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Improving irrigation management on hillslope pastures

Improving irrigation management in hillslope catchments to mitigate water and nutrient losses requires a good understanding of soil hydrology. A field trial was set up in a small catchment on a centre-pivot irrigated dairy farm in South Canterbury. Runoff was monitored over two irrigation seasons across a hillslope and at the catchment outlet. Seasonal rainfall variability influenced the starting date, frequency and amount of irrigation applied. Nearly 40% of the runoff events in the second season were directly linked to irrigation. The APSIM model was used to investigate the effects of irrigation management on runoff and pasture production. Simulations suggested that changing irrigation could increase pasture yield by 10%, but led to potentially greater runoff. Using soil moisture to control irrigation improved performance, although the location of monitoring within the landscape was important. Using frequent and smaller application depths resulted in yield increase and less runoff. Simulation of variable rate irrigation did not produce further gains. The best approach to reduce overall runoff losses was to maintain a soil moisture deficit after irrigation. The impact of changing irrigation management over the whole farm system in these complex landscapes needs further research.

Introduction
The area of irrigated land in Canterbury, New Zealand, has nearly doubled in the last two decades, to approximately 467,000 ha (StatsNZ Tatauranga Aotearoa 2021). One of the main drivers for this change was the fast-growing dairy industry, due to high international demand for dairy products. From 1990 to 2019, the number of dairy cattle increased by 82% in New Zealand (StatsNZ Tatauranga Aotearoa 2021). In the Canterbury region, cow numbers increased from 112,999 to 1,099,567, representing a remarkable increase of 973% in the last 30 years (StatsNZ Tatauranga Aotearoa 2021). This expansion and intensification has had a large impact on the freshwater quality in New Zealand (Larned et al., 2020;Gray et al., 2021). As part of this expansion, irrigated dairy farming has moved into more marginal areas, such as rolling hills (Houlbrooke et al., 2011). Landscapes with hillslopes are prone to surface and subsurface water runoff (Bircham and Gillingham 1986;Bretherton et al., 2010). There is evidence that irrigating grazed hillslopes with impeded drainage, such as slowly permeable fragipans present in loess sheets in South Canterbury and North Otago, can promote water losses via surface runoff (Laurenson et al., 2018;Langer et al., 2020). However, hydrological understanding of these systems is still limited. Furthermore, surplus runoff can contain agricultural contaminants and sediment that compromise water quality of surrounding surface water bodies.
Previous studies have revealed that on-farm runoff can compromise pasture water use efficiency, leading to loss of nutrients and reduced pasture production. Sheath and Boom (1985) and Gillingham et al. (2003) found that differences in pasture growth in different slope positions were related to declining soil moisture and storage. Variability in physical properties in hillslope soil is one of the key drivers for surface runoff. Runoff processes are complex and, in most catchments, different mechanisms can be observed. Common mechanisms for soils containing fragipans (layers with restrictions to water flow and root penetration) are either infiltration excess (IE) when precipitation rate is higher than the soil infiltration rate, or saturation excess (SE) when water storage of the soil is exceeded. Runoff from both IE and SE have been observed in several studies in New Zealand (Adams et al., 2005;Müller et al., 2006), and there is evidence that both mechanisms show dependency on slope position or seasonal changes. Srinivasan et al. (2002) found that SE occurred predominantly on the lower portion of hillslopes. Seasonal changes have been especially observed after long dry periods in summer, when the soil surface can develop water repellence (Müller et al., 2006;Leh et al., 2008) which reduces water infiltration significantly, leading to more IE runoff.
Other important factors to consider in grazed hillslope are the effects of animals. Their behaviour is impacted by slope gradient and position and this affects the distribution of nutrients to the soil via excreta (Saggar et al., 1990), as well as soil physical properties due to trampling (Dodd et al., 2016). Understanding the drivers and effects of runoff in hillslopes will lead to improved irrigation practices on farms and the development of strategies for freshwater resource management.
A collaborative research programme was established in 2019 to improve the understanding of the effects of irrigation on runoff losses in the loess rolling hill landscape of South Canterbury. Results from the first year showed that water use efficiency varied for the different slope classes (Langer et al., 2020). This suggested that irrigation management could be improved to ensure a better water supply for plants. Monitoring the site continued, extending the data collection to include the winter period and a second irrigation season. The following study reports and compares data from runoff plots and at catchment level, with the objective to identify the extent to which irrigation and rainfall affected runoff. Based on these measurements, the APSIM model was then used to investigate potential approaches to improve irrigation management for rolling hills. In addition, the merits and problems with a long-term monitoring site and the outlook for future work are discussed.

Site and experimental design
In 2019 the experiment site was established at a small catchment located at an irrigated dairy farm in Otaio, South Canterbury (-44.5S, 171.2E). The catchment drained an area of approximately 6.15 ha and was bounded by hillslopes in the North and South, which flattened off towards the headwaters in the West. Water drained into an ephemeral stream in an adjacent forest area. The farmed area was irrigated by a full circle centrepivot (T-L Irrigation, NE), 340 m long, with rotating sprinklers (Nelson Irrigation, WA) set approximately 6 m apart. The monitoring site was located under the last of the system's six spans (Figure 1).
Soils in the area were characterised as an impermeable fragipan layer overlain by well-drained topsoil, whose thickness varied between 30 cm at the top of the slope to 60 cm at the bottom of the slope. The soils were classified as Fragic Perch-Gley Pallic and Mottled Fragic Pallic according to the New Zealand soil classification (Hewitt 2010). Plant available water capacity (PAWC), defined as the water retained above the fragipan between the matric potential of -1500 kPa and -10 kPa, was estimated at 57 mm, 42 mm and 94 mm for the top, mid, and bottom plots respectively. Detailed soil descriptions are shown in Langer et al. (2020). Catchment surface runoff (Catchment) was measured through a weir at the outflow. The weir was constructed with barriers made of timber (H4 treated 200 mm x 50 mm gauged retaining timber), 8 m and 12.7 m long, installed in January 2019, on both sides of an H-flume device. The water level in the flume was monitored using a laser system (LWL6001-S, NIWA).
To investigate runoff on the hillslope within the catchment (Hillslope), three 40 m 2 runoff plots were installed in September 2019 along the south facing slope, approximately 65 m away from the catchment outflow weir. Langer et al. (2020) provides in-depth information on the runoff plot setup and monitoring. The surface-hydrologically isolated plots were positioned at the top (slope angle of 4º), mid (slope angle of 18º) and bottom (slope angle of 15º) of the south facing slope parallel to the tracks of the centre pivot irrigator. Surface runoff was drained into a 100 l collection bin for each individual plot. Precipitation from rainfall and irrigation was monitored using duplicate rain gauges in each plot (6463M, Davis Instruments, US; 0.2 mm per tip). Cows were excluded from the runoff plots with electric fences for the duration of the experiment. The area inside the runoff plots was cut regularly to a height of about 5 cm using a lawn mower. Dry matter yield was determined from two strips (0.47 m wide and 9 m long) for each plot. The grass cuts and fertilizer applications were carried out approximately following the farm's grazing and fertiliser management. After each harvest, 18 kg N/ha were applied in form of urea (Ballance Agri-Nutrients SustaiN®) for the grazing season 2019/2020 and 23 kg N/ha for the grazing season 2020/2021.

Data collection and analyses
Data was collected between 01/09/2019 and 19/04/2021. This period covered two irrigation seasons (01/09/2019 to 19/04/2020 and 01/09/2020 to 19/04/2021; totalling 231 days each) and one winter season (20/04/2020 to 31/08/2020; 133 days). For consistency, monitoring in both irrigation seasons were set to start and end such that the time span was the same, even though, in practice, the timing of the first application differed between years. No irrigation was applied during winter.
Hourly rainfall, temperature, humidity, wind speed and direction, were measured at the NIWA Otaio weather station (no. 43845, cliflo.niwa.co.nz) about 400 m north of the research area. No radiation and potential evapotranspiration (PET) values were available for the period between September 2019 and November 2020 due to equipment failure. For this period, values measured at the Timaru Aero Aws weather station (no. 5086, cliflo.niwa.co.nz) were used, with PET values adjusted (0.3 + 0.8*PET Timaru ) as the difference between stations, inferred from the period with overlap, was significant. Precipitation measurements for each plot rain gauge were recorded every 10 minutes from October 2019 to September 2020 and every minute from October 2020 onwards. Irrigation was calculated as the difference between the precipitation measured at each plot and the amount of rain measured at the Otaio weather station.
Catchment runoff data were recorded continuously every minute, enabling surface water runoff data to be linked precisely to rain and irrigation events. Surface water runoff at the Hillslope plots was automatically recorded once the volume of water that was collected reached about 35 l (approx. 0.9 mm depth). At this volume, switches triggered a pump sending the water through a flowmeter. Volumes that did not trigger the switches were manually measured during monthly maintenance visits. Because of the lag between runoff initiation and the collection of enough water to trigger the pump, runoff events recorded at the runoff plot level could not always be directly assigned to specific irrigation or rainfall events.

