Skip to main content
Environmental Sciences Europe
Download PDF

Impact of soil moisture regimes on greenhouse gas emissions, soil microbial biomass, and enzymatic activity in long-term fertilized paddy soil

Environmental Sciences Europe volume 36, Article number: 120 (2024) Cite this article

Abstract

Two potent greenhouse gases that are mostly found in agricultural soils are methane and nitrous oxide. Therefore, we investigated the effect of different moisture regimes on microbial stoichiometry, enzymatic activity, and greenhouse gas emissions in long-term paddy soils. The treatments included a control (CK; no addition), chemical fertilizer (NPK), and NPK + cattle manure (NPKM) and two moisture regimes such as 60% water-filled pore spaces (WFPS) and flooding. The results revealed that 60% water-filled pore spaces (WFPS) emit higher amounts of N2O than flooded soil, while in the case of CH4 the flooded soil emits more CH4 emission compared to 60% WFPS. At 60% WFPS higher N2O flux values were recorded for control, NPK, and NPKM which are 2.3, 3.1, and 3.5 µg kg−1, respectively. In flooded soil, the CH4 flux emission was higher, and the NPKM treatment recorded the maximum CH4 emissions (3.8 µg kg−1) followed by NPK (3.2 µg kg−1) and CK (1.7 µg kg−1). The dissolved organic carbon (DOC) was increased by 15–27% under all flooded treatments as compared to 60% WPFS treatments. The microbial biomass carbon, nitrogen, and phosphorus (MBC, MBN, and MBP) significantly increased in the flooded treatments by 8–12%, 14–21%, and 4–22%, respectively when compared to 60% WFPS. The urease enzyme was influenced by moisture conditions, and significantly increased by 42–54% in flooded soil compared with 60% WFPS while having little effect on the β-glucosidase (BG) and acid phosphatase (AcP) enzymes. Moreover DOC, MBC, and pH showed a significant positive relationship with cumulative CH4, while DOC showed a significant relationship with cumulative N2O. In the random forest model, soil moisture, MBC, DOC, pH, and enzymatic activities were the most important factors for GHG emissions. The PLS-PM analysis showed that soil properties and enzymes possessed significantly directly impacted on CH4 and N2O emissions, while SMB had indirect positive effect on CH4 and N2O emissions.

Introduction

The emission of greenhouse gases (GHGs), which are released from the agriculture sector, is an important issue in research on total global warming GHG emissions. The total agricultural emissions are approximately 10–12% of the global anthropogenic greenhouse gas emissions [1,2,3]. China emits approximately 40% of atmospheric CH4 and 60% of N2O, accounting for approximately 17% of global emissions. Furthermore, 50% of methane and 25% of nitrous oxide emissions are produced by the agricultural sector [4, 5]. In the agriculture sector, the primary source that emits these greenhouse gases is paddy soil [6, 7]. The total paddy soil area in China is approximately 3 × 107 hectares, emitting 7.7–8.0 Tg methane and 138–154 Gg nitrous oxide per year [8, 9].

Soil moisture is a significant factor that affects N2O and CH4 emissions in soils. A typical soil moisture indicator is the water-filled pore space (WFPS), which provides extensive information on the water content and total porosity of the soil system [10]. Different irrigation methods, especially alternating wetting and drying methods, have a great impact on soil moisture, which in turn plays an important role in methane and nitrous oxide emissions [10, 11]. Methanogenesis, which occurs in anaerobic situations, and methanotrophy, which arises in aerobic environments, are the two main mechanisms that determine methane emissions [8, 12]. Flooded soils create anaerobic conditions for methanogenic bacteria, while an aerobic environment is produced during the drying process, resulting in methane oxidation [12]. During rice farming with the alternating wetting and drying irrigation method, soil moisture remains elevated, potentially creating a constrained anaerobic environment for methane formation, particularly in the deep soil profile [13, 14].

Compared to upland soil, flooded soils also differ in the production of nitrous oxide due to variations in oxygen (O2) concentrations. The anaerobic environment of paddy soil limits the processes of mineralization, nitrification, and denitrification [15]. In long-term experiments, continuous flooding induces a serious anoxic environment in soils, resulting in complete denitrification and low N2O fluxes. According to studies by Xu et al., higher emissions of nitrous oxide are released when WFPS increases from 45 to 90% [16]. The nitrification process dominates the production of nitrous oxide when the WFPS increases from 60 to 70%. Amnat et al. found that soils that produce N2 limit nitrous oxide when WPFS exceeds 80% [17]. Wetland soil generates methane through methanogenesis, which occurs under anaerobic conditions during the decomposition of organic matter. Soil moisture actively maintains the moisture content at a precise level or rewets soils [18]. We need to evaluate how soil moisture affects CH4 and N2O emissions, soil microbial stoichiometry, and soil enzymes to figure out how different levels of moisture change the microbial biomass stoichiometry and enzyme activity in paddy soil and how these things relate to greenhouse gas (CH4 and N2O) soil properties and soil activities. The availability of soil nutrients, which are microorganisms' primary energy source, controls microbial biomass stoichiometry [19, 20]. The stoichiometry of soil microbial biomass indicates these microorganisms’ respective nutritional requirements for growth [21]. Mooshammer et al. compare this stoichiometry to readily available soil resources to determine if the microorganisms’ nutrient needs balance with the availability of nutrients in their surroundings. If there is a stoichiometric imbalance in the supply and demand of resources, a specific nutrient may limit microbial activity [22, 23]. Nutrient supplementation can reduce imbalances in C: N, C: P, or N: P ratios, thereby reducing microbial nutritional constraints [24]. The availability of soil nutrients, according to ecological stoichiometric theory, has a direct impact on microbial activity and growth. In other words, nutrients limit microbial growth [25]. Long-term fertilization application increases nutrient availability, especially nitrogen and phosphorous, which boosts soil microbial biomass and may reduce nutrient stoichiometry [25, 26]. By synthesizing extracellular enzymes, soil bacteria can get limited nutrients from the decomposition of soil organic materials [27, 28]. As a result, variations in enzyme activity can reflect nutritional restriction in soil bacteria to some degree [28]. Here, we evaluated the impact of moisture regimes on methane and nitrous oxide fluxes, microbial biomass stoichiometry, and enzymatic activities. Specifically, we tested the following hypotheses: (1) find out how the soil moisture content affects the stoichiometry of microbial biomass succession; (2) uncover the rate of GHG (CH4 and N2O) emissions under different moisture conditions; and (3) explore the dynamics of enzymatic activity in paddy soils.

