Nitrogen and phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements.

Rice cultivation usually involves high water and fertilizer application rates leading to the nonpoint pollution of surface waters with phosphorus (P) and nitrogen (N). Here, a 10-year field experiment was conducted to investigate N and P losses and their impact factors under different irrigation and fertilization regimes. Results indicated that T2 (Chemical fertilizer of 240 kg N ha-1, 52 kg P ha-1, and 198 kg K ha-1 combined with shallow intermittent irrigation) decreased N loss by 48.9% compared with T1 (Chemical fertilizer of 273 kg N ha-1, 59 kg P ha-1, and 112 kg K ha-1 combined with traditional flooding irrigation). The loss ratio (total N loss loading/amount of applied N) of N was 9.24-15.90%, whereas that of P was 1.13-1.31% in all treatments. Nitrate N (NO3--N) loss was the major proportion accounting for 88.30-90.65% of dissolved inorganic N loss through surface runoff. Moreover, the N runoff loss was mainly due to high fertilizer input, soil NO3--N, and ammonium N (NH4+-N) contents. In addition, the N loss was accelerated by Bacteroidetes, Proteobacteria, Planotomycetes, Nitrospirae, Firmicutes bacteria and Ascomycota fungi, but decreased by Chytridiomycota fungi whose contribution to the N transformation process. Furthermore, T2 increased agronomic N use efficiency (AEN) and rice yield by 32.81% and 7.36%, respectively, in comparison with T1. These findings demonstrated that T2 might be an effective approach to ameliorate soil chemical properties, regulate microbial community structure, increase AEN and consequently reduce N losses as well as maintaining rice yields in the present study.

Introduction

n class="Species">Rice (pan> class="Species">Oryza sativa L.) is one of the main staple crops and feeds over 65% of the world’s population with 11% of cultivated land [1,2]. Because the population is steadily increasing, rice production must increase by 1% annually [3]. High rice yields depended on higher inputs of nitrogen (N) and phosphorus (P) fertilizers, however, which inevitably increased the risk of potential eutrophication in the surrounding water bodies through surface runoff from paddy soils [3,4]. Eutrophication is the excessive growth of algae in response to N and P additions and consequently leads to a heavy mortality of other aquatic plants and animals resulting from the decomposition of algae [4]. To date, water—quality deterioration as a consequence of eutrophication was observed in many regions such as Europe, America and China [4-6]. In addition, a main N and P loss pathway is the direct loss of manure, fertilizer and/or soil to surface water by runoff [7]. Moreover, surface runoff is determined primarily by high irrigation and precipitation events [8]. Minimizing N and P concentrations in runoff is therefore important to protect receiving waters from eutrophication. A widely used method to achieve this is to optimize water and fertilizer management. For example, water-saving irrigation techniques could maintain rice yields despite 50% of the irrigation volume, compared to traditional irrigation [9]. Reduction of chemical fertilizer input is also a potential solution to lower nutrient export fluxes [10]. Therefore, nutrient runoff losses could be reduced by optimizing fertilizer and water management practices during the rice growing seasons.

The wet—dry cycles of n class="Chemical">water savinpan>g irrigation combinpan>ed with optimizinpan>g pan> class="Chemical">fertilization also changed soil properties, N and P transformation. These changes directly resulted in different characteristics of N and P use efficiency and loss from paddy fields [11]. It has been reported that soil moisture and temperature were important factors influencing seasonal variations in losses of available N and P in simulated freeze-thaw conditions [12]. In addition, optimal applications of water and fertilizers affected soil microbial communities, consequently leading to variations in N and P losses by surface runoff in field conditions [13,14]. Related studies have suggested that arbuscular mycorrhizal fungi (AMF) can not only scavenge P resources by improving P uptake of rices, but also reduce N losses from paddy soils through denitrification [15,16]. The combination of inoculation with AMF and 80% of the local norm of fertilization reduced N runoff by 27.2% [17]. Additionally, ammonia-oxidizing bacteria (AOB) played an important role in the ammonia oxidation which was crucial for N and P runoff losses [18]. These N cycling processes were closely linked to N and P losses. Hence, understanding the response of microbial communities to fertilization and irrigation is important to select the optimum water and fertilizer management to minimize nutrient inputs in paddy soils.

Soil microbial community composition and diversity were reportedly altered over a wide range of soil factors associated with n class="Chemical">water anpan>d pan> class="Chemical">fertilizer managements [14,18]. The present studies have mostly focused on the impacts of either irrigation management or fertilizer application alone on the microbial communities [14,19], but few studies have evaluated microbial community structure in response to the combination of water and fertilizer management, particularly in subtropical paddy soils. However, different irrigation and fertilization regimes tended to shape distinct microbial communities [14,19]. In addition, N and P runoff losses varied temporally, and little information about nutrient runoff losses from paddy fields was available in this region. The subtropical paddy field is one of the major rice production bases of South China. Importantly, the rice growing season in this area extends from May to September each year which corresponds with the main rainy and hydrologically active period of the year. The surrounding water bodies were vulnerable to pollution from N and P in paddy fields. To date, N and P runoff losses and their influencing factors while maintaining or enhancing rice yields in the paddy fields in southeastern China are currently unclear under different irrigation and fertilization regimes. Thus, we hypothesized that different irrigation and fertilization practices could alter soil chemical properties and microbial community structure, which would subsequently affect N and P runoff losses. To test the hypothesis, a 10-year plot experiment was conducted to estimate N and P runoff losses and uptake, soil chemical properties, microbial diversity, and community composition under different fertilization and irrigation regimes. In general, the purpose of this study was to ⑴ verify an optimal irrigation and fertilization practice in order to minimize N and P runoff losses, and ⑵ explore the factors influencing N and P losses in surface runoff from paddy fields in southeastern China.

Materials and methods

Experiment design

Field trial was initiated in 2008 and cropped by double-cropping n class="Species">rice (pan> class="Species">Oryza sativa L.) annually at Baisha Experimental Station, Fuzhou, Fujian Province, China (26°13′31″N, 119°04′10″E). The early and late cultivars of rice are conventional rice varieties 78–30 and 428, respectively. This region has a subtropical monsoonal climate with an average annual temperature of 19.5°C and mean annual precipitation of 1 350 mm. The soil is a typic Hapli-Stagnic Anthrosol (USDA soil system). At the beginning of the experiment, the soil had a pH (1:2.5) 6.19, 14.16 g kg-1 soil organic matter (SOM), 0.66 g kg-1 Total N (TN), 0.30 g kg-1 total P (TP), 3.8 mg kg-1 NO3-–N, 12 mg kg-1 NH4+–N, 3.358 and 0.83 mg kg-1 of available P (AP) and K (AK), respectively. A randomized complete block design with three treatments was conducted in 9 plots (4.0 m long × 5.0 m wide). Each treatment had three duplicates. The treatments consisted of control (no chemical fertilization with traditional flooding irrigation, T0), traditional chemical fertilization with traditional flooding irrigation (T1, based on local practices), and optimum fertilization with water-saving irrigation (T2, based on both fertilizer recommendation from local agriculture committee and water saving by shallow intermittent irrigation). The water and fertilizer practices used in this experiment are described in Table 1. The chemical compound fertilizer containing 15% N, 7% P, and 12% K was produced by China Petroleum and Chemical Co., Ltd. N, P, and K fertilizers were applied in the form of urea, superphosphate, and potassium chloride and rated according to each treatment as shown in Table 1. The proportion of N, P, and K was estimated at 46.4% of N in urea, 5% of P in calcium superphosphate, and 50% of K in potassium chloride, respectively. The 100% of the total amount of P, 60% of N, and 40% of K fertilizers were applied as basal fertilizers before planting, whereas the 40% N and 60% K fertilizers as topdressing fertilizers after tillering, respectively (Table 1). Annual fertilizer application rates were the same since 2008. Traditional flooding irrigation was needed for the rice season to be maintained at a depth of 1.0 − 6.0 cm, and water-saving irrigation at a depth of -3.0 to 3.0 cm in the paddy field. In order to prevent the exchange of water and nutrients between adjacent plots, each plot was surrounded by a concrete cement border, 40-cm deep by 30-cm width, leaving 20 cm above the soil surface for separation. At the base, a tank (2.0 m long×1.0 m wide×1.8 m deep) with vertical scale was placed to collect surface runoff beside the plot through a piping system (Fig 1). The early rice was transplanted with a 20.0 cm × 23.0 cm hill spacing on 21 April and harvested on 25 July 2018. The late rice was transplanted with the same hill spacing on 30 July and harvested on 1 December 2018.

Table 1

The water and fertilizer practices used in this experiment.

Treatment	Fertilization	Irrigation
T0	No chemical fertilization	Traditional flooding irrigation
T1	Conventional level of nitrogen (273 kg N ha−1), phosphorus (59 kg P ha−1), and potassium (112 kg K ha−1) fertilizer application	Traditional flooding irrigation
T2	Optimum level of nitrogen (240 kg N ha−1), phosphorus (52 kg P ha−1), and potassium (198 kg K ha−1) fertilizer application	Shallow intermittent irrigation

Fig 1

Device for the collection of runoff water in experimental plots in 2018.

Notes: Entrance 1 for water into tank during irrigation period; Entrance 2 for water into tank during paddy drying or fallow period.

Notes: Entrance 1 for n class="Chemical">water into tank during irrigation period; Entrance 2 for n class="Chemical">water into tank during paddy drying or fallow period.

Water sampling and analysis

The rainfall amount was recorded by an automatic meteorological station. After each runoff-producing-rain event, the depth in the runoff n class="Chemical">water inpan> a tanpan>k was recorded to assess runpan>off volume by modellinpan>g volume—depth relationships. Inpan> addition, five runpan>off sub-samples of about 100 mL were collected from each plot anpan>d mixed to make a composite sample inpan> 500 mL pan> class="Chemical">polyethylene bottles, then delivered on ice to the laboratory for analysis. The rest of the water was discharged to a nearby canal. The empty tanks were cleaned to prepare for the subsequent runoff collection. Prior to analysis the sample was divided into two parts. One part was filtered through a 0.45 - μm membrane to analyze NH4+–N, NO3-–N, and dissolved P (DP). The other part was filtered through a cellulose filter paper (≤11 μm; ash content < 0.01%) to analyze TN and TP. TN was measured by alkaline potassium persulfate ultraviolet spectrometric method, NO3-–N was analyzed by using dual wave length ultraviolet spectrophotometric method, and NH4+–N by the indophenol blue method [20]. TP and DP were determined by the molybdate blue method after the surface water samples were digested with potassium persulfate [21]. Cumulative N (P) runoff (kg ha-1) = sum of runoff volume (m3 ha-1) × N (P) concentration in runoff water (mg L-1), where runoff water volume is calculated as the base area of the water tank (2.0 m × 1.0 m) × the runoff depth in the water tank.

