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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 3: 630 - 641

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.03.003

Human Activities and Sustainable Development

Spatio-temporal Dynamics of Greenhouse Gas Emissions among Four Types of Rice in China

  • ZHANG Bingbin , 1, 2 ,
  • YANG Lun , 1, *
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  • 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
* YANG Lun, E-mail:

ZHANG Bingbin, E-mail:

Received date: 2025-01-24

  Accepted date: 2025-04-15

  Online published: 2025-05-28

Supported by

National Natural Science Foundation of China(42001249)

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Abstract

Rice is not only the most basic staple crop, but also a major contributor to greenhouse gas emissions (GHGs). Negative emission options that can guarantee food security are urgently needed. In this study, we analyzed the temporal and spatial dynamics of GHGs from four types of rice in China, namely early indica rice, mid-season indica rice, late indica rice, and japonica rice, and estimated their mitigation potentials. The main results are fourfold. (1) The annual average GHGs per hectare was 4513.5 kg CO2-eq ha-1, increasing gradually from 2005 to 2020, while there was an opposite trend in annual average GHGs efficiency (0.4 kg CO2-eq yuan-1). The GHGs intensity was 0.9 kg CO2-eq kg-1 and remained constant in the same period. (2) The GHGs per unit (sowing area, output or output value) in central and southeastern China were higher than those in the north and west. (3) The GHGs per unit (area, yield or output value) of late indica rice were the highest while the corresponding values for japonica rice were the lowest. (4) The GHGs per hectare might be able to achieve a 20% reduction from the 2020 level, providing that cropland redistribution and mitigation measures are adopted. Finally, we put forward policy proposals and available measures for emission reduction to promote the sustainable development of agricultural systems.

Key words： rice; double-cropping rice; greenhouse gas; mitigation; cropland redistribution

Cite this article

ZHANG Bingbin , YANG Lun . Spatio-temporal Dynamics of Greenhouse Gas Emissions among Four Types of Rice in China[J]. Journal of Resources and Ecology, 2025 , 16(3) : 630 -641 . DOI: 10.5814/j.issn.1674-764x.2025.03.003

1 Introduction

Global climate change has become a primary challenge for humanity and has already crossed its boundaries in the theory of Planetary boundaries (Rockström et al., 2009). This trend has shown adverse effects on ecosystems as well as economic activities and increased the frequency of extreme weather events (Bao et al., 2023; Cai et al., 2023; Fu et al., 2023; Geng et al., 2023). The reduction of greenhouse gas emissions (GHGs), which are the leading cause of climate change, has become a global consensus for meeting climate change mitigation targets such as carbon neutrality. Among the global anthropogenic GHGs, the food system represents about one-third of total emissions, estimated at 18 Gt CO2-eq in 2015 (Crippa et al., 2021). Agricultural land is the primary source of non-CO2 gas emissions, contributing about three-quarters of CH4 emissions and almost all N2O emissions to the total GHGs of the agrifood system (FAO, 2020). Thus, the key to climate mitigation is reducing GHGs from the agricultural sector that are predominantly composed of potent non-CO2 greenhouse gases.
To achieve agricultural emission reduction, reducing the emissions from rice is vital. Rice is not only one of the most basic staple crops, but also a dominant contributor to agricultural GHGs. The global CH4 emission from rice production was estimated to be 962±2170 Tg CO2-eq, accounting for 48% of GHGs from all croplands (Carlson et al., 2017). Meanwhile, in terms of GHGs per unit (yield, sowing area, or protein), rice still ranks first among all cereal crops (Tilman and Clark, 2014). As far as CH4 emissions, global rice cultivation witnessed an upward trend from 23324 kt in 2000 to 24384 kt in 2022 (FAO, 2022). Rice planting produces massive amounts of CH4 gas primarily due to the anaerobic decomposition of organic matter (Zhang et al., 2018).
From a national perspective, China is the top emitter in terms of annual average CH4 emission from rice cultivation, generating 5267.9 kt CH4, which accounts for approximately one-fifth of global CH4 emissions. The average emission in Indonesia is 2390.3 kt, which is around half the figure for India. The emissions from Thailand, Philippines, Viet Nam, Myanmar and Bangladesh are similar, with figures ranging from 1100 kt to 1800 kt (FAO, 2022). China holds the top position mainly because it ranks first in rice yield, contributing one-third of the global share (FAO, 2021). Thus, abatement efforts in China are crucial for realizing the CO2 neutrality of global agrifood systems.
From a regional perspective, rice cultivation in China includes various cropping systems due to vast territory. Four categories of rice varieties are grown in these systems, including early indica rice (EIR), mid-season indica rice (MIR), late indica rice (LIR), and japonica rice (JR). For example, rice is cropped once a year in Heilongjiang Province (JR) but twice in Hainan Province (EIR and LIR). In addition, management practices and planting techniques also vary between the cropping systems. For instance, single-cropping rice in northern China (JR) has a higher level of mechanization than double-cropping rice in the south (EIR and LIR). As a result, the GHGs may vary among these rice cropping systems.
The mainstream rice GHG calculation methods include life cycle assessment (Liang et al., 2021), input-output analysis (Kander et al., 2015; Caro et al., 2017), and field experiments (Ladha et al., 2016; Runkle et al., 2019). Specifically, the emission coefficient method and mechanism model have been widely used in life cycle assessment because of their comprehensiveness and simplicity (Foong et al., 2022; Ma et al., 2023). Yet, previous studies on GHGs have rarely considered the perspective of the rice cropping systems, which may limit the formulation and implementation of emission reduction measures (Wang et al., 2016; Cui et al., 2018). In this study, we quantified the spatial and temporal dynamics of rice GHGs in China from 2005 to 2020 among the four categories of rice (EIR, MIR, LIR and JR).
In the future, greater efforts to reduce rice emissions are necessary. Common mitigation strategies applicable to carbon sources and sinks in rice paddy fields include conservation tillage (Knapp and van der Heijden, 2018; Yadav et al., 2019), advanced crop establishment (Chakraborty et al., 2017), advanced management of agricultural inputs (Jiang et al., 2017), and land use changes (Ladha et al., 2016; Ramanathan et al., 2020; He et al., 2023). In this study, we put forward specific mitigation measures targeted for the different cropping systems and cropping provinces to provide insights on policy making in China and other countries with diversified rice cropping systems.