Methods and materials

Soil samples, location, and properties

We took soil samples from the farming experimental station at Qiyang, southern China, at the National Observation and Research Station (26°45′42" N, 111°52′32" E), at a height of approximately 160 m above sea level. The annual average temperature of the air is between − 8.4 and 40 °C, and approximately 1259 mm of precipitation. This experiment started in 1982 and the cropping system was early rice, late rice, and winter fallow. We select three different fertilization treatments: control (CK), chemical fertilizer (NPK), and NPK fertilizer plus cattle manure (NPKM) (NPKM = NPK + 22.5 t/ha fresh cattle manure). All the treatments had three replicates followed by a random block group arrangement. The soil, which was initially formed from quaternary red clay, is a typical ultisol with low fertility. Its pH is 5.97, and its SOC content is 12.2 g kg−1. In the original soil, TN, TP, TK, AN, AP, and AK in the original soil were 1.5 g kg−1, 0.48 g/kg, 14.2 g kg−1, 158 mg kg−1, 9.6 mg kg−1, and 65.9 mg kg−1, respectively. We collected soil samples from three distinct locations within each plot, ranging in depth from 0 to 20 cm. Afterward, the soil samples were air-dried, pulverized, and sieved through a 2-inch mesh sieve.

Incubation and treatment details

The sieved soil was pre-incubated at 30–40% water-holding capacity and maintained at 25 °C in an incubator for one week. The purpose of pre-incubation was to stabilize the soil microbial activity. We took 200 g of sieved soil from every field and placed it in a 1-L glass jar. The collected soils had been treated with two levels of moisture (60% and flooding) [30] with long-term fertilization treatments (CK, NPK, NPKM) and were mixed well. To calculate the daily flux rates for nitric oxide and methane emissions, One-liter glass bottles containing 200 g of soil were filled and placed to incubate for 60 days at 25 °C. Before gas sampling, the glass jars were covered with a polyethylene to reduce moisture loss and permit gas exchange. The jars were weighed daily to keep the moisture constant, and distilled water was added. Separate sets of soil samples were generated to collect the gas samples and analyze soil characteristics. The physical and chemical parameters of the soil samples were examined after completing the incubation study.

N2O and CH4 emission sampling method

Gas samples were regularly taken from the jars for the first eight days and then at two-day intervals from the 9th to the 17th. From the 18th to the 32nd day, gas samples were taken from each treatment after four-day intervals and after the 32nd day once a week until the end of the incubation study. At 0, 12, and 24 h following the beginning of the test, gas samples were extracted from the jars containing each treatment. The glass jars were left open for thirty minutes to let outside air into them before beginning the gas sample. The jars were then closed with corks of rubber, fitted with three-way valves. Gas samples were taken at two different times (0 and 1 h) using a 50 ml gas-tight syringe (BD Luer-LokTM Tip, China). An Agilent greenhouse gas chromatograph (7890 GC, Agilent Technologies Australia) was used to analyze the nitrous oxide and methane emission samples immediately after they were collected. The equation that was used for calculating the fluxes was provided by [29].

$$F = \rho \times \Delta {\text{C}} \times {\text{V}} \times \left( {\frac{273}{{273 + {\text{T}}}} \times W^{ - 1} } \right),$$
$$E = \mathop \sum \limits_{i = 1}^{n} \frac{{F_{{i{ }}} + F_{i + 1} }}{2} \times \left( {t_{i + 1} - t_{i} } \right) \times 24,$$

where F denotes the nitrous oxide (µg/kg soil/h) and methane (µg/kg soil/h) emissions; ρ represents the density of a gas at room temperature. The variables W, T, V, and ∆C represent the weight (kg) and gas space volume (m3) of the soil used in the experiment, as well as the change in gas concentration between 0 and 2 h of incubation. Fi and Fi + 1 represent the methane and nitrous oxide emission rates at times ti and ti + 1, respectively, while E represents the total methane and nitrous oxide emissions.

Laboratory analysis of soil

After 60 days of incubation, 5 g of fresh soil samples was extracted with 0.05 M K2SO4 agitated for an hour, filtered using Whatman #42 filters, and tested for exchangeable ammonium and nitrate using a flow injection analyzer (Lachat Instruments, Loveland, CO, USA). The incubated soil’s pH was measured using a pH meter at a ratio of 1:2.5 for soil to deionized water. The potassium dichromate method was utilized for the determination of organic carbon [30]. Available phosphorus was measured following Murphy`s procedures [31].

For the determination of microbial biomass phosphorus (MBP), nitrogen (MBN), and carbon (MBC), we used the fumigation method [32, 33]. For the precise measurement of MBC, MBN, and MBP, incubated soil samples were subjected to chloroform for 24 h. To determine the carbon and nitrogen microbial biomass, fumigated and non-fumigated soil samples were extracted using 0.05 M K2SO4, shaken for an hour, filtered, and then sent to TOC-VCPH for determination. The values of MBC and MBN were determined by dividing the extraction efficiency (0.5) by the variance among the levels in under fumigation and non-fumigated soils. Soil samples from fumigated and non-fumigated were mixed with 1 mL KH2PO4 (250 Gp m/L) for an hour before extraction to extract microbial biomass phosphorus. Using an extraction efficiency of 0.40, the MBP was computed. The soil microbial quotient (SMQ) was determined by applying the following formula: SMQ = SMBC/SOC. A microplate methodology was used to measure the activity of these extracellular enzymes. For urease enzymes, we used the method described by Yang et al. [34]. Acid phosphatase and β-glycosidase activities were measured by the standard protocol of Tabatabai et al. [35]. The enzyme activity was measured in nmol/g soil/h.

Statistical investigation

We used SPSS software version 21 (IBM SPSS Statistics; Chicago, USA) for statistical analysis and Sigma Plot to generate data visualizations. The duplicated data were analyzed using a two-way ANOVA, with LSD testing used to find significant differences between the treatments and their interactions with N2O, CH4, and soil microbial biomass activity. We investigate relationships between soil properties using regression analysis. We performed a random forest analysis using the “random forest” package in R to evaluate the pivotal and credible predictors of soil greenhouse gases among different soil factors.

