Effects of controlled-release fertilizer on N2O emissions in wheat under elevated CO2 and temperature

doi:10.21203/rs.3.rs-2075794/v1

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Research Article

Effects of controlled-release fertilizer on N2O emissions in wheat under elevated CO2 and temperature

https://doi.org/10.21203/rs.3.rs-2075794/v1

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published 10 Mar, 2023

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[Aims] N₂O emitted by agricultural ecosystems has a great impact on global warming and climate change. The use of controlled-release fertilizers (CRF) can reduce greenhouse gas emissions. However, the general patterns and variability of nitrogen functional genes in response to CRF associated with N₂O emission have not been synthesized under climate change.

[Methods] We investigated the effects of elevated CO₂ concentration (EC), elevated temperature (ET), and their combination (ECET) on enzyme activities and gene abundances.

[Results] We found that the cumulative N₂O emissions was in the range of 0.39–1.65 kg·ha⁻¹ in the wheat-growing season, accounting for 0.54%–2.29% of the total nitrogen input. EC remarkably reduced the cumulative N₂O emissions. The greenhouse gas emissions under ECET were less than the sum of the individual effects. ECET had no synergistic effect with or without CRF. N₂O emissions were considerably positively correlated with ammonia-oxidizing bacteria (AOB) at elongation stage. CRF can reduce N₂O emissions by reducing enzyme activities. Decreased gene abundances of ammonia-oxidizing archaea, AOB, nirS, and nosZ at the elongation stage and increased ratio of N₂O production to reduction at the two stages were observed when compared ET+CRF with ET. CRF can reduce N₂O emissions under climate change, especially under EC alone or ET alone. CRF and EC remarkably reduced the global warming potential.

[Conclusions] The results of our study enable the establishment of effective mitigation measures for N₂O emissions under future climate scenarios.

greenhouse gas emissions

controlled-release fertilizers

elevated CO2 concentrations

elevated temperature

nitrifiers and denitrifiers

Global climate change is one of the major threats to terrestrial ecosystems (Schmidhuber and Tubiello, 2007; Clayton et al., 2015). Mauna Loa Observatory observed the highest CO₂ concentration in the atmosphere on a monthly record of 417.1 µmol⋅mol^− 1 in May 2020 (World Meteorological Organization, 2020). The CO₂ concentration in the atmosphere is expected to rise to 550 µmol⋅mol^− 1 in 2050 and reach or exceed 700 µmol⋅mol^− 1 by the end of the 21st century because of the increase in population, energy production and utilization, deforestation, and other intensive human activities (IPCC, 2013). The earth's surface temperature has risen by 1.2°C since the industrial revolution (Chen, 2021). Global warming is expected to exceed 2°C by the end of the 21st century (IPCC, 2013). As one of the most important greenhouse gases, N₂O has a warming potential 265 times that of CO₂ (IPCC, 2014). To date, the average N₂O concentration in the global atmosphere has reached 331.1 nmol⋅mol^− 1 and will continue to rise (World Meteorological Organization Global Atmosphere Watch Programme, 2019).

Soil processes, particularly nitrification and denitrification processes, are responsible for approximately two-thirds of global N₂O emissions (Thomson et al., 2012). N₂O is derived from aerobic autotrophic nitrification (NH₄⁺ → NH₂OH → NO₂⁻ → NO₃⁻) as a byproduct and anaerobic denitrification (NO₃⁻ → NO₂⁻ → NO⁻ → N₂O → N₂) as an intermediate product. N₂O production is involved in abiotic processes, including chemical reactions, and biotic processes via reactions mediated by microbes (Butterbach-Bahl et al., 2013; Hu et al., 2015). The first rate-limiting step in the nitrification process is the oxidation of ammonia to hydroxylamine, in which ammonia monooxygenase is the key enzyme (Leininger et al., 2006). The amoA gene encodes its catalytic subunit, and ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) contain this gene. Moreover, AOA and AOB have niche differentiation in fertilized soil (Prosser and Nicol, 2012), and this niche differentiation may affect N₂O emissions because of different physiological processes (Hink et al., 2018). Nitrite reductase encoded by nir genes (nirK and nirS genes) is the rate-limiting step in the denitrification process, and some studies suggest that the nirK gene is the main denitrification gene and has a remarkable positive correlation with N₂O emissions (Li et al., 2016b). NosZ encoding nitrous oxide reductase consumes N₂O (Maeda et al., 2011); therefore, the ratio of (nirK + nirS) to nosZ can reflect the dynamic change process of N₂O (Sun et al., 2018).

