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Article

Effects of Drainage on Greenhouse Gas Emissions and Yields of Lowland Rice—Wheat Rotation System in East China

by
Hao He
1,†,
Dandan Li
1,†,
Feifan Pan
1,
Ze Wu
1,
Fengwen Wang
1,
Dong Wu
1,
Sheng Wu
1,
Shuyun Yang
1,2,3,* and
Youhua Ma
1,2,3,*
1
College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
2
Hefei Agricultural Environmental Science Observation and Experiment Station, Ministry of Agriculture, Hefei 230036, China
3
Anhui Key Laboratory of Farmland Ecological Conservation and Pollution Prevention and Control, Hefei 230036, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2022, 12(8), 1932; https://doi.org/10.3390/agronomy12081932
Submission received: 22 July 2022 / Revised: 11 August 2022 / Accepted: 15 August 2022 / Published: 17 August 2022
(This article belongs to the Special Issue How to Achieve Carbon Neutrality in Agroecosystem?)
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Abstract

:
The subtropical region of East China is characterized by abundant water and temperature resources conducive to crop cultivation, and large areas of lowland have been widely used for agricultural planting. The objectives of the study were to explore feasible methods of greenhouse gas (GHG) reduction for rice–wheat rotation systems and to explain the mechanism underlying the effect of drainage on GHG reduction. Shallow ditch (SD) and deep ditch (DD) treatments in the wheat season were set up for drainage to control the paddy soil water content, with conventional non-ditching as the control group (CG). CH4 and N2O emission fluxes were continuously measured, and related soil physical and chemical properties were also measured in this study. The results showed that CH4 emissions from paddy soil accounted for most of the global warming potential (GWP) in the rice–wheat rotation system. Drainage led to a significant reduction in cumulative soil CH4 emissions during the rice and wheat seasons; however, the overall cumulative N2O flux increased significantly. The total GWP produced by SD and DD in the three years was reduced by 58.21% and 54.87%, respectively. The GHG emission intensity (GHGI) of SD and DD declined by 60.13% and 56.40%, respectively. The CH4 emission flux was significantly positively correlated with the 5 cm ground temperature but negatively correlated with the soil redox potential (soil Eh). The drainage decreased the soil water and soil organic matter contents and increased soil pH, which were the mechanisms that reduced the CH4 emissions. The drainage increased the soil nitrogen content, which is the main reason for regulating N2O. The findings indicate that SD and DD not only ensured a stable increase in production but also effectively reduced GHG emissions, and we recommend SD treatment for agricultural production.

1. Introduction

Methane (CH4) and nitrous oxide (N2O) are important greenhouse gases (GHGs) that contribute more than 20% to the global increase in radiative forcing [1,2]. Agricultural production has been recognized as one of the dominant sources of CH4 and N2O emissions [3,4], and paddy soils are the main source of CH4 emissions, which is attributed to the high moisture content and high organic carbon content of paddy soils [5,6]. The annual total emissions of CH4 in global paddy fields account for approximately 5–19% of the total, while N2O emissions from paddy fields contribute up to 6–10% to global warming [7].
Rice–wheat rotation is a common planting system in East Asia and Southeast Asia. Winter wheat is planted during the winter season so that the maximum benefits of winter farmland can be fully utilized through crop rotation. The areas of farmland under the rice–wheat rotation pattern account for more than half of the total rice areas in China [8]. This pattern includes irrigated paddy fields and dryland wheat fields, so it is difficult to measure the GHG emissions in the entire rotation system [9,10]. The yield of rice and wheat in eastern China contributes nearly 30% to China’s grain production, making an important contribution to ensuring the safety of food production. With available water and temperature resources conducive to crop cultivation, rice–wheat rotation is an important planting system in fields along the middle and lower reaches of the Yangtze River among the main grain-producing areas in China [11,12], which to a certain extent, also represent the watershed of China’s humid and semi-humid regions. The Chao Lake area is a typical polder area with a rice–wheat rotation pattern. A polder area refers to the low-lying drainage area formed by an embankment network of plain rivers and lakes along the rivers, which comprises a lower terrain, a shallow soil tillage layer, and a high groundwater level. Large polder areas have been widely used for agricultural planting, whose distribution is related to rivers and lakesides [13]. In the context of the increasing demand for grain production, it is of great global significance to research and promote the reduction of GHGs in rice–wheat rotation systems to mitigate and adapt to climate change.
In paddy fields, soil water control is a highly important factor affecting CH4 and N2O emissions [14,15]. Under the anaerobic environment formed by flooding, fertilizer, organic matter, and rice root exudates provide substrate for methanogens, which are the main contributors to CH4. Related research has shown that a higher soil water content in winter will make the soil remain in an extremely anaerobic state, thus promoting the emissions of CH4 [16,17,18]. According to Jain et al. [19], water control in the non-rice growing period is effective in controlling CH4 emissions during China’s rice growing period. Another report pointed out that if drainage is used in the flooded farmland in southwestern China in winter, the total CH4 emissions during the growth period of the next rice season will be effectively reduced by approximately 63–72% [20]. Drainage helps reduce CH4 emissions by eliminating the extremely anaerobic environment of methanogens, but this may cause the paddy soil to have a wet-dry process, which leads to the production of N2O [21]. Linquist et al. [22] showed that drainage in the wheat season leads to an increase in N2O emissions from rice fields and a significant decrease in CH4 emissions. Liu et al. [23] showed through meta-analysis that midseason drainage in paddy fields is an effective way to mitigate the combined global warming potential (GWP) of CH4 and N2O compared with continuous flooding.
Furthermore, Hou et al. [12] reported that water management had a significant impact on GHG emissions from rice–wheat rotation systems, and long-term flooded rice fields had lower N2O emissions. However, it remains unclear whether the increase in N2O emissions can partially offset the CH4 mitigation, leading to an increase in GWP [24]. Most studies on the impact of water management on N2O and CH4 emissions are restricted to the rice season. Relatively few studies have been devoted to the effects of water control during the wheat season on the GHG emissions of the entire annual rice–wheat rotation [12]. Moreover, it cannot be ignored that with the change in soil moisture conditions, the soil temperature environment will inevitably change, which will have a direct and indirect impact on the nitrogen content of paddy soil. It is necessary to study the correlation between the soil water content, soil temperature, CH4 and N2O emissions, and the trends of the soil nitrate and ammonium nitrogen contents.
To explore a feasible plan for mitigating GHG emissions under the rice–wheat rotation pattern, a three-year study was conducted, with the rice–wheat double-cropping farmland of the Chao Lake polder area as the research object. Two water control treatments that were easy to implement and a control treatment were designed. In addition, the potential factors influencing CH4 emissions from paddy soils were analyzed, such as the soil water content, soil organic matter content, 5 cm ground temperature, soil Eh, and the relevant soil nitrogen content. The results are applicable to the emission reduction and efficiency enhancement technology of subtropical rice–wheat rotation farmland cultivation.

