Supporting the Pilot Low Methane Emission for Rice at the Qingshan Village December 2025 Danying Wang and Kai Yu China Rice Research Institute, Chinese Academy of Agricultural Science Contents | Page 1 of 11 CGIAR Contents 1. Introduction……………………………………………………………………….…….2 2. Materials and methods…………………………………………….………………….2 Figure1: Maps of the measurement sites…………………………….………………..2 Figure2: Automatic monitoring-water layer instrument……………….………………3 Table 1 Emission factors of agricultural material input……………….………………4 3. Result……………………………………………………………………….……………4 Table 2 Basic soil physicochemical properties of the experimental field….……….5 Figure3: Seasonal variation of air temperature (a), water layer (b), growth of rice plants (c) and CH4 fluxes (d) across field treatments in the rice-growing season…………………………………………………………………………………….6 Figure4: The rice yield (a) and seasonal cumulative CH4 emissions under different treatments (b) ........................................................................................................ 7 Table 3 Direct CH4, N2O emissions and the contribution rate of different management regimes............................................................................................ 7 Figure5: The composition diagram of C footprint from major categories of different paddy field management regimes ......................................................................... 8 Figure6: The percentage of carbon footprint components during different rice growth stages ........................................................................................................ 8 Figure7: The percentage of carbon footprint components of different paddy field management regimes (a) and paddy field operation (b) ........................................ 9 Table 4 The direct and indirect carbon footprint (CFp) per unit area and carbon footprint per unit yield of different field management regimes ............................... 9 4. Conclusion………………………………………………………………………………9 5. References…………………………………………………………………………….10 CGIAR Contents | Page 2 of 11 1. Introduction Rice is one of China’s three major staple food crops, accounting for approximately 60% of total staple food consumption, and rice paddies are among the largest emission sources in China’s crop production systems. According to statistics from the Food and Agriculture Organization of the United Nations (FAO), China ranked first globally in total rice production in 2022, with output exceeding 210 million tons, accounting for 28.0% of global rice production. The rice planting area reached nearly 450 million mu, representing 18.5% of the global rice planting area, second only to India. Meanwhile, global carbon emissions from rice paddies account for about 52% of total emissions from croplands, and in China, rice paddy emissions contribute approximately 60% of emissions from the crop production sector, with methane being the dominant greenhouse gas. China attaches great importance to agricultural carbon sequestration and emission reduction, and in recent years has successively issued policy documents such as the “Action Plan for Carbon Peaking Before 2030”, the “14th Five-Year National Plan for Green Agricultural Development”, the “Technical Guidelines for Green Agricultural Development (2018-2030)”, and the “Implementation Plan for Emission Reduction and Carbon Sequestration in Agriculture and Rural Areas”, all of which emphasize the critical role of agriculture in carbon mitigation. Therefore, under the context of global climate change, how to reduce carbon emissions from rice production has become a key issue of widespread concern in both the scientific community and society at large. Previous studies have demonstrated that rice paddy methane (CH4) emissions are strongly influenced by nitrogen fertilizer type, straw return, and straw management practices. The form of nitrogen fertilizer plays an important role in regulating CH4 emissions by altering soil redox conditions and microbial activity. Compared with ammonium- based fertilizers, nitrate-containing or slow-release fertilizers often suppress CH4 production by enhancing soil oxidation and stimulating methanotrophic activity. Straw return generally increases CH4 emissions due to the additional supply of labile carbon substrates for methanogenesis; however, the magnitude of this effect varies with straw return mode. Incorporation of fresh straw before transplanting typically leads to a pronounced increase in CH4 emissions, whereas straw removal, composted straw application, or partial straw return can mitigate this stimulatory effect. Moreover, practices such as straw incorporation combined with mid-season drainage or straw placement in deeper soil layers have been shown to reduce CH4 