Long-term effects of weed control on arbuscular mycorrhizal fungal community and grain yield in a rice-rotated wheat cropping system

doi:10.21203/rs.3.rs-3609647/v1

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Long-term effects of weed control on arbuscular mycorrhizal fungal community and grain yield in a rice-rotated wheat cropping system

https://doi.org/10.21203/rs.3.rs-3609647/v1

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Rational weed control in arable land is a key contributor to food security and sustainable agricultural development. However, little is known about how weed control practices affect the root-associated arbuscular mycorrhizal fungi (AMF), especially in rice-wheat cropping systems. Here, the effects of different weed control measures on AMF community structure, wheat yield and soil properties were determined based on a long-term weed control experiment established since 2001 in a rice-wheat annual double cropping system. The weed control treatments include: (ⅰ) control (CK): No weeding; (ⅱ) manual weeding + herbicide application (MH); (ⅲ) Hindering seeds dispersed from water + herbicide application (WaH); (ⅳ): Hindering seeds dispersed from water + manual weeding (WaM); (ⅴ) Hindering seeds dispersed from water + manual weeding + herbicide application (WaMH). Results demonstrated that weed control markedly affected soil properties, shifted AMF community structure (diversity and composition), and notably increased wheat yield by 52.6%-62.2% compared with CK. The values of Pielou evenness index, Shannon-Weiner index and Simpson’s index were significantly lower under MH compared with CK. The Mantel test analysis indicated that AMF diversity was dominantly controlled by both total nitrogen and soil organic carbon. Similarly, AMF composition was markedly affected by soil water content, total nitrogen, nitrate nitrogen, ammonium nitrogen, and microbial biomass nitrogen. Mantel test also revealed that wheat yield was significantly correlated to AMF composition and Pearson correlation analysis also showed that some key indicator taxa were significantly correlated with wheat yield, suggesting that the composition of AMF community might play an important role in wheat yield formation. In addition, redundancy analysis (RDA) showed that soil properties and wheat yield were closely related to AMF community characteristics. Taken together, our findings suggest that weed control can mediate the relationships among soil quality, AMF community and wheat yield, providing valuable guidance for integrated weed management in the Yangtze River basin regions in China.

Weed control

Arbuscular mycorrhizal fungi

Soil properties

Wheat yield

Rice-wheat cropping system

Weeds can evolve and expand rapidly in the human-mediated environments as one of the biotic components of agroecosystems (Kreiner et al. 2022; Vigueira et al. 2013). Usually, weeds inhibit the plant growth and development by competing with crops for natural resources such as water, nutrients and light (Patzold et al. 2020). Therefore, controlling weeds in arable land is an important issue for maintaining high crop yields (El Omari and El Ghachtouli 2021; Zhang et al. 2021). However, there are several positive effects of weeds on agricultural ecosystems, such as improving field microclimate environment (Ferrero et al. 2017), and maintaining weed diversity to help mitigate crop yield losses (Adeux et al. 2019). Appropriate weed control practice can improve soil nutrient availability (Hosseini Bai et al. 2012; Ibell et al. 2010) and enhance soil microbial diversity and functions (Hosseini Bai et al. 2012; Wang et al. 2022).

Arbuscular mycorrhizal fungi (AMF) are a type of soil beneficial microorganisms with a wide distribution and an abundant quantity (van der Heijden et al. 2015). They are able to establish symbiotic relationships with roots of most crops and improve their productivity and nutritional quality by improving nutrient availability to the plant through biological nitrogen fixation and phosphate solubilization processes in the soil and rhizosphere (Sheteiwy et al. 2021a; Sheteiwy et al. 2021b), which can effectively satisfy the demands of human beings for food security and sustainable agricultural development (Genre et al. 2020). Moreover, AMF is beneficial for improving soil structure (Morris et al. 2019), promoting the synergistic effects among soil microorganisms (Cao et al. 2022; Zhu et al. 2018), thus enhancing the uptake of soil nutrients by host crops (Dierks et al. 2022; Wang et al. 2017), strengthening crop stress resistance (Chen et al. 2018) and helping to increase crop yield (Zhang et al. 2019). Therefore, the composition and diversity of AMF communities are important indicators for evaluating agricultural sustainability (Guzman et al. 2021; Verbruggen and Kiers 2010).

The structure of AMF community is sensitive to different agricultural practices, including fertilization, tillage, pesticide application, cropping system, and so on (Gosling et al. 2006; Verbruggen and Kiers 2010). Such practices are variable and have either positive (Guzman et al. 2021; Xiao et al. 2022) or negative (Edlinger et al. 2022) effects on AMF communities. These effects of agricultural practices could be originated induced by from the both direct or indirect effects on the soil environment and host plants (Luo et al. 2021; Ngosong et al. 2010). In the cropping systems, complex interactive relationships between both AMF and weeds were previously observed (El Omari and El Ghachtouli 2021; Jordan et al. 2000), in which AMF may have some influence on the weeds’ community (Rinaudo et al. 2010). The weed’s community may also affect AMF community in turn (Herzog et al. 2019; Kubota et al. 2015). Thus, weed control may produce large impacts on AMF community (Ramos-Zapata et al. 2012; Xiao et al. 2022; Yang et al. 2007). However, how weed control affect AMF communities is still poorly understood under different cropping systems.

