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Article

Multiple Nitrogen Sources Application Inhibits Increasing Ammonia Volatilization Under Reducing Irrigation

by
Taotao Chen
1,2,†,
Erping Cui
1,†,
Ke Sun
3,
Chao Hu
1,
Siyi Li
4,
Ping Li
1,
Zhijuan Zhao
1,
Chuncheng Liu
1,
Bingjian Cui
1 and
Xiangyang Fan
1,*
1
Institute of Farmland Irrigation of Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
2
Graduate School of Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Xinxiang Hydrology and Water Resources Reporting Subcenter of Henan Province, Xinxiang 453002, China
4
Zhejiang Tongji Vocational College of Science and Technology, Hangzhou 311200, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(12), 2927; https://doi.org/10.3390/agronomy14122927
Submission received: 12 November 2024 / Revised: 29 November 2024 / Accepted: 6 December 2024 / Published: 8 December 2024
(This article belongs to the Section Water Use and Irrigation)
Browse Figures
Versions Notes

Abstract

:
Farmland ammonia (NH3) volatilization is an important source of NH3, and the application of chemical fertilizer nitrogen (N) is the main factor affecting NH3 volatilization. The optimal substitution of chemical fertilizer with organic manure and straw reportedly reduces NH3 volatilization, while reducing irrigation increases NH3 volatilization. However, the combined effect of nitrogen fertilizer substitution and reducing irrigation on NH3 volatilization and the role of microorganisms in this process remains unclear. In a soil column experiment, NH3 volatilization and microbial composition were measured under both multiple N sources and different irrigation levels by the vented-chamber method and metagenomic sequencing. The results revealed that multiple N sources application reduced cumulative NH3 volatilization by 16.5–75.4% compared to single chemical fertilizer application, and the decreasing trend of NH3 volatilization under reduced irrigation conditions was greater. Microorganisms had a more important effect on NH3 volatilization of reduced irrigation than conventional irrigation. The abundance of nirA, arcC, E3.5.1.49, and E3.5.5.1 (ammonia-producing) genes varied significantly at the two irrigation levels. Overall, multiple N sources could inhibit NH3 volatilization increasing under reducing irrigation compared to a single chemical fertilizer. Our findings contribute valuable insights into the combined effect of reduced irrigation and multiple N sources on NH3 volatilization.