Climate changes, including elevated CO₂ concentration (EC) and elevated temperature (ET), affect N₂O emissions, but this relation remains controversial. In cropland ecosystems, several studies find that EC remarkably increases N₂O emissions (Bhattacharyya et al., 2013; Dijkstra et al., 2012). However, the decrease in N₂O emissions under EC has also been reported (Phillips et al., 2001). Nitrogen limitation reduces N₂O emissions when the unstable carbon inputs from EC increase the competition between plant and microbes for nitrogen. The impact of ET on N₂O emissions is not completely consistent; studies find that ET has positive (Hu et al., 2019), negative (Hu et al., 2010), or no remarkable impact on N₂O emissions (Li et al., 2016a). In the case of sufficient nitrogen fertilizer, ET increases soil moisture and unstable carbon input and therefore increases N₂O emissions (Dijkstra et al., 2012). Although N₂O emissions under ET are not limited by nitrogen, ET affects multiple processes in plants. ET boosts the nitrogen uptake by plants, which reduces N₂O emissions, but ET also induces nitrogen mineralization, which increases N₂O emissions (Dijkstra et al., 2012).

In cropland ecosystems, the application of nitrogen fertilizers has also increased N₂O emissions. Nitrogen application is the most effective and irreplaceable way to increase crop yield. As the most commonly used nitrogen fertilizer, urea accounts for 73.4% of the global nitrogen application (Zhang et al., 2022). However, the rapid release of nitrogen from urea has led to remarkable nitrogen losses, such as greenhouse gas emissions, nitrate leaching, and runoff (Li, 2000). China has low nitrogen use efficiency and serious nitrogen loss (Cui et al., 2010); hence, appropriate measures to improve nitrogen use efficiency and crop yield are urgently needed. The Panel on Climate Change in the third assessment reports of an international organization report that nitrogen fertilizer management, such as the use of nitrification inhibitors (NIs), controlled-release fertilizers (CRF), and organic fertilizers, can reduce N₂O emissions from nitrogen fertilizer use by 30% globally (Moomaw and Moreira, 2001). Among them, CRF can reduce nitrogen loss by slowing the release rate of nitrogen fertilizer, increasing nitrogen absorption by crops, and reducing the hydrolysis of urea (Liu et al., 2020). Moreover, CRF can also reduce environmental pollution (Oertli, 1980). The results of a meta-analysis show that CRF can remarkably reduce N₂O emissions (− 35%, from − 58% to − 14%) compared with conventional nitrogen application (Akiyama et al., 2010). In addition, the report finds that the effect of CRF on the emission reduction varies with soil types; CRF is effective in poorly drained soils, such as Gleysol grassland, but not in well-drained soils, such as Andosol. This finding indicates that the main pathways of N₂O production are different in different soil types; an aerobic environment is required for nitrification, and an anaerobic environment is required for denitrification (Pan et al., 2022).

Current research on CRF has focused on the effects of CRF on nitrogen use efficiency, N₂O emissions, and nitrifiers and denitrifiers. However, little has been reported on the impact of CRF on N₂O emission and nitrifiers and denitrifiers under climate change scenarios. Hence, the objectives of this study were to identify the potential determinants of N₂O emissions under EC and ET and CRF application, to do this, we measured soil enzyme activity and microbial abundance under different treatments, which reflected the dynamics of N₂O production.

