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Article

CH4 and N2O Emission and Grain Yield Performance of Three Main Rice-Farming Patterns in Central China

by
Chengwei Li
1,
Jie Zhu
2,
Xinyu Li
1,
Jiao Deng
1,
Wei Yang
1,
Yong Zhou
3,
Shaoqiu Li
1,
Lixia Yi
1,*,
Zhangyong Liu
1 and
Bo Zhu
1
1
Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou 434025, China
2
Xiangyang Academy of Agricultural Sciences, Xiangyang 441057, China
3
College of Life Science and Technology, Hubei Engineering University, Xiaogan 432000, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(6), 1460; https://doi.org/10.3390/agronomy13061460
Submission received: 6 April 2023 / Revised: 18 May 2023 / Accepted: 23 May 2023 / Published: 25 May 2023
(This article belongs to the Special Issue Agricultural Management for Climate Change Adaptation, Greenhouse Gas Mitigation, and Agricultural Productivity)
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Abstract

:
This study focuses on the development of more cropping systems in response to global warming and food security concerns. A two-year field experiment (2017–2018) was conducted to investigate the effects of greenhouse gases (GHGs), soil environmental factors and yield on traditional double-cropping rice (DR), maize rice (MR) and ratooning rice (Rr). The results showed a significant annual effect of temperature and rainfall on GHG emissions under different cropping systems. Annual CH4 emissions under MR and Rr were significantly lower than under DR. Compared to DR, the highest cumulative N2O emissions were observed in MR (14.9 kg·ha−1) with a reduction of 23.7% in Rr. In addition, the upland crops significantly reduced CH4 emissions for late rice, while N2O emissions increased by 20.6%. Compared with DR and Rr, global warming potential (GWP) and greenhouse gas intensity (GHGI) were significantly lower for MR (p < 0.05). Meanwhile, the annual yield of MR (16.40 t·ha−1) was 8.1% and 2.4% higher than that of DR and Rr, respectively. This study further found that soil temperature and NH4+-N content were positively correlated with CH4 and N2O emissions, and soil moisture was positively correlated with N2O emission. Thus, we concluded that MR has the greatest potential to improve crop yield and mitigate GHG emissions in central China.

1. Introduction

CH4 and N2O are two major greenhouse gases (GHGs) in the atmosphere and increasing their concentration leads to the changes in atmospheric components and accelerates the rate of global warming [1]. Atmospheric concentrations of CH4 and N2O are increasing at a rate of 0.6% and 0.2–0.3% per year, respectively [2]. Their global warming potential (GWP) on a 100-year scale is 29.8 and 273 times greater than those of CO2, respectively [3]. Rice fields have been identified as a major agricultural source of CH4 and N2O emissions, accounting for 7–11% of soil N2O emissions and 10.5% of total CH4 emissions, respectively [4]. China accounts for 22% of the global rice area and 34% of rice output [5], and CH4 and N2O emissions from rice cultivation are estimated to be about 6.4 Tg·y−1 and 180 Gg·y−1, respectively [6]. Therefore, it is particularly important to evaluate CH4 and N2O emissions in China’s rice fields and to propose effective reduction measures through field activities.
Increasing extreme weather events, such as waterlogging, are threatening crop security [7], prompting farmers to switch from traditional double-cropping rice to rice-upland patterns or ratooning rice. Rice and maize are two key food crops in China. The different planting conditions not only improve the utilization rate of agricultural land, but also result in different levels of GHG emissions [8]. Ratooning rice (Rr) is the practice that uses what is left over from previous main rice for budding, booting and harvesting again. The ratooning rice area gradually increases due to its low expenditure, high income and lower water demand. Compared with rice-wheat rotation, the total greenhouse gas emissions and GHGI of Rr were reduced by 36.3% and 15.9%, respectively [9]. Zhang et al. [10] found that CH4 emissions and N2O emissions yielded from ratooning rice were 33.89% and 23.02% lower than those from double-cropping rice, respectively. Both CH4 and N2O emissions from rice-based systems contribute significantly to GHG emissions, with aerobic cereal crops releasing mostly N2O and little CH4 [11]. The crop rotation with maize rice and sweet sorghum rice significantly reduced CH4 emissions by 78~84%, and net CO2-eq emissions were reduced by 68~78% compared to double rice [12]. Although annual N2O emissions increased two to threefold with the incorporation of upland crops, the larger reduction in CH4 resulted in a significantly lower annual GWP compared to the double-rice-cropping system [13]. In addition, previous studies have reported that CH4 emissions are closely related to soil temperature [14], especially in 5 cm soil layers [15]. This is mainly because anaerobic microbial activity in soil is related to the production and consumption of CH4. Studies have observed that CH4 emissions increase significantly in paddy fields under flood conditions, while N2O emissions are low [16]. Therefore, it is particularly important to reduce CH4 emissions from paddy fields by controlling water flooding. Furthermore, nitrogen fertilizer is an important factor in regulating N2O production and emission [17]. Zhou et al. [18] found that high N rates contribute to greater N2O emissions with improved N substrate availability.
Therefore, we conducted a two-year study for the following reasons: (i) to verify the impact of different crop rotation systems on greenhouse gas emissions; (ii) to estimate CH4 and N2O in the overall impact of global warming potential; (iii) and to estimate the impact of the management activities of paddy fields on soil properties. These provide the theoretical basis to support China’s emissions reductions and to assess the economic benefits or risks of diversified rice-cropping systems.

