Regenerative Farming Enhances Human Health Benefits of Milk and Yoghurt in New Zealand Dairy Systems

Abstract

This on-farm study evaluated the effects of a regenerative (plant polyculture) as compared to conventional (monoculture) pasture-based New Zealand dairy production system on milk and yoghurt nutraceutical properties and environmental impact. Milk and yoghurt produced by two adjacent regenerative and conventional farms were sampled throughout the year and analyzed for chemical composition, metabolomics, and microbiome. Milk samples were also collected over four consecutive days (one day after herbage sampling) on four occasions throughout lactation: early lactation (October), peak lactation (December/January), mid-lactation (March), and late lactation (May). Overall, the regenerative system had a lower environmental impact while maintaining a similar yield and the same milk composition compared to conventional systems. Furthermore, milk and yoghurt from the regenerative system had a more favourable profile of phytochemical antioxidants with potential positive benefits to human health (anti-inflammatory and antioxidant).

1. Introduction

Plants contain thousands of phytochemicals (such as polyphenols, tannins, flavonoids, terpenoids, and alkaloids) that have known anti-inflammatory, anti-oxidative, and anti-carcinogenic benefits to human health [1]. In addition, they have been reported to reduce cholesterol and reduce risk of metabolic syndrome (e.g., diabetes, obesity, and cardiovascular disease)—which has been termed by the world health organization as a key indicator of the global health crisis [2,3,4]. While the pharmacological actions of plant bioactive compounds in humans have been explored for millennia, the evaluation of their effects in livestock and the subsequent transfer of pharmacological actions to the human consumer of meat or milk products has become of greater interest in modern times [1].

Ruminants naturally evolved on landscapes that provide a wide diversity of plant species that are taxonomically and biochemically diverse and which have enabled livestock to self-medicate specific biochemicals that have anti-inflammatory, antibacterial, and anthelmintic properties which domesticated livestock do not have access in ‘monotonous-conventional’ intensive grazing systems [5,6]. Emerging research indicates that allowing livestock to self-medicate on diverse forages can improve animal welfare and health promoting qualities of products (e.g., meat and milk) [7]. At the farm level it is essential to ascertain the advantages of diverse plant species on soil health, animal physiology, and human health within regenerative agriculture systems. Additionally, it is crucial to determine whether the specific arrangement of plant functional groups is key to the ‘healthier’ animal products. Regenerative agriculture describes holistic farming systems that, among other benefits, improve water and air quality, enhance ecosystem biodiversity, produce nutrient-dense food, and store carbon to help mitigate the effects of climate change [8,9]. On the other hand, pasture-based New Zealand intensive dairy systems (herein described as conventional or status quo systems) have been known for their simplicity, high stoking rates, lack of diversity, dietary monotony, and dependency on artificial N applications.

This on-farm research project aims to evaluate—for the first time—the effect of ‘regenerative grown’ complex swards, as compared to a ‘conventional ryegrass-based grazing system’ on metabolomic composition and properties of milk and yoghurt, in the context of human health. Further evaluations included the effect of these two systems on soil, milk, and yoghurt microbiome. As our understanding of metabolites and holobionts, and their effects on human health, increases, it is expected that greater influence will be placed upon the metabolomic profile of foods when considering their nutritional and nutraceutical, as well as prophylactic, value.

2. Materials and Methods

2.1. Research Site, Pastures, and Animals

The experiment was conducted at the Align Clareview dairy farm (Westerfield district, Canterbury, New Zealand) from September 2023 (early lactation) to May 2024 (late lactation). Clareview farm was established in 2013 with 296 hectares and milks 1080 kiwicross (Friesian × Jersey). Since then, the farm is split 50:50 for a farm system trial, with one half of the farm area running a regenerative system (REG), with 40% of the cows, based on complex mixed swards (

Table 1. Botanical compositions of swards of the two farm systems (regenerative and conventional, REG and CON) at Align Clareview dairy farm (Westerfield district, Canterbury, New Zealand) during the 2023 and 2024 lactation.

Plant Species (% of Total DM)	Early Lactation		Peak Lactation		Mid-Lactation		Late Lactation
	CON	REG	CON	REG	CON	REG	CON	REG
Italian ryegrass (Lolium multiflorum)	63.10	23.75	6.16	2.69	4.13	3.41	78.19	25.49
Perennial ryegrass (Lolium perenne)	19.69	19.09	55.48	9.29	64.10	11.77
White clover (Trifolium repens)	2.93	10.93	8.73	15.73	5.73	23.37	3.13	7.53
Red clover (Trifolium pratensis)		1.94		1.61		4.77		1.07
Persian Clover (Trifolium resupinatum)				3.33
Chicory (Cichorium intybus)	0.39	8.71	2.80	3.88	1.15	1.35	0.58	21.87
Plantain (Plantago lanceolata)	2.18	9.90	5.22	19.48	12.45	10.80	3.68	13.61
Cocksfoot (Dactylis glomerata)		7.50		14.59	0.48	25.55	0.02	10.65
Fescue (Festuca arundinacea)		5.37		2.59		1.28	0.05	0.70
Timothy (Phleum pratense)		0.40		0.85				0.03
Subterranean clover (Trifolium subterraneum)								0.01
Crimson clover (Trifolium incarnatum)							0.06	1.44
Dandelion (Taraxacum officinale)	6.85	4.96	9.07	4.50	4.47	8.62	5.75	6.73
Dock (Rumex obtusifolius)	2.60	6.11	6.01	15.65	2.48	1.65	3.14	9.21
Weeds *	2.22	0.8	3.16	1.29	0.71	2.92	0.45	0.12
Dead plant material		0.47	3.15	4.44	4.26	4.44	4.75	2.06

REG, regenerative; CON, conventional. * Plants that have not value in the context of the sward.

) and the other a conventional system (CON), based on a ryegrass–white clover-based sward. Both systems are managed separately, and milk is collected in different individual vats.

The REG system has a stocking rate of 3.0 cows/ha. Average peak daily production is 2.1 kg/MS (milk solids fat + protein), and a per year, per cow production is 391.2 kg/MS. Cows are grazed under rotational grazing with a daily pasture break allocation averaging pre-grazing herbage mass (determined by plate meterverages) of 2810 kg of dry matter per hectare (DM/ha). Supplements are fed at a yearly average per cow of 240 kg DM of grass silage, 470 kg DM barley grain, and 39 kg DM maize silage. Two pasture paddocks (10 ha) are also fed during the non-lactating period in the form of stockpiled herbage. REG system pastures have a longer round length, which varies through season, with the shortest round length being 27 days. REG swards are fertilized with 11.4 kg of synthetic N (calcium ammonium nitrate) and 1.3 kg of organic N (fish fertiliser) and biological amendments like fulvic acid, humic acid, seaweed, and compost added to soil.

