Does replacing chemical fertilizer with ryegrass (Lolium multiflorum Lam.) mitigate CH4 and N2O emissions and reduce global warming potential from paddy soil?

Does replacing chemical fertilizer with ryegrass (Lolium multiflorum Lam.) mitigate CH4 and N2O emissions and reduce global warming potential from paddy soil? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Does replacing chemical fertilizer with ryegrass (Lolium multiflorum Lam.) mitigate CH4 and N2O emissions and reduce global warming potential from paddy soil? Wei Yang, Lai Yao, Xueru Ji, Mengzhen Zhu, Chengwei Li, Shaoqiu Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1784777/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract ( Purpose ) The incorporation of ryegrass ( Lolium multiflorum Lam.; RG), a winter grass manure, could partly replace chemical N and reduce N loss during the succeeding rice seasons, but little is known about its impact on greenhouse gas emission. This study investigated the effect of different RG-urea substitution ratios (0%, 25%, 50%, 75% and 100%) on C and N release, CH 4 and N 2 O emissions in a paddy soil. ( Methods ) Gas samples for CH 4 and N 2 O fluxes measurement were collected by using a closed chamber and determined by chromatograph method. C and N release from the incorporated RG residue were tested by a mesh bag method. ( Results ) C and N release from RG followed a single exponential decay model, with 95.5%-97.8% of the original C and 98.7%-99.3% of N released during 192 days. RG-urea substitution ratio increased CH 4 emission, but was negatively correlated with N 2 O emission. In comparison with 0% substitution, global warming potential (GWP) and greenhouse gas intensity (GHGI) were not significantly different for the 25% and 50% RG substitutions, but were significantly higher for the 75% and 100% substitutions ( P <0.05). Soil redox, C and N remaining in litter residue were key characteristics explaining CH 4 emission, while NH 4 + -N and NO 3 - -N concentrations were correlated with the variation of N 2 O emission. ( Conclusion ) The increased CH 4 emission by RG incorporation could be offset by the reduced N 2 O emission when RG-urea substitution ratio was 50% or less. Ryegrass CH4 N2O Green manure substitution ratio C and N release Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Methane (CH 4 ) and nitrous oxide (N 2 O) are important greenhouse gases (GHGs) responsible for global climate change (Stocker 2014 ). Rice paddy soil is one of the important sources for CH 4 and N 2 O production, and accounts for a significant proportion of global GHG emissions (Carter et al. 2014 ). Rice paddies are responsible for 48% of total anthropogenic CH 4 emission from agriculture (Carlson et al. 2016 ), while N 2 O emission from paddy soils contributes 19%-25% of the of the global N 2 O emissions (Van Groenigen et al. 2011 ). Therefore, it is crucial to mitigate GHG emissions from paddy soils in most rice production regions to maintain a sustainable grain food supply. Green manure incorporation and chemical nitrogen (N) fertilizer applications are common soil management practices that may impact CH 4 and N 2 O emissions. First, the release of organic carbon (C) and inorganic N from these fertilizers determines the availability of substrates for CH 4 and N 2 O releasing microorganisms, such as methanogenic bacteria (Liu et al. 2022 ) and methanogenic Archaea (Zhang et al. 2018 ), ammonia oxidizers (Gao et al. 2020 ) and denitrifiers (Huang et al. 2020 ). Several studies have reported increased CH 4 emission after organic amendments such as green manure (Haque et al. 2012 ), crop residue and animal manure incorporation (Bhattacharyya et al. 2012 ; Nguyen et al. 2020 ). N 2 O emission peaks are usually observed immediately after intensive chemical N fertilizer application (Shaukat et al. 2019 ). Meanwhile, soil conditions that drive CH 4 and N 2 O formation and consumption may be greatly changed following organic matter returning and fertilizer application. For example, during the early stage of organic matter decomposition, the reduced dissolved oxygen concentration and soil pH and the increased dissolved organic C availability in the paddy soil (Xu et al. 2017 ) provide a favorable condition for methanogens, thereby enhancing CH 4 production during the rice growing seasons (Zhou et al. 2020 ). The rapidly rising soil NH 4 + -N and NO 3 − -N concentrations after chemical N fertilizer application benefit nitrification and denitrification, and increase the potential of N loss in the form of N 2 O (Cecilio et al. 2022 ; Zhu et al. 2013 ). Replacing chemical N with green manures has potential to reduce N 2 O emission because inorganic N release from the incorporated organic matter is much slower than from chemical fertilizers (Mehnaz et al. 2019 ; Zhu et al. 2014 ). However, acetate released during anaerobic decomposition of organic matter acts as a terminal electron acceptor for respiration and provides substrate for methanogens activities, resulting in increased CH 4 emissions (Amin et al. 2021 ). The ratio of substitution of organic for chemical fertilizer is an important factor for determining C and N dynamics after fertilization (Lashermes et al. 2010 ) as well as the consequent changes in soil environment (Hou et al. 2022 ). In addition, the complex interaction between organic material and inorganic N under different organic-chemical N substitution ratio greatly affects CH 4 and N 2 O emissions (Zou et al. 2009 ). An important aspect concerning GHG production in paddy soil is the comparison of radiative forcing from different GHGs. The global warming potential (GWP) calculation integrates the radiative forcing of CH 4 and N 2 O emissions into “CO 2 -equivalents” (Robertson and Grace 2004 ). Greenhouse gas intensity (GHGI) is calculated by dividing GWP by grain yield and is used to relate GHG emission and food production (Shang et al. 2011 ). Ryegrass ( Lolium multiflorum Lam.; RG), a commonly used green manure in paddy soils, has the potential to partly replace chemical N fertilizers in most Asian countries. Increased rice yield and N use efficiency have been observed after ryegrass incorporation alone or in combination with urea (Asagi and Ueno 2009 ; Zhu et al. 2014 ). However, the impacts of RG-urea substitution ratio on CH 4 and N 2 O emissions remains poorly understood. The objectives of this study were to: (1) estimate changes in CH 4 and N 2 O emissions during rice growing seasons under different RG-urea substitution ratios; (2) quantify C and N release from RG residue under different RG-urea substitution ratios; (3) measure the relationship between GHGs and soil properties after RG incorporation. Materials And Methods Experimental description Two open glasshouse pot experiments were conducted at the experimental station of Yangtze University, Jingzhou, Hubei Province, China (30°20′N, 112°12′E) in 2021. The regional climate was subtropical with annual mean air temperature 16.5℃ and precipitation 1200 mm. The paddy soil (0-15cm) was collected from a nearby farmer’s paddy field followed by manual removal of stems, roots, stones, etc. The soil was then air-dried and sieved to pass 2 cm mesh. Soil properties were: sand 265 g kg − 1 , clay 134 g kg − 1 , silt 601 g kg − 1 , pH 6.2, total N 1.4 g kg − 1 , organic C content of 14.2 g kg − 1 , available phosphorus 15.6 mg kg − 1 and available potassium 87.0 mg kg − 1 . The fresh RG ( cv . Muteli) aboveground litter had moisture content 79%, dry matter N content 4.94% and dry matter C content 38.1%. Experiment 1: effects of RG-urea substitution ratio on CH 4 and N 2 O emission The experiment comprised five RG-urea substitution ratios: 0%, 25%, 50%, 75% and 100% with 3 replicates for each treatment. All the treatments received a total amount N at a rate of 100 mg N kg − 1 air-dried soil. The details for RG and urea application amount in each treatment were shown in Table 1 . Plastic pots (20 cm in diameter and 15 cm in height), each containing 3 kg air-dried soil was used. The aboveground litter of RG was weighed, cut into 2–3 cm pieces and thoroughly mixed with soil in mid-April 2021. Superphosphate and potassium chloride were applied at 0.61 g kg − 1 and 0.21 g kg − 1 air-dried soil, respectively. On April 25, three rice seedlings (cv. Tianliangyou 616, 30 d old) were transplanted per pot. The seedlings were watered to 2 cm flooding depth and received no topdressing fertilizer during the whole growing season. The plants were harvested at 40 cm cutting height for the first maturity, leaving the stubbles for ratooning to get a second harvest. Table 1 The amount of C and N contributions for each treatment by RG or urea application Treatment N applied from urea (g pot − 1 ) N applied from RG (g pot − 1 ) C applied from RG (g pot − 1 ) 0% RG 0.3 0 0 25% RG 0.225 0.075 0.58 50% RG 0.15 0.15 1.16 75% RG 0.075 0.225 1.74 100% RG 0 0 2.31 Experiment 2: C and N release during RG decomposition In experiment 2, the mesh bag method (Zhu et al. 2014 ) was used to measure C and N release dynamics from the incorporated RG litter. Treatments were the same as in experiment 1. RG aboveground litter was cut and placed in 0.5mm mesh size nylon bags (10 cm × 10 cm) and buried in the soil with 39 replications for each treatment. Sampling and analysis In Experiment 1, gas samples for CH 4 and N 2 O fluxes measurement were collected using a static chamber (Zhang et al. 2020 ) at 3–7 d intervals after RG incorporation until the harvest of the ratoon crop. The pot with plant was placed in a closed PVP pipe chamber (height 110 cm, diameter 25 cm). An electric fan at the top of the chamber was used