*Article* **Soil Bacteria Mediate Soil Organic Carbon Sequestration under Different Tillage and Straw Management in Rice-Wheat Cropping Systems**

**Lijin Guo 1,2,†, Jie Shi 3,†, Wei Lin 4, Jincheng Liang 2, Zhenhua Lu 5, Xuexiao Tang 2, Yue Liu 2, Purui Wu <sup>2</sup> and Chengfang Li 6,\***


**Abstract:** Soil organic carbon (SOC) largely influences soil quality and sustainability. The effects of no-till (NT) and crop straw return practices (SR) on soil organic carbon sequestration have been well documented. However, the mechanism of soil bacterial community in regulating soil organic carbon under NT and SR remains unclear. In this study, we investigated the impacts of tillage (conventional tillage (CT) and NT) and crop straw return practices (crop straw removal (NS) and SR) on topsoil layer (0–5 cm) bacterial community, CH4 and CO2 emissions and SOC fractions in rice-wheat cropping system. Overall, in the wheat season following the annual rice-wheat rotation in two cycles, NT significantly increased SOC by 4.4% for 1–2 mm aggregates in the 0–5 cm soil layer, but decreased CO2 emissions by 7.4%. Compared with NS, SR notably increased the contents of SOC in the topsoil layer by 6.5% and in macro-aggregate by 17.4% in 0–5 cm soil layer, and promoted CH4 emissions (by 22.3%) and CO2 emissions (by 22.4%). The combination of NT and NS resulted in relatively high SOC and low CH4 emissions along with high bacterial community abundance. The most abundant genus under different treatments was *Gp6*, which significant impacted SOC and MBC. Bacterial communities like Subdivision3 had the most impact on CH4 emissions. Structural equation modeling further suggested that the soil bacterial community indirectly mediated the SOC through balancing SOC in 1–2 mm aggregates and CH4 emissions. This study provides a new idea to reveal the mechanism of short-term tillage and straw return on SOC.

**Keywords:** no-till; straw return; soil organic carbon fractions; soil aggregate; bacterial diversity

#### **1. Introduction**

Enhancing soil organic carbon storage is vital to achieving sustainable agriculture and alleviating the negative impacts of climate change [1–5]. Tillage and straw return practices greatly affect the storage of organic carbon in the soil [6–8]. Conventional intensive tillage (CT), which accompanies removing crop straw from the field, results in soil organic carbon decline, soil structural degradation, and greenhouse gas (GHG) emission increase [7,9,10]. On the contrary, no-till (NT) and crop straw return (SR) are regarded as effective ways to increase soil organic carbon (SOC) sequestration [11–13].

**Citation:** Guo, L.; Shi, J.; Lin, W.; Liang, J.; Lu, Z.; Tang, X.; Liu, Y.; Wu, P.; Li, C. Soil Bacteria Mediate Soil Organic Carbon Sequestration under Different Tillage and Straw Management in Rice-Wheat Cropping Systems. *Agriculture* **2022**, *12*, 1552. https://doi.org/10.3390/ agriculture12101552

Academic Editor: Pavel Krasilnikov

Received: 11 August 2022 Accepted: 22 September 2022 Published: 26 September 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/).

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Compared with CT, NT can increase SOC content by improving soil aggregation and decreasing CH4 and CO2 emissions in the topsoil layer [14,15]. NT reduces soil disturbance, prevents the soil macro-aggregate from being destroyed, provides better physical protection for SOC, and slows down SOC decomposition [2,16]. Moreover, NT can increase the input of organic residues in soil surface, which can be broken down by soil microbes and thus provide microbial binding agents for macroscopic aggregates to form [17,18]. NT can also reduce soil temperature and enhance soil humidity, which leads to the decline in microbial activity and the emissions of CH4 and CO2, reducing the loss of SOC [19,20]. However, the effect of NT on SOC content was regulated by complex biochemical processes, such as the GHG emissions and the formation of soil aggregate, and thus consistent conclusions were not obtained [6,21]. The soil microbial regulation mechanism of NT affecting SOC content is still unclear.

Crop straw is a source of organic carbon, and crop straw return is shown to result in enhancing SOC sequestration. Compared to no straw (NS), SR may have positively influenced the SOC content in the topsoil layer by elevating organic carbon input and improving soil microbial community and aggregate stability [9,12,22]. However, straw return also stimulates GHG emissions [17,23], which may offset the positive effects of SR on SOC sequestration [12]. Studies reported that soil microbial community [21], soil aggregate size [7,18] and GHG emissions [24] are closely related to SOC sequestration [25]. Yet, no experiment has so far been conducted to reveal the relationships among soil microbial community, the SOC in soil aggregates, GHG emissions and SOC sequestration as influenced by SR. Further study is needed to reveal the soil microbial regulation mechanism of SR affecting SOC sequestration.

The objectives of this study were to evaluate the effects of NT and SR on SOC content and soil microbial communities in a rice-wheat cropping system and to reveal the mechanisms that enable soil bacterial communities to regulate SOC under NT and SR. Therefore, we studied the effects of tillage (CT and NT) and crop straw return practices (NS and SR) on soil bacterial communities, CH4 and CO2 emissions, crop yields, and SOC aggregates in rice-wheat cropping systems. During the experiment, we found that tillage and straw return management had significant effects on SOC content in the 0–5 cm soil layer after two cycles of the rice-wheat rotation, but no significant effect on soil SOC in the 5–10 cm and 10–20 cm soil layers. Therefore, we focused on the 0–5 cm soil layer in this study. We hypothesized that soil bacteria could mediate SOC content through affecting soil aggregate SOC and CH4 emissions under tillage and straw return management.

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

#### *2.1. Experimental Site*

The experiment site lies at Dafashi Town (30◦01 N, 115◦34 E), Hubei province, China, and was established in June 2012. The area has a humid mid-subtropical monsoon climate, in which the annual mean air temperature is 16.8 ◦C, and the average annual precipitation from 2012 to 2014 is 1408.7 mm (Figure 1). The experimental soil (0–20 cm) is a silty clay loam (containing clay 40%, sandy 25%, and silt 40%), which is defined as Gleysol (FAO classification). Besides, the total organic carbon content is 1.64%, total nitrogen content is 0.24%, the pH is 5.9, and the bulk density is 1.20 g cm<sup>−</sup>3. This site has been dominated by a cropping system of rice (HHZ, *Oryza sativa* L.) and wheat (ZM9023, *Triticum aestivum* L.).

**Figure 1.** Average daily temperature and rainfall at the experimental site from 2012 to 2014 [2].

#### *2.2. Experimental Design*

This experiment was set up in a split-plot design, where tillage (CT and NT) and straw treatment (NS and SR) were set as the main plot and subplots, respectively (Figure 2). Four treatments including: (i) CTNS; (ii) CTSR; (iii) NTNS; and (iv) NTSR were arranged, and each treatment was conducted in triplicates. The area of each plot was 90 m<sup>2</sup> (9 m × 10 m). Under CT treatment, the soil was moldboard plowed twice in one year at a depth of 20 cm before planting rice and wheat. Moldboard plowing was omitted in NT treatment. Crop straw was removed from the field for both CTNS and NTNS treatments. A 6 cm length of crop straw harvested from each plot, was covered on the soil surface under NTSR treatment and incorporated into the soil under CTSR treatment. For all treatments, crop stubbles were kept in the fields. Rice was thrown manually at the rate of 190,000 seedlings per hectare in June and reaped in October. Wheat was directly sown at 150 kg ha−<sup>1</sup> in October and harvested in May the following year.

**Figure 2.** Design drawing of field experiment. Note: CT, conventional tillage; NT, no tillage; CTNS, conventional intensive tillage with straw removal; CTSR, conventional intensive tillage with straw return; NTNS, no tillage with straw removal; NTSR, no-tillage with straw return.

For all treatments, weeds were controlled by spraying 30% chlorpromazine emulsifiable oil containing 10% fenorim. The application rate of chemical fertilizer was 180 kg N ha<sup>−</sup>1, 90 kg P2O5 ha−<sup>1</sup> and 180 kg K2O ha−<sup>1</sup> in rice season and was 144 kg N ha−1, 72 kg P2O5 ha<sup>−</sup>1, and 144 kg K2O ha−<sup>1</sup> in wheat season. Commercial compound fertilizer (N:P2O5:K2O = 15%:15%:15%) were used in rice and wheat seasons. P and K fertilizers were applied

immediately as basal fertilizers after throwing or sowing. N fertilizers were applied with four splits (seedling stages: tillering stages: jointing stages: booting stages = 25:10:6:9) for the rice season, and three splits (seedling stages: tillering stages: boosting stages = 5:3:2) for the wheat season. During the rice growing season, the depth of waterlogging was kept at 8 cm, except for during the tillering and maturing stages. The wheat season was not irrigated, except after sowing.

#### *2.3. Soil Sampling and Physicochemical Analysis*

Soil samples were collected in May and October after the rice and wheat harvests from 2012 to 2014. The soil samples were taken from eight locations in each plot at a depth of 0–20 cm using a soil sampler with a diameter of 5 cm, before being divided into three categories (0–5 cm, 5–10 cm and 10–20 cm depths). Then part of soil samples in 0–5 cm soil layer were separated into the 1–2 mm, 0.25–1 mm, 0.053–0.25 mm, and <0.053 mm aggregate with a nest of sieves mounted (including 1 mm, 0.25 mm, and 0.053 mm). These soil samples were used to measure SOC, soil microbial biomass carbon (MBC) and dissolved organic carbon (DOC). The remaining soil samples were placed at −20 ◦C for DNA extraction.

The dry-sieving method was used to separate soil aggregates following the descriptions of Garzia-Bengoetxea et al. [26]. In the present study, the dry sieving method was used as the mechanical pressure exerted from outside is the main cause of soil aggregate destruction, and compared with the wet sieving method, the dry sieving method is less destructive to the soil. Furthermore, drying at a low temperature of 4 ◦C minimizes the effect on the soil microbial community and activities. Retsch AS200 control (Retsch Technology, Düsseldorf, Germany) was used to separate soil aggregates. Air-dried soil fragments (5 mm) were prepared for separation, and soil samples were separated into 1–2 mm, 0.25–1 mm, 0.053–0.25 mm and <0.053 mm soil aggregates by mechanical shaking (amplitude 1.5 mm) for 2 min.

The SOC content was measured with a FlashEA 1112 elemental analyzer (Thermo Finnigan, Milan, Italy). Fumigation-extraction method was used to measure MBC. MBC was calculated as the ratio of differences in organic carbon extracted from fumigated and non-fumigated soil and the conversion coefficient was 0.38. DOC could be measured by the methods of Jiang et al. [27].

#### *2.4. Phospholipid Fatty Acid Pattern*

Phospholipid fatty acids were extracted from a 3 g freeze-dried soil sample using the methods of Frostegård et al. [28]. Briefly, lipids were extracted in a single-phase chloroform– methanol–phosphate buffer system in a ratio of 1:2:0.8 (*v/v/v*). A stream of N2 was used for drying the different phases. Separation of extracts was performed on solid phase extraction columns (Supelco Inc., Bellefonte, PA, USA). The phospholipid fractions were saponified and methylated to fatty acid methyl esters. Internal standard 19:0 fatty acid methyl esters were added to calculate the absolute amount of fatty acid methyl esters before measurement. We employed a gas chromatograph/mass spectrometry system (6890–5973N series GC/MS Agilent Technologies, Palo Alto, CA, USA) outfitted with a Flame Ionization Detector and HP-5 capillary column (30 m × 0.25 mm × 0.25 μm) with ultra-purified helium as carrier gas for the extraction. The quantification of fatty acid methyl esters was performed.

#### *2.5. Measurement of Crop Grain Yields*

Crop grains harvested from the 2012 rice season to the 2014 rice season were measured at the central position in each plot using a 5 m2 frame. The rice and wheat grains were air dried, weighed, and adjusted to 14.0% and 12.5% moisture content, respectively.

#### *2.6. Measurement of CH4 and CO2 Emissions*

Static closed steel chamber method was used to monitor soil CH4 emission [14]. After the crop straw was returned to fields, continuous gas sampling was conducted until the crop harvest. The inner diameter of the chamber was 34 cm and the height of the chamber was 50 or 120 cm (depending on the rice height). To mix the air in the chambers well, four fans were installed on the top of the chamber. During sampling, two rings were placed in each plot, the chambers were placed temporarily in the groove of rings, and water was added to create a sealed environment. After 0, 5, 10 and 15 min of chamber closure (according to the IGAC recommendations [29]), gas samples were gathered from each chamber. The gas samples were collected using a syringe (20 mL) at the chamber's headspace, and then transferred to 20 mL vacuum glass bottles. The gas samples were collected between 9:00 and 11:00 am once every 7–10 and 10–15 days (recommended by Buendia et al. [30]) in rice and wheat seasons, respectively. A chromatograph equipped with a flame ionization detector (Shimadzu GC-14B) was used to measure CH4 fluxes [31].

The measurement of soil CO2 flux was conducted according to the method proposed by Li et al. [31]. The CO2 fluxes were measured three times for each plot with an 8100–103 short-term chamber connected to a LI-8100A soil CO2 flux system (Li-Cor Inc., Lincoln, NE, USA). The final value of soil CO2 flux was obtained by averaging the values of three separate measurements. The calculation of CH4 and CO2 fluxes was based on the linear variation in CH4 and CO2 fluxes [32]. The cumulative seasonal CH4 and CO2 emissions were derived by sequentially accumulating emissions from each of the two adjacent measurement intervals [31].