Plant sampling and analysis

The n class="Species">rice straw and grain were sampled and their yields were measured at harvest from each plot, separately (n class="Species">rice grain weights were adjusted to 13.5% moisture content). The rice samples were oven dried at 70°C for 72 h, weighed, and finely ground with a small ball mill for chemical analysis. Total N, P, and K in plants were determined by using the methods of diffusion, molybdenum blue colorimetry, and flame photometry, respectively [22].

Soil sampling and analysis

Soil physiochemical properties

Soil samples were collected at 0−20 cm depth after late n class="Species">rice harvest. For each plot, five soil cores were taken anpan>d homogenized as a composite sample. Onpan>e subsample was air-dried anpan>d then sieved to < 2.0 mm before physiochemical anpan>alysis. Soil moisture was calculated as the difpan> class="Chemical">ference between oven-dry (24 h at 105°C) and fresh weight. Soil pH was measured with a glass electrode (EL20 K, Mettler-Toledo, Greifensee, Switzerland) in 1:2.5 soil:water suspension. Soil organic C (SOC) was determined by the K2Cr2O7 oxidation-reduction titration technique. The TN content was measured spectrophotometrically after potassium persulfate digestion. Both NH4+–N and NO3−–N in 2 M KCl soil extracts (1:10 soil/extract (wt:vol)) were measured by using UV spectrophotometry. TP was measured by the alkaline fusion molybdenum-antimony colorimetric method. Olsen P in 0.5 M NaHCO3 soil extracts was determined by using the molybdate blue colorimetric method [23]. The TK and AK contents were determined by flame photometry [22].

DNA extraction and PCR amplification

A subsample of fresh soil was stored at—80°C for molecular analysis. Total microbial n class="Chemical">DNA was extracted from 0.5 g fresh soil by usinpan>g anpan> E.Z.N.A Soil pan> class="Chemical">DNA Kit (Omega Bio-tek, Norcross, Georgia, USA), according to the manufacturer’s protocol [24]. A NanoDrop-2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) was used to determine the purities and concentrations of extracted DNA, and the V3 − V4 region of the bacterial 16S rRNA gene was amplified by PCR (95°C for 3 min, followed by 25 cycles at 95°C for 30 s, 55°C for 30 s, 72°C for 45 s and a final extension at 72°C for 10 min) by using the forward primer 338F (5’-barcode- ACTCCTACGGGAGGCAGCA -3’) and the reverse primer 806R (5’-GGACTACHVGGGTWTCTAAT-3’), where a barcode is an unique eight-base sequence for each sample [25]. Meanwhile, The V4 − V5 region in the 18S ribosomal RNA gene of the fungi was amplified by PCR (95°C for 3 min, followed by 25 cycles at 95°C for 30 s, 55°C for 30 s, and 72°C for 45 s and a final extension at 72°C for 10 min) using primers SSU0817F 5’-barcode- TTAGCATGGAATAATRRAATAGGA)-3’ and 1196R 5’-TCTGGACCTGGTGAGTTTCC-3’, where a barcode is an unique eight-base sequence for each sample [26]. The PCR mixture (20 μL) contained 4 μL 5 × FastPfu Buffer, 2 μL 2.5 mmol L-1 dNTPs, 0.8 μL each primer (5 μmol L-1), 0.4 μL FastPfu Polymerase, and 10 ng template DNA [26].

Illumina MiSeq sequencing

The amplified n class="Chemical">DNA was subjected to horizontal electrophoresis on 2% pan> class="Chemical">agarose and purified with an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, California, USA) according to the manufacturer’s instructions and quantified by using QuantiFluo-ST (Promega, Madison, Wisconsin, USA). The purified amplicons were pooled in equimolar concentrations and paired-end sequenced (2 × 250) on an Illumina MiSeq platform according to the standard protocols [27]. The raw reads were deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database with accession number SRP293735.

Illumina data analysis

Raw fastq files were demultiplexed, quality-filtered, and analysed by using Quantitative Insights Into Microbial Ecology (QIIME) 1.17 [28]. These sequences were clustered into operational taxonomic units (OTUs) at 97% sequence similarity by the UPARSE pipeline (version 7.0.1090) [29]. Using the UPARSE (version 7.0.1090), we also removed singleton sequences (i.e., sequences appearing only one time in the entire n class="Chemical">data set). Inpan> addition, chimeric sequences were identified anpan>d removed by usinpan>g UCHIME. The taxonomy of 16S anpan>d 18S rRNA gene sequences was anpan>alyzed by Rpan> class="Chemical">DP Classifier (http://rdp.cme.msu.edu/) against the Silva rRNA database (version 1.30.2) using a confidence threshold of 70% [30]. As the number of sequence reads in each sample varied, the OTU table was rarified (holding the same sequence number in each sample) prior to microbial community diversity calculations. Rarefaction curves and other OTUs-based analyses such as the abundance-based coverage estimators (ACE) and Chao1, Shannon-Wiener index (H′), and Simpson’s index (D) were conducted by the mothur software package (version 7.0.1090) [25]. Chao1 and ACE were calculated to estimate the richness of microbial community based on sequence dissimilarity. The diversity within each sample was estimated by H′ and D [31].

Data analysis

Statistical analyses were done by using SAS software, version 8.02 (SAS Institute Inc., Carey, North Carolina, USA). All values were expressed as means ± SD (n = 3). The one-way analysis of variance and the Duncan multiple—range test were applied to determine the differences inpan> N anpan>d P runpan>off losses, uptake, microbial diversity, epan> class="Chemical">daphic characteristics, and rice yields at three water and fertilizer regimes in 2018. To better compare microbial community similarities, partial least squares discriminant analysis (PLS—DA) was performed by PLS regression methods. In addition, the similarities and differences among microbial communities were also described by using the number of shared and unique OTUs in the three treatments by a Venn diagram. To compare the top 10 microbial genera, a heatmap analysis was performed, and the result was plotted in Vegan packages in R software (version 2.15.3) [32]. Furthermore, a heatmap of correlations between the relative abundances of microbial taxa and edaphic characteristics (e.g., pH, SOC, and TN) was tested by using the Canoco software for Windows Version 4.5 [33]. In addition, environmental factors were selected by the functions of envfit (permu = 999) and variance inflation factor (vif).cca, and the environmental factors with vif > 10 were removed from the following analysis. The vif values of SOC, NH4+–N, NO3-–N, TP, and AK were higher than 10 and removed. Additionally, Pearson correlations were performed between the microbial abundances and N and P runoff losses. The unweighted UniFrac distance—based redundancy analysis (db‐RDA) was processed by R software (version 2.15.3) to determine which soil variables were related to soil microbial community structures [32]. Additionally, Pearson correlations were performed between the microbial abundances and N and P runoff losses. Furthermore, RDA was selected, depending on the length of gradient calculated by detrended correspondence analysis (DCA). In this study, the gradient length was smaller than 3.0, so RDA was chosen to analyze the correlations between soil N and P runoff losses and their impact factors [34]. The influencing factors included the runoff volume, fertilizer inputs, and soil chemical properties. The method of the rank analysis was performed by using Canoco for Window 4.5.

Results

Rice yields and soil fertility

The n class="Chemical">T1 anpan>d T2 treatments inpan>creased grainpan> yield by 65.9% anpan>d 90.4%, respectively, compared to the pan> class="Chemical">T0 treatment in the early rice season, while increased grain yield by 91.9% and 93.0% compared to the T0 treatment in the late rice season (Table 2). However, there were no significant differences in the grain yield between T1 and T2 treatments. In addition, K+ uptake in rice plants of T1 and T2 treatments in the late rice season was about 1.37 and 1.06 times higher than that in the early rice season, respectively (Table 2). Meanwhile, the T2 treatment had higher contents of soil pH, SOC, TK and AK than those in the T1 treatment. Nevertheless, soil NO3-–N content significantly (P < 0.05) increased in the T1 treatment but decreased in the T2 treatment as compared to that of the T0 treatment. Additionally, all three treated paddy soils were acidic (Table 2).

Table 2

Soil properties and plant traits as influenced by fertilization and irrigation in 2018.

Treatment	Soil properties									Plant traits
	pH	SOC	TN	TP	TK	NO3-−N	NH4+−N	Olsen−P	AK	Early rice				Late rice
										Grain yield	N	P	K	Grain yield	N	P	K
		g kg-1				mg kg-1				kg ha-1	g kg-1			kg ha-1	g kg-1
T0	5.97±0.08b	15.16±0.10b	2.18±0.25a	0.29±0.01c	20.31±0.73ab	13.20±0.77b	35.14±6.12b	0.96±0.06c	64.99±4.62b	2971±374b	81.64±4.03b	14.14± 0.84b	56.96± 3.08a	2795±165b	83.95 ±1.66c	10.60 ±0.45b	52.76 ±2.19c
T1	6.01±0.08b	15.33±0.13b	2.00±0.12a	0.41±0.01b	19.68±0.59b	18.74±2.94a	59.64±5.53a	2.75±0.19a	57.87±3.38b	4929±518a	96.88±4.27a	18.84± 0.68a	45.40±1.73b	5363±119a	108.16± 0.83a	21.36± 0.40a	107.80± 7.64b
T2	6.24±0.11a	15.98±0.18a	1.84±0.16a	0.48±0.02a	21.55±0.55a	6.37±0.96c	51.55±7.43a	2.41±0.17b	86.15±6.17a	5656±134a	88.39±2.74b	20.47± 0.55a	60.81±1.82a	5393±105a	101.04 ±2.57b	20.62 ±1.56a	125.14 ±4.49a

Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. SOC: Soil organic carbon; N: Nitrogen; TN: Total N; NO3− −N: Nitrate N; NH4+ −N: Ammonium N; P: Phosphorus; TP: Total P; K: Potassium; TK: Total K; AK: Available K. Values (means ± SD) with different lower-case letters in a column are significantly different at P < 0.05 according to the Duncan test.