2 Methods

2.1 Greenhouse gas emissions inventory

2.1.1 System boundary

Based on previous research, this study defined the total GHGs from rice cultivation as the sum of direct and indirect emissions of CO2, CH4, and N2O from rice planting to harvest (Table 1). The GHGs were converted to their CO2 equivalents (CO2-eq) according to their 100-year global warming potentials (IPCC, 2021).
Table 1 Accounting system of GHGs from rice cultivation
Gas type Emission source Indicators
CO2 Urea input (1) Fertilizer application rate
(2) Urea content
Manual labor respiration Number of rice-farmers
Diesel fuel combustion Fuel consumption
CH4 Rice cultivation (1) Sowing area
(2) Growing season length
(3) Organic amendment
(4) Water regime
Enteric fermentation Number of farm cattle
Diesel fuel combustion Fuel consumption
N2O Soil management (1) Fertilizer application rate
(2) Straw inputs
Diesel fuel combustion Fuel consumption

2.1.2 Emissions inventory

Given that there are four types of rice cropping in China, the total rice GHGs are the sum of the emissions from each of the four types. The total GHGs of each type of rice was calculated as follows:
U E   i = U E CO 2 i + U E CH 4 i + U E N 2 O i
where UEi represents the total GHGs of the i type of rice; U E CO 2 i , U E CH 4 i   and   U E N 2 O i are the emissions per hectare from CO2, CH4 and N2O, respectively (kg CO2-eq ha-1); and i represents the four types of rice, where i=1, 2, 3, 4 indicate early indica rice (EIR), mid-season indica rice (MIR), late indica rice (LIR), and japonica rice (JR), respectively. Full details are reported in the Appendix (www.jorae.cn).
GHGs emission intensity was calculated as follows:
G I i = T E i Y i × A r e a i
where GIi is the emission intensity of each type of rice (kg CO2-eq kg-1); TEi, Yi and Areai are the total GHGs (Tg CO2-eq), rice output per hectare (kg ha-1) and sowing area (ha), respectively.
GHGs emission efficiency was calculated as follows:
G E i = T E i F   O u t p u t   v a l u e i × A r e a i  
where GEi is the emission efficiency of each type of rice (kg CO2-eq yuan-1); and F   O u t p u t   v a l u e i is the output value of each type of rice (yuan).

2.1.3 Data sources

The data were obtained from the China Rural Statistical Yearbook Collection Yearbook, National Agricultural Product Cost-Benefit Data Collection, and China Price Statistical Yearbook from 2005 to 2020. Further information and data processing are described in the Appendix. Note that the Hong Kong and Macao Special Administrative Regions and Taiwan Province are not included in this study due to the difficulty of data access.