Modelling
Computer modelling was used to complement the analyses of observed data and to investigate how variations in irrigation management were likely to affect pasture production and runoff losses. Simulations were performed using the Agricultural Production Systems Simulator (APSIM) framework, version 7. 10 (Holzworth et al., 2014). The simulations were setup with the SoilWat module (Probert et al., 1998) used to describe water movement and solute transport. This module uses the curve number (CN) approach, from the USDA-Soil Conservation Service (SCS 1985;Ponce and Hawkins 1996), to estimate overland runoff. Sub-surface flow was computed based on slope and assumed that lateral hydraulic conductivity was the same as that measured vertically. Soil C and N cycling were described using the SoilN and SurfaceOM modules (Probert et al., 1998). Pasture growth over the different slopes was simulated using AgPasture with a ryegrass/white clover mix, as described in Vogeler and Cichota (2016). Pasture grazing rules were set to mimic the current rotation of the farm (grazing at intervals between 20 and 30 days from mid-August until May, and wintering off farm). To better describe the effects of terrain on pasture growth (i.e., effects of aspect and slope angle), solar radiation values were adjusted based on the methodology described by Allen et al. (2006). For each hillslope, three different areas were simulated, corresponding to the three runoff plots of the field trial. Soil parameters were setup based on measurements and the slope angle of each area (detailed soil descriptions are given in Langer et al. (2020)). Values of CN for runoff calculations were gathered from standardised tables (SCS 1985) with minor adjustments based on measurements for the first irrigation season, which resulted in values of 88, 92, and 90 for top, middle and bottom plots. The simulation outputs from the plots were aggregated to the Catchment based on the relative proportion of area in each slope class, rounded to 80% flat (top plot), 5% steep (middle plot), and 15% medium slope (bottom plot), which were determined based on a digital elevation map of the catchment.
Several different irrigation management scenarios, using a centre pivot, were considered ( Table 1). The base scenario mimicked the management over the first irrigation season, which was used for validating the model. Another used a fixed irrigation schedule (applying 12 mm every four days, which corresponded to an average ET of 3 mm/day estimated for the summer months at the site), with pauses when rainfall occurred (for 3 days when rainfall surpassed 10 mm over four days). This roughly mimicked a somewhat extreme management that ignored soil moisture entirely. All the remaining scenarios took soil moisture into account to control irrigation, with monitoring site taken to be either the top or the bottom runoff plot (defined as TM and BM). Simple management alternatives were set up to consider different soil moisture levels to trigger irrigations, combined with variations in the irrigation amount applied each time. Three application depths were used, 12 mm, 6 mm and 4 mm, which corresponded to return periods for the irrigator of 3, 2, and 1 day, respectively (marked as HD, LD, and SD). Finally, two scenarios were defined to mimic variable rate irrigation (VRI), attempting to maintain a soil water deficit of 5 mm and 15 mm (identified as D5 and D15). In these scenarios, the irrigator movement was controlled by soil moisture status at the top plot, but application varied for each of the three plots, according to their moisture status, but was limited to zero or between 2 mm and 8 mm (mimicking limitations to control irrigation at low rates and to avoid applying at a too high instantaneous intensity). For all scenarios, irrigation was assumed to occur at an instantaneous intensity of 22 mm/h, which was the average rate for the centre pivot at the location where the runoff plots were installed. Because the amount of application and/ or deficit maintained in the soil varied with scenario, the trigger point to start irrigation varied for each management scenario (Table 1).

Surface runoff measurements
There were differences in the number of recorded Catchment runoff events between the two irrigation seasons. More surface runoff events, 41, were measured during irrigation season 2 at the Catchment level, whereas 31 events were detected in irrigation season 1, and only three over the winter season (Table 2). Despite this, irrigation season 1 had more runoff events triggered by irrigation alone (about 39%) compared to irrigation season 2 (27%). This reflected the fact the Table 1 Main characteristics defining the scenarios used to simulate different irrigation management. Validation (base) 3-14 n/a n/a n/a

Scenario
In the scenario description, HD was high application depth (12 mm), LD was low depth (6 mm), and SD was small depth (4 mm); TM was the top plot moisture, BM was bottom plot moisture; VRI was variable rate irrigation, where the soil moisture was returned to either a 5 mm (D5) or 15 mm deficit (D15) post irrigation. NB. For VRI the irrigation amount varied between 2 mm and 8 mm, depending on soil moisture status of each plot. For the validation run, actual application depths applied on-farm were used and these varied at different dates and even between plots. Irrigation and rain triggered events 9 8 0 Journal of New Zealand Grasslands 83: 135-144 (2021) soil was at greater deficit during irrigation season 1 and the use of less irrigation by the farmer in irrigation season 2. The three runoff events in winter were linked to events in which rainfall totalled 9.4 mm and 8.8 mm over 4 and 2 days consecutively, and one day with 7.4 mm of rain. During the winter period, rainfall was considerably lower than normal, only 55 mm compared to the historical mean of 206 mm (1973-2019, cliflo. niwa.co.nz).
In agreement with the number of events per season, the total amount of surface runoff was larger in irrigation season 2 (Table 3). This reflected about 100 mm more rainfall compared to season 1, as well as variations in rainfall intensity. During irrigation season 1 a total of 83 days with rain were counted with only 56 days during irrigation season 2. Irrigation started much earlier in season 2, following a dry winter the first application was on the 1 st of September 2020, whereas the first irrigation in season 1 was on the 29 th of October 2019. Nonetheless, about 60 mm more water was applied during irrigation season 1 (Table  3). In total, 34 events were counted during irrigation season 1, with an average of 8 mm and a maximum of 23.7 mm. Only 27 irrigation events, with an average of 7 mm and a maximum of 13 mm, were measured during irrigation season 2. Irrigation measured at the Hillslope level revealed that more water was applied on the bottom slope compared to the mid and top slopes in both seasons (Table 3). About 60 mm more water was applied on the bottom slope compared to the mid and top slopes during irrigation season 1 and about 20 mm during season 2.
The measured runoff amounts at the Catchment level was approximately five times greater in the second irrigation season compared to the first (Table  3). However, the difference between irrigation seasons was much smaller for the runoff measured in the plots. The largest amount of surface runoff at a Hillslope level were measured at the bottom slope and the lowest amount at the top slope consistently for both irrigation seasons (Table 3). There was only slightly more runoff measured for all plots in irrigation season 2, which likely reflected changes in the soil and grass cover following the exclusion of cows at the start of the first season. The amount of irrigation varied depending on the slope position in both seasons. As shown in Table 3, more irrigation was applied at the bottom of the slope compared to the mid and top of the slope. In total, 1% of the total precipitation was discharged as surface runoff for the whole catchment during the first and 5% during the second irrigation season.

Model scenarios
The APSIM model simulated the general trends measured on the monitoring site reasonably well. Calculated cumulative runoff amounts agreed well with measurements (Figure 2A), while pasture growth was only slightly underestimated, but the relative differences between plots was well captured ( Figure 2B). Only values for the first year are shown and discussed, because there were considerable changes over time in the grass and soil surface of the runoff plots. The Table 3 Summary of water balance components measured at a dairy farm in South Canterbury over two irrigation seasons and the winter period in between. Values are given for the whole catchment (6.1 ha) and for three runoff plots installed at three locations along the south facing slope of the catchment. Irrigation seasons were defined between 1 st September and 19 th April for both years and the remainder winter season of 2020. sward was in much better state, with higher and more homogenous plant cover. To obtain good agreement for simulations for the second season, soil parameters had to be changed to reflect non-grazed conditions (lower CN for runoff and more uniform distribution of nutrients across the slope). As this did not reflect the overall paddock and catchment area, it was postulated that only the first irrigation season represented the general conditions of the paddocks under grazing. The summary of results using the APSIM model to simulate various scenarios for irrigation management are shown in Table 4. All simulations suggested that the site was under-irrigated in general, with more water being used compared to the validation run, which was amount measured on the farm. All simulations produced more pasture than the validation run. However, the measured amount applied in the plot at the bottom of the slope (218 mm) was larger than several other scenarios, which had higher estimated yields. This suggested that that the plot was slightly over-irrigated, or that the timing of irrigation was not right.

Location
Fixed schedule irrigation used the largest amount of water, more than twice the validation, representing almost 25% less water use efficiency (WUE, kg, DM/ mm water), and with the highest amount of runoff being three and a half times the validation run.
The results from the other scenarios, that used soil moisture to control irrigation, clearly showed that using the top plot to monitor water deficit resulted in more irrigation being applied which achieved higher yields. This reflected the fact that the flat areas represented most of the catchment area, and, thus, management tailored to these areas was likely to have the greatest impact on WUE. However, such management always resulted in considerably more runoff losses than the scenarios where soil moisture at the bottom plot was  Table 4. All simulations suggested that the site 94 was under-irrigated in general, with more water being used compared to the 95 validation run, which was amount measured on the farm. All simulations produced 96 more pasture than the validation run. However, the measured amount applied in the 97 plot at the bottom of the slope (218 mm) was larger than several other scenarios, 98 which had higher estimated yields. This suggested that that the plot was slightly over-99 irrigated, or that the timing of irrigation was not right.

00
Fixed schedule irrigation used the largest amount of water, more than twice the 01 Figure 2 Observed (symbols) and predicted (lines) cumulative amount of runoff (A) and harvested biomass (B) for three runoff plots installed along the top, middle, and bottom of a slope in a South Canterbury dairy farm.
used to trigger irrigations. There was a clear trend for less runoff, especially as a proportion of irrigation, when less irrigation depth was applied. The scenarios using VRI produced results that were quite similar to those when irrigation was triggered by soil moisture (Table 4). This suggested that further gains in WUE or reduction in runoff are limited. Considerable reductions in losses in the VRI scenarios was estimated for the mid plot only (data not shown), which had the steeper gradient, but represented only a small proportion of the total area. Maintaining a larger soil water deficit limited runoff with no pasture yield penalties.

Discussion
There were large differences in runoff responses across the hillslope and from the catchment between the irrigation seasons and winter. Rainfall variability and irrigation inputs were both important factors. In both irrigation seasons, disproportionally more surface runoff was generated at the catchment and plot level, compared to the winter season. Such a large difference may have been atypical, given that average winter rainfall is normally four times greater than recorded at the site. A more typical rainfall pattern would cause wetter soil conditions, generating more SE runoff due to rainfall exceeding plant uptake. With such an exceptionally dry winter, and PET of about 120 mm, there was a relatively large soil water deficit at the start of the second irrigation season. This led to earlier irrigation application dates in season 2 compared to season 1.
Irrigation was directly responsible for a large proportion (27-39%) of the Catchment runoff events depending on the season, although the majority was directly related to rainfall alone. In particular, it was noted that substantial surface runoff was mostly linked to large rainfall events in the second irrigation season. This finding was consistent with findings by Buda et al. (2009) andMcDowell et al. (2006), which showed that the generation of surface runoff events were linked to higher rainfall intensities. Indirectly, irrigation may be expected to contribute to rainfall-driven events, as it maintains soil water content closer to saturation levels.
The variability in timing and amounts of rainfall required different irrigation management responses. The farmer adapted his irrigation schedule somewhat based on soil moisture monitoring. Overall, more irrigation was required in the drier season 1 to keep up with the plant demand. While irrigation needed to be applied earlier in season 2, less water was applied over the course of the whole season. In contrast, during irrigation season 1, the majority of irrigation water was applied later in the season. The farmer's response to the variable rainfall supported the importance of using soil moisture measurements to schedule irrigation, although, to be effective, this should be supported by good soil water balance.
Although not quantified in this study, soil hydrophobicity (or soil water repellence) is a key factor in increased risk of runoff from hillslope pasture. Hydrophobicity can induce runoff through IE, as the soil's capacity to absorb water is reduced considerably (Müller et al., 2016;Bretherton et al., 2018). Often hydrophobicity develops after prolonged dry periods. This can be expected to occur on irrigated pastures during periods where plant water use exceeds irrigation supply, leading to soil water deficits. Consequently, it is expected that hydrophobic responses of soil to irrigation will vary over the season. For example, rapid Table 4 Summary from simulations of different scenarios with varying irrigation management. Values shown are an aggregation of simulated results based on the proportion of area for the three slope classes, corresponding to the runoff plots of the monitoring site, representing the 2019-2020 irrigation seasons. Relative differences (%) of each option to the validation (base) scenario. WUE is water use efficiency, HD is high application depth (12 mm), LD is low depth (6 mm), and SD is small depth (4 mm); TM signifies top plot moisture, BM is bottom plot moisture; VRI is variable rate irrigation, maintaining either a 5 mm deficit (D5) or 15 mm deficit (D15) post irrigation.