1. Introduction

Nitrogen is a crucial macronutrient for high agricultural yields. Ammonia (NH3) volatilization plays an important role in the nitrogen cycle [1], pollutes the atmospheric environment, and threatens ecology and human health. To enhance productivity, farmers often apply additional nitrogen to their fields. However, research indicates that the use of chemical nitrogen fertilizers is a major contributor to NH3 volatilization, and NH3 volatilization loss accounts for 10–19% of chemical fertilizer applications [2,3,4,5]. Fertilization management accounts for 47% of NH3 emission flux at the point level [6]. The higher the application rate of chemical fertilizer nitrogen, the higher the NH3 volatilization [7,8]. In addition, NH3 volatilization is affected by irrigation, temperature, pH, microorganisms, etc. [9,10,11,12]. Globally, NH3 volatilization from rice, wheat, and maize fields in 2018 was 4.3 ± 1.0 Tg N yr−1, and the trend is likely to increase with climate change [13]. NH3 volatilization loss decreases nitrogen use efficiency and damages atmospheric quality [14]. Environmental protection has received extensive attention from all over the world. As the main precursor of 2.5-micrometer particulate matter (PM2.5), NH3 gas results in air quality deterioration and a threat to human health [15,16]. In order to achieve the goal of environment-sustainable development, reducing NH3 volatilization is essential.
Reducing the application of chemical fertilizer is seen as a primary method for decreasing NH3 volatilization while maintaining crop yields. Research suggests that substituting chemical fertilizer with manure and straw is effective [17,18,19,20]. However, previous studies have also shown that whether straw retention and manure substitution can reduce NH3 volatilization depends on the carbon-nitrogen (C/N) ratio [21,22,23]. The substitution of chemical fertilizer with both straw and green manure is capable of regulating the C/N ratio, enhancing hydrolase activities, maintaining crop yields, and reducing NH3 volatilization. However, the replacement of chemical fertilizer with only straw leads to a decrease in yields [24]. Compared to green manure, manures such as cattle and sheep manure have lower C/N ratios and higher nitrogen content, making them important organic substitutes for chemical nitrogen (N) fertilizer; therefore, co-incorporating straw and manure may yield better results.
Irrigation plays a vital role in ensuring agricultural production and maintaining crop yields. As population growth, economic development, and societal changes exacerbate water scarcity, reducing irrigation has become a common agricultural practice. Studies have shown that suitably reduced irrigation can increase water use efficiency and maintain crop productivity [25,26,27,28], as well as improve crop quality [29,30,31]. Chilundo et al. (2016) showed that reduced irrigation could improve nitrogen use efficiency and affect soil properties [32]. Sun et al. (2022) indicated that reduced irrigation under chemical fertilizer application would decrease the α-diversity of bacteria [33]. Researchers have found that reduced irrigation affects microbial biomass and enzyme activities [34,35]. Regulated deficit irrigation with transfer water improved bacterial biomass and shifted microbial composition and biomass [36,37]. High irrigation can increase greenhouse gas emissions, and reduced irrigation decreases global warming potential and greenhouse gas intensity [38,39]. Meanwhile, reduced irrigation can also decrease nitrate leaching [40,41]. Researchers indicated that reduced irrigation can lead to increased NH3 volatilization [9,42,43].
In conclusion, previous studies have shown that rational organic substitution (multiple N sources) can reduce NH3 volatilization, and reduced irrigation will increase NH3 volatilization [17,18,19,20,25,26,27,28]. However, the characteristics of NH3 volatilization under both multiple N sources and reduced irrigation are still unclear. Whether multiple N sources have an effect on regulating the increase of NH3 volatilization under reduced irrigation remains to be explored. The role of microorganisms in this process is still unknown. In this study, NH3 volatilization and soil microorganisms were measured in a soil column incubation experiment under multiple N sources and two irrigation levels. The primary objectives were as follows: (1) to explore the characteristics of NH3 volatilization under the combination of multiple N sources and various irrigation levels; (2) to explore whether multiple N sources can regulate the increase of NH3 volatilization under reduced irrigation; (3) to investigate the role of microorganisms in NH3 volatilization under reduced irrigation and conventional irrigation. The novelty of this research is to evaluate the combined effects of reducing irrigation and multiple nitrogen sources application on NH3 volatilization, based on environmental protection and improving resource utilization rate aspects.

2. Materials and Methods

2.1. Soil Column Incubation Experiment

The soil utilized in the column incubation experiment was sourced from an agricultural area in Xinxiang, Henan Province, China (35°08′40″ N, 113°45′10″ E). According to the U.S. Department of Agriculture (USDA) classification standards [44], the soil was categorized as silt loam with proportions of 13.05% clay (<0.002 mm), 62.46% silt (0.002–0.02 mm), and 21.49% sand (0.02–2 mm). After air-drying, the soil was sieved through a 2 mm mesh. Its basic properties were organic matter of 2.37%, pH of 8.42, total nitrogen of 0.72 g kg−1, available phosphorus of 9.44 mg kg−1, and available potassium of 134.87 mg kg−1. The soil column incubation study was carried out in a greenhouse made of polyvinyl chloride. Each column (30 cm diameter, 120 cm height) was filled from the bottom up with 8.45 kg of 3-mm gravel (10 cm of column height) and 106.20 kg of soil (100 cm of column height) [45]. The diagram of the trial device is shown in Figure S1.
To prevent disturbances associated with field experiments and to manage the experimental conditions, we opted for a soil column experiment. The study included straw total retention (S) and straw non-retention, manure substitution of 30% chemical nitrogen (M) and no manure substitution, reduced irrigation (W1, 105 m3 hm−2) and conventional irrigation (W2, 140 m3 hm−2), three factors and two levels of a total of eight treatments, each performed in triplicate. The eight treatments were chemical fertilizer, straw retention and reducing irrigation/conventional irrigation (SW1/SW2); only chemical fertilizer and reducing irrigation/conventional irrigation (W1/W2); chemical fertilizer, straw retention, manure substitution and reducing irrigation/conventional irrigation (SMW1/SMW2), and chemical fertilizer, manure substitution and reducing irrigation/conventional irrigation (MW1/MW2). Based on the equal nitrogen input principle (240 kg N hm−2), multiple N sources, including maize straw (1–2 cm length), manure (air-dried cow manure), and chemical fertilizer (urea), were evenly mixed into the surface layer soil (0–20 cm depth). During the 30-day incubation period, varying volumes of groundwater were applied every 6 days. The amount of irrigation is determined according to local irrigation habits. The basic properties of the nitrogen sources and groundwater are presented in Table S1.