Rice-wheat annual double cropping is the main farming system in the major grain production regions of the Yangtze River basin in China, and appropriate weed management is very important for maintaining food security in these regions (Zhang et al. 2021). Therefore, selecting the most appropriate integrated weed management practices, such as chemical, agronomic and ecological control measures, could effectively reduce weed seed bank and thus ensure crop production and food safety (MacLaren et al. 2020). Previous research showed that sustainable weed control in rice-wheat cropping system in the Yangtze River basin could be achieved through an integrated weed management using a series of physical, chemical and ecological methods, including reducing weed population, hindering water dispersed seeds, and exhausting the weed seed banks in the long term (Zhang 2013). However, the effect of such weed control practices on AMF community remains unclear and needs further studies.

In the present study, the long-term different weed control practices by which AMF community composition and diversity could be affected in a rice-rotated wheat cropping system were investigated. We also evaluated the relationships between soil properties, wheat yield and AMF community characteristics (dominant genera, diversity and composition) in response to different weed control practices. The current study hypothesized that long-term different weed control practices can significantly alter soil properties and AMF community structure. Besides, the effects of different types of weed control practices were distinctive on soil properties and AMF community structure. The current study also supposed that wheat grain yield could be affected depending on relationship between AMF community and soil properties.

Study site

This study was conducted at the farm of Institute of Agricultural Sciences along the Yangzi River of Nantong City, Jiangsu Province (32°07′N, 120°37′E). The farming system has long been a double cropping rotated between summer rice and winter wheat. This region is located nearby the Donghai Sea, where the subtropical monsoonal climate condition. Mean annual temperature at the experimental location varies from 14 to 15℃, mean annual rainfall is over 1000 mm, which mainly occurs in summer, and annual total radiation is approximately 2100 h. The soil texture at the experimental field is sandy loam with 18.1 g kg^− 1 organic matter, 1.24 g kg^− 1 total nitrogen, 0.57 g kg^− 1 total phosphorus and 15.77 g kg^− 1 total potassium in the early stage of experiment (Gao et al. 2020).

Experimental design and field management

A completely randomized block design was adopted for this weed control field experiment with four replicates of five treatments: (I) Control (CK, no weeding); (II) Manual weeding + herbicide application (MH, no filtering of water dispersed seeds in the irrigation water, but the plots were weeded by manual weeding combined with herbicide application); (III) Hindering water dispersed seeds + herbicide application (WaH, water dispersed seeds were filtered from the irrigation water, and the plots were weeded with herbicide application only); (IV) Hindering water dispersed seeds + manual weeding (WaM, weed seeds control was consistent with III, and the plots were weeded with manual weeding only); (V) Hindering water dispersed seeds + manual weeding + herbicide application (WaMH, nylon net was used to filter water dispersed seeds in the irrigation water, and the plots were weeded manually combined with herbicide application). In details, water dispersed seeds were done by placing an 80-mesh nylon net at the outlet of the irrigation ditch to filter the irrigation water in the experimental plot. Manual weeding was to uproot and remove weeds twice at tillering and jointing stages respectively during both wheat and rice growing seasons. Herbicide application was conducted in each crop season. In the winter wheat growing season, 1500 mL ha^− 1 of starane + isoproturon were applied half a month after sowing wheat seeds. In the summer rice growing season, 1000 g ha^− 1 of benzamide + pyrazosulfuron-ethyl + carfentrazone-ethyl were applied one week after transplanting rice seedlings. All herbicides were purchased from Jiangsu Kuaida Agricultural Chemical Co., Ltd. Each experimental plot was 13.5 m² (4.5 m × 3.0 m).

The field experiment was initially carried out during the wheat season of November 2001. Winter wheat (Yangmai 13) and summer rice (Nangeng 5055) were grown in the experimental plots. Winter wheat seeds were broadcast manually in early November with a density of 150 kg ha^− 1. Summer rice seedlings were transplanted manually in middle June with a density of 3.0 × 10⁵ plants·ha^− 1. Fertilization was applied in both two crop seasons with compound fertilizer (N: P₂O₅: K₂O = 15:15:15) and urea (CO(NH₂)₂, N = 46.4%). During the summer rice and winter wheat seasons, N fertilization rates were 300 kg ha^− 1 and 180 kg ha^− 1, respectively. The N application ratio was basal fertilizer: tillering fertilizer: filling fertilizer = 6:3:1 for wheat, and it was 7:1:2 for rice, respectively. Both phosphate (P₂O₅) and potassium (K₂O) fertilizers were applied as basal fertilizers. The P₂O₅ fertilization rates were 130 kg ha^− 1 for summer rice and 80 kg ha^− 1 for winter wheat. The K₂O fertilization rates were 250 kg ha^− 1 for summer rice and 150 kg ha^− 1 for winter wheat. Other field management practices such as irrigation and pest management were similar to those applied by farmers’ practices rice and wheat production in the Jiangsu province of China.

