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Article

Carbon Budget of Paddy Fields after Implementing Water-Saving Irrigation in Northeast China

1
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, China
2
Key Laboratory of Agricultural Water Resources Use, Ministry of Agriculture and Rural Affairs, Northeast Agricultural University, Harbin 150030, China
3
School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150006, China
4
College of Agricultural Science and Engineering, Hohai University, Nanjing 210024, China
5
School of Agriculture and Hydraulic Engineering, Suihua University, Suihua 152001, China
*
Authors to whom correspondence should be addressed.
Agronomy 2022, 12(6), 1481; https://doi.org/10.3390/agronomy12061481
Submission received: 8 May 2022 / Revised: 16 June 2022 / Accepted: 17 June 2022 / Published: 20 June 2022

Abstract

:
Water-saving irrigation is recognized as an effective agricultural management due to water security and environmental protection problems. In Northeast China, an increasing number of paddy fields are shifting from conventional irrigation to water-saving irrigation. However, there is limited knowledge regarding the carbon (C) budget of paddy fields after implementing water-saving irrigation in Northeast China. A 2-year consecutive field study was performed from 2018 to 2019 using three different irrigation regimes (conventional irrigation (FI), controlled irrigation (CI), and intermittent irrigation (II)) and two nitrogen (N) fertilization levels (110 and 165 kg N ha−1) in a paddy field of Northeast China. The present study aimed to quantify the net ecosystem C budget (NECB) and net global warming potential (net GWP) after the implementation of water-saving irrigation in Northeast China. Both CI and II enhanced the C sequestration capacity of this paddy field. The net primary productivity (NPP) under CI and II was higher than FI by 18–38% and 11–33%, respectively, when the same N fertilization level was applied. The NECB ranged from 1151 to 2663 kg C ha−1, indicating that all treatments acted as net C sinks. II increased the NECB through increasing NPP, which exceeded increased removal of harvest and C mineralized losses. Under II, the NECB was significantly higher than FI and CI when the same N fertilization level was applied (p < 0.05). The net GWP under II and CI was significantly lower than FI (p < 0.05). The net GWP under II was lower than CI when the N fertilization level was 165 kg N ha−1, but no significant differences were detected. These results demonstrated that the II with 165 kg N ha−1 of paddy fields strongly decreased net GWP in Northeast China to combat global climate change.

1. Introduction

Rice is a main food for >50% of the world’s population. As the largest rice-producing country, China shares ~19% of the rice-growing area and 32% of the rice yield in the world [1]. Given the decline in the availability of water resources and increasing demands and environmental benefits for water-saving irrigation, several paddy fields with conventional irrigation (FI) have been replaced by water-saving irrigation in China [2,3]. Some widely used water-saving irrigation regimes, including rain-gathering irrigation (RGI), controlled irrigation (CI), and intermittent irrigation (II), have been applied across China, of which CI and II are the main regimes in Northern China [4,5,6]. Previous studies demonstrated that water-saving irrigation regimes enhance C sequestration by affecting plant growth due to differences in the amount of irrigation, irrigation frequency, and irrigation intervals during the rice cropping season [7,8]. Akter et al. reported that appropriate water-saving irrigation regimes promoted root activity, which thereby benefited other physiological processes and resulted in rice growth [9]. Wang et al. found that the application of water-saving irrigation reduced N loss, and more N was absorbed for rice growth, which thereby increased crop yields [10]. Moreover, some studies have reported that water-saving irrigation will affect plant growth and greenhouse gas (GHG) emissions after its implementation [11,12,13].
The effects of irrigation on GHG emissions are a result of soil moisture regulation. It is well-known that soil moisture is a key factor in GHG emission regulation as it affects microbe activities and other relevant processes [14,15]. Water-saving irrigation improves soil aeration by regulating soil moisture. Soil aerobic conditions strengthen microbial activities and soil respiration and thus promote CO2 production, which is emitted from the soil through the roots and soil respiration [16]. Qi et al. reported that the irrigation mode, irrigation volume, and irrigation frequency influence CO2 emissions [17]. Yang et al. reported that CI strongly increased soil respiration, which resulted in a decrease in NEE in paddy fields [18]. Nevertheless, soil aerobic conditions significantly inhibit CH4 emissions as they impede methanogen activities and all related processes and increase the redox potential [19]. The results of two experiments indicated that CI decreased CH4 emissions by 34% and 83.5%, respectively, compared to FI in Northeast and Southeast China [20,21]. Previous studies investigated GHG emissions and C sequestration under different water-saving irrigation regimes. However, the C budget of paddy field ecosystems remains uncertain after water-saving irrigation is implemented. Paddy field ecosystems may act as either a C sink or C source after water-saving irrigation is implemented. Therefore, it is important to understand the balance between C sequestration and GHG emissions in paddy fields, as well as enhance the C sequestration capacity to establish water-saving agricultural production.
Aiming to meet the needs of rice yields in China, rice-growing areas have increased rapidly in Northeast China, especially in Heilongjiang Province [22]. The National Bureau of Statistics of China reported that the total paddy field area was 2.68 × 106 ha in 2000 and increased at a rate of 0.16 × 106 ha year−1. In 2017, the total paddy field area of Northeast China reached 5.62 × 106 ha. Currently, FI is used in most areas of Northeast China. Such a rapid increase in paddy field areas requires a great deal of water for agricultural irrigation. Moreover, the population growth, urbanization, and industrialization of Northeast China, along with water resource quality decline, have aggravated the dire shortage of agricultural irrigation water. Due to the severe shortage of agricultural irrigation water, government departments have attempted to promote water-saving irrigation through training and other means every year. In 2018, the rice water-saving irrigation area of Heilongjiang Province reached 1.24 × 106 ha, and the annual water savings was 1.85 × 108 m3 [23]. Additionally, the effects of environmental factors and basic soil physical-chemical properties on rice growth and C mineralized losses vary across different regions. Therefore, selecting the most appropriate irrigation regime is vitally important for Northeast China.
This study aimed to address the following questions: (1) Which irrigation regimes are best for Northeast China? (2) How much of an effect does water-saving irrigation have on the balance between C mineralized losses and C sequestration? We aimed to quantify the net ecosystem C budget (NECB) and the emissions of CH4, as well as their contributions to the net GWP after implementing water-saving irrigation. The aims of this study were to (1) explore whether paddy fields act as a C sink or C source after implementing water-saving irrigation, and (2) identify which water-saving irrigation regime can decrease net GWP in Northeast China to combat global climate change.