2.2 Mitigation potentials

Based on the temporal and spatial dynamics of GHGs from rice cultivation, we explored how to effectively reduce GHGs from rice cultivation without compromising yield and sowing area. Many empirical studies have revealed that cropland redistribution (Xie et al., 2023; Yin et al., 2023) and advanced agricultural techniques (Ladha et al., 2016; Frank et al., 2018; Jat, et al., 2020) can not only increase food output but also lower GHGs. Therefore, this study set up two scenarios to evaluate the mitigation potentials for rice cultivation in China: (i) Business-as-usual scenario (BAU), where traditional management practices would remain; and (ii) Mitigation scenario (S), where mitigation measures would be applied according to previous studies and take place in the following two steps.
Step 1: Cropland redistribution of the four types of rice. To achieve a lower level of GHGs than in 2020, the sowing areas of each type of rice would be adjusted without reducing total yield or farmland area. This adjustment was made through a linear optimization model:
$ \begin{array}{l} Y=1000 \times\left(e_{1} X_{1}+e_{2} X_{2}+e_{3} X_{3}+e_{4} X_{4}\right) \\ \left\{\begin{array}{l} X_{1}+X_{2}+X_{3}+X_{4}=S \\ a_{1} X_{1}+a_{2} X_{2}+a_{3} X_{3}+a_{4} X_{4} \geqslant C \\ 1000 \times\left(e_{1} X_{1}+e_{2} X_{2}+e_{3} X_{3}+e_{4} X_{4}\right) \leqslant E \end{array}\right. \end{array}$
where Y is the total rice GHGs in one particular province after cropland redistribution (kg CO2-eq); Xi is the sowing area of each type of rice (ha); S is the total sowing area for rice in on particular province in 2020; ei is the GHGs per hectare of each type of rice (kg CO2-eq ha-1); ai is the rice output per hectare in 2020 (kg ha-1); C is the total rice output in one particular province in 2020 (kg); and E is the total GHGs of rice in one particular province in 2020 (kg CO2-eq).
Step 2: Agricultural practice improvement. Based on the cropland optimization, three types of feasible and advanced agricultural practices would be applied (Table 2). (i) Water-saving irrigation technologies can shorten the flooding period in the growing season and increase drainage times to lower CH4 emissions. (ii) Precision fertilization based on crop demand and the environment is expected to reduce applications by 15%. (iii) High efficiency operation of agricultural machinery as well as the use of clean energy can save 30% of diesel consumption.
Table 2 Advanced agricultural practices
Agricultural
input		 Adjusted-
indicator		 Value Mitigation measures
Irrigation water SFw 0.55 Precision fertilization and water-saving irrigation (Cui et al., 2018)
Fertilizer Application rate 15% reduction
Diesel fuel Consumption 30% reduction Effective machinery operation and the use of clean energy (Chel and Kaushik, 2011; Xu et al., 2012)

3 Results

3.1 Spatial-temporal dynamics of rice GHGs

3.1.1 GHGs per hectare

The GHGs per hectare of rice (defined as the area-weighted average of GHG emission per unit area of the four rice types) fluctuated upward from 2005 to 2020, with an annual average of 4513.5±173.1 kg CO2-eq ha-1 (mean±standard deviation, the same hereafter) and an average annual growth rate of 33.2 kg CO2-eq ha-1 yr-1 (Figure 1).
Figure 1 Total GHGs per hectare of the four types of rice in China from 2005 to 2020
The CH4 and N2O emissions basically remained stable, with annual average values of 3240±24.1 kg CO2-eq ha-1 and 754.2±41.2 kg CO2-eq ha-1, respectively. However, CO2 emissions started to rise rapidly from 2005 and reached a maximum value of 23.9 kg CO2-eq ha-1 in 2020, with an average annual value and growth rate of 519.1 kg CO2-eq ha-1 and 25.3 kg CO2-eq ha-1 yr-1 respectively.
In terms of the spatial pattern, the GHGs per hectare were higher in central and southeastern China and lower in the north and west (Figure 2). Specifically, the GHGs per hectare was the highest in the southern coastal provinces (Fujian, Guangdong, Guangxi, and Hainan), with an average value of 6301.8 kg CO2-eq ha-1, mainly due to EIR and LIR cultivation. In northern and western China, most provinces emitted less than 4000 kg CO2-eq ha-1 and were dominated by the cultivation of MIR and JR. In the middle and lower reaches of the Yangtze River (Hunan, Hubei, Anhui, Jiangxi, Jiangsu, and Zhejiang), GHGs per hectare ranged from 4600 to 5500 kg CO2-eq ha-1. Various types of rice were cropped in each province with different contributions to total GHGs. For example, EIR and LIR accounted for most emissions in Jiangxi, while JR contributed the most for Zhejiang Province.
Figure 2 Provincial average annual GHGs per hectare from the four types of rice in China from 2005 to 2020

Note: The pie charts show the proportions of total emissions of each type of rice.

3.1.2 Gas components

The emissions composition of rice production in China was calculated based on the results of GHGs per hectare (Figure 3), and there were different proportions of the three gases in the total GHGs. Specifically, CH4 emission, the main component of total GHGs, presented a downward trend, with its annual average contribution rate declining from 75.8% to 68.0%. The contribution rate of N2O emission remained stable at 16.7±0.3% during 2005-2020. Although CO2 emissions accounted for the smallest share, its contribution rate had been steadily increasing, with an annual average value of 11.4±2.2%.
Figure 3 Gas composition of rice GHGs per hectare from 2005 to 2020
From the perspective of agricultural activities, the top three contributors in descending order were: CH4 emissions from rice field cultivation (70.8%) > direct N2O emissions from soil management (12.6%) > CO2 emissions from diesel combustion (7.8%). These three activities cumulatively contributed 91.2% of the total GHGs.
Three types of agricultural inputs made varying contributions to the total GHGs. The GHGs caused by fertilizers and returned straw (excluding CH4 emissions) accounted for 18.8% of total GHGs, which was 2.2 times that of diesel input, and 10.4 times that of labor input.