Soil sample collection

Soil sampling in the experimental field was conducted at the grain-filling stage of winter wheat in May 2015. Topsoil (0–20 cm) was collected and mixed in each plot using randomized five-point sampling method with a columnar soil sampler (diameter: 2.5 cm). Each fresh soil sample was immediately transported to laboratory, and sieved through 2 mm mesh. Part of the soil samples was naturally air-dried for measuring soil organic carbon (SOC), another part of soil samples was stored at 4℃ for measuring soil properties, while the rest of the soil samples was stored at -20℃ for DNA extraction.

Soil properties analysis

Soil pH was analyzed by an acidometer in the soil-water slurry (m/m: 1:5). Soil water content (WC) measured by the aluminum-oven drying at 105 ℃. Total nitrogen (TN) was determined by Kjeldahl method. Nitrate nitrogen (NO₃⁻-N) was determined by ultraviolet spectrophotometry correction factor method. Ammonium nitrogen (NH₄⁺-N) was determined by indophenol blue colorimetry method. Available phosphorus (AP) was determined by sodium bicarbonate method. Soil organic carbon (SOC) was determined by high temperature potassium dichromate oxidation method. Microbial biomass carbon (MBC) and nitrogen (MBN) were determined by chloroform fumigation extraction method. For the detailed schemes of the above-mentioned determination for soil properties, please refer to Lu (2000). Soil total glomalin-related soil protein (TG) and easy extract glomalin-related soil protein (EEG) were determined by Bradford modified method (Emran et al. 2012).

AMF community composition analysis

High-throughput sequencing was used to analyze the AMF community. Briefly, 0.5g frozen soil sample was used for DNA extraction using the FastDNA Spin Kit (MpBio, USA), following the manual provided by the kit manufacturer. AMF specific primers (GC-AMV4.5NF, 5 '-AAGCTCGTAGTTGAATTTCG-3' and AMDGR, 5 '-CCCAACTATCCCTATTAATCAT-3') (Sato et al. 2005) were used to amplify 18S rRNA from soil DNA extracts. All PCR reactions were performed in 20-µL reaction mixtures containing 4 µL 5X FastPfu buffer, 2 µL deoxynucleotide triphosphate (2.5 mM), 2 µL AMF primers (5 mM), 0.4 µL FastPfu DNA polymerase, and 10 ng DNA extracts as template. The PCR reactions performed with initial incubation at 95℃ for 3 min, followed by 35 cycles (94℃ for 30 s, 55℃ for 30 s, and 72℃ for 60 s), and finally extended for 10 min at 72℃. PCR products were purified further by QIAquick PCR Purification Kit (QIAGEN), quantified and sequenced using the Miseq PE 300 platform (Illumina, USA). High-throughput sequencing data were processed by the QIIME pipeline (http://qiime.org/index-qiime1.html) (Caporaso et al. 2010). Primers were trimmed to remove, and paired-end reads were merged by Using FLASH (https://ccb.jhu.edu/software/FLASH/) (Magoč and Salzberg 2011). DNA sequences with quality score below 25, length < 200 bp, and ambiguous nucleotides were excluded. After assigning sequences to samples based on unique barcodes, noise data and chimeras were removed by USearch v.11 (http://www.drive5.com/usearch/)(Edgar 2010). Moreover, sequences similarity ≥ 97% were clustered into operational taxonomic units (OTUs). The taxonomic identity of each OTU representative sequence was blasting against the MaarjAM database (http://maarjam.botany.ut.ee/) (Opik et al. 2010).

Based on the OTUs dataset, Shannon-Weiner index (H), Simpson’s index (D), and Pielou evenness index (J) were calculated in each soil AMF to gain an insight into the status of AMF diversity. The calculation formulae are as follows:

\(H=-{\sum }_{i=1}^{S}\frac{Ni}{N}·\text{l}\text{n}\left(\frac{Ni}{N}\right)\)	(1)
\(D= 1-{\sum }_{i=1}^{S}{\left(\frac{Ni}{N}\right)}^{2}\)	(2)
\(J= \frac{H}{\text{l}\text{n}S}\)	(3)

Where, N is defined as abundance which is total sequence numbers of the OTUs dataset, Ni is the sequence number of one OTU. S is defined as richness which is the number of observed OTU.