2. Materials and Methods

2.1. Site Description

The field site was located in Suihua, Heilongjiang Province, China (46°57′28″ N, 127°40′45″ E; 168 m.a.s.l.), which is a typical black soil paddy field growing area at the intersection of the Songnen Plain and Xiao Xing’an Mountains. The site has a monsoon climate in a cold temperate zone. In this region, the average annual precipitation was 577 mm, and the average air temperature was 1.69 °C. The soil texture of this region was classified as sandy clay, and the physical-chemical properties of the 0–20 cm layer were pH 6.6 (H2O, 1:1), 40.6 g kg−1 organic matter, 186.8 mg kg−1 alkaline N, 34.9 mg kg−1 available p, and 106.8 mg kg−1 available K.
The dominant cropping system in the region is single cropping rice, which is cultivated ~120 days during the summer and autumn. Thereafter, the soil is frozen under extremely cold conditions during the fallow season. Prior to experimentation, the test field had rice planted for >20 years and received frequent farmyard manure input. Measurements were carried out in a 2-year field experiment from 2018 to 2019. Straw was shredded (<5 cm) by an automated pulverizer and returned to the soil at a depth of 20 cm at the time of harvest.

2.2. Experimental Design

The experiment was laid out in a spilt-plot design with a water-saving irrigation regime, and N fertilization was used as sub-plots. The irrigation regimes included (1) FI, (2) CI, and (3) II (Table 1). II sets the irrigation amount and intervals during the rice cropping season to make the paddy field in shallow water or no surface water conditions. CI keeps the paddy field in shallow water only in the turning green stage while allowing the paddy field under no surface water conditions throughout the rest of the cropping season. In the experimental area, the amount of N, P2O5, and K2O is recommended as 110, 45, and 80 kg ha−1, respectively [24,25,26,27]. The N fertilization levels consisted of (1) 110 kg N ha−1 (N110) and (2) 165 kg N ha−1 (N165) (>50% conventional N fertilization application; more N fertilizer is applied after returning straw) [28,29]. One day before rice transplanting, 45% of the N fertilizer was applied as basal fertilizer. Twenty-four and seventy-two days after transplanting, 20% and 35% of N fertilizer were applied as tillering and grain fertilizer, respectively. The application rates of P2O5 and K2O were the same in all treatments (45 kg ha−1 P2O5 and 80 kg ha−1 K2O). Before rice transplanting, 50% of the K2O was applied, and the other 50% of the K2O was applied at the leaf age of 8.5. Each treatment was laid out with 3 replicates. In total, there were 6 main-plots and 18 sub-plots. The size of the sub-plots was 10 × 10 m. Between each plot, a concrete barrier (height: 40 cm) was laid down as a barrier to prevent the exchange of water and fertilizer. Twenty-one-day-old rice seedlings (3 plants per hill, Longqing 3, China) were transplanted by automatic machine. The planting density was 16.67 cm × 30 cm. All other agricultural management practices, including raising seeding, pesticide application, and harvesting, were the same for each treatment [30]. In 2018 and 2019, the seedlings were transplanted on May 18 and May 19 and rice was harvested on September 22 and September 21.