to mix the air in the chamber. The gas samples were collected from the chamber at 0, 10 and 20 min between 9:00 and 11:00 am and transferred to 0.5 L sample bags. CH 4 and N 2 O concentrations were determined by gas chromatography (Agilent 7890 B, USA) with a flame ionization detector (FID) at 200℃ and an electron capture detector (ECD) at 330℃, respectively. Soil pH and redox state (Eh) were simultaneously measured by pH meter (Leici PHS-25, China) and ORP meter (Leici TR-901, China), respectively. In Experiment 2, RG residue in nylon mesh bags was collected 14, 24, 29, 34, 42, 48, 55, 69, 83, 111, 139, 167 and 192 days after incorporation (n = 3). The residue was carefully washed with tap water, oven-dried at 80℃ to constant weight, weighed, ground and sieved (100 mesh). C and N contents in RG residue were determined by an elemental analyzer (Costech ECS4010, Italy). Soil samples were collected at three random locations for each pot and mixed thoroughly for ammonium (NH 4 + -N) and nitrate (NO 3 − -N) concentrations measurements by using a UV spectrophotometer (Hash DR6000, China) after extraction with 2 mol L − 1 KCl. Calculations The CH 4 and N 2 O fluxes were calculated in detail as follows: $$F=p \times h \times dc/dt \times 273/(273+T)$$ where F is CH 4 flux (mg m − 2 h − 1 ) or N 2 O flux (µg m − 2 h − 1 ); p is density of CH 4 or N 2 O under the standard temperature and pressure (CH 4 0.714 kg/m 3 , N 2 O 1.964 kg/m 3 ); h is the height of the chamber (cm); dc/dt is the change of CH 4 or N 2 O concentration (mg m − 3 ) over time (h); T is the air temperature inside the chamber (℃). The seasonal CH 4 or N 2 O emission was calculated as follows: $${\text{S}\text{e}\text{a}\text{s}\text{o}\text{n}\text{a}\text{l} \text{C}\text{H}}_{4} \text{o}\text{r} {\text{N}}_{2}\text{O} \text{c}\text{u}\text{m}\text{u}\text{l}\text{a}\text{t}\text{i}\text{v}\text{e} \text{e}\text{m}\text{i}\text{s}\text{s}\text{i}\text{o}\text{n}\text{s}={\sum }_{i=1}^{n}\frac{{F}_{i}+{F}_{i-1}}{2}\times ({D}_{i+1}-{D}_{i})$$ where F is the CH 4 (mg m − 2 h − 1 ) or N 2 O (µg m − 2 h − 1 ) flux, i is the sampling time, D i+1 -D i is the number of days between two adjacent sampling time (h), and n is the total number of sampling intervals. Global warming potential (GWP) was calculated as the sum of N 2 O (GWP N2O ) and CH 4 (GWP CH4 ) as IPCC (2021): $$\text{G}\text{W}\text{P} (\text{g} {\text{C}\text{O}}_{2}-\text{e}\text{q} {m}^{-2})=29.8\times {\text{C}\text{H}}_{4}\left(\text{g} {\text{m}}^{-2}\right) + 273\times {\text{N}}_{2}\text{O}\left(\text{g} {\text{m}}^{-2}\right)$$ The greenhouse gas intensity (GHGI) was calculated as follows: $$\text{G}\text{H}\text{G}\text{I} \left(\text{g} \text{G}\text{W}\text{P} \text{p}\text{e}\text{r} \text{g} \text{o}\text{f} \text{y}\text{i}\text{e}\text{l}\text{d}\right)=\text{G}\text{W}\text{P}(\text{g} {\text{C}\text{O}}_{2}-\text{e}\text{q} {m}^{-2})/\text{g}\text{r}\text{a}\text{i}\text{n} \text{y}\text{i}\text{e}\text{l}\text{d} \left(\text{g} {m}^{-2}\right)$$ Percentage of C and N remaining in RG residues were calculated as described by Zhu et al. ( 2014 ): $$\text{Y}\text{c} \left(\text{%}\right) = \frac{\text{Y}\text{t}}{\text{Y}\text{i}}\times 100$$ where Yc is the C or N remaining percent of RG residue, Yt is the C or N content of RG residues at each sampling time, Yi is the original C or N content in RG. An exponential decay model was used to describe C and N release from RG litter over time as follows: $$\text{Y}\text{R} = {\text{e}\text{x}\text{p}}^{-\text{k}\text{t}}\times 100$$ where YR is C or N remaining in RG residue at time t and k is the C ( k C ) or N ( k N ) release rate. Statistical analysis Analysis of variance (ANOVA) for RG substitution ratio on CH 4 and N 2 O emission fluxes, GWP, GHGI, grain yield and soil properties was conducted with SPSS Statistics 19.0 (IBM Co., NY, USA). The multiple comparisons significant differences were set at P < 0.05. Pearson correlation analysis was used to examine the correlation between greenhouse gas emissions and soil properties. Canoco 5 (Beijing Huanzhong Ruichi Technology Co., LTD, China) was used to make the redundancy analysis (RDA) between GHG emissions and soil properties. Result CH 4 and N 2 O emissions A major peak was observed at 38 d after urea and RG application for CH 4 emissions (Fig. 1 a), and at 17 d for N 2 O emissions (Fig. 2 a). Several minor peaks were also detected for CH 4 and N 2 O emissions during the whole observing seasons. The highest CH 4 peaks were found in the 100% RG treatment (41.3 mg m − 2 h − 1 ), followed by 75% RG, 50% RG, 25% RG and 0% RG (Fig. 1 a). By contrast, N 2 O peaks were observed to be highest in 0% RG (784.9 µg m − 2 h − 1 ) and decreased with the rising RG-urea substitution ratio (Fig. 2 a). Most of the CH 4 (54.3%-63.7%) was emitted before the tillering stage and 73.7%-82.1% of the CH 4 emission took place before the first season maturity. Regression analysis showed that there was a significant quadratic relationship between RG-urea substitution ratio and total CH 4 emission ( R 2 = 0.99, p < 0.01; Fig. 1 b). When it came to N 2 O, 65.4%-79.6% of the seasonal emissions occurred before the tillering stage and 91.5%-98.3% of the total emissions were contributed before the first season maturity. A significant quadratic relationship was identified between RG-urea substitution ratio and total N 2 O emission (Fig. 2b; R 2 = 0.99, p < 0.01). Global warming potential and greenhouse gas intensity The GWP for seasonal CH 4 and N 2 O emissions showed a significant quadratic relationship with RG-urea substitution ratio (Fig. 3a; R 2 = 0.99, p < 0.05). The values for GWP were lowest in the 25% and 50% RG treatments (6.3% and 6.5% lower than 0% RG, respectively), which were significantly lower than those in 75% and 100% RG. As shown in Table 2 , contribution of CH 4 emission to the GWP (71.7%-96.7%) was much higher than that from N 2 O emission (3.3%-28.3%). CH 4 and N 2 O emissions from the first 45 d of the experiment accounted for 47.5%-56.4% of the total GWP while comparable total GWP (43.6%-52.5%) were detected in the relatively longer following 46–125 d (Fig. 3 b). Table 2 Total global warming potential (GWP), CH 4 and N 2 O-induced GWP, grain yield, greenhouse gas intensity (GHGI) as affected by ryegrass (RG)-urea substitution Treatment CH 4 -induced GWP a N 2 O -induced GWP b Total GWP (g CO 2 -eq m − 2 ) Grain yield (g m − 2 ) GHGI (kg CO 2 -eq kg − 1 yield) GWP (g CO 2 -eq m − 2 ) Account for total GWP c (%) GWP (g CO 2 -eq m − 2 ) Account for total GWP (%) 0% RG 277.1 d d 71.7 109.4 a 28.3 386.5 c 796.3 a 0.49 c 25% RG 291.6 d 80.5 70.5 b 19.5 362.1 c 773.7 a 0.47 c 50% RG 323.0 c 89.3 38.5 c 10.7 361.5 c 747.2 a 0.48 c 75% RG 455.0 b 93.7 30.8 c 6.3 485.8 b 639.2 b 0.76 b 100% RG 595.6 a 96.7 20.6 c 3.3 616.2 a 594.9 b 1.04 a a Global warming potential calculated from CH 4 emission b Global warming potential calculated from N 2 O emission c The percentage of CH 4 or N 2 O induced GWP to the total GWP d Different letters within the same column mean significant differences at p < 0.05 level. Rice grain yield in the 25% and 50% RG treatments was not significantly different from that in the 0% RG treatment, but was significantly higher than that in the 75% and 100% RG treatments (Table 2 ; p < 0.05). Similar as GWP, GHGI was higher in the 75% and 100% RG treatments as compared to the 0%, 25% and 50% RG treatments (Table 2 ). C and N release from RG residue The majority of C (83.2%-86.9%) and N (97.4%-97.5%) from the incorporated RG residue was released during the first 29 d after start of the experiment (Figs. 4 a and b). At the end of experiment, less than 4.5% of C and 1.3% of N remaining remained in RG residue. The decay exponential model successfully described C ( R 2 = 0.92–0.94, p < 0.05) and N ( R 2 = 0.98–0.99, p < 0.05) release dynamics after RG incorporation in the paddy soil (Table 3 ). The values for k N were generally higher than for k C (0.0877–0.1368 vs 0.0689-0.0800), indicating N release from RG residue was faster than C release. The values for k N increased with higher RG-urea substitution ratio while the opposite was true for k C (Table 3 ). Table 3 The rate of C ( k C ) and N releases ( k N ) from ryegrass (RG) residue under different RG-urea substitution ratios, estimated using a single exponential model Treatment K C r 2 K N r 2 25% RG 0.0800 0.9177 0.0877 0.9791 50% RG 0.0697 0.9400 0.1104 0.9948 75% RG 0.0706 0.9255 0.1368 0.9978 100% RG 0.0689 0.9251 0.1254 0.9975 Importance of soil properties for CH 4 and N 2 O emissions Soil NH 4 + -N concentrations were much higher than NO 3 − -N concentrations during the experiment (Figs. 5 a and b). Both NH 4 + -N and NO 3 − -N increased steadily and peaked 14–29 d after RG and urea application. NH 4 + -N and NO 3 − -N appeared to be highest in 0% RG and lowest in 100% RG treatment. Redox state (Eh value) decreased gradually within 30–40 d after the start of the experiment and increased steadily thereafter. Lower Eh values tended to be observed in higher RG-urea substitution ratios (Fig. 5 c). Soil pH was reduced during the first two months especially for the 25%, 50% and 100% RG treatments (Fig. 5 d). Distance-based RDA showed that the first two components accounted for 99.5% of the variation in CH 4 and N 2 O emissions (Figs. 6 a and c). C remaining (F = 49.8, p = 0.002), N remaining (F = 7.8, p = 0.014) and soil Eh (F = 6.9, p = 0.032) had a significant impact on CH 4 emission when NH 4 + -N (F = 15.4, p = 0.004) and NO 3 − -N (F = 6.6, p = 0.034) showed a significant impact on N 2 O emission (Table 4 ). Variance decomposition showed that 