Optimizing Fertilization Strategies to Reduce Carbon Footprints and Enhance Net Ecosystem Economic Benefits in Ratoon Rice Systems

Abstract

Ratoon rice is a planting system that efficiently utilizes temperature and light resources. However, multiple fertilization applications are typically required to maintain stable rice yields. Improper fertilization not only poses challenges to scarce labor resources but also increases carbon footprints (CFs). Research on the effects of different fertilization strategies on greenhouse gas (GHG) emissions, yield, CF, and ecosystem net economic benefits (NEEBs) in ratoon rice systems remains limited. A two-year field experiment was conducted to evaluate the effects of one conventional fertilization strategy and four optimized fertilization strategies on GHG emissions, yield, CF, and NEEBs in the ratoon rice system. The conventional fertilization strategy applied urea in five splits (FFP, 280 kg N·ha⁻¹). The optimized strategies included (1) one-time side deep application controlled-release fertilizer (CRF, 280 kg N·ha⁻¹); (2) CRF with 20% N replaced by organic fertilizer (OF + CRF1); (3) the same as (2) with a 10% N reduction (OF + CRF2, 252 kg N·ha⁻¹); and (4) the same as (2) with a 20% N reduction (OF + CRF3, 224 kg N·ha⁻¹). The results showed that compared with FFP, optimized fertilization treatments reduced CH₄ and N₂O emissions by 28.69% to 55.27% and 25.08% to 40.32%, respectively. They also increased the annual rice yields by 2.22% to 19.52% (except OF + CRF3). Optimizing fertilization treatments reduced annual CF, CF_Y, and CF_EC by 26.66% to 49.59%, 34.11% to 51.12%, and 25.35% to 41.47%, respectively. These treatments also increased NEEBs by 8.27% to 34.23%. Among them, OF + CRF1 and OF + CRF2 treatments achieved the highest NEEB. In summary, CRF treatments can balance ratoon rice yield and environmental benefits. Replacing part of the N with organic fertilizer further enhances annual yield and NEEBs.

1. Introduction

Addressing climate change and achieving food security represent some of the most significant global challenges of the 21st century. Agricultural activities account for approximately 20% to 23% of global greenhouse gas (GHG) emissions, underscoring their substantial contribution to climate change [1,2]. Nearly half of the world’s population relies on rice as a main food source. To obtain high yields, conventional rice production is typically achieved by relying on external inputs, such as agrochemicals and mechanization [3]. However, these inputs are largely derived from non-renewable resources, which will greatly increase GHG emissions and carbon footprints (CFs) in rice production, thereby threatening its long-term sustainability [4]. As the global population grows and arable land shrinks, it becomes increasingly important to balance higher food production with lower agricultural CF [4,5].

Ratoon rice is a cropping pattern that regenerates the ratoon crop by utilizing dormant buds on the rice stubble after the main crop is harvested [6]. Recently, this method has gained widespread adoption in suitable regions of China because it can increase harvesting frequency and total yield without requiring additional cultivated land [7,8]. It has been demonstrated that replacing double-season rice with ratoon rice cultivation reduces CF by 20.08% and increases net ecosystem economic benefits (NEEBs) by 30.95% [9]. Although ratoon rice has been extensively promoted in China, its potential as a sustainable cropping system is also being explored in other rice-growing regions. Studies have reported its application and potential in the Philippines, West Africa, and Japan [10,11,12]. In these regions, challenges such as limited arable land, labor shortages, and climate vulnerability have spurred interest in low-input, high-efficiency systems like ratoon rice. However, due to the long growth period of ratoon rice, five applications of nitrogen (N) fertilizer are usually required to obtain stable or higher yields [13]. These applications include basal, tillering, panicle, sprout-accelerating, and seedling-promoting fertilizers. This intensive fertilization regime results in low N use efficiency (NUE) and high GHG emissions [14]. Previous research on ratoon rice fertilization mainly focused on optimizing the key period and fertilization ratio [15,16]. For example, Zou et al. (2024) [15] found that a split N application (67.5% + 22.5%) after the main crop increased yield and reduced the CF by 36.64%. Despite such advances, the challenge of frequent fertilization events in ratoon rice systems remains unresolved. Therefore, developing a simplified fertilization strategy (such as one-time fertilization) is crucial for improving yield while reducing the CF. This study addresses this gap and contributes to sustainable agricultural practices.

In recent years, controlled-release fertilizer and deep application technology have gradually become effective strategies for optimizing rice N management [17,18]. Controlled-release fertilizers provide a stable and sustained nutrient supply [19]. This limits N substrates for soil nitrification and denitrification, thereby reducing N₂O emissions by 20% to 60% [20]. The integration of one-time side deep application technology with controlled-release fertilizer further enhances NUE and emission reduction effects. This approach involves the simultaneously deep placement of N fertilizer during rice transplanting, positioning the fertilizer near the root system [17]. Mitigation of CH₄ emissions was observed with deep placement N placement, attributed to stimulated plant growth and greater microbial presence in the soil [21]. Deep N placement promotes root elongation, accelerates N uptake, and minimizes N accumulation in surface soil. This shift relocates N₂O production from surface to deeper soil layers. In these deeper layers, extended diffusion paths and microbial assimilation facilitate further reduction of N₂O to N₂, thereby decreasing atmospheric emissions [22]. One-time side deep fertilization technology combined with controlled-release fertilizer can ensure a stable supply of nutrients throughout the rice growth cycle. This approach eliminates multiple fertilizations and reduces labor costs. In addition, replacing part of the N with organic fertilizer may be a promising strategy, as it simultaneously increases ratoon rice yield and enhances soil carbon sequestration. Organic fertilizer can enhance soil organic carbon (SOC) content, increase microbial diversity, and optimize the partitioning pattern of photoassimilates in the rice rhizosphere, thereby improving ratoon crop yield [23]. Although the application of organic fertilizer increases CH₄ emissions, its slow decomposition characteristics help reduce the risk of N excess and subsequent N₂O emissions [24]. At the same time, the potential of organic fertilizer to enhance SOC sequestration may indirectly reduce carbon emissions, which is a factor worth considering in the ratoon rice system [25].

