas, E_substrate, and E_{(gas-substrate)} represented the total energy of the CH₄ or N₂O, the substrates of C or Fe@C, and the energy of the CH₄ or N₂O adsorption on the different substrates. The differential charge density diagram was drawn by vesta [42].

2.2. Field Experiment Location

The experiment was conducted in Beishan Town, Changsha County, Hunan Province (112°56′15″ E, 27°54′55″ N). The test site is located in the middle of the East Asian monsoon region, which belongs to the subtropical monsoon humid climate, with mild climate, abundant heat, rainfall, sufficient sunshine, and four distinct seasons. It is 15–25 °C in spring, 18–36 °C in summer and 5–15 °C in winter. The annual rainfall is 1000–1200 mm. The rice variety is Yu Zhenxiang (regular medium-ripening late indica, with a fertility period of 114 days) purchased from Jin Se Nong Feng Company. Biochar was purchased from Henan Lize Environmental Protection Technology Co., Ltd. The main components are rice straw and rice husk. Sodium ferrate (the main component is sodium ferrate powder, content ≥99.5%, the molecular formula is Na₂FeO₄, purchased in Zhengzhou Zhenlai Chemical Products Co.). The basic properties of soil and biochar in the test site are shown in

Table 1. Chemical Properties of Soil and Biochar.

Material	TN (g kg−1)	TP (g kg−1)	TK (g kg−1)	AN (mg kg−1)	RAP (mg kg−1)	RAK (g kg−1)
Soil	0.68	0.74	6.45	77.30	15.77	13.50
Biochar	0.26	5.77	47.20	176.00	242.00	22.00

TN, TP, TK, AN, RAP, and RAK represented total nitrogen, total phosphorus, total potassium, available nitrogen, rapidly available phosphorus, and rapidly available potassium, respectively.

.

2.3. Experimental Design and Management

The experiment was conducted from May to October 2021 in a single-factor randomized group design with six treatments and three replications in a total of 18 plots, each with an area of 30 m², and the plots were separated by field ridges and mulch to prevent fertilizer cascading. The experimental treatments were as follows, CK: no sodium ferrate and biochar, as control. BC: Only biochar was applied at a rate of 15 t ha⁻¹. 2%Fe: Sodium ferrate only at 0.3 t ha⁻¹. 4%Fe: Only sodium ferrate was applied at a rate of 0.6 t ha⁻¹. BC + 2%Fe: Biochar and sodium ferrate were applied at the same time, the amount of biochar applied was 15 t ha⁻¹ and the amount of sodium ferrate applied was 0.3 t ha⁻¹. BC + 4%Fe: Biochar and sodium ferrate were applied at the same time, the amount of biochar applied was 15 t ha⁻¹ and the amount of sodium ferrate applied was 0.6 t ha⁻¹.

Sown on 15 May 2021, transplanted on 12 June and harvested on 15 September. The transplanting density was 15 cm × 25 cm, and protective rows were set up around the test area. Sodium ferrate and biochar were weighed and mixed and the ridge was made and then applied at once 2 days before the rice seedlings were transplanted, using machinery to turn them into the soil. Fertilizer application, irrigation, and other field management practices were consistent across all plots, with a single row and single irrigation. Nitrogen fertilizer (urea): 267 kg ha⁻¹; phosphorus fertilizer (phosphorus pentoxide): 97.5 kg ha⁻¹; potassium fertilizer (potassium chloride) 200 kg ha⁻¹. The proportion of nitrogen fertilizer is applied as base fertilizer: tiller fertilizer: spike fertilizer = 5:3:2; phosphorus fertilizer is applied as base fertilizer at one time; potassium fertilizer is applied as 50% of base fertilizer and 50% of tiller fertilizer. Base fertilizer was applied 5 d before transplanting and tiller fertilizer was applied 7 d after transplanting. Drain the field 30 d after transplanting, and dry the field for 5 d, then rehydrate and apply spike fertilizer.

