Animal performances, pasture biodiversity and dairy product quality: How it works in contrasted mountain grazing systems

Agriculture, Ecosystems and Environment 185 (2014) 231–244

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Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

Animal performances, pasture biodiversity and dairy product quality: How it works in contrasted mountain grazing systems Anne Farruggia a,b,∗ , Dominique Pomiès a,b , Mauro Coppa c , Anne Ferlay a,b , Isabelle Verdier-Metz d , Aline Le Morvan a,b , Arnaud Bethier a,b , Franc¸ois Pompanon e , Olivier Troquier f , Bruno Martin a,b a

INRA, UMR1213 Herbivores, F-63122 Saint-Genès-Champanelle, France Clermont Université, VetAgro Sup, UMR1213 Herbivores, BP 10448, Clermont-Ferrand F-63000, France c Department Agricultural, Forest and Food Sciences - DISAFA, University of Turin, Via L. da Vinci 44, Grugliasco 10095, Italy d INRA, UR 545 Fromagères, Route de Salers Aurillac F-15000, France e Université Joseph Fourier, Laboratoire d’Ecologie Alpine, CNRS, UMR 5553, 2233 rue de la Piscine, Grenoble Cedex 938041, France f INRA, UE1296 Monts d’Auvergne, Orcival F-63210, France b

a r t i c l e

i n f o

Article history: Received 22 April 2013 Received in revised form 2 January 2014 Accepted 4 January 2014 Available online 27 January 2014 Keywords: Dairy cow Pasture Animal performance Sensorial properties Fatty acid Biodiversity

a b s t r a c t The interactions between botanical composition of pasture, quality of herbage grazed, performances of dairy cows and sensory and nutritional properties of dairy products were investigated using an integrated system approach. Two contrasting grazing systems were evaluated from May to September in two years. The treatments included a continuous grazing system (DIV) managed at a lenient stocking rate (1.0 LU ha−1 ) on a botanically-rich permanent pasture, and a rotational grazing system (PROD) set up at a higher stocking rate (1.7 LU ha−1 ) on a former temporary grassland presenting moderate biodiversity. DIV aimed to maximize biodiversity and obtain high sensory and nutritional quality cheese, whereas PROD was oriented towards milk production and herbage quality. In each system, 12 non-feedsupplemented Montbéliarde cows were used. The DIV system led to higher milk production per cow in the early grazing season than the PROD system (22.2 vs. 19.9 kg d−1 ). At the beginning of summer, this milk production pattern was inverted following a decrease in grass nutritive value in the DIV system. In parallel, DIV cows showed a more marked loss of body condition than PROD cows over the season. In terms of milk fatty acid profile, the DIV system proved very interesting early in the grazing season but lost its value over time as the herbage matured. Cheese sensory properties differed between systems only after a long ripening period (6 months). Regarding the ecological performances, the DIV plot showed greater botanical and entomological biodiversity than the PROD plot. This study provides evidence that the balance between animal performances, dairy product quality and biodiversity in dairy systems is more complex than previously thought, since the expected benefits of each system vary markedly over periods. The evolution of herbage vegetation stage during the grazing season combined with the botanical composition of the pasture is a key component for understanding these variations. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Dairy systems in Europe are set to face several challenges that will prove difficult to conciliate. They need to gain efficiency in order to meet increasing global demand for food (Godfray et al., 2010), while minimizing their environmental impacts and paying greater attention to product quality, especially nutritional properties. Furthermore, mounting consumer concern over environmental issues, food origin and production methods, has led

∗ Corresponding author. Tel.: +33 473624144; fax: +33 473624429. E-mail address: [email protected] (A. Farruggia). 0167-8809/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2014.01.001

to an increasing preference for milk from pasture-based systems (Dawson et al., 2011). Finding acceptable compromises between milk production, human environment and dairy product quality is an increasingly pressing issue, forcing a rethink of dairy systems. The literature has reported several approaches for gaining insight into the trade-off between these components. Investigating attitudinal and economic criteria, Schmitzberger et al. (2005) introduced the concept of farming style. These authors showed that farming systems aimed at high-quality production in line with consumer demand and traditional rural culture are associated with high biodiversity levels within the farm area. Conversely, farming systems oriented towards maximizing production perform badly in terms of biodiversity maintenance. On the other hand, recent