2.2. Soil NH3 Volatilization Measurement

The vented-chamber method was used with a slight modification to measure NH3 volatilization [46]. The chamber consisted of a plastic frame (110 mm inner diameter, 200 mm height) and two sponges (110 mm diameter, 20 mm thickness). NH3 was collected with a phosphoglycerol solution in which the sponges were soaked, which was then extracted with 300 mL of 1 mol/L KCl. The extracts were analyzed with a UV-visible spectrophotometer (L9, Inesa, Shanghai, China) to determine the NH3 flow rate. NH3 flux was calculated based on the collection time per unit area of soil and the flow rate [47]. Gas samples were taken every day for the first 5 days after fertilizer application, with progressively increasing monitoring intervals thereafter.

2.3. Soil Sampling and Measurement

Based on previous studies of NH3 volatilization, soil samples were collected on the 15th day of the experiment (near the end of NH3 volatilization) for analyses and exploration [48,49]. Each soil treatment was sampled with a soil auger from the surface layer (0–20 cm). Portions of the fresh soil samples were used for NO3−-N and NH4+-N measurement. Additional portions were kept at −80 °C and used for DNA extraction. The rest of the soil was air-dried for the analysis of other soil physicochemical attributes.
Soil NO3−-N and NH4+-N were determined in 50 mL 1 mol/L KCl extracts from 10 g fresh soil samples via the spectrophotometric method in a UV-visible spectrophotometer (L9, Inesa, China). By a continuous flow analyser, the soil total nitrogen (TN) was determined by extraction with 5 mL of concentrated H2SO4 at 450 °C from air-dried soil (SEAL-AA3, Bran Luebbe, Norderstedt, Germany). Soil pH was measured with a pH meter (Star A211, Orion, NV, USA) at a soil-to-water proportion of 1:2.5 (w/v) [50].
Soil DNA was extracted from 0.5 g soil for metagenomic analysis using the Mag-Bind® Soil DNA Kit (M5635-02, Omega, GA, USA). The extracted DNA concentration and quality were determined using a Qubit™ 4 fluorometer (Q32856, Invitrogen, CA, USA) and agarose gel electrophoresis, respectively. Then, the extracted DNA samples were processed to construct metagenome sequencing on the PE150 Illumina NovaSeq platform (Illumina, CA, USA) at Personal Biotechnology Co., Ltd. (Shanghai, China).

2.4. Metagenomic Analysis

The raw sequencing reads were filtered by removing the adapter sequences, deleting the reads with lengths < 50 bp and containing fuzzy bases and pruning the low-quality reads (average quality value < 20 bp) to obtain quality reads using the software fastp (version 0.23.2, https://github.com/OpenGene/fastp (accessed on 7 December 2024)) [51]. Megahit (version 1.1.2) was used to assemble the reads using the meta-large preset parameters [52]. The gene prediction of open reading frame (ORF) was performed using Prodigal (version 2.6.3) for the contigs with sequence lengths of more than 300 bp [53]. The non-redundant gene set was clustered with a sequence identity of 95% and a coverage of 90% using the software MMseqs2 (version 13.45111, https://github.com/soedinglab/MMseqs2/ (accessed on 7 December 2024)) [54]. The abundance of genes was counted using Minimap2 (version 2.24, https://github.com/lh3/minimap2/ (accessed on 7 December 2024)) [55]. N functional genes abundance was expressed as relative abundance (the proportion of the reads number of the gene to the number of all reads in the sample). Nitrogen metabolism KEGG pathways were annotated based on the KEGG database (Kyoto Encyclopedia of Genes and Genomes, version 94.2, http://www.genome.jp/kegg/ (accessed on 7 December 2024)) using MMseqs2 software. In addition, species annotation was performed on Kaiju (version 1.9.0). The raw sequencing data were deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the accession number PRJNA1159393.