2.4. Methods of Sample Collection and Analysis

2.4.1. The Soil Chemical Properties Preparation and Quantification

Before the test and after rice harvest, 0–20 cm tillage layer soil was taken from each plot by a 5-point method and mixed into one soil sample. After the soil samples were dried naturally in the laboratory, the residual litter and roots were picked out and ground through a 0.25 mm sieve for the determination of soil chemical indexes. Total nitrogen was determined by the Kjeldahl method. Total phosphorus was determined by NaOH melting and molybdenum–antimony resistance colorimetry. Total potassium was determined by NaOH fusion-flame photometry. Effective nitrogen was determined by the alkali-hydrolyzed diffusion method. Effective phosphorus was determined by molybdenum–antimony resistance colorimetry. Effective potassium was determined by the neutral ammonium acetate extraction method [43].

2.4.2. Greenhouse Gas Acquisition and Measurement in Paddy Fields

A static closed chamber method was used for greenhouse gas sampling and analysis [44]. After rice transplanting, every 7 d, with a closed static box sampling, sampling box for the cylinder, made of glass fiber reinforced plastic material, box bottom square side length 55 cm, 120 cm high, in the rice planting before the plastic base fixed in each plot, the base has six cavities of rice, measurement with water into the bottom tank to seal. Greenhouse gas sampling time is fixed at 9:00–11:00 a.m. The sampling time is 0, 10, 20, and 30 min after the hood box, respectively, and 45 mL gas samples are taken each time. The gas samples were analyzed by Agilent 7890A (Agilent Techologies, Santa Clara, CA, USA) gas chromatograph, and the standard gases were provided by the National Center for Reference Materials. The gas emission rates were derived from the four gas sample concentration values by linear regression analysis. Calculating greenhouse gas emission fluxes from rice fields according to Equation (2).

$$\mathrm{F}=\mathsf{\rho }·\frac{273}{273+\mathrm{T}}·\mathrm{H}·\frac{\mathrm{dC}}{\mathrm{dt}}$$

(2)

F is the emission flux. ρ is the density of CH₄ and N₂O at standard atmospheric pressure, 0.714 and 1.98 kg m⁻³, respectively. T is the average temperature inside the sampling box during the sampling process, °C. H is the net height of the box cover of the sampling box, m. dC/dt is the rate of change in the greenhouse gas concentration in the sampling box.

Calculation method of total emission and mean emission flux: total CH₄ emission is the sum of emissions from each rice reproductive period. The mean value of emission flux is the ratio of total CH₄ emission to the field date during the field period. The seasonal cumulative emissions are calculated as follows Equation (3).

$$\mathrm{C}={\displaystyle \sum }\left({\mathrm{F}}_{\mathrm{i}+1}+{\mathrm{F}}_{\mathrm{i}}\right)/2\times ({\mathrm{t}}_{\mathrm{i}+1}-{\mathrm{t}}_{\mathrm{i}})\times 24$$

(3)

C denotes total greenhouse gas emissions (kg ha⁻¹). i indicates the number of tests, F is the greenhouse gas emission flux (mg m⁻² h⁻¹), t_i+1 $-$ t_i is the interval between two adjacent gas withdrawals, and 10⁻² is the conversion unit.

Global Warming Potential (GWP): The warming potential of a system is generally expressed in terms of CO₂ equivalent. On a timescale of 100a, the combined greenhouse effect per unit mass of CH₄ and N₂O is 34 and 298 times higher than that of CO₂, respectively. The warming potential is equal to the product of the cumulative seasonal emissions and the warming factor. The warming potential is Equation (4) to the product of the cumulative seasonal emissions and the warming factor.

GWP = 34 (CH₄) + 298 (N₂O)

(4)

GWP represents the combined greenhouse effect of CH₄ and N₂O gas emissions (kg CO₂ ha⁻¹). (CH₄) and (N₂O) represent the total cumulative emissions of CH₄ and N₂O, respectively (kg ha⁻¹). The emission intensity (GHGI) is the quotient of the warming potential and the yield.

2.4.3. Soil Acquisition and Soil Enzyme Activities Measurement

Soil samples were collected in layers from 0 to 20 cm in each plot during the tillering, gestation, flush, lactation, and maturity stages of rice. Mixed soil samples were collected from three points in each plot, picked up to remove crop roots and small stones, dried naturally, ground, and passed through a 0.1 mm sieve for the determination of soil urease and peroxidase activities. The urease activity was expressed as milligrams of NH₃-N in 1 g of soil after 24 h using the indophenol blue colorimetric method [45]. The amount of hydrogen peroxide decomposed by the enzymatic reaction was expressed by the difference between the number of milliliters of hydrogen peroxide required before and after the titration of the enzymatic reaction with 0.02 mol L⁻¹ potassium permanganate per gram of dry soil for 20 min using the KMnO₄ titration method [46]. In the process of measuring soil enzymes activities, the sample soil was incubated at constant temperature.