Statistics

The richness (S), abundance (N), Pielou evenness index (J), Shannon-Weiner index (H) and Simpson’s index (D) of AMF communities were calculated by diversity indices in PAST Version 2.00 (Hammer et al. 2001). Before calculating the diversity index, we performed rarefaction analysis (Hurlbert 1971) on the sequencing results of AMF by Vegan package (v.2.6-4) in R v.4.2.1(R Foundation for Statistical Computing, Vienna, Austria). Non-metric multidimensional scaling (NMDS) analysis was used to measure the beta diversity of AMF communities among different weed control treatments based on Bray-Curtis dissimilarities, and conducted with the metaMDS function (Oksanen 2011) of Vegan package (v.2.6-4) in R v.4.2.1. The ANOSIM analysis was conducted to test the statistically significant differences among AM fungal communities under different weed control treatments. To make OTUs data closer to normal distribution, the sequence numbers of OTUs data were converted by the following formula: log₁₀ (X + 1). Random forest classifier analysis (Breiman 2001) was conducted by RandomForest R package (v.4.7–1.1) to further discern whether weed control treatments share key taxa. The key OTUs were assigned using the calculated mean decrease in Gini index. The more important indicators were those OTUs with the higher mean decrease in Gini index. Then, key OTUs identified by random forest classifier analysis were tested for univariate nonparametric abundance differences. Pearson correlation was also performed between key OTUs and wheat yield. Redundancy analysis (RDA) was performed to decipher the relationships among AMF community characteristics and soil properties (Legendre and Gallagher 2001). RDA was implemented in Vegan package (v.2.6-4) in R v.4.2.1. AMF community characteristics include dominant genera (> 1% for the relative abundance multiplied by the frequency of occurrence for one specific genus), diversity indices, and community composition based on OTU data. Meanwhile, Mantel test was implemented to analyze the correlation between soil properties and AMF community characteristics (permu = 999). The abundances of AMF dominant genera also were converted using the following formula: log₁₀ (X + 1). To compare differences in soil properties, wheat yield, and AMF community diversity indices among different weed control treatments, one-way analysis of variation (ANOVA) and multiple comparisons based on Duncan test (P < 0.05) were performed with IBM Statistics SPSS 20 (International Business Machines Corp, USA). Before statistical analysis, normality test was conducted with Shapiro-Wilk analysis. If residuals were not normally distributed, data were log₁₀ X transformed.

Effects of weed control on soil properties

Weed control practices showed significant impacts on WC, TN, NO₃⁻-N, NH₄⁺-N, AP, SOC, and MBN (P < 0.05). Results showed that WaH had the maximum value in WC, TN, NO₃⁻-N, AP, and SOC among different weed control treatments. It has observed that WaM had the maximum value in NH₄⁺-N, but resulted in the minimum values in WC, NO₃⁻-N, AP, SOC, and MBN among different weed control treatments. Under control treatment (CK), maximum value of MBN was observed, while minimum value of TN was observed in MH, whereas NH₄⁺-N was found lower in WaMH among different weed control treatments. However, weed control showed no significant effect on pH, MBC, TG, and EEG (Table 1).

Table 1

Effect on soil properties by weed controls, values are means ± SE. The same letters in the same row represent no significant difference at P < 0.05. WC: Soil Water Content; TN: Total Nitrogen; NO₃⁻-N: Nitrate Nitrogen; NH₄⁺-N: Ammonium Nitrogen; AP: Available Phosphorus; SOC: Soil Organic Carbon; MBC: Microbial Biomass Carbon; MBN: Microbial Biomass Nitrogen; TG: Total Glomalin-Related Soil Protein; EEG: Easy Extract Glomalin-Related Soil Protein.
Treatment	CK	MH	WaH	WaM	WaMH
WC (%)	21.0 ± 0.6b	19.1 ± 0.4a	22.6 ± 0.2c	18.7 ± 0.1a	19.2 ± 0.1a
pH	7.1 ± 0.1a	7.1 ± 0.1a	7.1 ± 0.1a	7.2 ± 0.1a	7.1 ± 0.1a
TN (g/kg)	1.8 ± 0.1ab	1.5 ± 0.2a	1.9 ± 0.1b	1.6 ± 0.1a	1.6 ± 0.0ab
NO₃⁻-N (mg/kg)	61.4 ± 2.9a	57.4 ± 1.7a	72.2 ± 0.7b	56.8 ± 1.5a	58.9 ± 0.3a
NH₄⁺-N (mg/kg)	7.3 ± 0.8ab	8.5 ± 0.1b	6.4 ± 0.6ab	15.2 ± 3.3c	5.4 ± 0.2a
AP (mg/kg)	23.0 ± 0.1b	21.4 ± 0.4ab	25.9 ± 1.3c	20.5 ± 1.1a	25.5 ± 0.6c
SOC (g/kg)	13.3 ± 1.0ab	13.0 ± 1.1ab	14.9 ± 0.4b	11.5 ± 0.5a	12.7 ± 1.0ab
MBC (mg/kg)	232.9 ± 22.3a	222.1 ± 16.6a	256.7 ± 14.4a	235.7 ± 17.1a	232.8 ± 9.0a
MBN (mg/kg)	18.6 ± 2.2c	15.9 ± 1.2c	11.2 ± 0.4b	1.6 ± 0.3a	9.9 ± 0.7b
TG (g/kg)	1.7 ± 0.1a	1.6 ± 0.0a	1.7 ± 0.0a	1.5 ± 0.0a	1.6 ± 0.1a
EEG (g/kg)	0.73 ± 0.01a	0.75 ± 0.02a	0.74 ± 0.01a	0.72 ± 0.01a	0.73 ± 0.02a

Effects of weed control on crop yield

Compared with CK, four weed control treatments significantly increased wheat grain yield by 52.6%-62.2% (P < 0.05), without significant differences between them (P > 0.05, Fig. 1).