2.5. Statistical Analysis

Microsoft Excel 2016 was used for original data processing. The significant differences between treatments were determined in IBM SPSS 26 using one-way ANOVA with Duncan’s test at a p-value of 0.05. Origin 2024 was used to analyze the data and produce histograms and linear regression graphs. Other analyses were performed in R with the ‘car’, ‘rfpermute’, ‘vegan’, ‘psych’, ‘pheatmap’, and ‘plspm’ packages. The network of microbial communities was visualized using Gephi 0.10.1.

3. Results

3.1. Soil NH3 Volatilization

The dynamics of NH3 flux first increased and then decreased across all treatments (Figure 1A,B). NH3 volatilization occurred primarily in the first 7 days after the experiment. After 15 days, the NH3 volatilization flux was essentially stable, and there was no significant difference between treatments. With multiple N sources, the NH3 volatilization flux peaked earlier than with only chemical fertilizer as the nitrogen source (W1 and W2 treatments). Peaks occurred on the second, third, or fourth days after the experiment, respectively. The maximum emission flux of NH3 was 5.63 kg hm−2 d−1 in all treatments.
The W1 treatment’s cumulative NH3 volatilization was significantly higher than that of the W2 treatment, and the difference in the cumulative NH3 volatilization between the two irrigation levels decreased when multiple N sources were used (Figure 1C). All treatments cumulative NH3 volatilization ranged from 6.50 kg hm−2 to 26.39 kg hm−2. With reduced irrigation, the cumulative NH3 volatilization pattern was SMW1 (6.50 kg hm−2) < SW1 (14.09 kg hm−2) < MW1 (15.39 kg hm−2) < W1 (26.39 kg hm−2). The same pattern is held under conventional irrigation. NH3 losses were reduced by 46.6%, 41.7%, 75.4%, 30.6%, 16.5%, and 60.4% under the SW1, MW1, SMW1, SW2, MW2, and SMW2 treatments compared to single chemical fertilizer treatments, respectively. This affirmed that the combined application of straw retention and manure substitution was better than only straw retention or manure substitution to reduce NH3 volatilization. In addition, the effects of straw retention or manure substitution under reduced irrigation were better. NH3 volatilization with the straw retention or manure substitution treatments was significantly lower than that without straw retention or manure substitution.
There was a linear positive correlation between the cumulative NH3 volatilization and the amount of chemical fertilizer applied (Figure 1D). Different irrigation amounts had a substantial influence on the slope of the regression equation. When the chemical fertilizer input was greater than 125 kg hm−2, the cumulative NH3 volatilization was higher with reduced irrigation. There were interactions between the irrigation amount and straw (p < 0.01), the irrigation amount and manure (p < 0.001), and between all manipulations (p < 0.05) (Table S2). According to the random forest algorithm, irrigation had a significant effect on cumulative NH3 volatilization but less effect compared to straw and manure (Figure S2), and the effect of chemical fertilizer was the greatest.

3.2. Soil Properties Characteristics

NH4+-N in the reduced irrigation treatments was significantly higher than that in the conventional irrigation treatments (Table 1). The multiple N source treatments’ NH4+-N contents were significantly lower than those of only chemical fertilizer treatment at both irrigation levels. The W2 treatment NO3−-N content was significantly higher than that of the W1 treatment. When straw was retained, the NO3−-N content was not significantly different under different irrigation treatments but was significantly different from treatments without straw retention. The soil NH4+-N and NO3−-N were significantly lower under straw retention treatments than straw with no retention treatments. Except for SW2 and SMW2, and W1 and MW1 treatments that showed no significance, the soil NH4+-N and NO3−-N were also significantly lower under manure substitution than that under no manure substitution. The TN content of the soil was also not significantly different among treatments, but compared with conventional irrigation, it showed a decreasing trend under reduced irrigation.