Scenario
soil drying over dry summer periods might lead to hydrophobic conditions, increasing the risk of IE when irrigation is applied. While the amount of Catchment runoff over the irrigation season 2 was more than five times that of season one, the recorded runoff at plot level was only approximately 20% larger in season 2. In general, the relationship between surface runoff at the catchment and that measured at the plots was quite weak, and deviations were larger in irrigation season 2. This highlighted the differences in methods and scale, but was most likely a reflection of the impact that grazing cattle can have on soil surface conditions. Whilst most of the catchment area was grazed, the runoff plots had been fenced off for more than 1.5 years and it was noticeable that the ground cover changed over time. With no trampling damage, grass cover was much more uniform in the runoff plots. Reductions in soil macroporosity and saturated hydraulic conductivity can result from animal trampling (Gray et al., 2021), leading to conditions that promote surface runoff. The use of runoff plots provided valuable information to understand differences between locations and slopes. However, care must be taken when employing this methodology over long periods of time, as changes in surface conditions within the plots may not represent the overall condition of the catchment where grazing takes place. This breaks the hydrological flow across the landscape and does not capture the effect it may have on subsequent runoff.
Modelling was useful in exploring how irrigation management could be improved. One of the key findings was that there was likely to be an opportunity to increase DM production, albeit a small one. All simulated scenarios showed greater yields, although this was due to applying more water via irrigation. Several of the scenarios showed that runoff could be reduced compared to the base scenario. However, the scenarios where pasture production was maximised had increased runoff losses. For example, triggering irrigation based on soil moisture monitored at the top plot, which represented most of the flat areas, resulted in greater gains in production. But this scenario tended to generate greater runoff, due to steeper areas being generally over-irrigated. A compromise between increasing production and maintaining low levels of runoff is needed. As expected, applying low irrigation rates more often resulted in better outcomes. Maintaining soil deficit after irrigation resulted in better water usage and this practice tend to minimise runoff due to SE. It was important to note that this modelling exercise did not include the potential effects of water repellence by dry soil. Maintaining water deficit over the summer may induce surface hydrophobicity countering its benefits due to induced runoff via IE. This requires further study.
Despite the large variability in runoff and soil conditions observed for the hillslope plots, the catchment modelling VRI scenario did not improve production or reduce runoff better than some of the scenarios that simply used soil moisture to control irrigation (Table 4). While this may be surprising, it was a function of the relatively small area occupied by the slopes, with approximately 80% as flat land. This was a function of the differences in soil water balances on the slopes and was subject to large variability in rainfall that was difficult to predict. While VRI may be optimised further to improve production and reduce runoff, it is questionable how practicably this could be done in the field, or how to justify it economically. It is important to note that the current analysis of VRI used a relatively simple modelling setup and a more thorough investigation may show conditions in which it becomes a viable option.
The complexity and variability of hydrological conditions, as described by Langer et al. (2020) was reflected in the variation of generated surplus runoff at the different plot positions. The data collected from the monitoring sites contributed to the understanding of the hydrology in this challenging landscape, but further work is needed. Drainage was assumed to be low, due to the nature of the loess-derived soils (Webb and Burgham 1997), but sub-surface flow may have been significant, which would account for water losses in such catchments.
Understanding the various pathways for water loss is important to determine the risk of correlated environmental impacts such as sediment and nutrient losses. Changes in irrigation management, such as the scenarios modelled in this work, will have an impact on environmental outcomes. Increasing pasture production allows for increased stocking rates and, if runoff occurs (or increases as was predicted in some scenarios), sediment and nutrient losses will increase. Future analyses should consider the implications of changing irrigation practices on the whole farm to assess environmental risks.

Conclusions
The trial confirmed that irrigation triggers a considerable number of runoff events and that good management is important to avoid water losses, even though the total loss as surface runoff at the catchment level seemed relatively small. Large variation between seasons was observed, with greater runoff in the second year, but this was linked to large rainfall events. Modelling was a useful tool to explore irrigation management and trade-offs between pasture production and runoff, and understand where soil moisture should be measured in variable landscapes. Using the APSIM model enabled simulation of the conditions of the first irrigation season well. Based on this, scenarios using different irrigation management showed that using soil moisture monitoring improved pasture production, without increasing runoff losses. Using VRI did not considerably improve the performance of irrigation. The use of small application depths and maintaining a deficit after irrigation provided the best results. Further research is needed to understand the implications of changing irrigation management over the whole farm and its environmental impact.


provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

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Staphylococcus aureus from Minas Artisanal Cheeses: Biocide Tolerance, Antibiotic Resistance and Enterotoxin Genes

: Staphylococcus aureus is a common contaminant in artisanal raw-milk cheeses. Tolerance of S. aureus to biocides is a threat to disinfection in the cheese production environment, while antibiotic resistance and enterotoxin production are additional health concerns. This study aimed to evaluate the tolerance of S. aureus isolated from Minas artisanal cheeses to the biocides benzalkonium chloride, hexadecylpyridinium chloride, cetrimide, triclosan, hexachlorophene, and chlorhexidine, and the simultaneous occurrence of genes coding for antibiotic resistance ( mecA , aacA-aphD , and tetK ), efﬂux pumps [ qacA / B and smr ( qacC / D )], and enterotoxins ( sea , seb , sec , sed , see , seg , seh , sei , and sej ). Among the tested isolates, 38.2% were resistant to at least one biocide, and 73.1% were positive for one or more antibiotic resistance gene. Most of the biocide-tolerant and antibiotic-resistant isolates harbored efﬂux pump genes, and were positive for at least one staphylococcal enterotoxin gene. The study highlights the need for correct hygiene monitoring programs to ensure the safety of these products.

Introduction
Coagulase-positive staphylococci are commensal colonizers of the skin and mucous membranes of humans and other warm-blooded animals. They are potentially pathogenic, especially Staphylococcus aureus, which can cause a variety of infections, such as superficial infections of the skin and soft tissues, osteomyelitis, and septicemia [1]. They also cause staphylococcal food poisoning (SFP), one of the most frequent foodborne illnesses in the world [2]. SFP is a non-contagious acute gastrointestinal disease that results from the ingestion of foods containing preformed staphylococcal enterotoxins (SE), secreted mainly by coagulase-positive staphylococci, especially S. aureus. Several SE have been described, including the classical SEA and SED, responsible for the majority of SFP cases, in addition to the less common SEB, SEC, SEE, SEG, SEH, and SEI [3,4]. The incidence of SFP cases caused by methicillin-resistant S. aureus (MRSA) has increased in recent years, raising significant public health concerns [1].
Research and discovery of new antimicrobial compounds for use as stand-alone or combination therapy options must consider preventive strategies and measures to reduce the occurrence of resistant microorganisms. In this sense, effective disinfection and sanitation are critical in preventing the transmission of communicable diseases in human and animal environments [5].
The main biocides are quaternary ammonium compounds (QACs), such as benzalkonium chloride, cetrimide, and hexadecyl pyridinium chloride, as well as biguanides, such as chlorhexidine, and bisphenols, such as triclosan and hexachlorophene [6]. These antimicrobial compounds are used as active ingredients in specific formulations for the control of pathogenic organisms in livestock, food and beverage production, the pharmaceutical and textiles industries, consumer products, and health environments [8,9].
The incorrect use of biocides in the food production chain for the reduction of microorganisms of public health importance to safe levels may result in an increase in prevalence and spread of antimicrobial resistance [6]. When cleaning before disinfection is insufficient, and/or rinsing after disinfection is inadequate, bacteria may be exposed to sublethal concentrations of biocides, generating selective pressure for adaptation or the acquisition of tolerance genes, hampering the eradication of these bacteria from food processing environments [10][11][12].
Microbial tolerance to biocides is a complex network that includes phenotypic alterations, such as viable but not culturable state (VBNC) and cell surface modifications, as well as genetic factors, such as the activation of efflux pump systems and/or the acquisition of mobile genetic elements, such as plasmids and transposons carrying resistant genes [12]. The selection of biocide-tolerant microorganisms increases their ability to form biofilms, requiring treatments with increasingly higher concentrations of biocides for their elimination from the environment [13,14].
Thus, microbial tolerance to biocides compromises food safety and threatens public health, mainly because the mechanisms of transmission of this tolerance are similar to those observed for clinically important antimicrobials [12]. Biocides also activate efflux pump systems, and there is concern that this activation could lead to antibiotic resistance [6,15]. The intensive use of QACs in food processing environments can provide a natural selection of more resistant strains with enhanced multi-drug resistance efflux pump activity [16].
In food production systems, microorganisms from the environment and handlers may contaminate final products, endangering their safety and compromising their quality [17]. S. aureus is the most common causative agent of mastitis in dairy cows, and a frequent contaminant in raw milk [18]. In this sense, coagulase-positive staphylococci in the artisanal cheese production system deserve special attention, as these microorganisms are frequent contaminants in cheeses manufactured with raw milk, such as the Brazilian Minas artisanal cheese, one of the best known and most-consumed cheeses in the country [19][20][21].
As occurrence of S. aureus in Minas artisanal cheeses is frequently reported [19][20][21], this study aimed to investigate if S. aureus isolates, obtained from samples collected from retail establishments in the city of Sao Paulo, Brazil, present tolerance to biocides and/or harbor antibiotic resistance and staphylococcal enterotoxin genes. The final objective was to contribute to a better understanding of the role of good hygiene and manufacturing practices in safeguarding consumer health, not only at cheese production level, but also in retail environments.