2.4.4. Soil Acquisition and Soil Organic Carbon Measurement

Soil samples of 0–20 cm were collected in different plots of the field at the time of crop harvest. After pretreatment with soil enzymes, the samples were passed through a 0.25 mm sieve for the determination of soil organic carbon. Soil total organic carbon (TOC) was determined using a conventional method, namely the K₂Cr₂O₇ oxidation method [47]. The sampling method and sampling time for soil labile organic carbon (LOC) were the same as those for organic carbon, and the determination method was based on the 0.333 mol L⁻¹ potassium permanganate colorimetric method [48].

Soil carbon pool management indices were calculated according to Equation (5) to (9) using soils under the conditions without the application of sodium ferrate and biochar treatment as reference agricultural soils.

ROC (g kg⁻¹) = TOC − LOC

(5)

CPI = Sample TOC/Reference TOC

(6)

CPL = LOC/ROC

(7)

CPLI = Sample CPI/Reference CPI

(8)

CPMI = CPI × CPLI

(9)

ROC, CPI, CPL, CPLI, and CPMI are the soil recalcitrant organic carbon, the soil carbon pool index, the soil labile organic carbon pool, the soil organic carbon pool labile index, and the soil carbon pool management index, respectively.

2.4.5. Yield Determination

At the rice maturity, each plot was harvested within 2 m² (marked as yield measurement area after transplanting), manually threshed using a small threshing machine and then dried in the sun to obtain the sun-dried weight. Then take a small amount from it using the oven drying method (75 °C baking to constant weight) to determine the water content, so as to obtain the drying weight. The corrected water content was 13.5%, which was then converted into the actual yield.

2.5. Statistical Analyses

All data were collected using Excel 2013 (Microsoft Corporation, Redmond, USA). Origin 2021 (Origin Lab Corporation, Northampton, MA, USA) was used to draw the figures. The IBM SPSS statistics 20 software (International Business Machines Corporation, New York, NY, USA) was performed by Analysis of variance (ANOVA) and correlation analysis.

3. Results

3.1. Theoretical Prediction

Firstly, the adsorption abilities of substrates for CH₄ and N₂O were investigated. Previous studies have reported that the stronger adsorption ability is reflected by the more positive value of adsorption energy [49]. As shown in Figure 1a, the adsorption energies for CH₄ and N₂O on C or Fe@C are 0.13 and 1.41 or 0.21 and 2.11 eV, respectively. These results indicated that with the introduction of the iron compound, the Fe@C shows exceptional adsorption behavior on both CH₄ and N₂O. Then, we calculated the density of states (DOS) of different adsorption systems to describe the electronic properties of diverse elements. As shown in Figure 1b, there are no obvious orbital peak shifts during the CH₄ adsorbed on both C and Fe@C than before, which implied that has not existed the distinct electronic exchange between CH₄ and substrates. In the meantime, shifts in orbital peaks developed as the N₂O was adsorbed to C or Fe@C (Figure 1c). Compared with C, Fe@C shows the more obvious orbital peak shifts which ascribe to the larger electronic exchange. These phenomena are also confirmed by electron charge density difference, which is used to describe the charge redistribution of adsorbed on CH₄ and N₂O by C or Fe@C. Consistent with the DOS results, there are no distinct regions of charge exchange during CH₄ adsorption on C and Fe@C which means there main exists the physical adsorption between CH₄ and substrates (Figure 1d,f). However, different from N₂O-C systems, there are evidently depleted and accumulated areas of charge between N₂O and Fe@C which demonstrates that the Fe@C can restrict the N₂O with the physicochemical adsorption (Figure 1e,g). The results of the electron charge density difference are identical to the adsorption energy and DOS results which implied Fe@C will be an outstanding performance for CH₄ and N₂O restricted.

