Mao Fan
Xueting Yang1†
Jun Zhang- 1Chongqing Academy of Agricultural Sciences, Institute of Resources and Environment, Chongqing, China
- 2Department of Plant, Food, and Environmental Sciences, Dalhousie University Faculty of Agriculture, Cox Institute, Truro, NS, Canada
- 3Key Laboratory of Crop Physiology & Ecology, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
Methane (CH4) and nitrous oxide (N2O) are the two most important greenhouse gases following carbon dioxide (CO2). However, existing research on the relationship between rice plant morphological traits and GHG emissions remains relatively limited, often focusing only on individual or a few plant characteristics. To address this research gap, a field experiment was conducted in Chongqing from April to August 2024. Five locally promoted hybrid rice varieties, which are widely cultivated in the region, were selected as experimental materials. The CH4 and N2O emissions of these varieties throughout their entire growth cycle were continuously monitored using the static chamber-gas chromatography method. Concurrently, the morphological traits of both the above-ground components and root systems of the rice plants were quantified.The results revealed significant varietal differences in CH4 and N2O emissions. CH4 emissions followed a unimodal trend, peaking during the panicle emergence to full heading stage. In contrast, N2O emissions peaked after field drainage and drying. Cumulative CH4 emissions ranged from 314.6 to 443.4 kg·ha-1, with the variety ‘Qxiangyou 352’ exhibiting significantly lower emissions than the others. Cumulative N2O emissions ranged from -0.049 to 0.165 kg·ha-1, showing no significant differences among varieties. Correlation analysis indicated that CH4 flux was highly significantly positively correlated with plant height, leaf area index (LAI), aboveground dry biomass, and root dry biomass, but highly significantly negatively correlated with root oxidation activity (ROA). Similarly, N2O flux was highly significantly positively correlated with plant height, LAI, root volume, and root dry biomass, and significantly negatively correlated with ROA. Overall, ‘Qxiangyou 352’ not only achieved a relatively high yield of 11.2 t·ha-1, but also demonstrated the lowest global warming potential (GWP) of 8.8 t CO2e·ha-1 and the lowest greenhouse gas intensity (GHGI) of 0.8 t CO2e·t-1, highlighting its promising low-carbon and high-yield characteristics.
1 Introduction
Agricultural production activities constitute a pivotal source of anthropogenic greenhouse gas (GHG) emissions, with methane (CH4) and nitrous oxide (N2O) emissions contributing significantly, accounting for 51% and 58% of total anthropogenic GHG emissions, respectively. Projections indicate that agricultural production will persist as the foremost emitter by 2030 (Li et al., 2023; EPA, 2012). On a 100 year time scale, the radiative forcing potential of CH4 and N2O surpasses that of carbon dioxide (CO2) by factors of 28 and 265, respectively, exerting a profound influence on climate change dynamics (Xu et al., 2017). Therefore, China has proposed a “carbon peak, carbon neutral” strategy to fulfill its commitments under the Paris Agreement.
Rice (Oryza sativa L.), a staple food crop, sustains approximately half of the global population. As the global population continues to escalate, the demand for rice is projected to intensify further, with estimates indicating a 28% surge in global rice demand by 2050 (Aulakh et al., 2001; Sun et al., 2022). However, this crucial food source also poses significant environmental challenges. It should be noted that rice fields account for approximately 11% of the Earth’s arable land, yet they contribute 10% of global CH4 emissions and up to 20% of agricultural emissions (van Groenigen et al., 2013). Consequently, rice cultivation has been identified as the primary source of methane in the atmosphere (Wassmann and Aulakh, 2000). Lowland rice paddies, due to their prolonged submersion in an anaerobic environment, are conducive to the activity of methanogens. In contrast, the denitrification process is restricted, resulting in significantly higher CH4 emissions compared to upland rice paddies, while N2O emissions are relatively lower. In upland rice paddies, the well-ventilated soil suppresses the activity of methanogens, leading to a marked reduction in CH4 emissions (Das et al., 2025). At present, numerous studies have demonstrated that rice plants play a pivotal role in regulating CH4 and N2O emissions (Chen et al., 2018, 2019).This is mainly attributed to the differences in plant-mediated CH4 production, oxidation and transport capacity, resulting in a difference of 6 times and more than 14 times in CH4 and N2O emissions among different rice varieties (Riya et al., 2012). In anaerobic environments, approximately 60 to 90 percent of CH4 and N2O are transported from the soil to the atmosphere mainly through plant ventilated tissues(Setyanto et al., 2016). However, differences in plant type, tiller number, and root morphology among different rice varieties affect photosynthesis, respiration and nutrient uptake, resulting in varying CH4 and N2O emissions. Current research findings on the relationship between rice plant morphological characteristics and greenhouse gas emissions remain inconsistent. Aulakh et al. (2002) demonstrated that methane transport capacity (MTC) is closely associated with the physiological and morphological characteristics of rice varieties as well as their growth stages. Qin et al. (2015) conducted a comparative analysis of CH4 emissions across nine rice varieties and observed a significant positive correlation between emissions and tiller number, alongside a significant negative correlation with the rice harvest index. Specifically, rice varieties characterized by a high harvest index, low tiller count, minimal ineffective tillers, and strong root oxidation activity exhibit relatively low CH4 emissions. This emission reduction is attributed to enhanced oxygen transport to the rhizosphere, which facilitates methane oxidation and thereby mitigates CH4 emissions (Jiang et al., 2017; Bhattacharyya et al., 2019). In contrast, Zhang et al. (2015) found no significant correlation between aboveground morphological traits such as tiller number and plant height and CH4 emissions from paddy fields. Regarding belowground traits, Jiang et al. (2017) reported that rice varieties with well-developed root systems can promote CH4 oxidation and reduce CH4 emissions. Conversely, recent studies have presented opposing evidence: Lee et al. (2023) found that rice varieties with high root biomass exhibit higher CH4 emission fluxes. This phenomenon is explained by the fact that larger root systems secrete more organic substances, increasing rhizosphere organic carbon input and providing additional carbon sources for methanogens, thus stimulating methane production (Maurer et al., 2018).
Most of these studies have only focused on the influence of certain or a few plant characteristics on CH4 or N2O, lacking a comprehensive and systematic analysis of the synergistic effects of above-ground and underground morphological characteristics. Furthermore, the soil-climate-crop systems exhibit substantial variations across different regions. In particular, research conducted under the typical ecological conditions of the southwest region remains scarce. Chongqing is an important rice-growing area in China, with a perennial rice planting area of approximately 0.65 million hectares (Zhang et al., 2023), the planting mode is single-season rice.Climatically, it exhibits the typical traits of a subtropical monsoon climate, characterized by high temperatures, elevated humidity, and seasonal droughts. Such climatic conditions may regulate the emission processes of CH4 and nitrous oxide N2O by influencing rice growth. Nevertheless, the climatic characteristics of this region have not been thoroughly investigated.