2. Materials and Methods

2.1. Experimental Site

The experiment was conducted at the Chao Lake Agricultural Environment Experimental Station of Anhui Agricultural University from 2013 to 2015. The monitoring site was located in Tangzui Village, Qi Town, Chaohu city (117°41′6″ E and 31°39′50″ N with an elevation of 17 m), which is in the northern subtropical monsoon climate zone. The annual average precipitation is 1358 mm, the average temperature is 16.8 °C, the annual frost-free period is 247 days, and the sunshine duration is 2106 h. According to the statistics in 2014, the grain crop planting area was approximately 49,800 ha in Chaohu city, with a total output of approximately 319,000 tons of crops and rice production of approximately 256,000 tons.
This field was managed according to the traditional practices in the area, wherein the rice was flooded during the growing season, and the uncultivated fields were left after harvest until the next cultivation. The rice and winter wheat varieties used for the experiment were Huiliangyou 996 and Yangmai 16.
At the beginning of the experiment, rice soils were randomly selected to measure the relevant physicochemical properties. The typical soil type of this monitoring site in the Chao Lake lowland area is submerged rice soil with a pH value (H2O) of 6.19, an organic carbon content of 23.71 g kg−1, a total nitrogen content of 1.29 g kg−1, and a physical clay content of 488 g kg−1. The soil’s physical and chemical properties (0 to 20 cm) under different treatments are shown in Table 1.

2.2. Drainage and Fertilization Measurements

Two treatments and a control treatment were designed for the wheat season. Shallow ditch (SD): The depths of the field ditch, row ditch, and ditch beside the field were 20, 25, and 35 cm, respectively. Deep ditch (DD): The depths of the field ditch, row ditch and ditch beside the field were 30, 40, and 45 cm, respectively; control group (CG): no ditch (schematic diagram of the field ditches is shown in Figure 1). Each treatment was performed with three replicates. The designed depth of the ditch was determined based on actual situations. A large ditch outside the farmland was approximately 60–80 cm deep. The distance between the field drains was 3 m, and the ditches were interconnected.
The bund used to separate the experimental plots was made of cement. These plots were neatly arranged, each plot had water inlets and drains, and the drains were connected to one another. Local routine management was adopted for the field management of each treated plot. The wheat season land was plowed to 15 cm before planting. Rice was irrigated 4–5 times during the growth period and was irrigated for 1–2 days before the three fertilization events, and the depth was approximately 6–7 cm at the end of the field sunning stage.
The N, P, and K fertilizers were urea, superphosphate, and potassium chloride, respectively. The specific fertilization scheme and fertilization amount are shown in Table 2. The management of wheat and rice fields in the three years is shown in Table 3 and Table 4.