Results

Soil physiochemical properties

The chemical properties of soil that was treated with continuous fertilizer are listed in Table 1. Soil pH was significantly increased under flooding conditions compared with 60% WFPS after incubation for 60 days. The maximum pH of 5.89 was observed under flooding conditions in the NPKM treatment, while the lower pH of 5.3 was recorded at 60% WFPS in the control and chemical fertilizer treatments. The moisture had no significant effect on the SOC concentration, while significantly increased under long-term fertilization (without moisture treatment) and the highest SOC concentration was observed under NPKM followed by NPK and CK (Table 1). The DOC concentrations were significantly high with moisture and long-term fertilization. A high DOC content of 49.1 mgkg−1 was recorded in the NPKM flooded treatment, while the lower (19.7 mg kg−1) DOC was noted in the CK (60% WFPS) treatment. Different moisture regimes had non-significant effects on TP and AP, but under long-term fertilization, their effects were significant. Furthermore, the interactive effect between long-term fertilization and moisture was non-significant in all measured parameters.

Table 1 Soil chemical properties influence at 60% WFPS and flooding condition after various fertilization regimes
Full size table

Methane (CH4) and nitrous oxide (N2O) emissions

Soil CH4 emissions were significantly affected by soil moisture content and long-term fertilization. The combined NPK and manure (NPKM) treatments significantly increased CH4 emissions in flooded soil conditions compared with the CK treatment, and a decreasing trend was observed throughout the incubation period at 60% WFPS. Maximum CH4 fluxes of 3.8 µg kg−1 h−1, 3.2 µg kg−1 h−1, and 1.7 µg kg−1 h−1 were noticed on day 24 in NPKM, NPK, and CK flooded treatments, respectively (Fig. 1). Similarly, the 60% WFPS treatment, overall, showed less CH4 emissions such as on day 13 the NPKM emits 1.34 µg kg−1 h−1, whereas on day 11 small peak was also observed in CK (1.1 µg kg−1 h−1) and NPK (1.19 µg kg−1 h−1) (Fig. 1). However, the cumulative CH4 emissions were significantly increased in all treatments of flooded soil compared with 60% WPFS soil. The maximum cumulative emissions were recorded in the fertilized treatments in flooded soil compared with the control (Fig. 2).

Fig. 1
figure 1

CH4 and N2O emission fluxes in unfertilized and fertilized soils under 60% WFPS and flooded condition. Different letters show significantly different means at p < 0.05. Standard error is shown by bars; n = 3

Full size image
Fig. 2
figure 2

Cumulative CH4 and N2O emission in long-term fertilized and unfertilized soil under 60% WFPS and flooded condition. Significantly different means at p < 0.05 are represented by different letters

Full size image

The peaks of N2O emissions in all treated soils were higher at the start, and later followed a decreasing trend till the end of the incubation. Water regimes and long-term fertilizations both had a significant impact on N2O emissions. Figure 1 shows that 60% of WFPS treatments emit higher N2O emissions than flooded soil. On day 4 all the treatments (CK, NPK, and NPKM) in 60% moisture showed the highest peaks of 1.4, 6.7, and 7.1 µg kg-1 h-1, respectively (Fig. 1). Consequently, the N2O showed peaks till 10th day, while later showed no significant peak through the study period of 60 days. Similarly, in case of flooding treatment small peaks of 2.3, 3.1, and 3.5 µg kg−1 h−1 in the CK, NPK, and NPKM treatments, respectively, were observed. Cumulatively, the highest N2O emissions were recorded in NPKM (1.2 mg kg−1 h−1) at 60% WFPS, while the lowest N2O (0.4 mg kg−1) in flooded soil in CK treatment.

Soil microbial biomass stoichiometry and activity

All the microbial biomasses of carbon, nitrogen, and phosphorus (MBC, MBN, and MBP) significantly increased with different moisture regimes to long-term fertilized paddy soil. Compared with the 60% WFPS conditions, the flooded conditions significantly increased (Table 2). The MBC concentration in flooded treatments was 8–12% higher compared to 60% WFPS conditions. Under 60% WFPS conditions, the MBC values ranged from 117 to 160 mg C kg−1 (Table 2). Under long-term fertilization, the NPKM treatment showed higher MBC concentration of 182 and 160 mg C kg−1 for flooding and 60% WFPS soil, respectively (Table 2). Similarly, the MBN concentration in the present study was increased under both flooding and 60% WFPS treatment, particularly increased in flooding treatment which showed 14–21% higher MBN concentration that 60% WFPS. In case of MBP the flooding treatment (15–44 mg P kg−1) revealed higher MBP compared to 60% WFPS (15–34 mg P kg−1) (Table 2), which is 4–22% higher than 60% WFPS soil. Interestingly, the microbial quotient increased in flooded soil compared with 60% WFPS soil, while it decreased in fertilized soil compared with the control (Table 2); also, there was a non-significant change found between moisture regimes and microbial biomass stoichiometry (Table 2).

Table 2 Effect of soil moisture on soil microbial biomass stoichiometry after long-term fertilization
Full size table

Soil extracellular enzymatic activities varied between moisture regimes and long-term fertilization (Fig. 3). However, under flooded conditions, the urease activity significantly increased by 42–54% compared to 60% WFPS conditions. Under long-term fertilization, the NPKM treatment significantly increased β-glycosidase and acid phosphatase (BG and AP) enzyme activities, whereas the moisture content had little effect on BG and AP, which was 1.2–6.1% and 2–6.6%, respectively (Fig. 3). Furthermore, the highest values were observed for urease, BG, and AP in NKPM treatment.

Fig. 3
figure 3

Response of soil enzymes to long-term fertilization under two moisture levels. Significant differences between the treatments are shown by different lowercase letters at P < 0.05. β-glycosidase Glucosidase, AcP Acid Phosphatase

Full size image

Relationship between soil properties and enzymatic activities with cumulative CH4 and N2O emissions

In flooded and 60% WFPS soil, Figure S1 shows the Pearson correlation of microbial stoichiometry and soil properties with greenhouse gases. A linear relationship was identified between CH4 and N2O and other soil-related parameters. The DOC also exhibited a significant positive relationship with cumulative N2O, while MBC and pH revealed a non-significant relationship with cumulative N2O emissions (Fig. S2). Accordingly, cumulative CH4 emissions were significantly correlated with DOC by R2 = 0.55 (p < 0.05), with SMBC by R2 = 0.62 (p < 0.05), and with pH by R2 = 0.67 (p < 0.05) (Fig. S2). Soil extracellular enzymatic activities of urease, β-glycosidase, and acid phosphatase had a significant effect on N2O, while there was a non-significant effect on CH4 (Fig. S3).