Notes: n class="Chemical">T0 = Traditional irrigation; pan> class="Chemical">T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. SOC: Soil organic carbon; N: Nitrogen; TN: Total N; NO3− −N: Nitrate N; NH4+ −N: Ammonium N; P: Phosphorus; TP: Total P; K: Potassium; TK: Total K; AK: Available K. Values (means ± SD) with different lower-case letters in a column are significantly different at P < 0.05 according to the Duncan test.

Nitrogen and phosphorus losses

A total of 17 runoff-producing rainfall events were recorded during the n class="Species">rice growinpan>g season from 1 May to 9 September 2018, anpan>d they ranpan>ged from 7.0 to 101.7 mm. Among them, three extreme precipitation events with a pan> class="Chemical">daily rainfall greater than 60.0 mm were observed, 67.0 mm on June 21, 89.0 mm on July 10, and 101.7 mm on September 6 (Fig 2). High runoff fluxes of surface flow generally occurred from May to September 2018, when facing the high precipitation (Figs 2 and 3A). Moreover, the runoff flux had a close relationship with NO3- –N, TP and DP losses in all treatments (Fig 3A–3C). Meanwhile, the loss ratio of N was higher than that of P. In addition, the loss ratio of N from surface runoff in the T1 plots was the highest among the treated plots. By contrast, the T2 treatment reduced N loss from paddy fields by 21.21 kg N ha-1, especially because the N—fertilizer use efficiency was high in the T2 treatment in comparison with that in the T1 treatment (Table 3). Inorganic dissolved N loss accounted for 29.57–47.05% of N loss under different irrigation and fertilization regimes, and a greater proportion of the loss was in the NO3-–N form (Table 3). NO3-–N loss in the late rice season were higher than that in the early rice season (Fig 3B). Additionally, NO3-–N loss from the T1 treatment was significantly (P < 0.05) higher than that of the other treatments. Nevertheless, no significant difference was found in NH4+–N loss among three treatments (Table 3). Compared to the T0 treatment, the T1 and T2 treatments increased P loss by 30.92% and 33.07%, respectively (Table 3). However, the T2 treatment did not lead to significantly (P < 0.05) different P loss compared to the T1 treatment. DP was the major form of P loss, and accounted for 82.19%, 82.96%, and 79.41% for TP loss in T0, T1 and T2, respectively (Table 3). Moreover, DP loss was positively correlated with TP loss (Fig 3D). As the fertilizer level increased, the N and P runoff losses showed an upward trend (Fig 3A–3C). The cumulative P loss from paddy fields in the early rice season was greater than that in the late rice season in all treatments (Fig 3C). The highest P loss was also observed in all treatments on June 25 in the early rice season (Fig 3C).

Fig 2

Characteristics of 17 rainfall-runoff events in the experiment plots from May to September 2018.

Fig 3

Runoff Nitrogen (N) and phosphorus (P) losses from rice fields as influenced by the treatments T0, T1, and T2 from January to December 2018: Accumulated TN losses (A), AN and NN concentrations (B), accumulated TP and DP losses (C), and relationship between TP and DP concentrations (D). Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. RY: Runoff yields. TN: Total N; NN: Nitrate N; AN: Ammonium N; TP: Total P; DP: Dissolved P.

Table 3

Annual loads of nitrogen and phosphorus transported by surface runoff for the treatments T0, T1, and T2 from January to December 2018.

Treatment	Runoff (×105 L ha-1)	Fertilizer amount (kg ha-1)		Nitrogen and phosphorus losses in runoff (kg ha-1)					Loss ratio (%)		AEN (kg kg-1 N)	AEP (kg kg-1 P)
		TN	TP	TN	NO3--N	NH4+-N	TP	DP	TN	TP
T0	50.0±0.6a	0	0	24.67±2.40b	7.77±1.20b	1.03±0.13a	0.51±0.03b	0.42±0.02b	—	—	—	—
T1	50.6±0.3a	273	59	43.39 ±14.04a	11.63 ±1.15a	1.20 ±0.29a	0.67 ±0.04a	0.56±0.03a	15.89±5.14a	1.13±0.08a	16.58±0.42b	76.72±1.94b
T2	50.4±1.1a	240	52	22.17 ± 1.07b	9.25 ± 0.94b	1.18 ±0.32a	0.68 ± 0.08a	0.54± 0.08a	9.24±0.45b	1.31±0.16a	22.02±2.38a	101.61±11.00a

Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.N: Nitrogen; P: Phosphorus; Loss ratio, total nitrogen (phosphorus) loss loading/amount of applied nitrogen (phosphorus); AEN (P), agronomic N (P) use efficiency, increased grain yield/unit N (P) application. TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; TP: Total P; DP: Dissolved P. Values (means ± SD) with different lower-case letters in a column are significantly different at P < 0.05 according to the Duncan test.

Runoff n class="Chemical">Nitrogen (N) anpan>d pan> class="Chemical">phosphorus (P) losses from rice fields as influenced by the treatments T0, T1, and T2 from January to December 2018: Accumulated TN losses (A), AN and NN concentrations (B), accumulated TP and DP losses (C), and relationship between TP and DP concentrations (D). Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. RY: Runoff yields. TN: Total N; NN: Nitrate N; AN: Ammonium N; TP: Total P; DP: Dissolved P.

Notes: n class="Chemical">T0 = Traditional irrigation; pan> class="Chemical">T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.N: Nitrogen; P: Phosphorus; Loss ratio, total nitrogen (phosphorus) loss loading/amount of applied nitrogen (phosphorus); AEN (P), agronomic N (P) use efficiency, increased grain yield/unit N (P) application. TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; TP: Total P; DP: Dissolved P. Values (means ± SD) with different lower-case letters in a column are significantly different at P < 0.05 according to the Duncan test.

Microbial alpha diversity

High query coverage (>98.0%) suggested that this study captured the dominant OTUs of microbia in each soil sample (Table 4). Moreover, all of the rarefaction curves of bacterial 16S rRNA and fungal 18S rRNA sequences in soil samples reached saturation, suggesting that the number of sequence reads was sufficient to represent most of sequence types (S1A and S1B Fig). The numbers of 16S rRNA OTUs from bacteria at a 97% sequence identity were 1973, 2055, and 2018 as well as 266, 248, and 250 for fungal OTUs in soil samples in the n class="Chemical">T0, pan> class="Chemical">T1 and T2 treatments, respectively (Fig 4A and 4B). Most bacterial OTUs (85.79%) were shared (Fig 4A), while 179 of 323 fungal OTUs were shared among three treated soil samples (Fig 4B). Meanwhile, we also found that many of the alpha diversity indices were nonsignificantly (P>0.05) different among three treatments (Table 4). No variations in the soil microbial alpha diversity (except Chao 1) among different treatments may be explained by their response to natural mechanisms rather than by direct impacts of fertilization and irrigation treatments on bacteria and fungi. Additionally, bacterial alpha diversity indices (ACE, Chao 1 and Shannon-Wiener index) were significantly (P < 0.05) higher than those of the fungi in the paddy soil treated with different irrigation and fertilization strategies (Table 4).

Table 4

Microbial alpha diversity as affected by fertilization and irrigation in 2018.

Microbe	Treatment	Coverage (%)	Richness		Diversity
			ACE	Chao 1	H′	D (×10−3)
Bacteria	T0	98.71±0.07a(b)	1789±61a(a)	1812±37b(a)	6.23±0.15a(a)	5.57±0.78a(a)
	T1	98.77±0.02a(b)	1885±23a(a)	1892±26a(a)	6.41±0.11a(a)	4.62±1.16a(b)
	T2	98.72±0.05a(b)	1841±47a(a)	1858±48ab(a)	6.33±0.11a(a)	5.53±2.06a(b)
Fungi	T0	99.97±0.00a(a)	166±32a(b)	167±34b(b)	3.00±0.48a(b)	137.13±95.62a(a)
	T1	99.96±0.00a(a)	171±14a(b)	173±18a(b)	3.20±0.36a(b)	86.43±43.58a(a)
	T2	99.95±0.00a(a)	199±8a(b)	201±6ab(b)	3.15±0.23a(b)	95.38±51.71a(a)

Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. Operational taxonomic units (OTUs); Abundance-based coverage estimators (ACE); H′: Shannon-Wiener index; D: Simpson’s index. Values (means ± SD) with different lower-case letters inside and outside the parentheses in a column are significantly different between soil microbes or fertilizer treatments at P < 0.05 according to the Duncan test.

Fig 4

Venn diagram depicts bacterial (A) and fungal (B) operational taxonomic units (OTUs) that were shared or unique for T0, T1, and T2. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

Venn diagram depicts bacterial (A) and fungal (B) operational taxonomic units (OTUs) that were shared or unique for n class="Chemical">T0, pan> class="Chemical">T1, and T2. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

Notes: n class="Chemical">T0 = Traditional irrigation; pan> class="Chemical">T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. Operational taxonomic units (OTUs); Abundance-based coverage estimators (ACE); H′: Shannon-Wiener index; D: Simpson’s index. Values (means ± SD) with different lower-case letters inside and outside the parentheses in a column are significantly different between soil microbes or fertilizer treatments at P < 0.05 according to the Duncan test.

Microbial community composition

Each n class="Chemical">water anpan>d pan> class="Chemical">fertilizer treatment formed a unique microbial community structure by PLS—DA approach (Fig 5A and 5B). A total 34.85% of the variations in the composition of bacterial communities could be explained by the first two principal components, and a total 25.94% of the variations in the composition of fungal communities by the first two principal components (Fig 5A and 5B). Moreover, T2 increased the relative abundances of the bacterial phyla Actinobacteria, Cyanobacteria, and Verrucomicrobia compared to those in other treatments. Nevertheless, the T2 treatment decreased the abundance of Acidobacteria by 12.19% and 19.88%, respectively, compared to that in the T0 and T1 treatments (Fig 6A). Moreover, the predominant bacterial phyla in paddy soils were Proteobacteria (the number of classified sequences in this phylum ranged from 28.02 to 32.97% in all the samples), Chloroflexi (23.43–30.54%), and Acidobacteria (9.51–11.87%); the rare phyla were characterized by low Cyanobacteria, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia abundances (Fig 6A). Additionally, the classified sequences from each treated soil were affiliated with the fungal phyla: Ascomycota, Basidiomycota, Mucoromycota, and Chytridiomycota; the remaining sequences were unclassified fungi and other classified fungal phyla (Fig 6B). Ascomycota and Basidiomycota were the two most abundant fungal phyla in soils under different water and fertilizer treatments. Moreover, T2 increased the abundance of Mucoromycota by 30.72%, whereas T1 decreased Mucoromycota by 2.94% in comparison with that of T0 (Fig 6B).