emissions by limiting substrate availability and enhancing methane oxidation. Overall, the interaction between nitrogen fertilizer type and straw management determines carbon and nitrogen cycling in paddy soils, highlighting the importance of integrated nutrient and residue management for mitigating CH4 emissions from rice systems. We were established an annual field experiment from June 2024 to October 2024 with 9 various mitigation strategies involved in Qingshan village, Hangzhou City, and based on the 2024 experimental results, four treatments that exhibited relatively high rice yields and strong methane mitigation effects were selected for an additional one-year experiment in 2025. We aimed to: (i) assess the influences on rice productivity and CH4 emissions under the different mitigation strategies; (ii) give an insight into the net carbon budget of rice cultivation systems as affected by various treatments; (iii) provide a reference for achieving the strategic target of Carbon Neutral in agriculture of Qingshan village. 2. Materials and methods 2.1 Site description The field experiments were conducted in Qingshan village, Hangzhou, Zhejiang province, China (30o14’N, 118o09’E). This research location is representative of the rice rotation with decades history of rice cultivation. This study area has a subtropical monsoon climate with mean annual precipitation (MAP) of 581.7 mm and mean annual temperature (MAT) of 17.9 oC in 2025. Figure1: Maps of the measurement sites Image © 2024 CNES /Airbus, Map data © 2024 Hong Kong 20 meter Contents | Page 3 of 11 CGIAR 2.2 Field experimental design Our experimental plots were arranged in a random block design, and each field plot replicated three sample times. A total of three experimental treatments and one control treatment were established in the study. The first field experiment was designed with three different straw return mode treatments. These treatments included control with the traditional straw return regimes: the crop residues from the previous season were fully incorporated into the soil (Control), and the straw remove regime: the crop residues from the previous season were fully removed from the field (NS), the straw return with decomposing microbial agent, the crop residues from the previous season were fully incorporated into the soil in combination with the application of a straw decomposing microorganism inoculants (S+DMI). The second field experiment was designed with two different fertilizer application treatments. These treatments included the control with the traditional chemical N fertilizer (Control, seasonal N fertilizer rate: 150 kg N ha-1), and the controlled-release N fertilizer replaced the chemical N fertilizer (CRF, seasonal N fertilizer rate: 150 kg N ha-1). Throughout the entire rice growing season, automatic water level sensors, temperature and humidity sensors, and rice growth monitoring devices were installed in the paddy fields to continuously monitor air temperature, soil temperature, field water level, and rice growth conditions in real time (Fig.2). Figure2: Automatic monitoring-water layer instrument 2.3 Gas sampling and flux measurements The CH4 emissions were measured in situ and determined by the gas chromatograph (GC, Agilent 7890A, USA) method. Specially-made sampling collars were permanently installed (30 cm in soil depth) at each field plot with crop growth included to guarantee reproducible placement of gas collecting chambers throughout the whole cropping season. A well-shaped groove (5 cm in depth) at the top for each sampling collar was designed to seal the rim of the chamber with water when sampling. The sampling chambers were made of opaque PVC materials with an identical size of 0.5 m (length) × 0.5 m (width) × 0.5 m (height) (1.0 m high chamber was used during the late rice-growing stage) and a circulating fan was equipped inside to make it possible the complete gas mixing during gas sample collecting. To minimize the air temperature variations of inside the chamber during the sampling period, each chamber was wrapped with a layer of sponge and aluminum foil. In general, seasonal gas samples were sampled once a week, and increased sampling frequency following the fertilization and irrigation events. Five gas samples were collected at five-minute intervals following the sampling chamber closure between 08:30 and 10:30 am. Individual gas sample of 60 ml was sucked into gas bags from the chamber’s headspace by the specially-made air pump. The gas samples were brought back to the lab and measured using the gas chromatograph within five hours. Cumulative CH4 emissions were calculated using accumulating emissions between two continuous measurement intervals. Gas samples were analyzed with a modified GC (Agilent 