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research has used holistic approaches or multicriteria analysis taking into account not just farm performances and environmental impacts but also criteria such as animal welfare and milk quality. Muller-Lindenlauf et al. (2010) reported a significant advantage of low-input farms in terms of milk quality, ammonia emissions and animal welfare, whereas intensive farms performed better on greenhouse gas emissions and land demand. Limiting the analysis to a strictly animal production perspective, Delaby et al. (2003) demonstrated that basically only two opposing strategies co-exist in developed countries: maximization of grassland productivity at the expense of individual cow performances by a high stocking rate, and maximizing individual cow performances by reducing stocking rate and increasing the quantity of herbage offered and consumed. McCarthy et al. (2011) recently confirmed this strong positive relationship between stocking rate and milk production per ha to the detriment of milk production per cow. Finally, a large body of research has tackled the benefits of less intensive grazing management on biodiversity and on the nutritional and sensorial qualities of dairy products. The mechanisms underpinning these relationships are now well documented for biodiversity. Frequent sward disturbances associated with the high destruction of plant biomass caused by intensive grazing favour a small number of plant species characterized by a high potential growth rate (Grime, 1977; Tallowin et al., 2005) and are not beneficial to shelter and feeding resources for insects. Conversely, low grazing intensity tends to prove beneficial to plant and arthropod diversity, since swards develop mosaics of frequently-regrazed short grass patches and tall grass patches with ears and flowers avoided by animals, thus favouring broader plant diversity and providing available shelter (Dennis et al., 1998; Wallis De Vries et al., 2007) and key food resources as nectar for pollinators and live plant biomass for herbivorous insects (Öckinger et al., 2006; Dumont et al., 2009). The literature gives a much fuzzier picture of the relationships between plant diversity and dairy product sensory and nutritional characteristics. Marked modifications in the sensory properties of cheeses have been observed in on-farm studies when cows are moved between paddocks offering different vegetation (Bugaud et al., 2002; Buchin et al., 1999). However, little is known about the relative importance of grassland type and the associated mechanisms for cheese sensory properties (Farruggia et al., 2008). In terms of nutritional properties, particularly fatty acid (FA) concentrations, pasture feeding beneficially increases omega-3 FA and conjugated linoleic acid (CLA) contents in milk compared to hay or maize silage feeding (Chilliard et al., 2008; Coppa et al., 2011a). Further studies have shown that milk from grazing cows shows significant differences in FA profile when it is derived from highland pastures as opposed to lowland pastures (Collomb et al., 2002; Ferlay et al., 2008). The presence of plant secondary metabolites that can influence lipid ruminal metabolism in cows and thus affecting the nutritional quality of their dairy products is one of the major hypotheses to explain these differences in milk FA composition (Leiber et al., 2005). In this paper, we aim to demonstrate the chain of interactions between grassland botanical composition, herbage nutritive value, animal performances and dairy product sensory and nutritional characteristics in grazing systems. We set out with an integrated system approach with the aim of gaining a better understanding of the linkages between the factors involved rather than investigating each parameter of the system in isolation (Dawson et al., 2011). Indeed, most studies on dairy systems have tested effects of management strategy on only one type of grassland with one varying factor, e.g. effect of continuous vs. rotational grazing at the same stocking rate (Arriaga-Jordan and Holmes, 1986; Pulido and Leaver, 2003) or effect of hay-feeding vs. silage on nutritional and sensorial qualities (Verdier-Metz et al., 1998). Few have attempted to quantify time-course relationships between different components

of grazing systems closer to “real life” settings, i.e. grasslands where floristic composition reflects management, and with enough experimental animals to represent a real herd. Furthermore, this type of study is original in the literature because it fits within the boundaries of classic research on ruminant livestock, ecology and product quality, while mobilizing concepts from each discipline. Here, we implemented and analysed two contrasted experimental dairy grazing systems over the grazing season: (i) a continuous grazing system (DIV) with a lenient stocking rate set up on a broadly diversified permanent pasture, oriented towards biodiversity and (ii) a rotational grazing system (PROD) typically found in the upland region studied, featuring a higher stocking rate on previously temporary grassland displaying moderate biodiversity and oriented towards milk production and herbage quality. DIV is designed to offer animals a high level of botanical diversity and marked structural heterogeneity of vegetation over the season, whereas PROD offers leafy edible biomass throughout the grazing season. This multidisciplinary approach is based on a high number of surveys and measurements on different fields (i.e. sward measurements, animal measurements, animal GPS monitoring, botanical and entomological surveys, faecal, milk, and cheese samplings). Based on the literature, we made the assumption that the DIV system would achieve lower milk yield per ha but higher performances per animal, maintain higher ecological grassland potential, and produce milk and cheese with higher sensory and nutritional characteristics than the PROD system. Conversely, PROD would lead to higher milk yield per ha with lower performances per cow and maintain a lower ecological potential than the DIV system, but nevertheless yield milk and cheese with good nutritional characteristics due to the pasture-based diet.