Experimental design

The experimental study was conducted in climate-controlled open chambers at Shanxi Agricultural University, China (37.42°N, 112.58°E). The climate-controlled open chambers had four air chambers. One chamber had an ambient CO₂ concentration of 400 mmol⋅mol^–1 and ambient temperature (the same temperature as the outside of the chambers) (CK). The second chamber had ambient CO₂ concentration and a temperature 2 °C above the ambient temperature (ET). The third chamber had an elevated CO₂ concentration of 600 mmol⋅mol^–1 and an ambient temperature (EC). The last chamber had elevated CO₂ concentration and elevated temperature (ECET). The specific equipment operation was as described by Hao et al. (2017). Each treatment had two nitrogen fertilizer treatments: normal urea and CRF. The CRF was resin-coated urea developed by the School of Resources and Environment of Shandong Agricultural University. Therefore, eight treatments were used in total (Table 1), namely, CK, CRF, ET, ET+CRF, EC, EC+CRF, ECET, and ECET+CRF. Each treatment was set up with three repetitions.

Table 1 Experimental treatments

Treatments	Temperature	CO₂ concentration	Nitrogen fertilizer
CK	ambient temperature	400 mmol mol^–1	normal urea
CRF	ambient temperature	400 mmol mol^–1	controlled-release fertilizers
ET	ambient temperature + 2℃	400 mmol mol^–1	normal urea
ET+CRF	ambient temperature + 2℃	400 mmol mol^–1	controlled-release fertilizers
EC	ambient temperature	600 mmol mol^–1	normal urea
EC+CRF	ambient temperature	600 mmol mol^–1	controlled-release fertilizers
ECET	ambient temperature + 2℃	600 mmol mol^–1	normal urea
ECET+CRF	ambient temperature + 2℃	600 mmol mol^–1	controlled-release fertilizers

The winter wheat (Triticum aestivum L.) variety was Liangxing 99. The planting containers were polyethylene containers (60 cm×40 cm×28 cm, length × width × depth). Winter wheats were planted on October 15, 2018. In this experiment, clay loam soil was used. Crops were grown in rotation between wheat and soybeans. Fertilizers were applied at 1.73 g N (urea) and 1.31 g P (KH₂PO₄) per pot. Nitrogen fertilizer was half base fertilizer at sowing and half topdressing on February 26, 2019. KH₂PO₄was applied as the base fertilizer at sowing.

Soil properties and soil enzyme activity determination

The soil was sampled between the wheat rows on April 11 (elongation stage) and May 19, 2019 (booting stage). A 3 cm-diameter soil sampler was used to collect three parts of 0–20 cm soil from each pot to remove debris, such as straws and plant residues, and the samples were mixed well for later use. NH₄⁺-N and NO₃⁻-N were extracted from fresh soil with 2.0 M KCl at 1:10 (w/v) after sampling and quantified with colorimetric methods (Mulvaney, 1996) as soon as possible. Soil urease activity was determined by sodium phenolate–sodium hypochlorite colorimetric method (Guan, 1986). In this procedure, 10 mL of urea solution (10%) and 20 mL of citrate buffer (pH 6.7) were added to 5 g soil in hermetically sealed flasks and then incubated for 24 h at 37 ℃. Ammonium content was colorimetrically determined at 578 nm by indophenol blue reaction, and the activity was expressed as NH₃-N mg/ (g h). Protease activity was determined by the ninhydrin colorimetry method (Guan, 1986). In this procedure, 20 mL of casein solution (1%) and 1 mL of toluene were added to 4 g soil in hermetically sealed flasks and then incubated for 24 h at 30 ℃. After incubation, 2 mL of sulfuric acid (0.1 N) and 12 mL of sodium sulfate solution (20%) were added. The content was colorimetrically determined at 500 nm, and the activity was expressed as NH₂-N mg/ (g h). The reactions for hydroxylamine reductase, nitrite reductase, and nitrate reductase activities were determined by microassay kits (Solarbio, Beijing Solarbio Science & Technology Co., Ltd.).

Gas sample analysis

N₂O and CH₄ emissions were measured by using closed chambers (Cai et al., 1997). The chambers were made of opaque polyvinylchloride with two parts: the base and body. The base was 16 cm in diameter and was buried in the center of the soil of containers before planting wheat. The body was 16 cm in diameter and 20 cm in height. The chamber’s body top was fitted with an air outlet with a three-way valve. Gas samples were collected throughout the whole growing season of winter wheat from October 8, 2018 to June 9, 2019. Gas samples were collected every 4–7 days, but the collection frequency was increased to every 2 days after applying fertilization and following irrigation. The interface was sealed with water before sampling. Gas samples were collected with a 30 mL syringe and immediately transferred into vacuum serum bottles at 0 and 30 min after the body was placed on the base. The sampling time and temperature in the chambers were recorded. The temperature inside the chamber was measured using thermally sensitive probes. Gas samples were analyzed by a gas chromatograph (7890A GC System, Agilent Technologies, USA).