7890A) equipped with a flame ionization detector (FID). The oven and FID were operated at 55 °C and 200 °C, respectively. The flow rate of carrier gas (N2) was 30 mL min-1. A nonlinear fitting approach was adopted to determine the CH4 fluxes. Average fluxes and standard deviations of CH4 fluxes for each treatment were calculated from three replicates. The CH4 flux was calculated from the gas accumulated between sampling time according to the following equation: 𝐹 = 𝜌 × 𝑉 𝐴 × ∆ 𝐶 ∆ 𝑡 × 273 273 + 𝑇 (1) where, F is the CH4 flux (mg kg-1 h-1); ρ is the CH4 gas density (kg m-3) at standard temperature and pressure; V is the chamber volume (m3); A is the area of sampling collars (m2); ΔC/Δt is the change in CH4 concentration during a given time period for which the chamber was sealed (ppmv h-1); T is the temperature inside the chamber (°C). The CH4 flux over the sampling period represented the daily mean flux for the sampling day. Total CH4 emissions during the rice growing seasons were sequentially accumulated from the fluxes between every two adjacent measurement intervals, and the calculating equation was: CGIAR Contents | Page 4 of 11 Emission= ∑ { 𝐹𝑖 + 𝐹𝑖−1 2 × (𝐷𝑖 − 𝐷𝑖−1) × 24} 𝑛 𝑖=1 (2) Where, 𝐹𝑖 is the CH4 flux at the ith sampling date; 𝐷𝑖 denotes the day of the ith sampling; and n is the number of sampling periods. To provide an insight into the attribution of rice cropping system to climate change as influenced by N fertilization and irrigation regimes, we estimated the global warming potential (GWP) of CH4 and N2O emissions over the seasonal rice rotation system. According to the improved weight metrics, the combined GWP of CH4 and N2O was calculated using the following equation: 𝐺𝑊𝑃 (𝑡 𝐶𝑂2 − 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡 ℎ𝑎−1) = 34 × 𝐶𝐻4 + 298 × 𝑁2𝑂 (3) The carbon footprint 𝐶𝐹𝑝 of rice production (kg CO2-eq ha-1) was calculated by the following equation. 𝐶𝐹𝑝 = ∑(𝜃𝑖 × 𝐶𝑖) + 𝑁𝑓𝑒𝑟 𝑛 𝑖=1 × (𝐶𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 + 𝐶𝑑𝑖𝑟𝑒𝑐𝑡) × 44 28 × 𝐺𝑊𝑃𝑁2𝑂 + 𝐶𝐶𝐻4 × 𝐺𝑊𝑃𝐶𝐻4 (4) where, 𝜃𝑖 represents the rates of agricultural input 𝑖 (fertilizers, pesticides, diesel combustion, agriculture films and others), and 𝐶𝑖 denotes the EF of agricultural input 𝑖 (Table 1). 𝑁𝑓𝑒𝑟 is the rate of N fertilizer applied, and 𝐶𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 and 𝐶𝑑𝑖𝑟𝑒𝑐𝑡 indicates the indirect and indirect EFs of N2O. 𝐶𝐶𝐻4 is the CH4 emission in paddy fields measured from the in situ observation experiment (kg CH4 ha-1). 𝐺𝑊𝑃𝑁2𝑂 and 𝐺𝑊𝑃𝐶𝐻4 represent the GWPs of 298 and 34 for N2O and CH4. Table 1 Emission factors of agricultural material input Input Emission factors Uncertainty (%) Reference value unit N fertilizer 7.759 kg CO2-eq/kg 50 Chen et al., 2015 Urea 7.484 kg CO2-eq/kg 50 Chen et al., 2015 Ammonium bicarbonate 7.069 kg CO2-eq/kg 50 Chen et al., 2015 P fertilizer 2.332 kg CO2-eq/kg 50 Chen et al., 2015 Calcium superphosphate 0.715 kg CO2-eq/kg Chen et al., 2015 K fertilizer 0.660 kg CO2-eq/kg 50 Chen et al., 2015 Potassium chloride 0.616 kg CO2-eq/kg 50 Chen et al., 2015 Pesticides 14.319 kg CO2-eq/kg 12.43 Li et al., 2022 Diesel combustion 2.9778 kg CO2-eq/kg 2.62 Cao et al., 2014 Agriculture films 2.77 kg CO2-eq/kg 50 Li, 2014 Irrigation 0.35 kg CO2-eq/m3 11.44 Wang et al., 2012 Seed 0.795 kg CO2-eq/kg Cao et al., 2014 Electric 0.757 kg CO2-eq/kWh 17.90 Cao et al., 2014 3. Result Soil samples were collected from approximately 0-15 cm depths of fields before rice transplanting, with an initial pH of 5.25 ((1:2.5, soil/ water, w/v) and an average bulk density of 1.23 g cm−3. Total N and soil organic C contents were 2.5 g kg−1 and 36 g kg−1, respectively. The other soil physicochemical properties were shown in table 2. Contents | Page 5 of 11 CGIAR Table 2 Basic soil physicochemical properties of the experimental field Soil propertie s Total organic C (g kg-1) Total N (g kg- 1) Total P (g kg-1) Total K (g kg-1) Alkali- hydrolysable N (mg kg-1) Olsen- P (mg kg- 1) Olsen-K (mg kg- 1) pH 36 2.5 0.6 18.6 186.83 37.29 91.75 5.25 3.1 CH4 fluxes The seasonal pattern of methane flux did not differ among the field treatments but was highly sensitive to water regime (Fig. 3). During the initial flooding stage, methane flux increased continuously and reached a peak approximately three weeks after rice transplanting. Upon the onset of mid-season drainage, methane fluxes under all treatments declined sharply and subsequently remained at relatively low emission rates compared with the initial flooding period. After reflooding, methane flux gradually increased but did not exceed the peak observed during the vigorous tillering stage. With the implementation of intermittent irrigation during the ripening stage, methane emissions decreased again and remained low until