3.3. Core Microorganisms and N Functional Genes

Except for the observed species, the Chao1 and ACE indexes of the W1 treatment were significantly higher than those of the W2 treatment. There were no significant differences in the microbial richness among treatments (Figure S3). Over short periods, the microbial diversity could not be altered significantly by the application of multiple N sources (p > 0.05). However, the abundance of microorganisms showed a significant difference between the two irrigation levels according to a principal component analysis (PCA) (Figure S4). The relative abundance of microorganisms at the phylum was further explored, and the results are presented in Figure S5. Proteobacteria, Actinobacteriota, and Acidobacteriota were the dominant phyla, as reported in previous work [11,56].
At the genus level, network analysis was performed to show the correspondence between microorganisms and different nitrogen forms (p < 0.05, |R > 0.6|). As shown in Figure 2A,B, the number of microorganisms associated with nitrogen transformation was not significantly different between the two irrigation levels with multiple N sources, with nodes of 757 and 655, respectively. However, the number of edges was significantly higher under reduced irrigation than conventional irrigation. This suggests that microorganisms may have a more important effect on nitrogen transformation of reduced irrigation conditions compared to conventional irrigation. However, the network was made based on the weighted value, which reflects only the strength of the correlation between microorganisms and nitrogen forms. Therefore, the genus-level identification of the core microorganisms was performed via random forest analysis, and the importance of each predictor was estimated with specific feature vectors [57]. The degrees of microorganisms for NH3 volatilization were rated via IncMSE values. The top 20 genera are shown in Figure 2C. The results showed that the core microorganisms related to NH3 volatilization were g_ZC4RG42 (8.49), g_JAABQT01 (6.53), g_Devosia (5.04), g_Microbacterium (4.41), g_Nocardiopsis (4.28), and g_VFJQ01 (3.95) in the multiple N sources system. The network analysis at the genus level also showed that straw retention and manure substitution can significantly alter the microbial community. In a short time, the stability of the microbial community decreased under straw retention and manure substitution (Figure S6).
According to a previous study, nitrogen metabolism is mainly regulated by functional genes carried by microorganisms, and gene-level changes play an important role in nitrogen transformation [58]. The nitrogen metabolism pathway KO00910 has been proven to be associated with NH3 volatilization. The genes directly related to NH3 volatilization are shown in Figure 3A. The gene relative abundance heatmap showed that their abundance is significantly different under the two irrigation levels tested (Figure 3B). The nirA, arcC, E3.5.1.49, and E3.5.5.1 genes’ relative abundance under reduced irrigation were significantly lower than under conventional irrigation (Figure 3C–F). They all play important roles in causing NH3 volatilization. The W1 treatment had a trend higher than the SW1, SMW1, and MW1 treatments. In addition, the PCA analysis of gene abundance affirmed that irrigation level significantly affected the gene pattern (Figure S7).
Network analysis revealed a co-occurrence between the functional genes driving NH3 volatilization and their major hosts (Figure 4A,B). The total number of edges associated with functional genes was significantly higher under reduced irrigation than conventional irrigation, and NH3-producing genes played a major role in two irrigation systems. This indicated that NH3 volatilization was closely related to microorganisms and was expected to be higher under reduced irrigation. To identify the major hosts of key genes, a correlation analysis between key genes and core microorganisms for NH3 volatilization was performed. As shown in Figure 4C, the g_Metabacillus and g_VFJQ01 were significantly correlated with the key genes.