90.3% of the variance of CH 4 emission could be explained by C and N remaining in RG residue, soil Eh and other indicators (Fig. 6 b), while 84% of the variance of N 2 O emission was explained by soil NH 4 + -N, NO 3 − -N and C remaining in RG residue (Fig. 6 d). Table 4 Results of ANOVA RDA model Soil factors CH 4 N 2 O F p F p Eh 6.9 0.032 *a 3.6 0.092 pH 0.8 0.402 0.3 0.568 NH 4 + -N 1.8 0.214 15.4 0.004 ** NO 3 − -N 0.6 0.402 6.6 0.034 * C remaining 49.8 0.002 **b 5.1 0.056 N remaining 7.8 0.014 * 1.5 0.276 a Significant differences at p < 0.05 level b Significant differences at p < 0.01 level Discussion Effects of RG-urea substitution ratio on CH 4 emission Incorporation of organic amendments such as green manure and crop straw in an important soil practice to increase biological C fixing in agricultural fields, but the enhanced active C input may stimulate CH 4 production in paddy soils (Fu et al. 2018 ). This was confirmed by the rapidly increasing CH 4 emission after RG incorporation, especially in the treatments with high RG-urea substitution ratios (Fig. 1 ). Dramatically increased CH 4 emissions have also been reported in flooded paddy soils with fresh green manure incorporation (Lee et al. 2010 ; Liu et al. 2019 ). In this study, major CH 4 emissions took place at the time of rapid C release from RG residue, suggesting that C input by RG incorporation was an important source for CH 4 production (Fig. 1 a, Fig. 4 a). RG-urea substitution ratio affected the rate of green manure decomposition, which consequently determined the availability of soil C substrates for methanogens (Dalal et al. 2008 ; Xu et al. 2017 ). Soil condition is another factor affecting the activity of methanogens and methane-oxidizing bacteria which are responsible for CH 4 production and consumption in the soil. In the present study, Effects of soil condition changes by RG-urea substitution ratio on GHG emission could be better measured in a pot study than in field experiments, because some undesirable interferences such as rainfall and leaching could be well prevented. In the present study, rapid decomposition of RG litter may have reduced dissolved oxygen concentration in the flooded paddy soil, resulting in a decrease of soil Eh (Fig. 5 c). A low Eh provides a suitable condition for methanogens and reduces CH 4 oxidation in the soil (Muhammad et al. 2020 ). Significantly higher CH 4 emissions in low redox soils have also been reported by Fan et al. ( 2020 ) and Xu et al. ( 2017 ). About 5% of the variance in CH 4 emission was explained by changes in soil Eh while 0.6% was explained by pH (Fig. 6 b). Decreased soil pH by RG incorporation (Fig. 5 d) was attributed to the formation of small organic acids such as acetic acid and butanoic acid during the process of RG fermentation. This lower pH and increased acetic acid availability stimulates the activity of methanogens (Baumann et al. 2009 ; Qualls 2005 ). Effects of RG-urea substitution ratio on N 2 O emission Green manure applied alone or in conjunction with chemical fertilizers has been reported to improve total N use efficiency and decrease N losses into the environment (Gao et al. 2020 ; Liang et al. 2022 ; Zhu et al. 2012 ). However, the results of observations on N 2 O emission, a form of gaseous N loss in paddy fields, are inconsistent. Some reported significant N 2 O emissions after fertilization with chemical N (Shaukat et al. 2019 ; Xu et al. 2022 ). Others found negligible N 2 O emissions over the entire rice growing season (Chen et al. 2021 ; Cowan et al. 2021 ). In the current study, a major N 2 O peak was detected following urea application while several minor peaks were afterwards (Fig. 2 a). Similar as with Zhang et al. ( 2020 ), both the major peak and seasonal N 2 O emissions were significantly decreased with the increase of RG-urea substitution ratio, in spite that the soils received a same amount of total N input (Fig. 2 ). Soil mineral N concentrations, especially NH 4 + -N content, were significantly decreased with RG-urea substitution ratio in the peaking stage (Fig. 5 a). This may result in a reduced N available for nitrification or coupled nitrification-denitrification which are the main mechanisms for N 2 O production in agricultural soils (Gao et al. 2020 ; Nie et al. 2021 ). In addition, the lower redox condition which may reduce the activity of ammonia oxidizers (Tao et al. 2018 ). These results supported the beneficial effect of replacing chemical N with green manure on N 2 O mitigation. To trace N 2 O production from different N sources in a paddy ecosystem, 15 N labeled green manure or chemical fertilizer should be used in further studies. Global warming potential and greenhouse gas intensity The assessment of GWP is important because of the dramatically different effects of RG substitution on CH 4 and N 2 O emissions in this study. The majority of GWP (71.7%-96.7%) were caused by CH 4 emission, confirming that CH 4 is the dominant GHG responsible for radiative forcing in the studied paddy soil (Peyron et al. 2016 ; Wang et al. 2016 ). Nevertheless, GWP in 25% and 50% RG-urea substitution ratios were slightly decreased when compared with 0% RG (361.5-362.1g CO 2 -eq m − 2 vs 386.5 g CO 2 -eq m − 2 ) while CH 4 -induced GWP increased with RG-urea substitution ratio (Table 2 ). This confirms a positive trade-off between CH 4 emission and N 2 O emission for the 25% and 50% RG-urea substitution treatments (Shang et al. 2011 ). However, RG-urea substitution ratios higher than 50% tended to increase GWP due to the rising CH 4 emission. Nearly half of the GWP was observed during the first 45 d after fertilization (Fig. 3 b), suggesting that GHG mitigation strategies in paddy soil with organic matter incorporation should be focused on the early stages of organic material decomposition and rice growth. Shang et al. ( 2011 ) suggest incorporating organic matter when the soil is drained and before rice is planted in order to minimize CH 4 and N 2 O emissions. Because rice grain yields were not significantly decreased by 25% and 50% RG-urea substitution ratios, their GHGI values were comparable with 0% RG (Table 2 ). The increased GHGI under 75% and 100% RG-urea substitution could both be attributed to the promoted GWP and the reduced grain yields. Conclusions This study demonstrated that RG-urea substitution ratio played an important role in CH 4 and N 2 O emissions. CH 4 emissions were positively related with RG-urea substitution ratio, while the opposite was true for N 2 O emissions. Compared with 0% RG, the increased CH 4 emissions in the 25% RG and 50% RG substitution ratios were offset by decreased N 2 O emissions, leading to comparable GWP and GHGI values. The RG-urea substitution ratio affected C and N release from RG residues, which further affected CH 4 and N 2 O emissions in the paddy soil. Soil Eh, C and N remaining were key characteristics correlated with CH 4 emission while NH 4 + -N, NO 3 − -N and C remaining were main factors on N 2 O emissions. As CH 4 was the main contributor to GWP in paddy soils, further studies should be taken to reduce CH 4 fluxes, especially in the early stage of organic matter returning. Abbreviations RG, ryegrass; GWP, global warming potential; GHGI, greenhouse gas intensity. Declarations Acknowledgements The authors thank Dr. Paul Dijkstra from Northern Arizona University for his revision and comments on the manuscript. Thanks for the sponsorship and support of the National Natural Science Foundation of China (No. 31870424) and the Young Elite Scientists Sponsorship Program by CAST (2016QNRC001). Author Contributions Bo Zhu and Zhangyong Liu designed the experiments. Wei Yang, Lai Yao, Mengzhen Zhu, Xueru Ji, Chengwei Li and Shaoqiu Li performed the experiments and data collection. Wei Yang and Bin Wang analyzed the data and Bo Zhu wrote the first draft of the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors have no relevant financial or non-financial interests to disclose. References Amin FR, Khalid H, El-Mashad HM, Chen C, Liu G, Zhang R (2021) Functions of bacteria and archaea participating in the bioconversion of organic waste for methane production. 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Global Change Biol 15(21):229–242. https://doi.org/10.1111/j.1365-2486.2008.01775.x Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Jul, 2022 Reviewers invited by journal 01 Jul, 2022 Editor invited by journal 01 Jul, 2022 Editor assigned by journal 01 Jul, 2022 First submitted to journal 22 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1784777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":117759620,"identity":"838e12a3-3a31-4474-8dc8-13c299d1384e","order_by":0,"name":"Wei Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNvbG5gcf/0nIyfM3HyBOCx/P4TbDGWwWxoYzjiUQp0VOIr1BmoOtIrHhQI4BkQ7jOdhgzMAjwdjYcObjjTcMdnK6DYS0sDc2PC6QkGBmZ+7dbDmHIdnY7AAxtswwkGBjbDi7TZqH4UDiNoJaJBIbpHkSJICKc56RouWAhARQCxuRWngOthnObJAwAAayseUcAyL8It/e/vjBx4a6+vn8zQ9vvKmwkyOoBQVI8BAZNchaSNUxCkbBKBgFIwIAAI9JQeZMpaO6AAAAAElFTkSuQmCC","orcid":"","institution":"Yangtze University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yang","suffix":""},{"id":117759621,"identity":"4d24c3f1-1b71-47ac-9111-31b06e27aeb9","order_by":1,"name":"Lai Yao","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lai","middleName":"","lastName":"Yao","suffix":""},{"id":117759622,"identity":"84556751-6e59-41e1-82a9-62048860e71b","order_by":2,"name":"Xueru