2.3. C Mineralized Losses

Heterotrophic respiration (Rh) was measured in situ using LI-COR closed-chamber soil respiration system (LI-COR 8100; Li-cor Inc; Lincoln, MI, USA) [31]. PVC collars (10 cm inner diameter, 50 cm in height) were laid out with 3 replicates in each plot during the rice cropping season. In each collar, rice was not cultivated, and weeds were entirely removed. PVC collars were drilled to 5–50 cm, through which the management of water and fertilizer inside and outside the PVC collars were consistent, and roots were prevented from entering. PVC collars were inserted into the soil to 45 cm after rice was transplanted. Measurements were carried out from transplantation to harvest once per week. The measuring time was fixed between 9:00 and 11:00 a.m., as the Rh and CH4 fluxes during this time quantum were close to the mean daily Rh and CH4 fluxes [32]. In the event of heavy rainfall, the measurement time was delayed. Methane fluxes were measured at the same time on the same day using the static chamber-gas chromatography method. Static chambers were also laid out with 3 replicates in each plot during the rice cropping season. The static chambers consisted of a chamber and stainless-steel base. The chambers were made of transparent organic glass. Each chamber was equipped with an air thermometer and electronic fan. Chambers were removed from the base, except during gas collection. The stainless-steel base was embedded in each plot before transplanting with a sealing groove (3 cm width, 5 cm height) reserved at the top. While taking measurements, water was injected to seal the groove to avoid gas exchange in the chamber with outside air. At 0, 10, 20, and 30 min after chamber closure, 4 gas samples were collected using a 50 mL E-Switch gas bag via a rubber tube for each CH4 flux measurement. The temperature inside the chamber was also recorded [33]. All gas samples were analyzed within 24 h using gas chromatography (GC-2010 Plus; Shimadzu Corporation; Kyoto; Japan). The gas chromatograph was equipped with a flame ionization detector (FID); methane concentration was analyzed at 200 °C; the carrier gas was N2 with a purity of 99.99%.
Methane fluxes were calculated based on changes in their concentrations throughout the sampling period and estimated as the slope of the curve of the concentration versus time [34]. Then, CH4 fluxes were calculated as follows [35]:
F = ρ × h × d c / d t × 273 / 273 + Τ
where F is the CH4 fluxes (mg m−2 h−1), ρ is the density of CH4 under a standardized state (0.714 mg cm−3), h is the effective height of the chamber above the soil or surface water (m), dc/dt is the rate of increase of CH4 gas concentrations in the chamber (g m−3 d−1), and T is the mean air temperature inside the chamber at the time of sampling.
The seasonal Rh (kg C ha−1) and CH4 fluxes (kg C ha−1) for the entire cropping period were calculated as follows [36]:
S e a s o n a l   R h   a n d   C H 4 = i n ( E i × D i )
where Ei is the Rh (μmol m−2 s−1) and CH4 fluxes (mg m−2 h−1) at the ith interval during sampling, Di is the number of days between ith sampling and (i − 1)th sampling, and n is the sampling number.

2.4. NECB Calculation

Based on a mass C budget approach, the NECB (kg C ha−1) was calculated between the rate of C gain or loss in a cropping system. Previous studies demonstrated that the NECB with a positive value indicates that paddy fields act as a C sink, and a negative value indicates a C source. The NECB was calculated as follows [37,38].
R e = R a + R h
N E C B = C i n p u t C o u t p u t ( N P P + R a ) ( R e + H a r v e s t + C H 4 ) N P P R h H a r v e s t C H 4
where NPP (kg C ha−1) is the net primary production during rice cropping season and the fallow season [39], and Re is the sum of plant respiration Ra and Rh [40]; Rh was measured using the above closed-chamber soil respiration system, and CH4 fluxes were measured by the static chamber-gas chromatography method. In the event that all residues were returned to the field, only grain was moved from the field at the time of harvest. Soil C losses through runoff and leaching were typically excluded from the calculations and therefore not included in the current study.
Grain, stem, and leaf were harvested using sickle in each plot, and the size of the sampling frame was 100 × 100 cm. Roots were acquired from the 30 cm soil layer using a root extractor (5.8 cm inner diameter) in each plot and were washed using a pressure-water gun [41]. Grain, stem, leaf, and root were placed in a drying oven at 105 °C for 30 min, dried at 70 °C to a constant mass, and weighed. Grain, stem, leaf, and root were ground into a powder. Then, sample C was measured using a vario TOC elemental analyzer (Elementar vario TOC; Elementar; Hanau; Germany). The NPP of rice during rice cropping was calculated as follows [42,43,44,45]:
N P P = N P P g r a i n + N P P s t e m + N P P l e a f + N P P r o o t + N P P l i t t e r + N P P r h i z o d e p o s i t
in which rice NPP(grain, stem, leaf, root) was calculated using the dried matter weight multiplied by the C content at the time of harvest. NPPlitter and NPPrhizodepoait were estimated by other rules. Litter accounted for ~5% of the total dried matter weight, and the C leaf content was used to calculate NPPlitter [46]. NPPrhizodepoait mainly included exudates, exfoliated root hairs, and dead root and was calculated as 11% of the total C content [47].