Consequently, this study selects the paddy fields in Chongqing as the research subject and systematically measures the key morphological indicators of rice plants. The objectives of this study are threefold: (1) to unveil the quantitative relationship between the morphological characteristics of different rice varieties and the emission fluxes of CH4 and N2O; (2) to analyze the synergistic regulatory mechanism of aboveground and belowground traits on greenhouse gas emissions; (3) to evaluate the comprehensive contribution rate of plant morphological characteristics to greenhouse gas emissions, thereby providing a theoretical foundation for screening rice varieties with low carbon emissions.
2 Materials and methods
2.1 Experimental location
The study was conducted from April to August 2024 at Chongqing Modern Agricultural High-tech Park, Jiulongpo District, Chongqing (29°27′N, 106°21′E, elevation 357.7 m). The region’s subtropical monsoon humid climate features abundant water and heat resources, with rains and heat in the same season, an average annual temperature of 16~18°C, the average annual precipitation of 1000~1350 mm (with 70% faling between May and September), and a frost-free period of 340~345 days. During the test period, the average daily atmospheric temperature was 26.8°C, with a high of 41°C and a low of 12°C. The rainfall was 587.4 mm (Figure 1). The typical cropping system involves two to three crops per year. The soil type was purple soil of Shaximiao Formation, characterized by: soil organic matter, 25.2 g·kg-1; total nitrogen, 3.4 g·kg-1; ammonium nitrogen, 15.7 mg·kg-1; nitrate nitrogen, 12.1 mg·kg-1; pH, 7.1.
Figure 1. Variation characteristics of daily temperature, rainfall and soil temperature during sampling during rice growth period.
2.2 Test materials and design
This study selected five representative hybrid rice varieties for investigation: ‘Qxiangyou 352’, ‘Shen9you 28’, ‘Shennongyou 228’, ‘Yuxiangyou 8133’, and ‘Shennongyou 446’. Among these, ‘Shen9you 28’, ‘Yuxiangyou 8133’ and’Shennongyou 446’ are medium-early maturing three-line hybrid rice varieties, recommended for cultivation as single-season medium rice in regions below 800 m above sea level in Chongqing. ‘Qxiangyou 352’ is a late-maturing indica-type three-line hybrid rice variety, suitable for planting in late-maturing indica rice zones of Guizhou Province. ‘Shennongyou 228’ is an indica-type three-line hybrid rice variety, adaptable for early or medium rice cultivation in central and northern rice-growing regions of Guangxi, as well as for medium rice production in high-altitude mountainous areas. These varieties were selected due to their widespread promotion and extensive application in rice production systems in Chongqing in recent years.For experiment consisted of 15 plots, each measuring 6 m2 (2 m × 3 m), with three replicates per varieties. The planting density was 20 cm × 30 cm, with 2 seeds per hole. The experimental planting mode was for a single-season rice. The field management measures are in line with the local conditions.
The crop management practices included sowing on March 16, 2024, transplanting on April 25, 2024, and uniform harvesting after maturity on August 9, 2024. Fertilizer application consisted of pure nitrogen (150 kg·ha-1), superphosphate (135 kg·ha-1 P2O5), potassium sulfate (120 kg·ha-1 K2O). To ensure maximum fertilizer efficiency, nitrogen fertilizer was applied in three splits in a ratio of 5:2:3. Two days before transplanting, 50% of the total nitrogen amount was applied, plus full doses of P2O5 and K2O. Subsequently, at the tillering stage of rice, 20% of the total nitrogen was applied. Finally, at the heading stage of the rice, the remaining 30% of total nitrogen was applied. Base fertilizer was applied on April 23, tillering on May 17, and ear fertilizer on June 29, 2024.
2.3 Experimental field water management
The water management strategy for the experimental fields adopts a three-stage irrigation plan: flood irrigation is carried out during the seedling stage, and continuous flooding is applied from the tillering stage to the harvest stage, maintaining a water layer of 3~4 cm. One week before harvest, natural drainage is implemented. All experimental fields follow the principle of replenishing water when it is lacking, and the water layer is kept stable at 3~4 cm during irrigation. Before transplanting, the straw of the previous crop is removed and the soil is manually turned over. All concrete-structured experimental fields are covered with waterproof fabric to effectively prevent water and fertilizer leakage, ensuring the reliability of the experimental data.
2.4 Greenhouse gas sampling and measurement
Greenhouse gas emissions from rice were measured over the entire growth period (106 d) using static box-gas chromatography. Sampling occurred on sunny days (8:30~11:00 am) every 7 days, and postponed on rainy days, until harvest. Gas samples were collected from each plot and the CH4 and N2O concentrations were analyzed to calculate emission fluxes.
The static chamber is fabricated from transparent acrylic and is composed of a top box and a bottom base. The top box measures 100 cm in height, and 50 cm in both length and width. It does not contain an internal fan. The bottom base has a height of 15 cm and dimensions of 50 cm in length and width. There is a groove 4 cm deep on the top surface of the bottom base. During the sampling process, a specific quantity of water was maintained in the groove to guarantee a proper seal when the top box is placed on the base. Gas is extracted from the chamber by means of an automatic gas sampling device. Initially, the intake pipe of the automatic gas sampling device is connected to the ventilation hole on one side of the acrylic box. The air within the box is drawn through a rubber tube. The external end of the tube is connected to an aluminum foil gas bag (Delin, Dalian) via a three-way valve to collect the gas samples withdrawn from the box. The automatic gas sampling device is equipped with an internal temperature sensor. The temperature probe is inserted into the opening at the top of the box and then sealed to measure the temperature inside the box. This device is capable of automatically collecting four gas samples. Simultaneously, it measures the temperature during the sampling process. The time interval between each two consecutive samplings is 5 minutes. Sampling commences at 8:30, and the collected gas is stored in aluminum foil gas bags. After each sampling, the top box of the static chamber is removed, leaving only the bottom base in place. This is to ensure the normal growth of rice. Immediately following the gas collection, the aluminum foil gas bags used to gather the experimental gas are sent to the China National Rice Research Institute for concentration detection. The concentrations of CH4 and N2O gases are determined using a GC-2010 Plus gas chromatograph (Shimadzu Corporation, Japan).
2.5 Data collation and analysis
The calculation formula of greenhouse gas emission flux is as follows (Li et al., 2023) (Equation 1):
Where, F is the emission flux of methane and nitrous oxide, and the unit is mg·(m2·h) -1 and ug· (m2·h) -1,respectively; ρ is the gas density at standard atmospheric pressure, CH4 is 0.714 kg·m3, N2O is 1.25 kg·m3; H is the net height of the sampling box, m; dC/dt is the linear slope of gas concentration change with time during sampling time; T is the average temperature in the box during the sampling process, in °C. The CH4 and N2O emission fluxes are represented by three repeated averages.
The formula for calculating CH4, N2O emissions in each growth period is as follows (Kim et al., 2019) (Equation 2):
In the formula, Ri represents the mean of the emission of two adjacent measurements, Di represents the interval of two adjacent measurement dates, d. The total CH4 emission in the whole growth period was the sum of emissions in each growth period.