A systematic evaluation of fertilization strategies is essential to comprehensively understand their effects on GHG emissions, yield performance, and the environmental sustainability of ratoon rice systems. In recent years, CFs and NEEBs have become widely used indicators for evaluating the sustainability of agricultural practices [26]. However, studies on the integrated evaluation of CFs and NEEBs under optimized fertilization in ratoon rice systems remain limited. To address the knowledge gap, we conducted a two-year field experiment (2023–2024) in the ratoon rice growth cycle. This study focuses on (1) evaluating the impact of different fertilization strategies on greenhouse gas emissions; (2) comparing the CF, CF per kg of rice yield (CF_Y), CF per unit of economic output (CF_EC), and NEEBs under different fertilization strategies; and (3) identifying an optimal fertilization strategy that balances ecological and economic benefits in ratoon rice systems.

2. Methods

2.1. Experimental Site

The experiment was conducted from 2023 to 2024 at the Agricultural Science and Technology Industrial Park of Yangtze University in Jingzhou City, Hubei Province, China (30°23′46.68″ N, 112°29′7.71″ E). The region has a subtropical monsoon climate. Precipitation and temperature data during the experiment are presented in Figure S1. In 2023 and 2024, the annual average temperatures were 23.6 °C and 18.63 °C, respectively, with total precipitation of 954 mm and 905 mm. The experimental soil is a slightly sticky paddy soil of lacustrine origin. The initial soil properties (0–20 cm) were as follows: pH 6.9, ammonium nitrogen (${\mathrm{N}\mathrm{H}}_4^{+}$-N) 4.14 mg·kg⁻¹, nitrate nitrogen (${\mathrm{N}\mathrm{O}}_3^{-}$-N) 5.76 mg·kg⁻¹, total nitrogen (N) 2.44 g·kg⁻¹, total phosphorus (P) 0.64 g·kg⁻¹, total potassium (K) 8.44 g·kg⁻¹, available P 24.5 mg·kg⁻¹, available K 94.6 mg·kg⁻¹, and organic matter 28.6 g·kg⁻¹.

2.2. Experimental Design

The field experiment was conducted using a randomized complete block design with five treatments, each replicated three times. Each experimental plot covered an area of 50 m². The five experimental treatments were (

Table 1. Fertilizer types and application amount in different treatments (kg ha⁻¹).

Treatment	Total N Amount (kg N ha−1)	N Fertilizer Input (kg N ha−1)			P Fertilizer Input (kg P ha−1)		K Fertilizer Input (kg K ha−1)
		Organic Fertilizers Replace N	Urea	N from Controlled-Release Fertilizer	P from Controlled-Release Fertilizer	P2O5	K from Controlled-Release Fertilizer	K2O
FFP	280	-	280	-	-	150	-	180
CRF	280	-	-	280	28	122	30.8	149.2
OF + CRF1	280	56	-	224	22.4	127.6	24.6	155.4
OF + CRF2	252	50.4	-	201.6	20.2	129.8	22.2	157.8
OF + CRF3	224	44.8	-	179.2	17.9	132.1	19.7	160.3

Note: FFP, farmers’ familiar practice of common urea with a total N application rate of 280 kg·ha⁻¹; CRF, one-time side deep application of controlled release fertilizer with a total N application rate of 280 kg·ha⁻¹; OF + CRF1, organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 280 kg·ha⁻¹; OF + CRF2, 10% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 252 kg·ha⁻¹; OF + CRF3, 20% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 224 kg·ha⁻¹. In the FFP treatment, urea was applied in five-split surface broadcasting (basal fertilizer, tillering fertilizer, panicle fertilizer, sprout-accelerating fertilizer, and seedling-promoting fertilizer). In the CRF, OF + CRF1, OF + CRF2, and OF + CRF3 treatments, controlled-release fertilizer was applied once as a side deep placement during transplanting, with no additional topdressing thereafter. Organic fertilizers in the OF + CRF1, OF + CRF2, and OF + CRF3 treatments, along with phosphorus (P) and potassium (K) fertilizers in all treatments, were incorporated into the soil as basal fertilizers during tillage.