2. Material and methods 2.1. Site, animals, experimental design The experiment was conducted during the grazing season in 2008 and 2009 at the INRA farm of Marcenat in an upland area of central France on volcanic soils (45◦ 15 N, 2◦ 55 E; altitude 1135–1215 m). The region is characterized by a low annual mean temperature of 7.2 ◦ C (1975–2005), with a mean of 12.8 ◦ C from May to September, and a high mean annual precipitation of 1132 mm yr−1 , with a mean of 487 mm from May to September. During the trial, climate conditions from May to September were very close to average for temperature (12.7 ◦ C) but quite rainy (539 mm) in 2008, and rather hot (14.1 ◦ C) and dry (419 mm) in 2009. The grassland used for PROD was a 7.5 ha plot, previously temporary grassland sown in 1998, well fertilized (average 90 kg ha−1 yr−1 mineral nitrogen (N) and 120 kg organic N ha−1 yr−1 ) and cut once or twice a year before being grazed at the end of the season. For this experiment, the PROD plot was divided into 5 paddocks in 2008 and 6 paddocks in 2009 and received annually 80 kg N ha−1 mineral N fertilizer. The general rotational grazing management strategy was to use three paddocks for grazing during the first growth cycle in order to exert enough pressure to control grass growth. Two of these three PROD plot paddocks had been previously grazed for 9 and 5 days in 2008 and 4 and 7 days in 2009 (nights were spent in-barn) by 12 non-experimental dairy cows before the beginning of the experiment (5 cows ha−1 ) in order to control grass growth in PROD paddocks during the first cycle and have the same vegetative stage as in the DIV plot where grass growth started later. Unused paddocks in the first cycle were earlycut to be available for the summer increase in pasture area. If grass showed excessive growth, some paddocks could be “skipped” to be mowed. Conversely, if there was a grass shortage, either hay could

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be distributed on the plots in addition to the grass or an unplanned additional plot could be used. Paddock changes were controlled based on the milk production levels of the dairy cows, as suggested by Hoden et al. (1991). The herd was rotated when mean daily milk production fell below a threshold corresponding to 88% of the maximum milk production obtained on the paddock (average of the three highest daily values). The purpose of this decision rule was to control grazing by a quickly measurable criterion independent of vegetation stage. In the DIV system, only one large 12.6 ha plot was used. This pasture had been grazed at a low stocking rate and had received no fertilizer in the last 20 years. In 2007 before the beginning of the experiment, a botanist (see Acknowledgements) assessed and delimited two plant communities: one less diversified representing 37% of total area (4.7 ha), located on the more fertile areas of the plot (lower and upper flat parts), and one more diversified community located on the slope of the less fertile part of the plot (7.9 ha, 63%). In each system, 12 multiparous winter-calving Montbéliarde cows (average calving date: 2 January in 2008 and 15 January in 2009) grazed the plots at an average stocking rate of 1.7 livestock units (LU) ha−1 (1 LU = 600 kg live weight (LW)) in the PROD plot and 1.0 LU ha−1 in the DIV plot. Each year, the two groups of experimental cows were balanced before the beginning of the experiment for calving date, milk production, fat and protein milk contents, and LW. Cows were turned out to pasture from 22 May to 27 September in 2008 and from 15 May to 20 September in 2009. They grazed 24 h a day and were given no supplementation other than mineral blocks. 2.2. Biodiversity measurements As weak difference between years might be expected due to the slowness of the vegetation change in pasture, the botanist only evaluated the botanical composition of the two experimental plots in July 2009. All the species were determined in 40 1 m2 quadrats distributed homogenously across the DIV plot and 30 1 m2 quadrats in the PROD plot (5 per paddock), and their abundances were scored on a 0–100 scale. Species covering less than 1% within a quadrat were also identified. Abundance data was calculated as the average abundance of each species identified in each quadrat. Shannon index was used as a measure of plant species evenness at quadrat scale. As leaf dry matter content (LDMC) and vegetation height (VH) are both considered strong vegetation response traits (Garnier et al., 2004), plot trait means were calculated for LDMC and VH as means weighted by species abundance (Pakeman et al., 2009). E-florasys software (http://eflorasys.inpl-nancy.fr, Plantureux, 1996) was used to obtain individual species traits. Data collected at flowering peak given relevant information on ecological functioning (Hegland et al., 2010), measurements on flowering intensity and arthropod diversity were conducted in this period. Percentage cover of yellow, white and purple-pink flowers were visually evaluated each year in mid-July in virtual 30 m × 30 m squares distributed over the whole plots (149 and 157 squares in DIV plot and 71 and 83 squares in PROD plot in 2008 and 2009, respectively) using a method proposed by Farruggia et al. (2012). Flowering intensity per plot was estimated by summing the percentage cover values by each colour flower. Sward arthropods were trapped three times each year along three fixed 50 m-long transects per plot with 34 sweeps of a butterfly net. On the DIV plot, the three transects were located on flat, slope and top of slope, respectively. On the PROD plot, transects were located on three different paddocks, including one being grazed, one grazed just before and one scheduled to be grazed. The objective was to run measurements within a vegetation heterogeneity range that would have been expected to influence arthropod diversity, provided either by topography in the DIV system or