2. Materials and Methods

2.1. Experimental Site

The field experiments were conducted from March 2017 to November 2018 at Sanhu Experimental Farm (30°12’ N, 112°31’ E) in Jiangling County, Hubei Province. The region has a north subtropical monsoon humid climate, with mean annual rainfall of 900–1100 mm and temperature of 16.0–16.4 °C. The experimental field was cultivated under a rice-fallow system. The soil was loam, and the physical and chemical properties of the soil at 0–20 cm were as follows: organic matter, 28.59 g·kg−1; TN, 2.44 g·kg−1; TP, 0.38 g·kg−1; and pH, 6.9. The precipitation and daily air temperature during the 2017–2018 experimental period are shown in Figure 1.

2.2. Experimental Design

Three patterns, including double-cropping rice (DR), maize rice (MR) and ratooning rice (Rr), were selected in a completely random block design. Each plot area was 98 m2 (14.0 m × 7.0 m) with three replications, and a 0.40 m wide ridge was placed around the plot for isolation.
Maize was planted by direct seeding at 60 cm × 30 cm spacing. Rice was planted by artificial transplanting at a density of 27 cm × 17 cm. Water management was carried out by flooding in the pre-tillering stage, drying in the late tillering stage and alternate drying and wetting in the filling stage. The cultivars, including early rice Liangyou 287, late rice Jinyou 207, ratooning rice Liangyou 6326 and maize Zhengdan 958, were used in this study. The different field management activities are shown in Table 1.

2.3. Gas Sample Collection and Analysis

The CH4 and N2O fluxes were simultaneously monitored throughout the 2017 and 2018 growing seasons using the static chamber method [19]. The base of the closed chamber area was 45 cm × 45 cm and it was installed in the plot before planting. The height of the static chamber could be adjusted according to the plant stage. The outer layer was wrapped in aluminum foil to prevent rapid temperature rise inside the box. Bamboo rows were placed in each plot before planting to avoid field disturbance. Sampling was conducted at 7 d intervals during the rice and maize season, and 2–3 consecutive samples were taken after fertilization and rainfall. Samples were taken between 9:00 and 11:00 at regular intervals of 8 min (0, 8, 16 min) with 100 mL syringes repeatedly extracted 5 times and transferred to a 500 mL gas bag. The samples were analyzed using an Agilent 7890B gas chromatograph. CH4 and N2O emitted were calculated according to Equation (1):
F = ρ × h × ΔC/Δt × 273/(273 + T)
where F is the greenhouse gas emission rate (N2O, μg·m−2·h−1; CH4, mg·m−2·h−1); ρ is the gas density in the standard state (N2O, 1.964 kg·m−3; CH4, 0.714 kg·m−3); h is the height of the static chamber (m); ΔC/Δt is the gas mixing ratio concentration (mg·m−2·h−1); and T is the mean air temperature inside the chamber (°C).
The cumulative seasonal CH4 and N2O emissions were calculated according to Equation (2):
CE = ∑[(Fn + Fn+1)/2] × 24 × t
where CE is the seasonal emission of CH4 or N2O (kg·ha−1); Fn and Fn+1 are the emission rate of CH4 or N2O (mg·m−2·h−1 or μg·m−2·h−1) at two consecutive sampling days; and t is the number of days between two consecutive sampling days (d).
The global warming potential (GWP, t CO2 -eq ha−1) and GHGI (t CO2-eq t−1 yield) were calculated according to Equations (3) and (4) (IPCC, 2021):
GWP = CH4 × 29.8 + N2O × 273
GHGI = GWP/Y
Y: crop yield (t·ha−1)

2.4. Yield Measurement, Soil Sampling and Analysis

At maturity, three 1 m2 plots of rice in each plot were harvested, threshed and weighed. Two consecutive ears of twenty were harvested before the maize harvest in each plot, and grains per ear were weighed at 1000 grain weight.
Soil samples (0–20 cm) were collected randomly in each plot after harvest. The content of dissolved organic carbon (DOC) was analyzed using potassium the dichromate external heating method on fresh soil samples. Soil pH was measured in 1:2.5 (soil: water) suspension using the Mettler Toledo portable. The content of ammonium (NH4+-N) and nitrate (NO3−-N) was analyzed using the indophenol blue colorimetric method and the ultraviolet spectrophotometry method.

2.5. Statistical Analysis

Data were analyzed and sorted by Excel 2019 and plotted by Origin 2018, and LSD was used for mean separation by SPSS 24.0. Pearson’s correlation coefficient (r) was used to analyze the correlation between each environmental index and CH4 and N2O emitted.