#### *2.7. High-Throughput Sequencing*

According to the instructions, the FastDNA kit for soil (MP Bio-medicals, Santa Ana, CA, USA) was used to extract DNA from the soil samples and then stored at −20 ◦C. The bacterial hypervariable regions, including V3, V4 and V5 of 16S rDNA, were amplified by PCR using primers 357F (5 -CCTACGGGAGGCAGCAG-3 ) and 926R (5 - CCGTCAATTCMTTTRAGT-3 ) [33]. The forward primer was modified to include the FLXtitanium adaptor "B" sequence (5 -CCTATCCCCTGTGTGCCTTGGCAGTCTCAG-3 ), and the reverse primer was linked with the 454 FLX-titanium adaptor "A" (5 -CCATCTCATCCC TGCGTGTCTCCGACTCAG-3 ) [21].

DNA samples (10 ng) were applied as templates in the polymerase chain reaction. Polymerase chain reaction was conducted at 95 ◦C for 3 min, 94 ◦C for 30 s, 55 ◦C for 45 s, 72 ◦C for 1 min with 25 cycles and a final extension step of 72 ◦C for 7 min. Polymerase Chain Reaction Purification Kit (Axygen Bio, Union City, California, CA, USA) was used for purification of polymerase chain reaction products. The amplitudes from each sample were then combined in equimolar concentrations into one tube prior to 454 pyrophosphate sequencing. Pyrophosphate sequencing was performed by Shanghai Personal Biotechnology Co., Ltd. using the 454 GS-FLX Titanium System (Roche, Switzerland). To ensure analytical accuracy, the Quantitative Insights into Microbial Ecology (QIIME) pipeline [34] was employed to fetch high quality sequences, following the descriptions of Fierer et al. [35]. The unique sequence set was classified into operational taxonomic units OTUs (a threshold of 97% pairwise identity) by the QIIME implementation. Extraction of the longest sequences of the most abundant OTUs was used as a proxy for taxonomic identification for comparison with the Green Gene Database (release 13.8 http://greengenes.secondgenome.com/ (accessed on 10 January 2012)).

#### *2.8. Statistical Analysis*

All data were expressed as means and standard deviations of three replicates. The main effects and interactions of tillage and straw returning were conducted using general linear model analysis of variance with SAS 9.0 (SAS Institute 1999) designed for split plot with tillage practice and straw returning methods as fixed factors and replicates as random factors. The least significant difference test was conducted to examine whether the influence of tillage practices, straw return practices, or their interactions were significant at the level of 0.05. To test the effect of experimental treatments on bacterial composition, redundancy analysis was performed using the "vegan" package in R v. 3.1.2 (R Development Core Team, 2014) [36].

Structural equation modeling was performed to reveal the influence paths of tillage and straw return practices on SOC content from the perspectives of the soil bacterial community, DOC, microbial biomass carbon, CH4 and SOC in 1–2 mm aggregates. The use of structural equation modeling allowed the testing of complex path-relation networks. It should be noted that only the data for the 2013 rice season and 2014 wheat seasons were selected [37]. In the model, tillage (0 = no-till and 1 = tillage) and straw return (0 = straw removal and 1 = straw return) were considered as categorical variables. This approach allowed us to compare the effect of tillage and straw return practices on SOC content. Redundancy analysis results for bacterial communities in order level (relative abundance > 0.5%), were used as 'bacteria community' in the model. A 'robust' maximum likelihood estimation procedure of AMOS 20.0 (IBM SPSS, Chicago, IL, USA) software was conducted for the analysis. The chi-square test, goodness of fit index, comparative fit index, and root square mean error of approximation were used for testing the overall goodness of the fit of the model.

#### **3. Results**

#### *3.1. Soil Organic Carbon*

Tillage and straw return management significantly changed the SOC content in the 0–5 cm soil layer (*p* < 0.05, Table S1). Compared with CT treatment, NT treatment significantly increased SOC content in the 2013 wheat season (5.7%), 2013 rice season (15.3%) and 2014 rice season (4.4%). In comparison with NS treatment, SR treatment markedly enhanced SOC content in the 2012 rice season (6.6%), 2013 wheat season (8.3%), 2013 rice season (9.1%), 2014 wheat season (6.5%) and 2014 rice season (8.3%). Compared with CTNS, NTNS markedly increased SOC in the 2013 rice season, 2014 wheat season and 2014 rice season by 15.6%, 2.9% and 4.4%, respectively. In comparison with CTSR, NTSR significantly enhanced SOC in the 2013 wheat season, 2013 rice season and 2014 rice season by 7.1%, 15.1% and 4.5%, respectively. Interaction of tillage and straw return practices showed no remarkable effects on SOC content.

#### *3.2. Distribution of Soil Aggregates*

Tillage and straw returning methods had a significant effect on the distribution of soil aggregates in the soil layer within the topsoil layer (Table S2). Compared with CT treatment, NT treatment significantly increased the percentage of 1–2 mm soil aggregates in the 2014 wheat season (4.1%) (*p* < 0.05), whereas, there was a markedly reduced percentage of soil aggregates < 0.053 mm in the 2013 rice season (18.9%). Compared with NS treatment, SR treatment resulted in an increased the proportion of 1–2 mm soil aggregates in both wheat and rice seasons of 2013 (5.4%, 4.4%), and in the wheat season of 2014 (5.6%) (*p* < 0.05), but decreased the proportion of soil aggregates <0.053 mm in the 2014 wheat season (21%) (*p* < 0.05). NTNS significantly increased the proportion of 1–2 mm soil aggregates by 6.3% in the 2014 wheat season, and markedly reduced the percentage of soil aggregates < 0.053 mm in the 2013 rice season (7.6%) and 2014 wheat season (20.2%) compared to CTNS. NTSR, respectively, increased the proportion of 1–2 mm soil aggregates by 2.8%, 4.7%, 2.1% in the 2013 wheat season, 2013 rice season, 2014 wheat season, and markedly reduced the percentage of soil aggregates < 0.053 mm in 2013 rice season (29.9%) compared to CTSR. Interaction of tillage practices and straw returning methods showed no significant difference.

#### *3.3. Soil Organic Carbon Content within Aggregates*

Tillage and straw return practices greatly influenced the SOC content of aggregates in the 0–5 cm soil layer (Table 1). Compared to CT treatment, NT treatment increased the SOC content in 1–2 mm aggregates in the 2013 wheat season (17%), 2013 rice season (19.9%) (*p* < 0.05, Table 1). Higher SOC content in 0.25–1 mm aggregates was also observed under NT treatment than under CT treatment in the 2013 wheat (14.6%) and rice seasons (13.4%) (*p* < 0.05). Compared with NS treatment, SR treatment led to higher SOC content in 1–2 mm aggregates in the 2013 rice season (17.2%), and 2014 wheat season (17.4%) (*p* < 0.05). Moreover, higher SOC content in 0.25–1 mm aggregates was found in the 2013 rice season (7%) and 2014 wheat season (6.2%) under SR treatment than under NS treatment (*p* < 0.05). Compared to CTNS, NTNS significantly increased the SOC content in 1–2 mm aggregates in the 2013 rice season by 17.0%. Moreover, there were also significant differences in the SOC content of 0.25–1 mm aggregates in the 2013 rice season (16.5%), and in the 2014 wheat season (3.6%) between CTNS and NTNS. NTSR, respectively, increased the SOC content in 1–2 mm aggregates by 17.9%, 22.4%, 8.3% in the 2013 wheat season, 2013 rice season and 2014 wheat season, and enhanced SOC content in 0.25–1 mm aggregates in the 2013 rice season (10.6%) and 2014 wheat season (4.1%) relative to CTSR. In other soil layers, there was no remarkable difference between treatments. Interaction of tillage and straw return practices remarkably influenced the SOC content in 1–2 mm aggregates in the 2014 wheat season (*p* < 0.05).

**Table 1.** SOC contents (g kg<sup>−</sup>1) of aggregate fractions under different tillage and straw return practices (2012–2014).


Different letters in the columns denote statistical differences in the means of the variables between treatments by the least significant difference test (*p* < 0.05). \* *p* < 0.05; ns, not significant. CTNS, conventional intensive tillage with straw removal; CTSR, conventional intensive tillage with straw return; NTNS, no tillage with straw removal; NTSR, no-tillage with straw return. T, tillage; SR, straw return practices. T × SR, the interactions between tillage and straw return. Values are mean ± standard deviation (n = 3).

#### *3.4. Soil Dissolved Organic Carbon and Microbial Biomass Carbon*

Tillage and straw returning methods had significant effects on DOC contents in the 0–5 cm soil layer (Table S3). Compared to CT treatment, NT treatment markedly increased the DOC contents in both wheat seasons and rice seasons in 2014 (12.3%, 8.8%) (Table S3, *p* < 0.05). Similarly, higher DOC contents were found in both wheat and rice seasons in 2013 (23.7%, 23.8%) and 2014 (18.5%, 13%) (*p* < 0.05) under the SR treatment than under the NS treatment. Compared with CTNS, NTNS showed a significant increase in the DOC contents in the 2013 rice season (3.3%) as well as in the 2014 wheat (13.4%) and rice seasons (10.4%). NTSR showed significantly higher DOC contents in both wheat and rice seasons in 2013 (16.5%, 13.8%) and 2014 (11.4%, 7.4%) relative to CTSR. The interaction between NT and SR remarkably influenced the DOC during the whole 2013 season.

Relative to CT treatment, NT treatment significantly increased the MBC contents in the 0–5 cm soil layer in both wheat and rice seasons in 2013 (15.1%, 14.3%) and 2014 (21.5%, 39.8%) (Table S4, *p* < 0.05). SR treatment also resulted in higher MBC contents in both wheat and rice seasons in 2013 (27.8%, 18.1%) and 2014 (26.6%, 20.1%) (*p* < 0.05) than NS treatment. Compared to CTNS, NTNS displayed a statistically significant improvement

in MBC contents for both the 2013 (19.2%, 6.9%) and 2014 wheat and rice seasons (5.7%, 33.2%). NTSR also showed an improvement in the 2013 wheat season (12.0%), 2013 rice season (21.0%), 2014 wheat season (35.8%) and 2014 rice season (45.5%) against CTSR. Interactions of tillage and straw returning practices had significant effects on MBC contents with the exception of the 2012 and 2013 wheat seasons (*p* < 0.05).

#### *3.5. Greenhouse Gas Emissions*

Relative to CT treatment, NT treatment remarkably decreased CH4 emissions during the 2014 rice seasons (15.6%) (*p* < 0.05) (Table 2). SR treatment resulted in higher CH4 emissions during the rice (by 34.0–91.2%) and wheat (by 22.1–22.3%) seasons throughout all three experimental years (*p* < 0.05). Compared with CTSR, NTSR showed a significant reduction in CH4 emissions in the 2013 wheat season (18.0%), 2014 wheat season (10.8%), and 2014 rice season (16.6%). However, the NTNS showed the lowest CH4 emissions among all treatments in whole seasons. Interaction of tillage and straw return practices showed no significant effects on CH4 emissions.

**Table 2.** Seasonal CH4 emissions (kg hm<sup>−</sup>2) under different tillage and straw return practices (2012– 2014) (has been published by Guo et al. [2]).


Different letters in the columns denote statistical differences in the means of the variables between treatments by the least sign difference test (*p* < 0.05). \* *p* < 0.05; ns, not significant. CTNS, conventional intensive tillage with straw removal; CTSR, conventional intensive tillage with straw return; NTNS, no tillage with straw removal; NTSR, no-tillage with straw return. T, tillage; SR, straw return practices. T × SR, the interactions between tillage and straw return. Values are mean ± standard deviation (n = 3).

Compared with CT treatment, NT treatment lowered CO2 emissions in the 2014 wheat (7.2%) and rice reasons (21.4%) (*p* < 0.05) (Table 3). SR treatment induced more CO2 emissions (*p* < 0.05) than NS treatment in the 2012 rice season (91.2%), 2013 wheat (22.1%) and rice seasons (40.8%), and 2014 wheat (22.3%) and rice seasons (34.0%). Compared with CTNS, NTNS markedly elevated CO2 emissions in the 2012 rice season by 19.3%, whereas, it reduced CO2 emissions in the 2013 rice season by 22.7% and 2014 rice season by 10.9%. NTSR had lower CO2 emissions in the 2012 rice season, 2013 rice season, 2014 rice season (by 9.7%, 14.6% and 28.5%, respectively) against CTSR. Interaction of tillage and straw return practices showed a significant effect on CO2 emissions only in the 2012 rice seasons and 2014 rice seasons (*p* < 0.05). Meanwhile, the combination of NT and NS can reduce CO2 emissions compared to other treatments.


**Table 3.** Seasonal CO2 emissions (kg hm<sup>−</sup>2) under different tillage and straw return practices (2012– 2014) (has been published by Guo et al. [2]).

Different letters in the columns denote statistical differences in the means of the variables between treatments by the least sign difference test (*p* < 0.05). \*, *p* < 0.05; ns, not significant. CTNS, conventional intensive tillage with straw removal; CTSR, conventional intensive tillage with straw return; NTNS, no tillage with straw removal; NTSR, no-tillage with straw return. T, tillage; SR, straw return practices. T × SR, the interactions between tillage and straw return. Values are mean ± standard deviation (n = 3).

#### *3.6. Soil Microbial Community*

Tillage and straw returning methods significantly influenced bacterial biomass in 0–5 cm soil layer (Table S5). Compared with CT treatment, NT treatment increased the bacterial biomass in the 2013 rice seasons (35.6%) and 2014 wheat seasons (56.2%) (*p* < 0.05). SR treatment led to higher bacterial biomass than NS treatment in the 2013 wheat season (76.5%), 2013 rice season (54.9%), 2014 wheat season (75.7%), 2014 rice season (59.7%) (*p* < 0.05). Compared with CTNS, NTNS had a greater impact on bacterial biomass in the 2013 rice season (33.1%), and in the 2014 wheat season (38.3%), while for fungi, NTNS had a significant reduction in the 2013 wheat season (25.2%). Compared to CTSR, NTSR had a greater effect on microorganisms in both wheat and rice seasons in 2013 (70.1%, 37.1%) and 2014 (67%,75%), while for fungi, NTSR showed a significant improvement in the 2013 wheat season (23.8%). The interaction of tillage and straw return practices had a significant effect on bacterial biomass in the 2014 wheat season (*p* < 0.05). Tillage and straw returning practices had no significant effects on fungal biomass in the 2013 wheat seasons (Table S5). The fungal PLFAs were not detected in the 2013–2014 rice seasons.