Ji","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xueru","middleName":"","lastName":"Ji","suffix":""},{"id":117759623,"identity":"136a8b02-8104-4b74-a6c8-41a07d50ee47","order_by":3,"name":"Mengzhen Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengzhen","middleName":"","lastName":"Zhu","suffix":""},{"id":117759624,"identity":"87dd5603-d2ec-4b70-b186-b3ecb3b3088f","order_by":4,"name":"Chengwei Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chengwei","middleName":"","lastName":"Li","suffix":""},{"id":117759625,"identity":"47bf4ef7-ebcc-4afa-9083-ac0cb94c9257","order_by":5,"name":"Shaoqiu Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaoqiu","middleName":"","lastName":"Li","suffix":""},{"id":117759626,"identity":"a2be1c41-0eaa-4c23-8882-4d4d04f6c716","order_by":6,"name":"Bin Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":117759627,"identity":"ea8a5afd-e44a-4ba8-92d5-dcf8d02534e2","order_by":7,"name":"Zhangyong Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhangyong","middleName":"","lastName":"Liu","suffix":""},{"id":117759628,"identity":"fd033a9e-6485-47c5-90c3-c5158d5bd953","order_by":8,"name":"Bo Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2022-06-22 14:31:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1784777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1784777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24110394,"identity":"2d38bba5-06b5-47c5-99fc-11f3632f69b3","added_by":"auto","created_at":"2022-07-20 20:10:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257680,"visible":true,"origin":"","legend":"\u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e emission flux (a) and cumulative CH\u003csub\u003e4\u003c/sub\u003e emissions (b) of different RG-urea substitution ratios: 0% RG, 25% RG, 50% RG, 75% RG, 100% RG.\u003cstrong\u003e \u003c/strong\u003eTillering stage represents cumulative CH\u003csub\u003e4\u003c/sub\u003e emissions from the start of experiment to rice tillering stage, filling stage represents cumulative CH\u003csub\u003e4\u003c/sub\u003e emissions from tillering stage to rice filling stage, first maturity represents cumulative CH\u003csub\u003e4 \u003c/sub\u003eemissions from filling stage to first season rice maturity, second maturity represents cumulative CH\u003csub\u003e4 \u003c/sub\u003eemissions from first season harvest to the second season maturity. Bars indicate the SE (standard error). The quadratic linear regression relationship between CH\u003csub\u003e4\u003c/sub\u003e cumulative emission and RG-urea substitution ratio was significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/7868ab9f959040daf31d9236.png"},{"id":24109681,"identity":"665ab8b6-bae5-443a-a5c9-13fffbe3e09f","added_by":"auto","created_at":"2022-07-20 20:05:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224158,"visible":true,"origin":"","legend":"\u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO emission flux (a) and cumulative N\u003csub\u003e2\u003c/sub\u003eO emissions (b) of different RG-urea substitution ratios: 0% RG, 25% RG, 50% RG, 75% RG, 100% RG.\u003cstrong\u003e \u003c/strong\u003eTillering stage represents cumulative N\u003csub\u003e2\u003c/sub\u003eO emissions from the start of experiment to rice tillering stage, filling stage represents cumulative N\u003csub\u003e2\u003c/sub\u003eO emissions from tillering stage to rice filling stage, first maturity represents cumulative N\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e \u003c/sub\u003eemissions from filling stage to first season rice maturity, second maturity represents cumulative N\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e \u003c/sub\u003eemissions from first season harvest to the second season maturity. Bars indicate the SE (standard error). The quadratic linear regression relationship between N\u003csub\u003e2\u003c/sub\u003eO cumulative emission and RG-urea substitution ratio was significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/2d453aaa81d3d72469bd060e.png"},{"id":24111299,"identity":"6ca9f933-a4eb-496b-a14a-c76c2f4df06a","added_by":"auto","created_at":"2022-07-20 20:15:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130752,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal warming potential (GWP, a) of different RG-urea substitution ratios: 0% RG, 25% RG, 50% RG, 75% RG and 100% RG. GWP\u003csub\u003eCH4\u003c/sub\u003e represents GWP caused by CH\u003csub\u003e4\u003c/sub\u003e emission, GWP\u003csub\u003eN2O\u003c/sub\u003e represents GWP caused by N\u003csub\u003e2\u003c/sub\u003eO emission. Bars indicate the SE (standard error). The quadratic linear regression relationship between GWP and RG-urea substitution ratio was significant (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01). Percentage of GWP (b) before and after major CH\u003csub\u003e4\u003c/sub\u003e peaks. 0-45d represents GWP calculated by cumulated CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions from the start of fertilization to 45 days, 45-192d represents GWP calculated by cumulated CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions from 45 days to 192 days.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/5e5a09869043ca3916a2cc38.png"},{"id":24109682,"identity":"66d7376a-c5db-4723-b3c8-4c7ce27acf8b","added_by":"auto","created_at":"2022-07-20 20:05:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189071,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of C (a) and N (b) remaining in ryegrass residue during 192 days after fertilization under different RG-urea substitution ratios: 25% RG, 50% RG, 75% RG and 100% RG.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/acd1793ae33cec77acc38fdd.png"},{"id":24111789,"identity":"65cf1f7a-1266-4b0f-8e86-db2a4bafb564","added_by":"auto","created_at":"2022-07-20 20:20:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":502542,"visible":true,"origin":"","legend":"\u003cp\u003eSoil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (a), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N (b), Eh (c) and pH (d) changes of different RG-urea substitution ratios: 0% RG, 25% RG, 50% RG, 75% RG and 100% RG. Bars indicate the SE (standard error).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/a63a509af39b1bbaf2d352e1.png"},{"id":24111297,"identity":"5671b515-777b-47e2-bac7-88a3131347f4","added_by":"auto","created_at":"2022-07-20 20:15:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":261236,"visible":true,"origin":"","legend":"\u003cp\u003eDistance-based redundancy analysis (dbRDA) of composition of CH\u003csub\u003e4 \u003c/sub\u003e(a), N\u003csub\u003e2\u003c/sub\u003eO (c) and soil properties. Variance decomposition, with the percentages of CH\u003csub\u003e4 \u003c/sub\u003e(b) and N\u003csub\u003e2\u003c/sub\u003eO (d) variance explained by soil properties. Soil properties include NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N, Eh, pH, C and N remaining in ryegrass residue.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/48c91536e7059e6ead87fd66.png"},{"id":24111802,"identity":"a37b0df9-ccf3-4982-b719-ba5b61807c87","added_by":"auto","created_at":"2022-07-20 20:20:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1983645,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1784777/v1/98f95142-0527-4fe4-b521-84e91e4df328.pdf"}],"financialInterests":"","formattedTitle":"Does replacing chemical fertilizer with ryegrass (Lolium multiflorum Lam.) mitigate CH4 and N2O emissions and reduce global warming potential from paddy soil?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMethane (CH\u003csub\u003e4\u003c/sub\u003e) and nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO) are important greenhouse gases (GHGs) responsible for global climate change (Stocker \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Rice paddy soil is one of the important sources for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO production, and accounts for a significant proportion of global GHG emissions (Carter et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Rice paddies are responsible for 48% of total anthropogenic CH\u003csub\u003e4\u003c/sub\u003e emission from agriculture (Carlson et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while N\u003csub\u003e2\u003c/sub\u003eO emission from paddy soils contributes 19%-25% of the of the global N\u003csub\u003e2\u003c/sub\u003eO emissions (Van Groenigen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, it is crucial to mitigate GHG emissions from paddy soils in most rice production regions to maintain a sustainable grain food supply.