Bacterial Strains
One hundred samples of Minas artisanal cheeses, collected from randomly selected markets in the city of São Paulo, Brazil, were submitted to testing for coagulase-positive staphylococci using Petrifilm™ STX plates (3M, Sumaré, Brazil). As Minas artisanal cheeses are usually sold in pieces, as parts of the same cheese, the cheese samples selected for the study represented different lot numbers and manufacture/expiration dates, ensuring that they corresponded to different cheeses, and not pieces of the same cheese. For plating, cheese samples were homogenized with 0.1% peptone water (PW, Oxoid, Basingstoke, Hampshire, UK) and submitted to decimal dilutions in 0.1% PW. One to five typical colonies per cheese sample, obtained before and after the insertion of the revelation Petrifilm™ Staph Express Disk (3M, Sumaré, Brazil), were submitted to the coagulase test [22]. Prior to the coagulase test, colonies were transferred to tubes containing Brain Heart Infusion (BHI, Becton-Dickinson, Franklin Lakes, NJ, USA), incubated overnight at 35 • C, plated on Baird-Parker agar (Oxoid, Basingstoke, Hampshire, UK) and incubated overnight at 35 • C. Only typical colonies (shiny black, with and without halos) were submitted to the coagulase test.
For identification of coagulase positive colonies to species level, total DNA of five isolates per positive cheese sample was extracted using the GenElute™ kit (Sigma-Aldrich, Burlington, MA, USA) and submitted to 16S ribosomal DNA sequencing at the Center for Human Genome and Stem Cell Studies in the University of Sao Paulo, SP, Brazil. The obtained genomic sequences were analyzed with the BLAST algorithm, available at the National Centre for Biotechnology Information (NCBI, Bethesda, MA, USA).

Determination of Biocide Tolerance
All S. aureus isolates were tested for susceptibility to benzalkonium chloride (BC), hexadecylpyridinium chloride (HDP), cetrimide (CT), triclosan (TC), hexachlorophene (HC), and chlorhexidine (CD) (all from Sigma Aldrich, Spain) using the broth microdilution method and 96-well microtiter plates (Becton Dickinson, Franklin Lakes, NJ, USA) [23]. Biocides BC, HDP, CT, and CD were dissolved in Tryptic Soy Broth (TSB, Oxoid, Basingstoke, Hampshire, UK), while TC and HC were dissolved in 96% ethanol. These solutions were diluted to achieve concentrations of 256 mg/L, 128 mg/L, 64 mg/L, 32 mg/L, 16 mg/L, 8 mg/L, 4 mg/L, and 2 mg/L, respectively. For the tests, the isolates were activated in TSB at 37 • C for 24 h, and the cultures were diluted 1:10 in TSB. The biocide solutions, at the tested concentrations, were added to the wells of the microplates (180 µL per well), and 20 µL of each 1:10 diluted culture was added to the wells. The optical density (OD) at 620 nm was measured in a Sunrise microplate reader (Tecan, Grödig, Austria), at time zero and after 24 h at 30 • C. An isolate was classified as tolerant to a biocide when the difference between the ODs measured at time zero and after 24 h was ≥0.05 [17]. The MIC corresponded to the minimum concentration required to maintain the difference between the ODs measured at time zero and after 24 h below 0.05 [17]. Three controls were included in the tests: positive controls (pure cultures), negative controls (biocides at the tested concentrations, without added cultures), and sterility of culture medium (TSB). All tests were performed in triplicate.

Extraction of DNA
The DNA of the isolates was extracted using Quick-DNA™ Fungal/Bacterial Miniprep (Zymo Research, Irvine, CA, USA), following the recommendations of the manufacturer. The quality of the extracted DNA was considered satisfactory when the OD 260 nm /OD 260 nm ratio, measured with a NanoDrop 2000 UV-Vis Spectrophotometer (Thermo Scientific, Waltham, MA, USA), was in the range of 1.7 to 2.0. PCR was performed in a Mastercycler ® nexus thermocycler (Eppendorf, Hamburg, Germany).

Results
Based on the combinations of results of the tests for biocide tolerance, antibiotic resistance, efflux pump genes, and enterotoxin genes, 136 distinct S. aureus isolates were obtained. Isolates from the same cheese sample presenting identical results were considered as one isolate.

Tolerance to Biocides
Tolerance to at least one of the tested biocides was observed in 52 (38.2%) S. aureus isolates. The most tolerated biocides were those belonging to the QAC group: 39 (28.6%) were resistant to BC, 11 (8.0%) to HDP, and 3 (2%) to both. Among the isolates tolerant to BC (n = 39), the MIC was 32 mg/L for 35 isolates and 16 mg/L for four isolates. Five isolates were not inhibited by HC, four of which were also not inhibited by BC. The MIC for HDP and HC was 16 mg/L for both biocides. The other four isolates were tolerant to CD (MIC = 4 mg/mL). None of the isolates presented tolerance to TC or CT in the tested concentrations.

Antibiotic Resistance
At least one antibiotic resistance gene was detected in 113 (83.0%) S. aureus isolates, and almost half (n = 64, 47.1%) presented more than one. The most detected resistance genes were tetK (n = 74, 54.4%) and mecA (n = 71, 52.2%), followed by aacA-aphD, detected in 41 (30.0%) isolates. The occurrence of antibiotic resistance genes in the biocide-tolerant S. aureus isolates was high: 38 (73.1%) were positive for at least one gene, 23 (44.2%) were positive for two genes, and four (7.7%) harbored all three investigated genes (two isolates were tolerant to HDP, one isolate was tolerant to BC only, and one isolate was tolerant to BC and HDP, simultaneously).

Screening for Efflux Pump Genes
The great majority of biocide-tolerant isolates (n = 48, 92.3%) presented at least one of the two QAC efflux genes (smr-qacC/D and qacA/B), with greater prevalence of smr-qacC/D, detected in 43 (89.6%) of these isolates. The qacA/B gene was observed in 14 (29.2%) of the isolates harboring efflux pump genes. Ten isolates (20.1%) presenting an efflux pump gene were simultaneously positive for the two tested genes.
Thirty eight out of 39 BC-tolerant isolates were positive for the s smr-qacC/D gene, and 10 (25.6%) of them were also positive for the qacA/B gene. Both genes were detected in nine (23.1%) BC-tolerant isolates.
Eight HDP-tolerant isolates (n = 11) were positive for at least one of the two QAC efflux genes (72.8%). Among these isolates, smr-qacC/D was detected in four, qacA/B in five, and one isolate was positive for both genes.
All HC-tolerant isolates (n = 5) were positive for the qacA/B or smr genes. Three (60%) isolates contained only smr, and two (40%) were positive for both markers.
Among the isolates presenting at least one antibiotic resistance gene (n = 112), efflux pump genes were detected in 99 (88.4%), mainly smr, present in 92.9% of them. The qacA/B was much less frequent, detected in 26 of these isolates (26.3%). Nineteen isolates (19.1%) were positive for both genes simultaneously.

Tolerance to Biocides and Presence of Antibiotic Resistance and SE Genes
The simultaneous tolerance to at least one biocide and presence of at least one antibiotic resistance and one SE enterotoxin gene was observed in 38 (27.9%) isolates. The most frequent combination was tolerance to BC and positivity for tetK and seg and/or seh genes, observed in 22 isolates, corresponding to 57.9% of the isolates belonging to this group and 16.2% of the total number of isolates included in the study.

Discussion
In addition to the use of raw milk for production, artisanal cheeses undergo several processing steps where contact with handlers is unavoidable, and so, contamination with S. aureus can be expected [20,23]. Cross-contamination at retail locations can also be a source of S. aureus, as whole cheeses are cut into smaller pieces for sale, and cutting boards and utensils can transfer microorganisms from one cheese to another. Handlers with poor personal hygiene practices, in retail environments, can be additional sources of contamination [30].
A worrisome percentage (38.2%) of the S. aureus isolates obtained from Minas artisanal cheese samples collected from retail establishments in Sao Paulo was tolerant to at least one biocide, mainly to quaternary ammonium compounds (QACs), benzalkonium chloride (BC), and hexadecylpyridinium chloride (HDP). The wide use of BC in the formulations of products destined for cleaning utensils and other surfaces in the food industry may explain these results [31]. BC is also used as a component in pesticides, disinfectants, and detergents in animal husbandry, mainly dairy cattle [32]. HDP is used in personal hygiene products, and is also approved for decontaminating raw poultry, so its use in food processing environments is possible [33].
The frequent use and overuse of biocides in the food processing system can create selective pressure, resulting in the proliferation and abundance of disinfectant-tolerant strains [9], besides the appearance of strains tolerant to multiple biocides, as evidenced in this study and reported by other authors [34,35]. The reported tolerance to biocides suggests that any use that may provide a sublethal exposure represents a risk of developing adaptation to these compounds.
A high prevalence of the QAC efflux pump smr-qacC/D gene among the S. aureus isolates tolerant to biocides (89.6%) was detected, confirming previously published results [36,37]. Some studies have shown that efflux pumps decreased the effectiveness of QAC biocides, phenolic parabens, and intercalating agents, most notably in S. aureus expressing pumps such as QacA-D, QacG, and QacH [38]. In this study, none of the CDtolerant isolates were positive for the qacA/B and smr genes, suggesting that these genes do not play an important role in tolerance to this biocide, as also reported in other studies [35].
The co-existence of tolerance to biocides and potential resistance to antibiotics found in the S. aureus isolates obtained from artisanal cheeses can be explained by the positivity for smr and qacA/B genes encoding QAC efflux pumps. Despite having different mechanisms of action, studies indicate that the genetic systems for antibiotic and biocide resistance are similar [40]. Since similar genetic mechanisms are at play in antibiotic resistance, selection pressure may eventually contribute to the emergence of multi-resistant bacteria with the potential to impact public health [41], as evidenced in this study.
Previous studies have shown that decreased susceptibility to chlorhexidine in methicillin -resistant S. aureus (MRSA) was associated with the qac A, qac B, and smr genes [40]. The mechanisms underlying tolerance/resistance indicate that biocide tolerance genes and antibiotic resistance genes are located on the same mobile genetic elements [42]. Mutations in pmrB and the upregulation of efflux pump genes contributed to antibiotic resistance in Pseudomonas aeruginosa after exposure to benzalkonium chloride [42].
The most recent studies suggest that biocide-induced antibiotic resistance is more likely to become a contributing factor when bacteria are repeatedly exposed to sub-inhibitory concentrations of biocidal agents in disinfectants, increasing the chances of the co-selection of antibiotic-resistant bacteria [41,43]. Furthermore, these resistant bacteria were shown to be able to form strong biofilms on surfaces in the cheese production environment, with consequent hazards for public health and for the quality of the final products [37,44]. However, biocide-induced antibiotic resistance seems to be bacterial species-or strain-dependent: Listeria monocytogenes isolates obtained from food production plants and tolerant to BC did not present cross-resistance to clinically relevant antibiotics [45].
Most of the S. aureus strains isolated from the cheeses presented enterotoxin genes, predominantly for non-classical enterotoxins such as SEH and SEG. Despite the knowledge that the presence of these genes does not mean that the corresponding enterotoxins are produced, as genes require complex machinery for expression, these results are worrisome, and confer additional preoccupation to the current knowledge that the occurrence of enterotoxigenic S. aureus in Minas artisanal cheeses is common [26,[46][47][48]. The prevalence of non-classical enterotoxin seh gene in S. aureus isolated from Brazilian artisanal raw-milk cheeses, reported by previous studies, corroborates our findings [26,49]. The increasing predominance of non-classical staphylococcal enterotoxin genes demonstrates the importance of investigating these new enterotoxins in dairy foods when testing their safety.
Also worrisome is that nearly one third of the S. aureus isolates harboring enterotoxin genes were resistant to at least one biocide, and harbored genes of resistance to the main antibiotics used in clinical medicine. Tetracycline and methicillin are used in intensive livestock production, which may explain the resistance observed in bacteria obtained from animal products, such as cow's milk [26]. As pointed out for other types of cheese, S. aureus in these products deserves attention due to the potential risk to public health, especially when it accumulates resistance to antimicrobials, tolerance to biocides and production of enterotoxins that cause serious food poisoning [23]. The occurrence of S. aureus is mainly attributed to the inappropriate use of antimicrobial drugs and biocides in human and veterinary medicine, such as unnecessary use, incorrect choice, wrong dosage, short contact time, or irregular application [38]. Within the One Health perspective, it is necessary to address the possible transfer of microbial resistance genes between humans, animals, wildlife, plants, and the environment [50].