4. Discussion

4.1. NH3 Volatilization Character Under Different Irrigation Levels

With multiple N sources, the NH3 volatilization flux peaked earlier and lower than with chemical fertilizer. This may be because the multiple N source treatment involved applying less chemical fertilizer and a faster mineralization rate. Chemical fertilizer application plays an important role in NH3 volatilization, and the NH3 volatilization flux decreases with decreasing chemical fertilizer application [59]. Dai et al. (2023) also showed that when chemical fertilizer and manure were applied together, the proportion of NH3 volatilization from current season chemical fertilizer was greater with the application of only chemical fertilizer [60].
NH3 volatilization under reduced irrigation with a single chemical fertilizer nitrogen source was significantly higher than that under conventional irrigation, while NH3 volatilization under both irrigation levels was similar when multiple N sources were applied. According to previous studies, soil properties and microorganisms may drive this change [61,62]. According to the PCA analysis of microbial abundance, there was a significant difference in microbial abundance between the two irrigation levels (Figure S4). Therefore, a network analysis of microorganisms at the genus level under both irrigation levels was performed, and it was found that the relationship between microorganisms and nitrogen transformation under reduced irrigation was closer than that under conventional irrigation (Figure 2A,B). Further analysis of the genes directly related to NH3 volatilization found that the presence of the nirA, arcC, E3.5.1.49, and E3.5.5.1 genes was significantly different between the two irrigation levels, and the W1 treatment had a trend higher than SW1, SMW1, and MW1 treatments (Figure 3B). The network analysis of genes and microorganisms also identified more microorganisms related to NH3 volatilization under reduced irrigation (Figure 4A,B). Therefore, microorganisms affect NH3 volatilization more under reduced irrigation than under conventional irrigation.

4.2. Effects of Soil Mineral Nitrogen on NH3 Volatilization

Under reduced irrigation, the NH4+-N content was significantly higher, which is consistent with the research results of [9]. This increases NH3 volatilization [8,63]. Because NH4+ and OH− generate a reversible chemical reaction that produces NH3 and H2O, and soil water content under reduced irrigation is lower, NH3 volatilization will increase. On the other hand, the NO3−-N under reduced irrigation is lower than that under conventional irrigation with equal nitrogen input and the NH4+-N content will rise accordingly, which will increase NH3 volatilization [62,64]. The mineral nitrogen content increase led to higher NH3 volatilization. The soil NH4+-N and NO3−-N were lower under multiple N sources [65]. The main reason is that the chemical fertilizer is lower under multiple N sources when there is equal nitrogen input. In addition, the low application of chemical fertilizer resulted in a decrease in soil mineral nitrogen [66,67,68]. Therefore, multiple N sources decreased NH3 volatilization at the same irrigation level. TN had a decreasing trend under reduced irrigation. It may be caused by a higher NH3 volatilization loss under reduced irrigation.

4.3. Effects of Microorganism and N Functional Genes on NH3 Volatilization

Under reduced irrigation, more microorganisms related to NH3 volatilization were present (Figure 4A,B). Qi et al. (2022) showed that the reduction in NH3 volatilization was associated with the activities of soil microorganisms, and the application of biochar resulted in a reduction in the abundance of ammonia-oxidizing archaea (AOA) and bacteria (AOB) genes under controlled irrigation conditions, as well as a decrease in the abundance of the ammoxidation microbial community [69]. Microorganisms host various genes; each genus may contain genes that produce ammonia, consume ammonia or both. In addition, although the abundance of these microorganisms may be low, they play a key role regardless and the gene abundance can be high. The gene abundance heatmap showed that the abundance of key genes decreased under reduced irrigation. The nirA, arcC, E3.5.1.49, and E3.5.5.1 genes produce ammonia, and multiple N sources treatments had a trend lower than W1 treatment under reduced irrigation, so the production of NH3 under reduced irrigation will decrease more when multiple N sources are applied. Therefore, microorganisms negatively affect NH3 volatilization. This also explains why the NH3 volatilization under only chemical fertilizer treatment increased significantly, while it did not increase when multiple N sources were applied under reduced irrigation conditions. Previous studies have also shown that changes in N functional gene abundance are mainly driven by irrigation level [70,71]. Lower gene abundance was one of the reasons for the lack of the increase of NH3 volatilization in multiple N sources under reduced irrigation.

