*Article* **Integrated Organic-Inorganic Nitrogen Fertilization Mitigates Nitrous Oxide Emissions by Regulating Ammonia-Oxidizing Bacteria in Purple Caitai Fields**

**Daijia Fan 1,2, Cougui Cao <sup>1</sup> and Chengfang Li 1,\***


**Abstract:** Purpose Nitrogen (N) fertilizer application in agricultural soil is a primary anthropogenic nitrous oxide (N2O) source. Currently, the effect of the N fertilizer type on N2O emissions from upland soil has been rarely reported. To this end, impacts of various types of N fertilizer on N2O emissions in purple caitai (*Brassica campestris* L. ssp. *chinensis* var. *purpurea*) fields are investigated in this work. The field experiment was carried out with four treatments, including inorganic N fertilization (I), organic N fertilization (O), integrated organic-inorganic N fertilization (I+O) and no fertilization (CK). The nitrifier/denitrifier abundance was determined using absolute real-time quantitative PCR. Compared with I and O, I+O significantly increased dissolved organic C content, microbial biomass C and microbial biomass N by 24–63%, 12–38% and 13–36% on average, respectively. Moreover, the seasonal cumulative N2O-N emissions and fertilizer-induced N2O emission factor under I+O were significantly lower than those under I and O by 17–29% and 23–39%, respectively. The results indicate that N fertilizer type significantly affects the N2O emissions, and the integrated organic-inorganic N fertilization can mitigate the N2O emissions primarily by inhibiting the nitrification mediated by ammonia-oxidizing bacteria in purple caitai fields. Integrated organic-inorganic N fertilization is an ideal N fertilization regime to enhance soil fertility and yield and reduce N2O emissions in the upland fields.

**Keywords:** ammonia-oxidizing bacteria; integrated fertilization regime; N2O emission factor; N2O flux; purple caitai fields

#### **1. Introduction**

Nitrous oxide (N2O) is a bigger contributor to global warming compared with CO2 [1]. The N2O concentration in the atmosphere has risen by 20% since 1860 [1]. In addition, N2O is a dominant ozone-depleting substance and could remain the most threatening throughout the twenty-first century if its emissions are not controlled [2]. Considering that about 50–60% of N2O emissions are derived from agricultural soils [3], it is imperative to adopt proper agricultural practices to reduce N2O emissions.

Chemical nitrogen (N) fertilizers have been excessively applied worldwide to improve soil fertility and crop yields, consequently causing environmental losses such as soil degradation, water eutrophication and GHG emissions [4]. In recent years, it has been established that integrated inorganic-organic N fertilizer application could improve soil aggregation, soil structure and carbon (C) sequestration [5]. It has been found that N2O emissions is affected by the type and composition of the fertilizer [6]. Specifically, numerous studies have revealed that organic N fertilizers can lead to less N2O emissions than inorganic N fertilizers [7]. However, some other researchers observed greater N2O emissions under

**Citation:** Fan, D.; Cao, C.; Li, C. Integrated Organic-Inorganic Nitrogen Fertilization Mitigates Nitrous Oxide Emissions by Regulating Ammonia-Oxidizing Bacteria in Purple Caitai Fields. *Agriculture* **2022**, *12*, 723. https://doi.org/10.3390/ agriculture12050723

Academic Editor: Manuel Ângelo Rosa Rodrigues

Received: 20 April 2022 Accepted: 18 May 2022 Published: 20 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

organic N fertilization than under mineral N fertilization [8]. In addition, Ding et al. [9] proposed use of compost and urea to reduce N2O emissions, whereas Agegnehu et al. [10] discovered that organic amendments incorporated with conventional N fertilizer could significantly increase N2O emissions. These diverse and inconsistent conclusions indicate that the dynamic responses of N2O emissions to organic and inorganic N fertilization are still elusive and require further research.

The N2O in the soil is mainly biologically produced by nitrification and denitrification [11]. Nitrification is the oxidation of ammonia first to nitrite and then to nitrate, which is predominantly performed by aerobic nitrifying microorganisms including ammoniaoxidizing bacteria (AOB) and archaea (AOA) [12]. The initial step of nitrification is catalyzed by ammonia monooxygenase (AMO) [13]. Owing to its importance in the energy-generating metabolism, *amoA* is primarily applied as a marker gene in nitrification studies [14]. Reversely, denitrification is a facultative anaerobic pathway during which nitrate is reduced to nitrite and free nitrogen [15]. Nitrite reductase (NIR) is an essential enzyme that converts nitrite to N2O, and the most widely used marker genes for NIR are *nirK* and *nirS* [16]. All these microbial processes are highly susceptible to environmental parameters and agricultural practices including fertilization. Therefore, quantifying the microbial functional genes in the process of nitrification and denitrification can provide important information for the mitigation of N2O emissions [17].

Purple caitai (*Brassica campestris* L. ssp. *chinensis* var. *purpurea*), also known as zicaitai, is a traditional vegetable crop widely planted in the south of China and has become increasingly popular due to its high nutrient content [18]. Usually, high rates of N fertilizer are applied to promote purple caitai growth and development, inevitably leading to a considerable amount of N2O emissions from the soil. This environmental impact varies with different types of N fertilizers as mentioned above, which, however, has been scarcely studied in purple caitai fields. Hence, this paper studied the impact of various types of N fertilizers on N2O emissions in purple caitai fields. We hypothesized that the combined application of inorganic/organic N fertilizers could reduce N2O emissions through decreasing nitrification and denitrification.

#### **2. Material and Methods**

#### *2.1. Experimental Site*

The experimental site is located in Huazhong Agricultural University, Wuhan City, Hubei Province, China (30◦28 21 N latitude, 114◦20 48 E longitude), with an average annual temperature of 16.3 ◦C and an average annual precipitation of 1163 mm (rainfall mostly occurs between May and August) from 1961 to 2010 (Figure 1). The mean daily temperature and precipitation during the purple caitai growing seasons in 2016 and 2017 are shown in Figure 1. The total precipitation during the experimental period was 287.9 mm in 2016 and 66.1 mm in 2017, respectively. The soil is classified as Alisols with a clay loam texture (FAO soil clarification). The main soil properties before the experiment (measured in September 2016) were pH 7.03, organic C 9.13 g kg−1, total N 1.15 g kg−1, total phosphorus (P) 0.39 g kg<sup>−</sup>1, and total potassium (K) 8.67 g kg<sup>−</sup>1.

#### *2.2. Experimental Design*

An upland field experiment on purple caitai was carried out during the 2016 and 2017 growing seasons. The seedlings of purple caitai (HSCT, *Brassica campestris* L. ssp. *chinensis* var. *purpurea*) were transplanted in October and harvested in March of the next year, followed by a fallow season. Four treatments, including no fertilization (CK), inorganic N fertilization (I), organic N fertilization (O) and integrated organic-inorganic N fertilization (I+O) were implemented, and each treatment had three replications. Each plot was 12 m2 in area. Each treatment of the experimental field has been planted with purple caitai under the same N fertilization since 2011.

**Figure 1.** Changes in average daily temperature and rainfall during purple caitai growing seasons in 2016 (**A**) and 2017 (**B**).

The soil was moldboard plowed to a depth of 15 cm beforehand. Compound fertilizer (N:P2O5:K2O = 15%:15%:15%), urea (46% N), calcium superphosphate (17% P2O5), potassium chloride (60% K2O) and pelleted organic fertilizer (N:P2O5:K2O = 10%:3%:2%, living bacteria count ≥ <sup>2</sup> × 107 CFUs g<sup>−</sup>1; Compound Bio-NH4 +-fertilizer, Wuhan Heyuan Green Organism Co., Ltd., Wuhan, China) were selectively incorporated into the topsoil (0–20 cm depth) to provide 225 kg N ha−1, 112.5 kg P2O5 ha−<sup>1</sup> and 112.5 kg K2O ha−<sup>1</sup> for the plots under fertilization treatments throughout the growing seasons. The P and K fertilizers were used as basal fertilizers. As for the N fertilizer, 50%, 100% and 75%, respectively, were applied to the plots under I, O and I+O treatments as basal fertilizer, while the rest was applied at the bolting stage as topdressing fertilizer. No fertilizer was applied to the CK plots. The specific fertilization regimes are shown in Table 1. After basal fertilization, 20-day-old seedlings of purple caitai were manually transplanted on the same day. The field was only irrigated once to 3 mm immediately after the transplanting of seedlings. No irrigation was conducted after this time.


**Table 1.** Fertilization regimes of different treatments.

I, inorganic N fertilization; O, organic N fertilization; I+O, integrated organic-inorganic N fertilization; CK, no fertilization.

#### *2.3. Gas Sampling*

Soil N2O fluxes throughout the growing seasons of purple caitai were measured by the static chamber-gas chromatography method [19] (Li et al., 2013). In brief, the sampling chamber was a 1.1 m high steel barrel with a diameter of 0.3 m. Chamber bases with a groove of the ring were installed in each plot. The air-tightness of the chambers during the gas sampling was ensured by filling water in the groove of the ring. After shutting down the chamber, the air was mixed using four fans on the top of the chamber. A 30 mL syringe was used to extract the gas from the barrel through the three-way valve and inject it into a 30 mL glass vial that had been vacuumed beforehand. The sampling interval was 10 min, and the sampling time was 0 min, 10 min, 20 min and 30 min. At the same time, the barrel height and air temperature inside the chamber were recorded. The sampling was carried out after every N fertilization event or every ten days otherwise (from 15 October 2016 to 4 March 2017 and from 17 October 2017 to 6 March 2018) between 8:30 am and 11:00 am, with four samples being successively collected per plot at the interval of 10 min (at 0 min, 10 min, 20 min and 30 min).

The N2O concentrations were measured by gas chromatography (Shimadzu, GC-14B, Tokyo, Japan) as described by Li et al. [19]. The N2O flux was calculated according to the method of Li et al. [19], and seasonal cumulative N2O emissions were measured according to the following equations:

$$CE = \sum\_{i}^{n} ((F\_{i} + F\_{i+1})/2 \times 24 \times D\_{i})/10^{5} \times 2 \times \frac{14}{44} \tag{1}$$

where *CE* is the cumulative N2O emissions over the whole growing season of purple caitai (kg ha−1), *Fi* and *Fi*+1 represent the N2O fluxes measured on two adjacent sampling dates (μg m−<sup>2</sup> h−1), *Di* represents the length of the *i*th sampling interval (d), 14 represents the relative atomic mass of N, 44 is the relative molecular mass of N2O, and *n* represents the total number of sampling intervals.

The fertilizer-induced N2O emission factor (EFN2O) was calculated as the difference in seasonal cumulative N2O-N emissions between N fertilizer treatments and CK divided by the total amount of fertilized N [1].

#### *2.4. Soil Sampling and Measurement*

Five soil cores were collected from each plot on the same date as the gas sampling. After the removal of plant debris and stones, the soil cores from the same plot were mixed and homogenized into a composite sample. These composite samples were then divided into subsamples for chemical and biological analysis.

Chemical analysis was carried out for the soil samples obtained throughout the growing seasons of purple caitai (at an interval of one month). The contents of total C (TC) and N (TN) were measured using a FlashEA 1112 element analyzer (Thermo Fisher Scientific, Waltham, MA USA). The dissolved organic C (DOC) was extracted from a soil–water solution using the suction filtration method [20], and the microbial biomass C (MBC) and

microbial biomass N (MBN) were extracted using the chloroform fumigation-extraction method [21]. Soil DOC, MBC and MBN contents were measured using the Walkley–Black method [22]. The soil NH4 +–N and NO3 ––N were extracted by dissolving 20 g of fresh soil with 100 mL of 1 mol L-1 KCl solution and filtered through Whatman #1 filter paper after being shaken for one hour [23] (Zaman et al., 2009). The NH4 +–N and NO3 ––N contents in the soil extracts were then analyzed using a flow injection analyzer. The gross nitrification and denitrification rates were measured using the method proposed by Yao et al. [20]. In brief, fresh soils were amended with ammonium sulfate or KNO3. The treated soil was thoroughly homogenized in the bottle and added deionized water. The bottles were covered by a polyethylene film with tiny holes and incubated at 30 ◦C. After 15 days of incubation, the treated soil was extracted with 2 mol L−<sup>1</sup> KCl, and mineral N (NH4 <sup>+</sup> and NO3 −) was determined. The treated soils from the other bottles were incubated under anaerobic conditions at 30 ◦C for 5 days with NO3 −, extracted with KCl and determined using the continuous-flowing analyzer. Soils without ammonium sulfate or KNO3 were taken as the controls. The rates of nitrification and denitrification were calculated as the differences in mineral N concentrations or NO3 − contents between the 0 and 7 or 5 day samples divided by the amounts of added ammonium sulfate or KNO3.