Conclusions
This study evidenced that tolerance to biocides in S. aureus in Minas artisanal cheese from retail establishments is quite common, and that these isolates may also be resistant to antibiotics and harbor efflux pump genes that favor tolerance to biocides. Furthermore, the majority of the tested isolates were potentially enterotoxigenic. Improper use of biocides in artisanal cheese processing may result in the selection of enterotoxigenic S. aureus that are resistant to antibiotics. Biocide-tolerant strains may form biofilms on surfaces, constituting a threat to disinfection. The presence of antibiotic resistance and enterotoxin genes may be an additional health concern for consumers of artisanal cheeses. The implementation of correct monitoring programs is crucial to safeguard the safety of these products, not only at production level, but also in retail environments.


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Fermented Soy and Fish Protein Dietary Sources Shape Ileal and Colonic Microbiota, Improving Nutrient Digestibility and Host Health in a Piglet Model

Suitable protein sources are essential requirements for piglet growth and health. Typically, intestinal microbiota co-develops with the host and impact its physiology, which make it more plastic to dietary protein sources at early stages. However, the effects of fermented soybean meal (FSB) and fish meal (FM) on foregut and hindgut microbiota, and their relationship with nutrient digestion and host health remain unclear. In this study, we identified interactions between ileac and colonic microbiota which were reshaped by FSB and FM, and assessed host digestibility and host health in a piglet model. Eighteen weaned piglets (mean weight = 8.58 ± 0.44 kg) were divided into three dietary treatments, with six replicates/treatment. The level of dietary protein was 16%, with FSB, FM, and a mixture of fermented soybean meal and fish meal (MFSM) applied as protein sources. During days 1–14 and 1–28, diets containing MFSM generated higher piglet body weight and average daily gain, but lower feed to weight gain ratios when compared with the FM diet (P < 0.05). Piglets in MFSM and FM groups had lower apparent total tract digestibility (ATTD) of crude protein (CP) compared with the FSB group (P < 0.05). Serum immunoglobulins (IgM and IgG) in MFSM and FM groups were significantly higher on day 28, but serum cytokines (interleukin-6 and tumor necrosis factor-α) were significantly lower than the FSB group on days 14 and 28 (P < 0.05). When compared with FSB and FM groups, dietary MFSM significantly increased colonic acetic acid and butyric acid levels (P < 0.05). Compared with the FM and MFSM groups, the FSB diet increased the relative abundance of ileac Lactobacillus and f_Lactobacillaceae, which were significant positively correlated with CP ATTD (P < 0.05). Compared with the FSB group, the relative abundance of f_Peptostreptococcaceae and Romboutsia in MFSM or FM groups were increased and were significant positively correlated with total carbohydrate (TC) ATTD (P < 0.05). Piglets fed FSB had higher α-diversity in colonic microbiota when compared with other groups (P < 0.05). The relative abundance of colonic unidentified_Clostridiales and Romboutsia in MFSM and FSB groups were significantly higher than in the FM group (P < 0.05). Dietary MFSM or FM increased the relative abundance of colonic Streptococcaceae and Streptococcus, but decreased the relative abundance of Christensenellaceae when compared with the FSB group (P < 0.05). These bacteria showed a significantly positive correlation with serum cytokine and immunoglobulin levels (P < 0.05). Therefore, dietary FSB improved CP digestibility by increasing the relative abundance of ileac f_Lactobacillaceae and Lactobacillus, while dietary MFSM benefited TC digestibility by increasing f_Peptostreptococcaceae and Romboutsia. Dietary MFSM and FM enhanced immunoglobulin secretion by increasing colonic f_Streptococcaceae and Streptococcus prevalence, while dietary FSB promoted cytokine production by increasing microbiota diversity and Romboutsia and Christensenellaceae. Our data provide a theoretical dietary basis for young animals using plant and animal protein sources.

Suitable protein sources are essential requirements for piglet growth and health. Typically, intestinal microbiota co-develops with the host and impact its physiology, which make it more plastic to dietary protein sources at early stages. However, the effects of fermented soybean meal (FSB) and fish meal (FM) on foregut and hindgut microbiota, and their relationship with nutrient digestion and host health remain unclear. In this study, we identified interactions between ileac and colonic microbiota which were reshaped by FSB and FM, and assessed host digestibility and host health in a piglet model. Eighteen weaned piglets (mean weight = 8.58 ± 0.44 kg) were divided into three dietary treatments, with six replicates/treatment. The level of dietary protein was 16%, with FSB, FM, and a mixture of fermented soybean meal and fish meal (MFSM) applied as protein sources. During days 1-14 and 1-28, diets containing MFSM generated higher piglet body weight and average daily gain, but lower feed to weight gain ratios when compared with the FM diet (P < 0.05). Piglets in MFSM and FM groups had lower apparent total tract digestibility (ATTD) of crude protein (CP) compared with the FSB group (P < 0.05). Serum immunoglobulins (IgM and IgG) in MFSM and FM groups were significantly higher on day 28, but serum cytokines (interleukin-6 and tumor necrosis factor-α) were significantly lower than the FSB group on days 14 and 28 (P < 0.05). When compared with FSB and FM groups, dietary MFSM significantly increased colonic acetic acid and butyric acid levels (P < 0.05). Compared with the FM and MFSM groups, the FSB diet increased the relative abundance of ileac Lactobacillus and f_Lactobacillaceae, which were significant positively correlated with CP ATTD (P < 0.05). Compared with the FSB group, the relative abundance of f_Peptostreptococcaceae and Romboutsia in MFSM or FM groups were increased and were significant positively correlated with total carbohydrate (TC) ATTD (P < 0.05). Piglets fed FSB had higher α-diversity in colonic microbiota when compared with other groups (P < 0.05). The

INTRODUCTION
Dietary protein is a fundamental source of amino acids for mammals, and an essential requirement for physiological organ function and neurodevelopment in early life stages. In the gastrointestinal tract, dietary proteins interplay with a variety of substrates. Most are digested as peptides and amino acids by proteinases in the small intestine. Others enter the hindgut to undergo microbial fermentation (Walker et al., 2005;Blachier et al., 2007). High protein levels and poor protein digestibility in diets increase protein influx into the hindgut, cause excess fermentation, and release toxic metabolites which are detrimental to the host (Yao et al., 2016;Zhang and Piao, 2022). Thus, reducing dietary protein levels and optimizing existing protein sources may be effective in reducing abnormal fermentation processes in the hindgut.
In the pig industry, weaned piglets often experience a transition from highly digestible milk to solid protein diets which induce intestinal disorders and cause diarrhea or even death (Hu et al., 2020). The piglet model is suitable to investigate the effects of dietary protein sources on gut microbiota and health as they are susceptible to protein levels and quality (Li et al., 2019b). Fish meal (FM) has long been used as an ideal dietary protein source for animal production as it contains a balanced source of indispensable amino acids, and a rich source of longchain omega-3 fatty acids, vitamins, and minerals (Zhang et al., 2018a). Soybean meal is derived from plant proteins and is considered an excellent protein source approximately equivalent to animal proteins, and it rates as 1.0 on the protein digestibility corrected amino acid score scale (Hasler and Clare, 2002). Low protein diets with balanced amino acids can reduce diarrhea incidence and maintain intestinal health without affecting piglet performance (Wang et al., 2018). Reducing crude protein levels by 3% could shift colonic microbiota in pigs, decrease the relative abundance of Streptococcus but increase the relative abundance of Sarcina, Coprococcus, and Peptostreptococcaceae (Zhou et al., 2016). Fermentation improves soybean meal quality by decreasing anti-nutritional factors, such as protease inhibitors, and enhancing free amino acid and small peptide levels (Seo and Cho, 2016;Zhang et al., 2016). Also, fermented soybean meal (FSB) administration decreases the incidence of diarrhea in pigs by inhibiting intestinal pathogen colonization (Kiers et al., 2010).
The gastrointestinal tract contains a dense, dynamic, and highly complex microbial community, equating to appropriately 10 14 microbes and comprising thousands of individual species (Collins et al., 2012). Swine intestinal microbiota co-develop with the host and play important roles in nutrient digestion, absorption, metabolism, and intestinal health protection (Li et al., 2019b). Microbiota composition is in flux, but over time, diversity increases and converges toward an adult-like microbiota, with features in increasing overall number of taxa and functional genes (Koenig et al., 2011;Rodrguez et al., 2015). During early life stages, this microbial colonization period appears critical for both current-and whole-life health as the microbiota are more plastic and dynamic than in adults. Generally, the intestinal microbiota are resilient to changes in dietary protein sources (Rist et al., 2013). However, little is known about the effects of FSB and FM on host foregut and hindgut microbiota, while their relationship with nutrient digestion and host health remains unclear.
Therefore, we hypothesized that diets containing FSB and FM could alter ileal and colonic microbiota and contribute to host digestibility and health in early life. To this end, we used a piglet model to investigate dietary effects on growth performance and ileal and colonic microbial structures, and to understand the relationship between microbiota, nutrient digestibility, and serum immunity. Our data provide a theoretical dietary basis for young animals using different protein sources for overall health.