Fig 5

Partial least squares discriminant analysis (PLS—DA) is an adaptation of PLS regression methods to represent differences in the community structure of bacterial (A) and fungal (B) microbiota that was associated with T0, T1, and T2. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

Fig 6

Average relative abundance of dominant bacterial (A) and fungal (B) phyla (> 1.0%) in different fertilization and irrigation regimes. The abundance is expressed as the average percentage of the targeted sequences to the total high-quality bacterial and fungal sequences of samples from triplicate plots of each fertilization regime, respectively. Notes: ‘Others’ refer to those identified phyla with lower than 1.0% relative abundance in all the samples. T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

Partial least squares discriminant analysis (PLS—n class="Chemical">DA) is anpan> apan> class="Chemical">daptation of PLS regression methods to represent differences in the community structure of bacterial (A) and fungal (B) microbiota that was associated with T0, T1, and T2. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

Average relative abunn class="Chemical">dance of dominant bacterial (A) and fungal (B) phyla (> 1.0%) in different fertilization and irrigation regimes. The abundance is expressed as the average percentage of the targeted sequences to the total high-quality bacterial and fungal sequences of samples from triplicate plots of each fertilization regime, respectively. Notes: ‘Others’ refer to those identified phyla with lower than 1.0% relative abundance in all the samples. T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

Factors impacting N and P surface runoff losses

Soil pH (r2 = 0.8973, P = 0.003) and Olsen P content (r2 = 0.6609, P = 0.033) were significantly correlated with bacterial community structure by a db—Rn class="Chemical">DA (Fig 7A). Meanpan>while, soil pH (r2 = 0.8123, P = 0.007) anpan>d pan> class="Chemical">TN content (r2 = 0.67599, P = 0.024) were significantly correlated with fungal community structure (Fig 7B). In addition, the relative abundance of Desulfobacca bacteria was significantly (P < 0.05) positively related to SOC and Olsen P contents, whereas the relative abundance of Nitrospira bacteria was significantly (P < 0.001) negatively related to soil AK content (Fig 8A). The relative abundance of Leucosporidium fungi was significantly negatively related to soil AK content but positively correlated with soil Olsen P content (P < 0.05) (Fig 8B). Taken together, soil properties could alter microbial community composition, which was also the crucial contributing factor for N and P runoff losses under different fertilization and irrigation regimes. N and P losses in runoff were positively correlated with the relative abundances of the bacterial phyla Firmicutes, Bacteroidetes, and Gemmatimonadetes and the fungal phyla Ascomycota, whereas the nutrient runoff losses were negatively correlated with the abundances of the bacterial phyla Chloroflexi and the fungal phyla Basidiomycota and Chytridiomycota (Table 5). Meanwhile, the losses of TN and NO3-–N in the runoff were positively related to the abundances of the bacterial phyla Proteobacteria and Bacteroidetes, but negatively to the abundances of the bacterial phyla Planotomycetes and Verrucomicrobia (Table 5). Meanwhile, there was a significant (P < 0.05) and positive relationship between the abundance of Nitrospirae bacteria and TN runoff loss. In contrast, a negative correlation occurred between the abundance of Mucoromycota fungi and TN runoff loss (Table 5). In addition, there existed a positive association of TP and DP losses in the runoff with the abundance of Actinobacteria bacteria (Table 5). Meanwhile, the positive association of TP loss with the abundance of Cyanobacteria was found (Table 5). In addition, all the selected environmental factors interpreted the majority of N loss variations (94.5%), and P loss variations were totally interpreted by the environmental factors by using RDA (Fig 9A and 9B). Moreover, the N loss via surface runoff was mainly due to high N fertilizer input, soil NO3- –N, and NH4+–N content, whereas the P loss largely depended on high P fertilizer input, soil TP, and Olsen-P content (Fig 9A and 9B).

Fig 7

Distance-based redundancy analysis (db-RDA) of the bacterial (A) and fungal (B) communities based on environmental factors. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. N: Nitrogen; TN: Total N; P: Phosphorus; K: Potassium; TK: Total K.

Fig 8

Correlation heatmap of soil properties and relative abundances of bacterial (A) and fungal (B) communities at the genus level. *0.01 < P ≤ 0.05; **0.001 < P ≤ 0.01; ***P ≤ 0.001. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. SOC: Soil organic carbon; N: Nitrogen; TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; P: Phosphorus; TP: Total P; K: Potassium; TK: Total K; AK: Available K.

Table 5

Correlations of runoff losses of nitrogen and phosphorus and the abundances of bacteria and fungi.

Taxon	Nutrient runoff losses
	TN	NO3--N	NH4+-N	TP	DP
Bacteria
Proteobacteria	0.9999**	0.8937**	0.5450	0.3595	0.5034
Chloroflexi	-0.8407**	-0.9972**	-0.9080**	-0.8015**	-0.8864**
Acidobacteria	0.8851**	0.5558	0.0819	-0.1263	0.0331
Actinobacteria	-0.1358	0.3532	0.7633*	0.8805**	0.7939*
Planotomycetes	-0.9508**	-0.9834**	-0.7715*	-0.6230	-0.7395*
Nitrospirae	0.7163*	0.2967	-0.2047	-0.4030	-0.2523
Firmicutes	0.7606*	0.9782**	0.9559**	0.8744**	0.9404**
Cyanobacteria	-0.4201	0.0637	0.5405	0.7030*	0.5810
Bacteroidetes	0.6250	0.9217**	0.9941**	0.9500**	0.9876**
Gemmatimonadetes	0.6748*	0.9451**	0.9848**	0.9275**	0.9752**
Verrucomicrobia	-0.9398**	-0.6630*	-0.2157	-0.0088	-0.1678
Fungi
Ascomycota	0.3885	0.7810*	0.9861**	0.9991**	0.9930**
Basidiomycota	-0.8954**	-0.9994**	-0.8561**	-0.7305*	-0.8298**
Mucoromycota	-0.6521	-0.2115	0.2900	0.4819	0.3364
Chytridiomycota	-0.3341*	-0.7432*	-0.9747**	-0.9999**	-0.9844**

Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.N: Nitrogen; TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; P: Phosphorus; TP: Total P; DP: Dissolved phosphorus.

*, **Significant at the 0.05 and 0.01 probability level, respectively.

Fig 9

The impact factors determining N/P runoff losses by redundancy analysis (A)/(B). Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. SOC: Soil organic carbon; N: Nitrogen; TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; P: Phosphorus; TP: Total P.

Distance-based redunn class="Chemical">dancy analysis (db-Rn class="Chemical">DA) of the bacterial (A) and fungal (B) communities based on environmental factors. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. N: Nitrogen; TN: Total N; P: Phosphorus; K: Potassium; TK: Total K.

Correlation heatmap of soil properties and relative abunn class="Chemical">dances of bacterial (A) and fungal (B) communities at the genus level. *0.01 < P ≤ 0.05; **0.001 < P ≤ 0.01; ***P ≤ 0.001. Notes: n class="Chemical">T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. SOC: Soil organic carbon; N: Nitrogen; TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; P: Phosphorus; TP: Total P; K: Potassium; TK: Total K; AK: Available K.

The impact factors determining N/n class="Disease">P runoff losses by redunn class="Chemical">dancy analysis (A)/(B). Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. SOC: Soil organic carbon; N: Nitrogen; TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; P: Phosphorus; TP: Total P.

Notes: n class="Chemical">T0 = Traditional irrigation; n class="Chemical">T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.N: Nitrogen; TN: Total N; NO3−−N: Nitrate N; NH4+−N: Ammonium N; P: Phosphorus; TP: Total P; DP: Dissolved phosphorus.

Discussion

Effects of different water and fertilizer treatments on nitrogen and phosphorus losses

A total of 17 runoff-producing rainfall events occurred during the experimental period. Among them, three precipitation events with values over 60 mm were extreme events (Fig 2). Furthermore, a significant positive relationship between rainfall and runoff was reported for n class="Species">rice system [35]. Runpan>off pan> class="Chemical">DN loss was mainly in the form of NO3-–N than in NH4+–N in different water and fertilizer treatments (Table 3). The result is consistent with the previous report in which the N loss by surface runoff was mainly in the form of NO3−–N, respectively, from vegetable, upland crop, and rice systems under natural rainfall [35]. These results indicated that NO3−–N was the major form of N in the surface runoff. The reason was that NH4+–N was more easily adsorbed by soil colloidal particles than NO3−–N resulting in the slow migration of NH4+–N in soils [36], and NH4+–N could also be converted into NO3−–N by nitrification; hence, this would contribute to the preferential loss of easily mobile NO3−–N during successive rainfall events [37]. However, the loss ratio of P was only 1.13−1.31% (Table 3). The finding agreed with that obtained by Yi et al. (2018), who found that the loss ratio of P in surface runoff was lower than 1% [14]. The small amount of P runoff loss was mainly due to the studied soils, typic hapli—stagnic anthrosols, their enrichment of Fe and Al oxides, which was helpful to adsorb additional P resulting in less runoff loss of P from the paddy field [38]. Moreover, DP was the main P loss in the runoff in different water and fertilizer treatments (Table 3).