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by order of grazing in paddocks in the PROD system. The catches were carried out under good weather conditions between 10 a.m. and 3 p.m. at approximately 2-week intervals between mid-July and mid-August. Catches were preserved in ethanol prior to sorting and identification. All the individuals were counted and identified at insect (and arachnids) order level. Abundance per order (number of insects counted per order divided by total number of insects) was analysed, and the Shannon index was used as a measure of the evenness of the distribution of arthropod orders at transect scale. 2.3. Sward measurements Plot potential growth during the first vegetation cycle was assessed by measuring herbage biomass accumulation in 2008 and 2009 in a 30 m2 area excluded from grazing in PROD and DIV plots (in the most fertile area). From early May to late August, every 2 weeks, three strips of 5 m × 10 cm per area were mowed to 5 cm cutting height with a small lawnmower. The grass collected was dried (60 ◦ C, 72 h) then weighed to calculate dry biomass to 5 cm height per ha. Herbage mass (HM) was performed in the two experimental plots on the same day corresponding to a paddock change in the PROD rotation (8 times per grazing season each year). Four and ten 0.25-m2 quadrats were cut below 4–5 cm in PROD (in a fixed area within the paddock which will be grazed) and DIV (whole-plot) plots respectively. In 2009, four more quadrats were added in the DIV plot to better balance the localization of the quadrats in the two plant communities (5 in the less-diversified plant community and 9 in the more-diversified community). Green herbage mass was weighed per quadrat, and two subsamples were constituted. The first sub-sample was used to determine herbage dry matter (DM) by oven-drying (60 ◦ C, 72 h). Pasture allowance per cow (PA) was calculated at each sample period by multiplying herbage mass by the area of the whole plot for DIV and only the grazed paddock area for PROD, and then dividing by 12 cows. The second sub-sample was frozen to be subsequently sorted into grass, legumes, forbs and dead material, then into green grass leaves and ‘other’ for the grass compartment only. Each sub-sample was dried in order to estimate botanical composition and sward morphology over the course of the entire grazing season. Herbage nutritive value was then evaluated per quadrat using each entire sample once all its compartments had been reconstituted. These samples were ground through a 0.8 mm screen and analysed for nitrogen content [LECO combustion method (Northern Analytical Laboratory Inc., Merrimac, NH, USA); Sweeney and Rexroad, 1987], for fiber content (ADF, Van Soest and Wine, 1967) and for pepsin-cellulase DM digestibility (DCEL) according to Aufrère and Demarquilly (1989). These parameters were determined by near-infrared reflectance spectroscopy (NIR) according to the following procedures. All samples were scanned in monochromator NIR systems 6500, but laboratory determinations were performed on a subset of twenty samples that had been selected on their spectral information (Shenk and Westerhaus, 1993). These laboratory analyses were then used with the NIR spectra to expand the prediction equations for routine analysis. Crude protein (CP) was then obtained by multiplying the N data by 6.25. Organic matter digestibility (OMD) was calculated from DCEL values, ADF and CP using PrevAlim 4.1 software. In 2009, to better depict grazing management in the PROD plot, paddock height recordings were added and determined before and after each grazing using an electronic plate meter with a plastic plate (30 cm × 30 cm and 4.5 kg m−1 ; FITC model/Arvalis) by taking at least 200 measurements per ha along fixed transects defined at the beginning of the experiment. In parallel, DIV plot height was recorded throughout the grazing season, but given the size of the plot, the measurements were performed at every two changes of PROD paddock. The standard deviation (s.d.) of sward herbage