Ethical Considerations
This study was conducted in accordance with the "Guidelines on Welfare and Ethical Review for Laboratory Animals" (GB/T 35892-2018), and it was approved by the Institutional Animal Care and Use Committee of the Institute of Animal Science of the Chinese Academy of Agricultural Sciences, Beijing, China (IAS2019-25).

Materials
High-quality FSB was selected based on our previous study (Li et al., 2019c). Firstly, three FSB samples were selected from ten representative samples, based on the evaluation of crude protein (CP), amino acids, acid soluble proteins, and anti-nutrient factors, including glycinin, β-conglycinin, and trypsin inhibitors. The highest quality FSB was then selected based on dry matter (DM) and CP digestibility, and in vitro amino acid evaluation. FSB was fermented using a combination of Bacillus subtilis, Lactobacillus plantarum, and Saccharomyces cerevisiae at ≥10 9 CFU/mL, ≥C10 10 CFU/mL, and ≥10 9 CFU/mL concentrations, respectively. Also, imported fishmeal (Peru) containing 68.5% CP was selected as a highquality FM source.

Animals, Diets, and Experimental Design
Eighteen crossbred male weaned piglets (Duroc × Landrace × Yorkshire, weaned at 28 days), with an average body weight (BW = 8.58 ± 0.44 kg), were randomly allocated to three groups, consisting of six replicates, with one piglet/replicate. FSB (n = 6), FM (n = 6), and a mixture of fermented soybean meal and fish meal (MFSM, n = 6) were used as protein sources. To meet National Research Council (2012) nutrient requirements for 7-11 kg pigs, a non-medicated basal diet in mashed form was formulated, with CP levels at 16% in accordance with a previous study (Chen et al., 2018; Table 1). All diets contained equal standardized ileal digestible (SID) lysine, methionine, threonine, tryptophan, leucine, isoleucine, and valine, using added crystalline amino acids. Piglets were housed alone in one pen (1.8 m × 0.8 m) with a hard plastic, fully-slotted floor. Adjacent pens were separated by a closed baffle. Piglets were housed in an environmentally controlled room (28-30 and 25-28 • C between days 1-14 and 15-28, respectively). The study lasted 28 days, during which time meals were provided twice daily at 08:00 and 16:00 h, and pigs had ad libitum access to food and water.

Growth Performance
Taking each piglet as one unit, BW was recorded at study commencement and every fortnight. The feed intake of each piglet was recorded daily using an electronic feeding system (MXCD-15B, Yangzhou, China). Then, average daily feed intake (ADFI), average daily gain (ADG), and feed to weight gain ratios (F/G) were calculated between days 1-14, 15-28, and 1-28.

Apparent Total Tract Digestibility
Representative 1 kg feed samples were collected at study end and stored at 4 • C. Between days 25 and 28, approximately 200 g fresh feces from each pen was collected twice a day at 7:00 and 16:00, and immediately frozen at −20 • C. Feces was pooled by pen, dried at 65 • C for 72 h, and ground down to pass  Williams et al. (1962). In addition, organic matter (OM) and total carbohydrates (TCs) were calculated using the following equations: OM = 1 − ash and TC = DM − CP − EE − ash (Gerritsen et al., 2010). ATTD nutrient = 1 − (Cr diet × Nutrient feces )/(Cr feces × Nutrient diet ) (Long et al., 2017).

Ileal and Colonic Microbiota
After fasting overnight on day 28, piglets were humanely slaughtered 5 min after anesthetic injection. Ileal and colonic chyme was collected into 5 mL centrifuge tubes, snap frozen in liquid nitrogen, and stored at −80 • C. Bacterial genomic DNA was extracted from ileal and colonic chyme samples (Qiagen DNA stool mini kit, Hilden, Germany). DNA quantity and quality were assessed by NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA United States) and 1% agarose gels, respectively. The V3-V4 hypervariable region of 16S rRNA was amplified using specific primers (forward 5 -ACTCCTACGGGAGGCAGCA-3 and reverse 5 -GGACTACHVGGGTWTCTAAT-3 ) which contained barcodes. Polymerase chain reaction (PCR) was conducted in a total volume of 20 µL, including 1 × FastPfu buffer, 250 µM dNTP, 0.2 µM each primer, 1 U FastPfu polymerase (Beijing TransGen Biotech, Beijing, China), and 10 ng template DNA. Thermal cycling parameters: initial denaturation at 98 • C for 1 min, followed by 30 cycles of 98 • C for 10 s, annealing at 50 • C for 30 s, elongation at 72 • C for 30 s, and a final extension at 72 • C for 5 min. PCR products were electrophoresed on 2% agarose gels and purified using a Qiagen gel extraction kit (Qiagen, Hilden, Germany

Statistical Analysis
Data were analyzed using one-way analysis of variance using comparative averages in SPSS 22.0 (IBM Corp., Armonk, NY, United States). Differences between treatment means for growth performance, ATTD, and serum immune indices were analyzed using Duncan's multiple-range and least significant difference post hoc tests. Data were expressed as the mean ± standard deviation (SD). Correlations between differential gut microbiota and immune indices, and corresponding P-values were estimated using Spearman correlation analyzes using gplot and psych packages, respectively. P-values < 0.05 ( * ) were statistically significant and P-values < 0.01 ( * * ) were extremely significant.

Growth Performance
Dietary MFSM and FSB generated significantly higher piglet BWs on day 14 and ADG between days 1-14 (P < 0.05; Table 2). Dietary MFSM generated significantly higher piglet BWs on day 28 and ADG between days 1-28 (P < 0.05) when compared to FM animals. Between days 1-14, ADFI in the FSB group was significantly higher than the FM group (P < 0.05). Also, when compared to the FM group, piglets in FSB and MFSM groups had significantly lower F/G ratios between days 1-14 (P < 0.05), and piglets in the MFSM group had significantly lower F/G ratios between days 1-28 (P < 0.05).

Apparent Total Tract Digestibility
Apparent total tract digestibility (ATTD) of DM and TC were not influenced by dietary protein sources (P > 0.05; Table 3). FSBfed piglets had significantly higher CP ATTD than other groups (P < 0.05), whereas FSB and FM piglets had significantly higher OM ATTD than MFSM animals (P < 0.05). The EE ATTD in FM animals was significantly higher than MFSM and FSB groups (P < 0.05), and EE ATTD in the FSB group was significantly higher than the MFSM group (P < 0.05).

Serum Immune Globulins and Inflammatory Cytokines
On day 14, in MFSM and FM groups, serum IgA levels were higher (P < 0.05) and serum IL-6 and TNF-α levels were lower (P < 0.05) than the FSB group (Table 4). Serum IgG levels in the FM group were significantly increased and serum IL-1β levels in the MFSM group significantly decreased when compared with the FSB group (P < 0.05). On day 28, when compared with the FSB group, MFSM and FM piglets had significantly higher serum IgG and IgM levels (P < 0.05) and lower serum IL-1β, IL-6, and TNFα levels (P < 0.05). Piglets in the MFSM group had higher serum IgA levels than FSB and FM groups, and higher serum IgM levels than the FM group (P < 0.05).

Microbiota Diversity in the Ileum and Colon
In total, 2,294,708 effective sequences were generated from piglet gut microbiota samples, with an average 69,537 sequences/sample. Alpha-diversity analyzes showed varied community richness between groups in terms of colonic bacterial communities; the Chao 1 index in FSB animals was higher than FM and MFSM groups, and both Observed_species and ACE indices in the FSB group were higher than those in the MFSM group (P < 0.05; Table 5). However, no significant differences    in alpha diversity were observed in the ileum (P > 0.05). Beta-diversity NMDS analysis based on Bray-Curtis distances showed microbial communities were well separated between the ileum and colon (Figure 1A), and among groups in the ileum ( Figure 1B) and colon ( Figure 1C). Thus, piglet gut microbiota composition was shaped by different protein sources.

Ileal and Colonic Bacterial Community Structures
In the ileum, Firmicutes, Proteobacteria, and Actinobacteria were the three major bacterial phyla accounting for >95% of total ileal bacterial communities (Figure 2A). The relative abundance of Firmicutes in the MFSM-ileal (MFSM-I) group was 90.49%, and was higher than the 84.54 and 77.64% levels in FM-ileal (FM-I) and FSB-ileal (FSB-I) groups (P > 0.05), respectively. The relative abundance of Proteobacteria in the MFSM-I group was 7.87%, and was lower than the 14.17 and 11.08% levels in FM-I and FSB-I groups (P > 0.05), respectively. The relative abundance of Actinobacteria in FM-I and MFSM-I groups was 0.43 and 0.94%, respectively, but lower than the 9.40% in the FSB-I group (P > 0.05).
At the family level, the relative abundance of Lactobacillaceae decreased from 37.54 to 17.85 and 10.85% (FSB-I group to FM-I and MFSM-I groups, respectively), while unidentified_Clostridiales levels were opposite, with increases from 19.72 to 29.46 and 47.79%, respectively (P > 0.05; Figure 2B). When compared with FSB-I and MFSM-I groups, the FM-I group showed an increased relative abundance of Enterobacteriaceae, from 1.55 and 6.77 to 11.01%, respectively (P > 0.05). The relative abundance of Peptostreptococcaceae in MFSM-I and FM-I groups was 16.62 and 15.13% higher, respectively, than the 2.68% in the FSB-I group (P > 0.05). The relative abundance of Pasteurellaceae was lowest in the MFSM-I group at 0.77%, in comparison with FSB-I and FM-I groups at 9.36 and 2.96%, respectively (P > 0.05).