) (1) farmers’ familiar practice of common urea with a total N application rate of 280 kg·ha⁻¹ (FFP), (2) one-time side deep application of controlled release fertilizer with a total N application rate of 280 kg·ha⁻¹ (CRF), (3) organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 280 kg·ha⁻¹ (OF + CRF1), (4) 10% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 252 kg·ha⁻¹ (OF + CRF2), and (5) 20% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 224 kg·ha⁻¹ (OF + CRF3). The N application rates refer to pure N content. The CRF (plant oil-coated) had a nutrient composition of 25-10-11 (N + P₂O₅ + K₂O ≥ 46%) and a release period of six months. It was developed by Yangtze University and Jiayi (Guangzhou) Agricultural Technology Co., Ltd. (Guangzhou, China). The organic fertilizer was commercial organic fertilizer made from chicken manure (N 2.26%). All treatments used P fertilizer (12% P₂O₅, Hubei Sanning Chemical Co., Ltd., Yichang, Hubei Province, China) and K fertilizer (60% K₂O, Sinofert Holdings Co., Ltd., Beijing, China) as basal fertilizer once before transplanting, with the amount of 150 kg·ha⁻¹ and 180 kg·ha⁻¹, respectively. The insufficient P and K nutrients in the CRF were supplemented by P and K fertilizer. The rice was planted at a row spacing of 28 cm × 18 cm, and the variety used was Fengliangyouxiang 1.

In the FFP treatment, urea was applied five times throughout the growth period of ratoon rice. The amount of N applied in the main crop was 180 kg·ha⁻¹, of which 40% was used as basal fertilizer, 30% as tillering fertilizer, and 30% as panicle fertilizer. Additionally, 50 kg·ha⁻¹ of sprout-accelerating fertilizer and seedling-promoting fertilizer were applied before and after the harvest of the main crop, respectively. In other treatments, one-time side deep fertilization was employed. During rice transplantation, a 10 cm deep furrow was created using a seeder, positioned 5 cm away from one side of the seedling row. The CRF was evenly distributed into the furrow and covered with soil. P and K fertilizers, along with organic fertilizers, were applied as basal fertilizers and thoroughly mixed into the soil prior to rice transplantation.

The ratoon rice seedlings were raised on 22 March (2023) and 26 March (2024), transplantation on 20 April (2023) and 26 April (2024), main crop harvest on 13 August (2023) and 12 August (2024), and ratoon crop harvest on 23 October (2023) and 22 October (2024). At main crop harvest, the stubble height was maintained at 30 cm. Water management, pest and disease control, and field weeding were carried out in accordance with local cultivation practices throughout the growth period.

2.3. Gas Collection and Analysis

CH₄, N₂O, and soil heterotrophic respiration (R_h) fluxes were measured using an artificial static chamber combined with gas chromatography [27]. The chamber consisted of a stainless steel box body with 45 cm (length) × 45 cm (width) × 100 cm (height) and a base with 45 cm (length) × 45 cm (width) × 20 cm (height). The box body is wrapped with thermal insulation to prevent temperature rise due to solar radiation. A circulating fan inside the chamber ensured uniform gas mixing. After rice transplanting, the base, which featured a 2 cm groove, was securely placed at the center of each plot. Before gas collection, water was added to the groove to ensure an airtight seal. Once sealed, gas samples were immediately collected using a 100 mL syringe. Sampling occurred between 9:00 and 11:00 am, gas samples were taken at 0, 10, 20, and 30 min. During sampling, chamber temperature was recorded using an electronic thermometer. Gas samples were collected every 2 days during the first seven days following each fertilization (according to the fertilization schedule for the FFP treatment) and weekly thereafter.

The collected gas samples were transported to the laboratory and analyzed for CH₄, N₂O, and CO₂ concentrations using a gas chromatograph (Agilent 7890B, Agilent Technologies, Palo Alto, CA, USA). The gas emission flux and cumulative emissions detailed calculation process is described in Text S1.

2.4. Soil Collection and Analysis

The “five-point sampling method” was used to collect 0–20 cm soil samples from each plot at the tillering stage (TS), booting stage (BS), full heading stage of first-season rice (FHSFR), filling stage of first-season rice (FSFR), ripening stage of first-season rice (RSFR), full seedling stages of ratoon rice season (FSSRR), full heading stages of ratoon rice season (FHSRR), and ripening stages of ratoon rice season (RSRR). Soil ${\mathrm{N}\mathrm{H}}_4^{+}$-N content was analyzed using the indophenol blue colorimetric method; soil ${\mathrm{N}\mathrm{O}}_3^{-}$-N content was analyzed by dual-wavelength ultraviolet spectrophotometry [28]. Soil dissolved organic carbon (DOC) was analyzed using the potassium dichromate-sulfuric acid external calorific capacity method [29]. Standard solutions were used to calibrate analytical instruments before each set of measurements. All reagents used were of analytical grade, and procedural blanks were included to ensure accuracy and reliability of the results.

2.5. Yield Measurement

At harvest in both the main and ratoon crop, three 1 m² areas with uniform growth were selected in each plot for harvesting. The plants were manually harvested and threshed. The grains were air-dried to a constant weight and then weighed to determine grain yield. The final yield was adjusted to a standard moisture content of 14% [9]. Additionally, the effective panicle number per m² was counted in a 1 m² area, while other yield components—including filled grains per panicle, seed setting rate, and 1000-grain weight were measured from ten representative plants per plot (Table S1).

2.6. Soil Organic Carbon Balance

Based on the study of Smith et al. (2010) [27], this study used the dark static chamber method to estimate the carbon balance of agricultural ecosystems in a short period of time. The detailed procedure of the dark static chamber method is described in Section 2.3. Negative net ecosystem carbon budget (NECB) values indicate net carbon loss in the ecosystem, while positive values indicate net carbon uptake. NECB is calculated according to Text S2.

ΔSOC (soil organic carbon change) was calculated for each season (main crop and ratoon crop) using the coefficient 0.213 reported in the study [30]:

ΔSOC = 0.213 × NECB

where ΔSOC represents the soil organic carbon change, 0.213 represents the coefficient of change of SOC, and NECB is the net ecosystem carbon budget.