The warming potential of CH4 and N2O emissions in rice fields (GWP, GWP of CO2 is 1) was calculated according to the conversion coefficients of 28 and 265 on a 100-year time scale, respectively, and the formula is as follows (Guo et al., 2023) (Equation 3):
Where, GWP represents the warming potential (t CO2e·ha-1); CH4 and N2O represent the total emission of CH4 and N2O gas during rice cultivation (t·ha-1), respectively.
Greenhouse gas intensity (GHGI) is related to rice yield, and the formula is as follows (Haque et al., 2016) (Equation 4):
Where: GHGI is greenhouse gas emission intensity, t CO2e· t-1; Y is the yield of rice, t·ha-1.
2.6 Principles for processing concentration data
A total of 17 field samples were collected during rice growth period, yieldiing 255 theoretical CH4 and N2O emission fluxes. However, not all concentration values resulted in valid emission flux calculations. Following established data processing methods (Lu et al., 2000; Zou et al., 2004), only concentration data with a linear regression≥0.9 were considered valid. After analysis, 231 CH4 and 225 N2O emission fluxes were obtained, representing effective rates were 90.6% and 88.2%, respectively.
2.7 Plant sampling and determination of rice
Plant samples were collected at four critical growth stages of rice: tillering stage, heading stage, full heading stage and filling stage. Three rice plants with uniform growth were selected from each plot. A soil block of 30 cm×30 cm was dug around the root-stem junction of each plant and placed in a sieve. Then, the large soil clumps were washed clean with running water at the edge of the field. Subsequently, the roots of the rice plants were thoroughly rinsed with a high-pressure water gun to ensure that all adhering soil particles were removed as much as possible. After the initial field cleaning, the samples were brought back to the laboratory and further washed with pure water until no sediment remained on the roots. Then, the measurements of plant height, tiller number, leaf area index, aboveground dry biomass, root length, root volume, root porosity, underground dry biomass and root oxidation activity were conducted. The leaf area index (LAI) was determined using the length-width coefficient method (Amanullah and Inamullah, 2016). The longest root length and plant height were measured with a ruler. For measuring the root volume, the water displacement method was adopted. Given the actual size of the rice roots, a graduated cylinder of appropriate capacity was carefully chosen. It was essential that the graduated cylinder could not only fully enclose the rice roots but also provide sufficient space for accurate water-level readings. Initially, an appropriate volume of water was added to the graduated cylinder, and the water-level reading was precisely recorded. The cleaned and surface-dried rice roots were then carefully placed into the graduated cylinder, ensuring that the entire root system was completely submerged in water to avoid floating or entanglement, as these factors could introduce errors in the measurement. A glass rod or other suitable instrument was used to gently stir or press the roots, facilitating their complete submersion to the bottom of the graduated cylinder and expelling any adhering air bubbles. Once the water level in the graduated cylinder had stabilized, the water-level reading was accurately recorded once more. The root volume was then calculated as the difference between the two water-level readings. Regarding the determination of aboveground and underground dry biomass, the samples were first placed in a drying oven at 105°C for 30 min to halt the photosynthetic process, and then dried at 80°C until a constant weight was achieved. Root porosity was measured according to the method of Colmer (2003). The root oxidation activity (ROA) is determined through measuring the oxidation state of α-naphthylamine (α-NA). The detailed methodology can be found in the study conducted by Zhong (2017).
2.8 Rice yield determination
At the maturity stage of rice, three hills of rice were randomly sampled from each plot of the tested rice varieties. After harvest, the rice was manually threshed, and then the plump grains were effectively separated from the empty or shrivelled grains through a flotation method. Subsequently, the two types of grains were dried and counted separately. For the plump grains, the total weight of the entire sample was first measured. Then, a 50 g subsample was randomly selected for a detailed grain count. Regarding the empty or shrivelled grains, their actual quantity was directly counted.
The total number of grains was derived using the total weight of the sample and the calculated thousand-grain weight:
Total number of grains= (Total weight of the sample/Thousand-grain weight of the sample) × 1000.
2.9 Data analysis
The data calculation and chart drawing were conducted using WPS Office and Origin 2021 software. Subsequently, a one - way analysis of variance was carried out using IBM SPSS 25.0 software, and multiple comparisons were performed through Duncan’s test (P < 0.05). In this study, the methane and nitrous oxide emissions as well as the rice yield under different treatments were analyzed. Additionally, a principal component analysis (PCA) was implemented on the rice plants and greenhouse gas emissions. Moreover, the Pearson correlation coefficient was also calculated to assess the relationships between the selected factors and methane, nitrous oxide emissions, and rice yield.
3 Results
3.1 CH4 emission fluxes in paddy fields of different rice varieties
Throughout the rice growth cycle, CH4 emissions displayed a single-peak pattern, with the primary peak occurring during the heading to full-heading stage, followed by a gradual decline. However, the application of tillering fertilizer induced a secondary emission peak during the tillering stage (Figure 2A). At the tillering-stage peak, CH4 emission fluxes among varieties ranged from 16.4 mg·m-2·h-1 to 28.0 mg·m-2·h-1, ranked in descending order as: ‘Shen9you 28’>‘Qxiangyou 352’>‘Shennongyou 446’>‘Shennongyou 228’>‘Yuxiangyou 8133’. ‘Shen9you 28’ showed the highest peak (28.0 mg·m-2·h-1), exceeding ‘Qxiangyou 352’ (19.3 mg·m-2·h-1) by 45.1% and ‘Yuxiangyou 8133’ (16.4 mg·m-2·h-1) by 70.7%. The secondary emission peak—occurring later in the growth cycle—ranged from 26.7 mg·m-2·h-1 to 48.5 mg·m-2·h-1, with varieties ranked as:’Shennongyou 446’>‘Shen9you 28’>‘Shennongyou 228’>‘Yuxiangyou 8133’>‘Qxiangyou 352’. ‘Shennongyou 446’ reached the highest value (48.5 mg·m-2·h-1), which was 10.2% higher than Shen9you 28 (44.0 mg·m-2·h-1) and 81.7% greater than Qxiangyou 352 (26.7 mg·m-2·h-1).
Figure 2. Seasonal variations of greenhouse gas fluxes in paddy fields under five rice varieties. (A) Methane (CH4) flux and (B) nitrous oxide (N2O) flux after transplanting. Arrows indicate field management events (such as fertilization, drainage). A: Seedling stage - Tillering stage; B: Tiller stage - Heading stage; C: Heading stage - Full heading stage; D: Full heading stage - Filling stage; E: Filling stage - Mature stage.
3.2 Total CH4 emissions of different rice varieties
Cumulative CH4 emissions varied significantly among rice varieties, ranging from 314.6 kg·ha-1 to 443.4 kg·ha-1 (Table 1). ‘Qxiangyou 352’ exhibited the lowest emissions (314.6 kg·ha-1), which were 17.5% to 29.1% lower than all other cultivars. Emissions ranked in ascending order as follows: ‘Qxiangyou 352’<‘Shennongyou 228’<‘Yuxiangyou 8133’<‘Shennongyou 446’<‘Shen9you 28’. No significant differences were detected among ‘Shennongyou 228’, ‘Yuxiangyou 8133’, and ‘Shennongyou 446’.