\u003c/p\u003e \u003cp\u003eGreen manure incorporation and chemical nitrogen (N) fertilizer applications are common soil management practices that may impact CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions. First, the release of organic carbon (C) and inorganic N from these fertilizers determines the availability of substrates for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO releasing microorganisms, such as methanogenic bacteria (Liu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and methanogenic \u003cem\u003eArchaea\u003c/em\u003e (Zhang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), ammonia oxidizers (Gao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and denitrifiers (Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several studies have reported increased CH\u003csub\u003e4\u003c/sub\u003e emission after organic amendments such as green manure (Haque et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), crop residue and animal manure incorporation (Bhattacharyya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nguyen et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). N\u003csub\u003e2\u003c/sub\u003eO emission peaks are usually observed immediately after intensive chemical N fertilizer application (Shaukat et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Meanwhile, soil conditions that drive CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO formation and consumption may be greatly changed following organic matter returning and fertilizer application. For example, during the early stage of organic matter decomposition, the reduced dissolved oxygen concentration and soil pH and the increased dissolved organic C availability in the paddy soil (Xu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) provide a favorable condition for methanogens, thereby enhancing CH\u003csub\u003e4\u003c/sub\u003e production during the rice growing seasons (Zhou et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rapidly rising soil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentrations after chemical N fertilizer application benefit nitrification and denitrification, and increase the potential of N loss in the form of N\u003csub\u003e2\u003c/sub\u003eO (Cecilio et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Replacing chemical N with green manures has potential to reduce N\u003csub\u003e2\u003c/sub\u003eO emission because inorganic N release from the incorporated organic matter is much slower than from chemical fertilizers (Mehnaz et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, acetate released during anaerobic decomposition of organic matter acts as a terminal electron acceptor for respiration and provides substrate for methanogens activities, resulting in increased CH\u003csub\u003e4\u003c/sub\u003e emissions (Amin et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ratio of substitution of organic for chemical fertilizer is an important factor for determining C and N dynamics after fertilization (Lashermes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) as well as the consequent changes in soil environment (Hou et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the complex interaction between organic material and inorganic N under different organic-chemical N substitution ratio greatly affects CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions (Zou et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn important aspect concerning GHG production in paddy soil is the comparison of radiative forcing from different GHGs. The global warming potential (GWP) calculation integrates the radiative forcing of CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions into \u0026ldquo;CO\u003csub\u003e2\u003c/sub\u003e-equivalents\u0026rdquo; (Robertson and Grace \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Greenhouse gas intensity (GHGI) is calculated by dividing GWP by grain yield and is used to relate GHG emission and food production (Shang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRyegrass (\u003cem\u003eLolium multiflorum\u003c/em\u003e Lam.; RG), a commonly used green manure in paddy soils, has the potential to partly replace chemical N fertilizers in most Asian countries. Increased rice yield and N use efficiency have been observed after ryegrass incorporation alone or in combination with urea (Asagi and Ueno \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the impacts of RG-urea substitution ratio on CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions remains poorly understood. The objectives of this study were to: (1) estimate changes in CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions during rice growing seasons under different RG-urea substitution ratios; (2) quantify C and N release from RG residue under different RG-urea substitution ratios; (3) measure the relationship between GHGs and soil properties after RG incorporation.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eExperimental description\u003c/p\u003e \u003cp\u003eTwo open glasshouse pot experiments were conducted at the experimental station of Yangtze University, Jingzhou, Hubei Province, China (30\u0026deg;20\u0026prime;N, 112\u0026deg;12\u0026prime;E) in 2021. The regional climate was subtropical with annual mean air temperature 16.5℃ and precipitation 1200 mm. The paddy soil (0-15cm) was collected from a nearby farmer\u0026rsquo;s paddy field followed by manual removal of stems, roots, stones, etc. The soil was then air-dried and sieved to pass 2 cm mesh. Soil properties were: sand 265 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, clay 134 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, silt 601 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, pH 6.2, total N 1.4 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, organic C content of 14.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, available phosphorus 15.6 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and available potassium 87.0 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The fresh RG (\u003cem\u003ecv\u003c/em\u003e. Muteli) aboveground litter had moisture content 79%, dry matter N content 4.94% and dry matter C content 38.1%.\u003c/p\u003e \u003cp\u003eExperiment 1: effects of RG-urea substitution ratio on CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emission\u003c/p\u003e \u003cp\u003eThe experiment comprised five RG-urea substitution ratios: 0%, 25%, 50%, 75% and 100% with 3 replicates for each treatment. All the treatments received a total amount N at a rate of 100 mg N kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e air-dried soil. The details for RG and urea application amount in each treatment were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Plastic pots (20 cm in diameter and 15 cm in height), each containing 3 kg air-dried soil was used. The aboveground litter of RG was weighed, cut into 2\u0026ndash;3 cm pieces and thoroughly mixed with soil in mid-April 2021. Superphosphate and potassium chloride were applied at 0.61 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.21 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e air-dried soil, respectively. On April 25, three rice seedlings (cv. Tianliangyou 616, 30 d old) were transplanted per pot. The seedlings were watered to 2 cm flooding depth and received no topdressing fertilizer during the whole growing season. The plants were harvested at 40 cm cutting height for the first maturity, leaving the stubbles for ratooning to get a second harvest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe amount of C and N contributions for each treatment by RG or urea application\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN applied from urea (g pot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN applied from RG (g pot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC applied from RG (g pot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExperiment 2: C and N release during RG decomposition\u003c/p\u003e \u003cp\u003eIn experiment 2, the mesh bag method (Zhu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was used to measure C and N release dynamics from the incorporated RG litter. Treatments were the same as in experiment 1. RG aboveground litter was cut and placed in 0.5mm mesh size nylon bags (10 cm \u0026times; 10 cm) and buried in the soil with 39 replications for each treatment.\u003c/p\u003e \u003cp\u003eSampling and analysis\u003c/p\u003e \u003cp\u003eIn Experiment 1, gas samples for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO fluxes measurement were collected using a static chamber (Zhang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) at 3\u0026ndash;7 d intervals after RG incorporation until the harvest of the ratoon crop. The pot with plant was placed in a closed PVP pipe chamber (height 110 cm, diameter 25 cm). An electric fan at the top of the chamber was used to mix the air in the chamber. The gas samples were collected from the chamber at 0, 10 and 20 min between 9:00 and 11:00 am and transferred to 0.5 L sample bags. CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO concentrations were determined by gas chromatography (Agilent 7890 B, USA) with a flame ionization detector (FID) at 200℃ and an electron capture detector (ECD) at 330℃, respectively. Soil pH and redox state (Eh) were simultaneously measured by pH meter (Leici PHS-25, China) and ORP meter (Leici TR-901, China), respectively.\u003c/p\u003e \u003cp\u003eIn Experiment 2, RG residue in nylon mesh bags was collected 14, 24, 29, 34, 42, 48, 55, 69, 83, 111, 139, 167 and 192 days after incorporation (n\u0026thinsp;=\u0026thinsp;3). The residue was carefully washed with tap water, oven-dried at 80℃ to constant weight, weighed, ground and sieved (100 mesh). C and N contents in RG residue were determined by an elemental analyzer (Costech ECS4010, Italy). Soil samples were collected at three random locations for each pot and mixed thoroughly for ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N) and nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N) concentrations measurements by using a UV spectrophotometer (Hash DR6000, China) after extraction with 2 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e KCl.\u003c/p\u003e \u003cp\u003eCalculations\u003c/p\u003e \u003cp\u003eThe CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO fluxes were calculated in detail as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$F=p \\times h \\times dc/dt \\times 273/(273+T)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eF\u003c/em\u003e is CH\u003csub\u003e4\u003c/sub\u003e flux (mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) or N\u003csub\u003e2\u003c/sub\u003eO flux (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cem\u003ep\u003c/em\u003e is density of CH\u003csub\u003e4\u003c/sub\u003e or N\u003csub\u003e2\u003c/sub\u003eO under the standard temperature and pressure (CH\u003csub\u003e4\u003c/sub\u003e 0.714 kg/m\u003csup\u003e3\u003c/sup\u003e, N\u003csub\u003e2\u003c/sub\u003eO 1.964 kg/m\u003csup\u003e3\u003c/sup\u003e); \u003cem\u003eh\u003c/em\u003e is the height of the chamber (cm); \u003cem\u003edc/dt\u003c/em\u003e is the change of CH\u003csub\u003e4\u003c/sub\u003e or N\u003csub\u003e2\u003c/sub\u003eO concentration (mg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) over time (h); \u003cem\u003eT\u003c/em\u003e is the air temperature inside the chamber (℃).