High precipitation also caused large fluxes of n class="Chemical">DP, pan> class="Chemical">TP and NO3-–N in all treatments (Fig 3A–3C), indicating that rainfall was another risk factor for the increasing nutrient runoff losses, which was similar to that reported in other studies [14]. Additionally, DP loss was highly positively correlated with TP loss (Fig 3D). This result is consistent with that of Zhao et al. (2017), who indicated that the TP and AP concentrations in runoff had a strong correlation (R2 = 0.933), mainly because the P in the runoff water was mainly in the form of available P [39]. Consequently, DP runoff can be used to estimate TP runoff. Nevertheless, Liu et al. (2020) suggested that PP (particulate phosphorus) was mainly moved via surface flow, accounting for 69.4–79.7% of TP in a double rice-cropping system in the subtropical hilly region of China [10]. The process depended mainly on the paddy water, which had the strong adsorption of PP due to its high organic matter and clay contents [40]. In this study, the P loss in the early rice season was higher than that in the late rice season (Figs 2 and 3C). In addition, the average loss of P fractions in the surface runoff was lower than that of N fractions (Fig 3A–3C). Similar results were reported by Huang et al. (2020), who showed that the average runoff loss of TP and DP was lower than those of TN and DN in all treatments [7]. The results indicated that the risk of N loss in surface runoff was higher than that of P loss in the double rice cropping system in the subtropical region of China. Moreover, the N rather than P runoff losses in the T1 treatment significantly (P < 0.05) increased compared to those in the T2 treatment (Table 3). This result was consistent with the finding of Liu et al. (2020), suggesting that greater amounts of N and P fertilizers resulted in more substantial N loss through surface runoff from a paddy field [10]. That was because excessive N fertilizer applications to the intensive rice systems resulted in a large amounts of N accumulated in the paddy soil, consequently created a large soil N pool, which contributed to the preferential loss of easily mobile N runoff loss during successive rainfall events compared with the immobile and occluded P in rice paddy soils [41,42]. Furthermore, the greater N input led to a decrease in soil pH and thus enhanced P accumulation in the soil [43]. Overall, the optimal fertilization and irrigation for rice could reduce the N runoff loss from paddy fields.

Effects of different water and fertilizer treatments on soil microbial community

Numerous diversity indices, including species richness and evenness together, are also called heterogeneity indices. In this study, the acid pH-range in different pan> class="Chemical">water and fertilizer treatments was 5.97–6.24, but the pH changes did not result in alterations in microbial alpha diversity (except Chao 1) (Table 4). No variations in the soil microbial alpha diversity among the different treatments may be explained by their response to various natural and specific conditions (e.g., climatic factor and floristic composition) on microbes [44,45]. However, Joa et al. (2014) showed that soil pH was significantly (p < 0.05) positively related to bacterial species richness and diversity estimates such as Ace, Chao1 and Shannon index [46]. The inconsistent effects of soil pH on microbial alpha diversity might be a result along soil pH gradient. There was universal inhibition of all microbial variables below pH 4.5, probably because the release of free aluminum limited microbial growth in acidic soils [47]. In addition, the bacterial alpha diversity in the experimental plots was significantly (P < 0.05) higher that of the fungi (Table 4). Inherently much lower fungal diversity might be mainly caused by the growth-inhibiting effects of bacteria on fungi [48]. These findings indicate that different water and fertilizer treatments have a minor influence on microbial alpha diversity in acid paddy soils. However, microbial community structures were greatly affected by water and fertilizer treatments (Fig 5A and 5B), which was consistent with previous observations of a strong influence of N fertilization on microbial community composition [49]. The bacterial phyla Actinobacteria, Cyanobacteria, Verrucomicrobia and fungal phylum Mucoromycota were highly favoured, whereas the bacterial phylum Acidobacteria was repressed in the T2 treatment (Fig 6A and 6B). Thus, different irrigation and fertilization treatments altered soil microbial community structure, but not their alpha diversity in the paddy soil.

The influence of environmental factors on nitrogen and phosphorus losses

Different pan> class="Chemical">water and fertilizer treatments also altered microbial community structure (Fig 5A and 5B). This agrees with a previous report showing the change in microbial community structure might be caused by their responses to variations in soil properties associated with integrated water and fertilizer management [50]. In this study, the predominant factors controlling soil bacterial community structure were soil pH and Olsen P, while the main factors governing fungal community structure were pH and TN in different water and fertilizer treatments by using db—RDA (Fig 7A and 7B). The alterations in microbial community composition, in turn could affect N and P losses from paddy fields [17]. The ability of Firmicutes to fix N2 was used to produce large amounts of NH4+–N during growth as a well-known potential source of N for rice plants [51,52], which corresponded to the increased N uptake and runoff loss in the T1 and T2 treatments (Tables 2 and 5). In addition, Bacteroidetes bacteria as r—strategists [53], might be favored by higher soil fertility associated with N and P fertilizer application in the T1 and T2 treatments compared to that in the T0 treatment (Fig 6A). Moreover, Bacteroidetes belonged to one of the dominant denitrifiers that had a capacity for the reduction of NO3−–or NO2−–N to N2 as the end product in paddy soils, which increased soil TN, especially NO3−–N loss through surface runoff from paddy fields (Table 5) [54]. Proteobacteria was abundant and mainly included free-living N-fixing β-Proteobacteria [55], which provided an efficient N source for paddy soils and thus increased N runoff loss (Table 5). Conversely, Chloroflexi belonged to green bacteria, which was a diverse group of chlorophototrophic organisms. Most of these organisms synthesized bacteriochlorophylls c, d or e and utilized chlorosomes for light harvesting, and consequently improved rice growth and productivity [56]. This improved growth characteristics have stimulated root distribution and uptake of N and P nutrients, which led to a reduction in nutrient runoff losses from paddy fields (Table 5). In addtion, Planotomycetes, as oligotrophic bacteria, would be likely stimulated under nutrient-poor conditions, but their growth was inhibited by N and/or P inputs (Fig 6A) [53,57,58]. Moreover, some members of these anammox planctomycetes performed ammonium oxidation anaerobically, which led to an increase in NO3−-N in the T0—treated soil, and thus actually increased NO3−-N runoff loss risk [59]. An exception was Nitrospirae bacteria, which was present at the relatively lower abundance in the T2 treatment than that in the other two treatments (Fig 6A). This result is inconsistent with previous work which has shown that Nitrospirae was the dominant bacterial group under combined application of mineral and organic fertilizers in an irrigated farmland [60]. One possible explanation is that the periodic drought of the soils during the entire rice growing season in the T2 treatment, leading to an aerobic environment, especially in the harvest season, may significantly inhibit this facultatively anaerobic chemoautotrophic nitrite oxidizer [61]. Furthermore, Nitrospirae, as an ammonia-oxidizing bacterium, had high potential nitrification rates, thereby increasing NO3-–N runoff loss [18]. Our results further demonstrated that T2 had a small NO3-−N loss in surface runoff partly because of the low abundance of Nitrospirae (Table 5). The Actinobacteria were involved in supplying P to plants [62], which corresponded to the increased AEP (agronomic P use efficiency) in the T2 treatment owning to an increase in the abundance of Actinobacteria, and thus decreased P loss in surface runoff (Table 5 and Fig 6A). Likewise, some cyanobacterial taxa could also drive P cycling by accessing pools of P that are not generally available to plants [63]. The ability of Cyanobacteria contributed to increase AEP with an increase in their relative abundance in the T2 treatment, but simultaneously aggravated the P runoff loss (Table 5 and Fig 6A).

The dominant Ascomycota fungi has been described as litter decomposers [31], which increased soil N and P contents, in turn, accelerated nutrient runoff losses from paddy fields (Table 5). In addition, Chytridiomycota has been reported to inn class="Chemical">fect pan> class="Chemical">AMF spores [64]. Moreover, AMF promoted soil aggregate formation, which could protect organic N and P against decomposition from soil microbes [65] and consequently reduced N and P runoff losses (Table 5). Mucoromycota, as a saprotroph, most of them could degrade C sources ranging from simple sugars to pectins, hemicelluloses, lipids and proteins when colonizing different substrata [66]. The organic C degradation resulted in higher rice grain yields and TN uptake levels in the T1 and T2 treatments than those in the T0 treatment, and consequently reduced N runoff loss (Tables 2, 3 and 5).

Soil n class="Chemical">NH4+–N anpan>d NO3−–N contents were also the dominpan>anpan>t impact factors to inpan>terpret the difpan> class="Chemical">ference of N runoff loss among the treatments, followed by N fertilizer input, while the most important factor affecting P runoff loss was P fertilizer input, and secondly, they were soil Olsen P and TP (Fig 9A and 9B). Similarly, it has been reported that soil N pool contributed more than fertilizer input to increased N runoff loss, whereas fertilizer P input contributed more than soil P pool to increased P runoff loss [67]. Hence, these studies further demonstrated that N and P runoff losses were predominantly governed by edaphic factors and fertilization levels during rice-growing season under different water and fertilizer managements. Overall, the integrated strategy for rice irrigation and irrigation might play a major role in shaping soil microbial community structure by altering edaphic properties, which was responsible for N and P losses through surface runoff in paddy soils of subtropical China.

Conclusions

Our results demonstrated that the T2 (n class="Chemical">water-savinpan>g irrigation anpan>d optimizinpan>g pan> class="Chemical">fertilization) treatment increased agronomic N use efficiency and rice grain yield in the double rice cropping system, which reduced N runoff loss compared to the T1 (traditional irrigation and fertilization practice) treatment. The N loss in surface runoff was mainly in the form of nitrate N (NO3-–N) in all treatments. Furthermore, high N fertilizer input, soil NO3-–N, and ammonium N (NH4+−N) contents were important contributors to the N loss. In addition, different water and fertilizer treatments caused variations in soil microbial community structure, which might further affect N runoff loss. Bacteroidetes, Proteobacteria, Planotomycetes, Nitrospirae, Firmicutes bacteria and Ascomycota fungi contributed to an increase in the N runoff loss, but the N loss decreased by Chytridiomycota fungi. In summary, the T2 treatment should be a cost-effective and environmentally-friendly alternative to traditional fertilization and irrigation method in the present study.

Bacterial (A) and fungal (B) Shannon–Wiener curves for normalized number of reads at a 97% threshold in different fertilization and irrigation regimes. Notes: T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization.

(TIF)

Click here for additional n class="Chemical">data file.

7 Jan 2021

n class="Chemical">PONE-D-20-38799

n class="Chemical">Water and n class="Chemical">fertilizer managements affect nitrogen and phosphorus losses by surface runoff and microbial communities in a paddy soil

PLOS ONE

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Reviewer #1: Evaluation of nutrient losses from paddy soils under different irrigation anpan>d pan> class="Chemical">fertilization practices is important for sustainable management. Authors tried to assess nutrient losses under different water and fertilizer managements and to identify underlying mechanisms. However, the part of results and discussions are not robust. Besides, no significant highlight is established in the present form of manuscript. The present form of this manuscript is not recommended for publication in this journal.