On the other hand, the CON system is based on swards of ryegrass and white clover, with a stocking rate of 3.7 cows/ha, peak daily production 2.1 kg/MS, and 450 kg MS/cow. The CON cows are also grazed under rotational grazing with a daily pasture break allocation averaging pre-grazing herbage mass averages of 2720 kg DM/ha. Supplements are fed at a yearly average per cow of 181 kg DM of grass silage, 460 kg of DM barley grain, and 95 kg of DM maize silage. One pasture paddock (5 ha) is fed during the non-lactating period in the form of stockpiled herbage. Pastures have a shorter round length than REG, which varies through season, with the shortest round length being 20 days. Swards are fertilized with 170 kg of synthetic N in form of urea, with no biological amendments.

2.2. Herbage and Soil Sampling and Chemical Composition

Herbage samples were collected by hand-plucking over four consecutive days on four occasions throughout lactation: early lactation (October), peak lactation (December/January), mid-lactation (March), and late lactation (May) from paddocks from both systems. Each hand-plucked sample involved collecting ~20 handfuls of herbage cut to grazing height in randomly selected areas of the paddock. Herbage was then subsampled for DM%, botanical components, chemical composition, and metabolomics analyses. The DM, organic matter (OM), water soluble carbohydrates (WCS), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and digestibility and metabolizable energy (ME) were analyzed using near-infrared spectroscopy (Model: FOSS NIRS Systems 5000, FOSS, Hillerød, Denmark).

Sterilized, individual quick test augers were used to take soil samples at a 0–10 cm depth across the whole paddock following a “W” shape. In the laboratory, samples were subsampled into three Eppendorf tubes and stored in the −80 °C freezer until further microbiome analysis.

2.3. Milk and Yoghurt Sampling and Composition

Milk samples were also collected over four consecutive days (one day after herbage sampling) on four occasions throughout lactation: early lactation (October), peak lactation (December/January), mid-lactation (March), and late lactation (May). A composite sample of milk from each vat (conventional and regenerative) was collected following the morning and afternoon milking over four consecutive days. Milk was subsampled for milk components (protein %, fat %, lactose %, somatic cell count, milk urea nitrogen), fatty acid profile, metabolomics, and microbiome analyses, as described below.

Representative 100 mL subsamples of milk were taken for analysis of quality attributes (protein, fat, lactose, and somatic cell) using a CombiFoss 7 machine (Foss Electric, Hillerød, Denmark) by MilkTest New Zealand. Milk urea N measurements were determined on skimmed milk using an automated Modular P analyser [Roche/Hitachi, [10]]. Representative 50 mL subsamples of whole milk were freeze-dried and used to analyze FA methyl esters, which were prepared by trans-methylation and assessed through the use of gas chromatography (AOAC method 2012.13) using a Shimadzu GC-2010 gas chromatograph (Shimadzu, Tokyo, Japan).

Yoghurt was made with milk from mid- and late lactation for both REG and CON. Raw whole milk collected from the two systems was heat treated (approximately 83 °C for 20 min) then cooled to 40 °C and inoculated with standard yoghurt bacteria (S. Thermophilus, L. Bulgaricus). Yoghurt samples were collected from individual batches, REG and CON, and analyzed for metabolomics and microbiome.

2.3.1. Metabolomics Profiling

Chemicals: UHP-LC-MS-grade acetonitrile (ACN), dimethyl sulfoxide (DMSO), methanol (MeOH), tert-Butyl methyl ether (MTBE), formic acid (FA), and water (H₂O) (all, Supelco LiChrosolv^®) were ordered from Sigma-Aldrich (St. Louis, MO, USA). Non-labelled standards of compounds and stable-isotopically labelled (IS) standards including 4-hydroxybenzoic acid-¹³C₆, apigenin-D₅, benzoic acid-D₅, genistein-D₅, phenol-¹³C₆, and trans cinnamic acid D₇ were purchased from Cambridge Isotope Laboratories (Tewksbury, MA, USA) and/or Cayman Chemical (Ann Arbor, MI, USA). All individual standards of compounds were dissolved at a stock concentration between 1 and 10 mM in the reagents mentioned (or a combination thereof) depending on their solubility. All compounds were optimized one-by-one by direct infusion to determine optimal MS-parameters, such as declustering potential, collision energy, cell exit potential, and Q3-transitions (quantifier and qualifier).

Sample preparation: Prior to extraction, freeze-dried feed samples were pulverized in liquid nitrogen using an analytical mill (IKA^® A 11 basic). Milk yoghurt and plant samples were weighed out at 100 and 30 mg, respectively, and extracted with 1.25 mL of MTBE:MeOH (2:1, v:v) spiked with internal standards (1 µM of 4-hydroxybenozic acid 13C6 and Apigenin D5, and 5 µM of benzoic acid D5 and trans-cinnamic acid D7). Samples were homogenized at 30 oscillations/sec for 2 min twice using a Tissue Lyser II (Qiagen Retsch Tissue Lyser II, Germantown, MD, USA), sonicated in an ice-cold water bath for 15 min, and stored at −80 °C overnight to facilitate protein precipitation. The MTBE phase was removed by centrifugation at 16,000 rcf for 10 min at 4 °C after adding 750 µL of water. The organic phase of both the yoghurt and feed samples (free phenolics) were filtered (0.22 μm PTFE), centrifuged at 16,000 rcf for 10 min at 4 °C, transferred to an HPLC vial, and stored at −80 °C until LC-MS/MS analysis. Protein pellets of feed samples were further treated with internal standard-spiked 2 M NaOH (16 h at 30 °C) for bound phenolics extractions. pH was neutralized with formic acid and subjected to solid-phase extraction (Strata X-Pro, Phenomenex, Torrance, CA, USA) before liquid chromatograph tandem mass spectrometry (LC-MS/MS) analysis. Analysis was performed on a SCIEX Hybrid Triple Quad 7500 mass spectrometer coupled to a Shimadzu UPLC system Nexera–LC–40. Separation was achieved using an ACQUITY UPLC HSS T3 1.8 μm 2.1 × 100 mm (WatersTM) operated at 30 °C. The mobile phases consisted of 0.1% formic acid in water (A) and acetonitrile (B), and mobile phase flow rate was 0.6 ml/min. Gradient elution was 5% B from 0 to 2.1 min, then linearly increased to 95% B over 10 min, and returned to 5% B at 14.5 min. The system was equilibrated from 3.5 min in between injections. Five μL sample aliquots were injected in both positive and negative electrospray ionization modes, with ion spray voltages of –4000 V and +4500 V, respectively, with a source temperature of 450 °C. Then, 5 μL aliquots of samples were inject into system. Double blanks (internal standard free extraction solvent) along with internal standards blank were run every 20 samples for quality control. Chromatographic data was processed using SCIEX OS 3.1 software (AB Sciex, Framingham, MA, USA). Peak integration was performed using the MQ4 integration algorithm with the signal/noise filter at 3 and noise percentage at 40%. The area-under-the-curve method was used to integrate peak normalization, which was performed using isotopically labelled standards to account for any loss of material during sample preparation. The external standard curve used to quantify concentrations of compounds ranged from 0 to 10 uM injected as serial dilutions. For the feed samples, free and bound samples were summed to a total value for each sample. All phytochemical concentrations are expressed in µg/100 g.