FIGURE 2 | (A,B)
The effects of different protein sources on bacterial composition in the ileum, at phylum and family levels. (C) Taxa enrichment based on linear discriminant analysis effect size (LEfSe) analysis identified significant differences in microbial communities between groups. Bacterial taxa with a logarithmic LDA score > 2.5 were selected as biomarker taxa. FSB-I, fermented soybean meal-ileal group; FM-I, fish meal-ileal group; MFSM-I, mixture of fermented soybean meal and fish meal-ileal group. Data were presented as the mean of five replicates with one piglet each.
Linear discriminant analysis effect size showed that FM-I piglets had a higher relative abundance of the genera, unidentified_Enterobacteriaceae, Turicibacter, Romboutsia, Succiniclasticum, and Lachnospira when compared with FSB-I and MFSM-I piglets ( Figure 2C). Lactobacillus_reuteri, Lactobacillus_farciminis, Lactobacillus_ginsenosidimutans, and Clostridium_sp_K4410MGS_306 were dominant bacteria in the FSB-I group when compared with the other groups, whereas the relative abundance of Terrisporobacter and Eggerthella was significantly increased in the MFSM-I group when compared with FM-I and FSB-I groups.
At the family level, when compared with the FM-C group, both FSB-C and MFSM-C groups had a significantly increased relative abundance of unidentified_Clostridiales and Peptostreptococcaceae (P < 0.05; Figures 3B-D). The relative abundance of Streptococcaceae in the FM-C group was higher, and the relative abundance of Atopobiaceae, Veillonellaceae, and Christensenellaceae in the MFSM-C group was lower than the FSB-C group (P < 0.05).
Of the 35 most dominant colonic genera, the relative abundance of Butyricicoccus, Romboutsia, unidentified_Clostridiales, Terrisporobacter, and Sarcina in analysis of differences at the family level; t-tests were used to test for significant differences; P < 0.05 indicates a significant difference. (E) In the top 35 genera, Metastat was used to test for significant differences in genera relative abundance; deep pink diamonds indicate P < 0.05 and dark pink diamonds indicate P < 0.01 between two groups. FSB-C, fermented soybean meal-colonic group; FM-C, fish meal-colonic group; MFSM-C, mixture of fermented soybean meal and fish meal-colonic group. Data were presented as the mean of six replicates with one piglet each.
MFSM-C and FSB-C groups was significantly higher than the FM-C group (P < 0.05; Figure 3E). When compared with the FSB-C group, the MFSM-C group had a significantly decreased relative abundance of unidentified_Ruminococcaceae, Olsenella, and Megasphaera (P < 0.05), but a significantly increased relative abundance of Rothia (P < 0.05). The relative abundance of Streptococcus and Solobacterium in the FSB-C group was lower than MFSM-C and FM-C groups (P < 0.05), while the relative abundance of Solobacterium in the FSB-C group was higher than the MFSM-C group (P < 0.05).

Correlations Between Gut Microbiota and Apparent Total Tract Digestibility or Immune Parameters
Canonical correspondence analyses (CCA) suggested TC ATTD was negatively correlated with DM, CP, OM, and EE ATTD, and serum IgG was negatively correlated with IgA, IgM, IL-6, and TNF-α (Figure 4). Ileal microbes in FSB piglets were positively correlated with CP, OM, and DM ATTD. Ileal microbes in MFSM piglets were positively correlated with TC ATTD, and FM group microbes were positively correlated with EE ATTD.
Colonic microbes in the MFSM group were positively correlated with serum IgA and IgM levels, but negatively correlated with IL-1β and TNF-α. Colonic microbes in FM animals were positively correlated with serum IgG and IL-1β, and FSB group microbes were positively correlated with IL-6 and TNF-α levels.
A Spearman correlation matrix was used to explore relationships between predominant families and genera (35 most dominant genera) and ATTD or host immunity (Figures 5, 6). F_unidentified_Clostridiales and unidentified_Clostridiales were significantly negatively correlated with CP and OM ATTD (P < 0.05). Unidentified_Enterobacteriaceae,

FIGURE 4 | (A,B)
The Canonical correspondence analyses (CCA) of ileal segments between nutrient apparent total tract digestibility (ATTD) and the first 35 taxa at genus level, and the CCA of colonic segments between immune indices and the first 35 taxa at genus level. "" indicates the first 35 bacterial taxa in each group. Arrows indicate nutrient ATTD and immune indices, respectively. The closer the pendulum is to the arrow, the higher the positive correlation between bacteria and the index, and vice versa. If the angle between the arrows is acute, a positive correlation exists between measured indices, and the converse indicates a negative correlation. FSB-I, fermented soybean meal-ileal group; FM-I, fish meal-ileal group; MFSM-I, mixture of fermented soybean meal and fish meal-ileal group; FSB-C, fermented soybean meal-colonic group; FM-C, fish meal-colonic group; MFSM-C, mixture of fermented soybean meal and fish meal-colonic group. DM, dry matter; CP, crude protein; EE, ether extract; OM, organic matter; TC, total carbohydrate; IgA, immunoglobulin A; IgG, immunoglobulin G; IgM, immunoglobulin M; IL-1β, interleukin-1β; IL-6, interleukin-6; TNF-α, tumor necrosis factor-α. Data were presented as the mean of four (ileum) and six replicates (colon) with one piglet each. f_Peptostreptococcaceae, f_Erysipelotrichaceae, and Romboutsia were significantly negatively correlated with CP ATTD, while f_Lactobacillaceae, Lactobacillus, and Proteus were significantly positively correlated with CP ATTD (P < 0.05). F_Peptostreptococcaceae and Romboutsia showed significantly positive correlations with TC ATTD, but Dialister, Olsenella, and Solobacterium showed significantly negative correlations with TC ATTD (P < 0.05).
From statistical analyzes, colonic microbiota was more affected by protein sources than ileum microbiota, therefore, only correlations between colonic microbiota and immunity indices were investigated. Megasphaera, unidentified_Ruminococcaceae, and f_Christensenellaceae were significantly positively correlated with TNF-α, but negatively correlated with IgA (P < 0.05). F_Streptococcaceae and Streptococcus were significantly positively correlated with IgG, but significantly negatively correlated with IL-6 and IL-1β (P < 0.05). Pasteurella was significantly positively correlated with IgA and IgM, but negatively correlated with IL-6 and TNF-α. Also, Proteus was significantly positively correlated with IgM, but negatively correlated with IL-1β (P < 0.05). Romboutsia was positively correlated with IL-6 and TNFα, Turicibacter positively correlated with TNF-α, and Rothia negatively correlated with IL-6 and TNF-α (P < 0.05).

Short Chain Fatty Acid Levels in the Colon
Acetic and butyric acid levels in MFSM animals were significantly higher than levels in FSB and FM groups (P < 0.05; Table 6). Propionic acid levels in the MFSM group were significantly higher than those in the FSB group (P < 0.05). In terms of branched chain fatty acids, no significant differences were identified between groups (P > 0.05).