3. Results

3.1. CH4 Emissions

It was observed for two consecutive years that CH4 emissions contributed to the rice season, while it was lower in the maize season, and the two years had similar emission rules. CH4 emissions gradually increased after rice planting, rapidly reduced during the drainage drying stage and remained low until maturity.
During the first growing season, CH4 emissions ranged from 0.04 to 17.80 mg·m−2·h−1 in all treatments, and the seasonal mean CH4 fluxes of DR, MR and Rr were 4.92, 1.01 and 6.70 mg·m−2·h−1, respectively (Figure 2). Compared to DR, CH4 emissions increased by 64.68% in Rr and decreased by 82.65% in MR. (Table 2). The highest cumulative CH4 emissions were observed at 82.64~273.18 kg·ha−1 under DR in the second season, and there was no significant difference between MR and Rr in the second season (Table 2). In addition, CH4 emissions were 66.83% lower in MR than in DR in the late rice season. Annual CH4 emissions under different treatments were significantly higher in 2017 than in 2018, possibly due to the temperature and rainfall. As opposed to DR, annual CH4 emissions decreased by 72.63% and 18.12% under MR and Rr, respectively.

3.2. N2O Emissions

It was found that N2O emissions mainly occurred during the upland cropping season, after fertilization and during the alternate dry and wet stage of the paddy field, while it decreased during the long-term flood condition. For the first growing season, N2O emissions ranged from approximately −214.79 to 1052.73 ug·m−2·h−1, and the mean N2O emissions of MR was significantly higher than that of DR and Rr. The highest N2O flux was recorded at 5.28 and 10.96 ug·m−2·h−1 at MR in 2017 and 2018, respectively, likely due to the facilitation of soil nitrification under aerobic conditions (Figure 3). During the second growing season, the average N2O emissions were 231.32, 283.03 and 152.46 ug·m−2·h−1 at DR, MR and Rr, respectively, and the seasonal N2O emissions ranged from approximately 1.21 to 8.48 kg·ha−1 in 2017 and 2018. Compared with DR, MR significantly increased the annual cumulative N2O emissions by 70.31%, while Rr had a reduction of 23.66%.

3.3. Crop Yield

In a two-year experiment, the highest yield was observed in Rr during the first growing season, and it was significantly higher than MR and DR by 21.08% and 37.81%, respectively (Figure 4). There was no significant difference between DR and MR during the second growing season, and these were significantly higher than Rr. In general, the annual crop yields of DR, MR and Rr were higher in 2018 than in 2017, which may be due to the difference in climate. Compared with DR, the annual yield of MR and Rr was significantly higher by 8.06% and 7.30%, respectively.

3.4. GWP and Yield-Scaled GWP

The average GWP calculated for the different cropping systems is shown in (Figure 5A). The CH4 emissions during the rice season contributed 71.83% to the seasonal GWP, and a higher GWP was observed in 2017 than in 2018. Compared to DR, the annual GWP decreased by 40.81% and 26.59% for MR and Rr, respectively.
Yield-scaled GWP was identified as GHG emissions per unit of grain production. The highest YS GWP was observed in DR for 1.74 and 0.47 (t CO2-eq·t−1) in 2017 and 2018, respectively. The annual YS GWP decreased by 11.46%~54.26% compared to DR. MR, particularly, had the largest decrease of 30.97% over two years, and this was due to the increase in emission being much greater than the yield (Figure 5B).

3.5. Soil Characteristics and Correlation between CH4 and N2O Emissions

Compared with DR, the soil DOC concentration decreased by 20.75% and 44.64% in MR and Rr, respectively (Figure 6A). MR and Rr achieved NO3−-N concentration growth of 26.01% and 11.90% compared to DR, with NH4+-N concentration growths of 15.73% and 38.75%, respectively (Figure 6C,D). No obvious effect of the cropping systems on soil pH was observed.
There was a positive correlation between CH4 emissions and NH4+-N, soil temperature and soil water content, and a negative correlation between NO3−-N and pH. A positive correlation was found between N2O emissions and soil temperature, and a non-significant correlation was found with NH4+-N (Figure 7).

4. Discussion

4.1. Impact of Crop Pattern on CH4 Emissions

In the present study, it was found that CH4 emissions increased steadily after rice transplantation and decreased after reaching the peak (Figure 2), which is well explained by the fact that flood depth regulates the fluctuations in soil oxidation-reduction conditions [20,21]. The CH4 emission peak appeared during the tillering and filling stage during the rice season (Figure 2), possibly due to the rice roots that grew vigorously during the tillering stage with increased exudation, which provided substrates for methanogenic bacteria [22,23]. This second emission peak depended on the reduced oxidation of CH4 for massive root death, with CH4 emissions through the plant aerenchyma. In contrast to DR, the reduction in CH4 emissions for MR and Rr was 72.63% and 18.12%, respectively (Table 2). This may be because of the positive correlation between CH4 emissions and soil DOC content (Figure 6A). Some studies have shown that soil DOC provides sufficient substrates to produce CH4 [24,25], which is consistent with the higher soil DOC that is associated with higher CH4 emissions in this experiment. In the present study, CH4 emissions from MR were 66.83% lower than that from DR in late rice (Table 2), mainly influenced by the soil aeration status and soil moisture, which affect the activity of methanogenic and methane-oxidizing bacteria [26,27,28]. There was a significant positive correlation with soil moisture, NH4+-N concentration and soil temperature (Figure 7). In addition, the redox potential (Eh) gradually increased due to the regeneration of oxidants, e.g., Fe3+ failed to recover in a short period during the maize season, which in turn resulted in CH4 emission differences in the rice field [29]. In this study, the CH4 emissions of all treatments were higher in 2017 than those in 2018 (Table 2), which is related to the climatic and precipitation conditions [30].