LC-MS/MS conditions: Plant and yoghurt samples were run on a SCIEX Hybrid Triple Quad 7500 (Framingham, MA) coupled with a Shimadzu Nexera 40 Series (Kyoto, Japan) liquid chromatography system equipped with LC-40D X3 pumps. The samples were kept at 7 °C in a SIL-40CX3 auto-sampler, and compounds were separated at 30 °C using a reverse phase ACQUITY UPLC HSS T3 1.8 µm, 2.1 × 150 mm column from Waters (Milford, MA, USA). Binary mobile phases consisted of water (A) and acetonitrile (B), both containing 0.1% formic acid (v/v). Samples were injected separately in both negative and positive electrospray ionization modes. A purified standard mixture with a known concentration (1 µM) was used to determine each compound’s retention time using the following LC method: initial composition of 5% B for 2.1 min with a flow rate of 0.2 mL/min, ramping up gradually to 95% B and a maximum flow rate of 0.46 mL/min over 14 min to maintain constant pressure, before switching to 5% B for the final 4 min with a minimum flow rate of 0.175 mL/min. Scheduled methods were created for both positive and negative modes to decrease cycle time and increase dwell time. The OptiFlow^® Pro Ion Source was operated at 550 °C with a spray voltage of 3500 V, curtain gas at 40 psi, nebulizer gas (GS1) at 30 psi, and heating gas (GS2) at 50 psi for negative ionization mode. The spray voltage was set at 4500 V in positive ionization mode. In both modes, the cycling time in the scheduled method was set to 1000 ms, and the dwell time ranged from 3 to 250 ms depending on the number of MRMs triggered. Double blank (100% methanol), blank internal standard, and quality control samples were injected with each batch of 48 samples for quality control.

2.3.2. Microbiome Analyses

Amplicon sequencing: DNA was extracted from soil, milk, and yoghurt samples using the ZymoBIOMICS DNA Miniprep Kit (Zymo, D4300) according to the manufacturer instructions. Milk and yoghurt samples were pretreated with Proteinase K (20 µg/µL) for 30 min at 55 °C. Sample homogenisation was performed using a Precellys 24 Tissue Homogeniser (Bertin Instruments, Montigny-le-Bretonneux, France) for 5 × 1 min @ 6000 rpm.

A two-step PCR protocol was used to generate amplicons and index samples [11,12]. The 16S rRNA V4 region was amplified using the primers 515F (5′- GTGYCAGCMGCCGCGGTAA-3′) and 806R (5′- GGACTACNVGGGTWTCTAAT-3′) [13,14]. The fungal ITS2 region was amplified using the primers fITS7 (5′- GTGARTCATCGAATCTTTG-3′) and ITS4 (5′- TCCTCCGCTTATTGATATGC-3′) [15,16]. Paired-end 350 bp sequencing was performed by the Otago Genomics Facility (www.otago.ac.nz/genomics, 20 October 2024) on the Illumina MiSeq platform.

Processing raw amplicon reads: Raw amplicon reads were demultiplexed and quality filtered using QIIME2 [17]). Amplicon sequence variants (ASVs) were denoised using the DADA2 algorithm [18]. Taxonomy was assigned to ASVs using QIIME2 q2-feature-classifier [19] against SILVA_138.1_SSURef_NR99 [20] (Quast et al., 2012) and UNITE_ver8_dynamic reference databases.

2.4. Statistical Analyses

The effects of different production systems and stages of lactation on milk yield, MUN, milk solids, milk fatty acids, and herbage chemical composition were tested using generalized linear models in R [21]. The production system (CON or REG), stage of lactation (early, peak, mid, and late), and their interaction were included as predictors in the model. Effects were assessed using an analysis of deviance table based on a Type II Wald Chi-square test. Model assumptions were evaluated graphically to assess the normality and homoscedasticity of residuals. Statistical significance was set at p ≤ 0.05, while trends were discussed when 0.05 < p ≤ 0.10.

Metabolomic data and analysis: Sciex OS 3.1 software (AB Sciex, Framingham, MA, USA) was utilized to acquire and analyze chromatographic data. Peak integration was performed using MQ4 with a signal-to-noise ratio filter set at 10 and Gaussian smoothing at 3. Each analyte was normalized using the isotopically labelled standard with the closest retention time. Unlabeled external standard mixes, with known concentrations ranging from 10 µM to 0.02 nM in serial dilution, were run in parallel to the samples. Regression curve R² values were maintained at ≥0.9. Statistical analyses and graphical representations were generated using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/, accessed on 13 July 2025), as previously described. Group statistical differences were assessed using Welch’s t-test. Principal component analysis (PCA) was conducted to measure variation captured on PC1 and PC2. The top twenty-five compounds distinguishing the two groups were selected using Pearson distance measure and the Ward clustering algorithm and were displayed in a heatmap.

Bioinformatics: Microbiome data was analyzed using R, RStudio IDE (R Core Team, 2013) and the Ampvis2 package [22].

3. Results

3.1. Herbage, Milk, and Yoghurt Chemical Composition and Metabolomics

Chemical composition and nutritive value herbage consumed by the cows in both systems are presented in

Table 2. Chemical content of forage from a conventional system (CON) compared to regenerative system (REG) across different stages of lactation (Early, 90; Mid, 180; Late, 250; and End, 300 DIM).