2.5. Net Global Warming Potential

The net GWP (kg C ha−1) was used to estimate the climatic impact of the paddy field system after implementing water-saving irrigation. N2O emissions were not considered in this study. The net GWP was calculated as follows [48]:
N e t   G W P = 28 × S e a s o n a l   C   H 4 44 / 12 × N E C B
where the warming potential of CH4 was 28 on a 100-year time horizon [49], Seasonal CH4 represents cumulative fluxes of CH4 during the entire cropping period, and NECB represents the rate of C gain or C loss in a cropping system; 44/12 was the conversion coefficient of C to CO2.

2.6. Statistical Analysis

All formula calculations were calculated using Excel 2010. Statistical analyses were performed by SPSS v19.0 (IBM). Univariate multivariate analysis of variance was used to test the effects of irrigation regime, N fertilization, year, and their interactions on NPP and the components of NECB and net GWP. Three-way ANOVAs were used to evaluate significant differences in all treatments. The significant threshold of all statistical analyses was p < 0.05. All figures were visualized using ORIGIN v9.0 (OriginLab).

3. Results

3.1. Environmental Conditions

The typical climatic conditions of the study site were observed during the 2-year field study; environmental conditions did not differ considerably from 2018 to 2019 (Figure 1). During rice cultivation, the daily air temperature ranged from 2.0 °C to 37.6 °C in 2018 and from 1.7 °C to 33.7 °C in 2019. The seasonal precipitation in 2018 (563.6 mm) was higher during rice cultivation than in 2019 (542.5 mm).

3.2. Net Primary Productivity

The rice dried matter weight and their C contents are presented in Figure 2. Compared with FI, CI and II increased dried matter weight and rice C contents when the same N fertilization application level was applied. The increased intensity of rice dried matter weight of CI was strongest, and CI and II were higher than FI by 15–32% and 9–30%, respectively. The C contents of each part of the rice plant did not significantly differ among the three irrigation regimes. The dried matter weight and C contents of rice under N165 were higher than N110.
The NPP components are summarized in Table 2. NPPgrain, NPPstem, NPPleaf, NPPlitter, NPPrhizodeposit, and NPP under CI and II were significantly higher than FI when the same N fertilization level was applied (p < 0.05). Additionally, NPPgrain, NPPstem, NPPleaf, NPPlitter, NPPrhizodeposit, and NPP under CI were higher than II. NPP under CI and II were higher than FI by 18–38% and 11–33%, respectively. However, NPProot under FI was higher than CI and II. NPPgrain, NPPstem, NPPleaf, NPProot, NPPlitter, NPPrhizodeposit, and NPP under N165 were all significantly higher than N110. These changes exhibited similar trends in 2018 and 2019. Nonetheless, NPPgrain, NPPstem, NPPleaf, NPPlitter, NPPrhizodeposit, and NPP of each treatment in 2018 were higher than in 2019.

3.3. Carbon Mineralized Losses

Carbon mineralized loss mainly consisted of Rh and CH4 fluxes and was visualized during the rice cropping season (Figure 3). The CH4 fluxes of each treatment sharply increased after the turning green stage, where the highest CH4 fluxes were observed at the late tillering stage. Thereafter, CH4 fluxes dramatically decreased at the drainage stage and reached another peak at the jointing-booting stage. Except for the drainage period, CH4 fluxes under FI were higher than CI and II when the same N fertilization level was applied. Methane fluxes reached up to 55.53 mg m−2 h−1 in FN165. Rh in each treatment increased after the turning green stage, where the highest Rh fluxes were observed at the late tillering stage and decreased to a low level at the drainage stage. During rice cropping, Rh under CI and II was higher than FI when the same N fertilization level was applied. Rh reached 9.13 μmol m−2 s−1 in CN165. Furthermore, the Rh and CH4 fluxes under N165 were higher than N110 under all irrigation regimes.