4.2. Impact of Crop Pattern on N2O Emissions

A significant difference was found in N2O emissions among the different rice patterns. Because soil aeration conditions and N fertilization provide favorable conditions for nitrification and denitrification [29,31], N2O emissions are mainly concentrated in upland areas and after fertilization (Figure 3). In this study, annual N2O emissions from each cropping system in the rice season (3.10–8.48 kg·ha−1) were higher than the global average seasonal N2O emissions for rice (1.38 kg N ha−1) [16]. It also indicates that N2O emissions during the rice season need to be evaluated [32]. The annual N2O emissions of DR and Rr were 41.28% and 55.18% lower than those of MR (Table 2), respectively, associated with the prolonged anaerobic conditions limiting nitrification by microorganisms and the reduction in N2O to N2 [33,34]. In comparison, N2O emissions from maize were significantly higher than those from rice (Table 2), which was due to the alternation of wet and dry soil, which increased the mineralization of organic matter and promoted N2O emissions by stimulating microbial activity [31]. In the present study, N2O emissions from MR were 20.60% higher than DR during the late rice season, and soil microbial activity was observed by incorporation from upland crop root residue, which is contrary to Baldur et al. [33]. The reason for this could be attributed to the increasing NH4+-N content in rice due to the nitrogen surplus in maize (Figure 6D and Figure 7). The study found that N2O flux was positively correlated with NH4+-N content, as NH4+-N provides substrate for soil nitrification [35]. N2O emissions of all treatments were lower in 2017 than those in 2018 (Table 2), which is related to the temperature and precipitation conditions [32,36].

4.3. Impact of Crop Pattern on Yield, GWP and Yield-Scaled GWP

The combined impact of CH4 and N2O emissions, as calculated by GWP, was closely related to the crop component. In the present study, the GWP of the paddy pattern was determined by CH4 emissions with a contribution of 56.16%~84.01% (Figure 5A), which supports previous studies [32,37]. Switching from traditional double rice to maize rice reduces CH4 emissions but increases N2O emissions. In contrast to MR, the GWP for DR and Rr increased by 68.93% and 36.23%, respectively (Figure 5A). As a result of long-term flooding, favorable conditions for CH4 and N2O emissions have been created. In our study, the MR system achieved the significantly highest annual grain yield (16.40 t·ha−1) and lowest YS GWP (0.39 t CO2-eq·t−1) (Figure 4 and Figure 5B) among the high productivity of maize. The YS GWP in our study is in good agreement with estimates reported in recent meta studies for maize and rice [16,38].

5. Conclusions

Rice farmers are forced to change traditional rice cultivation from flooded double-rice systems to the new cultivation pattern due to the increasing focus on reducing double carbon emissions. The results showed that CH4 emissions accounted for 71.83% of GHG emissions from paddy fields. Compared to DR, annual CH4 and N2O emissions decreased by 18.12% and 23.66% under Rr, respectively. MR significantly reduced CH4 emissions while reducing N2O by 70.31%. The GWP for the different cropping systems was in the rank order of DR > Rr > MR, and it was lower for MR than for DR and Rr by 40.81% and 26.59%, respectively. MR had the highest annual yield and the lowest yield-scaled GWP was 0.39 (t CO2-eq·t−1). Overall, MR can achieve increased production and reduce the greenhouse effect, which is a better planting mode in Central China. In consideration of rice as the first food crop, it is suggested to study the balance relationship between the yield increase and greenhouse gas emissions of ratooning rice in the future. Water and fertilizer management and agronomic measures can be adopted, or suitable planting patterns can be found according to different land environments.

Author Contributions

L.Y., B.Z. and Z.L. conceived and designed the experiments. C.L., J.Z., X.L., J.D., W.Y., Y.Z. and S.L. carried out the experiments and data collection. B.Z. and L.Y. guided the idea of the manuscript. C.L. and L.Y. analyzed the data and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Key Program of Strategic Science and Technology Innovation Cooperation of China (2022YFE0209200-04), the Hubei Key Program of Research & Development, China (2020BBB089) and the Shishou Leading County Advanced Technology Integrated Demonstration Base Construction (SS202305).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships.