#### *2.5. Measurement of Yield and Calculation of Yield-Scaled N2O Emissions*

When the length of the red tender stems of the vegetable was more than 40 cm, the red tender stems from the plots were harvested and weighed.

The yield-scaled N2O emissions (t CO2-eq. t−<sup>1</sup> yield) were calculated as the ratio of cumulative N2O emissions (converted into CO2 equivalents) to purple caitai yields.

#### *2.6. Absolute Real-Time Quantitative Polymerase Chain Reaction (PCR) Analysis*

Genomic DNA was extracted from the composite soil samples and then analyzed using a NanoDrop spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). Absolute real-time quantitative PCR was carried out in 96-well PCR optical plates in triplicate per sample. The PCR protocol is shown in Table S1.

The proper dilution factor of the template DNA was determined by running quantitative PCR with different dilutions of template DNA in order to avoid PCR inhibition. Using a threshold cycle of 31 as the detection limit, a melting curve analysis was carried out to examine the specificity of the amplified products. Standards for all assays were prepared and then serially diluted for the construction of standard curves.

#### *2.7. Statistical Analysis*

One-way fixed effects ANOVA or two-way repeated measures ANOVA using the general linear model of SPSS software was conducted. Before ANOVA, the normality and homoscedasticity of data were tested, and the datasets that did not pass the tests were subjected to log transformation. One-way or two-way repeated measures ANOVA was conducted. If the ANOVA result was significant, the least significant difference test or Tukey's HSD test was carried out. The significance of the difference was defined as *p* ≤ 0.05. A similarity percentage (SIMPER) analysis was performed to analyze the relative contribution of each functional gene to the variations in nitrifier/denitrifier abundance among different treatments with Past (Øyvind Hammer, University of Oslo, Oslo, Norway; Version 3.26). The partial correlation coefficient (*r*) was calculated using SPSS [24].

#### **3. Results**

#### *3.1. Soil Chemical Properties*

Compared with CK, N fertilization treatments significantly enhanced the TC and TN contents at the harvest stages by 23–45% and 31–57%, respectively (Table S2). Furthermore, the N fertilizer type imposed a significant impact on TC, with significantly higher TC under I+O than under I and O by 7–18%. However, I+O only significantly increased TN in the

2017 growing season by 16–17% compared with I and O, while no significant differences were found between I, O and I+O in the 2016 growing season (Table S2).

The NH4 +–N, NO3 ––N, DOC, MBC and MBN contents under N fertilization treatments varied similarly in the 2016 and 2017 growing seasons of purple caitai, and all peaked immediately after the application of basal and topdressing N fertilizers (15 October and 14 December 2016; 17 October and 16 December 2017) (Figure 2).

**Figure 2.** Soil chemical properties including NH4 +–N (**A**), NO3 ––N (**B**), DOC (**C**), MBC (**D**) and MBN (**E**) contents under different treatments throughout two growing seasons of purple caitai. Different letters indicate significant differences at the level of 0.05. DOC, dissolved organic C; MBC, microbial biomass C; MBN, microbial biomass N; I, inorganic N fertilization; O, organic N fertilization; I+O, integrated organic/inorganic N fertilization; CK, no fertilization.

According to two-way repeated measures ANOVA, N fertilization treatments increased the contents of NH4 +–N and NO3 ––N as compared with CK, (Figure 2A,B). Moreover, there were significant differences in the contents of soil NH4 +–N and NO3 ––N among N fertilization treatments (Figure 2A,B). The NH4 +–N contents from four sampling stages (15 October 2016 to 13 January 2017 and 17 October 2017 to 15 January 2018) under I+O were significantly lower than those under I by 21–37%. The NH4 +–N contents on 14 December 2016, 4 March and 16 December 2017, and 14 February and 6 March 2018 under I+O 35–54% (*p* < 0.05) higher than under O. Meanwhile, the NO3 ––N contents from four sampling stages (15 October 2016 to 13 January 2017 and 17 October 2017 to 15 January 2018) under I+O were also significantly lower than those under I by 31–55%. The NO3 ––N contents on 12 December 2016 and 16 December 2017 under I+O were significantly lower than those under O by 40–44%.

As for the DOC, MBC and MBN contents, they were all significantly higher under N fertilization treatments than under CK (Figure 2C–E). Specifically, among N fertilization treatments, I+O caused higher DOC and MBC contents than I and O. In addition, in the 2016 growing season, the MBN contents were significantly higher under I+O than under I by 34% on average, while no significant differences were found between I+O and O. However, in the 2017 growing season, the MBN contents under I+O were significantly higher than under I and O by 13–36% on average.

#### *3.2. Soil Nitrification and Denitrification Rates, and Abundances of Nitrifier and Denitrifier Genes*

The nitrification and denitrification rates throughout two growing seasons of purple caitai showed similar variations to the N2O fluxes (Figure 3).

**Figure 3.** The N2O fluxes (**A**), gross rates of nitrification (**B**) and denitrification (**C**) in soil under different treatments throughout two growing seasons of purple caitai. Arrows indicate N fertilization. I, inorganic N fertilization; O, organic N fertilization; I+O, integrated organic/inorganic N fertilization; CK, no fertilization.

Both the nitrification and denitrification rates under N fertilization treatments peaked immediately after the application of basal and topdressing fertilizers. The nitrification rates were 125.6–403.4 μg kg–1 h–1, and the denitrification rates were 141.9–392.7 μg kg–1 h–1. N fertilization treatments increased the nitrification and denitrification rates by 24–77% and 26–40% on average compared with CK, respectively. Moreover, N fertilizer types were found to have a significant impact on the nitrification rate. The nitrification rate under I+O was significantly lower than that under I and O by 6–30% on average. However, no significant differences in denitrification rates were found among I, O and I+O.

As illustrated in Figure 4, the abundances of nitrifier and denitrifier genes peaked immediately after the application of basal and topdressing fertilizers. N fertilization treatments increased the abundance of nitrifiers and denitrifiers compared with CK (Figure 4). Besides, the abundances of nitrifiers and denitrifiers were also significantly affected by N fertilizer type. The abundance of the *AOB-amoA* gene under I+O was lower than I and O, while there was no significant difference in *AOA-amoA* gene abundance among I, O and I+O. In addition, the abundances of both *nirK* and *nirS* genes under I+O were significantly lower than those under I and O by 16–35% and 24–44%, respectively.

**Figure 4.** The abundance of nitrifier genes, including *AOB-amoA* (**A**) and *AOA-amoA* (**B**), and denitrifier genes including *nirK* (**C**) and *nirS* (**D**) at log-scale in soil under different treatments throughout two growing seasons of purple caitai. AOB, ammonia-oxidizing bacteria; AOA ammonia-oxidizing archaea; I, inorganic N fertilization; O, organic N fertilization; I+O, integrated organic/inorganic N fertilization; CK, no fertilization.

#### *3.3. N2O Emissions*

The N2O fluxes under N fertilization treatments showed an identical trend in the 2016 and 2017 growing seasons of purple caitai and peaked immediately after the application of basal and topdressing N fertilizers (Figure 3A). The N2O fluxes were 139.2–750.0 μg m–2 h–1 under I, 113.2–652.8 μg m–2 h–1 under O, 103.7–533.7 μg m–2 h–1 under I+O, and 72.9–139.9 μg m–2 h–1 under CK.

The N fertilizer type showed a significant impact on the seasonal cumulative N2O emissions in purple caitai fields (Table 2). N fertilization treatments increased the cumulative N2O-N emissions by 176–332% compared with CK. Among N fertilization treatments, I+O caused the lowest seasonal cumulative N2O-N emissions, which were significantly lower than those under I and O. In addition, the type of N fertilizers showed significant effects on the EFN2O in purple caitai fields as well (Table 2). The EFN2O under I+O was significantly lower than that under I and O in two growing seasons.

**Table 2.** Seasonal cumulative N2O–N emissions, EFN2O, yield and yield-scaled N2O emission under different treatments in two growing seasons of purple caitai.


Different small letters between treatments in a line mean significant differences at *p* < 0.05. CE stands for seasonal cumulative N2O–N emissions. EFN2O stands for fertilizer-induced N2O emission factor. Different letters in the same column indicate significant differences at the level of 0.05. I, inorganic N fertilization; O, organic N fertilization; I+O, integrated organic-inorganic N fertilization; CK, no fertilization.

#### *3.4. Correlation among Soil Chemical Properties, Nitrifier/Denitrifier Abundance, Nitrification/Denitrification Rate and N2O Flux*

The results showed that the *AOB-amoA* gene was the major contributor to the variations in nitrifier abundance among fertilization treatments, while *nirK* and *nirS* genes had roughly the same contribution to the variations in denitrifier abundance among fertilization treatments (Table 3). N2O flux was positively related to nitrification rate, while it showed no significant correlation with denitrification rate (Table 4). A SIMPER analysis was carried out to estimate the respective contributions of each functional gene to the variations in nitrifier/denitrifier abundance among different fertilization treatments.

**Table 3.** Respective contribution of each nitrifier (*AOB-amoA* and *AOA-amoA*) and denitrifier (*nirK* and *nirS*) gene to community abundance variations (%) among fertilization treatments during two growing seasons of purple caitai.


AOB, ammonia-oxidizing bacteria; AOA ammonia-oxidizing archaea; I, inorganic N fertilization; O, organic N fertilization; I+O, integrated organic-inorganic N fertilization.


**Table 4.** Partial correlation coefficients among soil chemical properties, nitrifier/denitrifier abundance and nitrification/denitrification rates in two growing seasons of purple caitai.

DOC, dissolved organic C; MBC, microbial biomass C; MBN, microbial biomass N; AOB, ammonia-oxidizing bacteria; AOA ammonia-oxidizing archaea; NR, nitrification rate; DNR, denitrification rate. ns, not significant; \* *p* ≤ 0.05; \*\* *p* ≤ 0.01.

Partial correlation coefficients were used to measure the correlations among soil chemical properties, nitrifier/denitrifier abundance, nitrification/denitrification rate and N2O flux in the 2016 and 2017 growing seasons of purple caitai (Table 4). There was a weak positive correlation between *AOB-amoA* and NO3 ––N in the 2016 growing season, while there were negative correlations between *AOB-amoA* and MBC/MBN in both growing seasons. *AOA-amoA* was in positive relation with NO3 ––N but in negative relation with DOC, though the correlations were not strong. *nirK* and *nirS* were both weakly positively correlated with NO3 ––N. Moreover, nitrification rate was in a moderate and a weak positive relation with NH4 +–N and NO3 ––N, respectively, but in a moderate negative relation with DOC, MBC and MBN. There was a strong positive relation between nitrification rate and *AOB-amoA*, while the correlations between nitrification rate and the other three functional genes were all weakly positive. However, no significant correlations of denitrification rate with other variables, except for the weak positive one with *nirS* in the 2016 growing season, were observed.

#### *3.5. Yield and Yield-Scaled N2O Emission*

The N fertilization significantly increased the yield by 82–172% compared with CK (Table 2). Among N fertilization treatments, I+O caused the highest yield in both 2016 and 2017, followed by I and O. Moreover, N fertilizer types significantly affected the yield-scaled N2O emissions. Among N fertilization treatments, I+O treatment resulted in the lowest yield-scaled N2O emission.

#### **4. Discussion**

#### *4.1. Effect of N Fertilizer Type on Soil Properties*

The N fertilizer type had a great influence on the soil properties in the purple caitai fields. The contents of available N in topsoil (0–20 cm) under integrated organic-inorganic N fertilization were significantly lower than that under inorganic N fertilization (Figure 2A,B), which was directly induced by the higher and faster mineral N input from inorganic fertilizers. Besides, the application of integrated organic and inorganic N fertilizers significantly increased the storage of labile soil organic C (SOC) and N (Figure 2) as compared with the single application of organic or inorganic N fertilizer. Research has been reported on the combined application of organic and inorganic N fertilizers, which could stimulate

soil microbial activity compared with the single application of organic or inorganic N fertilizer [25,26], which thereby boosts organic matter mineralization and facilitate SOC turnover and nutrient availability in soil. Moreover, compared with organic N fertilization, integrated organic-inorganic N fertilization could lead to an increase in labile SOC and N pool due to a stronger priming effect of root exudates on soil organic matter owing to the promoted plant growth under faster mineral N input [27]. So, higher DOC, MBC and MBN were observed under I+O than I and O (Figure 1), which also suggests that the application of integrated organic-inorganic fertilizers could help maintain soil fertility by enhancing the labile organic C and N. Similar results were reported by Ali et al. [28], who indicated that the combined application of inorganic-organic N fertilizers could improve soil quality with more labile SOC fractions. Interestingly, we observed higher DOC immediately after the N application. This may be because this study was preceded by continuous cultivation of purple caitai under different N fertilization for 5 years, thus leading to higher initial DOC contents under I+O at the beginning of this study.