5. Conclusions

The study revealed the combined effect of reducing irrigation and multiple nitrogen sources application on NH3 volatilization. This study’s results suggest that multiple N sources could inhibit NH3 volatilization under reducing irrigation compared to a single chemical fertilizer. The relationship between microorganisms and nitrogen transformation was closer under reduced irrigation than under conventional irrigation. The abundance of produce ammonia genes nirA, arcC, E3.5.1.49, and E3.5.5.1 was lower under reduced irrigation. Therefore, compared to conventional irrigation, microorganisms had a more important effect on NH3 volatilization of reduced irrigation conditions. Microorganisms played a more important role under reduced irrigation than under conventional irrigation. The NH3 produced by microorganisms under reduced irrigation was lower than that under conventional irrigation. This research emphasizes the important role of multiple nitrogen sources application in reducing NH3 volatilization for sustainable agriculture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14122927/s1. Table S1: The basic properties of nitrogen sources and groundwater; Table S2: The analysis of variance of independent variables on cumulative NH3 volatilization; Figure S1. The diagram of the trial device. Figure S2: The exogenous variables importance on cumulative NH3 volatilization; Figure S3: The indexes of specie α diversity; Figure S4: Principal Component analysis (PCA) showed the difference in the abundance of microorganisms at species levels; Figure S5: The relative abundance of the microorganism at the phylum level (top 10); Figure S6: Visualization of soil microbial community networks; Figure S7: Principal Component analysis (PCA) analysis of genes under different treatments.

Author Contributions

Conceptualization, T.C., K.S. and E.C.; methodology, T.C., B.C. and E.C.; software and investigation, T.C. and P.L.; formal analysis, T.C., C.H., S.L., Z.Z. and P.L.; visualization, T.C. and S.L.; resources, C.H. and K.S.; data curation, T.C., Z.Z. and C.H.; supervision, C.L. and X.F.; writing—original draft preparation, T.C. and E.C.; writing—review and editing, T.C., E.C., C.L., B.C. and X.F.; funding acquisition, E.C. and X.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the National Key Research and Development Program of China (2021YFD1700901), the Central Public-interest Scientific Institution Basal Research Fund (IFI2024-02), and the Scientific and Technological Project of Henan Province (232102320134).

Data Availability Statement

Data will be made available on request from the authors.

Conflicts of Interest

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.

