Cover Cropping Impacts Soil Microbial Communities and Functions in Mango Orchards

: Soil microbes play critical roles in nutrient cycling, net primary production, food safety, and climate change in terrestrial ecosystems, yet their responses to cover cropping in agroforestry ecosystems remain unknown. Here, we conducted a ﬁeld experiment to assess how changes in cover cropping with sown grass strips affect the fruit yields and quality, community composition, and diversity of soil microbial taxa in a mango orchard. The results showed that two-year cover cropping increased mango fruit yields and the contents of soluble solids. Cover cropping enhanced soil fungal diversity rather than soil bacterial diversity. Although cover cropping had no signiﬁcant effects on soil bacterial diversity, it signiﬁcantly inﬂuenced soil bacterial community compositions. These variations in the structures of soil fungal and bacterial communities were largely driven by soil nitrogen, which positively or negatively affected the relative abundance of both bacterial and fungal taxa. Cover cropping also altered fungal guilds, which enhanced the proportion of pathotrophic fungi and decreased saprotrophic fungi. The increase in fungal diversity and alterations in fungal guilds might be the main factors to consider for increasing mango fruit yields and quality. Our results indicate that cover cropping affects mango fruit yields and quality via alterations in soil fungal diversity, which bridges a critical gap in our understanding of the linkages between soil biodiversity and fruit quality in response to cover cropping in orchard ecosystems.


Introduction
Cover cropping (i.e., sown grass strips) has been used as an important and effective method to improve soil fertilizer and soil carbon stacks [1]. A 12-year field experiment indicated that cover cropping increased soil organic carbon (SOC) stocks [1]. A metaanalysis showed that cover cropping contributed to the changes in global cropland soil carbon, with an overall mean change of 15.5% [2]. Compared with monospecies cover crops, cover crop mixtures sequestered more SOC [2]. Elevated SOC is also associated with improved soil health and fertility; therefore, increasing SOC may help to enhance agricultural productivity [3]. However, not all studies found that cover cropping results in SOC accumulation. Some studies demonstrated that the introduction of cover crops resulted in losses of SOC due to the faster growth of cover crops [4]. In addition to an increased carbon input, cover crops have been shown to increase biodiversity [5].
Soil harbors a rich diversity of invertebrate and microbial life, which drives biogeochemical processes at local and global scales. Soil microbes play critical roles in the nutrient cycling, climate regulation, decomposition and turnover of soil organic matter [6]. Cover cropping alters soil quality and thereby influences soil microbial communities in agro-ecosystems. The potential of cover crops to increase soil biodiversity and specific microbial patterns has been highlighted in very few studies, especially in fruit orchards. For example, sowing plant seed mixtures promoted the growth of the bacterial community Soils were collected in July (summer) 2019. In the control, soils were collected tween the rows (M) and the drip line of mango trees (MS). Other soils (H, Z and B) w cover crops were collected between the rows. In each experimental plot, we collected surface soil cores (0-20 cm and 20-40 cm) randomly between rows ( Figure 1). All the samples in one experimental plot were mixed. In total, 18 samples per soil layer were lected. After removing the roots, stones and litters by hand, we sieved them through mm sieve and separated them into two portions. One portion (approximately 5 g) preserved in a 10 mL centrifuge tube (sterilized) and stored at −80 °C. The second por was air-dried and used to measure soil properties.

Mango Fruit Quality and Yield
When mango fruits were nearly matured (80%), all the mango fruits in each exp mental plot were harvested, and the masses were measured as yields. We randomly ch 20 mango fruits in each experimental plot and placed them into plastic bags for anal of the fruit quality. Total soluble solids content of mango fruit was measured by a dig refractometer and presented in percent Brix (%TSS). Organic acid concentration measured by an automatic titrator [22]. Vitamin C (Vc) content was determined via method of 2,6-dichlorophenol indophenol (Shao et al. 2013).