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height recorded in 2009 in the DIV plot was used as a heterogeneity indicator, while herbage sward height in the PROD plot was used to characterize grazing management. 2.4. Animal measurements In both years, faecal samples were collected from the rectum of each cow after the morning milking on one day in early June, early July and early August. The samples were oven-dried (60 ◦ C, 72 h) for N determination, and the data was used as an overall indicator of diet digestibility (Peyraud and Astigarraga, 1998). In mid-July, when dietary plant composition of the cows was expected to be highly diversified between grazing systems due to the evolution of the vegetation heterogeneity in the DIV plot, we implemented a novel DNA-based method of herbivore diet analysis (Valentini et al., 2008). Faecal samples were collected from each cow over two consecutive mornings. The equivalent of one tablespoon of each faecal sample was placed on a Whatman paper filter and then air-dried. After drying, the papers with the faecal samples were stored in 20 mL tubes with silica gel until DNA extraction. DNA fragments from the plant residues remaining in the faeces were identified using recent barcoding techniques enabling plant taxon identification based on DNA amplification and analysis (Taberlet et al., 2007). The total number of species identified per cow during the two sampled days and the classification of these species into grass, legumes and forbs were considered as indicators of diet diversity. This measurement was only performed in 2009 as only weak between-year differences could be expected in relation with the small evolution of the botanical composition of the plots between years. In 2009, GPS telemetry collars with differential correction (Schlecht et al., 2004) were used two days per period (3–4 June, 6–7 July and 3–4 September) to monitor cow locations in the DIV plot. Indeed, visual and informal observations made in 2008 on the grazed area over the grazing season have underlined the value of this parameter and the relevancy of its recordings. Individual milk production was recorded in both years at each milking to obtain daily milk production per animal. Fat and protein contents were measured weekly over the two years from individual sample of 4 consecutive milkings. They were estimated by interferometry integrated FTIR (Fourier transform infrared spectroscopy, FOSS, MilkoScan FT 6000) and expressed as g kg−1 of milk. Animals were weighed in both years every month from the start of the experiment to August, then every 2 weeks until the end of the experiment. Body condition score (BCS) was evaluated at the same time using a scale from 0 (skinny) to 5 (very fat) with steps of 0.25 (Rémond et al., 1988). Evolution of LW was calculated over the whole grazing season using the LW measured two weeks before the start of the experiment (data used for the balance between groups of cows) and at the end, and within period using the LW measured at the start and end of each period. 2.5. Measurements on milk and cheese samples In 2008, milk samples from the bulk milk of each system were collected at the beginning (9, 11 and 12 June), middle (8–10 July) and end of the grazing season (26, 27 and 28 August). At each period, the bulk morning milk was cooled to 4 ◦ C and pooled with the previous evening milk stored at 4 ◦ C. All 18 samples (2 systems × 3 periods × 3 days) were immediately stored at −20 ◦ C until FA analysis by gas chromatography according to Ferlay et al. (2008). Details are given in Coppa et al. (2011a). Selected FA results are reported and analysed in this paper in terms of their potential effects on human health. Saturated FA (SFA) that are mostly found in ruminant products (principally lauric, myristic and palmitic acids or C12:0, C14:0 and C16:0 respectively) and when consumed in excess