harvest. Therefore, although the magnitude of seasonal methane flux varied among treatments, the overall dynamic patterns were similar throughout the rice growing season. CGIAR Contents | Page 6 of 11 Figure3: Seasonal variation of air temperature (a), water layer (b), growth of rice plants (c) and CH4 fluxes (d) across field treatments in the rice- growing season Seasonal total CH4 emissions differed among field treatments (Fig. 4b). Compared with the control, the NS and S+DMI treatments significantly reduced cumulative seasonal methane emissions from the rice paddies by 22-26%. Although the methane mitigation effect of the CRF treatment was not statistically significant, it still resulted in an approximately 15% reduction in methane emissions (Fig. 4b, p < 0.01). Although the NS treatment exhibited the most pronounced methane mitigation effect, it resulted in a 12% reduction in rice yield (Fig. 4). Although the methane mitigation effects of the CRF and S+DMI treatments were less pronounced than that of NS, they increased rice yield by 7-17%. Among all treatments, the S+DMI treatment achieved the highest yield and significantly increased rice production compared with the Control treatment. Contents | Page 7 of 11 CGIAR Figure4: The rice yield (a) and seasonal cumulative CH4 emissions under different treatments (b) 3.2 Total carbon footprint This study showed that the GHG emissions of rice cultivated were 11.03-14.84 t CO2-eq ha-1, equivalent to 1.61- 2.22 t CO2-eq per ton of rice grain (Table 3 and Table 4). The direct emissions of GHGs were considered the major source during rice production process, accounting for over 77% of total C emissions throughout the whole rice production cycle. Specifically, methane emissions from paddy fields constitute over 90% of the total direct emissions, making it the predominant component of the carbon footprint in rice growth period (Table 4). As another major greenhouse gas emitted from rice paddies, N2O accounted for less than 4% of the carbon footprint of emissions in this study, compared with CH4 (Table 3). In addition, the field operations contributed over 14.5% of the total C emissions, serving as another major emission source, while the processing and storage of rice only contributed 0.36 t CO2-eq ha-1, accounting for a mere 1.2-2% of the total C emissions (Table 2 and Fig. 5). Table 3 Direct CH4, N2O emissions and the contribution rate of different management regimes Treatments CH4 N2O Total GHGs Contribution rate (%) t CO2-eq ha-1 t CO2-eq ha-1 t CO2-eq ha-1 CH4 N2O Control 14.26 0.58 14.84 96 4 NS 10.53 0.49 11.03 96 4 CRF 12.08 0.39 12.47 97 3 S+DMI 11.12 0.46 11.58 96 4 Average 12.00 0.48 12.48 96 4 CGIAR Contents | Page 8 of 11 Figure5: The composition diagram of C footprint from major categories of different paddy field management regimes 3.3 Carbon footprint of different rice growth stages There is a variability in the highest area-scaled C footprint during rice growth stage under different field management regimes, while the filling stage had the lowest C footprint over the whole rice growth period under all treatments (Fig. 6). The tillering stages had the highest area-scale C footprint of 9.31 t CO2-eq ha-1 on average, and the booting and heading stage had the second highest area-scale C footprint of 2.43 t CO2-eq ha-1 on average under different field management strategies. Compared to Control treatment, the CH4 primarily mitigated in the tillering stages of NS treatment. Therefore, reducing greenhouse gas emissions during the early stages of rice production is the most effective measure to decrease direct carbon emissions during the rice cultivation. Figure6: The percentage of carbon footprint components during different rice growth stages 3.4 Carbon footprint of different field management regimes and field operation Significant differences in the carbon footprint per unit area were observed among the different field management practices. The local conventional management exhibited the highest carbon footprint, reaching 18.01 t CO2-eq ha- 1 (Table 4). Although the NS treatment had the lowest carbon footprint, it also resulted in the lowest yield. Across Contents | Page 9 of 11 CGIAR the different management practices, the carbon footprint was mainly derived from methane emissions from paddy soils, nitrogen fertilizer application, and inputs of diesel and electricity, indicating that reducing methane emissions from rice paddies and decreasing nitrogen fertilizer inputs are effective approaches to lowering carbon emissions. Different agricultural operations resulted in varying levels of carbon emissions, with fertilization accounting for the largest proportion (Fig. 7b). The combined carbon emissions from nitrogen (N), phosphorus (P), and potassium (K) fertilizers