2.7. Calculation of Carbon Footprints

The CF represents the total GHG emissions associated with ratoon rice production, covering both direct emissions (from fields) and indirect emissions (e.g., from inputs like fertilizers, pesticides, and energy use) (Figure 1). Emissions were converted to CO₂ equivalents (CO₂-eq). Direct emissions comprised GHG fluxes from ratoon rice fields and SOC change. Indirect emissions include emissions from all agricultural inputs and field farming operations from sowing to harvesting of ratoon rice, mainly including pesticides, fertilizers, electricity used for irrigation, diesel consumed for farming, and seed inputs [31]. The calculation formula of CF is as follows:

CF = CF_Direct + CF_Indirect

where CF is the carbon footprint, CF_Direct represents the direct GHG emissions from ratoon rice fields, CF_Indirect represents the CO₂ equivalent of indirect GHG emissions generated by various agricultural inputs used in the production, processing, and transportation of agricultural production, such as fertilizers, pesticides, herbicides, fungicides, diesel, and electricity.

CF_Direct = N₂O × 273 + CH₄ × 27 − ΔSOC × 44/12

where CF_Direct represents the direct GHG emissions from ratoon rice fields, according to IPCC (2021) [1], 273 and 27 are the global warming potentials of N₂O and CH₄ relative to CO₂ over a 100-year timeframe, 44/12 means the conversion of C to CO₂.

$${\mathrm{CF}}_{\mathrm{Indirect}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{AI}}_{\mathrm{i}} \times { \mathrm{EF}}_{\mathrm{i}}\right)$$

where CF_Indirect represents the CO₂ equivalent of indirect GHG emissions generated, AI_i represents the type of agricultural inputs in the experimental system, and EF_i represents the emission factor (Table S2).

The calculation formula of CF per kg grain yield (CF_Y, kg CO₂-eq kg⁻¹) and CF per unit of economic output (CF_EC; kg CO₂-eq CNY⁻¹) are as follows:

$${\mathrm{CF}}_{\mathrm{Y}} = {\displaystyle \frac{\mathrm{CF}}{\mathrm{Grain} \mathrm{yield}}}$$

where CF_Y represents the carbon footprint per kg of rice yield.

$${\mathrm{CF}}_{\mathrm{EC}}={\displaystyle \frac{\mathrm{CF}}{\mathrm{Economic} \mathrm{output}}}$$

where CF_EC represents the carbon footprint per unit of economic output. Economic output is calculated as the total economic output minus the total economic input. The specific calculation parameters and economic output results are shown in Table S2.

2.8. Calculation of Net Ecosystem Economic Benefits

The net ecosystem economic benefit (NEEB) evaluates the economic viability of ratoon rice by accounting for income, production costs, and environmental costs. It was calculated according to the method of Xu et al. (2022) [9]:

NEEB = Grain income − Output cost − CF cost

where NEEB is the net ecosystem economic benefit. Grain income is calculated as rice production multiplied by the rice price, rice price based on the actual selling price in the corresponding year. Output costs include expenses for hired labor, fertilizers, pesticides, irrigation electricity, farmland, and diesel. Output cost details are shown in Table S3. CF cost is determined by multiplying CF by the carbon trading price. The carbon-trade price is 105.30 CNY t CO₂-eq [9].

2.9. Statistical Analysis

All statistical analyses were conducted using SPSS 22.0 (IBM, Inc., Armonk, NY, USA). Differences among treatments were evaluated using the least significant difference (LSD) test at a significant level of p < 0.05. A two-way analysis of variance (ANOVA) was performed to assess the effects of treatment (T), year (Y), and their interaction (T × Y) on cumulative CH₄ and N₂O emissions. All figures were generated using Origin 2024 (OriginLab Corp., Northampton, MA, USA).

3. Results

3.1. Direct Greenhouse Gas Emissions

Fertilization treatments significantly influenced the CH₄ and N₂O cumulative emissions (p < 0.05) (

Table 2. CH₄ and N₂O cumulative emissions under different fertilization treatments in 2023 and 2024.

Year	Treatment	CH4 Cumulative Emissions (kg ha−1)			N2O Cumulative Emissions (kg ha−1)
		Main Crop	Ratoon Crop	Annual	Main Crop	Ratoon Crop	Annual
2023	FFP	474.67 ± 8.85 a	75.50 ± 4.03 a	550.17 ± 5.70 a	2.34 ± 0.04 a	0.81 ± 0.00 a	3.15 ± 0.04 a
	CRF	214.01 ± 4.12 e	32.07 ± 3.01 c	246.08 ± 4.03 e	1.72 ± 0.03 b	0.64 ± 0.02 b	2.36 ± 0.02 b
	OF + CRF1	349.49 ± 6.21 b	42.83 ± 5.86 b	392.32 ± 7.22 b	1.64 ± 0.05 c	0.55 ± 0.03 c	2.18 ± 0.06 c
	OF + CRF2	335.07 ± 6.27 c	43.16 ± 3.73 b	378.23 ± 9.26 c	1.48 ± 0.03 d	0.50 ± 0.01 d	1.98 ± 0.02 d
	OF + CRF3	322.56 ± 2.26 d	38.27 ± 5.2 bc	360.83 ± 5.84 d	1.40 ± 0.03 e	0.48 ± 0.01 d	1.88 ± 0.03 e
2024	FFP	445.05 ± 9.72 a	110.18 ± 4.01 a	555.23 ± 9.67 a	2.45 ± 0.05 a	0.96 ± 0.05 a	3.42 ± 0.07 a
	CRF	209.63 ± 4.33 e	74.73 ± 6.59 c	284.35 ± 10.27 d	1.78 ± 0.06 b	0.75 ± 0.02 b	2.53 ± 0.08 b
	OF + CRF1	284.94 ± 3.48 b	85.38 ± 5.42 b	370.31 ± 7.63 b	1.78 ± 0.05 b	0.77 ± 0.04 b	2.55 ± 0.07 b
	OF + CRF2	266.1 ± 4.41 c	78.84 ± 3.27 bc	344.94 ± 2.32 c	1.74 ± 0.02 b	0.72 ± 0.03 b	2.46 ± 0.04 b
	OF + CRF3	254.92 ± 4.72 d	77.23 ± 6.3 bc	332.16 ± 9.18 c	1.59 ± 0.05 c	0.72 ± 0.03 b	2.31 ± 0.03 c
F value
Treatment		1413.48 ***	61.04 ***	1174.44 ***	422.51 ***	107.44 ***	506.02 ***
Year		480.589 ***	473.48 ***	8.73 **	99.16 ***	358.36 ***	339.26 ***
Treatment × Year		36.23 ***	0.87 ns	23.62 ***	4.85 **	5.36 **	8.65 ***