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Agronomy 14 02927 g001
Figure 1. Temporal variation of NH3 flux under reduced irrigation (A) and conventional irrigation (B), the NH3 cumulative volatilization (C) and the linear regression analysis of the cumulative NH3 volatilization and the amount of chemical fertilizer applied (D). S: straw retention; M: manure substitution; W1: reduced irrigation; W2: conventional irrigation. No uppercase S or M represents straw non-retention or no manure substitution. Different lowercase letters above the column meant significant differences between treatments (p < 0.05).
Figure 1. Temporal variation of NH3 flux under reduced irrigation (A) and conventional irrigation (B), the NH3 cumulative volatilization (C) and the linear regression analysis of the cumulative NH3 volatilization and the amount of chemical fertilizer applied (D). S: straw retention; M: manure substitution; W1: reduced irrigation; W2: conventional irrigation. No uppercase S or M represents straw non-retention or no manure substitution. Different lowercase letters above the column meant significant differences between treatments (p < 0.05).
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Figure 2. Visualization of soil microorganisms and nitrogen form networks (A), the nodes and edges of networks (B), and the random forest analysis of the core functional microorganisms affecting NH3 at the genus level (C). The IncMSE value represents the degree of core microorganisms.
Figure 2. Visualization of soil microorganisms and nitrogen form networks (A), the nodes and edges of networks (B), and the random forest analysis of the core functional microorganisms affecting NH3 at the genus level (C). The IncMSE value represents the degree of core microorganisms.
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Figure 3. The N functional genes are directly related to NH3 volatilization (A), gene abundance heatmap (B), and the gene relative abundance of nirA, arcC, E3.5.1.49, and E3.5.5.1 (C–F). S: straw retention; M: manure substitution; W1: reduced irrigation; W2: conventional irrigation. No uppercase S or M represents straw non-retention or no manure substitution. Different lowercase letters in Figure 3C–F indicate significant differences between treatments (p < 0.05).
Figure 3. The N functional genes are directly related to NH3 volatilization (A), gene abundance heatmap (B), and the gene relative abundance of nirA, arcC, E3.5.1.49, and E3.5.5.1 (C–F). S: straw retention; M: manure substitution; W1: reduced irrigation; W2: conventional irrigation. No uppercase S or M represents straw non-retention or no manure substitution. Different lowercase letters in Figure 3C–F indicate significant differences between treatments (p < 0.05).
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Figure 4. Network analysis showing the relationships between microorganisms at the genus level and NH3 volatilization functional genes under different irrigation levels (A), the edge numbers of different genes (B), and the correlation analysis between key genes and core microorganisms (C). * and ** represent significant differences at p < 0.05 and p < 0.01, respectively.
Figure 4. Network analysis showing the relationships between microorganisms at the genus level and NH3 volatilization functional genes under different irrigation levels (A), the edge numbers of different genes (B), and the correlation analysis between key genes and core microorganisms (C). * and ** represent significant differences at p < 0.05 and p < 0.01, respectively.
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Table 1. Soil properties on the 15th day of the experiment.
Table 1. Soil properties on the 15th day of the experiment.
TreatmentSW1W1SMW1MW1SW2W2SMW2MW2
NH4+-N (mg kg−1)2.82 ± 0.36 c7.06 ± 0.47 a1.55 ± 0.15 d5.49 ± 0.10 b1.03 ± 0.09 e2.72 ± 0.29 c0.83 ± 0.04 e1.51 ± 0.17 d
NO3−-N (mg kg−1)53.55 ± 1.54 cd69.08 ± 2.35 b43.90 ± 1.47 e64.78 ± 1.02 b55.50 ± 2.27 c87.45 ± 4.87 a45.81 ± 2.44 e50.87 ± 1.92 d
TN
(mg g−1)		0.81 ± 0.04 abc0.77 ± 0.05 abc0.76 ± 0.01 bc0.74 ± 0.01 c0.83 ± 0.02 ab0.80 ± 0.08 abc0.84 ± 0.02 a0.82 ± 0.01 ab
TN: total nitrogen; S: straw retention; M: manure substitution; W1: reduced irrigation; W2: conventional irrigation. No uppercase S or M represents straw non-retention or no manure substitution. The values represent the mean and standard deviation. Different lowercase lines indicated significant differences among treatments (p < 0.05).
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Chen, T.; Cui, E.; Sun, K.; Hu, C.; Li, S.; Li, P.; Zhao, Z.; Liu, C.; Cui, B.; Fan, X. Multiple Nitrogen Sources Application Inhibits Increasing Ammonia Volatilization Under Reducing Irrigation. Agronomy 2024, 14, 2927. https://doi.org/10.3390/agronomy14122927

AMA Style

Chen T, Cui E, Sun K, Hu C, Li S, Li P, Zhao Z, Liu C, Cui B, Fan X. Multiple Nitrogen Sources Application Inhibits Increasing Ammonia Volatilization Under Reducing Irrigation. Agronomy. 2024; 14(12):2927. https://doi.org/10.3390/agronomy14122927

Chicago/Turabian Style

Chen, Taotao, Erping Cui, Ke Sun, Chao Hu, Siyi Li, Ping Li, Zhijuan Zhao, Chuncheng Liu, Bingjian Cui, and Xiangyang Fan. 2024. "Multiple Nitrogen Sources Application Inhibits Increasing Ammonia Volatilization Under Reducing Irrigation" Agronomy 14, no. 12: 2927. https://doi.org/10.3390/agronomy14122927

APA Style

Chen, T., Cui, E., Sun, K., Hu, C., Li, S., Li, P., Zhao, Z., Liu, C., Cui, B., & Fan, X. (2024). Multiple Nitrogen Sources Application Inhibits Increasing Ammonia Volatilization Under Reducing Irrigation. Agronomy, 14(12), 2927. https://doi.org/10.3390/agronomy14122927

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