2.3. Collection and Measurements of N2O and CH4 Emissions

In this experiment, closed static boxes were used to monitor the farmland GHGs under the rice–wheat rotation pattern in the Chao Lake lowland area. The boxes were made of 5-mm-thick transparent glass (50 × 50 × 60 cm3 and 50 × 50 × 120 cm3). The former had a top cover, whereas the latter did not. The box of the first specifications was used when the crop plants were shorter than 60 cm. The two-layered box was used when the height of the crop exceeded 60 cm. Water was injected during the sampling process to keep the box sealed. Conventional gas sampling was carried out every day from 9:00 AM to 12:00 PM for a total of three hours; four sets of sampling were performed each hour, with a sampling interval of 15 min. Sixty milliliters of gas were sampled each time. The data were recorded, and the Eh value of the soil was monitored.
The samples were collected in the wheat season once a week and every 3 to 5 days in the rice season, which was adjusted rationally according to weather conditions. In the periods of fertilization, topdressing, and roasting, the samples were collected every two days. The CH4 and N2O concentrations of the collected gas samples were measured using gas chromatography (Brooker 450-GC, Brooker, Hatfield, PA, USA) within 24 h. An FID detector was used to detect CH4. The detection conditions were as follows: column temperature: 50 °C; detector temperature: 250 °C; nitrogen flow, 10 mL min−1; hydrogen: airflow 30 mL min−1, 300 mL min−1. N2O was detected with the Nl63ECD Detector under the following conditions: detector temperature of 300 °C and nitrogen flow rate of 300 mL min−1.
The CH4 and N2O emission fluxes were calculated by the following formula:
F = ρ × V A−1 × dc dt−1 × 273 T−1,
where F is the emission flux in units of mg m−2 h−1 (CH4 and N2O); ρ is the density of CH4 or N2O under standard conditions [0.714 kg m−3 (CH4) and 1.25 kg m−3 (N2O)]; V is the effective volume of the box (m3); A is the sampling box coverage area (m2); dc dt−1 is the change in CH4 or N2O concentration in the sampling tank per unit time [μL L−1 h−1 (CH4) and nL L−1 h−1 (N2O)]; and T is the temperature inside the box (K).

2.4. Calculation of Yield, GWP and GHGI

To analyze the actual effect of the different water control methods on crop yield in the wheat season, production measurements were conducted after crop maturation, and the average economic output of the three groups of repeated plots was determined.
GWP was calculated as CO2 equivalents (CO2-eq) over 100 years’ time and horizon using the radiative forcing potential of 265 for N2O and 28 for CH4 relative to CO2 (IPCC, 2013):
GWP = cumulative CH4 emission × 28 + cumulative N2O emission × 265
GHGI = GWP Y−1,
In the formula, GHGI is the GHG emission intensity (t hm−2, calculated as CO2); Y represents the yield of rice and wheat (kg hm−2).

2.5. Soil Properties

Soil properties were analyzed during a complete wheat season to clarify the mechanism of greenhouse gas production in paddy fields. We used an aluminum box to take soil samples and then dried these to determine the soil water content. An HW-type soil temperature automatic recorder was used to measure the 5 cm ground temperature, produced by the Nanjing Institute of Soil Research, Chinese Academy of Sciences.
The soil Eh value was determined by in situ measurement with an Eh meter. The model of the intelligent portable redox potential meter was QX6530, provided by the Nanjing Institute of Soil Science, Chinese Academy of Sciences. We used soil drills to randomly sample fresh soil samples of 0–20 cm from three locations. The Eh electrode was inserted 3–5 cm below the soil surface. After each removal, we quickly inserted the electrode into the soil sample to a depth of approximately 2 cm, and the Eh value was recorded after the reading became stable.
In each study area, a soil drill was used to collect field soil samples from five points in the 0–20 cm soil layer using the “Z” method. After sampling, the soil was oven dried and frozen, and the relevant soil physical and chemical properties were measured centrally. The soil pH was measured by the potentiometric method (water-soil ratio: 2.5:1), and soil organic matter was determined by the potassium dichromate volumetric method with oil bath heating. In addition, we explored the effect of drainage on the soil nitrogen content. We used a flow injection instrument to determine the contents of soil ammonium nitrogen and nitrate nitrogen; the Kjeldahl method was used to measure the soil’s total nitrogen content.

2.6. Data Statistics and Analysis

Microsoft Excel 2016 and IBM SPSS Statistics 22 software were used for statistical analysis in this study. Origin 2018 software was used to perform the figures in this study. Multiple comparisons between treatments were performed using the least significant difference test with a probability level of 0.05.

3. Results

3.1. CH4 Emissions

CH4 emissions from the rice–wheat rotation system occurred mainly in the rice season, while drainage during the wheat season effectively reduced the CH4 emissions in the middle and late stages of the rice season (Figure 2). The CH4 emission flux from CG rice fields showed many peaks after fertilizer application (Table 4), while the CH4 emission fluxes of SD and DD generally had single peaks during the rice season.
In the rice season, compared with that of CG, the cumulative CH4 emissions of SD and DD were reduced by 56.36% and 53.88% in 2013, by 74.95% and 72.03% in 2014, and by 73.19% and 70.97% in 2015 (Table 5). The results showed that the CH4 emissions of SD and DD paddy fields were significantly lower than those of CG (p < 0.05, Table 5), while there was no significant difference between SD and DD (p > 0.05). In addition, CH4 emissions from the rice season in the last two years declined significantly, and the cumulative CH4 emissions in 2014 and 2015 were reduced by 56.24% and 50.64% (average value for each treatment), respectively, compared with 2013. Relatively speaking, the CH4 emission reduction effect of SD in the rice season was slightly better than that of DD. Although the CH4 emissions in the wheat season were low, drainage also significantly reduced CH4 emissions during the wheat season. The total CH4 emissions of SD and DD were significantly reduced by 139.66% and 59.48% in 2013, by 93.11% and 94.09% in 2014, and by 82.59% and 74.34% in 2015 (p < 0.05).