Variables **	Early		Mid		Late		End		p-Value
	CON	REG	CON	REG	CON	REG	CON	REG	Treat	Stage	Inter	SD
DM%	24.08 b	24.73 b	24.18 b	25.58 b	21.73 b	20.98 b	30.52 a	29.01 a	0.98	<0.001	0.85	2.66
CP%	21.21 a	20.89 a	18.51 b	15.45 b	20.84 a	19.73 a	21.97 a	20.69 a	0.05	<0.001	0.65	1.79
OM%	90.01 A	89.18 B	90.15 B	91.02 A	89.67 B	89.96 A	90.34 A	89.82 B	0.57	<0.001	0.001	0.40
NDF%	35.91 A	30.63 B	32.73 B	39.10 A	29.06 AB	30.71 AB	29.20 AB	24.65 AB	0.61	<0.001	0.02	3.61
ADF%	21.02 B	20.04 B	22.18 A	25.36 A	19.17 AB	20.23 AB	18.01 AB	16.79 AB	0.49	<0.001	0.02	1.27
WSC%	19.48 b	18.72 b	15.94 c	15.14 c	20.49 b	19.02 b	23.17 a	24.04 a	0.62	<0.001	0.82	2.29
DMD%	82.02 A	82.21 A	78.62 B	74.92 B	82.68 AB	79.65 AB	84.64 AB	84.86 AB	0.02	<0.001	0.04	1.55
ME (MJ/kgDM)	12.24 b	12.16 b	11.77 c	11.34 c	12.29 b	11.89 b	12.67 a	12.62 a	0.01	<0.001	0.27	0.22

^A–B Upper case means interaction between the system and stage of lactation; ^a–c lower case means the difference between stages of lactation within the system. p < 0.05 means significant difference. ** Dry matter (DM), organic matter (OM), water soluble carbohydrates (WCS), neutral detergent fibre (NDF), acid detergent fibre (ADF), crude protein (CP), DM digestibility (DMD) and metabolizable energy (ME).

.

The chemical composition of herbage was affected by sampling time, i.e., season\stage of lactation (p < 0.05), but not by treatments (p > 0.05). Dry matter digestibility and ME were greater in CON than REG (p < 0.05), with only a tendency for protein content of herbage to be slightly greater in CON than the REG system.

Out of the 127 measured metabolites in herbage, 29 were different between systems, with a greater variability found in the REG than CON as presented in Figure 1, with the green cluster from the score plot spread more broadly as compared to the tight pink cluster representing CON. This is explained by the higher level of plant diversity in the REG herbage compared to the more limited diversity in the CON herbage.

The heatmap (Figure 2) provides a visualization of the differential abundance of metabolites between conventional and regenerative swards’ herbages. Key metabolites such as kaempferol-3-glucoside, a common isoflavonoid rich in legumes such as clover, and 5-feruloylquinic acid, a carboxylic acid involved in the shikimate pathway, are more abundant in conventional herbage, showing a 1.7- and 1.8-fold increase (both, p = 0.04) compared to regenerative herbage samples. In contrast, several compounds including apigenin, hydroxytyrosol, gentisic acid, daidzein, formononetin, and hesperidin are markedly elevated in regenerative herbage. For instance, hesperidin exhibits a 60.6-fold increase (p = 0.02), while formononetin, daidzein, and calycosin, three common isoflavonoids, are elevated 19.2- (p = 0.001), 4.2- (p = 0.001), and 49.5-fold (p = 0.03) in the regenerative herbage, respectively. Apigenin and linarin, two flavonoids, were elevated 2.1- (p = 0.02) and 51.4-fold (p = 0.03) in regenerative herbages (p = 0.02), reflecting not only distinct metabolic profiles associated with different systems, but also a greater phytochemical diversity for regenerative herbage.

Milk yield and chemical composition are presented in

Table 3. Milk yield (L/d) from cows in a conventional system (CON) compared to a regenerative system (REG) across different stages of lactation (Early, 90; Mid, 180; Late, 250; and End, 300 days in milk).

System	Early	Mid	Late	End	p-Value			SD
					Treat	Stage	Inter
CON	24.35 b	19.40 d	36.35 a	22.06 c	<0.01	<0.001	0.08	0.94
REG	23.59 b	18.32 d	31.30 a	21.47 c				2.13

^a–d lower case means the difference between stages of lactation within the system. p < 0.05 means significant difference.

and

Table 4. Milk composition from cows in a conventional system (CON) compared to a regenerative system (REG) across different stages of lactation (Early, 90; Mid, 180; Late, 250; and End, 300 days in milk).

Variable| Systems	Early		Mid		Late		End		p-Value
	CON	REG	CON	REG	CON	REG	CON	REG	Treat	Stage	Inter	SD
MUN mg/dL	28.55 a	25.65 a	24.85 b	21.82 b	26.22 ab	26.45 ab	26.07 b	25.30 b	<0.001	<0.001	0.17	2.26
Protein %	3.81 d	3.86 d	3.91 c	3.94 c	4.47 b	4.45 b	5.04 a	4.95 a	0.50	<0.001	0.10	0.48
Fat %	4.74	4.69	4.58	4.85	5.25	5.51	6.07	6.13	0.24	<0.001	0.70	0.66
Lactose %	5.11 a	5.11 a	4.83 b	4.92 b	4.71 b	4.74 b	4.63 b	4.65 b	0.11	<0.001	0.51	0.18

^a–d lower case means the difference between stages of lactation within the system. p < 0.05 means significant difference.

. Overall, milk yield was slightly greater in CON than the REG system and differed during lactation.

Milk from REG cows had a 5% greater content of saturated fatty acids (SFAs) (p < 0.05), 2% greater content of polyunsaturated fatty acids (PUFAs) (p < 0.05), 4.65% greater content of medium-chain fatty acids (MCFAs) (p < 0.05), 2% greater content of long-chain fatty acids (LCFAs) (p < 0.05), and 8% greater content of very long chain fatty acids (VLCFAs) as compared to milk from CON cows (

Table 5. Fatty acids content (Mg/g of dried sample) of milk from cows in a conventional system (CON) compared to a regenerative system (REG) across different stages of lactation (Early, 90; Mid, 180; Late, 250; and End, 300 days in milk).