3.1.3 GHGs intensity and emission efficiency

Emission intensity and emission efficiency were defined in this study as the emissions per unit yield and per output value of rice cultivation, respectively. Their annual average values were 0.9±0.1 kg CO2-eq kg-1 and 0.4±0.1 kg CO2-eq yuan-1, respectively (Figure 4).
Figure 4 GHGs intensity (GI) and efficiency (GE) of all four types of rice in China from 2005 to 2020
(1) Temporal variation trends. GHGs intensity held steady over the years. GHGs efficiency was just over 0.4 kg CO2-eq yuan-1 in 2005, before dropping to slightly more than 0.2 kg CO2-eq yuan-1 in 2014, after which the figure remained constant.
(2) Spatial distribution patterns. Generally, the GHGs intensity and efficiency in central and southeastern China were relatively higher than in the north and west (Figures 5 and 6). At the regional and provincial scales, the southern coastal areas (including Hainan, Guangxi, Guangdong, and Fujian) ranked high on the GHGs intensity and efficiency lists. In the northern and western regions, most provinces had GHGs intensities lower than 0.6 kg CO2-eq kg-1 and GHGs efficiencies lower than 0.2 kg CO2-eq yuan-1. Northeast China, represented by Heilongjiang Province, not only realized higher yield but also had lower GHGs efficiency. In the middle and lower reaches of the Yangtze River (including Hunan, Hubei, Anhui, Jiangxi, Jiangsu, and Zhejiang), the GHGs intensity values mainly fell between 0.6-0.8 kg CO2-eq kg-1, and the GHGs efficiency values were between 0.2-0.4 kg CO2-eq yuan-1.
Figure 5 Provincial average annual rice GHGs intensities in China from 2005 to 2020

Note: The bar charts show the yields of the four types of rice.

Figure 6 Provincial average annual rice GHGs efficiencies in China from 2005 to 2020

Note: The bar charts show the output values of the four types of rice.

3.2 Temporal dynamics among the four types of rice

3.2.1 GHGs per hectare

Late indica rice (LIR) ranked first among the four types of rice in terms of annual average GHGs per hectare from 2005 to 2020, estimated to generate 5574.3 kg CO2-eq ha-1, followed by early indica rice (EIR) (4994.2 kg CO2-eq ha-1), mid-season indica rice (MIR) (3861.2 kg CO2-eq ha-1), and japonica rice (JR) (3312.2 kg CO2-eq ha-1). The GHGs per hectare of all types of rice increased continuously due to the rapid growth of CO2 emissions and the slow growth of CH4 emissions (Figure 7).
Figure 7 GHGs per hectare among the four types of rice from 2005 to 2020

3.2.2 GHGs intensity and emission efficiency

LIR ranked first in terms of intensity (0.9 kg CO2-eq kg-1), followed by EIR and MIR. JR had the lowest GHGs intensity of 0.4 kg CO2-eq kg-1, which generally remained constant from 2005 to 2020 (Figure 8a). By contrast, the GHGs intensities of EIR and LIR fluctuated and rose significantly in 2019, mainly because of the yield reduction (Figure 8a).
Figure 8 GHGs intensities and efficiencies among the four rice types in China from 2005 to 2020
Regarding GHGs efficiency, EIR and LIR reached 0.4 kg CO2-eq yuan-1, while JR was the lowest at 0.2 kg CO2-eq yuan-1 (Figure 8b). The GHGs efficiencies of four types of rice showed rapid declines before 2011 due to the increasing output values, followed by slow recovery until 2020.

3.3 Spatial dynamics of the four types of rice

3.3.1 GHGs per hectare

The spatial patterns of GHGs per hectare differed among the four types of rice (Figure 9). LIR and EIR shared similar patterns, where Guangxi, Guangdong, Fujian, and Hainan had higher levels of GHGs and accounted for 45% of total emissions. For MIR, GHGs per hectare gradually decreased from southeast to northwest. The emission pattern for JR featured lower heterogeneity and levels ranging from 3000 to 5000 kg CO2-eq ha-1.
Figure 9 Provincial average annual GHGs per hectare among the four types of rice in China from 2005 to 2020

3.3.2 GHGs intensity and efficiency

The spatial patterns of GHGs intensity differed among the four types of rice (Figure 10). The GHGs intensities of EIR and LIR were both higher in the southern coastal region of China. The emission intensities of MIR were within a range of 0.2-1.0 kg CO2-eq kg-1 in all provinces. Most of the JR- cultivating provinces emitted less than 0.6 kg CO2-eq kg-1, Anhui had the highest GHGs intensity of 0.8 kg CO2-eq kg-1 while Jilin was the lowest of 0.2 kg CO2-eq kg-1.
Figure 10 Provincial average annual GHGs intensities among the four types of rice in China from 2005 to 2020
As for GHGs efficiency, the spatial patterns among the four types of rice were consistent with their corresponding patterns of GHGs intensity (Figure 11). EIR and LIR had the highest GHGs efficiency. Hainan was the highest, and the other provinces exceeded 0.3 kg CO2-eq yuan-1. The GHGs efficiency of MIR declined from east to west as the GHGs efficiency of Fujian was 0.4 kg CO2-eq yuan-1, nearly three times that of Shaanxi. About 70% of the provinces had GHGs efficiencies ranging from 0.2 to 0.4 kg CO2-eq yuan-1. Among JR-cultivating provinces, Anhui ranked at the top with a GHGs efficiency of 0.3 kg CO2-eq yuan-1,which was 4.4 times that of Jilin. More than two-thirds of the cultivating provinces had GHGs efficiencies lower than 0.3 kg CO2-eq yuan-1.
Figure 11 Provincial average annual GHGs efficiencies among the four types of rice in China from 2005 to 2020