\u003c/p\u003e \u003cp\u003eThe seasonal CH\u003csub\u003e4\u003c/sub\u003e or N\u003csub\u003e2\u003c/sub\u003eO emission was calculated as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${\\text{S}\\text{e}\\text{a}\\text{s}\\text{o}\\text{n}\\text{a}\\text{l} \\text{C}\\text{H}}_{4} \\text{o}\\text{r} {\\text{N}}_{2}\\text{O} \\text{c}\\text{u}\\text{m}\\text{u}\\text{l}\\text{a}\\text{t}\\text{i}\\text{v}\\text{e} \\text{e}\\text{m}\\text{i}\\text{s}\\text{s}\\text{i}\\text{o}\\text{n}\\text{s}={\\sum }_{i=1}^{n}\\frac{{F}_{i}+{F}_{i-1}}{2}\\times ({D}_{i+1}-{D}_{i})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eF\u003c/em\u003e is the CH\u003csub\u003e4\u003c/sub\u003e (mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) or N\u003csub\u003e2\u003c/sub\u003eO (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) flux, i is the sampling time, D\u003csub\u003ei+1\u003c/sub\u003e-D\u003csub\u003ei\u003c/sub\u003e is the number of days between two adjacent sampling time (h), and n is the total number of sampling intervals.\u003c/p\u003e \u003cp\u003eGlobal warming potential (GWP) was calculated as the sum of N\u003csub\u003e2\u003c/sub\u003eO (GWP \u003csub\u003eN2O\u003c/sub\u003e) and CH\u003csub\u003e4\u003c/sub\u003e (GWP \u003csub\u003eCH4\u003c/sub\u003e) as IPCC (2021):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{G}\\text{W}\\text{P} (\\text{g} {\\text{C}\\text{O}}_{2}-\\text{e}\\text{q} {m}^{-2})=29.8\\times {\\text{C}\\text{H}}_{4}\\left(\\text{g} {\\text{m}}^{-2}\\right) + 273\\times {\\text{N}}_{2}\\text{O}\\left(\\text{g} {\\text{m}}^{-2}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe greenhouse gas intensity (GHGI) was calculated as follows:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\text{G}\\text{H}\\text{G}\\text{I} \\left(\\text{g} \\text{G}\\text{W}\\text{P} \\text{p}\\text{e}\\text{r} \\text{g} \\text{o}\\text{f} \\text{y}\\text{i}\\text{e}\\text{l}\\text{d}\\right)=\\text{G}\\text{W}\\text{P}(\\text{g} {\\text{C}\\text{O}}_{2}-\\text{e}\\text{q} {m}^{-2})/\\text{g}\\text{r}\\text{a}\\text{i}\\text{n} \\text{y}\\text{i}\\text{e}\\text{l}\\text{d} \\left(\\text{g} {m}^{-2}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePercentage of C and N remaining in RG residues were calculated as described by Zhu et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e):\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\text{Y}\\text{c} \\left(\\text{%}\\right) = \\frac{\\text{Y}\\text{t}}{\\text{Y}\\text{i}}\\times 100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Yc is the C or N remaining percent of RG residue, Yt is the C or N content of RG residues at each sampling time, Yi is the original C or N content in RG.\u003c/p\u003e \u003cp\u003eAn exponential decay model was used to describe C and N release from RG litter over time as follows:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\text{Y}\\text{R} = {\\text{e}\\text{x}\\text{p}}^{-\\text{k}\\text{t}}\\times 100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere YR is C or N remaining in RG residue at time t and \u003cem\u003ek\u003c/em\u003e is the C (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e) or N (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e) release rate.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalysis of variance (ANOVA) for RG substitution ratio on CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emission fluxes, GWP, GHGI, grain yield and soil properties was conducted with SPSS Statistics 19.0 (IBM Co., NY, USA). The multiple comparisons significant differences were set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Pearson correlation analysis was used to examine the correlation between greenhouse gas emissions and soil properties. Canoco 5 (Beijing Huanzhong Ruichi Technology Co., LTD, China) was used to make the redundancy analysis (RDA) between GHG emissions and soil properties.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions\u003c/p\u003e \u003cp\u003eA major peak was observed at 38 d after urea and RG application for CH\u003csub\u003e4\u003c/sub\u003e emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), and at 17 d for N\u003csub\u003e2\u003c/sub\u003eO emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Several minor peaks were also detected for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions during the whole observing seasons. The highest CH\u003csub\u003e4\u003c/sub\u003e peaks were found in the 100% RG treatment (41.3 mg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), followed by 75% RG, 50% RG, 25% RG and 0% RG (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). By contrast, N\u003csub\u003e2\u003c/sub\u003eO peaks were observed to be highest in 0% RG (784.9 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and decreased with the rising RG-urea substitution ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eMost of the CH\u003csub\u003e4\u003c/sub\u003e (54.3%-63.7%) was emitted before the tillering stage and 73.7%-82.1% of the CH\u003csub\u003e4\u003c/sub\u003e emission took place before the first season maturity. Regression analysis showed that there was a significant quadratic relationship between RG-urea substitution ratio and total CH\u003csub\u003e4\u003c/sub\u003e emission (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). When it came to N\u003csub\u003e2\u003c/sub\u003eO, 65.4%-79.6% of the seasonal emissions occurred before the tillering stage and 91.5%-98.3% of the total emissions were contributed before the first season maturity. A significant quadratic relationship was identified between RG-urea substitution ratio and total N\u003csub\u003e2\u003c/sub\u003eO emission (Fig.\u0026nbsp;2b; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eGlobal warming potential and greenhouse gas intensity\u003c/p\u003e \u003cp\u003eThe GWP for seasonal CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions showed a significant quadratic relationship with RG-urea substitution ratio (Fig.\u0026nbsp;3a; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The values for GWP were lowest in the 25% and 50% RG treatments (6.3% and 6.5% lower than 0% RG, respectively), which were significantly lower than those in 75% and 100% RG. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, contribution of CH\u003csub\u003e4\u003c/sub\u003e emission to the GWP (71.7%-96.7%) was much higher than that from N\u003csub\u003e2\u003c/sub\u003eO emission (3.3%-28.3%). CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions from the first 45 d of the experiment accounted for 47.5%-56.4% of the total GWP while comparable total GWP (43.6%-52.5%) were detected in the relatively longer following 46\u0026ndash;125 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal global warming potential (GWP), CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO-induced GWP, grain yield, greenhouse gas intensity (GHGI) as affected by ryegrass (RG)-urea substitution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e-induced GWP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO -induced GWP\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal GWP\u003c/p\u003e \u003cp\u003e(g CO\u003csub\u003e2\u003c/sub\u003e-eq m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrain yield\u003c/p\u003e \u003cp\u003e(g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGHGI\u003c/p\u003e \u003cp\u003e(kg CO\u003csub\u003e2\u003c/sub\u003e-eq kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e yield)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003cp\u003e(g CO\u003csub\u003e2\u003c/sub\u003e-eq m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccount for total GWP\u003csup\u003ec\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003cp\u003e(g CO\u003csub\u003e2\u003c/sub\u003e-eq m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccount for total GWP (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277.1 d\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.4 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e386.5 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e796.3 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291.6 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.5 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e362.1 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e773.7 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323.0 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e361.5 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e747.2 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455.0 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.8 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e485.8 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e639.2 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e595.6 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.6 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e616.2 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e594.9 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eGlobal warming potential calculated from CH\u003csub\u003e4\u003c/sub\u003e