The emission fluxes of N₂O and CH₄ were calculated according to Sun et al. (2020). Cumulative emissions of N₂O and CH₄ were estimated from individual N₂O and CH₄ fluxes and the duration between two adjacent sampling periods (Sun et al., 2020).

The radiative forcing potentials for CH₄ and N₂O were 25 and 298, respectively, when the value of CO₂ was 1 (IPCC, 2007) according to a 100-year time frame (IPCC, 2007). Global warming potential (GWP) was calculated by N₂O and CH₄ in CO₂ equivalents (CO₂-eq).

GWP (kg CO₂-eq⋅ha⁻¹) = 298 × N₂O + 25 × CH₄

qPCR analysis

Soil DNA was extracted by using the Fast DNA Spin Kit for Soil (MP Biomedicals, Europe) and sent to Sangon Biotech (Shanghai) Co., Ltd. for gene sequencing and plasmid standard construction. Gene abundance measurements were subsequently performed by using a LightCycler480 real-time PCR instrument (Roche, Rotkreuz, Switzerland). The qPCR system (10 µL) consisted of DNA template (1 µL), 2×SYBR Green qPCR Master Mix (5 µL), 10 µM forward and reverse primers (0.2 µL each), and ddH₂O (3.6 µL). The functional gene primer sequences and qPCR program were shown in Table 2.

Table 2 Primer sets and qPCR programmer

Gene	Primers	qPCR programme	PCR programme reference
AOA-amoA	amoA-1F (GGG GTT TCT ACT GGT GGT)	1 cycles: 94℃ 4 min; 40 cycles: 94℃ 45 s, 55℃ 30 s, 72℃ 45 s, 80℃ 10 s^b; melting curve	Cui et al., 2013
	amoA-2R (CCC CTC KGS AAA GCC TTC TTC)
AOB-amoA	CrenamoA-19f (ATG GTC TGG YTW AGA CG)	1 cycles: 95℃ 4 min; 40 cycles: 94℃ 45 s, 55℃ 30 s, 72℃ 45 s, 80℃ 10 s^b; melting curve	Cui et al., 2013
	CrenamoA-616r (GCC ATC CAB CKR TAN GTC CA)
nirS	cd3aF (GT(C/G) AAC GT(C/G) AAG GA(A/G) AC(C/G) GG)	1 cycles: 94℃ 3 min; 5 cycles: 94℃ 20 s, 65℃ 40 s^a, 72℃ 40 s; 45 cycles: 94℃ 20 s, 60℃ 40 s, 72℃ 40 s , 80℃ 35 s^b; melting curve	Cui et al., 2016
	R3cd (GA(C/G) TTC GG(A/G) TG(C/G) GTC TTG A)
nirK	nirK1F (GG(A/C) ATG GT(G/T) CC(C/G) TGG CA)	1 cycles: 95℃ 3 min; 45 cycles: 95℃ 10 s, 60℃ 40 s, 72℃ 40 s, 80℃ 35 s^b; melting curve	Cui et al., 2016
	nirK5R (GCC TCG ATC AG(A/G) TT(A/G) TGG)
nosZ	nosZ-F (CG(C/T) TGT TC(A/C) TCG ACA GCC AG)	1 cycles: 95℃ 3 min; 45 cycles: 95℃ 20 s, 54℃ 40 s, 72℃ 40 s, 77℃ 45 s^b; melting curve	Cui et al., 2016
	nosZ-R (CAT GTG CAG (A/C/G/T)GC (A/G)TG GCA GAA)

^a Annealing temperature decreases by 1℃ after every cycle.

^b Fluorescence signal was read after the second extension step.

Statistical analysis

Advanced heatmap plots were constructed by using the OmicStudio tools at https://www.omicstudio.cn. ANOVA was performed in the SPSS 22.0 software. A significance level of P<0.05 was utilized for all tests. Figures were made using the Origin 8.0 software.