Note: FFP, farmers’ familiar practice of common urea with a total N application rate of 280 kg·ha⁻¹; CRF, one-time side deep application of controlled release fertilizer with a total N application rate of 280 kg·ha⁻¹; OF + CRF1, organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 280 kg·ha⁻¹; OF + CRF2, 10% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 252 kg·ha⁻¹; OF + CRF3, 20% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 224 kg·ha⁻¹. The data in each column for a given year are presented as mean ± standard error. Different lowercase letters following the values in the same column indicate significant differences among treatments (p < 0.05). For F values, an asterisk denotes significant effects of treatment, year, or their interaction: ** p < 0.01, *** p < 0.001 and ^ns indicates no significant effect.

). Compared with the FFP treatment, all optimized fertilization treatments significantly reduced CH₄ cumulative emissions by 26.37% to 54.91% (main crop), 22.51% to 57.52% (ratoon crop), and 28.69% to 55.27% (annual). This indicates that optimized fertilization altered soil redox conditions and the availability of carbon substrates, thereby reducing CH₄ emissions. Compared with the FFP treatment, the optimized fertilization treatments significantly reduced N₂O cumulative emissions by 27.35% to 56.50% (main crop), 19.79% to 40.74% (ratoon crop), and 25.08% to 40.32% (annual). Among optimized fertilization treatments, the OF + CRF3 treatment resulted in the lowest N₂O cumulative emissions. The average annual N₂O cumulative emissions over the two years showed OF + CRF3 < OF + CRF2 < OF + CRF1 < CRF < FFP. Optimizing fertilization reduces N₂O production by reducing excess N in the soil and improving the synchronization of N release. CH₄ and N₂O emissions were generally higher in 2023 than in 2024 across all treatments. In 2023, higher rainfall maintained prolonged soil saturation, favoring methanogenic activity and thus increasing CH₄ emissions, while also enhancing denitrification and N₂O release. In contrast, the relatively lower rainfall in 2024 led to more frequent drying–rewetting cycles, which suppressed CH₄ production but may have stimulated N₂O emissions during rewetting periods.

Two-way ANOVA showed that fertilization treatment and year had significant effects on the CH₄ and N₂O cumulative emissions (p < 0.05). Fertilization treatment and year also had significant interactive effects on the CH₄ and N₂O cumulative emissions in the main crop and annual (p < 0.05).

3.2. Yield

As shown in

Table 3. Ratoon rice yield under different fertilization treatments in 2023 and 2024 (t ha⁻¹).

Year	Treatment	Main Crop	Ratoon Crop	Annual
2023	FFP	8.05 ± 0.14 c	4.20 ± 0.07 b	12.25 ± 0.20 c
	CRF	8.36 ± 0.10 b	4.16 ± 0.05 b	12.52 ± 0.14 b
	OF + CRF1	8.82 ± 0.06 a	4.90 ± 0.07 a	13.72 ± 0.02 a
	OF + CRF2	8.80 ± 0.12 a	4.88 ± 0.05 a	13.68 ± 0.08 a
	OF + CRF3	8.31 ± 0.11 b	4.15 ± 0.07 b	12.46 ± 0.05 bc
2024	FFP	8.31 ± 0.05 c	4.30 ± 0.08 b	12.61 ± 0.08 c
	CRF	8.60 ± 0.08 b	4.41 ± 0.13 b	13.01 ± 0.19 b
	OF + CRF1	9.99 ± 0.08 a	5.09 ± 0.18 a	15.07 ± 0.19 a
	OF + CRF2	9.88 ± 0.19 a	4.92 ± 0.14 a	14.81 ± 0.07 a
	OF + CRF3	8.33 ± 0.09 c	4.33 ± 0.19 b	12.66 ± 0.15 c

Note: FFP, farmers’ familiar practice of common urea with a total N application rate of 280 kg·ha⁻¹; CRF, one-time side deep application of controlled release fertilizer with a total N application rate of 280 kg·ha⁻¹; OF + CRF1, organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 280 kg·ha⁻¹; OF + CRF2, 10% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 252 kg·ha⁻¹; OF + CRF3, 20% N reduction + organic fertilizer replacing 20% of N + one-time side deep application of CRF, with a total N application rate of 224 kg·ha⁻¹. Different lowercase letters following the values in the same column indicate significant differences among treatments (p < 0.05).