The paper seems technically sound, but I have some doubts regarding the scientific validity and the rigor in science.

(1) Surface runoff should be primarily determined by n class="Chemical">water depth inpan> the field anpan>d the size of rainpan>fall events, rather thanpan> soil microorganpan>ism compositionpan>. Inpan> general, a higher pan> class="Chemical">water level results in more nutrient losses through surface runoff. Compared with the water depth (1-6 cm) in T1 (traditional irrigation), the water depth (-3-3 cm) in T2 (shallow intermittent irrigation) was relatively lower. Therefore, the risk of nutrient loss under T1 should be higher than it under T2. Hence, the major result of the decline of nutrient losses in T2 is not significant and surprised.

(2) There is a lack of figure, such as SEM, which comprehensively shows the direct efn class="Chemical">fects of various environmental factors on nutrient losses, anpan>d its inpan>direct efpan> class="Chemical">fects through altering microorganism community composition. Besides studied environmental variables (Table 2), the size of rainfall events should be considered in investigating influencing factors of nutrient losses.

Specific comments:

Abstract

(1) The efn class="Chemical">fect of microorganism community composition on nutrient runoff losses should be demonstrated specifically.

(2) More details about n class="Chemical">T1 anpan>d T2 should be given.

Introduction

(1) Line 55-57, the relationship between n class="Chemical">ammonia oxin class="Chemical">dation and nitrogen losses should be elaborated.

(2) Line 81, the objective of this study was only to verify an existing irrigation practice. The “develop” is not appropriate.

Materials and methods

(1) Line 107, why did you choose 20 cm as the height of the concrete cement border? I think the quantity of runoff is mainly depended on the height of the border.

(2) Line 109, it’s better to show a figure of the runoff collection device.

Results

(1) Line 211, there are too many significant numbers.

(2) The content of 3.5 part is not closely related to the main purpose of this study. The efn class="Chemical">fects of abiotic factors on the microbial community composition should not be demonstrated in an independent part. The relationship between microorganisms and nutrient losses should be addressed specifically.

Discussion

(1) Line 285-286, please keep constant citation format in the text. Wang et a. (2019) is difn class="Chemical">ferenpan>t from others.

(2) Line 285, the sentence “n class="Chemical">NO3--N was the main form of n class="Chemical">TN” doesn’t seem scientific.

(3) Line 313, please delete one “the”.

(4) About 4.3 part, the relationship between microorganisms and environmental factors is not the critical issue in this study. Please rewrite and rename this part.

Tables and Figures

(1) Only n class="Chemical">fertilization treatment was given in the Table 1. Please add irrigation treatment.

(2) All tables and figures should be improved. The number and letters in the figures are too small.

(3) About Fig. 2 B, Y-axis number “0.000-8.000” should be changed into “0-8”. Please correct other similar issues.

(4) Figure resolution need to be improved.

Reviewer #2: Dear Editor and authors,

n class="Chemical">PONE-D-20-38799 entitled “n class="Chemical">Water and fertilizer managements affect nitrogen and phosphorus losses by surface runoff and microbial communities in a paddy soil” have done nice work but being poorly presented. The MS maybe accepted after major revision as followings:

1. The language should be revised deeply through the MS, even in Title, please ask a specialist for help. The title should be “n class="Chemical">Nitrogen anpan>d pan> class="Chemical">phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements”

2. Line 14, please clearly define of n class="Chemical">T1.

3. Line 28 What is the optimum n class="Species">rice yield?

4. Line 36 the total cultivated area in where?

5. In the introduction parts, the authors should clearly figure out the novelty of your study as there were plenty of literature on N and P losses from paddy fields with different n class="Chemical">water and fertilization managements.

6. Line 77 This knowledge gap is what? This should be clearly stated.

7. Table 1’ title should be n class="Chemical">Fertilization schemes for the different treatments. But the authors said that “The water and fertilizer practices used in this experiment are described in Table 1”, where is water managements? On other words, which kind of water-saving irrigation method you used in this study? Please clearly shown your experimental design, which is vital for the readers.

8. 2.2 n class="Chemical">Water sampling methods should be described in details.

9. Results this parts should be clearly and concise. Line 198 (2795 ± 165 t ha−1) should be deleted as the value has been shown in the Table 2, so many presentation in the results.

10. Why you only shown the relate n class="Chemical">data obtained from 2018? If you want to make concise, please at least to state this in the n class="Chemical">Data analysis.

11. Do not simply repeat the results in the Discussion parts.

12. The authors mentioned that “Runoff n class="Chemical">DN loss was mainpan>ly inpan> the form of pan> class="Chemical">NO3-–N than in NH4+–N under different water and fertilizer treatments” and given the reasons. I thinks the differences in N and P losses among the different treatments was your mainly focus, and should be deeply discussed.

13. Is there any relationship between soil physiochemical properties and N and P losses? This should be one important novelty of this study.

14. The conclusions should be further clarified.

15. All figures need to be clearly presented, and a lot of texts in figures are not visible.

Best wishes!

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, htn class="Chemical">tps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

17 Mar 2021

Reviewer #1:

The paper seems technically sound, but I have some doubts regarding the scientific validity and the rigor in science.

(1) Surface runoff should be primarily determined by n class="Chemical">water depth inpan> the field anpan>d the size of rainpan>fall events, rather thanpan> soil microorganpan>ism compositionpan>. Inpan> general, a higher pan> class="Chemical">water level results in more nutrient losses through surface runoff. Compared with the water depth (1-6 cm) in T1 (traditional irrigation), the water depth (-3-3 cm) in T2 (shallow intermittent irrigation) was relatively lower. Therefore, the risk of nutrient loss under T1 should be higher than it under T2. Hence, the major result of the decline of nutrient losses in T2 is not significant and surprised.

(2) There is a lack of figure, such as SEM, which comprehensively shows the direct efn class="Chemical">fects of various environmental factors on nutrient losses, anpan>d its inpan>direct efpan> class="Chemical">fects through altering microorganism community composition. Besides studied environmental variables (Table 2), the size of rainfall events should be considered in investigating influencing factors of nutrient losses.

I have added the Fig 9A-B to comprehensively show the direct efn class="Chemical">fects of various environmental factors on nutrient losses. Inpan> addition, runpan>off volume anpan>d pan> class="Chemical">fertilizer input were considered as the influencing factors of nutrient losses in Fig 9A-B.

Abstract

(1)The efn class="Chemical">fect of microorganism community composition on nutrient runoff losses should be demonstrated specifically.

The efn class="Chemical">fect of microorganism community composition on nutrient runoff losses was demonstrated specifically (lines 22-25 of revised manuscript with track changes).

(2) More details about n class="Chemical">T1 anpan>d T2 should be given.

More details about n class="Chemical">T1 and T2 have been given in lines 16-18, that is T2 (Chemical fertilizer of 240 kg N ha−1, 120 kg P ha−1, and 240 kg K ha−1 combined with shallow intermittent irrigation) and T1 (Chemical fertilizer of 273 kg N ha−1, 135 kg P ha−1, and 135 kg K ha−1 combined with traditional flooding irrigation).

Introduction

(1)Line 55-57, the relationship between n class="Chemical">ammonia oxin class="Chemical">dation and nitrogen losses should be elaborated.

Additionally, n class="Chemical">ammonia-oxidizing bacteria (AOB) played an important role in the n class="Chemical">ammonia oxidation which was crucial for N and P runoff losses [18]. Wang et al. (2017) found that the ammonia oxidation contributed to 37.5–67.6% of N losses in the phreatic zone, where AOB might be the major source of nitrite nitrogen (NO2-–N) for ammonia-oxidizing bacteria [19] (lines 56-59).

(2) Line 81, the objective of this study was only to verify an existing irrigation practice. The “develop” is not appropriate.

The “develop” was deleted in line 82.

Materials and methods

(1)Line 107, why did you choose 20 cm as the height of the concrete cement border? I think the quantity of runoff is mainly depended on the height of the border.

That was because the height of the ridge is 20 cm.

(2) Line 109, it’s better to show a figure of the runoff collection device.

A figure of the runoff collection device was shown in Fig 1A-B.

Results

(1)Line 211, there are too many significant numbers.

Many significant numbers were deleted in line 229.

(2) The content of 3.5 part is not closely related to the main purpose of this study. The efn class="Chemical">fects of abiotic factors on the microbial community composition should not be demonstrated in an independent part. The relationship between microorganisms and nutrient losses should be addressed specifically.

The content of 3.5 part is modified to “Factors impacting N and P surface runoff losses”, and thus rewrite this part. In addition, the relationship between microorganisms and nutrient losses should be addressed specifically (lines 294-306).

Discussion

(1)Line 285-286, please keep constant citation format in the text. Wang et a. (2019) is difn class="Chemical">ferenpan>t from others.

Citation format was modified to constant citation format in the text (Lines 323-324 of revised manuscript with track changes).

(2)Line 285, the sentence “n class="Chemical">NO3--N was the main form of n class="Chemical">TN” doesn’t seem scientific.

The sentence was modified to “NO3−–N was the major form of N in the surface runoff” in line 325.

(3)Line 313, please delete one “the”.

One “the”was deleted in line 348.

(4) About 4.3 part, the relationship between microorganisms and environmental factors is not the critical issue in this study. Please rewrite and rename this part.

The content of 4.3 part was changed into “The influence of environmental factors on n class="Chemical">nitrogen and n class="Chemical">phosphorus losses”, and thus rewrite this part (lines 379-435).

Tables and Figures

(1)Only n class="Chemical">fertilization treatment was given in the Table 1. Please add irrigation treatment.

n class="Chemical">Fertilization and irrigation treatment was given in the Table 1 in lines 114-115.

(2)All tables and figures should be improved. The number and letters in the figures are too small.

All tables and figures were improved according to your journal requirements.

(3)About Fig. 2 B, Y-axis number “0.000-8.000” should be changed into “0-8”. Please correct other similar issues.

Too many decimal places In Fig. 2B and other similar issues were corrected.

(4) Figure resolution need to be improved.

Ensure that our images have a resolution of at least 300 pixels per inch (ppi) according to the full details of the requirements of figure preparation guidelines.