Fatty Acids	Early		Mid		Late		End		p-Value
	CON	REG	CON	REG	CON	REG	CON	REG	Treat	Stage	Inter	SD
C4:0	5.49 b	5.28 b	5.36 ab	5.65 ab	5.42 ab	5.55 ab	6.02 a	6.09 a	0.48	<0.001	0.33	0.25
C6:0	5.27 b	5.23 b	5.06 ab	5.40 ab	5.31 ab	5.48 ab	5.77 a	5.88 a	0.12	<0.001	0.56	0.22
C7:0	0.08 a	0.08 a	0.02 b	0.03 b	0.07 a	0.06 a	0.07 a	0.07 a	0.56	<0.001	0.58	0.05
C8:0	3.80 a	3.83 a	3.45 b	3.67 b	3.74 ab	3.86 ab	4.05 ab	4.14 ab	0.09	<0.001	0.79	0.14
C9:0	0.11 A	0.12 A	0.06 AB	0.07 AB	0.10 B	0.09 B	0.10 AB	0.10 AB	0.48	<0.001	0.06	0.008
C10:0	10.46 a	10.94 a	8.96 b	9.63 b	9.96 a	10.36 a	10.97 a	11.28 a	0.01	<0.001	0.92	0.42
C10:1	0.73 d	0.72 d	0.83 c	0.86 c	1.02 b	1.00 b	1.17 a	1.19 a	0.83	<0.001	0.80	0.04
C11:0	0.23 a	0.26 a	0.13 c	0.14 c	0.21 b	0.19 b	0.21 b	0.22 b	0.30	<0001	0.08	0.01
C12:0	12.38 b	13.03 b	10.79 c	11.66 c	12.52 b	13.06 b	14.07 a	14.56 a	0.04	<0.001	0.93	0.51
C13:0 iso	0.18 d	0.19 d	0.21 c	0.22 c	0.29 b	0.28 b	0.36 a	0.37 a	0.50	<0.001	0.81	0.014
C12:1	0.09 b	0.08 b	0.12 a	0.12 a	0.11 a	0.10 a	0.11 a	0.11 a	0.57	<0.001	0.66	0.006
C13:0 anteiso	0.27 c	0.25 c	0.27 c	0.27 c	0.36 b	0.34 b	0.43 a	0.44 a	0.74	<0.001	0.56	0.01
C13:0	0.35 a	0.37 a	0.24 c	0.24 c	0.32 b	0.29 b	0.32 a	0.33 a	0.71	<0.001	0.13	0.01
C14:0 iso	0.24 b	0.23 b	0.31 a	0.28 a	0.29 a	0.28 a	0.28 a	0.30 a	0.30	<0.001	0.18	0.01
C14:0	39.79 c	41.13 c	40.80 c	42.96 c	43.94 b	46.07 b	48.96 a	49.35 a	0.03	<0.001	0.79	1.79
C15:0 iso	0.86 b	0.82 b	1.00 a	0.96 a	0.97 a	0.94 a	0.99 a	1.00	0.38	<0.001	0.85	0.05
C14:1 c9	2.15 d	2.15 d	2.73 c	2.76 c	3.54 b	3.49 b	4.40 a	4.45 a	0.92	<0.001	0.90	0.13
C15:0 anteiso	2.05 A	1.98 B	2.04 A	1.82 B	1.97 A	1.72 B	1.88	1.88	<0.001	<0.001	0.05	0.08
C15:0	4.02 b	4.07 b	4.08 b	3.95 b	4.42 a	4.27 a	4.40 a	4.43 a	0.44	<0.001	0.54	0.14
C16:0 iso	0.72	0.69	0.79	0.72	0.75	0.70	0.73	0.76	0.06	0.18	0.25	0.03
C16:0	108.2	112.1	126.6	139.8	140.6	152.6	150.5	152.3	<0.001	<0.001	0.04	4.46
C16:1 t9	0.59	0.52	0.52	0.39	0.50	0.35	0.45	0.42	<0.001	<0.001	<0.01	0.03
C16:1 c7	0.79 a	0.73 a	0.66 b	0.66 b	0.67 b	0.62 b	0.74 a	0.70 a	<0.01	<0.001	0.47	0.03
C16:1 c9	3.60 c	3.44 c	3.75 c	3.98 c	4.95 b	4.88 b	5.85 a	5.86 a	0.96	<0.001	0.17	0.17
C17:0 iso	1.47	1.41	1.44	1.45	1.36	1.36	1.36	1.43	0.93	0.34	0.68	0.08
C17:0 anteiso	2.07 a	2.03 a	1.80 b	1.70 b	1.43 c	1.36 c	1.38 c	1.40 c	0.38	<0.001	0.86	0.13
C17:0	1.92 a	1.78 a	1.70 b	1.81 b	1.70 b	1.83 b	1.63 b	1.78 b	0.03	<0.01	0.27	0.10
C17:1	0.90 A	0.82 A	0.71 B	0.74 B	0.76 B	0.78 B	0.80 B	0.86 B	0.57	<0.001	0.02	0.043
C18:0	40.51 a	38.44 a	34.89 b	36.13 b	32.10 b	33.71 b	32.95 b	34.52 b	0.62	<0.01	0.71	3.06
C18:1 t5-8	0.57 a	0.48 a	047 b	0.40 b	0.46 b	0.42 b	0.53 a	0.49 a	<0.001	<0.001	0.60	0.03
C18:1 t9	0.53 a	0.48 a	0.45 b	0.40 b	0.47 b	0.41 b	0.55 a	0.52 a	<0.001	<0.001	0.78	0.02
C18:1 t10	0.78 a	0.68 a	0.59 d	0.51 d	0.60 c	0.54 c	0.71 b	0.69 b	<0.001	<0.001	0.28	0.03
C18: t11	15.84 A	12.80 A	11.56 A	7.93 AB	10.32 A	6.75 AB	8.4 B	7.53 B	<0.001	<0.001	<0.001	0.62
C18:1 c6	1.21 a	1.08 a	1.01 c	0.93 c	1.11 abc	1.06 abc	1.33 b	1.22 b	0.001	<0.001	0.78	0.06
C18:1 c9	66.92 a	60.45 a	56.40 b	55.98 b	58.73 b	56.93 b	64.83 ab	65.11 ab	0.22	<0.001	0.51	4.23
C18:1 t15/c10	0.95 a	0.85 a	0.75 b	0.73 b	0.78 ab	0.84 ab	0.97 ab	0.89 ab	0.16	<0.001	0.14	0.05
C18:1 c11	1.57 A	1.41 A	1.04 B	1.07 B	0.99 AB	0.99 AB	0.97 B	1.09 B	0.87	<0.001	0.02	0.008
C18:1 c12	0.25 b	0.26 b	0.26 b	0.30 b	0.28 ab	0.33 ab	0.34 a	0.40 a	<0.001	<0.001	0.17	0.01
C18:1 c13	0.40 a	0.35 a	0.25 b	0.21 b	0.26 b	0.21 b	0.30 b	0.27 b	<0.001	<0.001	0.95	0.02
C18:1 c14/t16	1.65 ab	1.50 ab	1.43 b	1.31 b	1.48 ab	1.42 ab	1.70 a	1.55 a	<0.01	<0.001	0.84	0.08
C18:2 t9/12	0.57 a	0.50 a	0.47 b	0.38 b	0.49 b	0.37 b	0.47 b	0.43 b	<0.001	<0.001	0.16	0.04
C18:2 c9/t13	0.41 B	0.39 B	0.40 B	0.39 B	0.46 B	0.45 B	0.61 A	0.52 A	<0.01	<0.001	0.03	0.03
C18:2 c9 t12	1.10 b	0.95 b	0.88 c	0.78 c	1.06 b	0.94 b	1.28 a	1.14 a	<0.001	<0.001	0.77	0.05
C18:2 t9 c12	0.36 b	0.32 b	0.31 b	0.30 b	0.38 a	0.37 a	0.50 a	0.44 a	<0.001	<0.001	0.13	0.01
C19:0	3.02 a	2.75 a	2.82 b	2.40 b	2.97 b	2.47 b	3.32 a	2.96 a	<0.001	<0.001	0.60	0.15
C18:2 c9:12	2.85 B	2.90 A	2.72 A	3.47 B	2.86 A	3.30 B	2.75 A	3.27 B	<0.001	0.02	<0.001	0.14
C19:1	0.23 b	0.22 b	0.26 a	0.26 a	0.26 a	0.28 a	0.26 a	0.29 a	0.09	<0.001	0.44	0.015
C18:3 c6:9:12	0.07 b	0.07 b	0.08 b	0.08 b	0.09 a	0.08 a	0.10 a	0.10 a	0.82	<0.001	0.75	0.009
C18:3 c9:12:15	2.47 B	2.60 B	2.54 A	3.16 A	2.66 B	2.90 A	3.17 A	2.74 A	<0.001	<0.001	0.01	0.13
C20:0	0.2	0.32	0.30	0.37	0.29	0.35	0.28	0.34	<0.001	0.40	0.20	0.02
CLA:c9 t11	5.17 A	4.26 A	5.03 B	3.36 B	5.28 B	3.29 B	4.72 A	4.15 A	<0.001	<0.001	<0.001	0.21
C20:1 c8	0.25	0.22 A	0.25	0.18 B	0.25	0.18 B	0.25	0.23 A	<0.001	0.01	<0.01	0.01
C20:1 c9	0.21 B	0.21 B	0.24 A	0.27 A	0.26 AB	0.29 AB	0.27 A	0.32 A	<0.001	<0.001	0.01	0.014
C20:1 c11	0.10 A	0.09 A	0.06 A	0.06 AB	0.04 B	0.05 B	0.06 B	0.08 B	0.30	<0.001	0.04	0.01
C20:2 c11,14	0.08	0.05	0.00	0.01	0.00	0.00	0.00	0.00	0.20	<0.001	<0.001
C20:3 c8:11,14	0.12	0.13	0.12	0.15	0.14	0.16	0.12	0.14	<0.001	<0.01	0.47	0.01
C20:4 c5:8,11,14	0.17 A	0.16 B	0.16 B	0.18 A	0.17 A	0.18 AB	0.18 A	0.19 AB	<0.01	<0.01	0.06	0.009
C20:3 c11,14,17	0.04	0.04	0.06	0.07	0.04	0.06	0.06	0.07	0.15	0.06	0.58	0.014
C22:0	0.18 b	0.18 b	0.19 a	0.21 a	0.18 a	0.21 a	0.15 ab	0.19 ab	<0.001	<0.001	0.16	0.01
C22:1 c13	0.18 b	0.18 b	0.20 a	0.22 a	0.19 a	0.21 a	0.21 a	0.23 a	0.04	<0.001	0.62	0.01
C20:4 c8,11,14,17	0.13 b	0.09 b	0.10 c	0.08 c	0.11 c	0.10 c	0.16 a	0.13 a	<0.001	<0.001	0.10	0.009
C20:5 c8,11,14,17	0.23 b	0.25 b	0.24 b	0.26 b	0.24 b	0.24 b	0.28 a	0.29 a	0.01	<0.001	0.71	0.01
C23:0	0.10 b	0.10 b	0.12 b	0.13 b	0.13 a	0.14 a	0.10 b	0.13 b	<0.001	<0.001	0.33	0.01
C24:0	0.15	0.16	0.16	0.18	0.16	0.18	0.13	0.17	<0.01	0.09	0.43	0.01
C22:5 c7,10,13,16,19	0.35 b	0.35 b	0.36 b	0.40 b	0.41 a	0.46 a	0.45 a	0.48 a	<0.01	<0.001	0.44	0.02
C26:0	0.20 A	0.18 B	0.16 B	0.23 A	0.18 B	0.22 A	0.16 A	0.16 B	<0.01	0.01	<0.01	0.02