3.2. N2O Emissions

The seasonal variation in the N2O flux of SD and DD was approximately the same as that of CG (Figure 3). The N2O emissions in the wheat season were significantly higher than those in the rice season. In the wheat season, the SD and DD treatments led to no significant emission reduction compared with CG, SD reduced cumulative N2O emissions by 1.83%, and DD increased N2O emissions by 6.56% (three-year average). In some periods, the N2O fluxes of SD and DD were significantly higher than that of CG. Notably, during the rice season, SD and DD significantly increased cumulative N2O emissions by 38.15% and 49.40% (p < 0.05), respectively (three-year average).
Over the entire rice-wheat season, there was no obvious reduction in N2O emissions. Over the entire rice–wheat rotation system, the cumulative N2O emissions in the three years were ranked as DD > SD > CG (Table 6). Compared with CG, the cumulative N2O emissions of SD and DD increased by 9.18% and 18.36%, respectively.

3.3. GWP, Yield, and GHGI

The cumulative CH4 emissions during the rice season were the main contributor to the GWP of the rice–wheat rotation system, accounting for 88.26%, 72.22%, and 71.53% of the total produced by CG, SD, and DD, respectively (Table 7). With respect to the whole rice–wheat rotation system, compared with CG, SD, and DD both significantly reduced GWP by more than half (58.21% and 54.87%, respectively).
It is worth noting that the drainage ensured the crop yields of both rice and wheat fields. The yields increased by 3.28% (SD) and 2.88% (DD) in the rice season and by 7.64% (SD) and 4.71% (DD) in the wheat season. The GHGI can reflect the comprehensive impact of different treatments on crop yields and GHG emissions. From the perspective of the entire rice–wheat rotation system, relative to CG, the GHGI of SD and DD decreased by 60.70% and 54.48%, respectively (three-year total) (p < 0.05).

3.4. Soil Water Content and Soil pH

Both SD and DD treatments similarly decreased the soil water content and increased the soil pH (Figure 4). Ranges of the soil water content in the CG, SD, and DD treatments were 23.01–26.97%, 21.12–23.88%, and 20.61–23.97%, respectively, and the average soil water contents of the three treatments were 23.92 ± 1.03%, 21.13 ± 0.84%, and 21.86 ± 0.70%, respectively. The range of soil pH in the CG, SD, and DD treatments was 5.98–6.16, 6.01–6.22, and 5.95–6.20, respectively, and the average soil pH of the three treatments was 6.06 ± 0.05, 6.18 ± 0.04, and 6.17 ± 0.06, respectively.

3.5. Soil Organic Matter Content and Soil Nitrogen Content

As shown in Figure 5, compared with CG (27.97 ± 0.77) g kg−1, the soil organic matter contents of SD (24.71 ± 0.71) g kg−1 and DD (25.00 ± 0.82) g kg−1, decreased by 11.65% and 10.61%, respectively. The changes in the soil organic matter of CG were relatively more stable (26.65–29.63) g kg−1, and the soil organic matter content of SD and DD first decreased, then the trend became stable constant, and finally remained at 24.50 g kg−1.
Drainage significantly increased the soil nitrate nitrogen, ammonium nitrogen, and total nitrogen contents in the wheat season, and the changing trend of the soil nitrogen content in the SD and DD treatments was roughly the same. There were multiple peaks in the soil nitrogen content, all of which appeared after fertilization. The average soil nitrate nitrogen contents of the CG, SD, and DD treatments were 11.69, 13.34, and 13.85 mg kg−1, respectively; the average soil ammonium nitrogen contents were 1.97, 2.22, and 2.20 mg kg−1, respectively; and the average soil total nitrogen contents were 1.55, 1.60, and 1.62 g kg−1, respectively. Overall, there was no significant difference between SD and DD (p > 0.05).

3.6. Ground Temperature at 5 cm and Soil Redox Potential

As shown in Figure 6, there was a significant correlation between the change in the CH4 emission flux and the 5 cm ground temperature (p < 0.01). As the 5 cm ground temperature increased, CH4 emissions also increased gradually. The R2 value (determination coefficient) between the 5 cm ground temperature and the CH4 flux in the water-controlled farmland, the fitting degree of each treatment was higher (0.7 or higher), indicating that the 5 cm ground temperature played an important role in the change in the CH4 flux.
When the CH4 emission flux was high, the soil Eh value mostly stayed between −150~−300 mV; when the soil Eh value was lower than −150 mV, the CH4 emission flux increased significantly with the decrease in Eh, whereas the corresponding CH4 emission flux was close to zero when the Eh value was higher than −100 mV (Figure 7).