3.4 Mitigation potential

Our results showed that cropland optimization in conjunction with improved agricultural practices could cut GHGs by nearly 20%. In this case, the rice GHGs per hectare and total GHGs would be reduced to 4191.2 kg CO2-eq ha-1 and 134.1 Tg CO2-eq, respectively. Meanwhile, GHGs intensity and efficiency would be 0.69 kg CO2-eq kg-1 and 0.21 kg CO2-eq yuan-1, respectively.
On one hand, after cropland redistribution, the sowing areas of EIR and JR would increase by 93% and 41%, respectively, while the sowing areas of MIR and LIR across the entire country would decrease by 22% and 89%, respectively. On the provincial scale, emissions in 10 provinces are projected to decline, with their collective reduction estimated at 10.6% (or 12.7 Tg CO2-eq) below the 2020 levels (Table 3). Specifically, Anhui performed best with a 13.6% reduction, followed by Hubei (12.2%). This is primarily because the planting area of EIR might decline while that of JR might increase significantly. Similar emission reductions, just above 11.0%, are expected in Hainan, Jiangxi, and Fujian, if the sowing area of EIR is expanded and that of LIR shrinks. Meanwhile, the emissions are projected to be lowered by less than one-tenth relative to 2020 in Guangdong, Henan, Hunan and Guangxi. The abatement in Zhejiang would be negligible. It is plausible that their rice cropping patterns are already in their best state, where growers have made the most of their natural resources and realized ideal yields.
Table 3 Mitigation potentials after cropland optimization
Province Optimized sowing area (ha) Variation (ha) GHGs
(Tg)		 Reduction rate (%)
EIR MIR LIR JR EIR MIR LIR JR
Anhui 83.7 0 555.9 1872.5 -86.6 -1089.8 376.7 799.7 13.7 13.6
Hubei 0 1280.9 1509.2 2998.8 -122.4 1280.9 -718.1 1509.2 22.0 12.2
Guangdong 1834.4 0 0 0 965.3 0 -965.3 0 11.4 9.1
Hainan 227.5 0 0 0 117.6 0 -117.6 0 1.4 11.3
Henan 0 617.1 0 0 0 307.3 0 -307.3 2.0 8.4
Hunan 2762.0 1231.9 0 0 1536.2 -244.2 -1292.0 0 19.2 8.5
Jiangxi 1939.6 1502.2 0 0 722.1 556.4 -1278.5 0 18.2 11.6
Guangxi 1760.1 0 0 0 954.9 -133.7 -821.2 0 11.1 9.1
Fujian 368.6 233.1 0 0 270.9 -25.3 -245.6 0 4.1 11.6
Zhejiang 126.0 0 0 510.0 24.8 0 -89.6 64.8 3.7 3.7
Total 9101.9 4865.2 2065.1 5381.3 4382.8 -1347.3 -3083.2 1557.0 106.8 10.6

Note: The cropland redistribution is not an effective way to reduce GHGs, with the exceptions of Hubei, Hunan, Guangxi, Guangdong and Fujian provinces. In addition, cropland optimization always comes with high costs and a long transitional period for farmers.

On the other hand, mitigation measures tailored to fit local conditions can be carried out more easily. As a result, both Fujian and Hubei are predicted to achieve the highest emission reduction rates of 29% and 24%, respectively (Table 4). In addition, Zhejiang and Jiangsu could realize 20% reductions after applying agricultural techniques despite the poor effect of cropland redistribution. Moreover, in Chongqing and Yunnan, technological applications could realize high reduction rates.
Table 4 Mitigation potentials at the provincial level
Province GHGs per hectare
(kg CO2-eq ha-1)		 Total GHGs
(Tg CO2-eq)		 Reduction rate (%)
BAU S BAU S
Heilongjiang 3843.6 3457.4 14.9 13.4 10.0
Hubei 5489.7 4275.4 25.0 19.0 24.0
Anhui 6100.4 4670.3 15.8 12.1 23.5
Zhejiang 6072.3 4707.1 3.9 3.0 21.6
Jiangsu 5031.3 3877.7 11.2 8.6 23.1
Yunnan 4152.8 3398.4 3.2 2.6 18.9
Jilin 2248.8 1856.4 1.9 1.6 17.5
Liaoning 2639.0 2092.2 1.4 1.1 20.7
Henan 3528.6 3194.9 2.2 2.0 9.5
Shandong 5719.3 4363.4 0.6 0.5 23.7
Shanghai 4354.3 3413.0 0.4 0.3 21.6
Ningxia 5305.2 4209.9 0.3 0.3 20.6
Hebei 3827.7 3518.1 0.3 0.3 8.1
Xinjiang 4187.9 3709.8 0.2 0.2 11.4
Inner Mongolia 2689.5 2019.1 0.4 0.3 24.9
Tianjin 3513.6 3216.2 0.2 0.2 8.5
Shanxi 4897.1 4618.6 0 0 5.7
Fujian 7536.5 4970.1 4.6 3.3 29.2
Guangxi 6918.8 4735.5 12.2 10.5 14.1
Jiangxi 6106.6 4496.5 20.6 15.8 23.3
Hunan 5206.8 4139.9 21.0 17.0 19.2
Chongqing 3955.1 2948.4 2.6 1.9 25.5
Sichuan 3888.1 2971.5 7.3 5.5 23.6
Guizhou 3733.8 3322.5 2.5 2.2 11.0
Shaanxi 3052.7 2704.3 0.3 0.3 11.4
Hainan 7154.6 5021.9 1.6 1.4 17.1
Guangdong 6831.0 5046.5 12.6 10.8 14.1
Total 5228.1 4191.2 167.3 134.1 19.9

Note: “BAU” and “S” represent the business-as-usual scenario and the mitigation scenario, respectively.