^A–B Upper case means interaction between the system and stage of lactation; ^a–d lower case means the difference between stages of lactation within the system. p-value < 0.05 means significant difference.

) (p < 0.05). Furthermore, the content of omega-3 was 10.7% greater in milk from REG cows as compared to milk from CON cows (p < 0.05). Omega-6 was 25.3% greater in milk from the CON system (p < 0.05).

Only 3 out of the 84 metabolites measured were different between the milk from both systems (all p < 0.05); however, the regenerative samples displayed considerable variability (Figure 3). This is similar to the findings made in herbage samples and could indicate greater seasonal variability. Regenerative samples cluster distinctly, reflecting the influence of phenolic-rich REG herbage, while CON samples show unique patterns attributed to their respective practices. Overlapping regions suggest shared baseline characteristics.

The heatmap displayed in Figure 4 illustrates the differential abundance of metabolites between milk from different groups. Notably, several benzoic acid-derived metabolites, including 2-hydroxyhippuric acid and 4-hydroxyhippuric acid were 1.38- (p = 0.001) and 1.64-fold (p = 0.004) elevated in regenerative REG milk. Hippuric acids can be considered a marker of dietary polyphenol intake in mammals [23], and elevated levels in the REG milk likely indicate higher dietary polyphenol intake of dairy cows in the REG group. In addition, resorcinol was 1.6-fold more abundant under the regenerative system. These patterns reflect a shift in the phytochemical profile associated with farming practices, with regenerative milk showing greater representation of bioactive compounds, particularly those from the benzoic acid family, which are associated with reduced odds of metabolic syndrome in humans [24].

Regarding yoghurt, a total of 78 metabolites were identified and quantified in yoghurt samples. Of these metabolites, 47 showed significant differences between the two yoghurts coming from cows grazing in different systems. The Scores Plot (Figure 5) illustrates the distinct separation between CON and REG yoghurt samples based on their phytochemical profiles. The CON yoghurt samples are highlighted in pink and cluster to the right side of the plot, while the REG yoghurt samples are shown in green and form a separate cluster on the left. This grouping indicates that the metabolite profiles of CON and REG yoghurts are consistently different. The heatmap (Figure 6) provides a visualization of the differential abundance of metabolites between CON and REG yoghurt samples; for instance, the flavonoid luteoloside exhibited a 5.1-fold increase (p < 0.001) while benzoic acid and salicylic acid, a benzoic acid derivative, showed a 1.4- and 7.1-fold elevation (p < 0.01) in REG yoghurt, respectively.

3.2. Soil, Milk, and Yoghurt Microbiomes

There was difference between soil, milk, and yoghurt microbiome profiles, with some separation related to farming system, as illustrated in Figure 7.