#### *4.2. Effect of N Fertilizer Type on the Abundance and Activity of Nitrifiers/Denitrifiers*

Higher abundance and greater activity of nitrifiers/denitrifiers were observed immediately after the application of basal and topdressing fertilizers during the two growing seasons as well as under N fertilization treatment compared with CK (Figures 3B,C and 4). Moreover, the activity and abundance of nitrifiers/denitrifiers are positively correlated with the N fertilization intensity [29], and thus N addition significantly increased AOB abundance [30].

The abundances of *AOB-amoA*, *nirK* and *nirS* genes under I+O were all significantly lower than those under I and O, whereas there was no significant difference in abundance of *AOA-amoA* genes among various N fertilization treatments (Figure 4), indicating that AOA are less sensitive to the variations in N fertilizer type than other N-cycling microbial communities, which is consistent with the results of SIMPER analysis (Table 3). Similarly, many studies have shown that N fertilization can induce obvious changes in the AOB community but not in the AOA community [31]. Moreover, different N fertilizer types only altered the activity of nitrifiers but not that of denitrifiers (Figure 3B,C), contradicting the variations in denitrifier abundance (Figure 4). This inconsistency was probably because of the coupled effects of the denitrification-controlling abiotic factors such as oxygen (O2) content and organic C/N substrates [32].

The MBC and MBN contents under I+O were higher than those under I and O (Figure 2D,E), implying that the integrated N fertilization could enhance the microbial abundance in soil. It has been noted that heterotrophic bacteria usually have higher abundance and activity than nitrifying bacteria [33]. Thus, with more O2 being consumed by other microorganisms, the growth of AOB was inhibited, resulting in the lower abundance of AOB, which agrees well with the negative correlation between AOB and MBC/MBN (Table 4). Besides, the lower NH4 +–N availability under I+O compared with that under I may further inhibit the nitrification (Figure 2A). Moreover, the nitrification rate was only strongly correlated with AOB (Table 4), so the decline in AOB abundance was most responsible for the significantly lower nitrification rate under I+O compared with that under I and O. The abundances of *nirK* and *nirS* genes under I+O were both significantly lower than those under I and O (Figure 4C,D) probably owing to the significantly lower NO3 ––N contents (Figure 2B and Table 4), given that the availability of N oxides is key to denitrification [34]. However, the inhibiting effects of the decreased denitrifier abundance on denitrification rate under I+O might have been offset by the stimulating effects of O2 depletion under higher microbial activity and labile organic C amendment due to higher DOC contents (Figure 2C–E), considering that most denitrifiers are heterotrophic anaerobes [35].

#### *4.3. Effect of N Fertilizer Type on N2O Emissions*

The N2O fluxes in the soil under N fertilization treatments peaked immediately following N fertilization in both growing seasons (Figure 3A), which agrees well with previously reported results [36]. This is possibly due to a drastic boost in the nitrification rates (Figure 3B and Table 4). Moreover, the N2O emissions from CK ranged from 1.98 kg ha−<sup>1</sup> to 2.38 kg ha−<sup>1</sup> during the

purple caitai growing seasons. The emissions are higher than 0.68 kg ha−1−1.28 kg ha−<sup>1</sup> of background N2O emissions from Chinese vegetable soils [37], which could be attributed to the relatively higher air temperature and soil N availability in this study. Moreover, the total seasonal precipitation ranged from 66.1 to 287.9 mm during the experimental periods. Moreover, high rainfall and air temperature and neutral soil pH (7.03) can be beneficial to soil nitrification and denitrification [37,38], thus inducing relatively large N2O from CK in this study.

The integrated organic-inorganic fertilization significantly decreased the EFN2O (Table 2). The EFN2O in this study was within the range of 1.86–3.05% (Table 2), which is larger than the default IPCC value [1] as well as other estimates in Chinese croplands [39]. Aside from the possible systemic error caused by the choice of a linear regression model for flux calculation as well as the inaccuracies in measurements of sampling time, temperature and chamber volume [19], a high EFN2O value might result from the regional discrepancies such as temperature and soil type [40]. Besides, N2O emission factors actually increase with N additions [41]. Hence, the relatively high N inputs in our study might have led to higher EFN2O.

In line with previously reported results [42], the combined application of organic and inorganic N fertilizer significantly reduced the N2O emissions compared with the single application (Figure 3A and Table 2). As soil N2O emissions from nitrification and denitrification depend on soil available N, N fertilizer application is an important driver of N2O emissions in the soil [42]. Application of inorganic N fertilizer alone can increase soil mineral-N contents from the applied N fertilizers in excess of crop requirements [42]. Higher contents of NH4 +–N and NO3 ––N (Figure 2) under I than under I+O support this view in this study. Therefore, higher N2O emissions from I than from I+O were found (Table 2). Although O caused lower soil NH4 +–N contents than I+O (Figure 2), higher nitrification under O than under I+O (Figure 4) due to improved soil porosity under O [26] (Ali et al. 2014) ultimately results in more N2O emissions under O. Studies proposed that nitrification imposes a greater effect on N2O emissions [38]. In this study, there was a significant positive relation between N2O flux and nitrification rate, while no significant correlation existed between N2O flux and denitrification rate (Table 4). Besides, the nitrification rates significantly declined under I+O, whereas N fertilizer type had no significant impact on the denitrification rates (Figure 3B,C). The above results indicate that the declined nitrification plays a more important role than denitrification in lowering N2O emissions under I+O and the integrated organic-inorganic N fertilization diminishes N2O emissions primarily through the regulation of nitrification. In conclusion, integrated organic-inorganic N fertilization can be recommended as the optimum fertilization regime for enhancing soil fertility and mitigating N2O emissions in purple caitai fields.

#### *4.4. Effect of N Fertilizer Type on Yield and Yield-Scaled N2O Emission*

The effects of combined inorganic/organic N fertilizers on crop yield have been frequently studied [25,26]. Studies reported that combined inorganic/organic N fertilizers could significantly increase crop yield compared with the single N application. Similar results were observed in our study (Table 2). Enhanced yield in the present study may be ascribed to improved N use efficiency through increased soil N supply and improved soil microbial activity (Figure 2) [25]. Moreover, compared with I, O resulted in a lower purple caitai yield. This may be because organic N fertilizer alone does not provide enough available N for vegetable growth (Figure 2) due to low mineralization relative to chemical N fertilizer [26].

The yield-scaled N2O emission, an index for evaluating the source or sink of soil N2O per ton of yield [3], not only considers crop yield but also incorporates N2O emissions. Therefore, the index can be used to investigate the relationship between agronomic productivity and environmental sustainability (e.g., greenhouse gas emissions) in agricultural production [37]. In this study, among N fertilization treatments, I+O showed the lowest yield-scaled N2O emissions due to the highest yield and the lowest N2O emissions (Table 2), suggesting that combined inorganic/organic N fertilization could reduce soil N2O emissions and contribute to high purple caitai productivity. Moreover, the combination

could improve the soil fertility (Figure 2), and thus it can be concluded that the combined inorganic/organic N fertilization is a sustainable agricultural technology for reducing soil N2O emissions while improving soil fertility and yield of purple caitai.

#### **5. Conclusions**

The response of nitrous oxide (N2O) emissions to the combined application of inorganic and organic nitrogen (N) fertilizers from upland soils remains still unclear. In this view, this study hypothesized that the combined application could mitigate N2O emissions from purple caitai fields compared with the single application of inorganic or organic N fertilizers by decreasing nitrification and denitrification. The results showed that compared with the single application, the combined application significantly improved soil N availability, promoted microbial biomass, and diminished the N2O emissions. Moreover, partial correlation and similarity percentage analyses revealed that the ammonia-oxidizing bacteria (AOB) community was the major contributor to N2O production, and the integrated fertilization mitigated the N2O emissions primarily through inhibiting the nitrification by AOB in purple caitai fields. Therefore, we recommend the integrated organic-inorganic N fertilization as an optimum N fertilization practice to enhance the soil fertility and yield and mitigate the N2O emissions in the upland fields.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/agriculture12050723/s1, Table S1: Primers and protocols for RT-qPCR. Table S2: TC and TN contents in soil under different treatments at the harvest stages in the 2016 and 2017 growing seasons of purple caitai.

**Author Contributions:** C.L. conceived and designed the research; D.F. provided experimental data; D.F. conducted the measurement; D.F. and C.L. performed statistical analysis; D.F. wrote the manuscript; C.L. and C.C. commented on and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the Key Research and Development Project of Hubei Province (2021BBA224).

**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.

**Informed Consent Statement:** This paper does not contain any studies with human participants and/or animals. Informed consent was obtained from all individual participants included in this study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.

#### **References**


## *Article* **Side Deep Fertilizing of Machine-Transplanted Rice to Guarantee Rice Yield in Conservation Tillage**

**Qi-Xia Wu †, Bin Du †, Shuo-Chen Jiang, Hai-Wei Zhang and Jian-Qiang Zhu \***

College of Agriculture, Yangtze University, Jingzhou 434025, China; qixiawu@yangtzeu.edu.cn (Q.-X.W.); xiaobin@stu.scau.edu.cn (B.D.); 202073052@yangtzeu.edu.cn (S.-C.J.); 202071644@yangtzeu.edu.cn (H.-W.Z.)

**\*** Correspondence: 200572@yangtzeu.edu.cn

† These authors contributed equally to this work.

**Abstract:** Conservation tillage is an environmentally friendly and economical farming method, but its impact on rice yield is controversial. Artificially applied side deep fertilizing of machine-transplanted rice is when fertilizer is applied to the deep soil along with the machine transplantation of rice; this may improve the fertilizer utilization rate and rice yield and eliminate the possible negative effects of conservation tillage on rice yield. Using on machine-transplanted rice, this study aims to compare the effects of side deep fertilizing (SDF). We investigated the effects of artificially applying fertilizer (AAF) on rice growth and yield under conventional tillage (CT), reduced tillage (RT), and no tillage (NT). The rice root activity, root dry weight, leaf area index (LAI), net photosynthetic rate (Pn), chlorophyll content, panicle density, spikelets per panicle, and yield were all ranked as NT > RT > CT and SDF > AAF. The 1000-grain weight was also ranked as SDF > AAF. In addition, under NT conditions, the positive effect of SDF on rice growth and yield was higher than under RT and CT conditions. In general, conservation tillage combined with SDF saved costs and increased rice yield.

**Keywords:** reduced tillage; no tillage; side deep fertilizing of machine-transplanted rice; root function; photosynthesis

#### **1. Introduction**

Rice (*Oryza sativa* L.) is the main food source for more than half of the world's population and the most important food crop in Asia [1]. Tillage changes the distribution of soil nutrients and the stability of aggregates, reduces soil compactness, and allows air to enter deep soil [2]. Tillage can improve soil properties, promote rice growth, and increase rice yield [3]. However, the long-term use of conventional tillage (CT) has increased the degradation of organic matter, reduced the soil microbial content and enzyme activity, and caused a decline in soil function and soil erosion [4,5]. Since the 21st century, rural depopulation in China has not allowed the long-term development of traditional frequency tillage [6].

Conservation tillage such as reduced tillage (RT) and no tillage (NT) is a tillage technology for the protection of cultivated land. Hart et al. [7] reported that it has become apparent that the concomitant increase in losses of N and P from agricultural land is having a serious detrimental effect on water quality and the environment. Reportedly, conservation tillage promotes rice growth and increases rice yield by protecting soil aggregates [8] and by increasing soil organic matter contents [9], nutrient availability [10], microbial biomass [11,12], and enzyme activity [13]. In addition, conservation tillage has the effects of reducing the input of energy and labor [11] and reducing greenhouse gas emissions [14]. However, there are also reports that under conservation tillage, fertilizers are applied to the soil surface or the shallow soil layer, which increases fertilizer loss and reduces fertilizer use efficiency and rice yield [15].