#### *3.7. Soil Bacterial Community*

Soil bacterial community was mainly composed of phylum Acidobacteria, Verrucomicrobia and Proteobacteria in the 2013 rice season (Table S6), while it was mainly composed of phylum Acidobacteria, Chloroflexi and Proteobacteria in the 2014 wheat season (Table S7).

In the 2013 rice season, compared with CT treatment, NT treatment affected the abundance of *Gp1* (−17.7%*), Gp18* (−19.4%), *Gp4* (65.2%)*, Gp16* (21.6%), *Dehalogenimonas* (29.1%), *Caulobacterales* (27.6%), *Desulfuromonadales* (33.0%), *Myxococcales* (42.8%), and *Legionellale* (−9.7%) (Table S6, *p* < 0.05). Compared with NS treatment, SR treatment significantly affected the abundance of *Gp18* (−16.3%), *Gp4* (48.1%), *Gp17* (16.4%), *Chlamydiales* (−26.0%), *Caulobacterales* (19.7%), *Burkholderiales* (−37.6%) (*p* < 0.05). Compared with CTNS, NTNS significantly increased the abundance of *Rhizobiales* (7.0%), *Burkholderiales* (13.4%), and *Spartobacteria\_genera\_incertae\_sedis* (46.2%), whereas, it markedly decreased the abundance of order *Gp1* (1.5%), *Gp18* (25.2%), *Gp4* (38.3%), and *Dehalogenimonas* (17.2%). Compared with CTSR, NTSR significantly elevated the abundance of *Gp4* (267%), *Holophagales* (85.4%), *Dehalogenimonas* (103.4%), *Chlamydiales* (33.9%), *Caulobacterales* (50.9%), *Burkholderiales* (27.8%), and *Spartobacteria\_genera\_incertae\_sedis* (30.5%), whereas it significantly reduced the abundance of order *Gp1* (32.0%) and *Rhizobiales* (17.2%). Interaction of tillage and straw return practices significantly affected the abundance of *Gp1*, *Gp18*, *Gp4*, *Gp17*, *Holophagales*, *Dehalogenimonas*, *Chlamydiales*, *Caulobacterales*, *Syntrophobacterales*, and *Spartobacteria\_genera\_incertae\_sedis* (*p* < 0.05). NTSR showed the highest or the lowest bacterial community richness compared with the NTNS and CTSR.

In the 2014 wheat season, NT treatment led to a higher abundance of *Myxococcales* (206.3%) than CT treatment (Table S7, *p* < 0.05). In comparison with NS treatment, SR treatment brought out higher enrichment of *Gp4* (573.9%), *Gp10* (502.4%), *Gp18* (97.5%), *Sphingobacteriales* (164.7%) *Gp7* (303.8%), *Sphingobacteriales* (164.7%), *Flavobacteriales* (89.4%), and *Myxococcales* (173.2%), while it decreased the abundance of *Gemmatimonadales* (47.8%) and *Rhodospirillales* (42.0%) (*p* < 0.05). Compared with CTNS, NTNS significantly increased the abundance of order *Myxococcales* (176.0%), whereas it markedly decreased the abundance of order *Xanthomonadales* (5.5%). Compared with CTSR, NTSR significantly increased the abundance of *Gp4* (161.9%), *Gp10* (429.1%), *Gp18* (106.2%), *Gp7* (145.9%), *Xanthomonadales* (17.7%), and *Myxococcales* (218.1%). The interplay of tillage and straw return practices significantly influenced the abundance of *Gp18* and *Flavobacteriales* (*p* < 0.05).

#### *3.8. Crop Grain Yields and Their Relationship with Soil Properties*

Crop grain yields in this study were reported in our previous study (Table S8) [2]. NT treatment significantly reduced crop yields by 8.8% in the 2014 wheat season compared to CT treatment (*p* < 0.05). SR treatment showed no significant difference relative to NS. There was no significant difference in grain yields between NTNS and CTNS. NTSR had a remarkable increase in crop yields in the 2014 wheat season compared to CTSR (19.1%, *p* < 0.05). Interaction of tillage and straw return practices showed a significant effect on crop yields in the 2014 wheat season (*p* < 0.05). A significant correlation was observed between DOC and crop yields (Table S9).

#### *3.9. Relationship of Bacterial Community with Yield, Soil Aggregates and Soil Organic Carbon Fractions*

Redundancy analysis (RDA) showed that soil bacterial community was considerably influenced by SOC content in 1–2 mm aggregates, MBC and CH4 emissions (Figure 3, *p* < 0.05). MBC and SOC in 1–2 mm aggregates were closely related to *Gp6*, *Burkholderiales*, *Gp10*, *Sphingobacteriales*, *Myxococcales*, *Gp16*, *Flavobacteriales*, *Gp2*, *Gp3*, and *Xanthomonadales*. CH4 emissions were closely related to *Subdivision3\_genera\_incertae\_sedis*, *Gp18*, *Caulobacterales*, *Gp16*, and *Chlamydiales*. Besides, no significant correlation were found between crop yield and microbial community.

**Figure 3.** Redundancy analysis (RDA) ordination plot showing changes in bacterial community composition in 0–5 cm soil layer at order level (relative abundance > 0.5%) during the 2013 rice season and 2014 wheat season. SOC, soil organic C; MBC, microbial biomass C.

The structural equation modeling revealed that the predictors could explain 85.0% of the variances in SOC content (Figure 4). Soil bacterial community mediated SOC under tillage and straw systems through affecting SOC in 1–2 mm aggregates and CH4 emissions.

**Figure 4.** Selected structural equation modeling (data from 2013 rice season and 2014 wheat season were selected) for SOC in 0–5 cm soil layer (The chi-square test = 9.91; Goodness of fit index = 1.00; Comparative fit index = 0.91; Root square mean error of approximation = 0.00), based on the impact of tillage and straw return practices and SOC fractions. Values related to the solid arrows stand for the path coefficients. R<sup>2</sup> indicates the proportion of variance explained. Significance levels are as follows: \* *p* < 0.05; \*\* *p* < 0.01. Straw indicates straw systems; DOC indicates dissolved organic carbon; MBC indicates microbial biomass carbon; SOC indicates soil organic carbon.

#### **4. Discussion**

#### *4.1. Impact of NT and Straw Return on SOC Content in Aggregates*

NT can enhance SOC content by promoting the SOC sequestration in macro- aggregate [7,38,39]. In this study, a higher proportion of 1–2 mm soil aggregates (Table S2), and more SOC content in 1–2 mm aggregates, were found under NT than under CT (Table 1). Tillage operations breaks soil macro-aggregates and results in SOC losses [40]. Conversely, NT keeps soil undisturbed, which is conducive for accelerating the formation of macroaggregate, and reduces the degradation rate of SOC [41]. Moreover, NT can provide more physical protection for soil aggregates and promote the longevity of newly-formed macroaggregates, leading to stabilization of SOC in the micro-aggregates formed within stable macro-aggregates [42,43].

As an essential organic matter source, SR can promote the formation of soil macroaggregates and increase SOC content. Previous studies have well reported that SR can increase SOC content by increasing the input of organic carbon input [12,43]. In this work, higher SOC content in the topsoil layer (0–5 cm) was observed under SR than under NS (Table S1), which may be due to higher SOC sequestrated in 1–2 mm aggregates (Table S2). Straw degradation generates a large number of organic matter particles, contributing to the formation of macro-aggregates and the accumulation of SOC in macro-aggregate [7,17,44,45].

Some studies reported that interaction of tillage and straw return practices significantly affected SOC, possibly as NT can promote the accumulation of straw on the soil surface, thus enhancing SOC sequestration in the topsoil [21]. Similarly, we also found that both under NS or SR conditions, NT caused higher SOC in 1–2 mm and 0.25–1 mm aggregates than CT (Table 1). Moreover, the interaction of tillage and straw return practices significantly affected SOC in 1–2 mm aggregates in the 2014 wheat season (Table 1), suggesting that under straw return condition, NT can further promote SOC sequestration in 1–2 mm aggregates [7,21]. However, the interaction of tillage and straw return practices had no significant effect on SOC in other aggregates sizes (Table 1), possibly as 1–2 mm soil aggregates are more sensitive than soil aggregates of other smaller sizes [2,21].

#### *4.2. Effect of NT and Straw Return on Greenhouse Gas Emissions*

Emissions of CH4 and CO2 are important pathways of carbon loss from agricultural soil [24,31,46]. In this study, NT treatment reduced CH4 and CO2 emissions compared with CT treatment (Tables 2 and 3). CH4 emissions are primarily affected by the availability of organic matter and oxygen [12,17,46]. NT decreases soil disturbance and improves gas diffusion, inhibiting the growth of methanogenic bacteria and reducing the production of CH4 [23]. NT also can enhance soil moisture, reduce soil temperature, slow the organic residue degradation, and reduce the activity of soil microorganisms, thus reducing CH4 and CO2 emissions [12,19,20].

In contrast, straw return promoted CH4 and CO2 emissions (Tables 2 and 3) mainly by providing a large number of organic carbon for soil microorganisms [12,46]. Moreover, anaerobic degradation of crop residues can reduce soil Eh, thus increasing methanogenic populations and enhancing CH4 emissions [17,47,48]. Nevertheless, a large number of straw-derived carbon can be sequestrated in soil by the formation of resistant organic matter, which may offset the losses of SOC caused by CH4 and CO2 emissions.

In this study, the interaction of tillage and straw return practices had no effect on CH4 and CO2 emissions in the experiment (Tables 2 and 3), which may be due to SR and NT having the opposite effect on CH4 emissions. SR significantly enhanced CH4 and CO2 emissions, whereas, NT was found to have reduced CH4 and CO2 (Table 2). We also found that CTNS had no significant effect on CH4 emissions relative to NTNS, while NTSR had lower CH4 emissions than NTNS (Table 2). The reason may be the fact that NT leads to more straw being accumulated in the soil surface, which has better oxygen available than topsoil layers, thus inhibiting the production of CH4 from the soil [17,48]. Besides, CTNS had higher CO2 emissions than NTNS, and CTSR also had more CO2 emissions than NTSR. This can be attributed to there being a lower soil temperature under NT than under CT, thus leading to a decrease in the activity of soil microorganisms, and subsequently to a decrease in CO2 emissions [19,20].

#### *4.3. Effects of NT and Straw Return on Bacterial Community*

Microorganisms play a key role in regulating SOC turnover and sequestration [49]. The bacterial community accounts for the majority of soil microorganisms in the ricewheat cropping system (Table S5) [50], which is probably due to the long-term flooding of the field during the rice season resulting in the formation of an anaerobic environment, inhibiting the growth of the soil fungal community [21]. The bacterial community is sensitive to tillage and straw management [51]. Common dominant bacteria such as Phylum Actinobacteria, Proteobacteria and Actinobacteria are recognized to be remarkable plant biomass decomposers [52–54]. In this study, Phylum Acidobacteria, Verrucomicrobia and Proteobacteria phylum were dominated in the 2013 rice season (Table S5) and phylum Acidobacteria, Chloroflexi and Proteobacteria phylum were dominated in the 2014 wheat season (Table S6).

We found that NT significantly affected the bacterial community in the 2013 rice season and 2014 wheat season (Tables S5 and S6). NT can enhance some bacterial abundance related to the decomposition of crop residue [52,54,55], for example, phylum Actinobacteria (including order *Gp4*, *Gp16*), phylum Chloroflex (including order *Dehalococcoidetes*), phylum Proteobacteria (including order *Myxococcales*), phylum Alphaproteobacteria (including order *Caulobacterales*), phylum Chloroflexi (including order Dehalococcoidates), and phylum (including *Desulfuromonadales* and *Myxococcales*) (Tables S5 and S6). This is probably due to the fact that NT can provide more available substrates and nutrients for

soil microorganisms [56]. However, we also found NT decreased the abundance of bacteria such as order *GP1* and *Gp18* in 2013 rice season (Table S5), which may be due to *Gp1* and *Gp18* benefitting from a poor nutrition condition [57].

Straw return can input a large quantity of straw-derived carbon into soil, and thus affect the bacterial community [56,58,59]. In this study, straw return observably affected the bacterial community in the 2013 rice season and 2014 wheat season (Tables S6 and S7). Generally, SR can tend to increase the abundance of the bacterial community as SR can provide more metabolic substrates for bacteria [12]. SR can improve soil properties, such as soil permeability and water holding capacity, and provide comfortable habitat conditions for bacteria, thus improving the bacterial community [12,59]. However, in this study, SR decreased the abundance of some within the bacterial community, such as order *Caulobacterales*, which may be due to SR increasing the availability of oxygen and thus inhibiting the growth of *Caulobacterales* in the 2013 rice season [60]. SR also decreased the abundance of order *Gp18* in the 2013 rice season, which is probably due to the fact that order *Gp1* and *Gp18* could benefit from a poor nutrition condition [57].

In this study, the interaction of tillage and straw return practices significantly influenced the abundance of the bacterial community in 0–5 cm soil layer, such as order *Gp1, Gp18*, *Gp4, Gp17, Holophagales, Dehalogenimonas, Chlamydiales, Caulobacterales, Syntrophobacterales,* and *Spartobacteria\_genera\_incertae\_sedis* (Tables S6 and S7). SR and NT tended to increase the abundance of the soil bacterial community, and the combination of SR and NT can provide better habitat conditions, such as higher availability of oxygen and greater organic carbon for the soil bacterial community [12,59]. However, some within the bacterial community, such as order *G16* and *Rhodospirillales*, were not significantly affected by the interaction (Tables S6 and S7). This can be attributed to high diversity of soil bacterial community, and the difference in the preference of soil microorganisms regarding habitat conditions, such as oxygen availability and carbon and nitrogen sources. Moreover, crop rotation can also reduce the interaction effect of tillage and straw return practices on the soil bacterial community [21,26].