Table 1. CH4 emissions from different rice varieties during various growth stages (unit kg·ha-1).
Total CH4 emissions also differed across growth stages (Table 1). The heading to full heading stage was the period with the highest CH4 emissions and clear varietal differences, where ‘Shennongyou 446’ emitted significantly more CH4 than the other varieties. The filling to maturity stage had the lowest emissions, yet ‘Shen9you 28’ still showed significantly higher fluxes than all other varieties in this period. No significant differences among varieties were found in the other growth stages.
3.3 N2O emission fluxes in paddy fields of different rice varieties
As can be observed from Figure 2B, the N2O emission patterns of different rice varieties were fundamentally similar. During post-transplant growth, N2O flux remained low across all varieties, with periods of net uptake observed at various intervals. Emission curves exhibited fluctuating characteristics, with no significant differences between peak values. Toward the end of the growth period, following field drainage and drying, distinct N2O emission peaks occurred in all varieties. Peak emissions ranged from 28.3 to 50.9 μg·m2·h-1, ranked in descending order as:’Yuxiangyou 8133’>‘Shen9you 28’>‘Shennongyou 446’>‘Shennongyou 228’>‘Qxiangyou 352’. Among these, ‘Yuxiangyou 8133’ showed the highest peak (50.9 μg·m2·h-1), exceeding the second-highest emitter, ‘Shen9you 28’ (46.3 μg·m2·h-1), by approximately 9.9%. Compared to the lowest peak emitter, ‘Qxiangyou 352’ (28.3 μg·m2·h-1), ‘Yuxiangyou 8133’s emission rate was approximately 80.0% higher.
3.4 Total N2O emissions of different rice varieties
Cumulative N2O emissions across rice varieties ranged from -0.049 kg·ha-1 to 0.165 kg·ha-1, with no significant differences among varieties (Table 2). Emissions ranked in ascending order as follows:’Yuxiangyou 8133’<‘Shennongyou 446’<‘Qxiangyou 352’<‘Shennongyou 228’<‘Shen9you 28’. ‘Yuxiangyou 8133’ showed the lowest cumulative emission (-0.049 kg·ha-1), indicating net uptake, while ‘Shen9you 28’ exhibited the highest (0.165 kg·ha-1).
Table 2. N2O emissions from different rice varieties during various growth stages (unit kg·ha-1).
Significant temporal variations in N2O fluxes were observed across growth stages (Table 2). From tillering to heading, all varieties consistently consumed N2O, showing negative emission values. Notably, significant varietal differences emerged during the full heading to filling stage and the filling to maturity stage. In the latter stage, ‘Shen9you 28’ exhibited significantly higher N2O emissions than all other varieties.
3.5 Yield and its components of different rice varieties
The yields and their constituent factors varied significantly among the rice cultivars tested (Table 3). ‘Yuxiangyou 8133’ produced the highest number of effective panicles per hectare (2.9×106 ha-1), exceeding the other varieties by 7.4%~11.5%, while ‘Shennongyou 228’ and ‘Shen9you 28’ had the lowest (2.6×106 ha-1). ‘Yuxiangyou 8133’ also exhibited the highest thousand-grain weight, surpassing other cultivars by 14.5%~24.6%. In contrast, ‘Qxiangyou 352’ had the highest number of filled grains per panicle (200.2 grains), which was 17.0%~60.8% greater than that of the other varieties. ‘Shen9you 28’ achieved the highest seed setting rate (85.4%), whereas ‘Yuxiangyou 8133’ had the lowest (75.4%). No significant difference in seed setting rate was observed between ‘Qxiangyou 352’ and ‘Shennongyou 446’. Grain yield ranged from 7.6 t·ha-1 to 11.2 t·ha-1. Owing to the integrated contributions of panicle number, grain weight, filled grains per panicle, and seed setting rate, ‘Qxiangyou 352’ significantly outyielded all other varieties, with a yield advantage of 23.1%~47.4%.
Table 3. Comparison of yield and component factors of different rice varieties.
3.6 GWP and GHGI
As presented in (Figure 3), the GWP of each rice variety spans from 8.8~12.4 t CO2e·ha-1. In ascending order, they are: ‘Qxiangyou 352’<‘Shennongyou 228’<‘Yuxiangyou 8133’<‘Shennongyou 446’<‘Shen9you 28’. Specifically, ‘Qxiangyou 352’ exhibits the lowest GWP, measuring 8.8 t CO2e·ha-1, which is markedly lower than that of the other rice varieties. Conversely, there is no statistically significant difference in GWP among the remaining rice varieties. Notably, ‘Shen9you 28’ has the highest GWP, reaching 12.4 t CO2e·ha-1, which is approximately 6.9%~40.9% higher compared to the other varieties.
Figure 3. GWP and GHGI of different rice varieties.
Regarding the GHG, it ranges from 0.8~1.5 t CO2e·t-1 for each rice variety. Arranged from low to high, they are: ‘Qxiangyou 352’<‘Shennongyou 228’=‘Shen9you 28’<‘Shennongyou 446’<‘Yuxiangyou 8133’. ‘Qxiangyou 352’ has the lowest GHGI, being significantly lower than that of the other rice varieties by 42.9%~46.7%. Additionally, no significant difference in GHGI is observed among ‘Shennongyou 446’, ‘Shennongyou 228’, ‘Shen9you 28’, and ‘Yuxiangyou 8133’. However, ‘Yuxiangyou 8133’ has the highest GHGI, which is 7.1%~15.4% higher than that of ‘Shennongyou 446’, ‘Shennongyou 228’, and ‘Shen9you 28’.
3.7 Physiological characteristics of above ground parts in different rice varieties at different growth stages
Significant variations in aboveground physiological traits were observed among the rice varieties (Figure 4). Plant height increased rapidly during the early growth stages and stabilized thereafter. At the booting stage, ‘Qxiangyou 352’ was significantly taller than the other varieties. By the filling stage, ‘Shen9you 28’ exhibited the maximum plant height. Tiller number increased markedly from the tillering to the heading stage before stabilizing. At the tillering stage, ‘Shennongyou 446’ had a significantly higher number of tillers, whereas ‘Shen9you 28’ showed the lowest. No significant differences among varieties were detected in later growth stages. The LAI increased continuously through the tillering, heading, and booting stages, then plateaued. Although no significant differences were observed among varieties at any stage, ‘Shen9you 28’ showed a relatively higher LAI at tillering, while ‘Qxiangyou 352’ and ‘Yuxiangyou 8133’ led at the heading stage. Aboveground dry biomass accumulated consistently across all four growth stages. At the heading stage, ‘Shennongyou 446’ had significantly higher biomass than the other varieties. At the booting and filling stages, ‘Yuxiangyou 8133’ displayed numerically greater biomass, though differences were not statistically significant among varieties including ‘Shennongyou 446’, ‘Shennongyou 228’, and ‘Yuxiangyou 8133’.