Figure 8 presents the differences between CON and REG soil microbiomes at the different stages of lactation. For microbiome, a principal component analysis (PCA) was conducted, a multivariate analysis that considers several pieces of information and groups them into five main principal components. The plot illustrates the two main principal components which are the most important ones in terms of explaining the dataset. The first principal component (x-axis) is the most important one, so the differences between points along this axis are greater than those between points along the y-axis. Therefore, for soil bacteria population, although there is a difference between systems (y-axis), the differences between sampling dates (x-axis—stage of lactation) within each system are greater than differences between both systems per se. However, the combination of both axis is 16.5%, which means that those principal components only explain 16.5% of the data variation and restrained information also contributes to explaining the data.

The difference between the relative abundance of different bacteria families in the soil between different systems is presented in Figure 9.

Few differences were found for some of the families between the two systems. For instance, the Bacillaceae family was greater in the REG soil, while Gaiellaceae, and Micrococcaceae families were more abundant in the CON soil.

Few distinctions were detected in the PCA when both systems were compared in terms of milk microbiome (Figure 10). According to the heat map, Streptococcaceae and propionibacteriaceae families were more abundant in the CON milk, while the Staphylococcaceae family was more abundant in REG milk (Figure 11).

Yoghurt microbiome profile as compared to soil and milk (Figure 12). Principal component 1 explains 99.9% of the data variation. The microbial population of yoghurt was very different between the two systems (Figure 13). Most of the bacteria in the REG yoghurt belong to the Streptococcacea family, whereas the CON yoghurt is composed also of the Lactobacillaceae family. Some bacteria from both families are non-pathogenic bacteria used to make yoghurt and present probiotic activity.

Figure 14 illustrates a distinction between systems as compared to fungal populations in soil. Most of the relative abundance of fungal families in the soil is clearly different between the systems (Figure 15), with more noticeable distinctions for Halosphaeriaceae, Ophiocordycipitaceae, and Cucurbitariaceae.

4. Discussion

This study evaluated the effect of a regenerative (REG) as compared to conventional (CON) pasture-based New Zealand dairy production system on milk and yoghurt nutraceutical properties, and the environmental impact in the context of two distinct pasture bases: a multispecies sward for the REG and a monotonous ryegrass-based sward for the CON. The REG system produced milk and yoghurt with more favourable profile of bioactive compounds associated with human health compared to the CON system, while having a potential reduction in environmental impact. In the following section we discussed the details and implications of these results.

4.1. Human Health

Milk produced in the REG systems had a more favourable fatty acid profile associated with human health than CON. Three important fatty acids were found in greater amount: C18:3 c9,12,15—alpha-linolenic, C20:3 c8,11,14—Eicosatrienoic acid, and C22:5 c7,10,13,16,19—Docosapentaenoic acid. Alpha-linolenic acid (ALA) is an essential omega-3 fatty acid necessary for normal human growth and development [20]. Alpha-linolenic acid is thought to decrease the risk of heart disease by helping to maintain normal heart rhythm and pumping [21,22]. It might also reduce blood clots [23]. Eicosapentaenoic acid (EPA) is a dominant omega-3 fatty acid, and, as part of a healthy diet, can help lower the risk of CVD [24]. Docosapentaenoic acid has strong anti-inflammatory properties, and it has been suggested that it could be useful for the treatment of several clinical disorders including schizophrenia, bipolar disorder (manic depression), and possibly conditions such as Alzheimer’s disease as well as certain types of cancer [25,26]. Moreover, REG milk had greater content of long and very long-chain fatty acids. Dietary intake of long-chain fatty acids plays a potential protective role in insulin resistance and risk of diabetes [27]. Long and very long-chain fatty acids act as an antioxidant, protecting neuronal cell membranes from oxidative damage, and as an anti-inflammatory mediator in the brain [28]. Increasing long-chain fatty acid levels have also been shown to reduce triglyceride levels. Conjugated linoleic acid (CLA) c9t11 was greater in CON milk. CLA has been shown to reduce body fat, protect against cancer and atherosclerosis, and stimulate immune functions. A common metric to evaluate the fatty acid quality of animal products is through the omega-6-to-omega-3 ratio. A high omega-6-to-omega-3 ratio in the diet, as seen in many Western diets, can contribute to chronic inflammation in the brain, potentially leading to various health problems. Omega-3 fatty acids, especially EPA and DHA, have anti-inflammatory properties, while omega-6 fatty acids, like arachidonic acid, can promote inflammation at high levels [29]. Balancing the ratio, often through increased omega-3 intake, can help reduce inflammation and improve overall brain health [30]. The omega-6-to-omega-3 ratio is greater in CON milk as compared to REG milk, and REG milk has a higher concentration of important omega-3 as compared to CON milk; consequently, REG milk would ‘healthier’ for the consumer [30].

The REG system milk and yoghurt had more favourable profiles of bioactive compounds associated with human health compared to the CON system. Apigenin and formononetin were more abundant in REG forages and are associated with antioxidant and anti-inflammatory properties [30,31]. These compounds are likely the result of the diverse swards, with plant species, including legumes rich in isoflavones and flavones, underscoring the importance of phytochemically rich plant diversity in enhancing pasture nutraceutical value. Hydroxytyrosol and schaftoside were also more abundant in the REG system and are known for their health-promoting properties, including antioxidant and antimicrobial activities [32]. Other compounds related to antioxidants and antimicrobial activities were also enhanced in REG pastures such as gentisic acid, 3-aminosalicylic acid, and isovitexin [33,34]. Moreover, salicylic acid and resorcinol were more prominent in REG milk, adding to its antioxidant profile. Salicylic acid is a beta-hydroxy acid known for its keratolytic and anti-inflammatory effects, often used in skincare for acne and other skin conditions [35]. Resorcinol, a phenolic compound, exhibits antioxidant activity and has been used in various applications, including skincare and as an antiseptic [23]. Additionally, REG milk had elevated levels of 4-hydroxyhippuric acid and 2-methylhippuric acid. Hippuric acids are produced in the gut from polyphenols [36], and the higher levels of polyphenols found in the REG herbage likely underscore higher levels of these compounds in the REG milk. Kaempferol-3-glucoside and 5-Feruloylquinic acid are presented in greater amounts in CON pastures. Kaempferol-3-glucoside is a flavonoid with antioxidant properties, while kaempferol-3-glucoside is an isoflavone commonly found in legumes. Its prominence may result from specific plants like clover and ryegrass that dominate CON swards. The greater variety and concentration of phenolic compounds in REG pastures suggests that regenerative practices might offer and lead to a more diverse phytochemical array of health-promoting compounds, which is beneficial not only to livestock but potentially to the consumer.