Ammonium and nitrate nitrogen

The contents of ammonium nitrogen and nitrate nitrogen were remarkably affected by CRF, EC, and ET. However, the patterns for the two stages were different (Figs. 1A and B). For example, CRF inputs significantly reduced the nitrate nitrogen content at elongation stage (P<0.001, from −58% to −92%) but increased the contents of ammonium nitrogen (P<0.001, from 54% to 647%) and nitrate nitrogen (P<0.001, from 666% to 1708%) at booting stage, compared with normal urea. Compared with CK, the contents of nitrate nitrogen increased significantly by 34% and 122% under EC alone and ET alone at elongation stage, respectively. Compared with CRF, the contents of nitrate nitrogen under EC+CRF and ET+CRF were remarkably increased in the two stages.

Greenhouse gas emissions and emission intensity

During wheat growing season, the CRF inputs significantly reduced the cumulative N₂O emissions (P<0.001, from −29% to −66%) compared with normal urea. EC significantly reduced cumulative N₂O emissions (P<0.001, from −30% to −50%) compared with ambient CO₂ concentration (Table 3). However, ET had no significant effect on cumulative N₂O emissions compared with ambient temperature (P=0.14, from 109% to −25%). ECET alone remarkably reduced the cumulative N₂O emissions compared with CK. However, the effect of ECET+CRF on cumulative N₂O emissions was not remarkably different compared with CRF alone. EC significantly increased the CH₄ uptake compared with ambient CO₂ concentration (P=0.02, from 14% to 234%). The CH₄ uptake was the largest under ECET. GWP was maximal under ET. A remarkable reduction in GWP was observed in the CRF- or EC-grown wheats compared with the ambiently grown plants. ECET alone and ECET+CRF considerably reduced the GWP compared with CK.

Table 3 Effects of climate change and fertilizers on greenhouse gas emissions and global warming potential

Treatment		N₂O (kg·ha^-1)	CH₄ × 10^-4 (kg·ha^-1)	GWP (kg CO₂-eq ha⁻¹)
CK CRF ET ET+CRF EC EC+CRF ECET ECET+CRF		1.65±0.18a	-4.65±0.39ab	491.08±53.51a
		0.57±0.09cd	-1.19±1.17a	170.58±26.73cd
		1.73±0.29a	-1.83±1.33a	516.71±86.91a
		1.19±0.19b	-2.45±0.99ab	354.75±57.32b
		1.15±0.10b	-5.28±1.12ab	343.57±30.67b
		0.39±0.07d	-2.92±1.53ab	114.75±20.89d
		0.86±0.03bc	-6.11±1.42b	256.15±8.93bc
		0.61±0.08cd	-4.74±0.46ab	181.45±22.52cd
P value	[CO₂]	0.00	0.02	0.00
	temperature	0.14	0.76	0.14
	fertilizer	0.00	0.08	0.00
	[CO₂]×temperature	0.08	0.24	0.08
	[CO₂]×fertilizer	0.16	0.80	0.16
	temperature×fertilizer	0.02	0.16	0.02
	fertilizer×[CO₂]×temperature	0.97	0.39	0.97

Mean values with different letters are significantly different (P ≤ 0.05) according to Duncan’s multiple range test. The values represent the means of 3 replications. CK: 400 μmol mol^-1 CO₂ + normal temperature + normal urea; CRF: 400 μmol mol^-1 CO₂ + normal temperature + controlled-release fertilizers; ET: 400 μmol mol^-1 CO₂ + rising temperature (normal temperature+2℃) + normal urea; ET+CRF: 400 μmol mol^-1 CO₂ + rising temperature + controlled-release fertilizers; EC: 600 μmol mol^-1 CO₂ + normal temperature + normal urea; EC+CRF: 600 μmol mol^-1 CO₂ + normal temperature + controlled-release fertilizers; ECET: 600 μmol mol^-1 CO₂ + rising temperature + normal urea; ECET+CRF: 600 μmol mol^-1 CO₂ + rising temperature + controlled-release fertilizers. GWP: global warming potential; GWP = 298 × N₂O + 25 × CH₄

Soil enzymes activity

The urease activity was substantially increased from the elongation stage to the booting stage (Fig. 2A). At booting stage, CRF inputs significantly affected the activities of urease (P=0.01, from −5% to −14%) and protease (P<0.001, from 6% to 54%) compared with normal urea (Figs. 2A and B). The urease activity was decreased whereas the protease activity was increased under the CRF inputs at booting stage compared with normal urea.