4. Discussion

4.1. CH4 Emissions

In this study, the annual emissions of CH4 were lower than those in a study on subtropical permanently flooded rice paddy fields in China [25]. The cumulative CH4 emissions in the first year were extremely high in this study (Table 5), which could be attributed to a large number of anaerobic zones in the soil structure during the initial drainage stage, and drainage and drying will release CH4 originally stored in the rice soil [26,27]. Over the entire rice–wheat rotation system, the peaks of N2O flux in the rice season roughly coincided with the low value of the CH4 flux, all during the period of rice field sunning. CH4 tends to be generated under anaerobic conditions. During the period of rice field sunning, CH4 emissions were very low due to enhanced soil aeration, which destroys the extreme anaerobic environment of methanogens [28]. Therefore, rice field sunning can be regarded as an effective measure for CH4 emission reduction in paddy fields.
Soil pH significantly influences the activities and growth of methanogens and methane-oxidizing bacteria that control CH4 production and oxidation, respectively. Some studies have demonstrated that methanogens can adapt to acidic environments [18,29]. In this study, drainage in the wheat season led to an increase in soil pH, and lower CH4 emissions were measured in the rice season. Consistent with the findings of Jeffery et al. [30] that an increase in soil pH is conducive to the survival of methane-oxidizing bacteria, thus reducing CH4 emissions. The soil Eh is a characterization of the soil moisture status that corresponds to the soil oxygen availability. Gas exchange is reduced between the atmosphere and the soil when the soil is flooded. With the rapid decrease in the soil redox potential, CH4 emissions increase rapidly, but during the process of ditching and drainage, the soil becomes oxidated, and the oxidative properties of the soil are enhanced. Especially in the period when the water layer became shallow in the middle stage of rice growth, the drainage treatment increased the oxygen entering the soil surface, and the soil redox potential increased, which was more conducive to the oxidation of CH4. The corresponding Eh values were mostly between −150 and −300 mV when the CH4 emission flux was high in this study, indicating that −150 mV may be the critical value affecting the CH4 emission flux in lowland paddy soils [31]. Moreover, both soil temperature and soil water content are significantly positively correlated with soil CH4 emissions [32]. Under flooding conditions, a series of simple organic compounds are produced by the fermentation of soil organic matter. These simple compounds prone to mineralization are the carbon and energy sources for CH4 production. CH4 in rice fields is mainly produced by the decomposition of soil organic matter in those soils by methanogens in the soil under anaerobic conditions [33,34]. Drainage led to a decrease in the soil water content and inhibited the decomposition of soil organic matter due to the inactivity of soil microorganisms. Both SD and DD reduced the soil organic matter content in the study, and related research has revealed a significant positive correlation between CH4 emission flux and the soil organic matter content, which is consistent with our findings [35].

4.2. N2O Emissions

Soil N2O emission flux is sensitive to nitrogen fertilizer and soil moisture. In this field study, we observed that the peak N2O emissions appeared after nitrogen fertilizer application. Nitrogen fertilizer application provides a substrate for nitrifying and denitrifying microorganisms, and soil aeration is better when the rice is transplanted [36]. Drainage makes it easier for oxygen to enter the soil, speeding up the wetting-drying process.
Paddy fields are a weak source of N2O emissions, mainly because paddy fields are typically flooded, and soil redox potential is low, limiting the mineralization and nitrification of organic matter. Both SD and DD increased N2O emissions in this study, which is mainly attributed to the following reasons: drainage treatment reduced the soil moisture content and lowered the water level, which improved the extreme anaerobic conditions of the soil and promoted denitrification. Conventional tillage likely destroyed the soil aggregate structure, promoted the mineralization of the originally protected soil organic matter, and released a large amount of mineral nitrogen as a substrate for the nitrification and denitrification processes [37]. The oxygen consumption caused by the accelerated mineralization of soil organic matter easily formed anaerobic micro-zones, which promote the coupled process of soil nitrification and denitrification [38,39]. Relevant studies have confirmed that soil N2O emissions are positively correlated with soil nitrogen content and soil organic matter [38,40,41]. Although drainage reduced the soil organic matter content, it also released significant amounts of available nitrogen that was originally stored in the soil. It should be noted that the effect of soil pH on cumulative N2O emissions is very complex and affects not only the denitrification rate but also the distribution ratio of gaseous products [42,43]. For nitrification only, drainage led to an increase in soil pH, and cumulative N2O emissions should have decreased. Based on the above viewpoints, we infer that in the rice–wheat rotation system, soil NO3−-N and NH4+-N contents were the main factors regulating soil N2O emissions [40].
Dryland soil often has high porosity, making oxygen easily available, so the extreme anaerobic environment required for the survival of methanogens is blocked, while the activity of methanotrophic bacteria is preserved [44]. Liu et al. [45] reported that winter-wheat fields are often regarded as the sink of CH4. In this experiment, CH4 emissions in the wheat season were very low but should not be ignored. It is worth noting that the CH4 emissions of the rice–wheat rotation farmland in this study were extremely high, while the N2O emissions were considerably low. Wen et al. [46] reported that the substantial reduction of N2O to N2 under anaerobic conditions may lead to lower N2O emissions. The lowland polder areas along the middle and lower reaches of the Yangtze River are mostly derived from the development of water, and the groundwater level is strongly affected by the nearby main water bodies [47]. It has been reported that groundwater level fluctuation is a key predictor of CH4 and N2O emissions from cultivated lowlands [48]. Evans et al. [49] observed that a high groundwater level in lowland areas usually leads to CH4 increases and N2O decreases, which is a scientific issue that cannot be ignored in global climate change. Therefore, in arable lands with different groundwater levels, the effect of water control treatments on the total greenhouse effect caused by CH4 and N2O needs to be further assessed. For the further exploration of the GHG emission reduction technologies applicable to lowland polder areas, it is suggested that long-term observations be conducted on the soil water content and groundwater levels in the future to study the specific mechanisms of water control and emissions reduction.