Effects of weed control on AMF community composition

Significant changes in the OTU richness and abundance of AMF were observed under various weed control treatments (Fig. S1). A total of 141 OTUs were detected in all soil samples using high-throughput sequencing. The richness of AMF OTUs was 77, 88, 90, 93, and 113 OTUs under MH, CK, WaMH, WaM, and WaH treatments, respectively (Fig. S1A). The abundance of AMF OTUs was 9,560, 12,256, 13,115, 13,556, and 15,942 under WaM, MH, WaMH, CK, and WaH treatments, respectively (Fig. S1B).

In all soil samples, OTUs belonged to 1 phylum, 1 class, 5 orders, 8 families, and 8 genera. Glomeromycetes_unclassified, Paraglomus, and Glomus were the dominant genera under all weed control treatments. Their relative abundances were 92.0%-98.1%, 1.1%-8.6% and 0.4%-1.4%, respectively. The relative abundance of dominant genera exceeded 99.7% (Fig. S2). The other non-dominant genera, including Acaulospora, Archaeospora, Archaeosporales_unclassified, Diversispora, and Scutellospora were observed only in certain weed control treatments (Table S1).

Effects of weed control on Alpha diversity of AMF community

Combined with rarefaction analysis (Fig. S3), the sequencing results of this study could adequately reflect the diversity contained in the current samples. The alpha diversity indices of AMF community were calculated based on the abundance of OTUs under different weed control treatments (Fig. 2). The Pielou evenness index, Shannon-Weiner index, and Simpson’s index from different weed control treatments were 0.42-0.59, 1.82-2.79, and 0.61-0.86, respectively. In all weed control treatments, the Pielou evenness index (Fig. 2A) and the Shannon-Weiner index (Fig. 2B) showed a trend of MH﹤CK﹤WaMH﹤WaM﹤WaH. The Simpson’s index showed a trend of MH﹤CK﹤WaM﹤WaMH﹤WaH (Fig. 2C). The three indices in MH were significantly lower than the other four treatments (P < 0.05), but no significant difference was shown among CK, WaH, WaM, and WaMH (P > 0.05).

Effects of weed control on Beta diversity and key taxa of AMF community

AMF community was clearly separated on the NMDS ordination under different weed control treatments, indicating that the beta diversity of AMF community was different among different treatments (Fig. 3). Meanwhile, ANOSIM analysis showed that weed control significantly affected AMF communities (P = 0.001). The random forest classifier analysis and abundance differences test were combined to identify the key OTUs in the AMF community that led to discrepancies among weed control treatments. We identified a total of 19 key OTUs which did not belong to any currently defined lineage along AMF phylogeny (Glomeromycetes_unclassified) (Fig. 4A). Among them, only 2 OTUs (OTU159 and OTU39) had no significant difference among weed control treatments. The relative abundance of key OTU158, OTU720, OTU785, and OTU183 was the highest in CK. The relative abundance of key OTU445 was the highest in MH. The relative abundance of key OTU195, OTU39, OTU782, and OTU618 was the highest in WaH. The relative abundance of key OTU209, OTU548, OTU72, OTU605, OTU602, OTU159, and OTU96 was the highest in WaM. The relative abundance of key OTU58, OTU374 and OTU566 was the highest in WaMH (Fig. 4B). Therefore, long-term weed control could also alter the composition of AMF community and the relative abundance of key taxa.

Relationships among AMF community characteristics, soil properties and wheat yield

The RDA was used to analyze the correlation among AMF community characteristics, soil properties, and wheat yield. Regarding AMF dominant genera (Fig. 5A), alpha diversity indices (Fig. 5B), and AMF community composition (Fig. 5C) based on OTU data, the first axis (RDA1) explained 73.66%, 98.38% and 30.03%, respectively; the second axis (RDA2) explained 19.78%, 1.42%, and 25.21%, respectively, with a cumulative explanation of 93.44%, 99.80%, and 55.24%, respectively. A strong negative correlation was shown between Glomus and Paraglomus, but both had little correlation with Glomeromycetes_unclassified (Fig. 5A). The alpha diversity indices, including Pielou evenness index, Shannon-Weiner index and Simpson’s index, showed a strong positive correlation between each other (Fig. 5B). Mantel test showed that Glomeromycetes_unclassified, Glomus, and Paraglomus were not significantly correlated with soil properties (P > 0.05; Table 2). The alpha diversity indices were markedly affected by TN and SOC (P < 0.05; Table 2). AMF community composition was significantly affected by WC, TN, NO₃⁻-N, NH₄⁺-N, and MBN (P < 0.05; Table 2). Wheat yield had a significant correlation with AMF OTU composition (P < 0.05), but there was no significant correlation with the abundance of AMF dominant genera and alpha diversity indices (P > 0.05; Table 2). In addition, Pearson correlation analysis showed that five key OTUs identified by random forest classifier analysis were significantly correlated with wheat yield. OTU602 was significantly positively correlated with wheat yield, but OTU158, OTU720, OTU785, and OTU183 were significantly negatively correlated with wheat yield (Fig. 4C).