DISCUSSION
Given that high-quality protein and protein restriction can effectively decrease the amounts of proteins flowing into the hindgut, and decreased the risk of aberrant fermentation in the hindgut. Based on previous studies, no negative effects were reported on piglet growth performance when dietary CP levels were reduced by 3-4%, in contrast to the 2012 NRC recommendation of 20.5% in some piglet models (Liu et al., 2012;Gloaguen et al., 2014;Sirtori et al., 2014;Bandsma et al., 2015;Kalantar-Zadeh et al., 2016). Thus, in this study, a low-protein diet, with balanced amino acids, was used to determine interactions between dietary protein, gut microbiota, host digestibility, and health.
In our study, piglets fed a combination of FSB and FM had higher BW and ADG but a lower feed to weight gain ratio than piglets fed a single protein source, indicating piglets in the MFSM group had a higher growth rate when compared with other diets. This observation was consistent with a previous study; a combined soybean meal and fish meal diet significantly increased ADG and feed conversion ratios in piglets when compared with soybean meal or fermented soybean meal diets . Similarly, when compared with soy protein isolate or zein as the single dietary protein source, combination diets of soy meal, fish meal, and whey powder significantly increased piglet ADG and ADFI (Qi, 2011). A possible reason could be that ADFI was affected by different protein sources; ADFI in the FM group FIGURE 5 | Correlations between the ileal microbiota and nutrient apparent total tract digestibility (ATTD). Red represents a positive correlation and blue represents a negative correlation. *P < 0.05 and **P < 0.01 represent significant and extremely significant correlations. DM, dry matter; CP, crude protein; EE, ether extract; OM, organic matter; TC, total carbohydrate. Data were presented as the mean of four replicates with one piglet each.
was significantly lower than in the FSB group during days 1-14 which may have contributed to lower growth performance. This hypothesis putatively explained our observation that piglets fed a FM diet displayed a lower growth performance than FSB-fed piglets during days 1-14. This decreased ADFI in the FM group may be related to different protein sources affecting satiety signals, as satiety induced by fish meals declined more slowly over time when compared with other protein sources (Harvey and Moore, 2004). Another possibility could be the fishy smell of fish meal, but this requires further exploration. Faster absorption rates and hormonal responses to different protein sources could also be underlying mechanisms, such as amino acids, glucagon-like peptide, and peptides derived from proteins (Boirie et al., 1997;Gustafson et al., 2001;Hall et al., 2003). Soybean meal fermentation could eliminate most anti-nutritional factors and increase the proportion of small peptides, which has great potential to improve nutrient ATTD and growth efficiency in piglets Zhang and Piao, 2022). This was why CP ATTD in the FSB group was higher than in FM and MFSM groups, and why FSB-fed piglets had higher growth performance than FM-fed piglets at certain phases. However, EE ATTD in the FM group was higher than in other groups, which may explain why there was no significance in growth parameters in later phases among the three groups. Additionally, a higher piglet growth rate in the MFSM group may be attributed to the mixture of protein sources which reduced malnutrition risks and improved intestinal dysfunction (Engelsmann et al., 2022).
Most dietary nutrients are fully digested and absorbed at the end of the ileum, whereas undigested complex fragments are fermented in the hindgut (Zhang et al., 2018b). During this process, microbes promote nutrient application, and the protein sources can shape the composition of intestinal microbiota (Wu et al., 2022). The Proteobacteria phylum, as facultative anaerobes, cannot consume fiber, but can interfere with host nutrition by metabolizing fermentation products to carbon dioxide in the presence of oxygen (Litvak et al., 2018). As reported, the primary bacteria implicated in protein metabolism in the small intestine are: Escherichia coli, Streptococcus, Mitsuokella, and Succinivibrio dextrinosolvens (Ma et al., 2017). Piglets fed a highly digestible casein-based diet showed an increased abundance of FIGURE 6 | Correlations between the colonic microbiota and immune indices. Red represents a positive correlation and blue represents a negative correlation. *P < 0.05 and **P < 0.01 represent significant and extremely significant correlations. IgA, immunoglobulin A; IgG, immunoglobulin G; IgM, immunoglobulin M; IL-1β, interleukin-1β; IL-6, interleukin-6; TNF-α, tumor necrosis factor-α. Data were presented as the mean of six replicates with one piglet each.
Enterobacteriaceae when compared with piglets fed a soybean meal-based diet (Rist et al., 2014). Compared with degossypolized cottonseed protein, piglets fed a dried porcine protein diet showed increased intestinal Escherichia abundance, whereas piglets fed the degossypolized cottonseed protein diet showed a higher Lactobacillus abundance (Li et al., 2019a). Moreover, soy proteins and associated peptides modulated intestinal microbiota by enhancing Lactobacilli and Bifidobacteria prevalence (Huang et al., 2016). These observations were consistent with our study data; piglets fed a FM-based diet showed a higher relative abundance of ileac unidentified_Enterobacteriaceae and E. coli of the Proteobacteria phylum, while piglets fed FSBbased diet increased their relative abundance of Lactobacillaceae and Lactobacillus of the Firmicutes phylum. In addition, unidentified_Enterobacteriaceae negatively correlated with CP ATTD which suggested they competed with the host for nutrients and caused a lower piglet growth performance in the FM group. From previous studies, energy intake was positively associated with an increased relative abundance of Firmicutes, with a 20% increase in abundance associated with an additional energy harvest of 628 kJ (Hildebrandt et al., 2009;Reiner et al., 2018). These reports agreed with our data suggesting that FSB and FM combinations increased the relative abundance of Firmicutes and accelerated piglet growth. This positive correlation was also observed in previous piglet models (Pedersen et al., 2013;Mach et al., 2015), and in our study, was evidenced as a higher piglet growth performance in the MFSM group. Combined CCA and Spearman correlation analyzes indicated that f_Lactobacillaceae, Lactobacillus, and Proteus were dominant microbes in FSB animals and promoted CP digestibility, whereas f_Peptostreptococcaceae and Romboutsia were the main bacteria in MFSM animals and benefited TC digestibility. These bacteria may positively affect piglet growth performance.
Along the intestinal tract, between the ileum and colon, considerable variations in Bacteroidetes, Actinobacteria, Firmicutes, and Proteobacteria were observed. We showed that oxygen-tolerant Proteobacteria, such as Enterobacteriaceae grew well and were abundant in the ileum, but were dramatically decreased in the colon, while oxygen-sensitive Bacteroidetes adapting to a low oxygen environment were significantly increased in the colon. This observation agreed with a previous study showing that spatial changes in bacterial composition occurred as a result of altered microenvironments (Zhang et al., 2018b). Our colonic microbial composition data were consistent with previous findings showing that Lactobacilaceae, Peptostreptococcaceae, Veillonellaceae, Ruminococcaceae, Clostridiales, and Lachnospiraceae involving in Firmicutes, and Prevotellaceae belonging to Bacteroidetes were dominated taxa in the large intestine (Gill et al., 2006;Claus et al., 2011;Zhang et al., 2018b). These microbial abundance is important for complex carbohydrate digestion, amino acid biotransformation in the host.
Using excretory enzymes, Clostridia processes undigested dietary proteins and complex carbohydrates to produce amino acids and SCFAs (Zhu et al., 2017;Zhang et al., 2018b). Amino acids are further fermented via deamination in the colon to produce SCFAs and ammonia (Cummings and Macfarlane, 2010), which are key energy sources for colocytes. In our study, the relative abundance of Clostridiales in FSB and MFSM groups was higher than the FM group, while the relative abundance of Enterobacteriaceae was highest in the FM group when compared with the other groups. As previously reported, a host-protective mechanism against intestinal infection involves resistance between the obligate anaerobic Clostridia and the facultative anaerobic Enterobacteriaceae (Spees et al., 2013). Clostridiales, including Clostridium butyrate, stimulated immunoglobulins secretion in mice and Peyer's patch cell (Murayama et al., 1995;Wang et al., 1996). Immunoglobulins provide passive immune protection in early life by enhancing anti-bacterial, anti-infective, and anti-viral capabilities. In our study, serum IgA on day 14 and serum IgG or IgM on day 28 in FM and MFSM groups were higher than the FSB group. Bai et al. (2016) reported that feeding soy protein and raffinose combinations markedly increased cecum IgA levels. Gut bacteria and various antigens may be involved in these changes. In our study, the relative abundance of Streptococcus involved in f_Streptococcaceae in FM-C and MFSM-C groups was significantly higher than the FSB-C group, with immunoglobulins positively correlated with f_Streptococcaceae, Streptococcus, Pasteurella, and Proteus. When combined with data from the previous study, even though Streptococcus is generally considered a pathogen, a modest increase in their numbers could increase IL-10 levels (Bartholomeus et al., 2014). Interleukin 10 secreted by Th2 cells stimulate B lymphocyte proliferation and generate IgG antibodies (Chang et al., 2018). These results indicated that those microbes might be the under antigens for maintaining higher immunoglobulins to protect against infection when they are in an extremely lower relative abundance.
During piglet production, weanling stress may induce intestinal disorders with increased pro-inflammatory cytokine secretion such as IL-1β, IL-6, and TNF-α, which cause growth retardation (Bomba et al., 2014;Xiong et al., 2019). Our results showed that both FM and MFSM diets significantly decreased pro-inflammatory cytokine levels when compared with the FSB diet, indicating FM alleviated host inflammatory responses in weaned piglets. This observation may be associated with microbial diversity and composition. Theoretically, highly diverse gut microbiota reflect microbial maturity, which increases with age (Turnbaugh et al., 2007). In a previous report on children, microbial diversification was a gradual process, whereas the premature formation of an adult-type microbiota negatively affected host immune function (Maynard et al., 2012;Nylund et al., 2013). This observation accorded with our study; FSB diets enhanced host inflammatory responses by increasing microbial diversity. Distinct to microbial diversity, a study showed that dietary protein sources were major factors influencing microbial composition (Rist et al., 2013). Romboutsia is a bacterial genus of the family Peptostreptococcaceae, and are generally associated with protective leukocyte antigen haplotypes in autoimmune disease (Russell et al., 2019). Turicibacter was shown to have a strong association with immune function, as in the intestine, Turicibacter was almost abolished in innate and adaptive immunodeficiency mouse models (Dorottya and Richard, 2011;Dimitriu et al., 2013). At the family level, Christensenellaceae was positively correlated with gut metabolites implicated in protein catabolism (Waters and Ley, 2019). In our study, these bacteria were strongly correlated with serum TNF-α and IL-6 levels, suggesting that dietary FSB-enhanced host inflammation responses may be associated with these bacteria. This was distinct to dietary FM and the combined MFSM, which increased immunoglobulin secretion.
Clostridiales and Peptostreptococcus are predominant bacteria in the large intestine of healthy humans, and they produce SCFAs and are key drivers of amino acid utilization, including lysine, threonine, proline, and glutamate (Abdallah et al., 2020). When compared with animal protein sources, soy protein intake could induce more carbohydrate metabolism and generate SCFAs (Zhu et al., 2017). The role of SCFAs in immunity is well characterized; most studies show that SCFAs sustain a balance between commensal microbes and pathogen immunity by modulating Tregs and IL-10-producing T cells, and suppressing inflammatory cytokines (Singh et al., 2014). This regulatory mechanism is associated with SCFA-G protein coupled receptors or their histone deacetylase inhibiting ability (Ara et al., 2016). These observations agreed with our data showing that a combined MFSM diet increased SCFA levels in the colon, and had lower inflammation cytokines with tolerance to Streptococcus, Pasteurella, and Proteus. Another reason for this could be related to tryptophan metabolism by Peptostreptococcus and Clostridiales (Abdallah et al., 2020). Indole is a predominant tryptophan metabolite which reduces severe inflammation induced by lipopolysaccharide via IL-22 up-regulation, and also enhances aryl hydrocarbon receptor activation (Shijie et al., 2018). Thus, dietary FSB and FM sources affected host health by shaping different microbiota phenotypes, and combined dietary MFSM appeared to demonstrate better tolerance to commensals.

CONCLUSION
Diets containing MFSM significantly increased piglet growth performance when compared with FM-fed piglets, while diets containing FSB significantly increased CP ATTD when compared with MFSM and FM groups. Dietary MFSM and FM significantly enhanced serum immunoglobulin secretion and decreased serum cytokine production when compared with dietary FSB. Dietary FSB shaped the ileac microbiota, accelerated f_Lactobacillaceae and Lactobacillus prevalence, and increased CP digestibility, whereas dietary MFSM shaped the ileac microbiota, accelerated f_Peptostreptococcaceae and Romboutsia prevalence, and benefited TC digestibility. These microbiota phenotypes shaped by MFSM diets contributed to piglet growth performance. MFSM and FM diets also shaped a more tolerant phenotype in dominant microbiota, increasing the relative abundance of colonic f_Streptococcaceae and Streptococcus and enhancing immunoglobulin secretion. In contrast, dietary FSB shaped a more diverse microbiota phenotype, increasing the relative abundance of Christensenellaceae and Romboutsia and enhancing inflammation cytokine production. Therefore, dietary MFSM shaped particular microbiota, improving nutrient ATTD and host health which helped weaned piglets overcome weanling stress. Our study provides a theoretical basis for the application of different protein sources to the diets of young animals.

DATA AVAILABILITY STATEMENT
The datasets generated for this study can be found in the NCBI sequence read archive, accession number: PRJNA648691.

ETHICS STATEMENT
The animal study was reviewed and approved by Institutional Animal Care and Use Committee of the Institute of Animal Science of the Chinese Academy of Agricultural Sciences.

AUTHOR CONTRIBUTIONS
YQ and JZ: conceptualization, methodology, and project administration. YL and YH: animal experiments, chemical analysis, and data collection. YH: writing original draft. QZ, CT, and JZ: finished writing review. All authors agreed to the final manuscript and approved the submitted version.