Analysis of Soil Properties
Soil properties were measured by the standardized methods as described previou Soil bulk density (BD) was measured by the volume-mass relationship via a cutting r [23]. Soil pH was determined by a glass pH meter in a 1:2.5 soil-water suspension. organic matter (SOM) was digested with 5 mL concentrated H2SO4 and 5 mL 0.8 K2Cr2O7, and then determined by 0.2 M Ferrous Ammonium Sulfate [24]. Soil total ni gen (TN) was determined as described previously [25]. Soil available nitrogen (AVN) extracted by 1 mol/L KCl and then measured by a Seal Auto Analyzer3 [26]. Soil t phosphorus (TP) and soil available P (AVP) were determined by molybdenum, antim and scandium colorimetry. Total potassium (TK) and available potassium (AVK) in Soils were collected in July (summer) 2019. In the control, soils were collected between the rows (M) and the drip line of mango trees (MS). Other soils (H, Z and B) with cover crops were collected between the rows. In each experimental plot, we collected 20 surface soil cores (0-20 cm and 20-40 cm) randomly between rows ( Figure 1). All the soil samples in one experimental plot were mixed. In total, 18 samples per soil layer were collected. After removing the roots, stones and litters by hand, we sieved them through a 2 mm sieve and separated them into two portions. One portion (approximately 5 g) was preserved in a 10 mL centrifuge tube (sterilized) and stored at −80 • C. The second portion was air-dried and used to measure soil properties.

Mango Fruit Quality and Yield
When mango fruits were nearly matured (80%), all the mango fruits in each experimental plot were harvested, and the masses were measured as yields. We randomly chose 20 mango fruits in each experimental plot and placed them into plastic bags for analysis of the fruit quality. Total soluble solids content of mango fruit was measured by a digital refractometer and presented in percent Brix (%TSS). Organic acid concentration was measured by an automatic titrator [22]. Vitamin C (Vc) content was determined via the method of 2,6-dichlorophenol indophenol (Shao et al. 2013).

Analysis of Soil Properties
Soil properties were measured by the standardized methods as described previously. Soil bulk density (BD) was measured by the volume-mass relationship via a cutting ring [23]. Soil pH was determined by a glass pH meter in a 1:2.5 soil-water suspension. Soil organic matter (SOM) was digested with 5 mL concentrated H 2 SO 4 and 5 mL 0.8 M K 2 Cr 2 O 7 , and then determined by 0.2 M Ferrous Ammonium Sulfate [24]. Soil total nitrogen (TN) was determined as described previously [25]. Soil available nitrogen (AVN) was extracted by 1 mol/L KCl and then measured by a Seal Auto Analyzer3 [26]. Soil total phosphorus (TP) and soil available P (AVP) were determined by molybdenum, antimony and scandium colorimetry. Total potassium (TK) and available potassium (AVK) in soil were determined by a flame photometer.

Molecular Analysis
We used the PowerSoil kit to extract DNA from 0.5 g soils. The protocol was listed in the manufacturer's instructions. After extraction, the ratios of A260/A230 and A260/A280 were determined to assess the quality of extracted DNA.

Fungal Ecological Guilds Identification
We used FUNGuild to identify the fungal ecological guilds using fungal OTU dataset with taxon assignments [34]. This prediction method has been widely used in gaining insights into the distributions of soil fungal ecological groups [35,36]. In this study, we used the data with confidence levels of "highly probable" and "probable" to perform further analysis.

Statistical Analysis
We first used ANOVA to compare the differences of soil properties, mango fruit yield and quality, and bacterial and fungal diversity among different treatments on SPSS 20.0 (IBM Corporation, Armonk, NY, USA). We then conducted Pearson correlation analyses between the microbial diversity (Shannon diversity index) and soil properties. Prior to ANOVA and Pearson correlation analyses, the data were used to conduct a log transformation to meet the normality and homogeneity. Multiple regression models were constructed to compare the effects of soil properties on microbial diversity in R 3.5. Anosim analysis was performed to compare the effects of sown grass strips on the soil microbial community structure in R 3.5. The bacterial and fungal community structure was calculated based on the Bray-Curtis dissimilarity and visualized by a nonmetric multidimensional scaling (NMDS) plot in R 3.5 with the vegan package [37]. The effects of soil properties on the bacterial and fungal community structure were analyzed by the Mantel test in R 3.5. The associations between the soil microbial community structure and soil properties were determined by the Mantel test in R 3.5 [38].