4 Discussion

4.1 Mitigation strategies

In this study, the GHGs per hectare witnessed an upward trend from 2005 to 2020. Spatially, GHGs per unit are higher in central and southeastern China than in northern and western China. Similar increasing trends and distribution patterns have also been identified in other studies (Liu et al., 2018; Liang et al., 2021). The rising annual emissions have been mainly attributed to the lower efficiency of water and fertilizer use, and there are several reasons for the regional differences. For example, the cropping systems in northern regions are typically characterized by single-cropping, whereas they predominantly feature double-cropping in the south. Furthermore, the wet climate and continuous flooding irrigation in the central and southeastern regions further enhances bacterial activity which leads to higher GHGs.
We divided the mitigation strategies into two categories. First, cropland optimization and practice improvements are recommended to be applied simultaneously in Hubei, Hunan, Guangxi, Guangdong and Fujian, to achieve the best effect. Second, advanced agricultural practices should be carried out directly in the remaining provinces (e.g., Anhui, Zhejiang, Jiangxi, Jiangsu, Hainan, Sichuan, Chongqing, Heilongjiang, Inner Mongolia, Jilin, and Liaoning). For example, reducing fuel consumption and nitrogen fertilizer input to reduce GHGs should be the top priority in JR-cropping provinces like Heilongjiang and Inner Mongolia. However, priority should be given to the advanced water-saving irrigation technology measures in the MIR-cropping provinces such as Sichuan and Chongqing.
The recommended implementation methods are shown in Table 5, and previous studies have confirmed their beneficial results. Regarding water-saving irrigation, all the practices can provide steep cuts to seasonal CH4 emissions, whereas shallow irrigation and intermittent irrigation could stimulate N2O emissions. Both practices are likely to raise yields, while mid-season drainage might reduce the soil carbon content. As for precision fertilization, reducing fertilizer consumption through calculations and changing locations could significantly lower N2O emissions. Finally, using renewable and clean energy, and reducing dependence on fossil fuel, are promising ways to reduce GHGs.
Table 5 Specific and practical measures
Mitigation technology Impelmtation method Reference
Water saving irrigation technologies Mid-season drainage Haque et al., 2016
Alternating wet and dry irrigation Runkle et al., 2019
Shallow irrigation and intermittent irrigation Chen and Chen, 2023
Precision fertilization Integrated soil-crop system management Cui et al., 2018
Crop models or field experiments Cai et al., 2023;
Wu et al., 2015
Deep placement Liu et al., 2020
Effective energy
utilization		 Fuel consumption reduction Xu et al., 2012
Clean energy (e.g., solar photovoltaic water pumping power and solar dryers) Ghasemi-Mobtaker
et al., 2020
Biofuels Yang et al., 2018
Other than the practices mentioned above, many other mitigation measures could be taken outside the paddy fields, such as dietary shifts. For example, replacing rice with other cereals or potatoes can potentially reduce energy consumption and GHGs while enhancing nutrition (Gao et al., 2019; Rao et al., 2019; Huang et al., 2021).
Furthermore, every advanced management practice can affect both GHGs and carbon sequestration, which could moderate the greenhouse effect. For example, straw return was proven to enhance both CH4 emissions and the soil organic carbon content (Xiong et al., 2015; Jiang et al., 2017). Therefore, their impacts on both soil carbon and crop yield should be considered when assessing and adopting mitigation measures to realize net-zero emission agriculture.

4.2 Limitations

The uncertainty of our results is due to several factors. First, we ignored the GHGs from the production and transportation of agricultural inputs, as well as from processing and consumption. For example, the production stage of nitrogenous fertilizer involves coal mining, which can lead to CO2 and CH4 emissions (Zhang et al., 2013; Cui et al., 2018). Second, the discrepancy of irrigation regimes within a province was not included in this study. For example, Carlson et al. combined MIRCA 2000 data with ALGAS reports to finely account for the CH4 emissions from rainfed rice, dryland rice, and irrigated rice (Carlson, et al., 2017). Future studies could improve the accounting accuracy by refining coefficients like SFw and SFp at a small scale. Third, for our projections, atmospheric conditions were excluded from the driving factors. However, elevated atmospheric CO2 is expected to promote CH4 and NO2 emissions, and rising air temperatures and extreme rainfall are likely to cause reductions in rice yields (van Groenigen, et al., 2013; Fu et al., 2023). Fourth, due to data availability limitations, many provinces had to share the same returning rate of rice straw in the field although this leads to inaccurate estimations.