CRF alone (P<0.001, from 3% to 59%) and ET alone (P=0.01, from −25% to 13%) significantly affected hydroxylamine reductase activity at elongation stage compared with CK, and ET alone significantly affected hydroxylamine reductase activity at booting stage compared with CK (P<0.001, from −54% to 19%; Fig. 2C). Different from other treatments, the activity of hydroxylamine reductase was remarkably increased under ECET+CRF at booting stage compared with that under ECET.

CRF inputs significantly reduced nitrite reductase (P<0.001, from −24% to −41%) at elongation stage and nitrate reductase activity (P<0.001, from −8% to −49%) at booting stage compared with normal urea (Figs. 2D and E). Compared with CK, ET alone reduced the activities of nitrate reductase and nitrite reductase at the two stages.

Abundance of functional genes associated with N₂O emissions

At the elongation stage, CRF inputs remarkably affected the abundances of nitrification (AOA and AOB) and denitrification genes (nirS and nirK) compared with normal urea (Fig. 3). The abundance of the amoA gene was remarkably decreased under CRF, whereas the gene abundances of denitrification were different. No synergy under ECET treatment was also observed.

ET alone remarkably reduced the gene abundances of nitrification and denitrification at booting stage compared with CK (Fig. 4). ET+CRF had higher gene abundances of nitrification and denitrification than ET. CRF inputs decreased the ratio of (nirS+nirK) to nosZ at elongation stage (except ET) and increased the ratio of (nirS+nirK) to nosZ at booting stage compared with normal urea (Fig. 5).

Correlation coefficients between N₂O emissions and environmental properties

The relationships among N₂O emissions, soil properties, and functional microbial genes varied across time periods (Figs. 6 and 7). At the elongation stage, N₂O emissions were significantly positively correlated with ammonium nitrogen (rho=0.46, P<0.05), nitrate nitrogen (rho=0.53, P<0.01), nitrite reductase (rho=0.44, P<0.05), AOB (rho=0.55, P<0.01), nirS gene abundances (rho=0.51, P<0.05), and the ratio of (nirS+nirK) to nosZ (rho=0.54, P<0.01; Fig. 6) and significantly negatively correlated with hydroxylamine reductase (rho=−0.49, P<0.05). Hydroxylamine reductase was significantly negatively correlated with ammonium nitrogen (rho=−0.48, P<0.05), nitrate nitrogen (rho=−0.68, P<0.01), nitrite reductase (rho=−0.68, P<0.01), and AOB gene abundances (rho=−0.64, P<0.01). At the booting stage, N₂O emissions were significantly negatively correlated with nitrate reductase (rho=−0.56, P<0.01; Fig. 7).

In the present study, the general patterns and variability of nitrogen functional genes in response to CRF and their association with N₂O emission were studied under climate change scenarios. The study showed that CRF considerably decreased nitrate nitrogen content at elongation stage and remarkably increased nitrate nitrogen content at booting stage compared with CK. CRF also affected N₂O emissions by regulating the activity of soil enzymes and the gene abundance associated with nitrification and denitrification. Compared with CRF, ET+CRF increased N₂O emission by increasing the ratio of N₂O production and reduction, whereas EC+CRF reduced N₂O emission by lowering the ratio.

Nitrification and denitrification

NH₄⁺, the initial substrate for nitrification, is increased during nitrification (Barnard et al., 2005). During denitrification, NO₃⁻ is reduced to NO₂⁻ and then to gaseous NO, N₂O, or N₂. The patterns of ammonium nitrogen and nitrate nitrogen were consistent with the fertility release law of CRF, that is, their levels were lower at the elongation stage and higher at the booting stage (Figs. 1A and B). CRF inputs can reduce the environmental losses of nitrogen when their fertility release is consistent with plant nitrogen needs (Shaviv, 2001).