3.2. CH₄ and N₂O Emissions

The CH₄ emission flux varied greatly with the crop growing season (Figure 2a,c). From 0 to 20 d after transplantation, the CH₄ emissions were low. There were two obvious peaks in CH₄ emissions during 20–60 d after transplantation. The first was at approximately 28 d, and the second was at approximately 49 d. In the first emissions peak, the CH₄ emission fluxes of all treatments reached their maximum value for the entire rice-growing season, ranging from 60.75 to 213.42 mg m⁻² h⁻¹. The CH₄ emission fluxes of BC + 2%Fe were the largest, whereas those of BC + 4%Fe were the smallest. During the second emission peak, the CH₄ emission flux of 4%Fe (67.60 mg m⁻² h⁻¹) was higher than that of all other treatments. Overall, 4%Fe (30.39 mg m⁻² h⁻¹) and BC + 4%Fe (19.09 mg m⁻² h⁻¹) showed the highest and lowest average CH₄ fluxes, respectively. There is no significant difference in the average CH₄ emission fluxes between BC + 2%Fe and 4%Fe.

N₂O emission fluxes showed significant seasonal variations and continued to fluctuate until harvest (Figure 2b,d). The N₂O emissions peaked at 15–45 d. BC showed two emission peaks during this period, whereas the remaining treatments had their highest emission values at 35 d. The CK, 2%Fe, and BC + 2%Fe treatments showed no significant differences in N₂O emission fluxes. The highest and lowest mean N₂O fluxes were observed under BC (0.31 mg m⁻² h⁻¹) and 4%Fe (0.2 mg m⁻² h⁻¹), respectively (Figure 2d).

3.3. Greenhouse Effect and Yield

Cumulative CH₄ and N₂O emissions, GWP, GHGI, and yield differed among treatments (Figure 3). The cumulative CH₄ under each treatment ranged from 415.31 to 660.15 kg ha⁻¹. Among, 4%Fe (660.15 kg ha⁻¹) and BC + 2%Fe (632.16 kg ha⁻¹) exhibited higher cumulative CH₄ values than others, and lower values were identified under BC + 4%Fe (415.31 kg ha⁻¹) (Figure 3a). Additionally, the cumulative N₂O values varied significantly among these treatments. The highest cumulative N₂O emission was observed under BC (6.74 kg ha⁻¹), followed by BC + 4%Fe (5.97 kg ha⁻¹), and the lowest value was recorded for 4%Fe (4.37 kg ha⁻¹) (Figure 3b). The total GWP showed a similar trend to the CH₄ emissions, with low values observed for BC + 4%Fe (Figure 3c). Furthermore, the GHGI of BC + 4%Fe was the lowest and that of 4%Fe was the highest. In terms of yield, BC + 4%Fe (10.77 kg ha⁻¹) provided the highest yield, whereas 4%Fe (7.57 kg ha⁻¹) had the lowest (Figure 3d).

3.4. Soil Urease Activities

Urease activity fluctuated greatly throughout the growth stages (Figure 4a). Moreover, the changes in urease activity were not consistent among treatments throughout the growth period. In the CK treatment, urease activity increased steadily during the growth period and reached its highest level at maturity. The urease activity under BC + 4%Fe decreased continuously from the tillering stage to milk maturity but increased at maturity. The urease activity under 2%Fe and 4%Fe decreased with advancing growth stage, increased significantly at the milk maturity stage, and then decreased to its lowest level at the maturity stage. The urease activity under BC and BC + 2%Fe was the highest at the tillering stage and was lower but stable during later stages. The urease activity under 4%Fe was higher than that of other treatments from the tillering to milk maturity stage, whereas the urease activity under CK was the highest at the maturity stage. The 4%Fe treatment had the highest urease activity, whereas the BC + 4%Fe treatment had the lowest urease activity. The mean urease activity differed significantly between treatments, with 4%Fe having the highest urease activity, followed by CK, and 2%Fe. BC had the lowest urease activity.

3.5. Soil Catalase Activities

Catalase activity differed significantly at the tillering stage but not at other growth stages (Figure 4b). The soil catalase activity first decreased and then increased with the growth stage. It decreased from the tillering to booting stage, then increased, and tended to be moderate at the milk and maturity stages. Additionally, the enzymatic activity among all treatments tended to be consistent at each stage. The catalase activity of BC + 2%Fe slightly increased from the tillering to booting stage. The mean catalase activity of each treatment group was not significantly different.