Reviewer #2: Dear Editor and authors,

n class="Chemical">PONE-D-20-38799 entitled “n class="Chemical">Water and fertilizer managements affect nitrogen and phosphorus losses by surface runoff and microbial communities in a paddy soil” have done nice work but being poorly presented. The MS maybe accepted after major revision as followings:

1.The language should be revised deeply through the MS, even in Title, please ask a specialist for help. The title should be “n class="Chemical">Nitrogen anpan>d pan> class="Chemical">phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements”

The title and the language in the text were revised deeply through the MS withe track changes.

2.Line 14, please clearly define of n class="Chemical">T1.

More details about n class="Chemical">T1 and T2 have been given in lines 16-18, that is T2 (Chemical fertilizer of 240 kg N ha−1, 120 kg P ha−1, and 240 kg K ha−1 combined with shallow intermittent irrigation) and T1 (Chemical fertilizer of 273 kg N ha−1, 135 kg P ha−1, and 135 kg K ha−1 combined with traditional flooding irrigation).

3.Line 28 What is the optimum n class="Species">rice yield?

The “optimum” was deleted in line 29.

4.Line 36 the total cultivated area in where?

n class="Species">Rice (n class="Species">Oryza sativa L.) is one of the main staple crops and feeds over 65% of the world’s population with 11% of cultivated land [1-2] (lines 32-33).

5.In the introduction parts, the authors should clearly figure out the novelty of your study as there were plenty of literature on N and P losses from paddy fields with different n class="Chemical">water and fertilization managements.

To n class="Chemical">date, N anpan>d pan> class="Disease">P runoff losses and their influencing factors while maintaining or enhancing rice yields in the paddy fields in southeastern China are currently unclear under different irrigation and fertilization regimes. Thus, we hypothesized that the appropriate irrigation and fertilization practices could affect N and P runoff losses by environmental factor variations.

6.Line 77 This knowledge gap is what? This should be clearly stated.

N and n class="Disease">P runoff losses and their influencing factors while maintaining or enhancing n class="Species">rice yields in the paddy fields in southeastern China are currently unclear under different irrigation and fertilization regimes.

7. Table 1’ title should be n class="Chemical">Fertilization schemes for the different treatments. But the authors said that “The water and fertilizer practices used in this experiment are described in Table 1”, where is water managements? On other words, which kind of water-saving irrigation method you used in this study? Please clearly shown your experimental design, which is vital for the readers.

n class="Chemical">Fertilization and irrigation treatment was given in the Table 1 in lines 114-115.

8. 2.2 n class="Chemical">Water sampling methods should be described in details.

The detail description of n class="Chemical">water sampling methods is in lines 119-122.

9. Results this parts should be clearly and concise. Line 198 (2795 ± 165 t ha−1) should be deleted as the value has been shown in the Table 2, so many presentation in the results.

Many presentation, such as (2795 ± 165 t ha−1) in the results have been deleted.

10. Why you only shown the relate n class="Chemical">data obtained from 2018? If you want to make concise, please at least to state this in the n class="Chemical">Data analysis.

We wanted to make concise, and stated this in the n class="Chemical">Data analysis line 189.

11. Do not simply repeat the results in the Discussion parts.

The simply repeated results in the Discussion parts have been modified or deleted with track changes in the text.

12. The authors mentioned that “Runoff n class="Chemical">DN loss was mainpan>ly inpan> the form of pan> class="Chemical">NO3-–N than in NH4+–N under different water and fertilizer treatments” and given the reasons. I thinks the differences in N and P losses among the different treatments was your mainly focus, and should be deeply discussed.

The reasons were given (lines 325-328). Moreover, the difn class="Chemical">ferences in N and P losses among the difn class="Chemical">ferent treatments was deeply discussed (lines 348-356).

13.Is there any relationship between soil physiochemical properties and N and P losses? This should be one important novelty of this study.

The redunn class="Chemical">dancy analysis (Rn class="Chemical">DA) was conducted to determine which soil variables were related to N and P losses in Fig 9A-B, and described specially in lines 303-310 and 427-435.

14. The conclusions should be further clarified.

The conclusions were further clarified in lines 438-448.

15. All figures need to be clearly presented, and a lot of texts in figures are not visible.

Ensure that our images have a resolution of at least 300 pixels per inch (ppi) according to the full details of the requirements of figure preparation guidelines.

Submitted filename: Response to Reviewers.doc

Click here for additional n class="Chemical">data file.

19 May 2021

n class="Chemical">PONE-D-20-38799R1

n class="Chemical">Nitrogen and n class="Chemical">phosphorus losses  by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

PLOS ONE

Dear Dr. Huang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we n class="Chemical">feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I appreciate the revisions made. Although two reviewers agreed to accept the article, some additional comments are provided from an editorial stann class="Chemical">dpoint. Most are rather specific and should be easy to address.

Please submit your revised manuscript by Jul 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to htn class="Chemical">tps://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

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If you would like to make changes to your financial disclosure, please include your upn class="Chemical">dated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: htn class="Chemical">tp://journpan>als.plos.org/plosone/s/submission-guidelinpan>es#loc-laboratory-protocols. Additionally, PLOS ONE ofpan> class="Chemical">fers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

n class="Chemical">Dayong Zhao, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete anpan>d correct. If you have cited papers that have been retracted, please inpan>clude the rationale for doinpan>g so inpan> the manpan>uscript text, or remove these repan> class="Chemical">ferences and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

I appreciate the revisions made. Although two reviewers agreed to accept the article, some additional comments are provided from an editorial stann class="Chemical">dpoint. Most are rather specific and should be easy to address.

Specific comments:

Line 14. Change “Therefore” to “Here or In this study”.

Line 34. Please provide the full name of the abbreviation of N and P, though they have been defined in the abstract section.

Line 36-37. Please cite one ren class="Chemical">ferenpan>ce for this senpan>tenpan>ce.

Line 37. Please add “,” between “To n class="Chemical">date” anpan>d “pan> class="Chemical">water-quality”.

Line 49. Change “For example, Chen et al. (2018) reported that” to “It has been reported that”.

Line 53. Change “For instance,” to “Related studies have suggested that”.

Lien 57-60. Please consider the deletion of these sentences.

Line 64-66. We all know that high-throughput sequencing is widely used in determining the diversity and composition of soil microbes. These sentences did not provide very useful information. Thus, please consider removing the sentences.

Line 67. Remove the “Moreover”.

Line 79-84. According to the title and abstract, difn class="Chemical">ferent irrigation anpan>d pan> class="Chemical">fertilization practices could affect soil physicochemical properties and correspondingly influence soil microbial communities, and thereby contribute to N and P runoff losses. Am I right? However, this part has nothing to do with soil microorganisms. Your hypothesis also does not relate to microorganisms.

Line 174-183. What did you do with singleton sequences (i.e., sequence appearing only one time in the entire n class="Chemical">data set)? Moreover, how do you address uneven sequencing depth across samples? Please be clearer in your presentation.

Line 176-181. Please provide the versions of UPARSE, Silva rRNA n class="Chemical">database, Mothur software and insert ren class="Chemical">ferences for them.

Line 179. The authors mentioned that you obtained rarefaction curves using Mothur software, where is the result of rarefaction curves?

Line 193 and Line 200. Both Rstudio and R were used in this study to perform statistical analyses. Please provide their versions in your manuscript and insert ren class="Chemical">ference.

Line 194. Provide the version of the R pheatmap package and cite one ren class="Chemical">ference.

Line 199-200. Which distance did you use? Please make it clear.

Line 197. The “vif” is an abbreviation form. Please define it at its first mention.

Line 200. Provide the version of R vegan package, and then insert one ren class="Chemical">ference for it.

Line 241. Double “the”.

Line 263-267. According to the description of microbial alpha diversity indices in Line 180, change “Ace” to “ACE” and change “Chao” to “Chao1” in the table 4. Meanwhile, provide the explanation of ACE in the table notes in Line 264-267.

Line 306-307. Other environmental factor ren class="Chemical">fer to what? Anpan>d they would directly afpan> class="Chemical">fect the N and P losses. I think this sentence can be removed as what is needed in the results section is for the author to describe their findings objectively.

Figures

Figure 1. This image needs to be cropped appropriately as there is a lot of white space throughout the image.

Figure 3. As an example, the font of the words “Runoff yield” and “n class="Chemical">TN loss loads” inpan> the pan> class="Disease">vertical titles did not seem to correspond to the font of the horizontal title. Please unify the font of all the text in the figure A, B, C and D. In addition, there are up ticks and right ticks in the X-axis and Y-axis of Fig. 3D, respectively, while all ticks in Fig. 3A, 3B and 3C are not shown. Please unify the drawing style. As for Fig. 3D, my suggestion is that the authors can show down ticks and left ticks in X-axis and Y-axis, respectively.

Figure 3B. A point to note in the illustration of the types of line in the image is that the presentation of "n class="Chemical">NO3--N" and "n class="Chemical">NH4+-N" should be revised as they are not presented in a very aesthetically pleasing way.

Figure 3D. There are no n class="Chemical">data points between 0 and 0.6 mg/L of total n class="Chemical">phosphorus concentration, so the authors could have left the values of the horizontal coordinates not starting from 0.

Figure 4. The word “Venn” and three solid dots with “n class="Chemical">T0/n class="Chemical">T1/T2” could be removed from your figure as they are redundant. In addition, the size of the words (e.g., T0, T1 and T2) and numbers in the Venn plots needs to be slightly adjusted upwards. Figure 4A: The bar diagram has two ticks at the value 200 of the vertical coordinate, please deal with them.

Figure 5. Please delete the word “PLS-n class="Chemical">DA on OTU level” at the top of the figure.

Figure 6. Only major phyla are presented in each treatment. What is the relative abunn class="Chemical">dance of phyla below which they are classified as 'Others'? This needs to be made clear in the figure caption. In addition, please delete the word “Community barplot analysis” at the top of the figure.

Figure 7. Please delete the word “db-Rn class="Chemical">DA on OTU level” at the top of the figure.

Figure 8. Please delete the word “Spearman Correlation Heatmap” at the top of the figure.