reached 2.2 t CO2-eq ha-1, representing 74.1% of total emissions from agricultural operations. Nitrogen fertilizer was the dominant emission source, contributing approximately 64.6% of total carbon emissions from agricultural operations. Irrigation and processing/packaging were the other two major emission sources, accounting for 8.9% and 9.7% of total emissions, respectively. Figure7: The percentage of carbon footprint components of different paddy field management regimes (a) and paddy field operation (b) Table 4 The direct and indirect carbon footprint (CFp) per unit area and carbon footprint per unit yield of different field management regimes Treatments Direct CFp Indirect CFp Rice yield CFp per unit area CF per unit yield t CO2-eq ha-1 t CO2-eq ha-1 t ha-1 t CO2-eq ha-1 t CO2-eq grain t-1 Control 14.84 3.17 7.54 18.01 2.39 NS 11.03 3.15 6.66 14.18 2.13 CRF 12.47 2.45 8.10 14.92 1.84 S+DMI 11.58 3.17 8.80 14.75 1.68 Average 12.48 2.99 7.77 15.47 1.99 4. Conclusion This study systematically evaluated soil greenhouse gas emissions, carbon footprints, and crop yields under different management practices in the paddy ecosystem of Qingshan Village. Across the management practices, the carbon footprint was mainly derived from soil greenhouse gas emissions and nitrogen fertilizer application, which together accounted for approximately 80% of total carbon emissions over the entire rice production cycle. Therefore, reducing greenhouse gas emissions from rice paddies largely depends on mitigating methane emissions. Our results indicate that nitrogen fertilizer application patterns not only significantly influence methane emissions from rice paddies, but that nitrogen fertilizer production itself is also an important contributor to the overall carbon footprint. The application of control-released nitrogen fertilizers can directly reduce greenhouse gas emissions from paddies while also lowering indirect emissions associated with fertilizer production. In addition, straw removal significantly reduced the carbon footprint of rice paddies; however, this practice also led to a marked yield reduction, which is unfavorable from a farmer’s productivity perspective. Compared with direct straw removal, the practice of straw incorporation combined with the application of microbial decomposing agents not only reduced greenhouse gas emissions but also significantly increased rice yield, resulting in the lowest carbon footprint per unit yield among CGIAR Contents | Page 10 of 11 all treatments. Therefore, optimizing nitrogen management by replacing conventional nitrogen fertilizers with controlled-release fertilizers, together with the application of microbial decomposing agents, can reduce the carbon footprint per unit of rice production without significantly compromising yield. This integrated approach represents a win–win solution for achieving both yield stability and carbon mitigation, and is well suited to the low-carbon, high- yield development goals of Qingshan Village. 5. References Cao L, Li M, Wang X, et al., 2014. Life cycle assessment of carbon footprint for rice production in Shanghai[J]. Acta Ecologica Sinica, 34(2): 491-499. Chen S, Lu F, Wang X, 2015. Estimation of greenhouse gases emission factors for China’s nitrogen, phosphate, and potash fertilizers[J]. Acta Ecologica Sinica, 35(19): 6371-6383. Li F, 2014. Greenhouse gas emissions from major energy consumption in wheat production in China[J]. Journal of Agro-Environment Science, 33(5): 1041-1049. Li Y, Wu W, Yang J, et al., 2022. Exploring the environmental impact of crop production in China using a comprehensive footprint approach[J]. Science of The Total Environment, 824: 153898. Wang J, Rothausen S G S A, Conway D, et al., 2012. China’s water-energy nexus: Greenhouse-gas emissions from groundwater use for agriculture[J]. Environmental Research Letters, 7(1): 014035. Contents | Page 11 of 11 CGIAR Supporting the Pilot Low Methane Emission for Rice at the Qingshan Village December 2025 CGIAR is a global research partnership for a food-secure future, dedicated to transforming food, land, and water systems amidst a climate crisis. Its research is conducted by 13 CGIAR Centers/Alliances in close collaboration with hundreds of partners, including national and regional research institutes, civil society organizations, academia, development organizations, and the private sector. www.cgiar.org . We thank all funders who support this research through their contributions to the CGIAR Trust Fund: www.cgiar.org/funders. Climate Action is a CGIAR Science Program that drives science, innovation, and collaboration to transform food, land, and water systems for a climate-resilient, net-zero, and equitable future. 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