Regulation of CH4 and N2O emissions

We conducted random forest analysis to explore the relative importance of soil properties and microbial activities on greenhouse gas emissions (CH4 and N2O), and we conducted random forest analysis. In terms of soil properties, soil moisture, MBC, DOC (p < 0.001), and pH were the most significant factors for CH4 and N2O emissions (Fig. 4a). The DOC, NH4+, and NO3− concentrations (p < 0.01) were also important variables for GHG emissions. Furthermore, the activities of urease enzymes (p < 0.001) and the microbial quotient (MQ) (p < 0.001) were the most important factors for CH4 and N2O emissions, respectively (Fig. 4b). β-glycosidase and MBC:MBN (p < 0.01) were also vital signs for CH4 diffusive flux, whereas β-glycosidase and urease (p < 0.01) were identified as important variables for N2O diffusion.

Fig. 4
figure 4

The relative important (%) of predictor variables for the random forest model of cumulative CH4 and N2O emission. SM soil organic matter, DOC dissolve organic carbon, NO3 nitrate, NH4 ammonium, TP total phosphorus, AP available phosphorus, SOC soil organic carbon, Glucosidase β-glycosidase, MBC microbial biomass carbon, MBN microbial biomass nitrogen, MBP microbial biomass phosphorus, MQ microbial quotient, AcP acid phosphatase

Full size image

Direct and indirect effects of soil properties, microbial biomass, and activities on CH4 and N2O emissions

To explore the role of different explanatory indicators and complex interrelationships in the emissions of CH4 and N2O, we employed a partial least squares path model (PLS-PM) (Fig. 5). The results showed that the soil properties (0.43) and enzymes (0.46) had a prominently advantageous direct impact on GHGs (CH4 and N2O). Furthermore, the indirect influence of soil microbial biomass (0.43) proved to be more significant and beneficial than of soil properties (0.35) on GHG emissions, which was positively associated with soil enzymes (β-glycosidase, AcP, and urease). Moreover, soil properties (0.84) had an important direct effect on the soil microbial biomass. However, the direct effect of soil microbial biomass on GHG emissions was not significant. The loading scores suggested that NO3−, MBC:MBN, and urease were the most potent indicators of soil properties, soil microbial biomass, and soil enzymes, respectively, compared with other prospects for that potential variable.

Fig. 5
figure 5

Partial Least Squares Path Model (PLS-PM) and the effect of soil property legacy effect on GHG emissions and the related microbial stoichiometry during the study period. Each box represents observed variables or latent variables. Larger path coefficients are reflected in the width of the arrow with black indicating a positive effect. Path coefficients that were not significantly different from 0 are shown in black dashed lines. Path coefficients are calculated after 1000 bootstraps. The model is assessed using the Goodness of Fit statistic. SM soil organic matter, DOC dissolve organic carbon, NO3 nitrate, NH4 ammonium, TP total phosphorus, AP available phosphorus, SOC soil organic carbon, Glucosidase β-glycosidase, MBC microbial biomass carbon, MBN microbial biomass nitrogen, MBP microbial biomass phosphorus, MQ microbial quotient, AcP acid phosphatase

Full size image

Discussion

Paddy soil contributes to methane emissions, accounting for 31–112 Tg/y yearly, or 9–19% globally [36]. Soil moisture is one of the main factors that drives methane production and has a crucial effect on CH4 formation. This study showed that methane fluxes were lower in wet soil and higher in methane emissions than 60% WFPS. Our findings are consistent with previous research, which found that a high soil moisture level increased methane fluxes [37]. Higher levels of water-filled pore space in soil led in higher emissions of methane [38, 39]. In a recent study, methane fluxes were high compared to the 60% WFPS moisture in this study. The methanogenic activities in soil rise with soil moisture content, but methanotroph activities drop with soil oxidized zone reduction [40]. Increased soil moisture caused anaerobic conditions in the present study which promoted the activities of methanogenic rather than methanotrophic bacteria, resulting in the detected methane fluxes [41]. Increased soil moisture content assists in the breakdown of native SOM, which acts as a stimulant for methanogens to produce CH4 [40, 41].

The presence of both aerobic and anaerobic microsites in the soil is directly correlated with its water content; about 60–70% WFPS provides suitable conditions to assist nitrification and denitrification simultaneously and thus produce more N2O emissions [42]. According to current findings, the increase in N2O emissions in 60% WFPS soil was likely due to higher soil nitrate concentration and the reduction in NH4+-N during nitrification processes in all treatments. Microbial nitrification and denitrification processes consume soil ammonium and nitrate nitrogen as substrates, producing nitrous oxide as an intermediate product, while at 60% WFPS soil moisture content, the nitrous oxide emissions decrease [43]. Higher water-filled pore space improves anaerobic soil conditions, encouraging denitrification, and converting nitrous oxide into nitrogen, resulting in lower or no nitrous oxide emissions [44,45,46]. In flooded soil, denitrification is a crucial process that substitutes nitrogen oxides with oxygen as an electron acceptor. Current findings revealed that at 60% WFPS, more nitrous oxide was produced than in flooded soil (Figs. 1, 2). Flooding consistently creates an anaerobic environment in the soil and alters the chemical and biological processes that limit organic carbon and nitrogen mineralization, subsequently lowering substrates for N2O emissions [47]. Our results are consistent with the findings of Shang et al. who reported low N2O emissions from flooded soils [48]. The previous study's findings observed insignificant N2O emissions from flooded soils, which aligns with our results. Flooded soils are usually referred to as anaerobic because the water-filled soil pores limit the oxygen available [48]. The explanation for this is that the majority of denitrifying bacteria are facultative anaerobes, meaning they prefer to accept oxygen as an electron acceptor but will also absorb nitrogen oxides as an electron acceptor if oxygen becomes scarce [48, 49]. N2O emissions increased during the incubation study’s early phases and reduced as it carried on.