3.4. Net Ecosystem Carbon Budget

Seasonal Rh, CH4 fluxes, and NECB are summarized in Table 3. Seasonal Rh ranged from 703 to 2709 kg C ha−1 at the rice cropping stage. Seasonal Rh of CN165 was the highest in 2018, while FN110 was the lowest in 2019. Seasonal Rh under CI was significantly higher than FI and II when the same N fertilization level was applied (p < 0.05). Seasonal Rh under CI was 2.64–3.08 and 1.90–2.19-times higher than FI and II, respectively. Seasonal CH4 fluxes ranged from 389 to 960 kg C ha−1 at the rice cropping stage. Seasonal CH4 fluxes of FN165 were the highest, while CN110 was the lowest in 2018. Seasonal CH4 fluxes under FI were significantly higher than CI and II when the same N fertilization level was applied (p < 0.05). Seasonal CH4 fluxes under FI were 1.65–2.23 and 1.65–1.70-times higher than CI and II, respectively. Moreover, seasonal Rh and CH4 fluxes under N165 were higher than N110 under all irrigation regimes.
The NECB ranged from 1151 to 2663 kg C ha−1, indicating that all treatments acted as a net C sink. The NECB of IN165 was the highest in 2018, while CN110 was the lowest in 2019. Results revealed that NECB under II was significantly higher than FI and CI when the same N fertilization level was applied (p < 0.05). The NECB under II was higher than FI and CI by 9–57% and 60–91%, respectively. The NECB under FI was higher than CI when the same N fertilization level was applied. The NECB under N165 was significantly higher than N110 under all irrigation regimes (p < 0.05).

3.5. Net Global Warming Potential

The net GWP is summarized in Table 3. The net GWP of the NECB and CH4 fluxes ranged from 6037 to 19,377 kg C ha−1. The net GWP of FN110 was the highest in 2019, while IN165 was the lowest in 2018. When the same N fertilization level was applied, net GWP under II and CI were significantly lower than FI (p < 0.05). The net GWP under II was also lower than CI when the N fertilization level was 165 kg N ha−1, but no significant differences were detected. Moreover, the net GWP under N165 was lower than N110 under II.

4. Discussion

Whether paddy field ecosystems function as C sinks or sources is related to the balance of organic C inputs and outputs. It is well-known that appropriate agricultural practice can enhance the ability of C sequestration and mitigate CO2 emissions to combat climate change [50]. In Northeast China, due to the continuous expansion of paddy rice areas, it is imperative to implement water-saving irrigation. Government departments reported that the rice water-saving irrigation area of Heilongjiang Province will exceed 2.8 × 107 hm2 by 2025, accounting for ~70% of the rice-growing area [51]. However, the status of the C budget in paddy fields remains uncertain. In paddy field ecosystems, Re and C sequestration in rice plants are likely the main processes affecting the C balance. However, additional C inputs (only contain organic fertilizer) and outputs (grain, even straw, except for residues) also affect the C balance of paddy field ecosystems [52,53]. The calculation of NECB accounts for all C inputs and outputs to evaluate the C balances of paddy field ecosystems. The NECB has been recognized as a scientific evaluation index for determining short-term C balances [54,55].
In single cropping rice paddy ecosystems, more organic C inputs can increase rice dried matter weight, which indicates that SOC stock increases [56,57]. In this study, no additional C inputs were detected, and the C inputs only contained NPP. Results revealed that NPP under CI and II was higher than FI by 18%–38% and 11%–33%, respectively. This may be because CI and II increased the rice dried matter weight and C contents. These results confirmed that an increase in rice dried matter weight and C contents resulted in increased C inputs. Kim et al. reported that the higher-level N fertilization significantly increased NPP, mainly through the increase of rice dried matter weight [39]. These findings were consistent with the results of this study. This study showed that NPP under N165 was higher than N110.