5 Conclusions

In this study, we first analyzed the temporal and spatial dynamics of the GHGs among four types of rice. The results indicated that the GHGs per hectare showed an increasing trend from 2005 to 2020. Spatially, the GHGs per unit in central and southeastern China were higher than in other regions. In addition, the GHGs per unit of late indica rice were the highest. Next, we estimated the mitigation potentials of rice production at the provincial level under the premise of mitigation strategies. We proposed two mitigation strategies for different provinces which included cropland optimization and advanced agricultural techniques. By adopting these strategies, rice GHGs could be reduced by up to one-fifth of their 2020 emission levels.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Bao T, Jia G S, Xu X Y. 2023. Weakening greenhouse gas sink of pristine wetlands under warming. Nature Climate Change, 13: 462-469.

[2]
Cai S Y, Zhao X, Pittelkow C M, et al. 2023. Optimal nitrogen rate strategy for sustainable rice production in China. Nature, 615(7950): 73-79.

[3]
Carlson K M, Gerber J S, Mueller N D, et al. 2017. Greenhouse gas emissions intensity of global croplands. Nature Climate Change, 7(1): 63-68.

DOI

[4]
Caro D, Pulselli F M, Borghesi S, et al. 2017. Mapping the international flows of GHG emissions within a more feasible consumption-based framework. Journal of Cleaner Production, 147: 142-151.

[5]
Chakraborty D, Ladha J K, Rana D S, et al. 2017. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production. Scientific Reports, 7: 9342. DOI: 10.1038/s41598-017-09742-9.

PMID

[6]
Chel A, Kaushik G. 2011. Renewable energy for sustainable agriculture. Agronomy for Sustainable Development, 31(1): 91-118.

[7]
Chen Z D, Chen F. 2023. Irrigation schedule impact on greenhouse gas mitigation, carbon sequestration, and yield improvement of double rice-cropping systems in southern China. Soil & Tillage Research, 234: 105836. DOI: 10.1016/j.still.2023.105836.

[8]
Crippa M, Solazzo E, Guizzardi D, et al. 2021. Food systems are responsible for a third of global anthropogenic GHG emissions. Nature Food, 2(3): 198-209.

DOI PMID

[9]
Cui Z L, Zhang H Y, Chen X P, et al. 2018. Pursuing sustainable productivity with millions of smallholder farmers. Nature, 555(7696): 363-366.

[10]
FAO (Food and Agriculture Organization of the United Nations). 2020. Agrifood systems. https://www.fao.org/faostat/en/#data/GT/visualize.

[11]
FAO (Food and Agriculture Organization of the United Nations). 2022. Agrifood systems.

[12]
FAO (Food and Agriculture Organization of the United Nations). 2021. Crops and livestock products. https://www.fao.org/faostat/en/#data/GT.

[13]
Foong A, Pradhan P, Frör O, et al. 2022. Adjusting agricultural emissions for trade matters for climate change mitigation. Nature Communications, 13(1): 3024. DOI: 10.1038/s41467-022-30607-x.

[14]
Frank S, Beach R, Havlik P, et al. 2018. Structural change as a key component for agricultural non-CO2 mitigation efforts. Nature Communications, 9: 1060. DOI: 10.1038/s41467-018-03489-1.

[15]
Fu J, Jian Y W, Wang X H, et al. 2023. Extreme rainfall reduces one-twelfth of China’s rice yield over the last two decades. Nature Food, 4(5): 416-426.

[16]
Gao B, Huang W, Xue X B, et al. 2019. Comprehensive environmental assessment of potato as staple food policy in China. International Journal of Environmental Research and Public Health, 16(15): 2700. DOI: 10.3390/ijerph16152700.

[17]
Geng T, Jia F, Cai W J, et al. 2023. Increased occurrences of consecutive La Niña events under global warming. Nature, 619(7971): 774-781.

[18]
Ghasemi-Mobtaker H, Mostashari-Rad F, Saber Z, et al. 2020. Application of photovoltaic system to modify energy use, environmental damages and cumulative exergy demand of two irrigation systems—A case study: Barley production of Iran. Renewable Energy, 160: 1316-1334.

[19]
Haque M M, Kim G W, Kim P J, et al. 2016. Comparison of net global warming potential between continuous flooding and midseason drainage in monsoon region paddy during rice cropping. Field Crops Research, 193: 133-142.

[20]
He X Q, Batary P, Zou Y, et al. 2023. Agricultural diversification promotes sustainable and resilient global rice production. Nature Food, 4(9): 788-796.

DOI PMID

[21]
Huang Y F, Gao B, Huang W, et al. 2021. Producing more potatoes with lower inputs and greenhouse gases emissions by regionalized cooperation in China. Journal of Cleaner Production, 299: 126883. DOI: 10.1016/j.jclepro.2021.126883.

[22]
IPCC. 2021. Climate Change 2021:The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

[23]
Jat M L, Chakraborty D, Ladha J K, et al. 2020. Conservation agriculture for sustainable intensification in South Asia. Nature Sustainability, 3(4): 336-343.

[24]
Jiang Y, van Groenigen K J, Huang S, et al. 2017. Higher yields and lower methane emissions with new rice cultivars. Global Change Biology, 23(11): 4728-4738.