Figure 4. Characteristics of the aboveground parts of different varieties at different growth stages. (A) Plant height, (B) Tiller number, (C) LAI, (D) Aboveground dry biomass. Different letters within the same stage indicate significant differences among rice varieties (P < 0.05).
3.8 Physiological characteristics of the lower part in different rice varieties at different growth stages
As shown in Figure 5, root length increased during early growth stages and stabilized by later stages, with no significant differences observed among varieties across the tillering, heading, full-heading, and filling stages. Root volume increased continuously throughout the growth period. At tillering, ‘Qxiangyou 352’ exhibited significantly greater root volume than the other varieties. At heading, ‘Qxiangyou 352’, ‘Shennongyou 446’, and ‘Shennongyou 228’ all had significantly larger root volume than ‘Shen9you 28’ and ‘Yuxiangyou 8133’. At both full-heading and filling stages, ‘Qxiangyou 352’ again showed significantly greater root volume compared to the other varieties. No significant differences in root porosity were detected among varieties at tillering. From the heading stage onward, no significant differences were observed among ‘Qxiangyou 352’, ‘Shennongyou 446’, ‘Shennongyou 228’, and ‘Yuxiangyou 8133’, although ‘Shennongyou 446’ consistently exhibited relatively higher values. Below-ground dry biomass increased across all growth stages. At tillering, ‘Shennongyou 446’ and ‘Shennongyou 228’ had significantly higher biomass than the other varieties. At heading, both ‘Qxiangyou 352’ and ‘Shennongyou 446’ showed significantly greater biomass than the remaining varieties. By the filling stage, no significant differences among varieties were detected. ROA showed an initial increase followed by a decrease. No significant varietal differences were observed until the filling stage, during which ‘Qxiangyou 352’ and ‘Shen9you 28’ exhibited significantly higher ROA than the other varieties, with ‘Qxiangyou 352’ being the highest.
Figure 5. Characteristics of the underground parts of five rice varieties across different growth stages. (A) Root length, (B) Root volume, (C) Root porosity, (D) Underground dry biomass, (E) ROA. Different lowercase letters above bars within the same growth stage indicate significant differences among varieties (P < 0.05).
3.9 Correlation analysis of physiological characteristics of rice plants and greenhouse gas emissions
Correlation analysis (Figure 6) indicated that CH4 flux rates during both the heading and full-growth stages of the rice varieties were highly significantly positively correlated with key growth indicators measured in these periods. Specifically, CH4 flux showed significant positive correlations with plant height, LAI, and aboveground dry weight. Concurrently, CH4 flux was highly significantly negatively correlated with ROA during these growth stages. Moreover, N2O flux during these stages was also highly significantly positively correlated with plant height, LAI, root volume, and aboveground dry weight, while being significantly negatively correlated with ROA. In terms of cumulative emissions, cumulative CH4 emissions exhibited a highly significant positive correlation with tiller number and a significant positive correlation with aboveground dry biomass. Cumulative N2O emissions were positively correlated with LAI, root length, root volume, and root porosity. Furthermore, grain yield was significantly positively correlated with root porosity and belowground dry weight during this growth period.
Figure 6. Correlation analysis of CH4, N2O, yield and physiological characters of rice plants. The size and color saturation of the ellipse represent the correlation size. Red represents positive correlation and blue represents negative correlation.
3.10 PCA analysis of rice plant pysiological characteristics and greenhouse gas emissions
Principal component analysis (PCA) revealed complex relationships between rice plant traits and greenhouse gas emissions (Figure 7). In the aboveground compartment, PC1 and PC2 accounted for 29.6% and 18.6% of the total variance, respectively. The ordination plot indicated that plant height and LAI were positively associated with cumulative CH4 emissions, with samples distributed along the positive direction of PC1 suggesting that taller plants may be linked to higher CH4 release. For the belowground compartment, PC1 and PC2 explained 35.0% and 19.5% of the total variance, respectively. Root length and root porosity were correlated with both CH4 and N2O emissions. Patterns in sample distribution revealed intricate associations between root morphological traits and greenhouse gas fluxes, where root volume and belowground biomass exhibited negative correlations with N2O emissions along the negative PC1 axis.
Figure 7. PCA analysis of methane and nitrous oxide on rice plant physiological characteristics. (A) Aboveground parts (B) Underground parts.
4 Discussion
4.1 Greenhouse gas emission patterns
In this research, the seasonal variations of CH4 emission fluxes among different rice varieties demonstrated remarkable similarities. All five rice varieties under investigation exhibited a single-peak pattern in CH4 emissions. The peak occurred during the heading-booting stage, after which the CH4 emission fluxes showed a steadily declining trend (Figure 2A), which is consistent with the research by Hang et al. (2022). Notably, Jiang et al. (2013) reported two peaks in the rice growth period, one during the tillering stage and the other during the heading stage, which diverged from the findings of this study. The production and oxidation of CH4 are intricately regulated by methanogenic bacteria and methane-oxidizing bacteria. These microbial populations are highly sensitive to soil temperature, which, in turn, influences soil CH4 emissions by shaping the microbial community structure and function (Mohanty et al., 2007). In Chongqing, the early rice transplanting time results in relatively low soil temperatures and reduced soil biological activity during the early growth stage of rice. The slow oxygen consumption rate under such conditions is not conducive to the proliferation and activity of methane-producing bacteria. During the heading-booting stage, rice plants generate a substantial amount of root exudates and shed leaves. These provide a rich source of substrates for methanogenic bacteria, leading to an increase in the abundance of methanogenic bacteria and subsequently promoting CH4 emissions. As the rice reaches the mature stage, the abundance of methanogenic bacteria gradually diminishes, which corresponds to a decline in CH4 emission level (Fernandez-Baca et al., 2021). Furthermore, during the period from the heading stage to the full heading stage, the root volume, root dry weight, and ROA of ‘Qxiangyou 352’ were all greater than those of other rice varieties. Rice varieties characterized by well-developed root systems and strong oxygen-secreting capabilities can effectively promote CH4 oxidation and thus reduce CH4 production (Qi et al., 2024). Consequently, the root system of rice is a crucial factor contributing to the disparities in CH4 emissions among different rice varieties.
All rice varieties showed a certain peak in N2O emissions after the paddy fields were drained and sun-dried. This might be attributed to the long-term waterlogged conditions in the experimental fields. As nitrification and denitrification processes are mainly active during the soil’s wet-dry alternation stage, the N2O emissions from continuously waterlogged paddy fields usually remain at a low level or even negative throughout the growing season (Tokida et al., 2010; Perry et al., 2022). Moreover, under flooded conditions, the diffusion of N2O is inhibited, facilitating its further reduction to N2 via denitrification. This also explains the consistently low N2O emissions observed in the present study (Lin et al., 2017). Notably, significant alterations in soil moisture can expedite the nitrification and denitrification rates (Xiong et al., 2007), this leads to an emission peak after drainage. During the flooding period, due to oxygen deficiency, the N2O release is minimal. Conversely, the drainage of the paddy field enhances soil aeration, thereby triggering a release peak. Similar findings have been reported by previous scholars (Xiong et al., 2002; Xu et al., 1997).