In single cropping rice paddy ecosystems, harvest removal (grain, including straw, except for residues) and C mineralized loss are the main C output sources [58,59]. In China, the policy stipulates that anyone who burns straw will be punished, so most farmers return all straw to the field at the time of harvest. In this region, grain was removed at the time of harvest, and harvest removal only contained NPPgrain. Approximately 55–69% of C outputs were covered by harvest removal, while 30–45% were covered by C mineralized loss. These results indicated that C mineralized loss accounts for a small proportion of C outputs relative to harvest removal; thus, the contribution of C mineralized loss to NECB was smaller than NPPgrain.
Rh and CH4 are the main pathways of C mineralized loss from soil to the atmosphere in paddy field ecosystems [60]. Seasonal Rh under CI was 2.64–3.08 and 1.90–2.19-times higher than FI and II, respectively. No surface water management of water-saving irrigation resulted in a higher Rh in the paddy field. The soil oxygen content increases due to frequent alternate wet and dry conditions. The soil organic matter decomposition was promoted mainly through the increases of the soil oxygen content and the redox potential, and its products provide more substrates for Rh. Finally, water-saving irrigation keeps the paddy field in shallow water or no surface water conditions and reduces the distance of CO2 emissions into the atmosphere [18]. In this study, seasonal CH4 fluxes under FI were 1.65–2.23 and 1.65–1.70-times higher than CI and II, respectively. Methane production is mainly derived from anaerobic methane bacteria. FI provides a long-term anaerobic environment that promotes methane production [61]. However, water-saving irrigation improves soil aeration, which greatly reduces CH4 production.
The year, irrigation regime, and N fertilization application level of each factor had a significant effect on NECB, as well as the interaction between year and N fertilization application level, and the interaction between irrigation regime and N fertilization application level (Table 3). The NECB under N165 was significantly higher than N110 under all irrigation regimes. This indicates that the N fertilization level can promote the C sink capacity of paddy fields. The NECB under II was higher than FI by 9–57%. These results indicated that the NECB increased after the implementation of II, mainly because the increased NPP outweighed the increased harvest removal. However, the NECB under FI was higher than CI. This may be because the NPPgrain of CI was significantly higher than FI.
The year and irrigation regime of each factor had a significant effect on net GWP (Table 3). The net GWP decreased significantly after the implementation of water-saving irrigation due to increased C losses, which were far lower than the decreased CH4 fluxes. These results confirmed our hypothesis that water-saving irrigation could decrease net GWP. Wu et al. found that NECB with a positive value can contribute to mitigating GHG emissions [62]. This is consistent with our results, which showed that net GWP under II was lower than FI due to the higher NECB under II. However, the NECB under CI was lower than FI when the same N fertilization level was applied, but its net GWP was lower than FI. This may be because seasonal CH4 fluxes under CI were significantly lower than FI, indicating that CH4 fluxes can also contribute to mitigating GHG emissions. However, the current study had some limitations. For instance, we only evaluated direct GHG emissions. Emissions from irrigation power consumption, agricultural machinery fuel oil consumption, and other farm operations are also crucial for the C budget of paddy fields ecosystems [63,64]. All correlative GHG emissions should be considered to evaluate the C budget of paddy field ecosystems after the implementation of water-saving irrigation in future studies. At present, the optimal management pattern we obtained was very consistent with the water-saving and emission reduction policies advocated by the government. Our results also provide data support for the government to determine the appropriate irrigation methods and fertilizer amounts. At the same time, the government should strengthen the promotion to enhance farmers’ awareness of water-saving irrigation. On the one hand, water-saving irrigation reduces farmers’ planting costs; on the other hand, water-saving irrigation reduces the indirect emissions caused by irrigation involved in life cycle assessment (LCA).