According to Fig. 2 of the current investigation, N2O emissions were slightly lower in the CK treatment than in the NPK and NPKM treatments. According to Shaaban et al. (2015), N2O emissions were much lower at 55% WFPS than at 90% WFPS [45]. The amount of SOC breakdown produced by variable soil moisture levels might explain the differences in cumulative N2O emissions between moisture level treatments [49]. Furthermore, the 60% WFPS treatment provided aerobic soil conditions, but the flooding conditions created anaerobic soil conditions. Increasing the moisture level of soil from 60 to 100% resulted in a significant reduction in nitrous oxide (Fig. 2), which is consistent with previously reported findings [50].

Soil moisture plays an important role in regulating N2O emissions [51]. Results showed that the CK treatment significantly reduced N2O emissions compared to the NPK and NPKM treatments (Fig. 2). The main reason for the differences in total N2O emissions between treatments was the different amounts of SOC breakdown caused by different soil moisture levels [50, 51]. In addition, there were aerobic conditions under 60% WFPS, while flooding under WFPS resulted in anaerobic conditions. It is well established that repeated aerobic and anaerobic conditions result in nitrification and denitrification, respectively [51, 52]. A previous study revealed that lower N2O emissions were produced under nitrification than denitrification [46]. Our findings demonstrated that increasing the soil moisture content from 60 to 100% WFPS resulted in a considerable reduction in N2O emissions (Fig. 2), which is consistent with prior research findings.

Additionally, soil moisture contributes to the breakdown and solubilization of organic carbon, which releases readily accessible carbon and acts as a precursor for the development and metabolism of soil microorganisms [53]. The dissolved organic carbon and microbial biomass carbon stocks were greater under flooding conditions than under 60% WFPS (Tables 1, 2), showing that soil moisture promoted the solubilization and decomposition of indigenous organic matter. The favorable relationship among CH4 emissions, DOC, and MBC was further highlighted by correlation analysis. The results revealed that moisture content elevated soil pH from 5.3 to 5.7, 5.6, and 5.8 in the CK, NPK, and NPKM treatments, respectively. On the other hand, the soil pH of the 60% WFPS treatments declined throughout the incubation period. The current findings align with a recent study, which showed that the significant pH decline in soil originates from the transformation process, which produces two moles of protons for every molecule of NH4+ oxidized to NO3 [54].

One important measure for assessing the quality of soil is the biomass of soil microbes. SMB is less resistant to soil management practices and environmental variables than soil organic matter [55]. Although SMB is a small portion of OM, it plays a critical role in processes such as soil nutrient cycling and the transformation of soil organic matter and insoluble materials [44,45,46]. Similar to the findings of the present study, the enriched stocks of microbial biomass under long-term fertilization have been found to exhibit increased metabolic activity of microorganisms [56]. Accordance to previous studies, long-term inorganic fertilization combined with manure (NPKM) increased microbial biomass carbon and nitrogen as compared to the inorganic fertilizer and control treatments (Table 2). According to previous research, anthropogenic C input has effectively increased microbial biomass carbon and nitrogen due to the strong activation of microbes under high soil carbon concentrations [27]. Lower microbial biomass ratio between microbial biomass carbon and phosphorus may drive soil microorganisms to release nutrients and increase the availability of nitrogen and phosphorus pools [57, 58]. The findings of the present study also found a strong link between microbial biomass stoichiometry and nutrient inputs in the soil as described by [59]. In this study, compared with the NPK and NPKM treatments, the control (CK) treatment intensely reduced MBP (Table 2). Dai et al. (2019) discovered in an earlier investigation that N: P stoichiometry is the primary regulator of microbial biomass phosphorus. They also reported that adding phosphorus over time improved microbial phosphorus immobilization by lowering the relative abundance of phosphorus-depleted microbial communities [60].

The current study found that combining organic and inorganic fertilizers increased the soil SOC content, which influenced soil available nutrients and microbial biomass stoichiometry and its ratios (Table 2). These findings are in line with earlier research on the issue [59,60,61]. Additionally, in the present study, the control treatment substantially reduced MBP compared to the NPK and NPKM treatments (Tables 1 and 2).

Compared with the NPK and CK treatments, the application of inorganic fertilizers combined with manure (NPKM) caused the greatest increase in enzyme activities (Fig. 4) [59]. According to a prior study, pig dung combined with inorganic fertilizers substantially boosted extracellular enzymatic function in rice when compared with sole inorganic fertilizers and sole manure applications [61]. Increased soil acidity could contribute to reduced enzyme activity when applying inorganic fertilizer. Inorganic nitrogen input lowered soil pH, which resulted in lesser soil microbial activity and influenced the quantity of the phosphatase-solubilizing microbial community [60,61,62].

Conclusion

The findings of this study reveal that variation in moisture has a significant impact on both GHG emissions. Flooded soil resulted in lower N2O emissions than 60% WFPS and high CH4 emissions are found in flooded soil as compared to 60% WFPS, while long-term organic and inorganic treatments showed higher emissions in both water regimes. Increasing soil moisture content raised pH levels, which in effect raised nosZ gene transcripts and reduced soil emissions of nitrogen oxides. Cumulative CH4 emissions had a substantially enhancing effect on DOC, MBC, and pH, while cumulative N2O emissions had a favorable effect on soil enzymes. Our findings suggest that moisture is an important factor that affects GHG fluxes, soil nutrient availability, and activities. Therefore, we needed further research to understand the mechanism of methane, which involves the activities of methanogenesis and methanotrophs in different soil moisture levels.

References

  1. IPCC (2013) Climate change 2013, the physical science basis. Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  2. IPCC (2014) Climate change 2014-mitigation of climate change: working group I contribution to the fourth assessment report of the IPCC. Cambridge University Press, Cambridge

    Google Scholar 

  3. Hun YM, Tong Y, Gao Pe JX (2017) Coupled effects of straw and nitrogen management on N2O and CH4 emissions of rainfed agriculture in Northwest China. Atmos Environ 157:156–166

    Article  Google Scholar 

  4. Zou J, Huang Y, Jiang J, Zheng X, Sass RL (2005) A 3-year field measurement of methane and nitrous oxide emission from rice paddies in China: effects of water regime, crop residue, and fertilizer application. Glob Biogeochem Cycl 19:153–174

    Article  Google Scholar 

  5. Liu CA, Li FR, Zhou LM, Zhang RH, Lin SL, Wang LJ, Siddique KH, Li FM (2013) Effect of organic manure and fertilizer on soil water and crop yields in newly-built terraces with loess soils in a semi-arid environment. Agric Water Manag 117:123–132