Table 2

Mantel test of soil properties on AMF community characteristics. WC: Soil Water Content; TN: Total Nitrogen; NO₃⁻-N: Nitrate Nitrogen; NH₄⁺-N: Ammonium Nitrogen; AP: Available Phosphorus; SOC: Soil Organic Carbon; MBC: Microbial Biomass Carbon; MBN: Microbial Biomass Nitrogen; TG: Total Glomalin-Related Soil Protein; EEG: Easy Extract Glomalin-Related Soil Protein.
Soil properties	Dominant genera		Diversity indices		AMF composition
Soil properties	r	p	r	p	r	p
WC	-0.033	0.554	-0.054	0.625	0.290	0.001
TN	-0.171	0.914	0.463	0.017	0.179	0.005
pH	-0.063	0.724	-0.108	0.925	-0.111	0.982
NO₃⁻-N	-0.042	0.623	-0.057	0.618	0.275	0.003
NH₄⁺-N	-0.012	0.420	0.010	0.324	0.122	0.018
AP	-0.157	0.847	0.050	0.230	-0.035	0.767
SOC	-0.027	0.601	0.201	0.028	0.084	0.122
MBC	-0.156	0.855	0.081	0.255	-0.033	0.681
MBN	-0.069	0.663	0.013	0.390	0.370	0.002
TG	-0.028	0.531	0.062	0.177	0.040	0.245
EEG	-0.250	0.976	0.278	0.066	-0.042	0.731
Yield	0.114	0.216	-0.138	0.896	0.303	0.001

Long-term weed control practices in the rice-wheat cropping system of the Yangtze River basin show good control for various types of weeds, such as broadleaf and grasses (Zhang 2013), but the effect of weed control goes far beyond only influencing the weed community. After fourteen years’ treatments, weed control exerted profound effects on soil properties, AMF communities and crop yields during the wheat growing season. To some extent, the results agreed with our hypotheses that four weed control practices changed soil properties (e.g., WC, TN, SOC) and AMF diversity and composition compared with no weeding treatment; while the effects of four weed control practices on soil properties and AMF diversity and composition were different. We also found the AMF community diversity and composition were related to soil properties, and wheat grain yield was significantly correlated to AMF OTU composition. These findings suggest that rational weed control practice should be taken to sustain crop production and soil ecological environment of arable land in the rice-wheat double cropping region.

Effects of weed control on soil properties

Soil properties are an important axis for evaluating soil quality of arable land (Bünemann et al. 2018). Our findings suggest that weed control markedly alters soil properties, which is consistent with the results of weed control experiments in other regions (Hosseini Bai et al. 2012; Naeem et al. 2022; Promsakha Na Sakonnakhon et al. 2006; Wang et al. 2022), indicating that weed removal from the crop field is also an agricultural practice that could significantly change soil quality. Therefore, it is necessary to pay attention to changes in soil quality of arable land under long-term weed control for further understanding of weed-soil feedback mechanisms during long-term agroecosystem development for better crop protection (Patzold et al. 2020; Png et al. 2019).

In this study, TN and nitrogen fractions were significantly affected by weed control practices. Compared with CK, TN decreased in MH, WaM and WaMH, which may be because weed and crop association could effectively increase soil TN (Promsakha Na Sakonnakhon et al. 2006). However, TN increased in WaH, which might be mainly due to the intensity of weeding in WaH was moderate in this experiment (Zhang 2013), confirming that more intensive weed biomass is not better to maintain a higher nitrogen level in the field. This may be because most weeds are C4 plants in our experimental plots, which generally have higher nitrogen-use efficiency (Yuan et al. 2007), thus absorbing more nutrients than C3 crops. NH₄⁺-N and NO₃⁻-N are available inorganic nitrogen fractions, which can be directly assimilated by plants (Haynes and Goh 1978). In the present study, NH₄⁺-N was significantly decreased in WaMH and increased in MH and WaM, and NO₃⁻-N was significantly increased in WaH as compared with CK. Moreover, the current findings showed that NO₃⁻-N content was much higher than NH₄⁺-N content, and NO₃⁻-N was significantly positively correlated with TN, which suggests that reasonable weed control practice could increase soil nitrogen level by improving nitrogen availability. As a biological nitrogen component, soil MBN can comprehensively reflect the effects of soil microorganisms on nitrogen mineralization and retention (Shen et al. 1984). Soil MBN was reduced by weed control treatments reduced soil MBN as compared with CK. This was because weed control could affect the most growing weeds, leading to an inhibition of the biomass of nitrogen-transforming microorganisms, and thus significantly change soil nitrogen cycling (Promsakha Na Sakonnakhon et al. 2006).

Weed control practices significantly affected SOC. Compared with CK, SOC decreased in MH, WaM, and WaMH (Table 1), possibly because weed control reduced carbon input from root exudates (Ibell et al. 2010). However, SOC content was significantly increased in WaH, suggesting that moderately intensive weed management could promote soil carbon sequestration, possibly by maintaining a higher biomass of wheat without reducing carbon input from root exudates, while by reducing carbon output from soil respiration (Hosseini Bai et al. 2012).