CRF may slow down N₂O emission by inhibiting the abundance of genes related to nitrification, and CRF also increased the activity of hydroxylamine reductase (Fig. 2C). Correlation analysis showed that N₂O emissions were significantly positively correlated with AOB at elongation stage (rho=0.55, P<0.01; Fig. 6). Some studies also demonstrate that AOB abundance responds more strongly to nitrogen addition than AOA abundance (Carey et al., 2016; Ouyang et al., 2017; Ouyang et al., 2018). The opposite trend of gene abundance under ET treatment was observed at the two stages under CK and CRF (Figs. 3 and 4). However, this change did not affect the overall N₂O emission. This result also indirectly indicated that CRF can inhibit the increase in N₂O emission by reducing gene abundance under ET at booting stage. No synergistic effect on gene abundance was observed under ECET, which was similar to the N₂O emission pattern.

During denitrification, CRF affected N₂O emissions by changing gene abundance and soil enzyme activities and regulating the ratio of N₂O production to consumption. The ratio of (nirS+nirK) to nosZ can reflect the state of N₂O production and consumption during denitrification. A ratio of 1 means that the gene copy number of nitrite reductase is equal to the gene copy number of nitrous oxide reductase (Sun et al., 2018). The ratio of (nirS+nirK) to nosZ was less than 1 under CRF at the elongation stage, indicating that N₂O production was inhibited (except for the ET, Fig. 5). Although the gene copy number of nitrite reductase was less than that of the nitrous oxide reductase gene, N₂O production was promoted under CRF at booting stage. Under CRF application at elongation stage, ET may increase N₂O emission by increasing the ratio of (nirS+nirK) to nosZ, whereas EC may decrease N₂O emission by decreasing the ratio of (nirS+nirK) to nosZ. Similarly, correlation analysis also showed that N₂O emissions were significantly positively correlated with the ratio of (nirS+nirK) to nosZ at elongation stage (rho=0.54, P<0.01; Fig. 6).

N₂O emissions

A meta-analysis shows that CRF can remarkably reduce N₂O emissions (−35%, from −58% to −14%) (Akiyama et al., 2010). Our finding was in line with the results of this meta-analysis. The reduction in N₂O emission under CRF may be because the nitrogen released by CRF matches the needs of plants, but the emission is difficult to reduce when CRF releases more nitrogen than what the plants absorb (Akiyama et al., 2010).

Some studies report that ET has no considerable effect on soil N₂O emissions, whereas other studies show that ET increases N₂O emissions (Cui et al., 2018; Qiu et al., 2018). This study found that ET had no remarkable effect on N₂O emissions under normal urea but substantially increased N₂O emissions under CRF (Table 3). This result could be caused by several reasons. In fact, ET affects multiple processes. ET increases plant growth and nitrogen uptake, which reduces N₂O emissions, but ET also increases microbial activity and nitrogen mineralization, which increase N₂O emissions (Li et al., 2020). ET stimulates N₂O production by affecting the bacteria involved in nitrification and denitrification, but ET decreases soil water content, leading to the reduction in N₂O emissions (Li et al., 2020). Moreover, Hu et al. (2010) finds that the explanation rate of ET on N₂O production is less than 10%, indicating that the effect of ET on N₂O production is complex.

Many studies shows that EC can remarkably increase soil N₂O emissions (Dijkstra et al., 2012; Bhattacharyya et al., 2013; Liu et al., 2018). By contrast, the present study observed that EC considerably reduced N₂O emissions under normal urea, whereas other treatments with EC showed no substantial change in N₂O emissions. In addition, a meta-analysis shows that in farmlands, an EC level of less than 150 mmol⋅mol^–1 is conducive to increase N₂O production, and an EC level greater than 150 mmol⋅mol^–1 restricts N₂O emission (Wang et al., 2021). This outcome is attributed to the higher levels of soil carbon–nitrogen ratio induced by EC, which increases the competition for nitrogen between plants and soil microorganisms, resulting in a decrease in available nitrogen in the soil and a reduction in N₂O emissions (Leifeld, 2018).