    Article  Google Scholar 

  6. Peyron M, Bertora C, Pelissetti S, Said-Pullicino D, Celi L, Miniotti E, Romani M, Sacco D (2016) Greenhouse gas emissions as affected by different water management practices in temperate rice paddies. Agric Ecosyst Environ 232:17–28

    Article  CAS  Google Scholar 

  7. Zhang S, Chen J, Liu TQ, Cao CG, Li CF (2016) Effects of nitrogen fertilizer sources and tillage practices on greenhouse gas emissions in paddy fields of central China. Atmos Environ 144:274–281

    Article  CAS  Google Scholar 

  8. National Bureau of Statistics of China (2018) China statistical year book. China Statistics Press, Beijing

    Google Scholar 

  9. Sun HJ, Feng YF, Ji Y, Shi WM, Yang LZ, Xing BS (2018) N2O and CH4 emissions from N-fertilized rice paddy soil can be mitigated by wood vinegar application at an appropriate rate. Atmos Environ 185:153–158

    Article  CAS  Google Scholar 

  10. Adviento-Borbe MA, Linquist B (2016) Assessing fertilizer N placement on CH4 and N2O emissions in irrigated rice systems. Geoderma 266:40–45

    Article  CAS  Google Scholar 

  11. Chen H, Luo P, Wen L, Yang L, Wang K, Li D (2017) Determinants of soil extracellular enzyme activity in a karst region, southwest China. Eur J Soil Biol 80:69–76

    Article  CAS  Google Scholar 

  12. Zhang W, Xu M, Wang X, Huang Q, Nie J, Li Z, Li S, Hwang SW, Lee KB (2012) Effects of organic amendments on soil carbon sequestration in paddy fields of subtropical China. J Soils Sediments 12:457–470. https://doi.org/10.1007/s11368-011-0467-8

    Article  CAS  Google Scholar 

  13. Yao H, Campbell CD, Chapman SJ, Freitag TE, Nicol GW, Singh BK (2013) multi-factorial drivers of ammonia oxidizer communities: evidence from national soil survey. Environ Microb 15:2545–2556

    Article  CAS  Google Scholar 

  14. Yao Z, Zheng X, Wang R, Xie B, Butterbach-Bahl K, Zhu J (2013) Nitrous oxide and methane fluxes from a rice-wheat crop rotation under wheat residue incorporation and no-tillage practices. Atmo Environ 79:641–649

    Article  CAS  Google Scholar 

  15. Wu L, Tang S, He D, Wu X, Shaaban M, Wang M, Zhao J, Khan I, Zheng X, Hu R, Horwath WR (2017) Conversion from rice to vegetable production increases N2O emission via increased soil organic matter mineralization. Sci Total Environ 583:190–201

    Article  CAS  Google Scholar 

  16. Xue Y, Duan H, Liu L, Wang Z, Yang J, Zhang J (2013) An improved crop management increases grain yield and nitrogen and water use efficiency in rice. Crop Sci 53:271–284

    Article  Google Scholar 

  17. Chidthaisong A, Cha-un N, Rossopa B, Buddaboon C, Kunuthai C, Sriphirom P, Towprayoon S, Tokida T, Padre AT, Minamikawa K (2017) Evaluating the effects of alternate wetting and drying (AWD) on methane and nitrous oxide emissions from a paddy field in Thailand. Soil Sci Plant Nutr. https://doi.org/10.1080/00380768.2017.1399044

    Article  Google Scholar 

  18. Geisseler D, Horwath WR, Georg R, Ludwig B (2010) Pathways of nitrogen utilization by soil microorganisms: a review. Soil Biol Biochem 42:2058–2067. https://doi.org/10.1016/j.soilbio.2010.08.021

    Article  CAS  Google Scholar 

  19. Li P, Yang Y, Han W, Fang J (2014) Global patterns of soil microbial nitrogen and phosphorus stoichiometry in forest ecosystems. Glob Ecol Biogeogr 23:979–987

    Article  Google Scholar 

  20. Zhu Z, Ge T, Luo Y, Liu S, Xu X, Tong C, Shibistova O, Guggenberger G, Wu J (2018) Microbial stoichiometric flexibility regulates rice straw mineralization and its priming effect in paddy soil. Soil Biol Biochem 121:67–76. https://doi.org/10.1016/j.soilbio.2018.03.003

    Article  CAS  Google Scholar 

  21. Mooshammer M, Wanek W, Zechmeister-Boltenstern S, Richter A (2014) Stoichiometric imbalances between terrestrial decomposer communities and their resources: mechanisms and implications of microbial adaptations to their resources. Front Microbiol 5:22. https://doi.org/10.3389/fmicb.2014.00022

    Article  Google Scholar 

  22. Zechmeister-Boltenstern S, Keiblinger KM, Mooshammer M, Penuelas J, Richter A, Sardans J, Wanek W (2015) The application of ecological stoichiometry to plantmicrobial-soil organic matter transformations. Ecol Monogr 85:133–155. https://doi.org/10.1890/14-0777.1

    Article  Google Scholar 

  23. Yuan XB, Niu DC, Gherardi LA, Liu YB, Wang Y, Elser JJ, Fu H (2019) Linkages of stoichiometric imbalances to soil microbial respiration with increasing nitrogen addition: evidence from a long-term grassland experiment. Soil Biol Biochem 138:107580. https://doi.org/10.1016/j.soilbio.2019.107580

    Article  CAS  Google Scholar 

  24. Shi JY, Gong JR, Baoyin TGT, Luo QP, Zhai ZW, Zhu CC, Yang B, Wang B, Zhang ZH, Li XB (2021) Short-term phosphorus addition increases soil respiration by promoting gross ecosystem production and litter decomposition in a typical temperate grassland in northern China. CATENA 197:104952. https://doi.org/10.1016/j.catena.2020.104952

    Article  CAS  Google Scholar 

  25. Cleveland CC, Liptzin D (2007) C:N: P stoichiometry in soil: is there a“Redfield ratio” for the microbial biomass? Biogeochemistry 85:235–252. https://doi.org/10.1007/s10533-007-9132-0

    Article  Google Scholar 

  26. Ren F, Sun N, Xu M, Zhang X, Wu L, Xu M (2019) Changes in soil microbial biomass with manure application in cropping systems: a meta-analysis. Soil Tillage Res 194:104291. https://doi.org/10.1016/j.still.2019.06.008