**Citation:** Wu, Q.-X.; Du, B.; Jiang, S.-C.; Zhang, H.-W.; Zhu, J.-Q. Side Deep Fertilizing of Machine-Transplanted Rice to Guarantee Rice Yield in Conservation Tillage. *Agriculture* **2022**, *12*, 528. https:// doi.org/10.3390/agriculture12040528

Academic Editor: Manuel Ângelo Rosa Rodrigues

Received: 14 February 2022 Accepted: 3 April 2022 Published: 8 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Agricultural mechanization is an important process used to promote agricultural intensive management, adjust agricultural supply-side reforms, and accelerate the construction of modern agriculture, and it is also the basis for the development of smart agriculture in the future [16]. Machine-transplanted seedlings solve the disadvantages of traditional artificial direct-seeding rice, such as irregular growth, uneven row spacing, and susceptibility to lodging, reduces the labor input and production costs, and increases the planting speed, rice yield, and farmers' income [17]. Mechanical side deep fertilizing technology ensures that the fertilizer enters the soil cultivation layer, effectively reduces fertilizer loss, and improves fertilizer utilization and rice yield [18]. The side deep fertilizing (SDF) of machine-transplanted rice is an innovative approach, wherein rice seedlings are transplanted and granular fertilizers are applied simultaneously deep in the paddy soils [19]. This technique ensures close contact between nutrients and crop root systems, enhances nutrient absorption and utilization, and improves fertilizer use efficiency [20]. Zhong et al. [21] reported that SDF optimized agronomic traits and yield components, increased grain yield and economic return, and enhanced NPK fertilizer uptake but reduced their application rates. However, the application of SDF in rice production under conservation tillage has not yet been reported.

Aiming at the basal fertilizer application method for machine-transplanted rice, this study compared the effects of side deep fertilizing (SDF), artificially applying fertilizer (AAF), on rice growth and yield under three tillage management systems (CT, RT, and NT); thus, this study provides a reference for the sustainable development of conservation tillage and mechanized rice production.

#### **2. Material and Methods**

#### *2.1. Experimental Site*

Experiments were conducted during 2017–2019 at the Yangtze University farm, Jingzhou County (112◦04 N–112◦05 N, 30◦32 E–30◦33 E), Hubei Province, China. The area belongs to the northern subtropical agricultural climate zone. The annual average temperature was 16.5 ◦C, the accumulated temperature ≥ 10 ◦C was 5094.9–5204.3 ◦C, the annual average precipitation was 1095 mm, and the sunshine time was 1718 h. In the experimental field, the soil texture was clay loam, and the index of the soil agrochemical properties were a pH of 6.02, 32.31 g kg−<sup>1</sup> of organic matter, 243.05 mg kg−<sup>1</sup> of available N, 11.03 mg kg−<sup>1</sup> of available P, and 103.24 mg kg−<sup>1</sup> of available K.

#### *2.2. Experimental Design*

The experiments were arranged in a split-plot design, with the fertilization methods as the main plot and the tillage managements as subplots, in three replications. The plot size was 5 × 10 m2. Two fertilization methods (SDF and AAF) and three tillage management systems (RT, NT, and CT) were designed, which are briefly introduced as follows.

RT refers to only rotary tillage without plowing as the rice plants were planted; the field was irrigated to 3–4 cm deep after the wheat was harvested so as to prevent weeds (such as barnyard grass and tendon grass); at 3–5 days before rice transplanting, a paddy field stubble burying machine (HUHN RM320, Jining, Shandong, China) was used to press the rice stubble, and a rotary tiller (FENGYUAN 1GZL200, Huzhou, Zhejiang, China) was used to rotate (15 cm deep tillage) once, then field irrigation was performed and a water layer of 1–2 cm was kept in the field until transplanting.

NT means that the soil was not disturbed after the wheat harvest until rice planting. The residue of the wheat straw covered the ground until the rice harvest. At 3 weeks before planting, 60 mL ha−<sup>1</sup> *Roundup* (Organophosphorus herbicides, Monsanto Company, St. Louis, MI, USA) aqua herbicide was sprayed on the field as the soil was drying, and the field had te be kept dry for 5–7 days in order to eliminate weeds; after this, the field was irrigated and kept with a water layer of 1–2 cm until transplanting.

CT is a local conventional tillage method, which is carried out as follows: the paddy field was first soaked with water to a depth of 4–5 cm and then plowed once (with a tillage depth of 25 cm) about 5 days later, and a rotary tillage was conducted (with a tillage depth of 15 cm) 5 days before transplanting; after this, the rice plants were transplanted into the field with a water layer of 1–2 cm.

SDF is completed by an integrated machine used for rice transplanting and fertilizing (Yameike RXA-60TK, Changzhou, Jiangsu, China), by which a basal fertilizer is put at a depth of 20 cm of the topsoil while the rice transplanting is completed. Transplanting and fertilizing are done almost at the same time.

Under the tillage management of CT and RT, AAF is used to apply the basal fertilizer on the day of the rotary tillage, and the basal fertilizer mixes with the soil by rotary tillage, then, the rice seedlings are transplanted by a rice transplanter (Yameike RXA-60TK, Changzhou, China). As NT was adopted, AAF spread the basal fertilizer on the field one day before the mechanical rice transplantation.

Rice was sown on 1 May and transplanted on 1 June in 2019 and 2020. Harvest was on 16 September 2019 and 21 September 2020. The planting density was 25 hills m<sup>−</sup>2. The experimental plots in which the basal fertilizer was applied to the field using SDF and AAF had the same available nutrient dosage in each plot, with 120 kg ha−<sup>1</sup> of N, 59 kg ha−<sup>1</sup> of P2O5, and 120 kg ha−<sup>1</sup> of K2O, whereas 60% and 40% of N were applied, respectively, as the base fertilizer and tillering fertilizer. All the P2O5 and K2O was applied as base fertilizers. N, P2O5, and K2O were administered in the form of urea, disodium hydrogen phosphate, and potassium chloride, respectively. Rotary tillage was done twice before planting wheat in winter (HUHN RM320, Jining, Shandong, China).

#### *2.3. Measurements*

#### 2.3.1. Rice Yield and Its Components

Grain yields and panicle density were measured at maturity by taking 5 m2 plant samples at the center of each plot. The filled grains in each 5 m2 plant sample were separated from the straws. The filled grains were dried in an oven at 70 ◦C to a stable weight and weighed, and the grain yield was calculated at a 14% moisture content. Rice plant samples plots were taken from 5 planting pits per for the determination of yield components (spikelets per panicle, grain filling rate, and 1000-grain weight).

#### 2.3.2. Root Function

Rice plant samples were selected from 5 planting pits per plot to measure the root dry weight and root activity at the mid-tillering stage, heading stage, grain filling stage, and yellow ripe stage. For each root sample, a cube of soil (25 cm in length × 16 cm in width × 20 cm in depth) around each individual planting pit was removed using a sampling core, and such a cube contains about 95% of total root biomass [22]. The rice plants from 5 planting pits per plot formed a sample at each measurement, and the roots in each cube of soil were carefully rinsed with a hydropneumatic elutriation device (Gillison's Variety Fabrications, Benzonia, MI, USA). Portions of each root sample were used for the measurement of root activity, which was determined by measuring the oxidation of alpha-naphthylamine (α-NA) [23], whereas the other root samples were dried in an oven at 70 ◦C to stable weights and weighed. One gram of fresh roots was transferred into a 150 mL flask containing 50 mL of 20 ppm α-NA. The flasks were incubated for 2 h at room temperature in an end-over-end shaker. After this, the aliquots were filtered, and 2 mL of aliquot was mixed with 1 mL of 1.18 mmol−<sup>1</sup> NaNO and 1 mL of sulfanilic acid, and the resulting color was measured using a spectrophotometer.

#### 2.3.3. Photosynthetic Properties

Rice plants were selected from 5 planting pits per plot to measure the leaf area index (LAI), net photosynthetic rate (Pn), and total chlorophyll content at the mid-tillering stage, heading stage, grain filling stage, and yellow ripe stage. The LAI of the top fully expanded leaves in the main stem was calculated as the measured leaf area divided by the ground surface area. The Pn of the top fully expanded leaves in the main stem was determined by a

gas exchange analyzer (Li-6400, Li-COR Inc., Irving, TX, USA) between 9:30 and 11:00 a.m., when the photosynthetic active radiation above the canopy was 1200 mmol m−<sup>2</sup> s<sup>−</sup>1. After the determination of LAI and Pn, the measured leaves were cut, frozen immediately in liquid nitrogen, and stored at −80 ◦C for standby. The total chlorophyll content was extracted with about 0.2 g of fresh leaf disks using 25 mL of an alcohol and acetone mixture (*v*:*v* = 1:1) for 24 h in the dark at room temperature. The absorbance of the extract was measured at 663, 645, and 470 nm using a UV-VIS spectrophotometer (UV-2600, Shimadzu, Japan) to estimate the total chlorophyll content according to the method reported in [4].

#### *2.4. Statistical Analysis*

All the experimental data were collected in 2019 and 2020 and expressed as mean ± standard error (SE) of three replicates. The normal distribution and homogeneity variance of data were tested using Shapiro–Wilk's test and Levene's test on SPSS 21.0 (Statistical Product and Service Solutions, IBMLab) [24], respectively. The independent samples *t*-test was used to compare the differences in the relevant rice indicators between the two years (2019 and 2020). One-way analysis of variance was used to compare the differences between the relevant rice indicators among the tillage methods and fertilization methods, and twofactor analysis of variance was used to compare the impacts of the interaction of the tillage methods and fertilization methods on the rice indicators. In the statistical analysis, two significance levels were set at *p* < 0.05 and *p* < 0.01. The diagrams were drawn using the Origin 2017(*Origin* Lab) mapping software.

#### **3. Results**

#### *3.1. Root Activity and Root Dry Weight*

Figures 1 and 2 show the root activity and root dry weight under different fertilization and tillage systems. The results showed that there was no significant difference in root activity and root dry weight between the two years. With the growth of the rice, the root activity decreased, and the root dry weight increased first and then decreased. Different tillage and fertilization systems had significant effects on root activity and root dry weight. The root activity of SDF + NT was significantly higher than that of the other treatments in the mid-tillering stage, heading stage, full heading stage, and yellow maturity stage. Although AAF + NT was the second highest in the mid-tillering stage, SDF + RT was the second highest in the other growth stages, indicating that the SDF model was helpful in improving rice root activity. In the SDF model, the root activity in each growth stage was ranked as NT > RT > CT; in the AAF model, the root activity in each growth stage was ranked as NT > RT > CT; and the root activity of the NT treatment in the full heading stage and yellow mature stage was significantly higher than that of RT and CT. The results of the two fertilization models show that the NT treatment was helpful in improving the root activity of rice. The root dry weight of the SDF + NT model was significantly higher than that of the other treatments in the mid-tillering stage, heading stage, full heading stage, and yellow maturity stage. In the SDF and AAF models, the root dry weight at each growth stage was ranked as NT > RT > CT. It was concluded that the SDF model is better than the AAF model for rice root growth; the NT model is better than the RT and CT models for rice root growth, and the SDF + NT mode is better for rice root growth in the whole growth period.

#### *3.2. Leaf Area Index (LAI), Net Photosynthetic Rate (Pn), and Total Chlorophyll Content*

As shown in Figures 3–5, the interannual differences of LAI, PN, and chlorophyll contents were not significant. With the growth of rice, the photosynthetic rate and chlorophyll content showed a downward trend; the leaf area index first increased and then decreased, and was the highest at the heading stage. The LAI at each growth stage of the SDF and AAF models was ranked as NT > RT > CT, and SDF + NT was the highest. However, there was no significant difference between the mid-tillering stage, SDF + RT, and AAF + NT, and there was no significant difference between the yellow ripening stage and SDF + RT. SDF + NT in the other growth stages was significantly higher than that of the other treatments, indicating

that the SDF + NT model was helpful in improving rice leaf growth. In the SDF model, the chlorophyll content in each growth stage was ranked as NT > RT > CT, and NT was significantly higher than RT in the middle growth stage. There was no significant difference between NT and RT in the other growth stages, but they were significantly higher than the CT model. In the AAF model, the chlorophyll content in each growth stage was ranked as NT > RT > CT, and the chlorophyll content in the SDF model was significantly higher than that in AAF under the same tillage model, among which SDF + NT was the highest. However, there was no significant difference between SDF + NT and SDF + RT in the mid-tillering stage and booting stage in 2020; SDF + NT in the other growth stages was significantly higher than that in the other treatments. In the SDF and AAF models, the PN in each growth stage was ranked as NT > RT > CT, and under the same tillage mode, the PN in the SDF model was significantly higher than that in the AAF model. It was concluded that, in the SDF and AAF models, the leaf area index, chlorophyll content, and PN in each growth stage were ranked as NT > RT > CT, and the SDF model is significantly higher than the AAF model, among which the SDF + NT model is the highest, indicating that the SDF + NT model is helpful in improving the rice leaf area and chlorophyll content, so as to improve rice photosynthesis.