5. Conclusions

In Northeast China, we conducted a two-year field experiment to measure Rh, CH4 fluxes, and NPP and calculated NECB and net GWP. Results showed that single cropping rice paddy fields under each treatment act as net C sinks. The NECB was positive and ranged from 1151 to 2663 kg C ha−1. Compared to FI, single cropping rice paddy fields enhanced C sink intensity after the implementation of II. II increased the NECB through increasing NPP, which exceeded the increased removal of harvest and C mineralized losses. The NECB under II was higher than that of FI and CI by 9–57% and 60–91%, respectively. Compared to FI, the net GWP decreased after the implementation of CI and II. The net GWP under II was also lower than CI when the N fertilization level was 165 kg N ha−1 in 2018 and 2019. These results indicate that II with 165 kg N ha−1 is the optimal management pattern for paddy fields as it can decrease net GWP in Northeast China to combat global climate change. Furthermore, the LCA considering indirect GHG emissions should be applied to evaluate the C of paddy field ecosystems after the implementation of water-saving irrigation in future studies.

Author Contributions

Methodology, T.L.; software, J.L.; validation, Z.Z. (Zhongxue Zhang) and Z.Q.; formal analysis, T.N. and P.C.; investigation, Y.H. and L.J.; data curation, T.L.; writing—original draft preparation, T.L.; writing—review and editing, Z.Z. (Zhongxue Zhang) and Z.Z. (Zuohe Zhang); funding acquisition, Z.Z. (Zhongxue Zhang). All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Program on Key Basic Research Project of China (2016YFC0400108), General Projects of the National Natural Science Foundation of China (52079028), and the Opening Project of Key Laboratory of Efficient Use of Agricultural Water Resources, Ministry of Agriculture and Rural Affairs of the People’s Republic of China in Northeast Agricultural University (AWR2021002).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We would like to thank the Heilongjiang Province Hydraulic Research Institute for providing us access to the test sites, as well as for their valuable time providing us with management information. We would also like to acknowledge the National Bureau of Statistics of China for their data support.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Changes in maximum and minimum air temperature and precipitation in (a) 2018 and (b) 2019.
Figure 1. Changes in maximum and minimum air temperature and precipitation in (a) 2018 and (b) 2019.
Agronomy 12 01481 g001
Figure 2. Changes of (a) dried matter weight and (b) their C contents in paddy fields after the implementation of water-saving irrigation.
Figure 2. Changes of (a) dried matter weight and (b) their C contents in paddy fields after the implementation of water-saving irrigation.
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Figure 3. Changes in (a) CH4 fluxes and (c) Rh in 2018 and changes in (b) CH4 fluxes and (d) Rh in 2019 of paddy field after the implementation of water-saving irrigation.
Figure 3. Changes in (a) CH4 fluxes and (c) Rh in 2018 and changes in (b) CH4 fluxes and (d) Rh in 2019 of paddy field after the implementation of water-saving irrigation.
Agronomy 12 01481 g003
Table 1. Different water management regime at rice growth stages.
Table 1. Different water management regime at rice growth stages.
Irrigation RegimeTurning
-Green Stage
Early
Tillering Stage
Middle
Tillering Stage
Late
Tillering Stage
Jointing
-Booting Stage
Heading-Flowering StageMilk
Stage
Yellow -Ripe Stage
FI0~30 mm0~50 mm0~50 mmDrainage0~50 mm0~50 mm0~50 mmNaturally drying
CI0~30 mm0.7 θs~00.7 θs~0Drainage0.8 θs~00.8 θs~00.7 θs~0Naturally drying
II0~30 mm0~40 mm0~40 mmDrainage0~30 mm0~40 mm0~40 mmNaturally drying
Note: θs refers to soil saturated water content mass fraction in the root layer. FI, conventional irrigation; CI, controlled irrigation; II, intermittent irrigation.
Table 2. Changes in NPP components in paddy fields after the implementation of water-saving irrigation regime.
Table 2. Changes in NPP components in paddy fields after the implementation of water-saving irrigation regime.
Year2018 2019
TreatmentFN110FN165CN110CN165IN110IN165FN110FN165CN110CN165IN110IN165
NPPgrain
(kg C ha−1)
2824 ±
71e
3578 ±
126cd
3766 ±
89c
4765 ±
119a
3490 ±
106d
4290 ±
87b
1928 ±
42g
2538 ±
53f
2985 ±
98e
3580 ±
186cd
2829 ±
97e
3573 ±
114cd
NPPstem
(kg C ha−1)
1529 ±
38e
1934 ±
59b
1738 ±
49cd
2062 ±
61a
1685 ±
56cd
2031 ±
46ab
1285 ±
34f
1693 ±
58cd
1687 ±
53cd
2046 ±
41a
1648 ±
29d
1781 ±
46c
NPPleaf
(kg C ha−1)
514 ±
13g
664 ±
14cd
644 ±
20de
741 ±