Soil Properties of the Tested Orchard
The impacts of cover cropping with sown grass strips on soil properties depended on the types of sown grass and soil layers. The upper soils (0-20 cm) were more sensitive to the application of sown grass strips. B20 had the highest soil AVN, which was significantly higher than other sown grass types. The application of sown grass strips had no significant effects on soil pH, SOM and AVK at both soil layers (Table 1).

Yield and Fruit Quality of Mango under Different Sown Grass Trips
The application of sown grass trips had significant effects on mango fruit yield, with the highest yield in B ( Table 2). There were no significant differences observed between Z and M. The applications of B, H and Z significantly enhanced TSS content and decreased organic acid compared to M.

Variations in Soil Microbial Diversity under Different Sown Grass Strips
The observed bacterial Shannon diversity index ranged from 5.47 to 6.64, while phylotype richness (OTUs) varied from 1886 to 3509. Sown grass strips have no significant effects on soil bacterial α-diversity indices (Shannon diversity index and richness index). The soil fungal phylotype richness ranged from 419 to 1080, and the fungal Shannon diversity index ranged from 3.81 to 4.95. Among these soil properties, TN and AVN had significant associations with the bacterial Shannon diversity index ( Figure 2). A multiple regression models indicated that soil TN and AVN were the best predictors of the soil bacterial Shannon diversity index (with a relative importance of 0.54 and 0.42), followed by AVP, with a relative importance of 0.04. Soil properties had no significant correlations with the fungal diversity index.

Variations in Soil Microbial Community under Different Sown Grass Strips
Across all soils, a total of 1,506,304 quality bacterial sequences, and an average of 50,210 sequences per sample, were obtained. Four of the 28 phyla detected were dominant, including Acidobacteria (15.51%), Actinobacteria (17.55%), Proteobacteria (24.26%) and Chloroflexi (24.77%) (with an average relative abundance of >5%, n = 30), accounting for more than 82% of the bacterial sequences ( Figure 3A). The Proteobacteria taxa were dominated by Alphaproteobacteria (17.66%), followed by Deltaproteobacteria (4.12%) and Gammaproteobacteria (2.49%). The soil layer had no significant effects on the relative abundance of Acidobacteria and Proteobacteria. The upper soils (20.5%) had a higher relative abundance of Actinobacteria than that of the lower soils (14.6%).

Variations in Soil Microbial Community under Different Sown Grass strips
Across all soils, a total of 1,506,304 quality bacterial sequences, and an average of 50,210 sequences per sample, were obtained. Four of the 28 phyla detected were dominant, including Acidobacteria (15.51%), Actinobacteria (17.55%), Proteobacteria (24.26%) and Chloroflexi (24.77%) (with an average relative abundance of >5%, n = 30), accounting for more than 82% of the bacterial sequences ( Figure 3A). The Proteobacteria taxa were dominated by Alphaproteobacteria (17.66%), followed by Deltaproteobacteria (4.12%) and Gammaproteobacteria (2.49%). The soil layer had no significant effects on the relative abundance of Acidobacteria and Proteobacteria. The upper soils (20.5%) had a higher relative abundance of Actinobacteria than that of the lower soils (14.6%).  Across all mango soils, a total of 1,777,037 quality fungal sequences, and an average of 59,234 sequences per sample, were obtained. The dominant fungal phyla in soils were Ascomycota and Basidiomycota, with average relative abundances of 79.38 and 11.28%, respectively ( Figure 3B)  Across all mango soils, a total of 1,777,037 quality fungal sequences, and an average of 59,234 sequences per sample, were obtained. The dominant fungal phyla in soils were Ascomycota and Basidiomycota, with average relative abundances of 79.38 and 11.28%, respectively ( Figure 3B). Other minor phyla (Anthophyta, Cercozoa, Rozellomycota and Glomeromycota) were also found at a lower relative abundance (relative abundance < 1%). The soil layer and sown grass trip had no significant effects on the relative abundance of Ascomycota and Basidiomycota. Based on taxonomical classification at the class level, Sordariomycetes (36.69%), Eurotiomycetes (18.96%), Dothideomycetes (15.33%) and Agaricomycetes (9.50%) were more abundant than other groups (relative abundance > 1%), which accounted for 80.48% of the fungal sequences. Other fungal classes were less abundant in all the soils.
Anosim analysis indicated that the fungal community structure was strongly impacted by the application of sown grass strips (r 2 = 0.825, p = 0.001 for 0-20 cm soil layer; r 2 = 0.413, p = 0.001 for 20-40 cm soil layer). Soil bacterial community structure was not sensitive to sown grass strips (r 2 = 0.179, p = 0.095 for 0-20 cm soil layer; r 2 = 0.092, p = 0.203 for 20-40 cm soil layer). Different soil samples were clearly separated by the sown grass types in the NMDS plot ( Figure 4A