**Figure 1.** Root activity at different stages of rice under different tillage and fertilization treatments. (**a**–**d**) represent the root activity in mid-tillering stage, heading stage, full-heading stage and yellow ripe stage in 2019, respectively. (**e**–**h**) represent the root activity in mid-tillering stage, heading stage, full-heading stage, and yellow ripe stage in 2020, respectively. SDF + RT: Side Deep Fertilizing and Reduced Tillage. SDF + NT: Side Deep Fertilizing and No Tillage. SDF + CT: Side Deep Fertilizing and Conventional Tillage. AAF + RT: Artificially Applying Fertilizer and Reduced Tillage. AAF + NT: Artificially Applying Fertilizer and No Tillage. AAF + CT: Artificially Applying Fertilizer and Conventional Tillage. Different lowercase letters marked on the histogram mean significant differences among treatments at the 5% level according to Duncan's multiple-range test (0.05) (similarly hereinafter).

#### *3.3. Rice Yield and Its Compositions*

Table 1 shows the yield components of rice in 2019 and 2020 under different tillage and fertilization models, and the yield difference between the two years is not significant. The results of the variance analysis showed that the different tillage methods had significant or extremely significant effects on panicle density, grain number per panicle, and yield, and the different fertilization methods had significant effects on panicle density, grain number per panicle, 1000 grain weight, and yield. It can be seen from the data in the table that the yield under the SDF model was significantly higher than that under the AAF fertilization model. The NT yield under the SDF treatment was significantly higher than that of RT and CT, and there was no significant difference in yield among the three tillage methods of AAF treatment. It can be seen that the yield of SDF + NT is the highest, having increased by 14.03~15.17%, 22.06~30.22%, 26.99~30.22%, 19.35~34.43%, and 39.90~40.08%, respectively, compared with SDF + RT, SDF + CT, AAF + RT, and AAF + CT. In terms of yield components, there was no significant difference among the three tillage methods in the SDF model for the panicle density, grain number per panicle, seed setting rate, and 1000 grain weight, whereas NT in the SDF model was significantly higher than RT and CT, indicating that the main reason for the increase in SDF + NT yield was the increase in grain number per panicle.

**Figure 2.** Root dry weight at different stages of rice under different tillage and fertilization treatments. (**a**–**d**) represent the root dry weight in mid-tillering stage, heading stage, and full-heading Scheme 2019, respectively. (**e**–**h**) represent the root dry weight in mid-tillering stage, heading stage, fullheading stage, and yellow ripe stage in 2020, respectively. Different lowercase letters marked on the histogram mean significant differences among treatments at the 5% level according to Duncan's multiple-range test (0.05) (similarly hereinafter).



Within a column, different lowercase letters following numeric values mean significant differences among treatments at the 5% level according to Duncan's multiple-range test (0.05). \* and \*\* indicate significant differences at the *p* < 0.05 and 0.01 levels, respectively. "ns" means not significant between a certain indicator of rice and tillage or fertilization treatment.

**Figure 3.** Leaf area index (LAI) at different stages of rice under different tillage and fertilization treatments. (**a**–**d**) represent the leaf area index in mid-tillering stage, heading stage, full-heading stage, and yellow ripe stage in 2019, respectively. (**e**–**h**) represent the leaf area index in mid-tillering stage, heading stage, full-heading stage, and yellow ripe stage in 2020, respectively. Different lowercase letters marked on the histogram mean significant differences among treatments at the 5% level according to Duncan's multiple-range test (0.05) (similarly hereinafter).

**Figure 4.** Chlorophyll content at different stages of rice under different tillage and fertilization treatments. (**a**–**d**) represent the chlorophyll content in mid-tillering stage, heading stage, full-heading stage, and yellow ripe stage in 2019, respectively. (**e**–**h**) represent the chlorophyll content in midtillering stage, heading stage, full-heading stage, and yellow ripe stage in 2020, respectively. Different lowercase letters marked on the histogram mean significant differences among treatments at the 5% level according to Duncan's multiple-range test (0.05) (similarly hereinafter).

**Figure 5.** Net photosynthetic rate at different stages of rice under different tillage and fertilization treatments. (**a**–**d**) represent the net photosynthetic rate in mid-tillering stage, heading stage, fullheading stage, and yellow ripe stage in 2019, respectively. (**e**–**h**) represent the net photosynthetic rate in mid-tillering stage, heading stage, full-heading stage, and yellow ripe stage in 2020, respectively. Different lowercase letters marked on the histogram mean significant differences among treatments at the 5% level according to Duncan's multiple-range test (0.05) (similarly hereinafter).

#### **4. Discussion**

#### *4.1. Effects of Tillage and Fertilization Treatments on Root Function*

In this study, under different tillage management systems, the root function was ranked in the order of NT > RT > CT. Wang et al. [25] reported that compared with NT, both RT and CT mixed organic matter and fertilizers into deeper soils, which promoted the growth of rice roots in deeper soils. Wang et al. [26] reported that NT reduced the bulk

density of the topsoil (0–5 cm) but increased the bulk density of deep soil (>5 cm), and a higher and inconsistent bulk density of deep soil limited root system growth. However, the results of Wang et al. [27] concern long-term (14 years) tillage effects on soil and rice. Long-term NT or RT could increase soil compactness, restrict air from entering deep soil, accumulate reducing substances in deep soil, and limit the growth of rice roots [28]. Das et al. [11] reported that short-term (4 years) NT increased soil organic carbon, microbial biological carbon, and dehydrogenase activity, and provided superior conditions for root growth. This was consistent with our findings, indicating that short-term NT or RT could improve soil conditions and promote root growth, as well as support the finding that the effects of NT were more pronounced than those of RT. The organic matter covering the topsoil in NT decomposes slowly, and the continuously decomposed organic matter also continuously provides nutrients for the rice [8]. In addition, organic matter (rice stalks and dead weeds) cover the topsoil in NT, which could help in maintaining a constant temperature and moisture and improve the growth of microorganisms and the synthesis of soil enzymes [11]. In addition, RT and CT make the organic matter fully integrated with the soil, which promotes the decomposition of organic matter into inorganic nutrients, and the inorganic nutrients unused by plants are lost to the environment [13].

In our study, under different fertilization treatments, the root function was ranked as SDF > AAF, which is consistent with previous studies [29,30]. N is essential to the growth and development of rice, and it participates in many metabolic processes, such as protein hydrolysis and amino acid metabolism [31]. The lack of N in the basal fertilizer limited the supply of nutrients for root growth. Min et al. [30] reported that SDF reduced chemical N fertilizer input without any reductions in yield, whereas it increased nitrogen use efficiency and reduced NH3 volatilization and runoff N losses. In addition, the deep application of the fertilizer could make the fertilizer slowly dissolve in the soil and help retain it in the rice rhizosphere for a longer time such that the fertilizer can provide continuous nutrients for a longer period of time during rice growth [32]. SDF, as compared with AAF, had a better positive effect on root function under NT management, but such an effect did not occur under RT and CT; the reason may be that RT and CT brought organic matter into the deep soil. AAF causes the nutrients of the fertilizer to be retained in the topsoil under NT management, and side deep fertilization may bring fertilizer into the deep soil, which is advantageous for nutrient uptake by plant roots [27]. Therefore, side deep fertilization under NT management is very important for the growth of rice roots.

#### *4.2. Effects of Tillage and Fertilization on Photosynthesis*

Generally, under different tillage management systems, the photosynthesis in the four periods was ranked as NT > RT > CT. Our results agree with previous research stating that tillage could cause a decrease in photosynthetic capacity by limiting the nutrient uptake and growth of roots. In this study, under different fertilization treatments, the photosynthesis in the four periods was ranked as SDF > AAF, which is consistent with the findings of a previous study [33]. Leaves accumulated the most N in the plant, and as much as three quarters of leaf N was invested into the photosynthetic apparatus, which was the largest N sink in the plant [34]. It was reported that there was strong positive correlation between photosynthesis and leaf N content [35]. N application in the deep soil layer results in a higher NH4 +-N concentration in the soil in the prime stage of rice growth and prolongs the availability of N for 2 months, which improves photosynthesis [36]. In addition, root function is closely related to photosynthesis [37], and higher root activity and root dry weight result in higher LAI, chlorophyll content, and Pn.

#### *4.3. Effects of Tillage and Fertilization Treatments on Rice Yield and Its Compositions*

In this study, the spike density, spikelets per panicle, and yield under different tillage management systems were ranked as NT > RT > CT. Conservation tillage, especially NT, may improve soil properties, enhance root function (Figures 1 and 2) and photosynthesis (Figures 3–5), increase the absorption of nutrients by the roots and the amount of

carbohydrates assimilated by photosynthesis, and promote the formation of yield [38]. Conservation tillage also improves root function and photosynthesis at heading, full heading, and the yellow ripe stage, but only increases the rice "sink capacity" (that is, spikelet density) and does not increase the grain filling rate and 1000-grain weight. The reason may be the mutual restriction between the yield components [38]; the larger storage capacity formed in the early stage requires more carbohydrate filling. Therefore, on the basis of conservation tillage, increasing the nutrient supply in the later stage of rice growth could further increase the yield of rice. In our study, spike density, spikelets per panicle, 1000-grain weight, and yield under different fertilization treatments were ranked as SDF > AAF, which is consistent with the findings of a previous study [31]. The deep placement of N has a catalytic effect on roots, which provides more N in the deep root layer, ensures a longer availability of N, promotes plant N uptake, and increases crop yield [38].

#### **5. Conclusions**

From our findings, it can be concluded that under no tillage conditions, the positive effect of side deep fertilizing on rice growth and yield was higher than under reduced tillage and conventional tillage conditions. On the whole, side deep fertilizing under conservation tillage not only retains the advantages of conservation tillage for environmental protection, but also saves costs and maintains the high yield of rice, which is of value as a reference for the sustainable development of agriculture.

**Author Contributions:** Q.-X.W., B.D. and S.-C.J., conceptualization; Q.-X.W., B.D. and J.-Q.Z., methodology; B.D., S.-C.J. and Q.-X.W., formal analysis; Q.-X.W., B.D. and S.-C.J., investigation/writing—original draft/supervision; B.D., S.-C.J. and H.-W.Z., visualization; H.-W.Z., Q.-X.W. and S.-C.J., data curation; Q.-X.W., B.D. and S.-C.J., writing—review and editing; Q.-X.W., J.-Q.Z., B.D. and S.-C.J., project administration. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation (U21A2039) and National Key Research and Development Plan (2016YFD0300907).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Reasonable Nitrogen Regime in the Main Crop Increased Grain Yields in Both Main and Ratoon Rice**

**Qiang Zhang 1,2, Xiangchen Liu 2, Guilong Yu 2, Bin Duan 2, Hao Wang 2, Haiying Zhao 2, Daqing Feng 2, Mengxuan Gu <sup>2</sup> and Lijun Liu 1,\***


**Abstract:** Planting ratoon rice can realize one sowing and two harvests, which is of great significance for improving grain yield. However, the effects of nitrogen (N) regime in the main crop on the grain yield of ratoon rice and the associated physiological mechanisms are not clearly understood. The indica hybrid rice Liangyou 6326 was used, and three N fertilizer levels (100 kg ha−<sup>1</sup> (low N, LN), 250 kg ha−<sup>1</sup> (medium N, MN), and 400 kg ha−<sup>1</sup> (high N, HN)) and four different ratios of basal tillering fertilizer to panicle fertilizer (7:3, 6:4, 5:5, and 4:6) applied to the main crop were designed to investigate their effects on the grain yields of the main and ratoon crops. The results showed that excessive N application rate and panicle N application rate in the main crop was not conducive to the improvement of yield and agronomic nitrogen use efficiency (ANUE) in both seasons. The increased yield in the ratoon crop was attributed to the increase in the regeneration rate. Appropriate increasing of the panicle N application rate was beneficial for increasing the ROA and NSC concentration in the main crop, resulting in an increase in the number, length, and fresh weight of regenerated buds, which caused an improvement in the regeneration rate. However, when excessive panicle N was applied in the main crop, the excessive germination of regenerated buds decreased the length and fresh weight of the regenerated bud and resulted in a decrease in the regeneration rate. These results suggest that in the production of ratoon rice, reasonable N regime in the main crop could increase the yield and ANUE in both seasons.