References

  1. Zhang, Y.M.; Hu, C.S.; Zhang, J.B.; Dong, W.X.; Wang, Y.Y.; Song, L.N. Research advances on source/sink intensities and greenhouse effects of CO2, CH4 and N2O in agricultural soils. Chin. J. Eco-Agric. 2011, 19, 966–975. [Google Scholar] [CrossRef]
  2. Wang, C.; Shen, J.L.; Zheng, L.; Liu, J.Y.; Qin, H.L.; Li, Y.; Wu, J.S. Effects of combined applications of pig manure and chemical fertilizers on CH4 and N2O emissions and their global warming potentials in paddy fields with double-rice cropping. Environ. Sci. 2014, 35, 3120–3127. [Google Scholar]
  3. IPCC. Climate Change 2021: The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021. [Google Scholar]
  4. Liu, H.J.; Guo, Z.; Zhang, L.P.; Zhu, X.L.; Sun, G.F.; Chen, L.G.; Zheng, J.C. Effects of different combined application ratio of organic-inorganic fertilization on CH4 and N2O emissions in paddy season. Ecol. Environ. Sci. 2016, 25, 808–814. [Google Scholar]
  5. Liang, K.M.; Zhong, X.H.; Huang, N.R.; Rubennito, M.L.; Pan, J.F.; Tian, K.; Liu, Y.Z. Grain yield, water productivity and CH4 emission of irrigated rice in response to water management in south China. Agric. Water Manag. 2016, 163, 319–331. [Google Scholar] [CrossRef]
  6. Zhang, W.; Yu, Y.; Li, T.; Sun, W.; Huang, Y. Net greenhouse gas balance in China’s croplands over the last three decades and its mitigation potential. Environ. Sci. Technol. 2014, 48, 2589–2597. [Google Scholar] [CrossRef]
  7. Liu, K.; Harrison, M.T.; Yan, H.; Liu, D.L.; Meinke, H.; Hoogenboom, G.; Wang, B.; Peng, B.; Guan, K.Y.; Jaegermeyr, J.; et al. Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nat. Commun. 2023, 14, 765. [Google Scholar] [CrossRef]
  8. Zhou, W.; Lv, T.F.; Chen, Y.; Westby, A.P.; Ren, W.J. Soil physicochemical and biological properties of paddy-upland rotation: A review. Sci. World J. 2014, 2014, 856352. [Google Scholar] [CrossRef]
  9. Wang, T.Y.; Fan, D.; Song, K.F.; Zhang, G.B.; Xu, H.; Ma, J. Reduced methane and nitrous oxide emissions from ratoon rice paddy in Chaohu polder area. J. Agro-Environ. Sci. 2021, 40, 1829–1838. [Google Scholar]
  10. Zhang, L.; Xu, H.Q.; Li, L.L.; Chen, Y.W.; Zheng, H.B.; Tang, Q.Y.; Tang, J.W. Comparative study on CH4 emission from ratoon rice and double-cropping rice fields. Sci. Agric. Sin. 2019, 52, 2101–2113. [Google Scholar]
  11. Pittelkow, C.M.; Adviento-Borbe, M.A.; van Kessel, C.; Hill, J.E.; Linquist, B.A. Optimizing rice yields while minimizing yield-scaled global warming potential. Glob. Chang. Biol. 2014, 20, 1382–1393. [Google Scholar] [CrossRef] [PubMed]
  12. Strakova, P.; Penttilä, T.; Laine, J.; Laiho, R. Disentangling direct and indirect effects of water table drawdown on above-and belowground plant litter decomposition: Consequences for accumulation of organic matter in boreal peatlands. Glob. Chang. Biol. 2012, 18, 322–335. [Google Scholar] [CrossRef]
  13. Cha-un, N.; Chidthaisong, A.; Yagi, K.; Sudo, S.; Towprayoon, S. Greenhouse gas emissions, soil carbon sequestration and crop yields in a rain-fed rice field with crop rotation management. Agric. Ecosyst. Environ. 2017, 237, 109–120. [Google Scholar] [CrossRef]
  14. Yvon-Durocher, G.; Allen, A.P.; Bastviken, D.; Conrad, R.; Gudasz, C.; Stpierre, A.; Thanh-Duc, N.; Giorgio, P.A. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 2014, 507, 488–491. [Google Scholar] [CrossRef] [PubMed]
  15. Zhou, M.H.; Zhu, B.; Wang, X.G.; Wang, Y.Q. Long-term field measurements of annual methane and nitrous oxide emissions from a Chinese subtropical wheat-rice rotation system. Soil Biol. Biochem. 2017, 115, 21–34. [Google Scholar] [CrossRef]
  16. Linquist, B.; Groenigen, K.J.; Adviento-Borbe, M.A.; Pittelkow, C.; Kessel, C.V. An agronomic assessment of greenhouse gas emissions from major cereal crops. Glob. Chang. Biol. 2012, 18, 194–209. [Google Scholar] [CrossRef]
  17. Carlson, K.M.; Gerber, J.S.; Mueller, N.D.; Herrero, M.; MacDonald, G.K.; Brauman, K.A.; Havlik, P.; O’Connell, S.; Johnson, J.A.; Saatchi, S.; et al. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Chang. 2017, 7, 63–68. [Google Scholar] [CrossRef]