#### *4.4. Effect of NT and Straw Return on Crop Yields*

The effect of NT and SR on crop yields was discussed in our previous study [2]. In this study, NT had no significant effect on grain yields during 2012–2014, except that NT significantly reduced crop yields in the 2014 wheat season (Table S8). In general, less than five years of continuous NT is not enough to change crop yields [2,61]. Lower yields under NT than under CT in the 2014 wheat season can be attributed to high rainfall during the growth season of wheat (Figure 1). NT can promote the accumulation of straw residue on the soil surface, and enhance the soil anaerobic condition in the case of high rainfall, inhabiting the growth of wheat under NT [2,62–64]. Moreover, NT can decrease crop yield and may be due to decreased productive tillers and increased weed growth [2].

On the contrary, straw return often increases crop yields, as SR can enhance the input level of organic matter, thus improving soil nutrient conditions [61]. In this study, SR had no effect on crop yields (Table S8). The reason may be the fact that a long time is required, usually, for straw to be degraded and then change soil physical-chemical properties. Therefore, short-term straw return may have little effect on crop yields [2].

In this study, the interaction of tillage and straw return practices had no effect on crop yields, except in the 2014 wheat season (Table S8). Generally, the interaction of long-term tillage and straw return can increase crop yield, as long-term NT or SR can promote straw residue input into the soil, thus providing more nutrition for crops [3]. However, short-term NT and SR cannot significantly change crop yields [11].

#### *4.5. Relationships between Soil Organic Carbon and Bacterial Community under Different Tillage and Straw Return Practices*

The soil bacterial community largely contributes to aggregates stabilization and SOC sequestration [25,41]. In this study, the bacterial community (such as *Gp6*, *Gp10*, *Gp16*, *Gp18*), Planctomycetes (including *Burkholderiales* and *Subdivision3\_genera\_incertae\_sedis*) and Actinobacteria (such as *Sphingobacteriales*) were significantly affected by MBC and SOC in 1–2 mm macro-aggregates (Figure 3), which may be due to the fact that bacteria can metabolize organic matter and be stabilized as microbial residues in organic mineral complexes [53,54]. During the process of the decomposition of organic matter, a large quantity of broken organic carbon is released, contributing to the formation of soil macroaggregates [64].

We found that the bacterial community, such as *Subdivision3\_genera\_incertae\_sedis*, *Gp18*, and *Caulobacterales*, observably affected CH4 emissions (Figure 3). It was reported that the bacterial community could affect CH4 emissions through changing the availability of oxygen and organic carbon for methanogens [65–67]. The bacterial community can provide organic carbon for methanogens by degrading crop residue and thus enhance CH4 emissions [67,68]. Besides, methanotrophic bacteria are important mediators for CH4 consumption, which plays a significant role in controlling CH4 emissions [69]. Therefore, the bacterial community may contribute to the shift in SOC content in macro-aggregates and CH4 emissions, thus affecting the dynamics of SOC [17,41].

In this study, structural equation modeling analysis showed that SOC in 1–2 mm aggregates and CH4 emissions jointly affected SOC sequestration under tillage and straw return systems (Figure 4), suggesting that SOC content was regulated by the balance between the SOC sequestration in 1–2 mm aggregates and the SOC losses induced by CH4 emissions. Compared with CT, NT enhanced the formation of macro-aggregates (Table S2) and the accumulation of SOC in 1–2 mm aggregates (Table 1), while it reduced CH4 emissions (Table 2), resulting in an increase in SOC content in the topsoil layer [32,41,42]. Compared with NS, SR promoted the losses of SOC induced by CH4 emissions compared with NS (Table 2), and accelerated an increase in SOC sequestration in 1–2 mm aggregates (Table 1). Moreover, part of the straw could be sequestrated in soil by forming recalcitrant organic matter [7], which leads to increase in SOC content. Therefore, it can be concluded that both NT and SR increased SOC content, which may be the results of the balance between SOC accumulation in 1–2 mm aggregates and CH4 emissions.

#### **5. Conclusions**

Both NT and SR increased SOC content in 0–5 cm topsoil layers in a rice-wheat cropping system. Our study indicates that NT and SR increased SOC content in 1–2 mm soil aggregates. NT resulted in lower CO2 and CH4 emissions compared with CT. However, SR increased CO2 and CH4 emissions compared to NS. Bacterial communities (such as Gp6, Gp10, Gp16 and Gp18), had significant relationships with SOC in 1–2 mm aggregates and MBC. Bacterial communities like *Subdivision3\_genera\_incertae\_sedis*, *Gp18*, and *Caulobacterales* had *the most effect on* CH4 emissions. Our study highlights that 4.4–15.3% of increase in SOC contents under NT and straw return were mainly due to the balance between SOC accumulation in 1–2 mm soil aggregates and CH4 emissions in rice and wheat cropping systems.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/agriculture12101552/s1, Table S1: Changes of soil organic carbon contents (g kg−1) under different tillage and straw return practices from (2012–2014); Table S2: Changes in aggregate composition (%) under different tillage and straw return practices in 0–5 cm soil layer (2012–2014); Table S3: Changes of dissolved organic carbon (g kg−1) contents in 0–5 cm soil layer under different tillage and straw return practices during 2012–2014; Table S4: Changes of soil microbial biomass carbon (mg kg−1) contents in 0–5 cm soil layer under different tillage and straw return practices during 2012–2014; Table S5: Soil bacterial and fungal PLFA under different tillage practices and residue returning methods in 0–5 cm soil layer (2013–2014); Table S6: The change in bacterial community at order level (relative abundance > 0.5%) under different tillage and straw return practice in 2013 rice season; Table S7: The change in bacterial community at order level (relative abundance > 0.5%) under different tillage and straw return practice in 2014 wheat season. Bacterial community; Table S8: The change in bacterial community at order level (relative abundance > 0.5%) under different tillage and straw return practice in 2014 wheat season. Bacterial community; Table S9: The relationship between crop yield and soil properties.

**Author Contributions:** Data curation and writing-original draft preparation, L.G.; methodology, W.L. and Z.L.; review and editing, J.S. and J.L.; investigation, P.W.; formal analysis, X.T. and Y.L.; validation, C.L.; supervision, C.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is funded by the Key Research and Development Project of Hubei Province (2021BBA224), the Hainan Provincial Natural Science Foundation of China (320RC470, 2019RC111), and Priming Scientific Research Foundation of Hainan University (KYQD(ZR)1982).

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

**Data Availability Statement:** Not applicable.

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

#### **References**


## *Article* **Optimizing Nitrogen Application for Chinese Ratoon Rice Based on Yield and Reactive Nitrogen Loss**

**Ren Hu 1,2, Zijuan Ding 1,2, Tingyu Li 3, Dingyue Zhang 4, Yingbing Tian 1,2, Yuxian Cao <sup>5</sup> and Jun Hou 1,2,\***


**Abstract:** Ratoon rice (RR) has been regarded as a labor-saving and beneficial production system. Nitrogen (N) surplus and reactive N losses (Nr losses) are effective environmental indicators used to evaluate the performance of N management. Few studies have assessed N surplus and Nr losses for Chinese RR. In this study, Chinese RR planting areas were divided into South China (SC), the southern part of East China (SEC), Central China (CC), the northern part of East China (NEC), and Southwest China (SW). N surplus and Nr losses were also calculated based on 782 studies using a quadratic model under optimized N management for the highest yield (OPT-yield), the highest N-use efficiency (NUE) (OPT-NUE), and the highest grain N uptake (OPT-N uptake). The RR yields in the five regions ranged from 9.98 to 13.59 t ha<sup>−</sup>1. The high-yield record was observed in SEC, while the low-yield record was observed in NEC. The highest and the lowest Nr losses were found in NEC and SC, respectively. N surplus was reduced, while the yield was maintained in SEC, CC, NEC, and SW under OPT-yield and OPT-N uptake, and N surplus and Nr losses were reduced in the five regions when targeting the highest NUE. Farmers should be encouraged to plant RR in SEC and CC. RR was also a good choice when N management measures were conducted in three other regions. To achieve a win–win situation for both yield and the environment, OPT-yield could serve to improve the N management of current conventional practices.

**Keywords:** ratoon rice; nitrogen balance; reactive nitrogen losses; nitrogen surplus; nitrogen-use efficiency

#### **1. Introduction**

With the world population increasing, rice production needs to reach 519.50 million tonnes in order to meet the world population's demand for rice in 2022 [1], and China is not exempt from this. Rice is a staple food for more than 65% of Chinese people, and it is a subsistence crop for rice farmers and consumers in Chinese rural areas lacking resources. About 20% more rice needs to be produced by 2030 to meet domestic demands if rice consumption per capita is to be kept at the present level in China [2]. Therefore, it is imperative to increase rice yield per hectare in the limited planting area. Ratoon rice (RR) is a kind of rice that can be harvested twice in one crop; dormant sprouts that survive on rice stubble germinate into ears and can then be harvested for another season (ratoon crop) after the harvest of the first crop (main crop). Two harvests and a higher multiple cropping index can be realized using this rice farming system [3]. Grain yield in the RR system is higher than that in middle-season rice, and the net energy ratio and the economic profit in the RR system are higher than those in double-season rice [4].

**Citation:** Hu, R.; Ding, Z.; Li, T.; Zhang, D.; Tian, Y.; Cao, Y.; Hou, J. Optimizing Nitrogen Application for Chinese Ratoon Rice Based on Yield and Reactive Nitrogen Loss. *Agriculture* **2022**, *12*, 1064. https:// doi.org/10.3390/agriculture12071064

Academic Editor: Antonio Carlo Barbera

Received: 15 June 2022 Accepted: 13 July 2022 Published: 20 July 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/).

Nitrogen (N) is the main nutrient used to boost the growth and development of crops. The use of N fertilizer is necessary to obtain high crop yields [5]. Farmers usually input ample N fertilizer to ensure higher grain yields. The data released by the National Bureau of Statics in 2021 showed that the consumption of N fertilizer applied to crops in China was 51.91 Tg (1 Tg = 1012 g) [6], accounting for about 45.66% of the world's total N fertilizer consumption (113.70 Tg, International Fertilizer Industry) [7]. However, too much N does not increase the yield [8] but rather increases serious environmental pollution, including CH4 and N2O emissions in the RR system [9]. Therefore, increasing or maintaining the yield with a low input of N fertilizers has become a critical consideration for ensuring sustainability in RR production. To minimize environmental pollution, China achieved zero growth in using chemical fertilizers by 2020 [10]. For this purpose, N management practices that could sustain high yield and minimize Nr losses needed to be established. Many optimized N management (OPT) strategies can increase the yield and reduce Nr losses in RR systems, for example, special fertilizer for bud promotion [11] and optimal N application using a quadratic equation on yield and N application [12]. Besides N management, water-saving irrigation, such as alternative wetting and drying irrigation, has been found to be a promising option to mitigate environmental Nr losses while reducing irrigation water input in RR fields [9]. However, few studies have been conducted to assess environmental effects under different N applications in the RR cropping system.

Of the many indicators used to assess N management, N-use efficiency (NUE) and N surplus may be helpful in policymaking. The efficiency of all the N inputs transferring to harvested crop N is defined as NUE, and it is consistent with the definition used by Zhang et al. [13]. The difference between N input and harvested N output is defined as N surplus [14,15]. This helps provide guidelines for improvements in nutrient management within a specified boundary [16]. N surplus has been widely used as an indicator for N management by various countries and organizations [13], for example, the mineral accounting system in the Netherlands [17] and intensive farming in Denmark [18]. Several case studies have considered the effects of N surplus analyses in different systems, for example, understanding seasonal N dynamics in the maize–wheat double-cropping system [19] and determining the appropriate N rate and topdressing N ratio in rice–wheat rotation [20]. These studies have contributed to the efficient agricultural N management and helped in reducing Nr losses while maintaining or improving crop yields.

However, few studies have been conducted to assess the rice yield and the environmental load in different Chinese RR planting areas after optimal N application [8,13]. This study collected data from 782 studies on the RR system covering 16 provinces in China to quantify N surplus and Nr losses in the RR system. The aim was (1) to answer which region should be encouraged to develop RR by comparing yields, Nr losses, and NUE in five Chinese RR regions, and (2) to establish a model to simulate the N surplus and Nr losses under OPT for the highest yield, the highest NUE, and the highest grain N uptake.

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

#### *2.1. Main Cropping Regions*

According to the requirements of temperature, light, and water for RR growth, the critical meteorological indexes of suitable and unsuitable planting areas of RR were determined using the principal component analysis [21]. Then, the suitable RR planting zones in China were divided into 5 climatic ecological zones and 13 regions (Figure 1 and Table S1 in Supplementary Information). They were named South China (SC), the southern part of East China (SEC), Central China (CC), the northern part of East China (NEC), Southwest China (SW), and Others (Figure 1). For "Others", no data were available; therefore, these areas, which included Beijing, Tibet, Qinghai, Hong Kong, and Macao, were not included in this study [21].

**Figure 1.** Experiment sites and main regions for RR cultivation in China. indicates Beijing.

#### *2.2. Data Source*

We searched for peer-reviewed publications published between 2005 and 2020 on RR via Science Direct, Springer Journals, the Web of Science, and the China National Knowledge Infrastructure using the search terms ratoon rice, nitrogen fertilizers, and yield. All studies that met the following criteria were included: (1) the crops in all studies were RR; (2) the start and end years of the experiment were available; (3) the amount of N fertilizer applied to the main crop and the ratoon crop of RR in the experiment was stated; (4) the amount of N absorbed and taken away by crops or the crop yield of the ratoon crop and the main crop was given; and (5) the detailed location of the experiment sites was given. A total of 782 studies fit the criteria and were included in this study, comprising over 72 experiments conducted in 16 provinces throughout China. If the same data appeared in multiple publications, they were entered into the study only once.