4.3. Yield, GWP, and GHGI

Although there are related reports on assessing GHGs in the entire rice–wheat rotation system, that in the lowland polder area remains unclear [50]. China’s subtropical regions are widely planted with rice, but it has always been a difficult point in related research to ensure stable crop production or even production increases when reducing GHG emissions from paddy soils. Drainage can improve the soil permeability of a paddy field; the oxygen supply is sufficient and promotes the effective absorption of nutrients and water by crops, which is conducive to root growth, promoting the accumulation of nutrients and ensuring the yield of rice and wheat grains [12,51]. However, drainage is usually not significant for increased crop yields; for example, Sunohara et al. [52] reported that corn yielded an average growth rate of 4% with controlled drainage and soybean had an average yield of 3%, both of which were not significant compared to conventional farming.
In this study, the cumulative CH4 emissions from paddy fields were the major contributors to GHG emissions from the complete rice–wheat rotation system. The most effective way to reduce the total amount of GHGs in rice fields is to reduce CH4 emissions, which is consistent with the study of Lagomarsino et al. [53]. Jiang et al. [54] pointed out that compared with continuous submerged irrigation, non-continuous submerged irrigation effectively reduced the GWP of rice fields by 44%. In southern China, after the adoption of mid-term drainage in the double-cropping rice planting area, the GWP in the early and late rice seasons was reduced by 22% and 41%, respectively [14]. Drainage in the wheat season significantly reduced the GWP of the entire rice–wheat rotation system, even with increased N2O emissions in some years, and the net GWP of the entire system dramatically declined because of significantly reduced CH4 emissions. Finally, DD did not have the potential to reduce GHG emissions further or increase crop yields any further than SD, suggesting that deeper ditches for drainage treatments may have a limited effect. In general, based on the current situation where a large number of fertilizers are used to increase production and environmental protection is neglected, drainage is a planting technique worth promoting.

5. Conclusions

The CH4 emissions in the rice season were the main contributor to the global warming potential of the rice–wheat rotation cropping system. Compared with CG, the CH4 cumulative emissions of SD and DD were reduced by 65.80% and 63.42% (rice season) and 101.37% and 77.28% (wheat season), respectively, while the N2O emissions increased in some years. The CH4 emission flux was significantly positively correlated with the 5 cm ground temperature but negatively correlated with the soil Eh. When the soil Eh value was lower than −150 mV, the CH4 emission flux increased markedly with decreasing Eh. Drainage decreased the soil water content and soil organic matter content, increased the soil pH, and disrupted the extreme anaerobic environment required by methanogens, ultimately significantly reducing CH4 emissions. At the same time, however, drainage increased the soil nitrogen content and therefore increased cumulative N2O emissions. Both SD and DD ensured rice–wheat yields and significantly reduced the global warming potential of the total greenhouse gas emissions from the rice–wheat rotation system.

Author Contributions

Formal analysis, H.H. and D.L.; Investigation, H.H., D.L., F.P., Z.W., F.W., D.W. and Y.M.; Methodology, D.L., F.P., Z.W., F.W., D.W., S.W., S.Y. and Y.M.; Project administration, F.P., S.Y. and Y.M.; Resources, S.Y.; Writing—original draft, H.H.; Writing—review & editing, H.H., D.L., S.Y. and Y.M. All authors have read and agreed to the published version of the manuscript.