  18. Zhou, M.H.; Zhu, B.; Bruggemann, N.; Wang, X.G.; Zheng, X.H.; Butterbach-Bahl, K. Nitrous oxide and methane emissions from a subtropical rice-rapeseed rotation system in China: A 3-year field case study. Agric. Ecosyst. Environ. 2015, 212, 297–309. [Google Scholar] [CrossRef]
  19. Zhang, G.B.; Yang, Y.T.; Huang, Q.; Ma, J.; Yu, H.Y.; Song, K.F.; Dong, Y.J.; Lv, S.H.; Xu, H. Reducing yield-scaled global warming potential and water use by rice plastic film mulching in a winter flooded paddy field. Eur. J. Agron. 2020, 114, 126007. [Google Scholar] [CrossRef]
  20. Kraus, D.; Weller, S.; Klatt, S.; Haas, E.; Wassmann, R.; Kiese, R.; Butterbach-Bahl, K. A new Landscape DNDC biogeochemical module to predict CH4 and N2O emissions from lowland rice and upland cropping systems. Plant Soil. 2015, 386, 125–149. [Google Scholar] [CrossRef]
  21. Towprayoon, S.; Smakgahn, K.; Poonkaew, S. Mitigation of methane and nitrous oxide emissions from drained irrigated rice fields. Chemosphere 2005, 59, 1547–1556. [Google Scholar] [CrossRef]
  22. Feng, X.; Jiang, C.S.; Peng, X.L.; Li, Y.P.; Hao, Q.J. Effects of the Crop Rotation on Greenhouse Gases from Flooded Paddy Fields. Eco. Environ. 2019, 40, 392–400. [Google Scholar]
  23. Singh, S.K.; Bharadwaj, V.; Thakur, T.C.; Singh, P.P.; Mishra, A.K. Influence of crop establishment methods on methane emission from rice fields. Researchgate 2009, 97, 84–89. [Google Scholar]
  24. Wang, N.; Chang, Z.Z.; Xue, X.M.; Yu, J.G.; Shi, X.X.; Lena, Q.M.; Li, H.B. Biochar decreases nitrogen oxide and enhances methane emissions via altering microbial community composition of anaerobic paddy soil. Sci. Total Environ. 2017, 581–582, 689–696. [Google Scholar] [CrossRef]
  25. Qin, X.B.; Li, Y.E.; Wang, H.; Li, J.L.; Wan, Y.F.; Li, Y.; Liao, Y.L.; Fan, M.R.; Zhu, J.M.; Gao, Z.Q.; et al. Impact of biochar amendment on carbon emissions intensity in double rice field in South China. Trans. CSAE 2015, 31, 226–234. [Google Scholar]
  26. Weller, S.; Janz, B.; Jorg, L.; Kraus, D.; Racela, H.S.U.; Wassmann, R.; Butterbach-Bahl, K.; Kiese, R. Greenhouse gas emissions and global warming potential of traditional and diversified tropical rice rotation systems. Glob. Chang. Biol. 2016, 22, 432–448. [Google Scholar] [CrossRef] [PubMed]
  27. Breidenbach, B.; Blaser, M.B.; Klose, M.; Conrad, R. Crop rotation of flooded rice with upland maize impacts the resident and active methanogenic microbial community. Environ. Microbiol. 2015, 18, 2868–2885. [Google Scholar] [CrossRef]
  28. Breidenbach, B.; Brenzinger, K.; Brandt, F.B.; Blaser, M.B.; Conrad, R. The effect of crop rotation between wetland rice and upland maize on the microbial communities associated with roots. Plant Soil. 2017, 419, 435–445. [Google Scholar] [CrossRef]
  29. Conrad, R. Control of microbial methane production in wetland rice fields. Nutr. Cycl. Agroecosyst. 2002, 64, 59–69. [Google Scholar] [CrossRef]
  30. Cai, Z.C.; Xing, G.X.; Shen, G.Y.; Xu, H.; Yan, X.Y.; Tsuruta, H.; Minami, K. Measurements of CH4 and N2O emissions from rice paddies in Fengqiu, China. Soil. Sci. Plant. Nutr. 1999, 45, 1–13. [Google Scholar] [CrossRef]
  31. Zheng, X.H.; Wang, M.X.; Wang, Y.S.; Shen, R.X.; Gou, J.; Li, J.; Jin, J.S.; Li, L.T. Impacts of soil moisture on nitrous oxide emission from croplands: A case study on the rice-based agro-ecosystem in Southeast China. Chemosphere—Glob. Chang. Sci. 2000, 2, 207–224. [Google Scholar] [CrossRef]
  32. Shang, Q.Y.; Yang, X.X.; Gao, C.M.; Wu, P.P.; Liu, J.J.; Xu, Y.C.; Shen, Q.R.; Zou, J.W.; Guo, S.W. Net annual global warming potential and greenhouse gas intensity in Chinese double rice-cropping systems: A 3-year field measurement in long-term fertilizer experiments. Glob. Chang. Biol. 2011, 17, 2196–2210. [Google Scholar] [CrossRef]
  33. Baldur, J.; Sebastian, W.; David, K.; Heathcliff, S.R.; Reiner, W.; Klaus, B.B.; Ralf, K. Greenhouse gas footprint of diversifying rice cropping systems: Impacts of water regime and organic amendments. Agric. Ecosyst. Environ. 2019, 270–271, 41–54. [Google Scholar]
  34. Zou, J.; Huang, Y.; Zheng, X.; Wang, Y. Quantifying direct N2O emissions in paddy fields during rice growing season in mainland China: Dependence on water regime. Atmos. Environ. 2007, 41, 8030–8042. [Google Scholar] [CrossRef]