would be atherogenic (Givens, 2010). Nevertheless, other SFA such as odd- and branched-chain FA (OBCFA) could have anticarcinogenic properties (Shingfield et al., 2008). The main isomer of milk monounsaturated FA (MUFA, i.e. oleic acid or cis9-C18:1) is considered as neutral for human health in the case of dairy product consumption (Givens, 2010). Although the negative effect of certain trans MUFA is often mentioned, recent studies in animal models have not demonstrated that the major trans isomer of C18:1 in milk (vaccenic acid or trans 11-C18:1) is associated with risk of cardiovascular diseases (Wang et al., 2012). Furthermore, one CLA isomer (cis9trans11-CLA) belonging to the polyunsaturated FA (PUFA) and partly derived from the desaturation of vaccenic acid in the mammary gland could have antiatherogenic and anticarcinogenic properties, at least in animal models (Shingfield et al., 2008). Omega-3 PUFA (primarily linolenic acid or C18:3n-3 in milk) are considered beneficial as they reduce the risk of cardiovascular disease and develop the cognitive capacities of young mammals (Palmquist, 2009). Linoleic acid (C18:2 n-6) is an essential FA that belongs to omega-6 FA family. In human diet, nutritionists recommend keeping a linoleic acid/linolenic acid ratio down to less than 5 (Legrand et al., 2010). In 2009, small Cantal cheeses (10 kg instead of the usual 40 kg) were manufactured with the milk sampled during three consecutive days in July (6–8 July) when forb abundance is expected to be the highest. Cheeses were made from 100 L of PROD and DIV milk in parallel in two vats. Milks were partially skimmed to standardize fat/protein ratio to 1:10. Six cheeses (2 systems × 3 days) were thus produced. The cheeses were ripened for 12 weeks and then sampled for analysis. One half of each cheese was left and put back into the cellar for an additional 12-weeks ripening period. Details are given in Coppa et al. (2011c). Two triangular tests (cheeses ripened 12 or 24 weeks) were performed by a panel of 9–11 expert assessors who regularly perform sensory analyses on Cantal cheese. The cheese samples were served at room temperature (22 ◦ C). For each cheese-making day, three samples of cheese (two identical and one different) were presented simultaneously to each assessor in three opaque white plastic boxes. Each assessor was asked to identify the different sample from among the three. During each test, two replicates were performed to obtain 6 (3 days × 2 replicates) answers per assessor. Results were expressed as a percentage of correct answers. 2.6. Data analysis All the measurements recorded throughout the grazing season were analysed by distributing data according to three periods (P1, P2, P3), taking into account the increase in grazed area in PROD as well as the evolution of the vegetation in DIV: spring, when sward was both abundant and of high quality; summer, at flowering of the most abundant dicotyledons; end of summer, when cumulative treatment effects of the stocking rate were expected to be at a maximum. Periods thresholds were chosen according to beginning and end of the experiment and according to weeks of milk production: 5 weeks for P1 and 6 weeks for P2 and P3 in both 2008 and 2009 (P1: 22 May–25 June, P2: 26 June–6 August, P3: 7 August–17 September in 2008; P1: 25 May–18 June, P2: 19 June–30 July, P3: 31 July–10 September in 2009). Data obtained during the two years were analysed simultaneously using the SAS software procedures (SAS Inst., Cary, NC). A repeated-ANOVA model was used for animal data (milk, fat and protein contents, LW, BCS) with period (year) as repeated measures to take account of the dependence of data between periods in a year. System, period and year were considered as fixed factors, and cow as a random factor. The interactions system × period, system × year and system × year × period were tested. Data obtained before the start of the experiment to balance the groups of cows were used as

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Table 1 Abundance of plant species in DIV and PROD plots ranked in descending order (for species whose abundance is greater than 0.005). DIV

Agrostis capillaris Trifolium repens Festuca rubra ssp. rubra Plantago lanceolata Achillea millefolium Veronica chamaedrys Carex caryophyllea Helianthemum nummularium Poa pratensis Galium verum Dactylis glomerata Thymus pulegioides s.l. Holcus mollis Hieracium pilosella Festuca nigrescens ssp. nigrescens Meum athamanticum Chamaespartium sagittale Viola lutea Anthoxanthum odoratum Trifolium pratense Avenula pubescens Ranunculus acris Stachys officinalis Gentiana lutea Trisetum flavescens Briza media Cynosurus cristatus Rhinanthus minor Ajuga reptans Potentilla tabernaemontani Scabiosa columbaria Lathyrus pratensis Festuca lemanii Euphrasia rostkoviana Stellaria graminea Poa chaixii Cytisus scoparius

PROD

Mean

s.e.m

0.185 0.136 0.097 0.053 0.051 0.038 0.033 0.029 0.025 0.023 0.023 0.022 0.016 0.015 0.014 0.014 0.013 0.012 0.011 0.010 0.009 0.009 0.008 0.008 0.008 0.008 0.007 0.007 0.007 0.006 0.006 0.006 0.006 0.006 0.006 0.005 0.005

0.0133 0.0199 0.0087 0.0089 0.0084 0.0065 0.0058 0.0093 0.0064 0.0039 0.0092 0.0054 0.0047 0.0051 0.0042 0.0045 0.0045 0.0030 0.0034 0.0022 0.0029 0.0023 0.0045 0.0034 0.0022 0.0035 0.0029 0.0037 0.0027 0.0019 0.0021 0.0026 0.0031 0.0027 0.0023 0.0052 0.0028

Dactylis glomerata Taraxacum gr. officinale Trifolium repens Elymus repens Lolium perenne Holcus mollis Agrostis capillaris Poa pratensis Poa annua ou supina - vivace Poa trivialis Plantago major Festuca rubra ssp. rubra Plantago lanceolata Festuca arundineacea Rumex obtusifolius Bromus hordeaceus ssp. hordeaceus – – – – – – – – – – – – – – – – – – – – –