    Article  Google Scholar 

  27. Bowles TM, Acosta-Martínez V, Calderón F, Jackson LE (2014) Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agro ecosystems across an intensively managed agricultural landscape. Soil Biol Biochem 68:252–262. https://doi.org/10.1016/j.soilbio.2013.10.004

    Article  CAS  Google Scholar 

  28. Zhao FZ, Ren CJ, Han XH, Yang GH, Wang J, Doughty R (2018) Changes of soil microbial and enzyme activities are linked to soil C, N and P stoichiometry in afforested ecosystems. For Ecol Manag 427:289–295. https://doi.org/10.1016/j.foreco.2018.06.011

    Article  Google Scholar 

  29. Khan MN, Huang J, Li D, Alemu N, Han T, Du J, Qaswar M, Kofi C, Ali T, Haseeb A, Zhang L, Xu Y, He Z, Zhang H, Nú˜nez-delgado A (2022) Mitigation of greenhouse gas emissions from a red acidic soil by using magnesium-modified wheat straw biochar. Environ Res. https://doi.org/10.1016/j.envres.2021.111879

    Article  Google Scholar 

  30. Khan MN, Lan Z, Sial TA, Zhao Y, Haseeb A, Jianguo Z, Zhang A, Hill RL (2019) Straw and biochar effects on soil properties and tomato seedling growth under different moisture levels. Arch Agron Soil Sci 65:1704–1719. https://doi.org/10.1080/03650340.2019.1575510

    Article  CAS  Google Scholar 

  31. Murphy J, Riley JP (1962) A modified of single solution method for the determination of phosphate in nature water. Anal Chim Acta 27:31–36. https://doi.org/10.1016/S0003-2670(00)88444-5

    Article  CAS  Google Scholar 

  32. Brookes PC, Powlson DS, Jenkinson DS (1982) Measurement of microbial biomass phosphorus in soil. Soil Biol Biochem 14:319–329. https://doi.org/10.1016/0038-0717(82)90001-3

    Article  CAS  Google Scholar 

  33. Brookes PC, Landman A, Pruden G, Jenkinson DS (1985) Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol Biochem 17(6):837–842. https://doi.org/10.1016/0038-0717(85)90144-0

    Article  CAS  Google Scholar 

  34. Yang X, Liu J, McGrouther K, Huang H, Lu K, Guo X, He L, Lin X, Che L, Ye Z, Wang H (2016) Effect of biochar on the extractability of heavy metals (Cd, Cu, Pb, and Zn) and enzyme activity in soil. Environ Sci Pollut Control Ser 23:974–984. https://doi.org/10.1007/s11356-015-4233-0

    Article  CAS  Google Scholar 

  35. Weaver RW, Augle S, Bottomly PJ, Bezdicek D, Smith S, Tabatabai MA, Wollum A (1994) Method of soil analysis: part 2- microbiological and biochemical properties. Soil Science Society of America, Madison, pp 775–833

    Book  Google Scholar 

  36. Bach CE, Warnock DD, Van Horn DJ, Weintraub MN, Sinsabaugh RL, Allison SD, German DP (2013) Measuring phenol oxidase and peroxidase activities with pyrogallol, l-DOPA, and ABTS: effect of assay conditions and soil type. Soil Biol Biochem 67:183–191

    Article  CAS  Google Scholar 

  37. Chen H (2013) Methane emissions from rice paddies natural wetlands, lakes in China: synthesis new estimate. Glob Change Biol 19(1):19–32

    Article  Google Scholar 

  38. Sitaula BK, Bakken LR, Abrahamsen G (1995) CH4 uptake by temperate forest soil: effect of N input and soil acidification. Soil Biol Biochem 27:871–880

    Article  CAS  Google Scholar 

  39. Hiltbrunner D, Zimmermann S, Karbin S, Hagedorn F, Niklaus PA (2012) Increasing soil methane sink along a 120-year afforestation chronosequence is driven by soil moisture. Glob Chang Biol 18:3664–3671

    Article  Google Scholar 

  40. Luo GJ, Kiese R, Wolf B, Butterbach-Bahl K (2013) Effects of soil temperature and moisture on methane uptake and nitrous oxide emissions across three different ecosystem types. Biogeosciences 10:3205–3219

    Article  CAS  Google Scholar 

  41. Ji Y, Liu PF, Conrad R (2018) Response of fermenting bacterial and methanogenic archaeal communities in paddy soil to progressing rice straw degradation. Soil Biol Biochem 124:70–80

    Article  CAS  Google Scholar 

  42. Vilain G, Garnier J, Tallec G, Cellier P (2010) Effect of slope position and land use on nitrous oxide (N2O) emissions (Seine Basin, France). Agric Meteorol 150:1192–1202

    Article  Google Scholar 

  43. Hussain S, Peng S, Fahad S, Khaliq A, Huang J, Cui K, Nie L (2014) Rice management interventions to mitigate greenhouse gas emissions: a review. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-014-3760-4

    Article  Google Scholar 

  44. Shaaban M, Peng Q, Lin S, Wu Y, Zhao J, Hu R (2014) Nitrous oxide emission from two acidic soils as affected by dolomite application. Soil Res 52:841–848

    Article  CAS  Google Scholar 

  45. Shaaban M, Peng QA, Hu R, Wu Y, Lin S, Zhao J (2015) Dolomite application to acidic soils: a promising option for mitigating N2O emissions. Environ Sci Pollut Res 22:19961–19970

    Article  CAS  Google Scholar 

  46. Shaaban M, Yupeng W, Salman M, Peng Q, Xiangyu X, Lei W, Aneela Y, Saqib B, Liang YM, Shan L, Muhammad Z, Abid M, Hu R (2018) Reduction in soil N2O emissions by pH manipulation and enhancednosZ gene transcription under different water regimes. Environ Pollut 235(2018):625–631. https://doi.org/10.1016/j.envpol.2017.12.066

    Article  CAS  Google Scholar 

  47. Yang H, Sheng R, Zhang Z, Wang L, Wang Q, Wei W (2016) Responses of nitrifying and denitrifying bacteria to flooding-drying cycles in flooded rice soil. Appl Soil Ecol 103:101–109