Funding

Anhui Province Science and Technology Major Special Project: 2021d06050002; Young Talent Project of Anhui Academy of Agricultural Sciences: QNYC-202109; National Key Research and Development Program of China: 2017YFD0301301.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data analyzed during the study are included in this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Agronomy 12 01932 g001 550
Figure 1. Schematic diagram of the field ditches.
Figure 1. Schematic diagram of the field ditches.
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Agronomy 12 01932 g002 550
Figure 2. CH4 emissions in each treatment in the rice–wheat seasons in the three years. CG, SD, and DD represent ditches of different depths for drainage during the wheat season, CG: control group; SD: shallow ditch; DD: deep ditch.
Figure 2. CH4 emissions in each treatment in the rice–wheat seasons in the three years. CG, SD, and DD represent ditches of different depths for drainage during the wheat season, CG: control group; SD: shallow ditch; DD: deep ditch.
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Figure 3. Seasonal variation in N2O emission flux during the rice growth period in the three years. CG, SD, and DD represent ditches of different depths for drainage during the wheat season, CG: control group; SD: shallow ditch; DD: deep ditch.
Figure 3. Seasonal variation in N2O emission flux during the rice growth period in the three years. CG, SD, and DD represent ditches of different depths for drainage during the wheat season, CG: control group; SD: shallow ditch; DD: deep ditch.
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Figure 4. Effects of different treatments on the soil water content (a) and soil pH (b) in the wheat season. CG: control group; SD: shallow ditch; DD: deep ditch. Error bars represent standard errors.
Figure 4. Effects of different treatments on the soil water content (a) and soil pH (b) in the wheat season. CG: control group; SD: shallow ditch; DD: deep ditch. Error bars represent standard errors.
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Figure 5. Effects of different treatments on the soil organic matter content; (a), soil ammonium nitrogen content (b), soil nitrate nitrogen content (c), and soil total nitrogen content (d) in the wheat season. CG: control group; SD: shallow ditch; DD: deep ditch. Error bars represent standard errors.
Figure 5. Effects of different treatments on the soil organic matter content; (a), soil ammonium nitrogen content (b), soil nitrate nitrogen content (c), and soil total nitrogen content (d) in the wheat season. CG: control group; SD: shallow ditch; DD: deep ditch. Error bars represent standard errors.
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Figure 6. Relationship between the CH4 emission flux and 5 cm ground temperature in CG: control group (a), SD: shallow ditch (b), and DD: deep ditch (c).
Figure 6. Relationship between the CH4 emission flux and 5 cm ground temperature in CG: control group (a), SD: shallow ditch (b), and DD: deep ditch (c).
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Figure 7. Relationship between the Soil redox potential (Eh) value and CH4 emission flux in CG: control group (a), SD: shallow ditch (b), and DD: deep ditch (c).
Figure 7. Relationship between the Soil redox potential (Eh) value and CH4 emission flux in CG: control group (a), SD: shallow ditch (b), and DD: deep ditch (c).
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Table
Table 1. Physical and chemical properties of 0 cm to 20 cm soil in each treatment.
Table 1. Physical and chemical properties of 0 cm to 20 cm soil in each treatment.
TreatmentsOrganic MatterTotal NitrogenNitrate NitrogenAmmonium NitrogenAvailable NitrogenpH
(g kg−1)(g kg−1)(mg kg−1)(mg kg−1)(mg kg−1)(H2O)
CG27.38 ± 0.851.29 ± 0.128.62 ± 0.211.87 ± 0.1081.58 ± 3.326.04 ± 0.11
SD26.97 ± 0.531.27 ± 0.109.05 ± 0.181.77 ± 0.1081.22 ± 3.256.11 ± 0.08
DD27.46 ± 1.011.33 ± 0.178.97 ± 0.221.84 ± 0.0982.63 ± 2.386.08 ± 0.09
Note: CG is the control group treatment, SD is the shallow ditch drainage treatment, and DD is the deep ditch drainage treatment.
Table
Table 2. Fertilization measures in 2013–2015 (kg hm−2).
Table 2. Fertilization measures in 2013–2015 (kg hm−2).
Rice
season		Basal fertilizerTillering fertilizerPanicle fertilizerTotal fertilizer
NP2O5K2ONNK2ONP2O5K2O
67.567.567.567.545018067.567.5
Wheat
season		Basal fertilizerWinter fertilizerStriking root fertilizerTotal fertilizer
NP2O5K2ONNK2ONP2O5K2O
727272696902107272
Table
Table 3. Field management in the wheat season in 2012–2015.
Table 3. Field management in the wheat season in 2012–2015.