Variations in Soil Fungal Ecological Guilds under Different Sown Grass Strips
Soil functional fungal groups significantly altered under different cover cropping with different sown grass strips. Cover cropping enhanced the proportion of pathotroph

Variations in Soil Fungal Ecological Guilds under Different Sown Grass Strips
Soil functional fungal groups significantly altered under different cover cropping with different sown grass strips. Cover cropping enhanced the proportion of pathotroph fungi and decreased saprotroph fungi. Cover cropping increased the proportion of Arbuscular Mycorrhizal fungi and wood saprotroph fungi, while cover cropping had no significant effects on the plant pathogen or animal pathogen ( Figure 5).

Variations in Soil Fungal Ecological Guilds under Different Sown Grass Strips
Soil functional fungal groups significantly altered under different cover cropping with different sown grass strips. Cover cropping enhanced the proportion of pathotroph fungi and decreased saprotroph fungi. Cover cropping increased the proportion of Arbuscular Mycorrhizal fungi and wood saprotroph fungi, while cover cropping had no significant effects on the plant pathogen or animal pathogen ( Figure 5).

The Associations between Soil Microbial Community and Diversity and Mango Fruit Yields and Quality
The correlation between fungal diversity (richness) and mango fruit yields was significant (r = 0.75, p < 0.01) ( Figure 6). The soil fungal community structure (repressed by NMDS1) was significantly correlated with mango fruit yields (r = −0.79, p < 0.01). These also showed significant correlations between fungal diversity and mango fruit TSS (r = 0.71, p = 0.01) and organic acid (r = −0.76, p < 0.01). However, the soil bacterial diversity had no significant associations with mango fruit yields and TSS (data not shown).

The Associations between Soil Microbial Community and Diversity and Mango Fruit Yields and Quality
The correlation between fungal diversity (richness) and mango fruit yields was significant (r = 0.75, p < 0.01) ( Figure 6). The soil fungal community structure (repressed by NMDS1) was significantly correlated with mango fruit yields (r = −0.79, p < 0.01). These also showed significant correlations between fungal diversity and mango fruit TSS (r = 0.71, p = 0.01) and organic acid (r = −0.76, p < 0.01). However, the soil bacterial diversity had no significant associations with mango fruit yields and TSS (data not shown).

Discussion
Cover cropping has been widely used in agriculture systems to improve crop yield and quality as well as soil quality. In our study, the mango yield was significantly enhanced by 3-14% after the application of sown grass strips between rows. Similarly, winter cover cropping improved corn yields [39], and soybean yield significantly increased