3.6. Soil Organic Carbon

The dynamic changes in soil organic carbon and carbon pool characteristics during the rice maturation period are shown in Figure 5. The total organic carbon varied from 11.1 mg kg⁻¹ to 14.28 mg kg⁻¹ among the different treatments, with the highest value under BC (Figure 5a). Soil recalcitrant organic carbon had similar values to TOC, ranging from 9.7 mg kg⁻¹ to 12.68 mg kg⁻¹; BC + 2%Fe had the highest value (Figure 5b). However, the soil labile organic carbon differed, and BC + 4%Fe showed the highest value (Figure 5c). The proportion of LOC in TOC was lower than that in ROC. Dynamic changes in the carbon pool index and carbon pool labile index were also observed in different treatments (Figure 5d,e). BC + 2%Fe (1.11) exhibited the highest CPI value, whereas BC + 4%Fe (1.41) exhibited the highest CPLI. Moreover, the carbon pool management index differed significantly among the treatments, with the highest and lowest CPMI observed in BC + 4%Fe (1.48) and BC + 2%Fe (0.66), respectively (Figure 5f). The lowest CPI was observed in 4%Fe (0.85), and the lowest CPLI and CPMI values were found in 2%Fe.

3.7. Relationship Analysis of Greenhouse Gas Emissions, Yields, Organic Carbon Characteristics, and Soil Enzymes Activities

As shown in Figure 6, CH₄ and N₂O emissions were greatly influenced by yield and soil organic carbon. CH₄ emissions were negatively correlated with yield and CPLI. N₂O emissions were positively correlated with yield, TOC, LOC, CPI, and CPMI but negatively correlated with urease enzyme activity and GHGI. Furthermore, soil organic carbon characteristics were significantly negatively correlated with urease activity and negatively correlated with yield. GWP and GHGI were negatively correlated with CPLI.

4. Discussion

4.1. Soil Organic Carbon

Compared with that of the CK treatment, the SOC content significantly increased when biochar was applied alone or mixed with sodium ferrate, indicating that biochar promoted the accumulation of soil organic carbon (Figure 5a,c). This may be because biochar itself is rich in organic carbon, thus it directly increases the SOC content when applied to the soil [50]. Owing to the ROC, it degraded slowly in the soil; therefore, the SOC content of the rice fields increased over time [51]. These results were similar to those reported by Khan et al. [52]. However, compared with the CK treatment, all treatments with only Fe reduced the organic carbon content, indicating that the addition of sodium ferrate alone was not conducive to the accumulation of SOC (Figure 5). Under anaerobic conditions, sodium ferrate rapidly produces trivalent iron, which is subsequently reduced by biota or chemical reduction. Hence, the SOC protected by the trivalent iron may be released due to its reduction; this corroborates the increase in CH₄ emissions from Fe application alone under these test conditions [53]. In terms of LOC, BC + 2%Fe and BC + 4%Fe increased it significantly, indicating that the simultaneous application of high amounts of sodium ferrate and biochar was beneficial for the accumulation of LOC. This may be because of the attachment of sodium ferrate-derived colloidal substances to the biochar changes the original properties of the biochar and stimulates the conversion of inert organic carbon to LOC [54].

4.2. Soil Enzymes

The present study found that the addition of biochar significantly reduced urease activity, but its effect on catalase was not significant. Decreased activity may result from the adsorption of enzyme molecules by biochar, which protects the binding sites for enzymatic reactions and prevents them from proceeding [55]. The highest enzymatic activity was reached with the 4% addition of sodium ferrate (Figure 4a,b). This could indicate that sodium ferrate stimulates soil urease activity, while indirectly indicating that the combined application of sodium ferrate and biochar has insignificant toxic effects on rice fields. Ergang et al. used modified biochar and found a similar decrease in urease activity [56]. Urease activity was also significantly lower in BC + 2%Fe and BC+4%F e than that in CK (Figure 4a). Furthermore, urease showed a significant negative correlation with N₂O (−0.75 ***) and LOC (−0.48 *), whereas LOC showed a highly significant positive correlation with yield (0.65 **) and N₂O (0.54 **) (Figure 6). These results indicate that urease in the soil can inhibit N₂O and LOC, and the greater the accumulation of LOC, the more urase decreases N₂O emissions and yield.