**Keywords:** nitrogen rate; nitrogen ratio; yield; regenerative ability

#### **1. Introduction**

The global population continues to grow and is expected to reach 8.5 billion by 2030 and 10.9 billion by 2100, according to UN's 2019 World Population Prospects. Global food production must be further increased to meet the needs of the growing population [1,2]. Rice (*Oryza sativa* L.) is an important food crop species worldwide, supporting more than half of the global population [3,4]. Given that global urbanization is increasing and water resources are becoming increasingly scarce, there are two main ways to increase global rice production: One is to increase the grain yield per unit area, and the other is to increase the multiple cropping index [5]. Because achieving major breakthroughs in rice yield per unit area is difficult, developing ratoon rice comprising one plant with two harvest periods was an effective way to increase rice yield [6,7]. Ratoon rice is rice planted in such a way that dormant buds in the stubble survive after the main crop is harvested, and subsequently, regenerate into panicles [8]. The use of ratoon rice has been adopted in many countries [9]. For example, in China, the planting area of ratoon rice reached nearly one million ha in 2019, and according to reports, there are more than 3.3 million ha of fields that are suitable

**Citation:** Zhang, Q.; Liu, X.; Yu, G.; Duan, B.; Wang, H.; Zhao, H.; Feng, D.; Gu, M.; Liu, L. Reasonable Nitrogen Regime in the Main Crop Increased Grain Yields in Both Main and Ratoon Rice. *Agriculture* **2022**, *12*, 527. https://doi.org/10.3390/ agriculture12040527

Academic Editors: Chengfang Li and Lijin Guo

Received: 21 February 2022 Accepted: 6 April 2022 Published: 8 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

for planting ratoon rice in southern China [10]. Therefore, increasing research on ratoon rice and further improving its yield are of great significance to ensure global food security.

The yield of ratoon rice is affected by many factors, such as the height of the stubble [11], water management [12], variety [13], and nitrogen (N) fertilizer management [14], among which N fertilizer management has a critical influence on the yield of ratoon rice. N fertilizer management of ratoon rice mainly involves the application of N fertilizer to the main crop and the ratoon crop (bud fertilizer and seedling fertilizer). Many scholars have researched the effects of N fertilizer management in ratoon season on the growth and development of ratoon crops, and most agree that the application of bud and seedling fertilizers can promote the growth of regenerated buds and improve the regenerative capacity and yield of ratoon crops [15,16]. However, there are relatively few studies about the effects of N fertilizer management in the main crop on the growth and development of ratoon crops, and the results have been inconsistent or even contradictory. For example, Huang et al. (2022) reported that increasing N application rate in the late growth stage of the main crop could improve the effective tillering percentage, leaf area index, canopy light interception rates, and transport rate of stem and sheath in the main crop, leading to an increase in yields of both seasons [17]. Properly postponed N application in the main crop was beneficial to increasing the root activity and promoting the growth of rice, resulting in increasing the yields of both seasons [18]. Liu et al. (2014) found that increasing the amount of panicle N fertilizer in the main crop was beneficial for increasing the yields of the main and ratoon crops [19]. However, Chen et al. (2010) suggested that a moderate amount of delayed N fertilizer could increase the yield of the main crop but had little effect on the yield of the ratoon crop [20]. Wang et al. (2019) indicated that the N application rate in the main crop had little effect on the yield of the ratoon crop [14].

However, there is a lack of in-depth research on the effects of N regime in the main crop on the growth and development of regenerated buds and the regeneration rate. In addition, the N regime in the main crop on the yield of the ratoon crop under different N application rate have not been reported so far. Since the N application rates vary greatly in different areas in the field production of ratoon rice, it is necessary to study the effects of N regime in the main crop on the yield of ratoon rice under different N application rates. In this study, N was applied at three different levels in conjunction with four panicle N ratios to the main crops to investigate the effects of the N regime in the main crop on the yield of the main and ratoon crops and to determine rational N management practices for the high-yield cultivation of ratoon rice.

#### **2. Materials and Methods**

#### *2.1. Experimental Site and Materials*

The field experiment was conducted in a farmer's field at Xinyang (32◦07 N, 114◦05 E), Henan Province, North China, in 2018 and repeated in 2019. The soil in the experimental field was a clay loam with contents of organic carbon of 11.4 g kg<sup>−</sup>1, total N of 54.3 mg kg−1, Olsen-phosphorus (P) of 9.7 mg kg−1, and available potassium (K) of 79.8 mg kg−1. Two years' weather information during the rice-growing periods (from March to November) is shown in Figure S1.

The tested rice variety was Liangyou 6326, an indica two-line hybrid rice variety that is a result of the combination of Xuan 69S and Zhongxian WH26. This variety was released by the Xuancheng Agricultural Science Research Institute and passed the National Crop Variety Approval in 2007 [National Approved Rice 2007013].

#### *2.2. Experimental Design*

The experiments were arranged in accordance with a split-plot design, with four replicates (plot size: 4 × 5 m). The main plots were divided according to the rate of total N applied to the main crop, and the three N application rates were low N (LN), medium N (MN), and high N (HN), corresponding to 100 kg ha−1, 250 kg ha−1, and 400 kg ha<sup>−</sup>1, respectively. The N rates of MN and HN were based on the local standard

for ratoon rice cultivation "Technique rule for planting ratoon rice in the south of Henan Province" (DB41/T 1564-2018) and "Theory and technology of rice precise and quantitative cultivation" [21]. Subplots corresponded to different ratios of basal tillering fertilizer to panicle fertilizer application to the main crop. The ratios were 7:3 (PN30), 6:4 (PN40), 5:5 (PN50), and 4:6 (PN60). The details of the N treatments are summarized in Table 1. In addition, 0 kg ha−<sup>1</sup> N rate was set in both the main and ratoon crops to investigate the yield of N free. The seeds were sown in a greenhouse on March 4 in both 2018 and 2019. The seedlings were subsequently transplanted to the field on April 8 in both years, with a hill spacing of 0.33 × 0.15 m and 2 seedlings per hill. Before transplanting, all the plots were plowed and puddled. To prevent the flow of fertilizer between neighboring plots, the plots were separated by a 40 cm wide ridge created by a plastic film inserted into the soil to a depth of 30 cm. The technical drawing is shown in Figure S2.

**Table 1.** Nitrogen application rates (kg N ha<sup>−</sup>1) in the main and ratoon crops.


Note: LN, low N rate (the total amount of N applied was 100 kg ha−1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha−1). PN30, PN40, PN50 and PN60, correspond to N application ratio of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5 and 4:6, respectively. The numbers in () indicate the percentage of N application to the total N application of the main crop.

The N fertilizer used was urea, with an N content of 46%. The ratio of basal fertilizer to tiller fertilizer was 7:3. Basal fertilizer was applied one day before transplantation, and tiller fertilizer was applied 5 days after transplantation. Panicle fertilizer was applied twice at panicle initiation and at the beginning of spikelet differentiation, accounting for 60% and 40%, respectively. The same amounts of P (as calcium superphosphate, 12% P2O5) and K (as potassium chloride, 60% K2O) were applied in both years. All P and K were applied as basal fertilizer, and the amounts of P and K were 937.5 kg ha−<sup>1</sup> and 187.5 kg ha<sup>−</sup>1, respectively. At 15 days after heading of the main crop, 50 kg N ha−<sup>1</sup> was applied to each plot as bud fertilizer. After the main crop was harvested, 150 kg ha−<sup>1</sup> N was applied as a seedling fertilizer. Although the bud fertilizer was applied during the growth period of the main crop, it had a greater impact on the yield of the ratoon crop, so the bud fertilizer is usually included in the N management of ratoon season [22]. After transplanting, the field was kept flooded for 35 days, and then the water was drained. The field was reflooded at the jointing stage and drained again 7 days before the main crop was harvested. A water layer 1 to 3 cm above the soil surface was maintained during the entire ratoon crop growing season. Weeds were removed by hand. Diseases and insects were controlled by chemicals to avoid yield loss.

The main crop was harvested by hand on 12 August in both years with a stubble height of 0.35 m.

#### *2.3. Sampling and Measurements*

#### 2.3.1. Root Oxidation Activity (ROA)

At full heading, at the 15th day after heading, and at maturity of the main crop, after the mean stem number in each plot was recorded, representative plants from 10 hills were selected to measure the ROA in each plot. A block of soil (20 × 20 × 20 cm) around each individual hill was removed, and after rinsing with running water (Figure S3), the root subsamples were taken to measure the ROA via oxidation of alpha-naphthylamine (α-NA) according to the methods of Ramasamy et al. [23].

#### 2.3.2. Nonstructural Carbohydrate (NSC) Accumulation in the Stem and Leaf

At the same time as the procedures described above, after the mean stem number in each plot was recorded, representative plants from 10 hills (0.495 m2) were selected in each plot and removed. The plants were separated into stems (culms + sheaths), leaves, and panicles. All the samples were first dried at 105 ◦C for 30 min and then dried to constant weight at 75 ◦C in an oven. The dried samples were subsequently crushed and passed through a 0.15 mm sieve to ultimately determine the NSC concentration [24]. Briefly, 100 mg dry sample was placed into 15 mL distilled water and boiled for 20 min. After filtration and constant volume, 1 mL of the content was taken, and 5 mL of anthrone reagent was added. Then spectrophotometer (UV-1800, Shimadzu, Tokyo, Japan) was used to measure the soluble sugar and starch content. According to the above method, at harvest of the main crop, 10 representative stubbles were taken from each plot to determine the NSC concentration.

#### 2.3.3. Regenerated Buds

On the 15th day after heading, the 25th day after heading, maturity, and the 7th day after the main crop was harvested, 5 hills were selected from each plot according to the average level of seedlings in the whole field. The fresh weight and length of living regenerated buds at each internode were measured, and the number of living and dead regenerated buds per stem was investigated (buds larger than 1 cm were included) as described by Xu et al. [25] and Zhang et al. [26] (Figure S4).

#### 2.3.4. Yield and Its Components

Plants from 1 m<sup>2</sup> in each plot were harvested to measure the spikelet number per panicle and the panicles number per square meter. The filled grains were separated by submerging all spikelets in tap water after threshing. In addition, the grain yields of the main and ratoon crops were measured by hand-harvesting rice plants from 5 m2 in each plot (the moisture was adjusted to 14%).

#### 2.3.5. Statistical Analysis

The regeneration rate was calculated as the number of panicles per square meter in the ratoon crop/the number of panicles per square meter in the main crop.

Agronomic nitrogen use efficiency (ANUE) = (Grain yield − Grain yield of 0 N)/N application rate.

The minimum sample size of each indicator in this experiment is 4. Analysis of variance (ANOVA) was performed using SPSS (version 17.0; SPSS, Inc., Chicago, IL, USA) to detect the effects of year and variety. The means were subjected to least significant difference (LSD) tests at *p* < 0.05 (LSD0.05) and *p* < 0.01 (LSD0.01). The number, length, and fresh weight of regenerated buds, regeneration rate, ROA and NSC content in stem are continuous variable variables. Therefore, this experiment uses Pearson's correlation and regression analysis to analyze their relationship.

#### **3. Results**

#### *3.1. Differences in Experimental Factors*

The yields of the main and ratoon crops and their components did not significantly differ between years but did significantly differ among N application rates and ratios (Table 2). The length, fresh weight, and number of regenerated buds per stem (unless otherwise specified, the following regenerated buds refer to living buds) at maturity of the main crop, ROA and NSC concentration in the stems and leaves on the 15th day after heading, NSC concentration in the stubble, and the regeneration rate of ratoon crop were not significantly different between years but were significantly different among N rates and ratios (Table 3). Since year was not a significant factor in any experiment, this paper mainly used the mean values of the two years for analysis purposes.