19a
621 ±
21e
684 ±
16bc
402 ±
12h
613 ±
16e
562 ±
17f
702 ±
19b
522 ±
15g
632 ±
17de
NPProot
(kg C ha−1)
610 ±
13b
716 ±
22a
465 ±
17e
569 ±
21c
507 ±
15d
627 ±
17b
472 ±
17de
627 ±
15b
416 ±
17f
507 ±
16d
452 ±
14e
597 ±
18bc
NPPlitter
(kg C ha−1)
268 ±
6e
339 ±
10c
327 ±
8cd
402 ±
10a
314 ±
10d
371 ±
9b
205 ±
5f
270 ±
7e
279 ±
9e
339 ±
9c
272 ±
7e
321 ±
9cd
NPPrhizodeposit
(kg C ha−1)
602 ±
14e
758 ±
24c
727 ±
17cd
895 ±
22a
693 ±
22d
839 ±
18b
450 ±
11f
602 ±
15e
622 ±
20e
752 ±
27c
600 ±
16e
724 ±
20cd
NPP
(kg C ha−1)
6347 ±
148e
7988 ±
249c
7666 ±
181cd
9435 ± 234a7310 ±
229d
8841 ±
190b
4742 ±
119f
6343 ±
159e
6552 ±
213e
7926 ± 285c6323 ±
173e
7628 ±
211cd
Statistical analysis
Year
(A)
Irrigation regime (B)N fertilization (C)NPPgrain
(kg C ha−1)
NPPstem
(kg C ha−1)
NPPleaf
(kg C ha−1)
NPProot
(kg C ha−1)
NPPlitter
(kg C ha−1)
NPPrhizodeposit
(kg C ha−1)
A (year)******************
B (irrigation regime)******************
C (N fertilization)******************
A × B***ns**nsns
A × C*ns**nsnsns
B × Cns*****nsnsns
A × B × Cns*nsnsnsns
Different lowercase letters indicate significant differences between treatments from 2018 to 2019 (p < 0.05). Values are presented as the mean ± standard error of 3 replicates. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, non-significant F-values. NPP, net primary productivity; NPPgrain, NPPstem, NPPleaf, NPProot, NPPlitter, and NPPrhizodeposit, net primary productivity of grain, stem, leaf, root, litter, and rhizodeposit.
Table 3. Seasonal Rh, CH4 fluxes, NECB, and net GWP in a paddy field after the implementation of water-saving irrigation.
Table 3. Seasonal Rh, CH4 fluxes, NECB, and net GWP in a paddy field after the implementation of water-saving irrigation.
YearTreatmentC Input (kg C ha−1)C Output (kg C ha−1)NECB
(kg C ha−1)
net GWP
(kg C ha−1)
NPPSumHarvestRhCH4Sum
2018FN1106437 ±
148e
6437 ±
148e
2824 ±
71e
758 ±
155ef
869 ±
20b
4451 ±
244d
1896 ±
97c
17371 ±
252b
FN1657988 ±
249c
7988 ±
249c
3578 ±
126cd
1000 ±
150def
960 ±
28a
5538 ±
291c
2450 ±
76b
17915 ±
543ab
CN1107666 ±
181cd
7666 ±
181cd
3766 ±
89c
2338 ±
166b
389 ±
19e
6493 ±
188b
1173 ±
8f
4769 ±
528de
CN1659435 ±
234a
9435 ±
234a
4765 ±
119a
2709 ±
145a
482 ±
21d
7956 ±
281a
1478 ±
123d
8067 ±
749cd
IN1107310 ±
229d
7310 ±
229d
3490 ±
106d
1067 ±
142de
516 ±
17cd
5072 ±
247c
2238 ±
73b
6242 ±
246e
IN1658841 ±
190b
8841 ±
190b
4290 ±
87b
1324 ±
143d
564 ±
19c
6178 ±
249b
2663 ±
64a
6037 ±
416e
2019FN1104742 ±
119f
4742 ±
119f
1928 ±
42g
703 ±
140f
856 ±
40b
3488 ±
222e
1255 ±
115ef
19377 ±
819a
FN1656343 ±
159e
6343 ±
159e
2538 ±
53f
921 ±
164ef
885 ±
39b
4344 ±
256d
1999 ±
112c
17441 ±
862b
CN1106552 ±
213e
6552 ±
213e
2985 ±
98e
1931 ±
156c
484 ±
37d
5401 ±
271c
1151 ±
134f
9341 ±
1133c
CN1657926 ±
285c
7926 ±
285c
3580 ±
186cd
2430 ±
156ab
513 ±
37cd
6521 ±
378b
1404 ±
96de
9196 ±
962c
IN1106323 ±
173e
6323 ±
173e
2829 ±
97e
1008 ±
162def
513 ±
36cd
4351 ±
282d
1973 ±
122c
7131 ±
801de
IN1657628 ±
211cd
7628 ±
211cd
3573 ±
114cd
1277 ±
153d
535 ±
37cd
5386 ±
275c
2243 ±
113b
6757 ±
998de
Statistical analysis
A (year)**********ns********
B (irrigation regime)************************
C (N fertilization)*********************ns
A × Bnsns*ns**ns***ns
A × Cnsns*nsnsnsnsns
B × Cnsnsnsnsnsns**ns
A × B × Cnsnsnsnsnsnsnsns
Different lowercase letters indicate significant differences between treatments from 2018 to 2019 (p < 0.05). Values are presented as the mean ± standard error of 3 replicates. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, non-significant F-values. Rh, heterotrophic respiration; CH4, seasonal CH4 fluxes; NPP, net primary productivity; NECB, net ecosystem carbon budget; net GWP, net global warming potential.
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Li, T.; Nie, T.; Chen, P.; Zhang, Z.; Lan, J.; Zhang, Z.; Qi, Z.; Han, Y.; Jiang, L. Carbon Budget of Paddy Fields after Implementing Water-Saving Irrigation in Northeast China. Agronomy 2022, 12, 1481. https://doi.org/10.3390/agronomy12061481

AMA Style

Li T, Nie T, Chen P, Zhang Z, Lan J, Zhang Z, Qi Z, Han Y, Jiang L. Carbon Budget of Paddy Fields after Implementing Water-Saving Irrigation in Northeast China. Agronomy. 2022; 12(6):1481. https://doi.org/10.3390/agronomy12061481

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Li, Tiecheng, Tangzhe Nie, Peng Chen, Zuohe Zhang, Jiaxin Lan, Zhongxue Zhang, Zhijuan Qi, Yu Han, and Lili Jiang. 2022. "Carbon Budget of Paddy Fields after Implementing Water-Saving Irrigation in Northeast China" Agronomy 12, no. 6: 1481. https://doi.org/10.3390/agronomy12061481

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