, There were significant differences in yield among the different fertilization treatments (p < 0.05). The CRF, OF + CRF1, and OF + CRF2 treatments significantly increased the main crop yield by 3.85% to 9.56% in 2023 and 3.49% to 20.22% in 2024 compared with the FFP treatment. Under organic fertilizer replacement, compared with FFP treatment, OF + CRF1 and OF + CRF2 treatments significantly increased ratoon crop yield by 16.15% to 16.62% in 2023 and 14.34% to 18.16% in 2024. Compared with FFP treatment, CRF, OF + CRF1, and OF + CRF2 treatments resulted in significant increases in annual yield of 2.22% to 11.98% in 2023 and 3.17% to 19.52% in 2024. OF + CRF1 had the highest yield in the main crop, ratoon crop, and annual yield. This indicates that optimized fertilization treatments ensure adequate nutrient supply for ratoon rice, thereby improving yield.

3.3. Carbon Footprints

As shown in Figure 2a, fertilizers, particularly N fertilizers, were the primary contributors to CF_Indirect, accounting for 51.78% to 70.32% of its total. Due to the input of controlled-release fertilizer and organic fertilizer, the CF_Indirect of CRF and OF + CRF treatments was 3.37% to 19.91% higher than that of FFP treatment. The annual CF_Direct of ratoon rice ranged from 10,345.82 to 25,961.07 kg CO²-eq ha⁻¹ (Figure 2b). CH₄ emissions from ratoon rice dominate CF_Direct. Compared with the FFP treatment, the optimized fertilization treatments significantly reduced CF_Direct by 34.84% to 60.21% in the main crop, 31.48% to 58.58% in the ratoon crop, and 33.22% to 59.41% in the annual. The total CF of the main crop, ratoon crop, and annual showed the same trend in the two years, which was FFP > OF + CRF1> OF + CRF2 > OF + CRF3 > CRF (Figure 2c). Additionally, the CF in the ratoon crop was significantly lower than in the main crop. Compared with FFP treatment, the optimized fertilization treatments significantly reduced annual CFs by 26.66% to 49.59%. Compared with OF + CRF treatments, the CRF treatment significantly reduced annual CFs by 23.54% to 30.26%. These findings demonstrate that while organic and controlled-release fertilizers may increase indirect emissions slightly, their combined use substantially lowers direct emissions, leading to an overall reduction in total CF.

The Yield-scaled CF (CF_Y) and economic output CF (CF_EC) reflect the environmental and economic efficiency of crop production, respectively. As shown in Figure 3, compared to the FFP treatment, optimized fertilization treatments significantly reduced CF_Y by 28.45% to 44.10% in the main crop, 36.17% to 59.58% in the ratoon crop, and 34.11% to 51.12% in the annual (Figure 3a). Among them, the CRF treatment showed the lowest CF_Y. These reductions were primarily attributed to increased yield and reduced total carbon emissions. Optimized fertilization treatments enhanced the economic benefits of ratoon rice production (Table S3) while reducing CF_EC (Figure 3b). Specifically, compared with the FFP treatment, optimized fertilization treatments decreased CF_EC by 30.98% to 45.77% in the main crop, 13.04% to 31.60% in the ratoon crop, and 25.35% to 41.47% in the annual. The CRF treatment had the lowest CF_EC, indicating both environmental and economic superiority.

3.4. Net Ecosystem Economic Benefits

The benefits of grain income and output cost and CF cost are shown in Figure 4. Since no pesticides were used on the ratoon crops and labor demand was low, the output cost and CF cost were lower than in the main crop. Although the optimized fertilization treatments increased the fertilizer input cost, it reduced the labor input by reducing the number of fertilization times while increasing the yield, thereby obtaining a higher economic output (Table S2). The optimized fertilization treatments reduced the CF cost and ultimately increased the annual NEEBs of ratoon rice. Compared with the FFP treatment, the optimized fertilization treatments significantly increased the annual NEEBs by 10.45% to 24.73% in 2023 and 8.27% to 34.23% in 2024. Among them, the annual NEEBs of OF + CRF1 and OF + CRF2 were the highest, significantly increased by 9.71% to 10.72% in 2023 and 16.48% to 18.02% in 2024, compared with CRF treatment. Overall, optimizing fertilization treatments can increase economic benefits to farmers without increasing environmental costs.

4. Discussion

4.1. Effects of Different Fertilization Treatments on Direct Greenhouse Gas Emissions

Fertilization methods significantly affected CH₄ emissions [32]. The results of this study showed that both CRF and OF + CRF treatments significantly reduced CH₄ emissions (Table 2). This reduction is primarily attributed to the deep placement of fertilizer and the slow-release characteristics of CRF. CH₄ oxidation in paddy soil mainly occurs at the soil–water interface and within the oxidation zone of the rice rhizosphere [33]. Surface application of N fertilizer leads to ${\mathrm{N}\mathrm{H}}_4^{+}$ accumulation in the upper soil layer, inhibiting methanotrophic bacterial activity and promoting CH₄ production [21]. In contrast, a single deep application of N fertilizer precisely delivers nutrients to the rice root zone. N fertilizer applied to the root zone will produce more ${\mathrm{N}\mathrm{H}}_4^{+}$ and increase oxygen utilization in the rhizosphere, which may stimulate CH₄ oxidation [34]. In addition, controlled-release fertilizers further reduce CH₄ emissions by gradually releasing ${\mathrm{N}\mathrm{H}}_4^{+}$, limiting substrate availability for methanogens and reducing the abundance of dominant methanogenic archaea such as Methanosarcina [35]. In this study, compared with CRF treatment, the OF + CRF treatments led to higher CH₄ emissions (Table 2). Organic fertilizers contain abundant carbon matrices, which serve as substrates for methanogen growth, thereby stimulating CH₄ production [36].