**Table 2.** Analysis-of-variance of F-values of the grain yield and yield components of the main and ratoon crops between/among years, N regimes.


Note: Y, years. N, nitrogen rate. R, ratio of nitrogen application. \*\* represents a significant difference at the 1% level according to LSD tests, NS represents no significant difference at the 5% level according to LSD tests.

**Table 3.** Analysis-of-variance of F-values of key indices such as the growth and development of the main crop after full heading, growth of regenerative bud, and regenerative ability between/among years, N regime.


Note: Y, years. N, rate nitrogen application. R, ratio of nitrogen application. NSC, nonstructural carbohydrate. ROA, root oxidation activity. DAH, days after heading of the main crop. MS, maturity stage of the main crop. \*\* represents a significant difference at the 1% level according to LSD test, \* represents a significant difference at the 5% level according to LSD test, NS represents no significant difference at the 5% level according to LSD test.

#### *3.2. Yield and Its Components in the Two Seasons*

The yield of the main crop decreased in the order MN > HN > LN under different N levels. That is, a high N rate was not conducive to increasing the yield of the main crop. A significant decrease in the filled grain rate was the main reason for the decrease in yield in the main crop under HN (Figure 1A–C, and Table S1). Under the same N level, with increasing panicle N rate, the yield of the main crop decreased under LN, first increased and then decreased under MN (peaking at PN40) and increased under HN.

There was no significant difference in yield under MN and HN in the ratoon crops, but their yields were higher than that under LN. With the increasing proportion of panicle N fertilizer under the LN level, the yield increased. However, under MN and HN, the yield first increased and then decreased, and the highest yields were obtained at PN50 and PN40 (Figure 1D–F). According to the analysis of yield components of the ratoon crops, there were no significant differences in the number of spikes per panicle, filled grain rate, or 1000-grain weight among the different treatments, but there were significant differences in panicle numbers, which was the main reason for the changes in the yield of the ratoon crops (Table 4). Correlation analysis showed that there was a linear correlation between yield and panicle number of the ratoon crops and the coefficient of determination was high (Figure S5), indicating that an increased panicle number was the key to increasing the yield of the ratoon crops.

**Figure 1.** Effects of N regime in the main crop on the yield of the main crop (**A**–**C**), ratoon crop (**D**–**F**), and both seasons (**G**–**I**). Error bars are ±SE. Note: LN, low N rate (the total amount of N applied was 100 kg ha<sup>−</sup>1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha<sup>−</sup>1). PN30, PN40, PN50, and PN60 correspond to N application ratios of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5, and 4:6, respectively. The different lowercase letters at the same N application rate indicate significant differences at the 5% probability level according to LSD tests.

In terms of the total yield of the main crop and ratoon crop, the highest yields under the LN level were obtained at PN50 and PN60, both of which were 12.8 t ha<sup>−</sup>1. The highest yield under the MN level was obtained at PN40 and PN50 (15.6 t ha−1), and that under the HN level was obtained at PN40 (15.1 t ha−1) (Figure 1G–I). These results indicate that N application to the main crop at a reasonable rate and ratio had important effects on the yield of the main crop and ratoon crop in both seasons.


**Table 4.** Effects of N regime in the main crop on the yield components of the ratoon crop.

Note: LN, low N rate (the total amount of N applied was 100 kg ha<sup>−</sup>1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha−1). PN30, PN40, PN50, and PN60, correspond to N application ratio of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5, and 4:6, respectively. Data in a column followed by different lower-case letters indicate significant differences at the 5% probability level according to the LSD test. Means followed by different upper-case letters indicate significant differences between the three nitrogen fertilizer levels at the 5% probability level according to the LSD test.

#### *3.3. Regeneration Rate and Its Relationship with Panicle Number and Yield in the Ratoon Crop*

The regeneration rate reflects the regeneration ability of ratoon rice, and this factor and the number of mother stems together determine the panicle number of the ratoon crop. The regeneration rate showed a trend of first increasing and then decreasing with increasing N fertilizer levels (Figure 2). As the panicle N application rate increased, the regeneration rate increased under the LN level, while under MN and HN, it first increased and then decreased. The change trend was similar to that of panicle number in the ratoon crop (Table 4), and the correlation analysis showed that compared with the number of mother stems, the regeneration rate had a greater correlation with the panicle number and yield of the ratoon crop (Table 5), indicating that the change in regeneration rate was the main reason for the changes in the panicle number and yield of the ratoon crop.

**Table 5.** Effects of N regime in the main crop on the yield components of the ratoon crop.


Note: LN, low N rate (the total amount of N applied was 100 kg ha<sup>−</sup>1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha−1). \*\* represents the significant difference at the 1% level according to LSD test, \* represents a significant difference at the 5% level according to LSD test.

#### *3.4. Regenerated Buds and Its Relationship with Regeneration Rate*

#### 3.4.1. Number, Length, and Fresh Weight of Regenerated Buds

The total number of regenerated buds per unit area (including living and dead regenerated buds) increased from the 15th day after heading to the 7th day after harvesting of the main crop, but due to the increase in the number of dead regenerated buds, the number of living regenerated buds per unit area began to decrease after the main crop was harvested (Figure S6). During the growth of regenerative buds, due to the different growth rates of regenerative buds in different internodes, the regenerative buds that grow slowly

are likely to die [9,27]. Compared with the dead bud, the living bud can better reflect the regeneration ability, so this paper mainly focuses on the growth of the living buds. Before the main crop was harvested, the number of regenerated buds per stem increased with increasing N application rate and proportion of panicle N fertilizer (Figure 3A–C). On the 7th day after harvesting, there was no significant difference in the number of regenerated buds per stem under MN and HN levels, and both of them were higher than that under LN level. With the increasing proportion of panicle N fertilizer, the number of regenerated buds per stem on the 7th day after harvest increased under the LN level but first increased and then decreased under the MN and HN levels, with the greatest values occurring at PN50 and PN40, respectively.

**Figure 2.** Effects of N rate and the ratio of the main crop on the regeneration rate. Error bars are ± SE. Note: LN, low N rate (the total amount of N applied was 100 kg ha−1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha<sup>−</sup>1). PN30, PN40, PN50, and PN60 correspond to N application ratios of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5, and 4:6, respectively. The different lowercase letters at the same growth stage indicate significant differences at the 5% probability level according to the LSD test.

Under different N levels, the length and fresh weight of regenerated buds followed the order MN > HN > LN (Figure 3D–I). With the increasing proportion of panicle N fertilizer, the length and fresh weight of regenerated buds increased under the LN level and first increased and then decreased under the MN and HN levels, with the greatest values occurring at PN50 and PN40, respectively. At maturity of the main crop, the number of regenerated buds per stem was nonlinearly positively correlated with the length and fresh weight of regenerated buds (Figure S7). The development level of regenerated buds gradually improved with an increasing number of regenerated buds per stem and decreased with excess germination of the regenerated buds.

3.4.2. Correlations between the Regeneration Rate and the Length, Fresh Weight, and Number of Regenerated Buds

The regeneration rate was linearly correlated with the length and fresh weight of regenerated buds (Figure 4B,C), but was nonlinearly correlated with the number of regenerated buds (Figure 4A). The regeneration rate first increases and then decreases with an increase in the number of regenerated buds per stem.

**Figure 3.** Effects of N rate and the ratio of the main crop on the number (**A**–**C**), length (**D**–**F**), and fresh weight (**G**–**I**) of regenerated buds. Error bars are ± SE. Note: LN, low N rate (the total amount of N applied was 100 kg ha<sup>−</sup>1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha−1). PN30, PN40, PN50, and PN60 correspond to N application ratios of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5, and 4:6, respectively. DAH, days after heading of the main crop. MS, maturity stage of the main crop. DAHM, days after harvest of the main crop. The different lowercase letters at the same growth stage indicate significant differences at the 5% probability level according to the LSD test.

**Figure 4.** Relationships between the number (**A**), length (**B**), and fresh weight (**C**) of regenerated buds at maturity of the main crop and the regeneration rate.

#### *3.5. Effects of N Regime on the ROA and NSC Concentration in the Stem and Leaf of the Main Crop*

Under different N levels, the ROA at different growth stages decreased in the order MN > HN > LN (Figure 5A–C). Under the same N level, ROA at different growth stages increased with the increasing proportion of panicle N fertilizer, and this trend was most obvious at the maturity stage. At the maturity stage in the main crop, compared with that at PN30, the ROA at PN60 increased by 51.4%, 44.4%, and 43.1% under the LN, MN, and HN levels, respectively.

After full heading of the main crop, the NSC concentration in the stems and leaves decreased. The NSC concentration in stems and leaves and in stubble decreased in the order HN > MN > LN (Figure 5D–I). However, under the same N level, the concentration improved with an increasing proportion of panicle N fertilizer. At the maturity stage, compared with those at PN30, the stems and leaves NSC concentration at PN60 increased by 40.1%, 32.2%, and 33.8% under the LN, MN, and HN levels, respectively, and the stubble NSC concentrations increased by 55.1%, 35.2%, and 41.3%.

#### *3.6. Relationships between the ROA and NSC Concentration in Stem and Leaf of the Main Crop and Regeneration Rate of the Ratoon Crop*

The NSC concentration in the stems and leaves and the ROA in rice plants after heading of the main crop were linearly correlated with the maximum number of regenerated buds per stem but nonlinearly correlated with the length and fresh weight of regenerated buds and the regeneration rate (Figures 6 and 7). These results indicated that the number, length, fresh weight, and regeneration rate of regenerated buds increased with increasing NSC concentration in stems and leaves and increasing ROA. However, when the NSC concentration in stems and leaves and the ROA were too high, though the regenerated buds continued to increase, the development level of regenerated buds and regeneration rate decreased. Compared with ROA, the NSC concentration in stems and leaves had a higher coefficient of determination with the number, length, fresh weight, and regeneration rate of regenerated buds. Compared with the values at the full heading stage, the NSC concentration in stems and leaves and ROA on the 15th day after heading and at maturity were more strongly correlated with the number, length, fresh weight, and regeneration rate of regenerated buds.

**Figure 5.** Effects of N rate and the ratio of the main crop on root oxidation activity (**A**–**C**), NSC concentration in the stem and leaf (**D**–**F**) after heading of the main crop and in the stubble (**G**–**I**). Error bars are <sup>±</sup> SE. Note: LN, low N rate (the total amount of N applied was 100 kg ha−1); MN, medium N rate (the total amount of N applied was 250 kg ha<sup>−</sup>1); HN, high N rate (the total amount of N applied was 400 kg ha−1). PN30, PN40, PN50, and PN60 correspond to N application ratios of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5, and 4:6, respectively. HS, full heading stage of the main crop. DAH, days after heading of the main crop. MS, maturity stage of the main crop. The different lowercase letters at the same growth stage indicate significant differences at the 5% probability level according to the LSD test.

**Figure 6.** Relationships between root oxidation activity in the main crop and number (**D**,**H**,**L**), length (**C**,**G**,**K**), and fresh weight of regenerated buds (**B**,**F**,**J**) at maturity of main crop and regeneration rate (**A**,**E**,**I**). Note: HS, full heading stage of the main crop. DAH, days after heading of the main crop, MS, maturity stage of the main crop. ROA, root oxidation activity.

**Figure 7.** Relationships between NSC concentration of stem and leaf in the main crop and number (**D**,**H**,**L**), length (**C**,**G**,**K**), and fresh weight of regenerated buds (**B**,**F**,**J**) at maturity of main crop and regeneration rate (**A**,**E**,**I**). Note: HS, full heading stage of the main crop. DAH, days after heading of the main crop, MS, maturity stage of the main crop. ROA, root oxidation activity.

The stubble NSC concentration was nonlinearly correlated with the regeneration rate. With increasing stubble NSC concentration, the regeneration rate first increased and then decreased (Figure 8).

**Figure 8.** Relationships between stubble NSC concentration and regeneration rate.

#### *3.7. Effects of N Regime in the Main Crop on the Nitrogen Use Efficiency in Main and Ratoon Corp*

The ANUE in the main crop decreased with the N application rate in the main crop increased. As the panicle N application rate increased in the main crop, the ANUE in the main crop decreased under LN, increased under HN, first increased and then decreased under MN. In the ratoon crop, there was no difference in ANUE between HN and MN, but they were all higher than LN. And the ANUE increased under LN, first increased and then decreased under MN and HN with the panicle N application rate increased in the main crop. In terms of the ANUE in both seasons, the ANUE showed the order MN > LN > HN. And as the panicle N application rate increased in the main crop, the ANUE first increased and then decreased under LN, MN, and HN. The results showed that excessive N application rate and panicle N application rate in the main crop were not conducive to the increase of ANUE in both seasons.