DOI PMID

[25]
Kander A, Jiborn M, Moran D D, et al. 2015. National greenhouse-gas accounting for effective climate policy on international trade. Nature Climate Change, 5(5): 431-435.

[26]
Knapp S, van der Heijden M G A. 2018. A global meta-analysis of yield stability in organic and conservation agriculture. Nature Communications, 9: 3632. DOI: 10.1038/s41467-018-05956-1.

PMID

[27]
Ladha J K, Rao A N, Raman A K, et al. 2016. Agronomic improvements can make future cereal systems in South Asia far more productive and result in a lower environmental footprint. Global Change Biology, 22(3): 1054-1074.

DOI PMID

[28]
Liang D J, Lu X, Zhuang M H, et al. 2021. China’s greenhouse gas emissions for cropping systems from 1978-2016. Scientific Data, 8(1): 10. DOI: 10.1038/s41597-021-00960-5.

[29]
Liu T Q, Li S H, Guo L G, et al. 2020. Advantages of nitrogen fertilizer deep placement in greenhouse gas emissions and net ecosystem economic benefits from no-tillage paddy fields. Journal of Cleaner Production, 263: 121322. DOI: 10.1016/j.jclepro.2020.121322.

[30]
Liu W W, Zhang G, Wang X K, et al. 2018. Carbon footprint of main crop production in China: Magnitude, spatial-temporal pattern and attribution. Science of the Total Environment, 645: 1296-1308.

[31]
Ma L, Cui Y, Liu B, et al. 2023. A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data. Science of the Total Environment, 859(1): 159917. DOI: 10.1016/j.scitotenv.2022.159917.

[32]
Ramanathan K, Sangeeviraman V, Chandrahasan P, et al. 2020. Integration of fish culture and poultry rearing in transplanted rice for nutritional security in smallholder farms. Scientific Reports, 10(1): 10566. DOI: 10.1038/s41598-020-67657-4.

[33]
Rao N D, Poblete-Cazenave M, Bhalerao R, et al. 2019. Spatial analysis of energy use and GHG emissions from cereal production in India. Science of the Total Environment, 654: 841-849.

DOI

[34]
Rockström J, Steffen W, Noone K, et al. 2009. A safe operating space for humanity. Nature, 461(7263): 472-475.

[35]
Runkle B R K, Suvoscarev K, Reba M L, et al. 2019. methane emission reductions from the alternate wetting and drying of rice fields detected using the Eddy Covariance Method. Environmental Science & Technology, 53(2): 671-681.

[36]
Tilman D, Clark M. 2014. Global diets link environmental sustainability and human health. Nature, 515(7528): 518-522.

[37]
van Groenigen K J, van Kessel C, Hungate B A. 2013. Increased greenhouse-gas intensity of rice production under future atmospheric conditions. Nature Climate Change, 3: 291. DOI: 10.1038/nclimate1712.

[38]
Wang W, Wu X H, Chen A L, et al. 2016. Mitigating effects of ex situ application of rice straw on CH4 and N2O emissions from paddy-upland coexisting system. Scientific Reports, 6: 8. DOI: 10.1038/srep37402.

[39]
Wu L, Chen X P, Cui Z L, et al. 2015. Improving nitrogen management via a regional management plan for Chinese rice production. Environmental Research Letters, 10: 095011. DOI: 10.1088/1748-9326/10/9/095011.

[40]
Xie W, Zhu A, Ali T, et al. 2023. Crop switching can enhance environmental sustainability and farmer incomes in China. Nature, 616: 300-305.

[41]
Xiong Z Q, Liu Y L, Wu Z, et al. 2015. Differences in net global warming potential and greenhouse gas intensity between major rice-based cropping systems in China. Scientific Reports, 5: 17774. DOI: 10.1038/srep17774.

PMID

[42]
Xu L, Yang M, Huang Y. 2012. Analysis on influencing factors of fuel consumption for mechanical paddy rice production indouble-cropping areas in southern China. Transactions of the Chinese Society of Agricultural Engineering, 28(23): 33-39. (in Chinese)

[43]
Yadav G S, Das A, Lal R, et al. 2019. Impact of no-till and mulching on soil carbon sequestration under rice (Oryza sativa L.)-rapeseed (Brassica campestris L. var. rapeseed) cropping system in hilly agro-ecosystem of the Eastern Himalayas, India. Agriculture, Ecosystems & Environment, 275: 81-92.

[44]
Yang Y, Tilman D, Lehman C, et al. 2018. Sustainable intensification of high-diversity biomass production for optimal biofuel benefits. Nature Sustainability, 1(11): 686-692.

[45]
Yin L, Tao F, Chen Y, et al. 2023. Novel cropping-system strategies in China can increase plant protein with higher economic value but lower greenhouse gas emissions and water use. One Earth, 6(5): 560-572.

[46]
Zhang G, Wang X K, Zhang L, et al. 2018. Carbon and water footprints of major cereal crops production in China. Journal of Cleaner Production, 194: 613-623.

[47]
Zhang W F, Dou Z X, He P, et al. 2013. New technologies reduce greenhouse gas emissions from nitrogenous fertilizer in China. Proceedings of the National Academy of Sciences of the USA, 110(21): 8375-8380.

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