#### *2.3. Data Calculation*

#### 2.3.1. Calculation of N-Use Efficiency and N Stored in Soil

The main external N inputs to the RR system in China included fertilizer N, atmospheric N deposition, biological N fixation, seed N, and N from irrigation water (irrigation N). The internal N cycle of the soil and a small amount of N input were not taken into account (straw returning to the field, soil organic matter humification, and mineralization). At the same time, NUE and N surplus were calculated. Since 2000, under the strict prohibition of the government and economic incentives, it has been assumed that all the straw returns to the soil [13,22,23]. Irrigation N has been considered for N surplus calculation in some studies, e.g., in greenhouse vegetables in the North China Plain (water-deficient area) [24]. However, RR is usually planted in an area with abundant rain (Figure 1 [21]), where both the amount of irrigation water and its N content are minor. Thus, irrigation N was not considered in this study.

The N partial factor productivity (PFPN) and NUE were calculated using the following equation [13,25]:

$$\text{PFPN} = \frac{\text{Yield}}{\text{N}\_{\text{fer}}}$$

$$\text{NUE} = \frac{\text{N}\_{\text{har}}}{\text{N}\_{\text{fer}} + \text{N}\_{\text{dep}} + \text{N}\_{\text{fix}}}$$

where Nhar is the grain N uptake, kg ha−1; N uptake by grain was calculated by multiplying yield (kg ha−1) by the N content of the grain (%). Nfer, Ndep, and Nfix represent the N input from fertilization, atmospheric deposition, and non-symbiotic N fixation, respectively (kg ha−1). Details about the calculation of Nhar, Ndep, and Nfix can be found in Tables S2–S4.

NΔsoil was calculated as follows [26,27]:

$$\mathbf{N\_{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\cdots}\cdots}\right{\cdots}}}}}}}}}}}}}}}}}}}}}}}}}}}}}$$

where NΔsoil is the N stored in soil kg ha−1. Nfer, Ndep, Nfix, and Nseeding represent the N input from fertilization, atmospheric deposition, biological fixation, and seedlings, respectively (kg ha−1). Nhar is the N in harvested grain, kg ha−1. Nvol, Nlea, and Nrun represent the N output from NH3 volatilization, N leaching, and N runoff, respectively (kg ha<sup>−</sup>1). Nnit represents the N output from denitrification losses, which was estimated to be 21.6% of N fertilizer based on the mean value calculated from the published literature [28–31]. Details about the calculation of Nseeding, Nvol, Nlea, and Nrun can be found in Tables S3 and S4.

#### 2.3.2. Calculation of Nr Losses and N Surplus

Nr losses include NH3 volatilization, N2O, nitrate leaching, and runoff, but N2 is not harmful to the environment, and, hence, it was not counted in the Nr losses [13] where crop seeds absorb only a small proportion of the total nutrient input [32]. Nr losses and N surplus were calculated as follows:

$$\mathbf{N\_{f} losses} = \mathbf{N\_{nit}} + \mathbf{N\_{vol}} + \mathbf{N\_{ka}} + \mathbf{N\_{run}}$$

$$\mathbf{N\_{sur}} = \mathbf{N\_{fer}} + \mathbf{N\_{dep}} + \mathbf{N\_{fix}} - \mathbf{N\_{har}}$$

where Nnit, Nvol, Nlea, and Nrun represent the N input from nitrification or denitrification loss, NH3 volatilization, N leaching, and N runoff, respectively (kg ha<sup>−</sup>1). Nfer, Ndep, Nfix, and Nhar represent the N input from fertilization, atmospheric deposition, non-symbiotic N fixation, and grain N uptake, respectively (kg ha<sup>−</sup>1), and grain N uptake was calculated by multiplying the dry matter content (kg ha<sup>−</sup>1) by the N content of the grain (%). Details about the calculation of N uptake can be found in Table S2.

#### 2.3.3. Optimized N Based on the Highest Yield, Highest NUE, and Grain N Uptake

Grain N uptake was calculated by multiplying the dry matter content (kg ha−1) by the N content of the grain (%) [33]. Table S2 presents the details about the calculation of N uptake. The effect of N application on crop yield is divided into two stages: one is yield increase and the other is yield stabilization or even reduction with the increased N fertilization rate [34]. The diminishing marginal effect of N on yield can be observed empirically, which is mainly because of the cumulative effect of various physiological processes during plant growth [35]. Thus, a quadratic model was used to calculate the optimal N applications [36]. The optimal N application was calculated when the inflection point of the curve was met, following which the maximum yield was obtained. This method was used to calculate the optimal N application under the highest NUE (Table S6) and grain N uptake (Table S7) [33]. Therefore, the optimal N applications for the highest yield, the highest NUE, and the highest grain N uptake were defined as OPT-yield, OPT-NUE, and OPT-N uptake in this study, respectively. The un-optimized N management was defined as Un-OPT. To make the N application more in line with farmers' field management, outliers that exceeded three times of the average value were eliminated.

#### 2.3.4. Data Analysis

Excel 2010 (Microsoft., Redmond, WA, USA) was used for data processing. SPSS 26.0 (IBM Corp., Chicago, IL, USA) was used for one-way ANOVA, and Arc Gis 10.0 (ESRI Inc., Redlands, CA, USA) and Excel 2010 was used for drawing.

#### **3. Results**

#### *3.1. Comparison of N Application, Yields, and NUE in Different Regions*

The results show that N application to the main crop was the highest in SEC (Figure 2). N application to the ratoon crop was the highest in NEC, indicating that a large amount of N fertilizer was used to obtain a high yield. The yield of the main crop in SEC was the highest (8.95 t ha<sup>−</sup>1), and the yields of the ratoon crop in SEC and CC (4.66 and 4.60 t ha−1) were higher than those in other regions (*p* < 0.05) (Figure 2). Total yield was defined as the sum of the yields of the main crop and the ratoon crop. The total yields in SEC and CC were significantly higher than those in SC, NEC, and SW, and the total yield in NEC was the lowest. The PFPN was the lowest in NEC, and the NUE in CC was 60%, which was 11−122% higher than that in the other four regions (Figure 2).

**Figure 2.** Estimation of yield, N application, and N efficiency of RR in different regions Values are means ± SD of three replicates. Different lowercase letters on bars indicate significant differences at *p* < 0.05.

#### *3.2. N Stored in the Soil in Different Regions*

N fertilizer is the main source of N input. The highest amount of N fertilizer was used in NEC, followed by SEC (Table 1). CC and NEC had the largest N deposition. Grain N in CC accounted for 60.81% of the total N output, while that in NEC accounted for only 43.80%. Besides grain N, NEC had the largest denitrification N loss and ammonia volatilization. SEC had the largest N output due to the largest crop uptake (57.21%). The NEC region showed the highest Nr losses, and the SC region showed the lowest Nr losses. Apparent N stored in the soil (NΔsoil) of SE, SEC, and CC was 20, 24, and 12 kg N ha−1, respectively, while that of NEC and SW was 131 and 82 kg N ha<sup>−</sup>1, respectively. The results indicate that the N input was close to the N output in planting areas, such as SE, SEC, and CC.

**Table 1.** Estimation of the seasonal N stored (NΔsoil) in soil in RR system in five areas in China.


Note: Deposition indicates atmospheric N deposition, and biological fixation indicates biological N fixation. Different letters indicate significant difference among treatments in the same site (*p* < 0.05); "±" followed by the standard deviation.

#### *3.3. Correlation between N Application and Yield, NUE, and Grain N Uptake*

As shown in Figure 3, the results indicate that the yield was significantly related to N application. When other conditions were constant, the yield first increased and then gradually decreased with the increased N application, with a turning point (the optimal N application). The equation *<sup>Y</sup>* <sup>=</sup> −<sup>9</sup> × <sup>10</sup>−5*x*<sup>2</sup> + 0.0574*<sup>x</sup>* + 3.3637 (*<sup>p</sup>* < 0.01) can express the relationship between yield and N application. Therefore, the RR yield attained the highest point (12.87 t ha−1) when the N application rate was 319 kg ha−1. Below a specific N application rate, NUE decreased when the N application rate exceeded 257 kg N ha−<sup>1</sup> based on the relationship equation between NUE and N application (*<sup>Y</sup>* <sup>=</sup> −0.0006*x*<sup>2</sup> + 0.3087*<sup>x</sup>* + 19.677, *<sup>p</sup>* < 0.01). Therefore, NUE reached the highest point (59%) when the N application rate was 257 kg ha−1. Grain N uptake generally increased with N application (Figure 2). Generally, grain N uptake showed a significant correlation with N application, which could be described using a quadratic equation (*<sup>Y</sup>* <sup>=</sup> −0.0015*x*<sup>2</sup> + 0.9623*<sup>x</sup>* + 52.616, *<sup>p</sup>* < 0.01). Moreover, the yield, NUE, and grain N uptake demonstrated a close relationship with N application in five typical Chinese RR regions (Tables S5–S7).

**Figure 3.** Relationship between N application and yield, NUE, and grain N uptake.

#### *3.4. Performance under Optimized N Managements (OPTs) and Un-OPT Practice*

The results show that the optimal N application was different in the five regions under the same indicator (Table 2). The yields of OPT and Un-OPT were 11.08–13.51 t ha−<sup>1</sup> and 9.98–13.16 t ha−1, respectively. The RR yield of OPT was 11% higher than that of Un-OPT. Compared with Un-OPT, the N surpluses of SEC, CC, NEC, and SW were reduced by 2–72 kg N ha−<sup>1</sup> and 27–98 kg N ha−<sup>1</sup> under OPT-yield and OPT-N uptake, respectively. After OPT-NUE, NUE was 22% higher than that of Un-OPT, and N surplus and Nr losses were also reduced in the five regions. Expressing Nr losses on a yield-scaled basis provides an indication of Nr losses per ton of grain yield. The average yield-scaled Nr losses for Un-OPT (12.35 kg N t<sup>−</sup>1) were 6%, 24%, and 4% higher than those for OPT-yield, OPT-NUE, and OPT-N uptake, respectively.


**Table 2.** Yield, NUE, N surplus, and yield-scaled Nr loss responses to three optimal N applications in China.

Note: Nr losses denote reactive N losses, NH3 indicates NH3 volatilization, N2O indicates denitrification losses, L&R indicates the sum of N leaching and N runoff, and yield-scaled Nr losses indicate Nr losses/yield.

#### *3.5. Assessment of N Management*

The N input and harvested N of RR in China under OPT-yield, OPT-NUE, OPT-uptake, and Un-OPT are shown in Figure 4, and the desirable ranges for NUE (50–90%) that were suggested by the EU Nitrogen Expert Panel [35] are also shown in Figure 4. The N inputs of SEC and NEC under Un-OPT were exceeded by 400 kg N ha<sup>−</sup>1yr−1, but the N harvest in SEC was 40% higher than that in NEC. The N harvest of RR under OPT-yield, OPT-NUE, OPT-uptake, and Un-OPT were above the minimum productivity level (80 kg N ha−<sup>1</sup> yr<sup>−</sup>1) suggested by the EU Nitrogen Expert Panel [35], especially for the N harvest values in CC, which were much higher. The NUE values for OPT-yield, OPT-NUE, and OPT-N uptake were 17%, 28%, and 15% higher than those for Un-OPT, respectively, and the NUE values of RR under OPT-yield and OPT-uptake were within the desirable ranges (50–90%), showing that a high yield (high N harvest) was obtained together with a desirable NUE level.

**Figure 4.** Comparison of N input and N harvest under different N managements of RR in China. N harvest indicates grain N uptake; N input includes fertilizer N, N deposition, biological N fixation, and seed N; the yellow and orange parts indicate high-NUE and low-NUE areas (data from Zhang C [13]); open circles denote data under Un-OPT in the five RR planting areas of China. The desirable ranges NUE = 90% and NUE = 50%, and the desired minimum yield level (N harvest = 80 kg ha−1yr−1) were suggested by the EU Nitrogen Expert Panel (EU Nitrogen Expert Panel 2015 [37]).

#### **4. Discussion**

#### *4.1. Yield, NUE, Nr Losses, and N Surplus in Main RR Production Areas in China*

Dense planting has been recommended as a promising practice to achieve higher grain yields [38,39]. Fujian is the main RR planting area in SEC. SEC had the highest yield (13.59 t ha−1) at a higher planting density (27.15 × 104 hills ha−1), followed by CC (13.16 t ha<sup>−</sup>1; 25.33 × 104 hills ha<sup>−</sup>1) (Table S8), indicating that SEC and CC were dominant in RR-growing areas.

There are several reasons for a low yield, and the specific reason in different planting areas was different, i.e., Sichuan, Guangxi, and Anhui provinces. The largest planting area of RR is Sichuan province in China [39], but it had a low RR yield (10.91 t ha <sup>−</sup>1). The reasons for this are as follows: (i) the altitude in Sichuan rice planting areas is 200–800 m [40], and RR yield decreased when the altitude exceeded 350 m [41]; (ii) the average planting density was 21.65 × 104 hills ha−<sup>1</sup> (Table S8), which resulted in low effective panicles and RR yield [39]; (iii) a high incidence of rice disease (e.g., sheath blight) decreased RR yield [39]. The low RR yield in Guangxi province was mainly caused by the frequently high temperature [42]. In Anhui province, rainstorms, floods, drought, hail, and typhoon disasters are frequent, causing serious losses to agricultural production [43].