Weed control practices also significantly altered soil WC. compared with CK, WC decreased in MH, WaM and WaMH, indicating that the presence of weeds could preserve soil moisture (Zhang et al. 2020). However, WC increased significantly in WaH compared with CK, which may be due to the increased transpiration water loss induced by excessive weeds in CK (Sen et al. 2021). In addition, WC, TN and SOC showed significantly positive correlation between each other, which further indicated that moderate weed control intensity may achieve the synergistic improvement of water and nutrient‑use efficiency and soil carbon sequestration. Therefore, it is unnecessary to completely remove weeds in arable land. A sustainable weed management approach can be applied to improve soil quality and thus sustain soil ecological environment in agroecosystem (MacLaren et al. 2020; Wortman 2016).

Effects of weed control on AMF community

At present, soil rhizosphere and endosphere are the hotpots to study AMF community (Guzman et al. 2021; Xiao et al. 2022). Unfortunately, we did not investigate AMF community in these hotpots, but only in bulk soil. Non-rhizosphere soil is also a very important part for plant growth (Angst et al. 2016). AMF can help host plants absorb nutrients from non-rhizosphere soil through their mycelium expansion (Dierks et al. 2022; Wang et al. 2017). Thus AMF community structure in non-rhizosphere soil has also attracted attention (Luo et al. 2021). Moreover, our weed control treatment has been lasted for 14 years at the sampling date, indicating that weed control may exert a long-term impact on soil environment. However, the crop rhizosphere effect usually works for a short term on soil process, perhaps only a few months. Actually, most soil microorganisms need sufficient time to adapt to soil environmental changes (Reardon et al. 2014). However, rhizosphere microorganisms are mainly recruited from non-rhizosphere soil (Ling et al. 2022). Therefore, the long-term effects of weed control on AMF community structure could be better revealed by studying AMF community composition in non-rhizosphere soils.

The findings released by NMDS ordination analysis showed that long-term weed control largely altered AMF community structure. Glomus is generally considered to be widespread and the most abundant genus in most natural and artificial ecosystems (Oehl et al. 2003; Pereira et al. 2014), and is often found in rice-wheat cropping region in China (Qin et al. 2015; Zhu et al. 2018). However, the current study showed that Glomeromycetes_unclassified was the most dominant genus than Glomus, indicating that our experimental site has a unique AMF community, but long-term weed control does not change the dominant genena’s abundance in AMF community. Interestingly, the relative abundance of key OTU taxa of AMF community was significantly altered among weed control treatments. This suggested that weed control practices could influence the key indicator taxa of AMF as significantly as other crop cultivation practices, such as tillage and fertilization (Lang et al. 2022; Liu et al. 2022; Luo et al. 2021).

The abundance, richness and α-diversity indices of AMF community are also significantly affected by weed control. Previous studies showed that AMF species diversity is positively correlated with plant diversity (Burrows and Pfleger 2002; Guzman et al. 2021). Based on this, we can infer reasonably that AMF diversity should be the highest under no weeding treatment, which has the highest weed species diversity. However, our results showed that AMF diversity was the highest under WaH treatment. Which could be explained by the intermediate-disturbance hypothesis, indicating that that species diversity is higher with intermediate-disturbance than with frequent disturbance or no disturbance (Fox 1979). In this experiment, the intensity of weeding in the field with WaH is moderate. Based on the investigation of soil weed seed banks, it was found that the species richness of weed seed banks in WaH was moderate (WaMH < MH < WaH < WaM < CK) (Zhang 2013), thus possibly maintaining the highest diversity of AMF. These is findings are consistent with the findings obtained by Ba et al. (Ba et al. 2012) who observed that the highest richness and evenness of AMF in the moderately grazed areas. Therefore, maintaining a certain abundance of weeds may play an important role in protecting species diversity in arable land (van der Heijden et al. 2008). In addition, with the exception of MH treatment where AMF diversity was significantly reduced, no decrease in AMF diversity was observed in other pesticide application treatments (WaH and WaMH). Thus, through the current findings still not clear whether the application of pesticides could exert negative effects on AMF community (Edlinger et al. 2022; Ramos-Zapata et al. 2012), which need a further investigation.

This is well confirmed by our results here AMF community structure is strongly correlated with soil properties (Guzman et al. 2021; Wakelin et al. 2012). The abundance of dominant genera and diversity of AMF can be explained by soil properties for more than 90%, and AMF community composition can be explained by soil properties over 50%. Mantel test showed that TN and SOC were significantly positively correlated with AMF diversity, which is consistent with the results of Yang et al. (2021) and Xiao et al. (2022). However, Qin et al. (2015) and Liu et al. (2022) found a reverse trend. On the one hand, the above-mentioned paradoxical phenomenon might be caused by the possible threshold effect of TN and SOC on AMF diversity, which only increases AMF diversity within a certain range (Wu et al. 2023; Xiao et al. 2022). On the other hand, this might be due to the complex interactions among many ecological and environmental factors (Moebius-Clune et al. 2013). For example, compared to no-fertilization control, AMF diversity of both wheat and maize harvest stages significantly decreased in the topsoil (0–15 cm) while it was increased in the subsoil (15–30 cm) under long term fertilization in wheat-maize double cropping region of southwest China. This phenomenon was not attributable to changes of SOC and TN, possibly as AMF diversity was also controlled by temperature, crop biomass and other soil factors (Luo et al. 2021). In this study, TN and nitrogen fractions (NO₃⁻-N, NH₄⁺-N, and MBN) were closely related to AMF OTU composition, suggesting that weed control might influence AMF community composition through regulating soil nitrogen availability. The amount of available nitrogen in the soil can affect AMF spore density and root colonization (Li et al. 2010; Yang et al. 2011), thus possibly influencing the trade-off between AMF adaptation strategies and field environment conditions (Babalola et al. 2022; Bakhshandeh et al. 2017). Hence, weed control might indirectly affect AMF community characteristics through altering soil properties.