emission\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eGlobal warming potential calculated from N\u003csub\u003e2\u003c/sub\u003eO emission\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003eThe percentage of CH\u003csub\u003e4\u003c/sub\u003e or N\u003csub\u003e2\u003c/sub\u003eO induced GWP to the total GWP\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ed\u003c/sup\u003eDifferent letters within the same column mean significant differences at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRice grain yield in the 25% and 50% RG treatments was not significantly different from that in the 0% RG treatment, but was significantly higher than that in the 75% and 100% RG treatments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar as GWP, GHGI was higher in the 75% and 100% RG treatments as compared to the 0%, 25% and 50% RG treatments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eC and N release from RG residue\u003c/p\u003e \u003cp\u003eThe majority of C (83.2%-86.9%) and N (97.4%-97.5%) from the incorporated RG residue was released during the first 29 d after start of the experiment (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b). At the end of experiment, less than 4.5% of C and 1.3% of N remaining remained in RG residue. The decay exponential model successfully described C (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and N (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) release dynamics after RG incorporation in the paddy soil (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The values for \u003cem\u003ek\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e were generally higher than for \u003cem\u003ek\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (0.0877\u0026ndash;0.1368 \u003cem\u003evs\u003c/em\u003e 0.0689-0.0800), indicating N release from RG residue was faster than C release. The values for \u003cem\u003ek\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e increased with higher RG-urea substitution ratio while the opposite was true for \u003cem\u003ek\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe rate of C (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e) and N releases (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e) from ryegrass (RG) residue under different RG-urea substitution ratios, estimated using a single exponential model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100% RG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eImportance of soil properties for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions\u003c/p\u003e \u003cp\u003eSoil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentrations were much higher than NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentrations during the experiment (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and b). Both NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N increased steadily and peaked 14\u0026ndash;29 d after RG and urea application. NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N appeared to be highest in 0% RG and lowest in 100% RG treatment. Redox state (Eh value) decreased gradually within 30\u0026ndash;40 d after the start of the experiment and increased steadily thereafter. Lower Eh values tended to be observed in higher RG-urea substitution ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Soil pH was reduced during the first two months especially for the 25%, 50% and 100% RG treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eDistance-based RDA showed that the first two components accounted for 99.5% of the variation in CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and c). C remaining (F\u0026thinsp;=\u0026thinsp;49.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), N remaining (F\u0026thinsp;=\u0026thinsp;7.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and soil Eh (F\u0026thinsp;=\u0026thinsp;6.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) had a significant impact on CH\u003csub\u003e4\u003c/sub\u003e emission when NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (F\u0026thinsp;=\u0026thinsp;15.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N (F\u0026thinsp;=\u0026thinsp;6.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) showed a significant impact on N\u003csub\u003e2\u003c/sub\u003eO emission (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Variance decomposition showed that 90.3% of the variance of CH\u003csub\u003e4\u003c/sub\u003e emission could be explained by C and N remaining in RG residue, soil Eh and other indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), while 84% of the variance of N\u003csub\u003e2\u003c/sub\u003eO emission was explained by soil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and C remaining in RG residue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of ANOVA RDA model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003csup\u003e*a\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC remaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003e**b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN remaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eSignificant differences at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003eSignificant differences at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEffects of RG-urea substitution ratio on CH\u003csub\u003e4\u003c/sub\u003e emission\u003c/p\u003e \u003cp\u003eIncorporation of organic amendments such as green manure and crop straw in an important soil practice to increase biological C fixing in agricultural fields, but the enhanced active C input may stimulate CH\u003csub\u003e4\u003c/sub\u003e production in paddy soils (Fu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This was confirmed by the rapidly increasing CH\u003csub\u003e4\u003c/sub\u003e emission after RG incorporation, especially in the treatments with high RG-urea substitution ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Dramatically increased CH\u003csub\u003e4\u003c/sub\u003e emissions have also been reported in flooded paddy soils with fresh green manure incorporation (Lee et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, major CH\u003csub\u003e4\u003c/sub\u003e emissions took place at the time of rapid C release from RG residue, suggesting that C input by RG incorporation was an important source for CH\u003csub\u003e4\u003c/sub\u003e production (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). RG-urea substitution ratio affected the rate of green manure decomposition, which consequently determined the availability of soil C substrates for methanogens (Dalal et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil condition is another factor affecting the activity of methanogens and methane-oxidizing bacteria which are responsible for CH\u003csub\u003e4\u003c/sub\u003e production and consumption in the soil. In the present study, Effects of soil condition changes by RG-urea substitution ratio on GHG emission could be better measured in a pot study than in field experiments, because some undesirable interferences such as rainfall and leaching could be well prevented. In the present study, rapid decomposition of RG litter may have reduced dissolved oxygen concentration in the flooded paddy soil, resulting in a decrease of soil Eh (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). A low Eh provides a suitable condition for methanogens and reduces CH\u003csub\u003e4\u003c/sub\u003e oxidation in the soil (Muhammad et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Significantly higher CH\u003csub\u003e4\u003c/sub\u003e emissions in low redox soils have also been reported by Fan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Xu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). About 5% of the variance in CH\u003csub\u003e4\u003c/sub\u003e emission was explained by changes in soil Eh while 0.6% was explained by pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Decreased soil pH by RG incorporation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) was attributed to the formation of small organic acids such as acetic acid and butanoic acid during the process of RG fermentation. This lower pH and increased acetic acid availability stimulates the activity of methanogens (Baumann et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Qualls \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffects of RG-urea substitution ratio on N\u003csub\u003e2\u003c/sub\u003eO emission\u003c/p\u003e \u003cp\u003eGreen manure applied alone or in conjunction with chemical fertilizers has been reported to improve total N use efficiency and decrease N losses into the environment (Gao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the results of observations on N\u003csub\u003e2\u003c/sub\u003eO emission, a form of gaseous N loss in paddy fields, are inconsistent. Some reported significant N\u003csub\u003e2\u003c/sub\u003eO emissions after fertilization with chemical N (Shaukat et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Others found negligible N\u003csub\u003e2\u003c/sub\u003eO emissions over the entire rice growing season (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cowan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the current study, a major N\u003csub\u003e2\u003c/sub\u003eO peak was detected following urea application while several minor peaks were afterwards (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Similar as with Zhang et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), both the major peak and seasonal N\u003csub\u003e2\u003c/sub\u003eO emissions were significantly decreased with the increase of RG-urea substitution ratio, in spite that the soils received a same amount of total N input (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Soil mineral N concentrations, especially NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N content, were significantly decreased with RG-urea substitution ratio in the peaking stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This may result in a reduced N available for nitrification or coupled nitrification-denitrification which are the main mechanisms for N\u003csub\u003e2\u003c/sub\u003eO production in agricultural soils (Gao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nie et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the lower redox condition which may reduce the activity of ammonia oxidizers (Tao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These results supported the beneficial effect of replacing chemical N with green manure on N\u003csub\u003e2\u003c/sub\u003eO mitigation. To trace N\u003csub\u003e2\u003c/sub\u003eO production from different N sources in a paddy ecosystem, \u003csup\u003e15\u003c/sup\u003eN labeled green manure or chemical fertilizer should be used in further studies.