    Article  Google Scholar 

  48. Shang Q, Yang X, Gao C, Wu P, Liu J, Xu Y, Shen Q, Zou J, Guo S (2011) 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 Change Biol 17:2196–2210

    Article  Google Scholar 

  49. Jones CM, Spor A, Brennan FP, Breuil MC, Bru D, Lemanceau P, Griffiths B, Hallin S, Philippot L (2014) Recently identified microbial guild mediates soil N2O sink capacity. Nat Clim Change 4:801–805

    Article  CAS  Google Scholar 

  50. Chapuis-Lardy L, Wrage N, Metay A, Chotte JL, Bernoux M (2007) Soils, a sink for N2O? A review. Glob Change Biol 13:1–17

    Article  Google Scholar 

  51. Bateman E, Baggs E (2005) Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space. Biol Fertil Soils 41:379–388

    Article  CAS  Google Scholar 

  52. Feng K, Yan F, Hütsch BW, Schubert S (2003) Nitrous oxide emission as affected by liming an acidic mineral soil used for arable agriculture. Nutr Cycl Agroecosyst 67:283–292

    Article  CAS  Google Scholar 

  53. Dalal R, Allen D, Livesley S, Richards G (2008) Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes: a review. Plant Soil 309:43–76

    Article  CAS  Google Scholar 

  54. Li W, Guo S, Liu H, Zhai L, Wang H, Lei Q (2018) Comprehensive environmental impacts of fertilizer application vary among different crops: implications for the adjustment of agricultural structure aimed to reduce fertilizer use. Agric Water Manag 210:1

    Article  Google Scholar 

  55. Liu B, Frostegård Å, Bakken LR (2014) Impaired reduction of N2O to N2 in acid soils is due to a posttranscriptional interference with the expression of nosZ. MBio 5:0138314

    Article  Google Scholar 

  56. Geisseler D, Scow KM (2014) Long-term effects of mineral fertilizers on soil microorganisms—a review. Soil Biol Biochem 75:54–63. https://doi.org/10.1016/j.soilbio.2014.03.023

    Article  CAS  Google Scholar 

  57. Fließbach A, Oberholzer HR, Gunst L, Mäder P (2007) Soil organic matter and biological soil quality indicators after 21 years of organic and conventional farming. Agric Ecosyst Environ 118:273–284. https://doi.org/10.1016/J.AGEE.2006.05.022

    Article  Google Scholar 

  58. Ai C, Liang G, Sun J, Wang X, Zhou W (2012) Responses of extracellular enzyme activities and microbial community in both the rhizosphere and bulk soil to long-term fertilization practices in a fluvo-aquic soil. Geoderma 173:330–338

    Article  Google Scholar 

  59. Khan KS, Joergensen RG (2019) Stoichiometry of the soil microbial biomass in response to amendments with varying C/N/P/S ratios. Biol Fertil Soils 55(3):265–274

    Article  CAS  Google Scholar 

  60. Qaswar M, Ahmed W, Jing H, Hong Zhu F, Xiao Jun S, Xianjun J, Kailou L, Yongmei X, Zhongqun H, Asghar W, Shah A, Huimin Z (2019) Soil carbon (C), nitrogen (N) and phosphorus (P) stoichiometry drives phosphorus lability in paddy soil under long-term fertilization: a fractionation and path analysis study. PLoS ONE. https://doi.org/10.1371/journal.pone.0218195

    Article  Google Scholar 

  61. Ye G, Lin Y, Liu D, Chen Z, Luo J, Bolan N, Fan J, Ding W (2019) Long-term application of manure over plant residues mitigates acidification, builds soil organic carbon and shifts prokaryotic diversity in acidic Ultisols. Appl Soil Ecol 133:24–33

    Article  Google Scholar 

  62. Zang H, Blagodatskaya E, Wang J, Xu X, Kuzyakov Y (2017) Nitrogen fertilization increases rhizodeposit incorporation into microbial biomass and reduces soil organic matter losses. Biol Fertil Soils 53:419–429. https://doi.org/10.1007/s00374-017-1194

    Article  CAS  Google Scholar 

Download references

Funding

This research was financially supported by the National Natural Science Foundation of China (No. 42207398), the Project funded by China Postdoctoral Science Foundation (2022M723441), and the Special Project of JingGangShan High Technology Demonstration Area [20222–051246].

Author information

Authors and Affiliations

  1. State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Asad Shah, Jing Huang, Tianfu Han, Kiya Adare Tadesse, Nano Alemu Daba & Huimin Zhang

  2. institute of Agricultural Resources and Regional Planning, National Observation Station of Qiyang Agri-Ecology System, Chinese Academy of Agricultural Sciences, Qiyang, 426182, Hunan, China

    Jing Huang & Huimin Zhang

  3. CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China

    Muhammad Numan Khan

  4. Department of Botany, Abdulwali Khan University, Mardan, KPK, Pakistan

    Sajeela Khan

  5. Ministry of Agriculture Key Laboratory of Plant Nutrition and Fertilizer, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, People’s Republic of China

    Sami Ullah & Muhammad Fahad Sardar

  6. Key Laboratory of Ecological Prewarning, Protection and Restoration of Bohai Sea, Ministry of Natural Resources, School of Life Sciences, Shandong University, Qingdao, 266237, China

    Shah Fahad

  7. College of Agriculture, Abdulwali Khan University, Mardan, KPK, Pakistan

    Shah Fahad

Authors
  1. Asad Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianfu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Numan Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kiya Adare Tadesse
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nano Alemu Daba
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sajeela Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sami Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Muhammad Fahad Sardar
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shah Fahad
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Huimin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AS contributed to conceptualization, data curation, formal analysis, methodology, writing—original draft, and writing—reviews and editing. HZ was involved in conceptualization, resources, writing—review and editing, and supervision. MNK performed data curation and methodology. KAT contributed to resources and methodology. TH contributed to formal analysis and resources. MFS and SF were involved in writing—review and editing. JH performed investigation and writing—original draft preparation. NAD was involved in writing—reviews and editing. SK performed writing—review and editing.

Corresponding author

Correspondence to Huimin Zhang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shah, A., Huang, J., Han, T. et al. Impact of soil moisture regimes on greenhouse gas emissions, soil microbial biomass, and enzymatic activity in long-term fertilized paddy soil. Environ Sci Eur 36, 120 (2024). https://doi.org/10.1186/s12302-024-00943-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12302-024-00943-4

Keywords