Nr losses in the five regions ranged from 113 to 177 kg N ha−<sup>1</sup> (average 146 kg N ha<sup>−</sup>1). SC had the lowest Nr losses (113 kg N ha<sup>−</sup>1), and NEC had the highest Nr losses (177 kg N ha−1) (Table 1), which are higher than those of double-season rice under OPT in the Taihu region in China (102 kg N ha−1) in the study conducted by Ju et al. [44]. The NUE of RR ranged from 27% to 60% (Figure 2), and the highest NUE was in CC (60%), which is close to that of the Chinese double-cropping system (68%) under OPT proposed by Zhang et al. [13]. Moreover, the average NUE was 47%, which is lower than the average predicted NUE (60%) of rice for 2050 [45], indicating that N application needs to be optimized for RR.

#### *4.2. NUE and N Surplus under Three Optimal N Application Rates*

Many methods (i.e., integrated soil–crop system management [46], response curves of N application and yield [47], and N balance management [48]) were used to determine the best N application amount, and the most common method was the recommended method based on the effective function of N application [12]. The relationship between yield and N application at specific locations or different scales has been examined in many studies [49,50], which include quadratic equations, quadratic-plus-plateau models, square roots, and exponential equations. The quadratic equation has been the method most commonly used to calculate the optimal N application in China [33,51]. The quadratic model between N application rate and yield (Table S5), NUE (Table S6), and crop N uptake (Table S7) can be established. Then, obvious inflection points and mutation points can be used to determine the optimal N application under different indicators. We can determine the minimum amount of N application needed to ensure a certain yield or gain [12,33,34]. The quadratic model recommended an optimal N application for RR of 319 kg ha−<sup>1</sup> in order to obtain the highest yield (12.78 t ha−1) under OPT-yield in this study (Table 2), which is lower than that in the study conducted by Cao et al. [8] (13.67 t ha−1) under the optimal N application rate. This difference is mainly due to the fact that the quadratic model was selected in this study while the linear-plus-plateau model was used in the study of Cao et al. [8]. The theoretical optimal N surplus under the highest yield was 180 kg N ha<sup>−</sup>1, which is higher than the N surplus benchmark (120 kg N ha−1) determined by Zhang et al. [13]. This difference is mainly due to the fact that the crops researched were different. RR was studied in this study, while all the main Chinese rice-based systems (rice, double rice, rape-rice, and wheat-rice) were used in the study conducted by Zhang et al. [13], and different crops have different N surplus benchmarks. The highest NUE could be achieved with the lowest N application in this study (Table 2), which is consistent with the findings of Zhang et al. [12]. The highest NUE (59%) for Chinese RR estimated in our study was lower than the NUE (64%) for the rice–rice system proposed by Zhang et al. [13]. Crop N uptake was supposed to be an indicator for estimating the N utilization rate [52]. The N application rate under the highest grain N uptake was higher than that under the highest yield and NUE, and this result is similar to that of Zhang et al. [33].

The NUE of RR in China based on different indicators ranges from 53% to 59% (Table 2); this is close to the mean NUE target for 2050 suggested by Zhang et al. [45], in which higher NUE targets were set for rice (60%). N surplus (133–183 kg N ha<sup>−</sup>1) (Table 3) based on different indicators in this study was higher than the average surplus target of all main grain crops in China (65 kg N ha−1) and the worldwide average value for 2050 (53 kg N ha<sup>−</sup>1) [45] (Table 3). The biggest differences between our study and that conducted by Zhang et al. [45] was based on data. First, the data of different crops were used in the study conducted by Zhang et al. [45], while the data of only RR in the five main RR regions were used in this study. Second, the data from the Food and Agriculture Organization (FAO) and International Fertilizer Industry Association (IFA) statistical databases were used by Zhang et al. [45], while data from on-farm experiments were obtained in this study (Table 3).



#### *4.3. Policy Suggestions*

Farms from some developed countries (e.g., the Netherlands and Europe) have achieved lower Nr losses than those under the fertilization plan [52], suggesting that our N surplus could be further reduced. The amount of N fertilizer needs to be reduced while the yield is maintained or improved in order to achieve the proposed N surplus for RR. The required improvements could be expressed as the full adoption of the "4R" of nutrient stewardship (right source, right rate, right time, and right place) [54]. Enhancedefficiency fertilizers (e.g., controlled-release urea) can significantly increase rice yields by 26%, and reduce NH3 volatilization (23–62%) and N surface runoff losses (8–58%) [55,56]. The rice nutrient expert system has been used to provide the correct N fertilizer amount based on the yield response of rice in the previous season, and it recommend a more accurate amount of N fertilization for rice [57]. For RR, the N fertilizer used in the first season had a significant effect on the yield of the main crop but little effect on the yield of the ratoon crop [8], while the N fertilizer used for bud promotion and seed promotion had significant effects on the yield of ratoon crops [58]. Therefore, the fertilization time should be precise. The deep placement of urea can better match the N demand of rice plants and effectively minimize NH3 volatilization compared with broadcast [58,59]. New irrigation technology (e.g., dry–wet alternate irrigation) [60] and moldboard plowing with direct seeding [61] have also been found to realize higher yields with lower Nr losses, and they should also be used for RR. In addition, pest, weed, and disease control technologies also help farmers achieve high RR yields, for example, validamycin to eliminate pests (sheath blight) in RR, special herbicides to remove weeds (echinochloa crusgalli) in RR, and isoprothiolane to control disease (rice blast) in RR [21,39].

#### **5. Conclusions**

SEC and CC are the dominant regions for RR with higher yields and lower Nr losses. Hence, policy incentives should be implemented in these two regions for food security and environmental protection. Appropriate N surplus (180 kg N ha−1) and NUE (54%) values under OPT-yield can not only increase yield but also reduce Nr losses. The "4R" of nutrient stewardship can be fully adopted to achieve N surplus in different regions under OPT-yield when the sustainable development of RR is encouraged in China.

**Supplementary Materials:** https://www.mdpi.com/article/10.3390/agriculture12071064/s1. References [13,21,42,43,62–67] are cited in the Supplementary Materials. Table S1: Information of zone, province and experiment sites; Table S2: Protein and nitrogen content of grain for ratoon rice in five areas of China; Table S3: Nutrient source (atmospheric deposition, biological fixation of nitrogen and rice seeding) into cropland; Table S4: Models for calculating reactive nitrogen (Nr) loss; Table S5: Relationship between N application and yield for RR in five areas of China; Table S6: Relationship between N application and NUE for RR in five areas of China; Table S7: Relationship between N application and grain N uptake for RR in five areas of China; Table S8: The accumulated temperature and planting density in different province.

**Author Contributions:** R.H.: conceptualization, methodology, writing—original draft, data curation, and writing—review and editing. Z.D.: validation and supervision. T.L.: conceptualization, methodology, and writing—review and editing. D.Z.: methodology. Y.T.: investigation and software. Y.C.: investigation. J.H.: supervision, funding acquisition, conceptualization, methodology, and validation. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by the Engineering Research Center of Ecology and Agricultural Use of Wetland, the Ministry of Education (KF202109), and the Hubei special fund for agricultural science and technology innovation (2018skjcx01).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** We acknowledge the very helpful comments by C. Zhang (College of Tropical Crops, Hainan University) for the revision of this manuscript.

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

#### **References**


## *Article* **Studying the Effect of Straw Returning on the Interspecific Symbiosis of Soil Microbes Based on Carbon Source Utilization**

**Yucui Ning 1, Xu Wang 1, Yanna Yang 1, Xu Cao 2, Yulong Wu 1, Detang Zou <sup>3</sup> and Dongxing Zhou 1,\***


**Abstract:** Heilongjiang province has made great contributions to ensuring the food security of China. Grain production has increased year by year, followed by a large amount of straw—especially the production of corn straw. Straw returning is the best treatment method from the perspective of ecology. This study simulated modern mechanized operation conditions from the field of soil biological characteristics to explore the impact of straw decomposition on the changes in the soil microbial community. In this study, in the black soil region of Northeast China (45◦45 27~45◦46 33 N, 126◦35 44~126◦55 54 E), the orthogonal experimental design was used to experiment for two years (2019–2020), using straw length, amount, and buried depth as returning factors. The carbon source utilization intensity algorithm that was developed by our team was used to extract a single carbon source. A compound mathematical model was constructed based on path analysis and grey relation analysis. This study analyzed the interspecific symbiotic relationship of soil microbes in the process of straw returning and explored the regulatory methods and schemes with which to promote straw decomposition. The results showed that in the first year after straw returning, the cumulative decomposition rate of straw could reach 55.000%; the supplement of the carbon source was glycyl-L-glutamic acid, which was helpful for the decomposition of straw. It was found that cyclodextrin should be added within 90–120 days after straw returning to promote decomposition. In the second year of straw returning, the cumulative decomposition rate of straw can reach 73.523% and the carbon sources α-D-lactose and D-galactonic acid γ-lactone should be supplemented appropriately to promote straw decomposition. This study provides an experimental basis for corn straw returning to the black soil of the cold regions, along with the scientific and technological support for the sustainable development of agriculture and a guarantee of national food security.

**Keywords:** straw returning; soil microbes; carbon source utilization; grey relational analysis; path analysis

#### **1. Introduction**

As the main corn-producing area in China, the cold black soil region plays an important role in stabilizing the balance of grain supply and demand along with ensuring national food security [1,2]. However, the abandonment or random burning of corn straw has increased haze [3], the frequency of fires, and the waste of resources [4]. Therefore, determining how to efficiently deal with straw has become a critical concern.

Returning straw to the field can improve the soil environment [5], increase the content of soil organic matter [6], and enhance the ability of soil to retain water and fertilizer [7]. It can also supply necessary elements in plants [8], promote crop growth and development [9,10], and help with nitrogen fixation and emission reduction in the agricultural ecosystem [11,12]. Moreover, the rice yield can be effectively maintained by partially replacing mineral fertilizer with straw returning [13,14]. Recently, many scholars have conducted

**Citation:** Ning, Y.; Wang, X.; Yang, Y.; Cao, X.; Wu, Y.; Zou, D.; Zhou, D. Studying the Effect of Straw Returning on the Interspecific Symbiosis of Soil Microbes Based on Carbon Source Utilization. *Agriculture* **2022**, *12*, 1053. https:// doi.org/10.3390/agriculture12071053

Academic Editors: Chengfang Li and Lijin Guo

Received: 6 July 2022 Accepted: 15 July 2022 Published: 19 July 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/).

extensive research on straw returning to explore the best scheme of the process. These include studies on the degree of straw crushing, the amount of straw returning [15,16], the research and development of applied materials [17,18], the selection of farming methods [19,20], and the impact of soil types in the straw returning area on the straw decomposition effect [21,22]. Despite multiple studies, the research rarely involved studies on the regulation of interspecific symbiosis and the cooperation of soil microbes in the process of straw returning.

Based on this research gap, this study simulated the operating conditions of modern agricultural machinery, designed a three-factor orthogonal experiment using the amount, length, and buried depth of straw return as the factors, and carried out a two-year straw returning experiment in the cold black soil area. Using the carbon source utilization intensity algorithm that was developed by our team [23], the study extracted a single carbon source and analyzed the impact of straw returning on the carbon source utilization intensity of soil microbes. This study used the path analysis model (PA) and grey relational analysis model (GRA) to analyze the interspecific symbiotic relationship of soil microbes in the process of straw returning and find the regulatory methods and schemes with which to promote straw decomposition. This study provided scientific and technological support for the sustainable development of agriculture and to guarantee national food security.

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

#### *2.1. Test Materials*

The test was conducted at the teaching experimental base of Northeast Agricultural University (45◦45 27~45◦46 33 N, 126◦35 44~126◦55 54 E). The test area belongs to the temperate continental monsoon climate with an average annual temperature of 3.6 ◦C, annual precipitation of 500–600 mm, an average annual frost-free period of 135–140 days, and an effective accumulated temperature of 2700 ◦C [24]. The detailed meteorological data during the test are given in the Supporting Information.

The soil was typical black soil with a pH of 6.30 ± 0.06. It was composed of 39.06 ± 0.42 g/kg of organic matter; 2.20 ± 0.08 g/kg of total nitrogen; 2.71 ± 0.08 g/kg of total phosphorus; and 183.25 × <sup>10</sup>−<sup>3</sup> ± 0.16 × <sup>10</sup>−<sup>3</sup> g/kg of available potassium.

The tested straw was corn straw with total carbon of 479.0 ± 0.23 g/kg; total nitrogen of 13.16 ± 0.09 g/kg; total phosphorus of 4.56 ± 0.06 g/kg; and total potassium of 15.36 ± 0.07 g/kg. The C: N ratio was 36.13–36.78.

The mesh bag was cut from 100-mesh polyamide fiber, and the bag was 35 cm long and 25 cm wide.

#### *2.2. Test Design*

According to the three-factor five-level quadratic orthogonal rotation test design, the straw length, amount, and buried depth were taken as the test factors. Referring to the previous research results [24,25], the maximum and minimum values of each test factor are determined, that is, the actual value when the coding value is 1.682 and −1.682. Then, the actual value under other coding levels is determined according to the equivalent conversion between the coded value and the actual value. The test design result is shown in Table 1.

The straws with different weights and lengths were put into mesh bags (35 cm × 25 cm) and then soaked with water to enable the moisture content of the straw to reach 40%. The bags were buried in the soil horizontally. Each treatment was randomly arranged and repeated four times, with a total of 20 plots. Each plot was 15 m long and 1 m wide. During the straw returning period, no farming is carried out.


**Table 1.** Test design.

According to the three-factors five-levels quadratic orthogonal rotation experimental design, fifteen groups of experiments were carried out. The straw amount in each experimental plot (1 m2) is given in the Supporting Information.

Two kinds of decomposition tests were set up. The first was a one-year decomposition period while the second was a two-year decomposition period. In view of the climate impact of the cold black soil area, all the straws were buried in the spring on 6 May 2019. After 15 days of adaptation in the soil, 30 days cycles were taken for sampling in the one-year decomposition period until the end of autumn on 21 October. Thus, a total of five cycles were considered in the one-year decomposition period. The samples for the two-year decomposition period were taken on the same date of the next year (2020), as shown in Table 2.