  35. Zhang, A.F.; Cheng, G.; Qaiser, H.; Zhang, M.; Feng, H.; Miles, D.; Sun, B.H.; Zhao, Y.; Chen, H.X.; Chen, J.; et al. Contrasting effects of straw and straw–derived biochar application on net global warming potential in the Loess Plateau of China. Field Crops Res. 2017, 205, 45–54. [Google Scholar] [CrossRef]
  36. Yao, Z.S.; Zheng, X.H.; Xie, B.H.; Mei, B.L.; Wang, R.; Butterbach-Bahl, K.; Zhu, J.G.; Yin, R. Tillage and crop residue management significantly affects N-trace gas emissions during the non-rice season of a subtropical rice–wheat rotation. Soil Biol. Biochem. 2009, 41, 2131–2140. [Google Scholar] [CrossRef]
  37. Wang, W.; Wu, X.; Chen, A.; Xie, X.; Wang, Y.; Yin, C. Mitigating effects of ex situ application of rice straw on CH4 and N2O emissions from paddy-upland coexisting system. Sci. Rep. 2016, 6, 37402. [Google Scholar] [CrossRef]
  38. Zheng, H.; Huang, H.; Yao, L.; Liu, J.; He, H.; Tang, J. Impacts of rice varieties and management on yield-scaled greenhouse gas emissions from rice fields in China: A meta-analysis. Biogeosciences 2014, 11, 3685–3693. [Google Scholar] [CrossRef]
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Figure 1. Daily air temperature and precipitation in the cropping system during the 2017–2018 experimental period.
Figure 1. Daily air temperature and precipitation in the cropping system during the 2017–2018 experimental period.
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Figure 2. Average daily CH4 flux (n = 3) of three different cropping systems from 2017 to 2018: (a) DR: double-cropping rice; (b) MR: maize rice; (c) Rr: ratooning rice. The shaded part represents the standard deviations of the means.
Figure 2. Average daily CH4 flux (n = 3) of three different cropping systems from 2017 to 2018: (a) DR: double-cropping rice; (b) MR: maize rice; (c) Rr: ratooning rice. The shaded part represents the standard deviations of the means.
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Figure 3. Average daily N2O flux (n = 3) of three different cropping systems from 2017 to 2018: (a) DR: double-cropping rice; (b) MR: maize rice; (c) Rr: ratooning rice. The arrows indicate the time of N fertilizer application in the different cropping systems. The shaded part represents the standard deviations of the means.
Figure 3. Average daily N2O flux (n = 3) of three different cropping systems from 2017 to 2018: (a) DR: double-cropping rice; (b) MR: maize rice; (c) Rr: ratooning rice. The arrows indicate the time of N fertilizer application in the different cropping systems. The shaded part represents the standard deviations of the means.
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Figure 4. Seasonal yield under different cropping systems from 2017 to 2018. Significant differences of grain yield at p < 0.05 are indicated by different letters.
Figure 4. Seasonal yield under different cropping systems from 2017 to 2018. Significant differences of grain yield at p < 0.05 are indicated by different letters.
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Figure 5. Global warming potential (GWP) (A) and yield-scaled GWP (B) under four different cropping systems in 2017–2018. Significant differences of GWP and GHGI at p < 0.05 are indicated by different letters.
Figure 5. Global warming potential (GWP) (A) and yield-scaled GWP (B) under four different cropping systems in 2017–2018. Significant differences of GWP and GHGI at p < 0.05 are indicated by different letters.
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Figure 6. Soil-dissolved organic carbon (DOC) (A), pH (1:2.5H2O) (B), NO3−-N (C) and NH4+-N concentration (D) of different treatments after harvesting. Significant differences of soil characteristics at p < 0.05 are indicated by different letters.
Figure 6. Soil-dissolved organic carbon (DOC) (A), pH (1:2.5H2O) (B), NO3−-N (C) and NH4+-N concentration (D) of different treatments after harvesting. Significant differences of soil characteristics at p < 0.05 are indicated by different letters.
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Figure 7. Correlation analysis of different factors with respect to CH4 and N2O (* p < 0.05, ** p < 0.01).