Few comprehensive studies on the effect of ECET on N₂O emissions have been conducted, and synergistic effects have been observed in a few meta-analyses (Wang et al., 2021). Larsen et al. (2011) shows no remarkable change in N₂O emissions under ECET. In the present study, ECET reduced N₂O emissions or had no remarkable effect on N₂O emissions (Table 3). A synergistic effect occurs when the carbon input caused by EC and the nitrogen mineralization caused by ET relieve the limitations of carbon and nitrogen in denitrification, but the performance shows an antagonistic effect when the nitrogen limitation of denitrification is transformed into water limitation (Dijkstra et al., 2012). N₂O emissions are still limited by a single factor when multiple factors are combined (Zhou et al., 2008).

GWP

The annual average GWP was 114.75–516.71 kg CO₂-eq⋅ha⁻¹in wheat cultivars (Table 3). This value is within the range of a meta-analysis (32 –4349 kg CO₂-eq⋅ha⁻¹) in wheat (Linquist et al., 2012). The present study found that CRF application considerably reduced GWP. Lan et al. (2021) finds that CRF application reduces GWP by 5%–17%. Liu et al. (2022) also finds that CRF substantially affects GWP. This finding suggests that CRF, as an environmentally friendly fertilizer, can reduce the warming potential and reduce environmental pollution. A substantial decrease in N₂O emissions was observed under CRF compared with the changes in CH₄, indicating that CRF affected GWP by reducing N₂O emissions in wheat fields. Studies have shown that all ET, EC, and combined treatments lead to varying degrees of increase in GWP in rice–wheat rotation systems, and the increase in GWP under EC conditions is due to a remarkable increase in N₂O emissions (Wang et al., 2018). In the present study, ET increased GWP, whereas EC and ECET decreased GWP. This result may be caused by the different ECs, which increases nitrogen limitation when EC is greater (Wang et al., 2021). N₂O production is also limited by the availability of inorganic nitrogen (Dijkstra et al., 2012); therefore, EC reduced N₂O emissions, resulting in lower GWP.

ET probably significantly increased N₂O emissions by increasing N mineralization. However, EC greatly reduced the cumulative N₂O emissions, which might be related to the increase of the C/N ratio by EC, thereby increasing the N competition between plants and microorganisms. CRF can reduce N₂O emission by reducing the enzyme activities and gene abundance related to nitrification and denitrification and decreasing the ratio of (nirS + nirK) to nosZ. CRF and EC remarkably reduced the GWP. Furthermore, our study provided a preliminary understanding of the effect of the combination of EC and ET on N₂O emissions, which was an antagonistic effect and not the sum of the individual effects. The relationship between N₂O emissions and combined treatment can be used as a basis for the regulation of global N₂O emissions. Our study provides evidence of how CRF affects N₂O emissions under climate change scenarios.

Funding

This project was funded by the National Natural Science Foundation of China (31971773), the State Key Laboratory of Sustainable Dryland Agriculture, Shanxi Agricultural University (No. 202105D121008-3- 7) and the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (20210041).

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Qi Liu, Yajing Liu, and Xingyu Hao. The first draft of the manuscript was written by Qi Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Effects of controlled-release fertilizer on N2O emissions in wheat under elevated CO2 and temperature

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Experimental design

Soil properties and soil enzyme activity determination

Gas sample analysis

qPCR analysis

Statistical analysis

Results

Ammonium and nitrate nitrogen

Greenhouse gas emissions and emission intensity

Soil enzymes activity

Abundance of functional genes associated with N₂O emissions

Correlation coefficients between N₂O emissions and environmental properties

Discuss

Nitrification and denitrification

N₂O emissions

GWP

Conclusion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

Effects of controlled-release fertilizer on N2O emissions in wheat under elevated CO2 and temperature

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Experimental design

Soil properties and soil enzyme activity determination

Gas sample analysis

qPCR analysis

Statistical analysis

Results

Ammonium and nitrate nitrogen

Greenhouse gas emissions and emission intensity

Soil enzymes activity

Abundance of functional genes associated with N2O emissions

Correlation coefficients between N2O emissions and environmental properties

Discuss

Nitrification and denitrification

N2O emissions

GWP

Conclusion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1

Abundance of functional genes associated with N₂O emissions

Correlation coefficients between N₂O emissions and environmental properties

N₂O emissions