**Table 2.** Sampling period.


Note: A, year.

#### *2.3. Sample Collection and Index Determination*

#### 2.3.1. Determination of Soil Microbial Community

According to the sampling method of rhizosphere soil, the soil around the mesh bag should be taken to the laboratory at 4 ◦C. The samples were activated at 25 ◦C for 24 h. After that, 10 g of sample was weighed and added to 90 mL of sterilized 0.85 mol/L NaCl solution. It then oscillated at 250 r/min for 30 min and was gradually diluted to 10−<sup>3</sup> after standing for 10 min. The bench was clean in the vertical flow, followed by the inoculation of 150 μL of soil suspension into an ECO plate, and finally cultured in a constant temperature incubator at 28 ◦C. The absorbance value (OD value) at 590 nm was measured by taking 24 h as a culture cycle. The measurements of seven culture cycles (168 h) were continuously taken.

In the *t* culture cycle, the total change of 31 carbon sources in the ECO plate was given by the following:

$$\mathbf{x}\_{t} = \sum\_{i=1}^{31} \mathbf{x}\_{i\prime}^{t} \text{ and } \mathbf{x}\_{i}^{t} = \left(OD\_{i}^{t} - OD\_{i}^{t-1}\right)^{2} / \left|OD\_{i}^{t-1}\right| \tag{1}$$

where *yt* is the total change of 31 carbon sources; *OD* is the absorbance value of carbon source; and *i* indicates the type of carbon source, *i* = 31; *t* = 1, 2, ··· , 7. The distribution of carbon sources is given in the Supporting Information.

Therefore, the utilization intensity of carbon source by microbes was given by

$$Z\_i = \sum\_{t=1}^{7} Q\_i^t \tag{2}$$

where *Q<sup>t</sup> <sup>i</sup>* is the dimensionless data and *<sup>Q</sup><sup>t</sup> <sup>i</sup>* = *<sup>x</sup><sup>t</sup> i* /*yt* × 100%.

#### 2.3.2. Calculation of Straw Decomposition Rate

The straw, with the mesh bag, was placed into a sterile bag, stored at 4 ◦C and taken back to the laboratory. The mud and grassroots, which adhered to the mesh bag, were washed with deionized water and then dried in a constant temperature oven at 60 ◦C. The straw in each sampling period was accurately weighed and used to calculate the straw decomposition rate according to the Equation (3):

$$G\_T = (M\_0 - M\_T) / M\_0 \times 100\% \tag{3}$$

where *GT* is the straw decomposition rate; *M*<sup>0</sup> is the initial dry weight of the straw; and *MT* is the dry weight of the straw after *T* days of returning.

#### *2.4. Data Analysis*

#### 2.4.1. Path Analysis Model (PA)

PA studies the direct effect, indirect effect, and total effect by decomposing the correlation between the independent variables and dependent variables [26]. In this study, the utilization intensity of the carbon source by soil microbes is the independent variable: *Z*1, *Z*2, ··· , *Z*31; the straw decomposition rate is the dependent variable: *G*. *Rαβ* represents the simple correlation coefficients (spearman) of *Z<sup>α</sup>* and *Zβ*; *Rα<sup>g</sup>* represents the correlation coefficient of *Z<sup>α</sup>* and *G*; *Pα<sup>g</sup>* is the direct path coefficient, which indicates the direct effect *Z<sup>α</sup>* on *G* when the other variables are fixed. *Rα<sup>g</sup>* can be decomposed into the following equations:

$$\begin{cases} P\_{1\S} + r\_{12}P\_{2\S} + r\_{13}P\_{3\S} + \dots + r\_{1k}P\_{k\S} = r\_{1\S} \\ r\_{21}P\_{1\S} + P\_{2\S} + r\_{23}P\_{3\S} + \dots + r\_{2k}P\_{k\S} = r\_{2\S} \\ r\_{31}P\_{1\S} + r\_{32}P\_{2\S} + P\_{3\S} + \dots + r\_{3k}P\_{k\S} = r\_{3\S} \\ \dots \\ r\_{k1}P\_{1\S} + r\_{k2}P\_{2\S} + r\_{k3}P\_{3\S} + \dots + P\_{k\S} = r\_{k\S} \end{cases} \tag{4}$$

In this study, the absolute value of the path coefficient can be directly used to compare the importance of various microbial populations to straw decomposition. Among these, the direct path coefficient reflects the direct effect of this microbial population. Microbes can also affect the straw decomposition through the interaction with other microbial communities, which is expressed by the indirect path coefficient.

The indirect path of *Z<sup>α</sup>* to dependent variable *G* through other variables *Z<sup>β</sup>* is *RαβPβg*, and the determination coefficient of *Zα* to *G* is calculated as follows:

$$\mathbf{C}\_{(a)}^{2} = P\_{a\mathbf{g}}^{2} + 2 \sum\_{a \neq \beta} P\_{a\mathbf{g}} R\_{a\beta} P\_{\beta\mathbf{g}} = 2R\_{a\mathbf{g}} P\_{a\mathbf{g}} - P\_{a\mathbf{g}}^{2} \tag{5}$$

where *C*<sup>2</sup> (*α*) is the determination coefficient.

Secondly, the path residual effect *PRg* is calculated. If the residual effect is minute (generally bounded by 0.20), it indicates that the PA included the main influencing factors; otherwise, variables need to be added to improve the model.

$$P\_{\mathcal{R}\mathcal{g}} = \sqrt{1 - \left(P\_{1\mathcal{g}}R\_{1\mathcal{g}} + P\_{2\mathcal{g}}R\_{2\mathcal{g}} + P\_{3\mathcal{g}}R\_{3\mathcal{g}} + \dots + P\_{k\mathcal{g}}R\_{k\mathcal{g}}\right)} \tag{6}$$

In this study, the residual path coefficient was less than 20.00% as the judgment standard for extracting carbon sources *Z* = [*Z*1, *Z*2, *Z*3, ··· , *Zk*].

#### 2.4.2. Grey Relational Analysis Model (GRA)

GRA is a quantitative evaluation method based on grey system theory. It reflects the similarity of the development process between sequences through displacement difference. It can make up for the defect of the mathematical statistics method having a linear relationship with the sequence, which is irrelevant. It can overcome the deficiency of relying exclusively on the model for quantification and directly find the primary and secondary factors in the process of system development [27,28]. The specific process of GRA model construction was as follows:

In this study, the straw decomposition rate, *G*, is set as the parent sequence, and the carbon source extracted by path analysis, *Z* , is set as the sub-sequence.

Calculate the difference and take the absolute value, that is Δ*η*(*k*) = *Gμ*(*k*) − *Z <sup>η</sup>*(*k*) . Calculate the maximum and minimum values for all absolute values, that is: max *<sup>η</sup>* max*<sup>k</sup>* Δ*η*(*k*) and min *η* min Δ*η*(*k*).

*k* Calculate the relational coefficient according to the following formula.

$$\xi\_{\eta\eta}(k) = \left\{ \min\_{\eta} \min\_{k} \Delta\_{\eta}(k) + \varepsilon \left[ \max\_{\eta} \max\_{k} \Delta\_{\eta}(k) \right] \right\} / \left\{ \Delta\_{\eta}(k) + \varepsilon \left[ \max\_{\eta} \max\_{k} \Delta\_{\eta}(k) \right] \right\} \tag{7}$$

where *ε* ∈ {0, 1} is the resolution coefficient. The smaller the *ε* value, the greater the difference between the relational coefficients and the stronger the discrimination ability. Referring to the previous research results [29], in this paper, *ε* = 0.5.

Calculation of grey comprehensive correlation degree (*GCD*): *ψαβ* = <sup>1</sup> *ρ ρ* ∑ *k*=1 *ξμη*(*k*).

#### **3. Results and Analysis**

#### *3.1. Decomposition Rate of Straw with Different Returning Ways*

As shown in Figure 1A, with the extension of straw returning time, the straw decomposition rate of each treatment group gradually increased. After 150 days of straw returning, in the T10 treatment, the cumulative decomposition rate of straw was the largest, 55.000%; in the two-year decomposition test ((A + 150) day), the cumulative decomposition rate of straw in the T10 treatment was still the largest, reaching 73.523%. In the process of straw decomposition, there was a trend of fast decomposition in the early stage and slow decomposition in the late stage. In the first two months of the one-year decomposition test, the monthly average decomposition rate was 9.310–11.000%; meanwhile, the monthly average decomposition rate of the last two months was 3.167–7.167%. The reason for this result is that, on the one hand, at the late stage of decomposition, the easily degradable organic matter in the straw gradually decreases, and the remaining part is

mainly the difficult to decompose organic matter. On the other hand, it may be that the soil temperature decreases in the late stage of decomposition, resulting in the reduction in microbial activity, which is not conducive to the decomposition of straw [30]. As shown in Figure 1B,C, at the end of the test, in the two kinds of straw decomposition tests, the straw decomposition rate showed an inverted "U" shape with the increase in the coding value. High or low straw returning causes the imbalance of the soil carbon–nitrogen ratio, affects the number and activity of microbes, and leads to the reduction in straw decomposition rate [31]. Therefore, it is necessary to explore the evolution of soil microbial communities in the process of straw decomposition and find methods and schemes with which to promote straw decomposition.

**Figure 1.** Cumulative decomposition rate of straw with different returning ways. (**A**) Including all straw returning periods; (**B**) after 150 days of straw returning, the change of straw decomposition rate caused by the interaction of straw amount and straw length; (**C**) after (A + 150) days of straw returning, the change of straw decomposition rate caused by the interaction of straw amount and straw length. Notes: The changes of straw decomposition rates caused by the interaction of straw amount and buried depth (Figure S1), and the interaction of straw length and buried depth (Figure S2) are given in Supporting Information.

It can be seen from Table S2 that the minimum value of the determination coefficient appeared in the 60 days of straw returning, which was 0.967, indicating that the relative contribution of the six variables which entered the PA to the straw decomposition rate had reached 96.7%, and the remaining path coefficient was 0.182, which met the judgment standard. The results showed that the PA was suitable for analyzing the relationship between straw decomposition and the soil microbial community in the cold black soil areas.

In the 30-day straw returning test group, the carbon sources L-arginine and *N*-acetyl-D-glucosamine had high direct path coefficients in the positive axis direction, which were 0.846 and 0.837, respectively. However, their total effect values, which were 0.315 and 0.101, respectively, were not high due to the counteraction of their negative indirect path effect. This was lower than that of the carbon source glycyl-L-glutamic acid, which had a total effect value of 0.371. In the 60-day group, the carbon source L-phenylalanine had the largest positive direct path effect of 0.437, with a total effect of 0.343. Although the carbon source D-malic acid had the largest positive indirect path effect of 0.741, due to the offset of the negative direct path effect of −0.581, its total effect was lower than that of the carbon source L-phenylalanine, which was 0.161. After 90 days of straw returning, the carbon source α-ketobutyric acid had the highest direct path coefficient of −1.239 and indirect path coefficient of 1.253, but the total effect value was only 0.014 due to the opposite effect between them. Simultaneously, the total effect of the carbon source *N*-acetyl-D-glucosamine was the largest in the negative direction with a total effect value of 0.512. The carbon source glycyl-L-glutamic acid had the largest total effect value of 0.379 in the positive direction. As shown in Figure 2, the carbon source α-ketobutyric acid played a major role through the indirect effect of the carbon source glycyl-L-glutamic acid with a value of 0.824.

After 120 days of straw returning, the carbon source L-asparagine ranked first with a positive direct effect of 0.549 and the carbon source α-cyclodextrin ranked second with a value of 0.536. However, the former counteracted the negative indirect effect of the carbon source D-glucosaminic acid, so its total effect value was lower than the latter. Simultaneously, as shown in Figure 2, the indirect effects between the carbon sources L-asparagine and α-cyclodextrin were negative and occupied large components, which were −0.107 and −0.110, respectively. In the last stage of the one-year straw returning test, the direct path effect of the carbon source glycyl-L-glutamic acid was the largest, which was 0.577. The indirect path effect of carbon source 4-hydroxy benzoic acid was the largest, which was 0.666, but the total effect value was negative at −0.469, due to the counteraction of the negative direct effect of −1.135. Although the indirect path effect of the carbon source D-xylose was also offset by the negative direct effect, its total effect value was the largest positive at 0.246.

As shown in Table 3, for the two-year straw returning test group, in the treatment of A + 30, the carbon source α-D-lactose had the maximum direct path effect of 0.506, the minimum indirect path effect of 0.052, and the maximum total path effect value of 0.558. Although the carbon source β-methyl-D-glucoside had a direct path effect of 0.500, its total effect value was only 0.062 due to the counteraction of its indirect path effect with a value of −0.439. In the treatments of A + 60 and A + 90, the total effect values of the carbon source D-galactonic acid γ-lactone were 0.714 and 0.648, respectively. These values ranked first in each group with a much higher total effect value than that of other carbon sources. This depended on them having the largest direct path effect. In the treatments of A + 150, however, the absolute values of the direct and the indirect path effect coefficients of each carbon source were large; due to the offset between positive and negative effects, only the carbon source tween 40 had a small positive total effect with a value of 0.059.