Effects of weed control on crop yield

The current study showed that weed control has significantly affected on wheat yield, in which wheat yield could increase by more than 50%. This is in line with results from similar weed control treatments at other sites (Zhang et al. 2021). The yield-increasing effect can be directly explained by inhibiting the growth of weed communities from weed control practices, which could avoid competition from weeds to wheat for light and nutrient resources.

Our findings suggest that wheat grain yield can be explained by AMF OTU composition, suggesting that AMF might play important roles in wheat grain formation. In agroecosystems, AMF can drive soil nutrient cycling to support crop growth (Kim et al. 2022; Sheteiwy et al. 2021a; Sheteiwy et al. 2021b). A similar finding also was confirmed by meta-analysis, which suggests that AMF can promote grain crop yield (Zhang et al. 2019). In this study, sampling was conducted at wheat grain filling, a critical stage for wheat to assimilate carbon dioxide into organic matter such as starch and protein, which is closely related to the high yield (Aranjuelo et al. 2013). However, wheat could be more inclined to cooperate with AMF at this stage, because the absorption function of wheat roots gradually declined during wheat grain development, leading to wheat being unable to rely on its own roots to complete nutrient supply (Sawers et al. 2018). In other words, the symbiotic relationship between AMF and wheat root system at the grain filling stage could increase wheat yield through improving nutrient utilization efficiency (Yang et al. 2022). In a sense, the potential relationship between AMF community and wheat yield could be effectively revealed by studying AMF community composition during wheat grain filling. Similarly, previous studies reported that wheat yield could directly or indirectly affected by AMF diversity and community structure (Liu et al. 2022; Zhu et al. 2018). Therefore, weed control could increase wheat grain yield through regulating AMF community. Moreover, we found that some key OTUs taxa were significantly associated with wheat yield, suggesting that special AMF groups might play an important role in regulating crop productivity.

Weeds, crops and AMF are known to interact with each other (Jordan et al. 2000). AMF could promote the growth of strong host crops, not only directly by suppressing weaker or non-mycorrhizal weeds, but also indirectly by enhancing competitive ability of host crops to weaken the growth of some strong host weeds (El Omari and El Ghachtouli 2021; Li et al. 2016). However, various experiments, including this study, showed that weed control practices could significantly affect the characteristics of AMF community to alter the interactions among AMF, weeds and crops (Brito et al. 2013; Ramos-Zapata et al. 2012). Therefore, it is necessary to reduce the competition between weeds and crops, while concomitantly improve the cooperation between AMF and crops to ensure high crop yield in agricultural practices.

This study suggests that fourteen years of continuous weed control can alter soil properties and AMF community structure, and increase wheat yield by more than 50%. TN and SOC are key factors controlling AMF diversity. Apparent changes in WC, TN, and nitrogen fractions suggest that weed control can change soil water conditions and nitrogen cycling, which further markedly controls AMF OTU composition. Furthermore, wheat yield is closely related to AMF composition. These findings imply that weed control can not only directly increase wheat yield, but also shift AMF community to affect wheat yield indirectly. Consequently, it is worthy paying attention to the interaction among crops, weeds and AMF for considering integrated weed management in rice-wheat double cropping systems.

Author contribution: L.Q.-Writing the original draft, Data curation, Investigation. L.L.-Conceptualization, Methodology, Supervision. S.J., X.Y., and W.Y.-Investigation. Z.Z.-Funding acquisition. E.K.A., S.M., F.A., and Y.H.-Revising the manuscript. All authors reviewed the manuscript.

Funding: This study was supported by the National Natural Science Foundation of China (No. 32272563).

Data Availability: The data generated in this study are available from the corresponding author upon reasonable request.

Competing interests: The authors declare no competing interests.

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No competing interests reported.

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Long-term effects of weed control on arbuscular mycorrhizal fungal community and grain yield in a rice-rotated wheat cropping system

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Abstract

Figures

Introduction

Materials and methods

Study site

Experimental design and field management

Soil sample collection

Soil properties analysis

AMF community composition analysis

Statistics

Results

Effects of weed control on soil properties

Effects of weed control on crop yield

Effects of weed control on AMF community composition

Effects of weed control on Alpha diversity of AMF community

Effects of weed control on Beta diversity and key taxa of AMF community

Relationships among AMF community characteristics, soil properties and wheat yield

Discussion

Effects of weed control on soil properties

Effects of weed control on AMF community

Effects of weed control on crop yield

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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