\u003c/p\u003e \u003cp\u003eGlobal warming potential and greenhouse gas intensity\u003c/p\u003e \u003cp\u003eThe assessment of GWP is important because of the dramatically different effects of RG substitution on CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions in this study. The majority of GWP (71.7%-96.7%) were caused by CH\u003csub\u003e4\u003c/sub\u003e emission, confirming that CH\u003csub\u003e4\u003c/sub\u003e is the dominant GHG responsible for radiative forcing in the studied paddy soil (Peyron et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nevertheless, GWP in 25% and 50% RG-urea substitution ratios were slightly decreased when compared with 0% RG (361.5-362.1g CO\u003csub\u003e2\u003c/sub\u003e-eq m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e \u003cem\u003evs\u003c/em\u003e 386.5 g CO\u003csub\u003e2\u003c/sub\u003e-eq m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) while CH\u003csub\u003e4\u003c/sub\u003e-induced GWP increased with RG-urea substitution ratio (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This confirms a positive trade-off between CH\u003csub\u003e4\u003c/sub\u003e emission and N\u003csub\u003e2\u003c/sub\u003eO emission for the 25% and 50% RG-urea substitution treatments (Shang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, RG-urea substitution ratios higher than 50% tended to increase GWP due to the rising CH\u003csub\u003e4\u003c/sub\u003e emission. Nearly half of the GWP was observed during the first 45 d after fertilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), suggesting that GHG mitigation strategies in paddy soil with organic matter incorporation should be focused on the early stages of organic material decomposition and rice growth. Shang et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) suggest incorporating organic matter when the soil is drained and before rice is planted in order to minimize CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions. Because rice grain yields were not significantly decreased by 25% and 50% RG-urea substitution ratios, their GHGI values were comparable with 0% RG (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The increased GHGI under 75% and 100% RG-urea substitution could both be attributed to the promoted GWP and the reduced grain yields.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that RG-urea substitution ratio played an important role in CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions. CH\u003csub\u003e4\u003c/sub\u003e emissions were positively related with RG-urea substitution ratio, while the opposite was true for N\u003csub\u003e2\u003c/sub\u003eO emissions. Compared with 0% RG, the increased CH\u003csub\u003e4\u003c/sub\u003e emissions in the 25% RG and 50% RG substitution ratios were offset by decreased N\u003csub\u003e2\u003c/sub\u003eO emissions, leading to comparable GWP and GHGI values. The RG-urea substitution ratio affected C and N release from RG residues, which further affected CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions in the paddy soil. Soil Eh, C and N remaining were key characteristics correlated with CH\u003csub\u003e4\u003c/sub\u003e emission while NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and C remaining were main factors on N\u003csub\u003e2\u003c/sub\u003eO emissions. As CH\u003csub\u003e4\u003c/sub\u003e was the main contributor to GWP in paddy soils, further studies should be taken to reduce CH\u003csub\u003e4\u003c/sub\u003e fluxes, especially in the early stage of organic matter returning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRG, ryegrass; GWP, global warming potential; GHGI, greenhouse gas intensity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr. Paul Dijkstra from Northern Arizona University for his revision and comments on the manuscript. Thanks for the sponsorship and support of the National Natural Science Foundation of China (No. 31870424) and the Young Elite Scientists Sponsorship Program by CAST (2016QNRC001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBo Zhu and Zhangyong Liu designed the experiments. Wei Yang, Lai Yao, Mengzhen Zhu, Xueru Ji, Chengwei Li and Shaoqiu Li performed the experiments and data collection. Wei Yang and Bin Wang analyzed the data and Bo Zhu wrote the first draft of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmin FR, Khalid H, El-Mashad HM, Chen C, Liu G, Zhang R (2021) Functions of bacteria and archaea participating in the bioconversion of organic waste for methane production. 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Global Change Biol 15(21):229\u0026ndash;242. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2008.01775.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2008.01775.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ryegrass, CH4, N2O, Green manure substitution ratio, C and N release","lastPublishedDoi":"10.21203/rs.3.rs-1784777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1784777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003e(\u003cem\u003ePurpose\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe incorporation of\u003cem\u003e \u003c/em\u003eryegrass (\u003cem\u003eLolium multiflorum\u003c/em\u003e Lam.; RG), a winter grass manure, could partly replace chemical N and reduce N loss during the succeeding rice seasons, but little is known about its impact on greenhouse gas emission. This study investigated the effect of different RG-urea substitution ratios (0%, 25%, 50%, 75% and 100%) on C and N release, CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions in a paddy soil.\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(\u003cem\u003eMethods\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eGas samples for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO fluxes measurement were collected by using a closed chamber and determined by chromatograph method. C and N release from the incorporated RG residue were tested by a mesh bag method.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(\u003cem\u003eResults\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eC and N release from RG followed a single exponential decay model, with 95.5%-97.8% of the original C and 98.7%-99.3% of N released during 192 days. RG-urea substitution ratio increased CH\u003csub\u003e4\u003c/sub\u003e emission, but was negatively correlated with N\u003csub\u003e2\u003c/sub\u003eO emission. In comparison with 0% substitution, global warming potential (GWP) and greenhouse gas intensity (GHGI) were not significantly different for the 25% and 50% RG substitutions, but were significantly higher for the 75% and 100% substitutions (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Soil redox, C and N remaining in litter residue were key characteristics explaining CH\u003csub\u003e4\u003c/sub\u003e emission, while NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N concentrations were correlated with the variation of N\u003csub\u003e2\u003c/sub\u003eO emission.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(\u003cem\u003eConclusion\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe increased CH\u003csub\u003e4 \u003c/sub\u003eemission by RG incorporation could be offset by the reduced N\u003csub\u003e2\u003c/sub\u003eO emission when RG-urea substitution ratio was 50% or less.\u003c/p\u003e","manuscriptTitle":"Does replacing chemical fertilizer with ryegrass (Lolium multiflorum Lam.) mitigate CH4 and N2O emissions and reduce global warming potential from paddy soil?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-20 20:05:20","doi":"10.21203/rs.3.rs-1784777/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-07-01T07:18:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-01T06:22:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2022-07-01T06:10:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-01T05:42:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2022-06-22T10:28:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"147663a4-fb94-43fc-b066-e78741dca8e6","owner":[],"postedDate":"July 20th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-09-25T11:40:58+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-20 20:05:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1784777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1784777","identity":"rs-1784777","version":["v1"]},"buildId":"oE6Zbj460LM0Up2FdVbMZ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}