**Figure 2.** Indirect path effect between carbon sources. (A, year; Z0, water; Z1, β−methyl−D−glucoside; Z2, <sup>D</sup>−galactonic acid γ−lactone; Z3, <sup>L</sup>−arginine; Z4, pyruvic acid methyl ester; Z5, <sup>D</sup>−xylose; Z6, <sup>D</sup>−galacturonic acid; Z7, <sup>L</sup>−asparagine; Z8, tween 40; Z9, I−erythritol; Z10, 2−hydroxy benzoic acid; Z11, <sup>L</sup>−phenylalanine; Z12, tween 80; Z13, <sup>D</sup>−mannitol; Z14, 4−hydroxy benzoic acid; Z15, <sup>L</sup>−serine; Z16, α−cyclodextrin; Z17, N−acetyl−D−glucosamine; Z18, γ−hydroxybutyric acid; Z19, <sup>L</sup>−threonine; Z20, glycogen; Z21, <sup>D</sup>−glucosaminic acid; Z22, itaconic acid; Z23, glycyl−L−glutamic acid; Z24, <sup>D</sup>−cellobiose; Z25, glucose−1−phosphate; Z26, α−ketobutyric acid; Z27, phenylethyl−amine; Z28, α−D−lactose; Z29, D,L−α−glycerol phosphate; Z30, <sup>D</sup>−malic acid; Z31, putrescine.)


**Table 3.** The results of path analysis and grey correlation analysis.

Note: A, year. Z1, Z2, ... , Z31 are the utilization intensity of the carbon source by soil microbes, the detailed information is given in Supporting Information. *GCD*: Grey comprehensive correlation degree.

#### *3.2. Relational Analysis between Microbes and Straw Decomposition*

In the 30-day straw returning group, the carbon source glycyl-L-glutamic acid with the largest total path effect value ranked second, and the carbon source glycogen had the same correlation degree of 0.710. Meanwhile, the carbon source L-arginine had the largest correlation degree of 0.758. In the 60-day straw returning group, the correlation degree of each carbon source entering the PA model was lower than 0.700 and the differences between the carbon sources were small. In the 90-day straw returning group, the correlation degree of the carbon source glycyl-L-glutamic acid was the largest, with a value of 0.751, indicating that it was closely related to the straw decomposition. Simultaneously, the correlation degree of the D-malic acid was the second largest, with a value of 0.739. This was similar to the total effect that was obtained by PA in the positive axis direction. A similar phenomenon occurred in the 120-day straw returning group where the correlation degree of carbon source α-cyclodextrin was the largest, with a value of 0.782. Meanwhile, the carbon sources N-acetyl-D-glucosamine and D-glucosaminic acid, with the maximum negative total effect in PA, had the minimum correlation degree with values of 0.651 and 0.690, respectively (Table 3). After 150 days of straw returning, the correlation degree of the carbon sources D-xylose and L-serine ranked first at 0.726, followed by the value of the correlation degree of the carbon source glycyl-L-glutamic acid at 0.724.

In the two-year straw returning test groups, in the treatments of A + 30, A + 60, and A + 90, the carbon sources with the largest positive direct effect had the largest correlation degrees, which were 0.760, 0.805, and 0.818, respectively. The correlation degree of the carbon source α-cyclodextrin with the largest positive direct effect of 0.696 was second only to the first carbon source γ-hydroxybutyric acid with a value of 0.702 in the treatment of A + 120. However, in the treatment of A + 150, D-malic acid, the carbon source with the largest positive indirect effect, had the highest correlation degree with a straw decomposition at a value of 0.738, while phenylethylamine, the carbon source with the second positive indirect effect, also had the second correlation degree of 0.735.

#### **4. Discussion**

Combined with the results of PA and GRA, the path map was drawn to analyze the interspecific symbiotic relationship of soil microbes during straw returning, and to find the methods and schemes for promoting straw decomposition.

In the one-year straw returning of the 30-day group, the carbon source L-arginine had the largest direct path effect value and correlation coefficient, but its total effect value was lower than that of the carbon source glycyl-L-glutamic acid. It can be seen from Figure 2 that the ranking of the correlation degree is affected through the indirect effect of the carbon source D-galactonic acid γ-lactone. Simultaneously, the carbon source D-galactonic acid γ-lactone had a negative maximum direct path effect and total effect. Therefore, when accelerating the decomposition of returning straw, it can be considered to reduce the input of the carbon source D-galactonic acid γ-lactone, and supplement glycyl-L-glutamic acid appropriately. As an amino acid carbon source, glycyl-L-glutamic acid plays an important role in the early stage of straw returning, which may be to balance the "carbon–nitrogen ratio" in the soil and provide suitable environmental conditions for the proliferation of microbial communities [32,33]. After 60 days of straw returning, the correlation degree difference between each carbon source entering the PA model and straw decomposition was small, indicating that the soil microbial community was in the stage of rapid reproduction and expanding population size at the time. This can be seen in Figure 3 showing that the direct demand for all kinds of carbon sources was large.

**Figure 3.** Path diagram.

In the 90-day straw returning group, although the results of GRA were consistent with the total effect that was obtained by PA in the positive axis direction, the first two were carbon source glycyl-L-glutamic acid and the carbon source D-malic acid. The former mainly promoted straw decomposition through the direct effect. While the latter promoted straw decomposition through an indirect effect on the former. Simultaneously, in the indirect impact of the carbon source α-ketobutyric acid on straw decomposition (Figure 2), the carbon source glycyl-L-glutamic acid played a major role. Therefore, glycyl-L-glutamic acid was the necessary carbon source for the soil microbial community within 60–90 days of straw returning. During the decomposition of straw, cellulose and hemicellulose, formed by hexose and pentose through a single bond, were decomposed first, followed by lignin, which was linked by benzene ring compounds through the δ bond and π bond [34]. It was found that the lignin-degrading microbes had a high demand for amino acid carbon sources [35,36]. Therefore, amino acid carbon sources should be supplemented in the later stage of straw returning to accelerate the straw decomposition.

As shown in Figure 3, after 120 days of straw returning, the direct path effect of itaconic acid on straw decomposition was offset by its indirect path effect through L-asparagine. Simultaneously, the antagonistic effect between the carbon source L-asparagine and the carbon source α-cyclodextrin, and its indirect path effect through the carbon source Dglucosaminic acid made the total effect value and the correlation degree of the carbon source L-asparagine lower than that of the carbon source α-cyclodextrin. On one hand, the existence of L-asparagine may inhibit the synthesis of some substances, thus slowing down the decomposition of straw by the microbial community. L-asparagine hydrolyzes the acylamino into aspartic acid and ammonia under the action of L-asparaginase [37]. Glucosamine can be used as the starting material for the asymmetric synthesis of various amino acids [38], and it is also a special component of the lipopolysaccharide of *Rhizobium leguminosarum*, which is crucial for the nitrogen cycle in organisms [39]. Alternatively, the unique external hydrophilic and internal hydrophobic structures of cyclodextrin can not only increase the biological activity of microbes to accelerate the straw decomposition [40], but also increase the permeability of the cell membrane to promote microbes to absorb nutrition more effectively [41]. Simultaneously, the cylindrical three-dimensional structure with one large side and another small side is conducive to the adsorption of ammonia [42]. Moreover, given the effect of cyclodextrin on the comprehensive improvement of the physical properties of soil [27,43], it can be supplemented to the soil within 90–120 days after the straw is returned to the field.

It can be seen from the path map that the carbon source glycyl-L-glutamic acid is also a necessary carbon source for the soil microbial community within 120–150 days of straw returning. However, it can be seen from Figure 2 that the indirect path effect of the carbon source 4-hydroxy benzoic acid through the other carbon sources was the largest at this stage, and the indirect path effects of the other carbon sources through 4-hydroxy benzoic acid were also at a high level. 4-hydroxy benzoic acid has strong allelopathy on rhizosphere microbes, can inhibit the function of root mitochondria [44], promote the growth of pathogens, and lead to the occurrence of soil-borne diseases [45,46]. Studies have found that straw returning well inhibits the soil-borne pathogens, including *Fusarium oxysporum* [47], *Rhizoctonia cerealis* [48], *Verticillium dahliae Kleb* [49], and *Plasmodiophora brassicae Woronin* [50]. Therefore, it is speculated that straw returning improves the living environment of microbes [51], promotes the proliferation of microbial populations that can use the carbon source 4-hydroxybenzoic acid in the soil to form dominant species, consumes 4-hydroxybenzoic acid in the soil, and alleviates the soil-borne diseases of crops. Yang et al. [52] confirmed that the insufficient ability of microbes to metabolize 4-hydroxybenzoic acid in the soil is an important factor that causes tobacco root rot. Zhang et al. [53] found that there are microbes which can metabolize 4-hydroxybenzoic acid in the specific disease inhibiting soil of tobacco bacterial wilt.

In the two-year straw returning test of A + 30 day treatment, seven carbon sources were mentioned to enter the PA model, but only the direct and the indirect path effect coefficients of the carbon source α-D-lactose were positive, and the correlation coefficient of α-D-lactose was the largest, indicating that α-D-lactose is the characteristic carbon source of straw decomposition at this stage. α-D-lactose, as the energy source of rod-shaped strain, can effectively improve the removal efficiency of lignin [54], and as a co-metabolic substrate, it can promote the degradation of polycyclic aromatic hydrocarbons by *Pseudomonas* [55]. Moreover, α-D-lactose can be used as an inducer to promote *Escherichia coli* to produce cyclodextrin glucosyltransferase, and then convert starch into cyclodextrin through a cyclization reaction [56,57]. Therefore, in the treatments of A + 30 and A + 60, the carbon sources α-D-lactose and α- cyclodextrin were extracted into the PA model (Table 3), while in the treatments of A + 60 and A + 90, the carbon source D-galactonic acid γ-lactone played an important role in straw decomposition, which may be related to lignin degradation. It was found that D-galactonic acid γ-lactone could be transformed into D-galactonic acid under the action of glucolactonase and enter the glycolysis process [57] to accelerate the degradation of lignocellulose [58,59].

Table 3 shows that for the treatments of A + 120 and A + 150, among the carbon sources that were extracted from the PA model, only three had a positive effect, but their total effect values were small, and the total effects between the other carbon sources and straw decomposition were negative. The results showed that the effect of the microbial community on straw decomposition was weakened at this stage. It may be possible that nutrients such as nitrogen, phosphorus, and potassium in the straw were released, and microbes no longer relied on the straw to provide the carbon source, nitrogen source, and energy. A long-term location returning test in Songnen Plain, conducted by Gong et al. [60], showed that after two years of straw returning, the degradation rates of cellulose and hemicellulose exceeded 80%, and lignin was low at 78.63%. The study by Chen et al. [61] confirmed that after half a year of straw returning, the release rate of nitrogen and phosphorus exceeded 70%, while the release rate of potassium was more than 90%.

Figure 2 shows that the indirect path effect between carbon sources increased, but the positive correlation ratio decreased, indicating that the symbiotic relationship between soil microbial species due to the straw decomposition decreased with the continuous decomposition of straw, which was consistent with the research results of Schmid et al. [62]. Simultaneously, Figure 3 shows that the complexity of the soil microbial network at this stage increased significantly. Tang et al. [63] found that the straw returning increased the network complexity to enhance the defense ability of crops against *Fusarium* wilt. Ma et al. [64] confirmed that straw returning can stimulate the growth of specific species clusters and inhibit the activity of pathogens by regulating the interaction between microbial populations.

#### **5. Conclusions**

In this study, straw length, amount, and buried depth were taken as the straw returning factors, and the two-year straw returning experiment was carried out by orthogonal design. The single carbon source was extracted by the carbon source utilization intensity algorithm, combined with a path analysis and grey correlation analysis to build a composite mathematical model to analyze the interspecific symbiotic relationship of soil microbes in the process of straw returning. The path map was drawn to explore the regulatory methods and schemes with which to promote straw decomposition.

It can be seen from the path map that in the black soil region of Northeast China (45◦45 27−45◦46 33 N, 126◦35 44~126◦55 54 E), in the first year of straw returning, the cumulative decomposition rate of straw can reach 55.000%. Further, supplementing the carbon source glycyl-L-glutamic acid to the soil was conducive to the decomposition of straw—especially within 90–120 days of straw returning, adding the carbon source cyclodextrin. In the second year of straw returning, the cumulative decomposition rate of straw could reach 73.523%, and carbon sources α-D-lactose and D-galactonic acid γ-lactone needed to be supplemented appropriately to promote straw decomposition. Additionally, 4-hydroxybenzoic acid degrading bacteria can be screened in the peripheral soil within 120–150 days of straw returning.

This results of this study provide an experimental basis for corn straw returning to the black soil of the cold regions, along with the scientific and technological support for the sustainable development of agriculture and a guarantee of national food security.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/agriculture12071053/s1, Figure S1: Straw decomposition rate caused by straw amount and buried delth: (A) 150 days of straw returning, (B) (A + 150) days of straw returning; Figure S2: Straw decomposition rate caused by straw length and buried delth: (A) 150 days of straw returning, (B): (A + 150) days of straw returning; Table S1: Test design (1 m2); Table S2: The determination coefficient and residual path coefficient of PA; Table S3: The meteorological conditions of the experimental site during the test. Section S1: the distribution of carbon sources in ECO plates; Section S2: the straw amount in each experimental plot (1 m2); Section S3: the changes of straw decomposition rates caused by the interaction of straw amount and buried depth; Section S4: the changes of straw decomposition rates caused by the interaction of straw length and buried depth; Section S5: the partial results of the path analysis (determination coefficient and residual path coefficient); Section S6: The meteorological conditions of the experimental site. Data S1: original data.

**Author Contributions:** Y.N.: writing—original draft preparation; data analysis; mathematical modeling. X.W. and Y.Y.: indices determination—soil microbial community. X.C. and Y.W.: indices determination—straw decomposition rate. D.Z. (Detang Zou): overall design; reviewing and editing. D.Z. (Dongxing Zhou): overall design; writing—reviewing and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the National Natural Science Foundation of China (42107327), Heilongjiang Provincial Natural Science Foundation of China (YQ2021D002), and the Project funded by China Postdoctoral Science Foundation (2022M710650), Heilongjiang Postdoctoral Science Foundation (LBH-Z21120).

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

**Informed Consent Statement:** This article does not contain any studies with human applicants or animals performed by any of the authors.

**Data Availability Statement:** All data generated or analysed during this study are included in the online version of this article as Supplementary Material.

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

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