Regarding yoghurt, from a total of 78 metabolites identified and quantified, 47 showed significant and consistent differences between the yoghurt coming from different systems, potentially indicating a consistently nutraceutical profile. Some important human health-associated compounds were more abundant in REG yoghurt. In regenerative yoghurt, metabolites such as salicylic acid and luteoloside were found to be more abundant. Salicylic acid, a plant and key component of ‘aspirin’ (the genericized trademark for acetylsalicylic acid) offers pain-relieving, anti-inflammatory, antithrombotic, fever-reducing properties, and has been linked to reducing the risk of stroke and heart attack, as well as some types of cancer [35]. Moreover, salicylic acid has shown various effects on brain health, including neuroprotective and anti-inflammatory properties. Studies suggest it can enhance neuronal excitation, potentially by reducing GABAergic transmission, and may have neuroprotective effects against Parkinson’s disease [35]. Luteoloside is a flavonoid with several potential health benefits including antioxidant, anti-inflammatory, and anti-viral properties, as well as having neurocognitive and cardiovascular protective effects [3]. Moreover, uteoloside has been used as hepatoprotection and even as an anti-diabetic [36,37].

Conversely, conventional yoghurt showed higher levels of metabolites like 2,6-dihydroxybenzoic acid and catechol, which are linked to microbial activity and polyphenol metabolism [38]. These findings suggest that regenerative farming may contribute to a more favourable metabolite profile in yoghurt, with potential health benefits for consumers.

4.2. Soil, Milk and Yoghurt Microbiomes

Few differences were found for some of the families between the two systems. For instance, Bacillaceae family was greater in the REG soil, while Gaiellaceae and Micrococcaceae families were more abundant in CON soil. Bacillaceae species play a significant role in plant growth by releasing beneficial compounds and producing phytohormones that contribute to plant stress tolerance and overall growth. These bacteria can directly promote plant growth through mechanisms like phytohormone production and nutrient solubilization, while indirectly enhancing plant defences and stress resilience [10,39]. They also contribute to the organic matter and nutrient cycle in the soil. Gaiellaceae plays a role in both decomposing organic matter and the nutrient cycle in soil [40]. Micrococcaceae are known for their ability to degrade organic matter, contribute to nutrient cycling, and potentially inhibit the growth of plant pathogens. Moreover, the Cucurbitariaceae family was less abundant in the REG than CON soil, with some species of this family being pathogenic to plants, animals, and humans. On the other hand, a greater abundance of Ophiocordycipitaceae is found in the REG soil, and species of this family work with beneficial soil organisms and are used as biological control against pests in agriculture. They also help with organic matter decomposition and nutrient transportation, indicating a healthy and biodiverse soil ecosystem. Although further research at species level is needed, these few significant differences in the soil microbiome between the systems may help explain some preliminary results on regenerative agriculture increasing plant growth and health at low levels of artificial fertilizers [41,42].

Few distinctions between systems were also detected in milk microbiomes. Streptococcaceae and propionibacteriaceae families were more abundant in the CON milk, while Staphylococcaceae family was more abundant in the REG milk. Streptococcaceae in the milk can indicate bovine mastitis contamination and can cause foodborne illnesses. Propionibacteriaceae are a family of Gram-positive bacteria that are commonly found in dairy products like milk and cheese, and they have been recognized for both their antimicrobial and probiotic properties [43]. Staphylococcaceae, a family of bacteria that includes species like Staphylococcus aureus, can have both benefits and risks in dairy products by producing bacteriocins, which are antimicrobial peptides that can inhibit the growth of other bacteria, potentially reducing the risk of spoilage or contamination in dairy products [44]. As stated before, and in this case for milk, further research at species level is needed. These few differences in milk microbiomes may suggest differences between the systems that may also reflect cow health.

The microbial population of yoghurt was different between the two systems. Most of the bacteria in the REG yoghurt belong to the Streptococcacea family, whereas the CON yoghurt is also composed of the Lactobacillaceae family. Lactobacillus bulgaricus and Streptococcus thermophilus work together symbiotically, but their individual roles contribute to the overall flavour, texture, and fermentation process. S. thermophilus is generally considered the “starter”, initiating fermentation by consuming lactose and producing lactic acid, lowering the pH, and aiding milk coagulation. L. bulgaricus then builds upon that, further lowering the pH. S. thermophilus produces some diacetyl, which contributes to the creamy or buttery flavour of yoghurt, while L. bulgaricus produces acetaldehyde, which is responsible for sharp flavour characteristic in yoghurt [45]. Consequently, yoghurt coming from these two systems would have different texture and flavour. The REG herbage was higher in polyphenols and several polyphenol derived metabolites were subsequently higher in the REG yoghurt. Polyphenols can enhance the growth of S. thermophilus in yoghurt. Studies have also shown that certain polyphenols can stimulate the growth of S. thermophilus and the acidification rate of yoghurt, leading to improved fermentation [46,47,48]. Consequently, it is expected that REG yoghurt may be creamier.

While both bacteria are important in yoghurt, Lactobacillus strains, particularly L. bulgaricus, were generally considered to be the probiotic one that offers health benefits. However, relatively recent studies [49] indicate that Streptococcus thermophilus, known as the fermentation starter, offers various health benefits, particularly for digestive health and potentially for lactose intolerance, breaking down lactose, thus making yoghurt more digestible. It can help with lactose digestion, improve gut motility, help with regular bowel movements, and potentially reduce acute diarrhea and antibiotic-induced diarrhea. Moreover, yoghurt cultures, including S. thermophilus, may stimulate the gut’s immune system and reduced symptoms of inflammatory bowel disease [49,50].

4.3. Environmental Impact

Greater yield and slightly greater protein content of herbage from the CON system may have led to the 9.3% greater MUN, but no difference was found in milk protein content (Table 4). This indicates that at similar milk yield, the REG system’s milk contributes to reducing negative environmental impact by decreasing N excretion in the urine, as milk urea N is linearly associated with urinary N excretion [51,52]. Approximately 82% of the urinary N excreted by dairy cows in pasture-based systems is discharged onto pastures [53,54], with 20–30% of the N lost in this manner leached to the waterways and 2% lost as N₂O. Increments of N in waterways have been related with environmental pollution and detrimental effects to human health [55,56,57,58]. ‘Blue baby syndrome’ has been associated with increasing levels of nitrates in drinking water, resulting in methemoglobinemia in infants, which can be fatal [59]. Other studies indicate an increased risk of developing colorectal cancer [60], thyroid disease [61], and neural tube defects [62] from high levels of nitrates consumed in drinking water.

5. Conclusions

Overall and under the conditions and the term of this study, the regenerative system led to milk and yoghurt with a more favourable profile of bioactive compounds and phytochemicals, as well as a microbiome associated with positive benefits to human health. Moreover, the regenerative system has the potential to reduce environmental impact in terms of N pollution, all of which while maintaining a similar milk yield than the conventional system. Further and longer-term research is needed to keep validating these benefits of regenerative systems in New Zealand pasture-based dairy production systems.