N₂O is an intermediate product of nitrification and denitrification, and N fertilizer is an important factor affecting N₂O emissions from rice fields [37]. In this study, both CRF and OF + CRF treatments significantly reduced N₂O emissions (Table 2) compared to FFP treatment, aligning with findings from previous studies [37,38,39]. Under FFP, fast-acting N fertilizer applied near the soil–water interface is rapidly nitrified, with the resulting ${\mathrm{N}\mathrm{O}}_3^{-}$ undergoing denitrification under fluctuating redox conditions, leading to substantial N₂O emissions [37]. In contrast, deep application of N fertilizer can ensure that the rice roots absorb more N, reducing soil nitrification and denitrification by reducing the supply of N substrates, and thus reduce N₂O emissions [39,40,41]. Deep fertilizer placement leads to N accumulation in deeper soil layers, lowering N concentrations in the surface soil. This effectively suppresses ${\mathrm{N}\mathrm{H}}_4^{+}$-N conversion to ammonia and decreases the nitrification rate [42]. Furthermore, controlled-release fertilizer regulates urea dissolution, ensuring stable N release. This prevents excessive inorganic N accumulation in the soil, thereby limiting its transformation into gaseous N. According to Akiyama et al. (2013) [43], controlled-release fertilizer reduces amoA gene expression in nitrifiers and inhibits denitrifiers with nirS and nirK genes, thereby lowering N₂O emissions. In this study, compared with CRF treatment, OF + CRF treatments reduced N₂O emissions (Table 2). This may be attributed to enhanced oxygen consumption during organic matter decomposition, which suppresses nitrification and limits N₂O formation [44].

4.2. Effects of Different Fertilization Treatments on Carbon Footprints

Agricultural inputs cause a large amount of GHG emissions and CFs [45]. In this study, compared to FFP treatment, optimized fertilization treatments reduced the annual CF by 26.66% to 49.59% (Figure 2c), and CRF exhibited the lowest CF. Deep fertilization and controlled-release fertilizer reduce CF by improving NUE, reducing N losses and GHG emissions [34,37]. The contribution of CF_Indirect to the total CF was substantially lower than that of CF_Direct. N fertilizer played a dominant role in CF_Indirect, accounting for 51.78% to 70.32% of the total CF_Indirect (Figure 2a), which is similar to the results of Deng et al. (2024) [46]: that is, indirect GHG from N fertilizer in ratoon rice systems accounted for 44.3% of the total indirect GHG. Compared with the FFP treatment, both the CRF and OF + CRF treatments exhibited higher CF_Indirect. This increase was mainly attributed to the higher emission factor of controlled-release fertilizers in the CRF treatment (Table S2). In the OF + CRF treatment, the additional use of organic fertilizers further increased indirect GHG emissions. Although CRF increased CF_Indirect, the one-time deep application of controlled-release fertilizer can also reduce CF_Direct by increasing SOC (Figure 2b) [47].

CF_Direct was the primary contributors to the total CF in ratoon rice system in this study (Figure 2b). CRF and CRF+ OF reduce CF_Direct, which outweighs the increase in CF_Indirect, therefore reducing total CF. CH₄ emissions were the main contributor to CF_Direct (Figure 2b), which is consistent with the results reported by Liu et al. (2020) [37]. Deep application of controlled-release fertilizers can promote rice root growth and increase rhizosphere oxygen utilization, thereby enhancing CH₄ oxidation in the underground soil and reducing CH₄ emissions [34,37]. The organic fertilizer treatments (OF + CRF) in this study led to higher CH₄ emissions compared with the CRF treatment, thereby increasing CF_Direct. However, the long-term environmental benefits of organic fertilizer (such as increased soil organic carbon storage and reduced fertilizer dependence) may not be fully covered by short-term CF assessments. Studies have shown that after long-term organic fertilizer input, there may be a saturation point or equilibrium point, and even if the amount of organic fertilizer input increases, it will not lead to an increase in CH₄ emissions [48]. Therefore, this effect should be further considered and verified in future studies. Previous studies have also demonstrated that replacing N fertilizers with controlled-release fertilizer or organic fertilizers can sustain crop yield, enhance soil carbon sequestration, and reduce the CF [49]. In this study, OF + CRF has a stronger SOC storage capacity, and its long-term SOC benefits may offset the disadvantage of increasing CH₄ emissions (Figure 2b).

This study comprehensively evaluated the environmental effects of different fertilization treatments from two perspectives, CF_Y and CF_EC, to avoid the limited conclusions based on a single indicator [46]. The optimized fertilization treatments significantly reduced annual CF_Y by 34.11% to 51.12% in the ratoon rice systems (Figure 3a). This was mainly attributed to the one-time side deep application of controlled-release fertilizer, which increased the ratoon yield by improving nutrient utilization and reduced the CF by 34.11% to 51.12% (Table 3 and Figure 3). Optimized fertilization can achieve the synergy of “increasing production and reducing emissions”. Furthermore, optimized fertilization treatments reduced the annual CF_EC by 25.35% to 41.47%, as the CRF and OF + CRF treatments lowered labor input while increasing rice yield, thereby improving economic benefits (Table S2) [50].