Mean

s.e.m

0.225 0.201 0.198 0.066 0.064 0.052 0.045 0.044 0.019 0.017 0.014 0.007 0.007 0.006 0.005 0.005 – – – – – – – – – – – – – – – – – – – – –

0.0235 0.0258 0.0280 0.0183 0.0218 0.0114 0.0153 0.0113 0.0056 0.0080 0.0054 0.0077 0.0046 0.0053 0.0019 0.0016 – – – – – – – – – – – – – – – – – – – – –

s.e.m: Standard error of the mean.

covariates. The same model, without covariates, was used to analyse faeces content and herbage data at plot and plant community level, with cows and quadrats, respectively, considered as random factor. A repeated measures analysis was also used to assess insect abundance and Shannon index, with transect as random factor and trapped day (year) as repeated measures. Data on botanical composition were analysed using the PROC GLM procedure of SAS, taking the system as fixed effect and the quadrat as individual unit. Botanical data obtained at plant community level in the DIV plot were analysed using the same model, taking plant community as fixed effect. The number of species identified (by barcoding) per cow and their classification into grass, legumes and forbs were also analysed with the same model, with cow as statistical unit. Data on FA of bulk milk collected in 2008 were analysed using a mixed model with repeated data, considering system, period (repeated measures) and their interaction as fixed effect. The day of producing was the statistical unit. For the cheese triangular test, the values from the table calculated binomial law parameter p = 1/3 with n repetitions (AFNOR) were used. In all statistical models, structure of the covariance matrix of repeated measurements was chosen based on the smallest Akaike information criterion. In all analyses, non-significant interactions were removed from the model. All proportional data were arcsine-root transformed to stabilize variance. All differences between treatments, years and periods were detected using the Tukey–Kramer correction for multiple comparisons. To highlight

the evolutions of all the criteria between periods within the DIV and PROD systems, significant differences are presented in the tables in a same system per period, whereas figures highlight significant differences between systems at a same period. Lastly, a Principal Component Analysis (PCA) was performed using SPSS for Windows software (version 17.0; SPSS Inc., Chicago, IL) to highlight the relationships among data on herbage characteristics, animal behaviour and animal performances, pooling the two years and the three periods together. A total of 12 variables was used, including 7 variables describing plot vegetation and nutritive value (HM, percentages of grass, forbs, dead material and green grass leaves in HM, OMD and CP of the HM), one variable depicting animal behaviour (CP content in faeces), and 4 variables describing animal performances (milk production, fat and protein content in milk, LW evolution). To show the distribution of the systems along years and periods, individuals identified by the grazing system, period and year were projected on the two principal components. 3. Results 3.1. Ecological performances The PROD plot was characterized by higher plant height and lower LDMC than the DIV plot (VH: 0.45 vs. 0.36 m; LDMC: 177 vs. 181 mg g−1 ). A total of 99 species were identified in summer 2009 on all quadrats in the DIV plot, including 75 species whose abundance was over 1%, against 69 and 33 species in the PROD plot. The DIV plot presented twice as many plant species per m2 than the PROD plot (23.8 vs. 12.2, p < 0.001) and its Shannon index was higher (3.59 vs. 2.41, p < 0.001). Forb cover was much greater on the DIV plot than the PROD plot (39.8 vs. 24.4% of plot area, p < 0.001). The dominant species on the DIV plot were Agrostis capillaris

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Table 2 Effect of system on number of individuals per catch, Shannon index and percentage of Heteroptera, Homoptera, Coleoptera, Lepidoptera, Hymenoptera, Diptera, Thysanoptera, Orthoptera and Arachnids caught in DIV and PROD plots. Effect DIV mean Number of individuals per catch Shannon index Heteroptera (%) Homoptera (%) Coleoptera (%) Lepidoptera (%) Hymenoptera (%) Diptera (%) Thysanoptera (%) Orthoptera (%) Arachnids (%)

77.9 2.114 11.9 19.7 3.9 2.6 5.1 43.7 0.7 2.4 6.9

PROD mean 89.7 1.697 2.6 30.1 1.1 0.2 5.8 44.6 0.5 0.9 1.3

s.e.m 9.77 0.0997 1.84 2.86 0.76 0.70 1.06 4.58 0.24 0.59 1.27

Syst ns **

Yr

Tday

Syst × Tday

Syst × Yr

Syst × Yr × Tday

ns ns

***

*

ns ns ns ns ns ns ns ns ns ns ns

ns ns ns ns ns ns ns

**

ns

**

**

ns

†

*

ns ns ns ns ns

***

ns ns ns ns

**

†

*

*

ns ns

ns ns

ns ns ns

** **

ns ns ns † ***

ns ns ns

*

ns ns ns

ns (non significant): p ≥ 0.10; syst: system (DIV or PROD); Tday: day trapped (day 1, 2 or 3); Yr: year (2008 or 2009); s.e.m: standard error of the mean. *** p < 0.001. ** p < 0.01. * p < 0.05. † p < 0.10.