#### **4. Discussion**

#### *4.1. Reasonable N Regime in the Main Crop Improved the Growth and Development of Regenerated Buds*

There are few reports on the effect of N regime in the main crop on the growth and development of regenerated bud. The growth and development of regenerated buds are usually reflected by their number, length, and fresh weight. The number, length, and fresh weight were closely related to the NSC content in stems and ROA in plants after full heading in the main crop [28–30]. Before the main crop was harvested, the growth of regenerated buds depended on the nutrients stored in the stems and leaves of the main crop [31]. Therefore, under a high stubble cutting height, the NSC concentration in stems and leaves after heading in the main crop was positively correlated with the number and length of regenerated buds [30]. Zhang et al. (2005) reported that increasing the ROA in plants after full heading in the main crop could promote the growth of regenerated buds [27]. In this paper, the NSC concentration in the stems and leaves and ROA in the main crop were positively correlated with the number, length, and fresh weight of regenerated buds (Figures 6 and 7).

Previous studies reported that appropriately increasing the panicle N application rate reduced the number of ineffective tillers and increased the percentage of productive tillers, leading to an increase in the dry matter weight per stem [32–34]. The canopy transmittance and root activity were improved by applying more N in the late stage of rice [17,35]. Our

result is consistent with those of our predecessors. Increasing the panicle N application rate increased the NSC concentration in stems and leaves and ROA after heading of the main crop (Figure 5). Therefore, increasing panicle N application rate and N application rate could improve the number, length, and fresh weight of regenerated bud by improving NSC concentration in stems and leaves and ROA (Figures 6 and 7). However, our study also showed that the length and fresh weight of regenerated buds were nonlinearly correlated with the ROA and NSC in the main crop (Figures 6 and 7). With the increase of panicle N application rate and N application rate, the length and fresh weight first increased and then decreased. The decrease in length and fresh weight of regenerated buds was related to the number of regenerated buds. The length and fresh weight of regenerated buds first increased and then decreased with the increasing number of regenerated buds (Figure S7). This may have occurred because nutrient competition among regenerated buds intensified after excessive germination, leading to low development in regenerated buds.

#### *4.2. Reasonable N Regime in the Main Crop Improved the Regeneration Rate*

The regeneration rate is the ratio of the panicle number in the ratoon crop to that in the main crop and can directly reflect the regeneration ability of individual plants. It was closely related to the growth and development of regenerated buds. Xu et al. (2021) found that the fresh weight and length of regenerated buds were significantly and positively correlated with the regeneration rate [31]. Increasing the length and fresh weight of regenerated buds could reduce the mortality of regenerated buds and increase the regeneration rate [36]. Our result also showed that the regeneration rate was linearly correlated with the length and fresh weight of regenerated buds (Figure 4B,C). Since the ROA and NSC in the stem and leaf of the main crop were nonlinearly correlated with the length and fresh weight of regenerated buds, the regeneration rate had a similar trend with ROA and NSC in the stem and leaf of the main crop (Figures 6 and 7). However, there are few studies on the relationship between the number of regenerated buds and regeneration rate. This research showed that the regeneration rate was nonlinearly correlated with the number of regenerated buds (Figure 4A). This may be due to the nonlinear correlation between the number of regenerated buds and the length and fresh weight of regenerated buds (Figure S7). The length and fresh weight of regenerated buds decreased after regenerated bud excessive germination, and the regenerated buds with slow growth tended to die at the later stage of the ratoon crop. Therefore, although excessive N application rate and panicle N application rate were beneficial to increase the number of regenerated buds, it would reduce the length and fresh weight of regenerated buds, leading to a decrease in regeneration rate. We can conclude that a reasonable N regime in the main crop could simultaneously increase the number of regenerated buds and the development of regenerated buds, improving the regeneration rate and the yield of the ratoon crop.

#### *4.3. Effects of N Regime in the Main Crop on the Yield of the Main and Ratoon Crops*

The N regime strongly affected rice yields. Zhu et al. (2017) and Liang et al. (2021) revealed that rice yield first increased and then decreased with increasing N application rate [37,38], and we reached a similar conclusion (Figure 1A–C). Ye et al. (2019) indicated that N application at a later stage of rice reduced the panicle number and was not conducive to increasing grain yields [39]. However, Li et al. (2018) reported that increasing the proportion of panicle N fertilizer improved N use efficiency and increased grain yield [40]. Our results are different from those of previous studies. With increased panicle N application, we found that the yield of the main crop decreased under the LN level, increased under the HN level, and first increased and then decreased under the MN level (Figure 1A–C). These results indicated that the yield was not only related to the N application rate but also closely related to the application time and N ratio.

There have been few studies on the effects of the N regime in the main crop on the yield of ratoon crops, and the results have been inconsistent. Zhang et al. (2019) revealed that N application rate and N application method in the main crop had a great effect on the yield of the ratoon crop [12]. Some scholars reported that increasing N application rate in the late growth stage of the main crop was beneficial to promoting the growth and development of the aboveground and underground organs of rice, which could improve the yield of the main and ratoon crops [17–19]. Nakano et al. (2009) indicated that N application rate of 22.5 g N m−<sup>2</sup> in the main crop had a higher dry matter yield for the ratoon crop than with 15.0 g N m<sup>−</sup>2, and more N was applied early in the main crop could obtain higher dry matter yield [41]. However, Chen et al. (2010) and Wang et al. (2019) showed that an increase in total N application or panicle N fertilizer in the main crop had little effect on the yield of the ratoon crops [14,20]. Our research showed that, the effect of increasing panicle N application on the yield of the ratoon crops was different under different N application rates. With increased panicle N application, the yield in the ratoon crop increased under LN level, while it first increased and then decreased under MN and HN levels, with the greatest values occurring at PN50 and PN40, respectively (Figure 1D–F). High N application rate in the main crop was not conducive to increasing yield in the main and ratoon crops (Figure 1). Further analysis showed that the N regime in the main crop mainly affected the yield of the ratoon crop by changing the regeneration rate in the ratoon crop (Table 5). In this experiment, the yield of the ratoon crop was mainly affected by the panicle number in the ratoon crop (Figure S5). The panicle number in the ratoon crop is usually determined by the number of mother stem and regeneration rate. Although the number of mother stems and the regeneration rate were both changed by different N regime, the correlation analysis revealed that compared with the number of mother stems, the regeneration rate had a greater correlation with panicle number in the ratoon crop (Table 5). Especially under the LN and MN levels, when the number of mother stems was reduced by increased panicle N application, the improvement in regeneration rate still led to a higher yield in PN60 and PN50 (Figure 1 and Table 4). Therefore, the appropriate way to obtain a high yield in the ratoon crop should be to continuously improve the regeneration rate through breeding methods and cultivation techniques.

We can conclude that an appropriate proportion of basal tillering fertilizer to panicle fertilizer under different N rates could improve the regeneration rate and yield of the ratoon crop and simultaneously increase the yield of the main crop, achieving the purpose of high yield in both seasons.

#### *4.4. Effects of the N Regime in the Main Crop on the N Use Efficiency of the Main and Ratoon Crops*

N use efficiency (NUE) is also an important factor in determining whether the application of N fertilization is reasonable. N uptake rate and NUE are closely related to the N application rate. Xu et al. (2015) reported that appropriately increasing N application rate was beneficial to improving N uptake and NUE, leading to an increase in rice yield [42]. However, under high N application rate, the N recovery efficiency decreased, the ANUE first increased and then decreased, and rice yield did not increase. Zhang et al. (2019). revealed that with the increase of N application rate, the N uptake rate and ANUE first increased and then decreased [43]. Our result showed that with the N application rate increased, ANUE decreased in the main crop, there was no increasing trend in HN in the ratoon crop, and it first increased then decreased in both seasons (Table 6). Excessive N application reduces the number and physiological activity of roots [44] and reduces the absorption of inorganic N in paddy soil [43], resulting in less N absorbed by plants and a lower N uptake rate and NUE.

The N application ratio also has a great influence on the ANUE. Because the seedlings had less root system and less N was absorbed in the early stage of rice growth, appropriately reducing basal N and appropriately increasing panicle and tiller N could improve the NUE [45]. Our result is consistent with that (Table 6). In addition, Xu et al. (2011) reported that N uptake and N use efficiency were not only related to N regime, but also to soil characteristics [46]. Under high N supply of soil, reducing the N application rate in basal was beneficial to improve NUE and rice yield. However, under low N supply of soil, reducing the N application rate in basal would result in decrease in NUE and rice yield [47]. Liu et al. (2005) indicated that compared with low N supply of soil, the effect of N rate on rice yield was reduced, and NUE was also decreased under high N supply of soil [48]. In China, the yield of rice is usually 5–6 t ha−<sup>1</sup> in N-free areas of paddy soils [49]. In this experiment, the yield of N free area was 5.28 t ha−<sup>1</sup> in the main crop and 8.43 t ha−<sup>1</sup> in the total yield of two seasons, belonging to the area with medium and upper fertility. Therefore, during the ratoon rice production of the area, appropriately reducing the application rate of base tillering fertilizer and increasing the application rate of panicle fertilizer is beneficial to improve the NUE and the yield of both seasons.


**Table 6.** Effects of N regime in the main crop on the nitrogen use efficiency in the main and ratoon crop.

Note: ANUE, agronomic nitrogen use efficiency. LN, low N rate (the total amount of N applied was 100 kg ha<sup>−</sup>1); MN, medium N rate (the total amount of N applied was 250 kg ha−1); HN, high N rate (the total amount of N applied was 400 kg ha−1). PN30, PN40, PN50, and PN60, correspond to N application ratio of basal tillering fertilizer to panicle fertilizer of 7:3, 6:4, 5:5, and 4:6, respectively. Data in a column followed by different lower-case letters indicate significant differences at the 5% probability level according to the LSD test. Means followed by different upper-case letters indicate significant differences between the three nitrogen fertilizer levels at the 5% probability level according to the LSD test. ANUE in the ratoon cop = (Grain yield of ratoon crop − Grain yield of 0 N in ratoon crop)/N rate in ratoon crop. ANUE in both seasons = (Grain yield of both seasons − Grain yield of 0 N in both seasons)/N rate in both seasons. The grain yield of 0 N in the main and ratoon crops was 5.28 t ha−<sup>1</sup> and 3.15 t ha−1, respectively.

#### **5. Conclusions**

The rate and ratio of N application in the main crop had an important influence on the rice yield and ANUE of the main and ratoon crops. Excessive total and panicle N application rate in the main crop were not conducive to improving the grain yield and ANUE in both seasons. Under different N application rate, appropriate increasing the panicle N application rate was beneficial for increasing the ROA and NSC concentration in stem and leaf in the main crop, resulting in improving the growth and development of regenerated buds, which caused an increase in the regeneration rate and yield of the ratoon crop. A total N rate of 250 kg ha−<sup>1</sup> and a ratio of basal tillering fertilizer to panicle fertilizer of 5:5 in the main crop could increase grain yields and ANUE in both seasons.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/agriculture12040527/s1, Figure S1: Mean temperature (A), sunshine hours (B) and rainfall (C) monthly during rice growth in 2018 and 2019. Figure S2: Technical drawings for the experimental program. Figure S3: The process of obtaining root samples from the field. Figure S4: Field investigation on the growth and development of regenerated buds. Figure S5: Relationships between the yield and panicle number in the ratoon crop. Figure S6: Effects of N regime in the main crop on the number of living (A–C) and dead (D–F) regenerated buds per square meter. Figure S7. Relationships between number, fresh weight, and length of regenerated buds at maturity of the main crop. Table S1: Effects of N regime in the main crop on the yield components of the main crop.

**Author Contributions:** Conceptualization, L.L. and X.L.; Writing-original draft, Q.Z.; Writing—review and editing, Q.Z. and L.L.; Project administration, X.L.; Data curation, G.Y. and H.W.; Investigation, D.F. and H.Z.; Formal analysis, B.D. and M.G.; Supervision, L.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by Modern Agricultural Industrial Technology System of Henan Province (Z2012-04-G01), the National Natural Science Foundation of China (32071947,31871557), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