4.3. Trends of CH₄ Emission Fluxes

Some studies have shown that sodium ferrate or biochar applications can reduce CH₄ emissions from rice fields [10,31]. However, little research has been conducted on the effects of applying combined sodium ferrate and biochar on CH₄ in rice fields. In this study, all treatments showed a consistent pattern of change at each fertility stage, with the first, highest peak in CH₄ emissions approximately 28 d after transplantation (Figure 2a). This may be because rice plants grow luxuriantly during the tillering stage, with a high number of tillers, and rice roots and aeration tissues grow vigorously. Rice aeration tissues are the main pathway for CH₄ emission to the atmosphere; thus, they facilitate emissions by increasing CH₄ emission channels [57]. Simultaneously, rice is irrigated by long-term flooding during the tillering period, soil permeability is poor, and there is sufficient methanogenic substrate in the soil. Thus, soil methanogenic bacteria can produce large amounts of CH₄. The second peak period occurred 49 d after transplantation. The spike fertilizer was applied at this time, which stimulated CH₄ emissions [58]. CH₄ emission fluxes gradually decreased and leveled off 63 d after transplantation owing to improved soil aeration caused by reduced moisture, which inhibited CH₄ production and promoted CH₄ oxidation.

4.4. Differences in Cumulative CH₄ Emissions between Treatments

Consistent performance was observed in terms of average greenhouse gas emission fluxes and seasonal cumulative emissions from rice fields (Figure 2c,d and Figure 3a,b). Strictly speaking, no clear and consistent conclusion exists regarding the effects of biochar on CH₄ emissions. Nevertheless, in this study, biochar application alone was found to have a facilitative effect on CH₄ production (Figure 3a), compared with that of the control. Biochar contains nitrogen, and the experimental treatment did not reduce nitrogen (Table 1), which contributed to CH₄ emissions [59]. Additionally, the application of 2%Fe and 4%Fe alone increased the cumulative CH₄ emissions (Figure 3a). Compared with those of the CK treatment, BC + 2%Fe significantly promoted CH₄ emissions, whereas BC + 4%Fe significantly reduced CH₄ emissions. This suggests that sodium ferrate modified biochar does significantly reduce CH₄ emissions; however, this may be related to the ratio of biochar to sodium ferrate. Biochar rapidly adsorbs Fe(OH)₃ colloids generated from sodium ferrate, modifying the surface carbon pore structure of the biochar, thus enhancing emissions reduction. This coincides with the CH₄ emissions reduction observed for the modified biochar [60,61].

4.5. Differences in N₂O Emissions and Greenhouse Effect between Treatments

From 0 to 45 d, N₂O emission fluxes increased in all treatments, probably because N₂O emissions are highly sensitive to N fertilizer and soil moisture [62]. From 45 d to harvest, the N₂O emissions gradually decreased as soil aeration improved owing to field moisture and nutrient reductions. Figure 3b shows the cumulative N₂O emissions, which contrast with the results of Liu et al. [63]. Similar to the CH₄ emissions, biochar application alone promoted N₂O emissions, which increased following the addition of biochar to the soil [64]. Some studies on biochar have correlated the reduction in N₂O emissions with urease activity, which was the main driver of the increase in N₂O emissions in this case. In this study, 4%Fe was found to cause the most significant N₂O reduction, which was also reported by Ciais et al. [35]. Greenhouse gas emission intensity is a comprehensive index of the greenhouse effect used to evaluate the greenhouse effects of the rice fields. The magnitude of GWP was mainly influenced by CH₄, and the magnitude of GWP for all treatments was the same as the respective cumulative CH₄ emissions. Compared with those of CK, the BC + 4%Fe treatment had the lowest gas emission intensity but the highest yield, indicating that the BC + 4%Fe treatment had the best combined greenhouse gas reduction and the best yield under the production conditions.

5. Conclusions

In summary, we demonstrated a method for theoretical calculations to shorten the study time in rice paddies by predicting the ability of sodium ferrate-modified biochar to adsorb CH₄ and N₂O through first-principles calculations. Guided by theoretical simulations, modifications were selected for carbon sequestration and reduction in the rice paddies. The field test data confirmed the advantages of sodium ferrate-modified biochar. Compared with that of CK, BC + 4%Fe reduced CH₄ and increased the yield by 14.57% and 17.45%, respectively. Moreover, it increased the accumulation of LOC. This method, verified by theoretical calculations and rice field experiments, directly reflects the effects of carbon sequestration and emissions reduction via high-valent iron-modified biochar. This provides a new avenue for future research and the development of new materials for greenhouse gas reduction in rice fields.