4.2 Differences in rice yield
In this study, the grain yield of the rice varieties under test spanned from 7.6 to 11.2 t·ha-1. Among them, ‘Qxiangyou 352’ exhibited the highest yield. This could be largely attributed to its remarkable performance in terms of the number of effective panicles, the number of grains per panicle, the thousand-grain weight, and the seed setting rate. Notably, this variety had the highest thousand-grain weight and seed setting rate among all the tested varieties. Furthermore, the yield differences were significantly associated with phenotypic traits including plant height, leaf area index, and root characteristics.
Plant height is a crucial factor influencing rice yield (Badshah et al., 2014). A body of research has demonstrated that a proper increase in plant height can contribute to enhanced yield (Quan et al., 2024; Fernández et al., 2018). Specifically, within a certain range, rice yield shows a positive correlation with the increasing in plant height. The LAI of rice directly impacts the light interception capacity of the biological population, thereby affecting the synthesis of photosynthetic products in rice (Wang et al., 2025). As a result, the LAI of rice is of great significance in the production and accumulation of dry matter as well as the formation of yield. Notably, in this particular study, no significant differences in LAI were observed among various rice varieties. This finding suggests that LAI might not be the primary determinant of rice yield. Furthermore, yield variations among different rice varieties are closely associated with root characteristics. Roots, being the vital organs for rice to absorb water and nutrients (Zhu et al., 2025), their development level and activity directly influence the growth state and ultimate yield of rice. Liu et al. (2020) indicated that the characteristics of high root activity (such as root dry weight and root oxidative activity) are of great significance in promoting the formation of large panicles in super-high-yield hybrid rice. Specifically, the increase in root activity facilitates the absorption of soil nitrogen. This contributes to biomass accumulation and enhances nitrogen utilization efficiency, ultimately boosting rice yield (Zhu et al., 2022). In this study, among all the tested rice varieties, the ‘Qxiangyou 352’ variety exhibited the greatest plant height, along with a higher leaf area index and more favorable root characteristics during each growth stage. These combined advantages firmly established a solid foundation for its highest yield performance. Moreover, Denier van der Gon et al. (2002) showed that a negative correlation exists between the CH4 emission flux in paddy fields and the rice yield. Jiang et al. (2017) likewise discovered that high-yield rice varieties generally exhibit lower CH4 emissions. This is consistent with our research findings.
4.3 Effects of rice plants on greenhouse gas emissions
This study demonstrates that the morphological and physiological traits of rice exert a significant influence on CH4 and N2O emissions from paddy fields. Specifically, rice varieties characterized by greater plant height, LAI, and higher aboveground/root biomass tend to exhibit elevated CH4 and N2O emissions. In contrast, enhanced ROA in rice correlates with a reduction in the emissions of these two greenhouse gases. This observation implies that robust ROA fosters an oxidative microenvironment in the rhizosphere, thereby facilitating CH4 oxidation and suppressing denitrification processes—ultimately mitigating net GHG emissions (Guan et al., 2024; Qin et al., 2015; Qi et al., 2024). The rice cultivar ‘Qxiangyou 352’ selected for this study possesses well-developed root systems and high aboveground biomass, with ROA significantly superior to other tested varieties. This trait may constitute a key mechanism underlying its ability to maintain high yields while achieving the lowest cumulative CH4 emissions, comprehensive GWP, and GHGI among the evaluated cultivars. However, the performance of the ROA exhibits variability across different regions. According to the research conducted by Liu et al. (2020) in Hunan province, a significantly positive correlation was identified between ROA and methane emissions. This phenomenon may be attributed to the discrepancies in rice varieties and planting environment. Furthermore, our findings reveal a significant positive correlation between root volume and N2O emissions, which aligns with the conclusions of (Fu et al., 2011). This relationship can be attributed to the fact that greater root biomass enhances the root system’s capacity for N2O uptake and translocation, thereby leading to increased N2O emissions (Fu et al., 2012).
5 Conclusions
In the Chongqing region, a comparative field-based study was conducted to investigate the effects of different rice varieties on CH4 and N2O emissions. The results indicated that the seasonal patterns of CH4 emissions were similar across the varieties, with peak emissions occurring during the booting to heading stage. In contrast, peak N2O emissions were observed after field drainage and drying. Correlation analysis revealed that CH4 flux was highly significantly positively correlated with plant height, LAI, aboveground dry biomass, and root dry biomass, while it was highly significantly negatively correlated with ROA. For N2O flux, a highly significant positive correlation was found with plant height, LAI, root volume, and root dry biomass, whereas a significant negative correlation was observed with ROA. Although ROA appears to be a general trait associated with lower emissions, its effectiveness across different regions remains to be verified. Among the tested varieties, ‘Qxiangyou 352’ not only achieved a relatively high grain yield of 11.2 t·ha-¹ but also exhibited the lowest global warming potential (8.8 t CO2e·ha-¹) and the lowest greenhouse gas intensity (0.8 t CO2e·t-¹), highlighting its promising low-carbon and high-yield characteristics. In conclusion, the ‘Qxiangyou 352’ variety demonstrates the lowest total greenhouse gas emissions, the highest yield, as well as the lowest warming potential and greenhouse gas emission intensity, making it suitable for large-scale adoption in Chongqing and other ecologically similar regions.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
Author contributions
MF: Investigation, Visualization, Writing – original draft, Data curation. XY: Writing – original draft, Investigation, Visualization, Data curation. AR: Writing – review & editing. JiZ: Supervision, Writing – review & editing. YW: Supervision, Writing – review & editing. JuZ: Methodology, Writing – review & editing. XH: Resources, Methodology, Writing – review & editing, Funding acquisition.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by Chongqing Natural Science Foundation “CSTB2023NSCQ-MSX0436” and Chongqing Special Project for Performance Incentive Guidance of Scientific Research Institutions “CSTB2024JXJL-YFX0015”.