Figure 7. Correlation analysis of different factors with respect to CH4 and N2O (* p < 0.05, ** p < 0.01).
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Table
Table 1. Crop management in this experiment.
Table 1. Crop management in this experiment.
Crop Management Activities 20172018
Crop cultivation
First seasonMaize29 Mar: sowing, 23 Apr: transplanting, 15 Jul: harvesting30 Mar: sowing, 22 Apr: transplanting, 16 Jul: harvesting
Early rice29 Mar: sowing, 2 May: transplanting, 20 Jul: harvesting25 Mar: sowing, 3 May: transplanting, 18 Jul: harvesting
Ratooning rice29 Mar: sowing, 2 May: transplanting, 15 Aug: harvesting25 Mar: sowing, 3 May: transplanting, 10 Aug: harvesting
Second seasonLate rice27 Jun: sowing, 27 Jul: transplanting, 3 Nov: harvesting22 Jun: sowing, 27 Jul: transplanting, 1 Nov: harvesting
Regeneration of season3 Nov: harvesting23 Oct: harvesting
Fertilizer application
First seasonMaize29 Mar: NPK (345 kg·ha−1)30 Mar: NPK (345 kg·ha−1)
Early rice1 May: NPK (563 kg·ha−1)2 May: NPK (750 kg·ha−1)
7 May: Urea (78 kg·ha−1)10 May: Urea (52 kg·ha−1)
14 Jun: Urea (117 kg·ha−1)2 Jun: Urea (78 kg·ha−1)
Main rice1 May: NPK (628 kg·ha−1)2 May: NPK (750 kg·ha−1)
7 May: Urea (88 kg·ha−1)10 May: Urea (69 kg·ha−1)
26 Jun: Urea (130 kg·ha−1)24 Jun: Urea (103 kg·ha−1)
28 Jul: Urea (163 kg·ha−1)1 Aug: Urea (162 kg·ha−1)
Second seasonLate rice26 Jul: NPK (450 kg·ha−1)26 Jul: NPK (450 kg·ha−1)
4 Aug: Urea (157 kg·ha−1)4 Aug: Urea (155 kg·ha−1)
30 Aug: Urea (78 kg·ha−1) 23 Aug: Urea (78 kg·ha−1)
Ratooning rice25 Aug: Urea (163 kg·ha−1)20 Aug: Urea (162 kg·ha−1)
The compound fertilizer NPK is a combination of N:P2O5:K2O (16:10:22). Urea is 46:0:0 of nutrient (NPK) contents.
Table
Table 2. Seasonal and annual cumulative of CH4 and N2O under different cropping systems from 2017 to 2018 (kg·ha−1).
Table 2. Seasonal and annual cumulative of CH4 and N2O under different cropping systems from 2017 to 2018 (kg·ha−1).
YearTreatment1st Season2nd SeasonAnnual
CH4N2OCH4N2OCH4N2O
2017DR129.96 ± 24.19 b1.32 ± 0.05 c273.18 ± 98.00 a4.19 ± 0.72 a403.15 ± 124.89 a5.51 ± 0.25 b
MR23.83 ± 4.00 c5.28 ± 1.35 a95.62 ± 2.57 b5.21 ± 1.88 a119.45 ± 11.22 c10.49 ± 0.05 a
Rr198.69 ± 46.55 a3.94 ± 0.40 b97.58 ± 17.34 b1.21 ± 0.18 b296.27 ± 13.70 b5.15 ± 2.96 b
2018DR76.23 ± 5.14 b4.89 ± 2.02 b82.64 ± 11.50 a7.18 ± 0.30 a158.87 ± 16.64 a12.06 ± 2.44 b
MR11.94 ± 1.07 c10.96 ± 2.50 a22.41 ± 3.26 b8.48 ± 2.21 a34.35 ± 7.95 b19.44 ± 2.58 a
Rr140.88 ± 3.68 a3.85 ± 0.08 b23.06 ± 4.04 b4.41 ± 1.49 b163.94 ± 4.89 a8.27 ± 2.22 b
AverageDR103.10 ± 9.53 b3.10 ± 0.99 b177.91 ± 43.25 a5.68 ± 1.51 a281.01 ± 52.78 a8.79 ± 1.04 b
MR17.89 ± 1.02 c8.12 ± 2.39 a59.01 ± 1.46 b6.85 ± 2.23 a76.90 ± 6.91 bc14.97 ± 3.43 a
Rr169.79 ± 24.78 a3.90 ± 1.41 b60.32 ± 19.83 b2.81 ± 1.51 b230.10 ± 66.09 a6.71 ± 1.11 b
Mean ± SD: different letters within the same column indicated significant differences in CH4 and N2O cumulative emissions among treatments during the 2017–2018 period (p < 0.05).
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Li, C.; Zhu, J.; Li, X.; Deng, J.; Yang, W.; Zhou, Y.; Li, S.; Yi, L.; Liu, Z.; Zhu, B. CH4 and N2O Emission and Grain Yield Performance of Three Main Rice-Farming Patterns in Central China. Agronomy 2023, 13, 1460. https://doi.org/10.3390/agronomy13061460

AMA Style

Li C, Zhu J, Li X, Deng J, Yang W, Zhou Y, Li S, Yi L, Liu Z, Zhu B. CH4 and N2O Emission and Grain Yield Performance of Three Main Rice-Farming Patterns in Central China. Agronomy. 2023; 13(6):1460. https://doi.org/10.3390/agronomy13061460

Chicago/Turabian Style

Li, Chengwei, Jie Zhu, Xinyu Li, Jiao Deng, Wei Yang, Yong Zhou, Shaoqiu Li, Lixia Yi, Zhangyong Liu, and Bo Zhu. 2023. "CH4 and N2O Emission and Grain Yield Performance of Three Main Rice-Farming Patterns in Central China" Agronomy 13, no. 6: 1460. https://doi.org/10.3390/agronomy13061460

APA Style

Li, C., Zhu, J., Li, X., Deng, J., Yang, W., Zhou, Y., Li, S., Yi, L., Liu, Z., & Zhu, B. (2023). CH4 and N2O Emission and Grain Yield Performance of Three Main Rice-Farming Patterns in Central China. Agronomy, 13(6), 1460. https://doi.org/10.3390/agronomy13061460

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