Wheat SeasonSowing, Applying Basal FertilizerApplying Winter FertilizerApplying Striking Root FertilizerHarvesting
2012–201331 October 201217 January 201310 March 20131 June 2013
2013–201426 October 201319 January 20148 March 201426 May 2014
2014–20156 November 201428 January 201513 March 20156 June 2015
Table
Table 4. Field management in the rice season in 2013–2015.
Table 4. Field management in the rice season in 2013–2015.
Rice SeasonTransplanting Seedlings, Applying Basal FertilizerApplying Tiller FertilizerField SunningIrrigation and RehydrationApplying Panicle FertilizerHarvesting
2012–201313 June 201328 June 201311 July 201320 July 201327 July 201327 September 2013
2013–201421 June 20148 July 201417 July 201421 July 201420 August 201410 October 2014
2014–201519 June 201518 July 20156 August 20158 August 201520 August 201517 September 2015
Table
Table 5. Cumulative CH4 emissions in each treatment during the rice–wheat season (kg hm−2).
Table 5. Cumulative CH4 emissions in each treatment during the rice–wheat season (kg hm−2).
Treatments201320142015
Rice SeasonWheat SeasonRice SeasonWheat SeasonRice SeasonWheat Season
CG330.44 ± 11.32 a2.32 ± 1.02 a180.08 ± 27.22 a3.05 ± 1.27 a198.64 ± 14.06 a3.39 ± 1.34 a
SD144.22 ± 14.22 b−0.92 ± 0.12 c45.03 ± 7.01 b0.21 ± 0.05 b53.25 ± 3.24 b0.59 ± 0.13 c
DD152.43 ± 18.10 b0.94 ± 0.32 b49.29 ± 13.90 b0.18 ± 0.07 b57.67 ± 3.52 b0.87 ± 0.12 b
Note: CG, SD, and DD represent ditches of different depths for drainage during the wheat season, CG: control group; SD: shallow ditch; DD: deep ditch. Lowercase letters indicate significant differences between treatments (p < 0.05); values following ± are the standard errors (n = 3) of the replicates, and different letters indicate that the differences between treatments reached a significant level.
Table
Table 6. Cumulative N2O emissions in each treatment during the rice–wheat season (kg hm−2).
Table 6. Cumulative N2O emissions in each treatment during the rice–wheat season (kg hm−2).
Treatments201320142015
Rice SeasonWheat SeasonRice SeasonWheat SeasonRice SeasonWheat Season
CG0.98 ± 0.16 b1.36 ± 0.46 b0.96 ± 0.52 b1.19 ± 0.54 a0.55 ± 0.20 c4.00 ± 1.76 c
SD1.08 ± 0.11 a,b1.33 ± 1.87 b1.34 ± 0.46 a,b0.82 ± 0.26 c1.02 ± 0.10 b4.28 ± 0.81 b
DD1.13 ± 0.09 a1.51 ± 1.21 a1.46 ± 0.33 a0.95 ± 0.44 b1.13 ± 0.23 a4.52 ± 1.62 a
Note: CG, SD, and DD represent ditches of different depths for drainage during the wheat season; CG: control group; SD: shallow ditch; DD: deep ditch. Lowercase letters indicate significant differences between treatments (p < 0.05); values following ± are the standard errors (n = 3) of the replicates, and different letters indicate that the differences between treatments reached a significant level.
Table
Table 7. Average yield (kg hm−2) and yield-scaled GWP (kg CO2-Eq kg −1) in each treatment in the rice–wheat season.
Table 7. Average yield (kg hm−2) and yield-scaled GWP (kg CO2-Eq kg −1) in each treatment in the rice–wheat season.
TreatmentsCO2–e (CH4)CO2–e (N2O)Total GWPYieldGHGI
Rice seasonCG6618.83 a219.95 c6838.78 a8104.42 a0.84 a
SD2263.33 b303.87 b2567.20 c8370.12 a0.31 b
DD2420.97 b328.60 a2749.57 b8337.74 a0.33 b
Wheat seasonCG81.76 a578.58 b660.34 a4433.31 a0.15 a
SD−1.12 c567.98 c566.86 c4772.22 a0.12 b
DD18.57 b616.57 a635.14 b4642.24 a0.14 a,b
Total seasonsCG6700.59 a798.53 b7499.12 a12537.73 c0.60 a
SD2262.21 b871.85 b3134.06 c13142.34 a0.24 b
DD2439.55 b945.17 a3384.71 b12979.98 b0.26 b
Note: CG: control group; SD: shallow ditch; DD: deep ditch. According to IPCC 2013, relative to CO2, the global warming potential (GWP) (calculated as the radiative forcing potential) of N2O is 265, and the radiative forcing potential of CH4 is 28. Different letters indicate significant differences within treatment (p < 0.05) based on Duncan’s multiple range test.
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He, H.; Li, D.; Pan, F.; Wu, Z.; Wang, F.; Wu, D.; Wu, S.; Yang, S.; Ma, Y. Effects of Drainage on Greenhouse Gas Emissions and Yields of Lowland Rice—Wheat Rotation System in East China. Agronomy 2022, 12, 1932. https://doi.org/10.3390/agronomy12081932

AMA Style

He H, Li D, Pan F, Wu Z, Wang F, Wu D, Wu S, Yang S, Ma Y. Effects of Drainage on Greenhouse Gas Emissions and Yields of Lowland Rice—Wheat Rotation System in East China. Agronomy. 2022; 12(8):1932. https://doi.org/10.3390/agronomy12081932

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He, Hao, Dandan Li, Feifan Pan, Ze Wu, Fengwen Wang, Dong Wu, Sheng Wu, Shuyun Yang, and Youhua Ma. 2022. "Effects of Drainage on Greenhouse Gas Emissions and Yields of Lowland Rice—Wheat Rotation System in East China" Agronomy 12, no. 8: 1932. https://doi.org/10.3390/agronomy12081932

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