Acknowledgments
We are grateful to the Chongqing Academy of Agricultural Sciences and the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, for providing the experimental sites and technical support. Special thanks are extended to the editors and reviewers for their constructive comments and suggestions, which greatly improved the quality of this manuscript.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Amanullah I. and Inamullah X. (2016). Dry matter partitioning and harvest index differ in rice genotypes with variable rates of phosphorus and zinc nutrition. Rice Sci. 23, 78–87. doi: 10.1016/j.rsci.2015.09.006
Aulakh M. S., Wassmann R., and Rennenberg H. (2001). Methane emissions from rice fields—quantification, mechanisms, role of management, and mitigation options. Adv. Agron. 70, 193–260. doi: 10.1016/S0065-2113(01)70006-5
Aulakh M. S., Wassmann R., and Rennenberg H. (2002). Methane transport capacity of twenty-two rice cultivars from five major Asian rice-growing countries. Agric. Ecosyst. Environ. 91, 59–71. doi: 10.1016/s0167-8809(01)00260-2
Badshah M. A., Tu N., Zou Y., Ibrahim M., and Wang K. (2014). Yield and tillering response of super hybrid rice Liangyoupeijiu to tillage and establishment methods. Crop J. 2, 79–86. doi: 10.1016/j.cj.2013.11.004
Bhattacharyya P., Dash P. K., Swain C. K., Padhy S. R., Roy K. S., Neogi S., et al. (2019). Mechanism of plant mediated methane emission in tropical lowland rice. Sci. Total Environ. 651, 84–92. doi: 10.1016/j.scitotenv.2018.09.141, PMID: 30223222
Chen J., Li S., Li C., Wang H., Geng Y., Kong D., et al. (2019). Post-seasonal effects of water-saving rice production regimes on N2O emissions in an annual rice-barley rotation system. Catena 182, 104112. doi: 10.1016/j.catena.2019.104112
Chen Y., Li S., Zhang Y., Li T., Ge H., Xia S., et al. (2018). Rice root morphological and physiological traits interaction with rhizosphere soil and its effect on methane emissions in paddy fields. Soil Biol. Biochem. 129, 191–200. doi: 10.1016/j.soilbio.2018.11.015
Colmer T. D. (2003). Aerenchyma and an inducible barrier to radial oxygen loss facilitate root aeration in upland, paddy and deep-water rice (Oryza sativa L.). Ann. Bot. 91, 301–309. doi: 10.1093/aob/mcf114, PMID: 12509350
Das R. S., Chatterjee D., Saha S., Sarkar D., Alam R., Dey S., et al. (2025). Enhancing carbon sequestration potential of lowland rice agroecosystems for environmentally clean production system: A review. CSA 2, 100054–100054. doi: 10.1016/J.CSAG.2025.100054
Denier van der Gon H. A. C., Kropff M. J., van Breemen N., Wassmann R., Lantin R. S., Aduna E., et al. (2002). Optimizing grain yields reduces CH4 emissions from rice paddy fields. PNAS 99, 12021–12024. doi: 10.1073/pnas.192276599, PMID: 12189212
EPA (2012). Global anthropogenic non-CO2 greenhouse gas emissions: 1990-2030 (EPA). Available online at: https://www.epa.gov/node/156809. (430-R-12-006) (Accessed October, 2025).
Fernández C., Vega J. A., and Fontúrbel T. (2018). Vegetative growth response of cytisus oromediterraneus to fuel reduction treatments. Plant Ecolog. 219, 251–259. doi: 10.1007/s11258-018-0793-7
Fernandez-Baca C. P., Rivers A. R., Kim W., Iwata R., McClung A. M., Roberts D. P., et al. (2021). Changes in rhizosphere soil microbial communities across plant developmental stages of high and low methane emitting rice genotypes. Soil Biol. Biochem. 156, 108233. doi: 10.1016/j.soilbio.2021.108233
Fu Z., Huang H., Zhu H., and Chen C. (2011). Relativity between CH4 and N2O emission and rice plant characteristics. J. Hunan Agric. Univ. (Nat. Sci.). 37, 356–360. doi: 10.3724/sp.j.1238.2011.00356
Fu Z., Zhu H., Chen C., and Huang H. (2012). Research on the correlation between the greenhouse gases emission from paddy field and the biological characteristics of rice root system. J. Agro-Environ. Sci. 30, 2416–2421. doi: 10.11654/jaes.2011.12.004
Guan S., Qi Z., Li S., Du S., and Xu D. (2024). Effects of rice root development and rhizosphere soil on methane emission in paddy fields. Plants 13, 3223. doi: 10.3390/plants13223223, PMID: 39599432
Guo L., Lin W., Cao C., and Li C. (2023). Integrated rice-crayfish farming system does not mitigate the global warming potential during rice season. Sci. Total Environ. 867, 161520. doi: 10.1016/j.scitotenv.2023.161520, PMID: 36646218
Hang X., Danso F., Luo J., Liao D., Zhang J., and Zhang J. (2022). Effects of water-saving irrigation on direct-seeding rice yield and greenhouse gas emissions in north China. Agriculture 12, 937. doi: 10.3390/agriculture12070937
Haque M., Kim G. W., Kim P. J., and Kim S. Y. (2016). Comparison of net global warming potential between continuous flooding and midseason drainage in monsoon region paddy during rice cropping. Field Crops Res. 193, 133–142. doi: 10.1016/j.fcr.2016.04.007
Jiang Y., Groenigen K. J. V., Huang S., Hungate B. A., Kessel C. V., Hu S., et al. (2017). Higher yields and lower methane emissions with new rice cultivars. Global Change Biol. 23, 4728–4738. doi: 10.1111/gcb.13737, PMID: 28464384
Jiang Y., Wang L., Yan X., Tian Y., Deng A., and Zhang W. (2013). Super rice cropping will enhance rice yield and reduce CH4 emission: A case study in nanjing, China. Rice Sci. 20, 427–433. doi: 10.1016/s1672-6308(13)60157-2
Kim G. W., Gutierrez-Suson J., and Kim P. J. (2019). Optimum N rate for grain yield coincides with minimum greenhouse gas intensity in flooded rice fields. Field Crops Res. 237, 23–31. doi: 10.1016/j.fcr.2019.04.011
Lee J. H., Lee J. Y., Kang Y. G., Kim J. H., and Oh T. K. (2023). Evaluating methane emissions from rice paddies: A study on the cultivar and transplanting date. Sci. Total Environ. 902, 166174. doi: 10.1016/J.SCITOTENV.2023.166174, PMID: 37562609
Li D., He H., Zhou G., He Q., and Yang S. (2023). Rice yield and greenhouse gas emissions due to biochar and straw application under optimal reduced N fertilizers in a double season rice cropping system. Agronomy 13, 1023. doi: 10.3390/agronomy13041023
Li C., Zhu J., Li X., Deng J., Yang W., Zhou Y., et al. (2023). CH4 and N2O Emission and grain yield performance of three main rice-farming patterns in central China. Agronomy 13, 1460. doi: 10.3390/agronomy13061460
Lin Y., Ding W., Liu D., He T., Yoo G., Yuan J., et al. (2017). Wheat straw-derived biochar amendment stimulated N2O emissions from rice paddy soils by regulating the amoA genes of ammonia-oxidizing bacteria. Soil Biol. Biochem. 113, 89–98. doi: 10.1016/j.soilbio.2017.06.001