Our results support the use of organic fertilizers to partially replace N fertilizers in farmers’ production, as it can both increase crop yields and reduce environmental impacts. However, in actual application, nutrient management strategies should be adjusted according to long-term application conditions. Using organic fertilizers in proper proportions/combinations not only reduces the dependence on chemical fertilizers but also improves soil fertility, soil health, and quality [25]. Despite the multiple benefits of organic fertilizers, appropriate demonstration, outreach, and training are still needed to guide farmers in implementing effective nutrient management practices in the field. To better assess or predict organic fertilizers’ long-term effects, future studies should adopt integrative approaches such as long-term field monitoring, life cycle assessment, and modeling-based prediction methods [2]. These approaches can help identify key influencing factors and simulate long-term outcomes under various management scenarios, thereby supporting data-driven decision-making in sustainable agriculture.

4.3. Effects of Different Fertilization Treatments on Net Ecosystem Economic Benefits

Enhancing grain yield while simultaneously reducing agriculture input and environmental costs has become a major challenge in agricultural production [51]. As a key indicator for assessing the sustainability of agricultural systems, NEEBs provide a comprehensive evaluation of the balance between economic returns and environmental impacts [26]. This study showed that the optimized fertilization treatments significantly increased NEEBs compared with the FFP treatment (Figure 4). This is mainly because the optimized fertilization treatments reduced labor input, increased the ratoon rice yield, and reduced GHG emissions. Deep fertilization can effectively reduce nutrient loss and retain nutrients in the root zone soil, providing a continuous nutrient supply for grain development in the later stage of rice growth. As shown in Figure S2, soil ${\mathrm{N}\mathrm{H}}_4^{+}$-N, ${\mathrm{N}\mathrm{O}}_3^{-}$-N, and DOC concentrations were higher under optimized fertilization during all growth stages of ratoon rice. This enriched nutrient environment stimulated root proliferation and enhanced nutrient uptake, thereby increasing yield [34,38,52]. At the same time, we also found that the NEEBs of organic fertilizer replacement treatments (OF + CRF1 and OF + CRF2) increased the ratoon rice annual NEEBs by 9.71% to 18.02% compared with CRF treatment (Figure 4). This was mainly because the input of organic fertilizer significantly increased the ratoon crop yield (Table 3). Lin et al., (2023) [23] found that the application of organic fertilizers in the main crop had residual benefits for the ratoon crop. The incorporation of organic fertilizers enhances root activity during the later growth stages, delays root senescence, and consequently promotes the sprouting and growth of ratoon buds [16,23]. The quality of the ratoon crop is better than that of the main crop, and its price is higher. The higher yield brings higher economic benefits [6]. Due to the lower cost of organic fertilizers and the reduced labor requirements associated with one-time fertilization, farmers can achieve higher economic returns at a lower overall cost. From a practical perspective, the side deep fertilization machinery purchase cost and operation requirements still limit farmers’ willingness to adopt them [9,37]. Although these technologies have been promoted to a certain extent, the government should still provide financial subsidies for machinery and fertilizers to reduce the economic burden on farmers. In addition, to achieve the carbon neutrality goal before 2060 [52], a carbon emission reduction subsidy incentive mechanism should be implemented for farmers. This would encourage farmers to adopt more environmentally friendly practices through positive incentives.

5. Conclusions

This study analyzed the effects of five fertilization strategies on greenhouse gas emissions, carbon footprint, and net ecosystem economic benefits in ratoon rice systems. The results showed that, compared with the FFP treatment, the optimized fertilization treatments (CRF and OF + CRF) reduced CH₄ and N₂O emissions by 28.69% to 55.27% and 25.08% to 40.32%. Optimizing fertilization treatments reduced the annual carbon footprint, carbon footprint per kg of rice yield, and carbon footprint per unit of economic output by 26.66% to 49.59%, 34.11% to 51.12%, and 25.35% to 41.47%, respectively. Optimizing fertilization treatments increased NEEBs by 8.27% to 34.23%. OF + CRF1 and OF + CRF2 achieved the highest NEEBs. Although the treatment OF + CRF3 involved a 20% nitrogen reduction, its performance was lower than OF + CRF1 and OF + CRF2. This suggests that in the short term, excessive nitrogen reduction may limit crop nutrient supply. Therefore, nitrogen reduction should be aligned with the long-term soil fertility improvements from organic fertilizer use.

This study demonstrates that organic fertilizer replacement can not only effectively reduce greenhouse gas emissions but also enhance organic carbon content and increase the ratoon rice yield. These combined effects contribute to both environmental sustainability and economic benefits. In practical applications, we recommend that farmers adopt an integrated fertilization strategy combining organic fertilizer with controlled-release fertilizer. This approach promotes cleaner and more sustainable rice production.

To optimize sustainable fertilization strategies, future research should investigate the long-term effects of organic fertilizer replacement on soil carbon sequestration, nitrogen cycling, and greenhouse gas emissions. Large-scale field demonstrations and economic assessments will be required to evaluate the scalability and practical feasibility of optimized fertilization strategies. Additionally, a deeper investigation into the dynamics of soil microbial communities will help elucidate the mechanisms by which fertilization regimes influence greenhouse gas emissions and nutrient transformation processes. These insights will support the development of precise and sustainable nutrient management policies in support of national carbon neutrality goals.