Discussion
Cover cropping has been widely used in agriculture systems to improve crop yield and quality as well as soil quality. In our study, the mango yield was significantly enhanced by 3-14% after the application of sown grass strips between rows. Similarly, winter cover cropping improved corn yields [39], and soybean yield significantly increased after the 3-year application with a multispecies mixture of legumes, grasses and Brassica spp [40]. These positive effects of cover cropping in crop yields suggested that cover cropping is an effective method to increase yields. In this study, we also found a significant increase in mango yields with B and H. However, Z had a slight increase in mango yields, suggesting that Z is not suitable to use in mango orchards. The positive effects of B and H on mango yields might be explained by the variations in nutrients and microbial community diversity in soils caused by cover cropping.
A meta-analysis found that global cropland soil carbon changes due to cover cropping, which increases SOC in near-surface soils by an average of 15.5% [2]. However, in our study, we found the sown grass did not enhance SOC in both soil layers. This difference might be explained by the duration of application of the sown grass. Sown grass strips caused the changes in available nutrients, such as AVN and AVP. The growth of sown grass could produce root exudates and litters, which were the main resources of soil nutrients, especially available nutrients. These variations in soil nutrients directly and indirectly influenced soil microbial diversity and community compositions, respectively.
In this study, we found that soil fungal diversity was sensitive to the application of sown grass strips. The introduction of sown grass between the rows enhanced the fungal diversity compared to the control (no sown grass). This might be explained by the higher decomposition ability of litters and roots than bacteria. Fungi are considered the primary decomposers of dead plant biomass in terrestrial ecosystems. The results from the litter-bag decomposition experiments in the field and the laboratory indicated the overwhelming advantage of fungi during the litter decomposition process [41][42][43][44]. Pascoal and Cássio (2004) showed that the contribution of fungi to litter decomposition greatly exceeded that of bacteria [45]. Despite the critic roles of fungi, the roles of bacteria could not be neglected [46], especially during the litter decomposition in which they mainly worked at the later decomposition stage. Therefore, the long-term introduction of sown grass strips might cause variations in soil bacterial diversity and community.
Different types of sown grasses also had different effects on soil fungal diversity. B and H soils had higher soil fungal diversity than Z. These variations might be explained by the quality of plant litter, roots and root exudates, which were the main factors impacting soil fungal diversity and community. For example, high quality litter decomposed faster than the low-quality litter [47,48]. The fast decomposition released a much higher amount of nutrients to the soil and resulted in the fast succession of soil fungi.
Nitrogen was considered as the main resource of soil microbes and strongly affected soil biodiversity and community structure [26,49]. Wang et al. (2018) found that tropical forest soil microbial community composition was shaped by N addition, with the increase in the proportion of arbuscular mycorrhizal fungi [50]. In the present study, we found that soil TN and AVN had significant effects on soil bacterial diversity, suggesting that soil N was the best predictor of the regulation of soil bacterial diversity in the mango orchards.
Soil microorganisms are considered as the main regulator of soil nutrient cycles and net primary production. In this study, we found that soil microbes enhanced mango fruit yields and quality via increasing fungal diversity and alterations in fungal community structure. Soil microbial diversity may improve crop yields through these mechanisms. First, soil biodiversity mediated nutrients available in the soil, which were the main resources of crop plant growth. Many previous studies have suggested that soil biodiversity enhances plant growth and drives crop yields [51,52]. Second, soil microorganisms help to maintain soil health and prevent the invasion of pathogens. Higher soil biodiversity provides higher ecosystem functions, such as net primary production and nutrient cycling. Therefore, soil microbial diversity, especially for fungal diversity, is vital in order to improve mango fruit yields.

Conclusions
This field experiment showed that cover cropping with sown grass had significant effects on the soil microbial community in a mango orchard. Fungal diversity was more sensitive than bacterial diversity in response to sown grass. The application of Brachiaria eruciformis (B) had the strongest effects on soil fungal diversity. The increase in fungal diversity suggested that sown grass had positive influences on soil biodiversity. Across soil properties, AVN and TN were the most important predictors affecting soil bacterial communities, while soil nutrients (TN and AVN) were the most important factors in mediating soil fungal communities. These results show that the sown grass strips in a mango orchard regulated the soil microbial community and diversity via soil organic matter and nitrogen, which might be of great significance in mango production and quality and the suitability of mango orchards. Mixtures of sown grasses might be more effective for soil biodiversity and soil function, and operations in mango orchards and future research should be focused on this aspect.