Liu L., Fu Z., Wang Y., Zhu Z., Huang Y., and Xu Y. (2020). Correlation analysis between methane emission and root characteristics of different early and late rice varieties. Southwest Chin. J. Agric. Sci. 33, 469–477. doi: 10.16213/j.cnki.scjas.2020.3.002
Liu K., Li T., Chen Y., Huang J., Qiu Y., Li S., et al. (2020). Effects of root morphology and physiology on the formation and regulation of large panicles in rice. Field Crops Res. 258, 107946. doi: 10.1016/j.fcr.2020.107946
Lu Y., Wassmann R., Neue H. U., and Huang C. (2000). Dynamics of dissolved organic carbon and methane emissions in a flooded rice soil. Soil Sci. Soc Am. J. 64, 2011–2017. doi: 10.2136/sssaj2000.6462011x
Maurer D., Kiese R., Kreuzwieser J., and Rennenberg H. (2018). Processes that determine the interplay of root exudation, methane emission and yield in rice agriculture. Plant Biol. (Berlin Ger.). 6, 951–955. doi: 10.1111/plb.12880, PMID: 30047200
Mohanty S. R., Bodelier P. L. E., and Conrad R. (2007). Effect of temperature on composition of the methanotrophic community in rice field and forest soil. FEMS Microbiol. Ecol. 62, 24–31. doi: 10.1111/j.1574-6941.2007.00370.x, PMID: 17725622
Perry H., Carrijo D., and Linquist B. (2022). Single midseason drainage events decrease global warming potential without sacrificing grain yield in flooded rice systems. Field Crops Res. 276, 108312. doi: 10.1016/j.fcr.2021.108312
Qi Z., Guan S., Zhang Z., Du S., Li S., and Xu D. (2024). Effect and mechanism of root characteristics of different rice varieties on methane emissions. Agronomy 14, 595. doi: 10.3390/agronomy14030595
Qin X., Li Y., Wang H., Li J., Wan Y., Gao Q., et al. (2015). Effect of rice cultivars on yield-scaled methane emissions in a double rice field in South China. J. Integr. Environ. Sci. 12, 47–66. doi: 10.1016/j.agee.2023.108409
Quan R., Wang J., Qin H., Chen L., Xiao D., Zhao Z., et al. (2024). Improving grain yield and salt tolerance by optimizing plant height with beneficial haplotypes in rice (Oryza sativa). J. Adv. Res. 12, 7. doi: 10.1016/j.jare.2024.12.007, PMID: 39674500
Riya S., Zhou S., Watanabe Y., Sagehashi M., Terada A., and Hosomi M. (2012). CH4 and N2O emissions from different varieties of forage rice (Oryza sativa L.) treating liquid cattle waste. Sci. Total Environ. 419, 178–186. doi: 10.1016/j.scitotenv.2012.01.014, PMID: 22289172
Setyanto P., Rosenani A. B., Boer R., Fauziah C. I., and Khanif M. J.. (2016). The Effect of rice cultivars on methane emission from irrigated rice field. Indones. J. Agric. Sci. 5, 20–31. doi: 10.21082/ijas.v5n1.2004
Sun Y., Lai Y., Wang Q., Song Q., Jin L., Zeng X., et al. (2022). Combination of water-saving irrigation and nitrogen fertilization regulates greenhouse gas emissions and increases rice yields in high-cold regions, Northeast China. Int. J. Environ. Res. Public Health 19, 16506. doi: 10.3390/ijerph192416506, PMID: 36554386
Tokida T., Fumoto T., Cheng W., Matsunami T., Adachi M., Katayanagi N., et al. (2010). Effects of free-air CO2 enrichment (FACE) and soil warming on CH4 emission from a rice paddy field: impact assessment and stoichiometric evaluation. Biogeosciences 7, 2639–2653. doi: 10.5194/bg-7-2639-2010
van Groenigen K. J., van Kessel C., and Hungate B. A. (2013). Increased greenhouse-gas intensity of rice production under future atmospheric conditions. Nat. Clim. Change. 3, 288–291. doi: 10.1038/nclimate1712
Wang L., Liu S., Lu S., Lv T., Ma D., Kang G., et al. (2025). Irrigation and nitrogen fertilizer increase wheat productivity by improving root-shoot population coordination. Agric. Water Manage. 316, 109589. doi: 10.1016/j.agwat.2025.109589
Wassmann R. and Aulakh M. S. (2000). The role of rice plants in regulating mechanisms of methane missions. Biol. Fertil. Soils. 31, 20–29. doi: 10.1007/s003740050619
Xiong Z., Xing G., Tsuruta H., Shen G., Shi S., and Du L. (2002). Measurement of nitrousoxide emissions from two rice-based cropping systems in China. Nutr. Cycling Agroecosyst. 64, 125–133. doi: 10.1023/a:1021179605327
Xiong Z., Xing G., and Zhu Z. (2007). Nitrous oxide and methane emissions as affected by water,soil and nitrogen. Pedosphere 2, 12–21. doi: 10.1016/s1002-0160(07)60020-4
Xu G., Liu X., Wang Q., Xiong R., and Hang Y. (2017). Effects of screenhouse cultivation and organic materials incorporation on global warming potential in rice fields. Environ. Sci. pollut. Res. 24, 6581–6591. doi: 10.1007/s11356-017-8397-7, PMID: 28078520
Xu H., Xing G., Cai Z., and Tsuruta H. (1997). Nitrous oxide emissions from threerice paddy fields in China. Nutr. Cycling Agroecosyst 49, 23–28. doi: 10.1023/a:1009779514395
Zhang W., Duan X., Yao X., Liu Q., Xiao R., Zhang X., et al. (2023). Progress and challenges of rice ratooning technology in Chongqing Municipality, China. Crop Environ. 3, 137–146. doi: 10.1016/J.CROPE.2023.05.003
Zhang Y., Jiang Y., Li Z., Zhu X., Wang X., Chen J., et al. (2015). Aboveground morphological traits do not predict rice variety effects on CH4 emissions. Agric. Ecosyst. Environ. 208, 86–93. doi: 10.1016/j.agee.2015.04.030
Zhong J. (2017). Roots Characteristics among Different Rice Varieties and their Effects on Plant Methane Transport Rate (Hunan Agricultural University), 12–14.
Zhu J., Chen S., Xu C., Liu Y., Yu K., Zhang X., et al. (2025). One-time application of polymer-coated urea increased rice yield and plant nitrogen uptake by optimizing root morphological and physiological traits. Agronomy 15, 282. doi: 10.3390/agronomy15020282
Zhu K., Yan J., Shen Y., Zhang W., Xu Y., Wang Z., et al. (2022). Deciphering the morpho–physiological traits for high yield potential in nitrogen efficient varieties (NEVs): A japonica rice case study. J. Integr. Agric. 21, 947–963. doi: 10.1016/s2095-3119(20)63600-0
Keywords: CH4, N2O, phenotypic difference, rice varieties, yield
Citation: Fan M, Yang X, Raheem A, Zhang J, Wang Y, Zhang J and Hang X (2026) The regulatory influence of variations in plant traits among different rice varieties on greenhouse gas emissions in paddy fields. Front. Agron. 8:1738324. doi: 10.3389/fagro.2026.1738324
Received: 05 January 2026; Accepted: 05 January 2026; Revised: 31 December 2025;
Published: 27 January 2026.
Edited by:
Sangeeta Lenka, Indian Institute of Soil Science (ICAR), IndiaReviewed by:
Amit Anil Shahane, Central Agricultural University, IndiaArti Bhatia, Indian Council of Agricultural Research (ICAR), India
Copyright © 2026 Fan, Yang, Raheem, Zhang, Wang, Zhang and Hang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jun Zhang, emhhbmdqdW5AY2Fhcy5jbg==; Xiaoning Hang, aGFuZ3hpYW9uaW5nQGNxYWFzLmNu
†These authors share first authorship