Figure 3-Figure 9: The numbers 0, 1 and 2 are below the letter T (i.e., the form of a subscript) in the figure captions and main text. However, in the figure, the author does not show the n class="Chemical">T0, n class="Chemical">T1 and T2 in a subscript form, so please standardize the format of presentation.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you n class="Chemical">feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommenn class="Chemical">dation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the n class="Chemical">data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with n class="Chemical">data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the n class="Chemical">data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all n class="Chemical">data underlying the findings in their manuscript fully available?

The PLOS n class="Chemical">Data policy requires authors to make all pan> class="Chemical">data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in stann class="Chemical">dard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, htn class="Chemical">tps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

16 Jun 2021

Additional Editor Comments (if provided):

I appreciate the revisions made. Although two reviewers agreed to accept the article, some additional comments are provided from an editorial stann class="Chemical">dpoint. Most are rather specific and should be easy to address.

Specific comments:

Line 14. Change “Therefore” to “Here or In this study”.

“Therefore”was changed into “Here”in line 14 of revised manuscript with track changes.

Line 34. Please provide the full name of the abbreviation of N and P, though they have been defined in the abstract section.

We provided the full name of the abbreviation of N and P in line 33 of revised manuscript with track changes.

Line 36-37. Please cite one ren class="Chemical">ferenpan>ce for this senpan>tenpan>ce.

We cited one ren class="Chemical">ference for this sentence in line 37.

Line 37. Please add “,” between “To n class="Chemical">date” anpan>d “pan> class="Chemical">water-quality”.

We added “,” between “To n class="Chemical">date” and “n class="Chemical">water-quality”in line 37.

Line 49. Change “For example, Chen et al. (2018) reported that” to “It has been reported that”.

We Changed “For example, Chen et al. (2018) reported that” to “It has been reported that” in line 48.

Line 53. Change “For instance,” to “Related studies have suggested that”.

We Changed “For instance,” to “Related studies have suggested that”in line 51.

Line 57-60. Please consider the deletion of these sentences.

We deleted these sentences of lines 57-60.

Line 64-66. We all know that high-throughput sequencing is widely used in determining the diversity and composition of soil microbes. These sentences did not provide very useful information. Thus, please consider removing the sentences.

We removed the sentences of lines 64-66.

Line 67. Remove the “Moreover”.

We removed the “Moreover”in line 67.

Line 79-84. According to the title and abstract, difn class="Chemical">ferent irrigation anpan>d pan> class="Chemical">fertilization practices could affect soil physicochemical properties and correspondingly influence soil microbial communities, and thereby contribute to N and P runoff losses. Am I right? However, this part has nothing to do with soil microorganisms. Your hypothesis also does not relate to microorganisms.

We Changed this part to “Thus, we hypothesized that different irrigation anpan>d pan> class="Chemical">fertilization practices could alter soil chemical properties and microbial community structure, which would subsequently affect N and P runoff losses. To test the hypothesis, a 10-year plot experiment was conducted to estimate N and P runoff losses and uptake, soil chemical properties, microbial diversity, and community composition under different fertilization and irrigation regimes. In general, the purpose of this study was to ⑴ verify an optimal irrigation and fertilization practice in order to minimize N and P runoff losses, and ⑵ explore the factors influencing N and P losses in surface runoff from paddy fields in southeastern China.” in lines 71-77.

Line 174-183. What did you do with singleton sequences (i.e., sequence appearing only one time in the entire n class="Chemical">data set)? Moreover, how do you address uneven sequencing depth across samples? Please be clearer in your presentation.

We revised this part according to editors' requirements in lines 169-181. Specifically, Using the UPARSE (version 7.0.1090), we also removed singleton sequences (i.e., sequences appearing only one time in the entire n class="Chemical">data set). As the number of sequence reads in each sample varied, the OTU table was rarified (holding the same sequence number in each sample) prior to microbial community diversity calculations.

Line 176-181. Please provide the versions of UPARSE, Silva rRNA n class="Chemical">database, Mothur software and insert ren class="Chemical">ferences for them.

We provided the versions of UPARSE, Silva rRNA n class="Chemical">database, Mothur software and insert ren class="Chemical">ferences for them in lines 171-179.

Line 179. The authors mentioned that you obtained rarefaction curves using Mothur software, where is the result of rarefaction curves?

We added the result of rarefaction curves in lines 251-253.

Line 193 and Line 200. Both Rstudio and R were used in this study to perform statistical analyses. Please provide their versions in your manuscript and insert ren class="Chemical">ference.

We provided the versions of R in line 191 and line 199.

Line 194. Provide the version of the R pheatmap package and cite one ren class="Chemical">ference.

We provided the versions of R in line 199.

Line 199-200. Which distance did you use? Please make it clear.

The unweighted UniFrac distance - based redunn class="Chemical">dancy analysis (db‐Rn class="Chemical">DA) was processed by R software (version 2.15.3) in lines 198-199.

Line 197. The “vif” is an abbreviation form. Please define it at its first mention.

We defined the “vif” as variance inflation factor in line 195.

Line 200. Provide the version of R vegan package, and then insert one ren class="Chemical">ference for it.

We provided the versions of R in line 199, and then inserted one ren class="Chemical">ference for it.

Line 241. Double “the”.

We deleted “the”in line 240.

Line 263-267. According to the description of microbial alpha diversity indices in Line 180, change “Ace” to “ACE” and change “Chao” to “Chao1” in the table 4. Meanwhile, provide the explanation of ACE in the table notes in Line 264-267.

We changed “Ace” to “ACE” and changed “Chao” to “Chao1” in the table 4. Meanwhile, provide the explanation of ACE (abunn class="Chemical">dance-based coverage estimators) in the table notes in Line 265.

Line 306-307. Other environmental factor ren class="Chemical">fer to what? Anpan>d they would directly afpan> class="Chemical">fect the N and P losses. I think this sentence can be removed as what is needed in the results section is for the author to describe their findings objectively.

This sentence “Other environmental factor ren class="Chemical">fer to what? And they would directly afn class="Chemical">fect the N and P losses” was removed in this manuscript.

Figures

Figure 1. This image needs to be cropped appropriately as there is a lot of white space throughout the image.

Figure 1 was cropped appropriately.

Figure 3. As an example, the font of the words “Runoff yield” and “n class="Chemical">TN loss loads” inpan> the pan> class="Disease">vertical titles did not seem to correspond to the font of the horizontal title. Please unify the font of all the text in the figure A, B, C and D. In addition, there are up ticks and right ticks in the X-axis and Y-axis of Fig. 3D, respectively, while all ticks in Fig. 3A, 3B and 3C are not shown. Please unify the drawing style. As for Fig. 3D, my suggestion is that the authors can show down ticks and left ticks in X-axis and Y-axis, respectively.

Figure 3 was revised according to editors' requirements.

Figure 3B. A point to note in the illustration of the types of line in the image is that the presentation of "n class="Chemical">NO3--N" and "n class="Chemical">NH4+-N" should be revised as they are not presented in a very aesthetically pleasing way.

"n class="Chemical">NO3--N" and "n class="Chemical">NH4+-N" in Figure 3 were revised.

Figure 3D. There are no n class="Chemical">data points between 0 and 0.6 mg/L of total n class="Chemical">phosphorus concentration, so the authors could have left the values of the horizontal coordinates not starting from 0.

Figure 3D was revised.

Figure 4. The word “Venn” and three solid dots with “n class="Chemical">T0/n class="Chemical">T1/T2” could be removed from your figure as they are redundant. In addition, the size of the words (e.g., T0, T1 and T2) and numbers in the Venn plots needs to be slightly adjusted upwards. Figure 4A: The bar diagram has two ticks at the value 200 of the vertical coordinate, please deal with them.

Figure 4 was improved according to editors' requirements.

Figure 5. Please delete the word “PLS-n class="Chemical">DA on OTU level” at the top of the figure.

The word “PLS-n class="Chemical">DA on OTU level” at the top of the figure 5 was deleted.

Figure 6. Only major phyla are presented in each treatment. What is the relative abunn class="Chemical">dance of phyla below which they are classified as 'Others'? This needs to be made clear in the figure caption. In addition, please delete the word “Community barplot analysis” at the top of the figure.

Fig 6. Average relative abunn class="Chemical">dance of dominpan>anpan>t bacterial (A) anpan>d funpan>gal (B) phyla (> 1.0%) inpan> difpan> class="Chemical">ferent fertilization and irrigation regimes. The abundance is expressed as the average percentage of the targeted sequences to the total high-quality bacterial and fungal sequences of samples from triplicate plots of each fertilization regime, respectively. Notes: ‘Others’ refer to those identified phyla with lower than 1.0% relative abundance in all the samples. T0 = Traditional irrigation; T1 = Traditional irrigation and fertilization practice; T2 = Water-saving irrigation and optimizing fertilization. In addition, we deleted the word “Community barplot analysis” at the top of the figure.

Figure 7. Please delete the word “db-Rn class="Chemical">DA on OTU level” at the top of the figure.

We deleted the word “db-Rn class="Chemical">DA on OTU level” at the top of the figure.

Figure 8. Please delete the word “Spearman Correlation Heatmap” at the top of the figure.

We deleted the word “Spearman Correlation Heatmap” at the top of the figure.

Figure 3-Figure 9: The numbers 0, 1 and 2 are below the letter T (i.e., the form of a subscript) in the figure captions and main text. However, in the figure, the author does not show the n class="Chemical">T0, n class="Chemical">T1 and T2 in a subscript form, so please standardize the format of presentation.

We stann class="Chemical">dardized the format of presentation of n class="Chemical">T0, T1 and T2 in both Figure 3-Figure 9 and in this manuscript.

Submitted filename: Response to Reviewers.doc

Click here for additional n class="Chemical">data file.

23 Jun 2021

n class="Chemical">Nitrogen and n class="Chemical">phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

n class="Chemical">PONE-D-20-38799R2

Dear Dr. Huang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at htn class="Chemical">tp://www.editorialmanpan>ager.com/pone/, click the 'Uppan> class="Chemical">date My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

n class="Chemical">Dayong Zhao, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

1 Jul 2021

n class="Chemical">PONE-D-20-38799R2

n class="Chemical">Nitrogen and n class="Chemical">phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

Dear Dr. Huang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the n class="Chemical">date of publication. For more inpan>formation please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. n class="Chemical">Dayong Zhao

Academic Editor

PLOS ONE