(18.5%), Trifolium repens (13.6%), Festuca gr. rubra (9.7%), Plantago lanceolata (5.3%) and Achillea gr. millefolium (5.1%), whereas Dactylis glomerata (22.5%), Taraxacum gr. officinale (20.1%) and Trifolium repens (19.8%) dominated the PROD plot (Table 1). As broom grew strongly in the DIV plot, especially in the slope, it has to be mechanically chopped out at the end of the grazing season in 2008 to curb its spread and to maintain the production potential of the system in the medium term. Significant differences were confirmed between the two plant communities of the DIV plot, with lower species number, more grass, less forb abundance and lower Shannon index in the more fertile area than in the less fertile area (21.4 vs. 26.5, p < 0.01; 48.5 vs. 36.5%, p < 0.01; 29.6 vs. 49.6%, p < 0.001; 3.34 vs. 3.83, p < 0.001, respectively). Over the two years, the DIV plot provided higher global flower intensity in midJuly than the PROD plot (8.1 vs. 1.0%, p < 0.001). Percentage cover by white, yellow and purple-pink flowers was also higher in the DIV plot than the PROD plot (2.1 vs. 0.7%, p < 0.001; 1.7 vs. 0.1%, p < 0.001; 4.4 vs. 0.2%, p < 0.001, respectively). Year and year × system interactions were found due to a higher flowering intensity in the DIV plot in 2008 than in 2009, except for percentage cover by yellow flowers which presented only a significant year effect. The number of insects captured per transect was not different between the two systems, but Shannon index was higher in the DIV plot (Table 2; p < 0.01). The DIV plot differed from the PROD plot by a higher number of Heteroptera, Coleoptera, Lepidoptera, Orthoptera and Arachnida, whereas PROD was characterized by more Homoptera and Collembola. 3.2. Grazing management, herbage production and herbage quality Herbage biomass accumulation per ha was significantly higher over the first grazing cycle, with a significantly higher DM percentage in the PROD plot than the DIV plot from the beginning of the grazing season, even though measurements were performed on the most fertile area in the DIV plot (Table 3). There was an average 1.5-fold higher herbage biomass accumulation in the PROD plot, allowing hay to be stored at the end of May in 2008 and 2009 (2.0 and 7.6 t DM). However, in the PROD plot, grass growth was reduced in mid-August 2008 and 2009, requiring 9 days of pasture on an additional unplanned 1.20 ha plot in 2008 and distribution of hay at pasture during 6 days in 2009. In 2009, paddocks from the PROD system were to an average pre-grazing height of 8.4 cm [s.d.: 4.1] and to a post-grazing height of 4.9 cm [2.1]. On the DIV plot, grass height averaged 7.7 cm [3.6] over the season. Height decreased from P1 to P3 in the more fertile plant community but only from P2 to P3 in the less fertile community (Table 4). Over the two years, HM per ha was not affected by grazing system but there was a strong period effect (p < 0.001; Table 3) and a significant interaction between system and period (p < 0.05). In the DIV plot, HM clearly increased between P1 and P2 (+1322 kg DM ha−1 , p < 0.05), then remained stable. In the PROD plot, HM was significantly steady over the three periods. Pasture allowance per cow was 9.1 times higher over the grazing season in the DIV plot than in the PROD plot (p < 0.001). An increase of differences of PA was observed between the two plots over the periods (Fig. 1a) due to the increase of PA in the DIV plot and the stability in the PROD plot. Proportion of green grass leaves was higher in PROD HM than DIV HM over the three periods (+15%, p < 0.01), with strong period and treatment × period effects (p < 0.001). However, the difference between DIV and PROD only became significant at P3 (p < 0.001; Fig. 1b). Proportion of green grass leaves decreased rapidly between P1 and P2 in DIV HM (−13 percentage points [pp], p < 0.05; Table 3) while it remained significantly steady and high in PROD HM. In parallel, mean proportion of dead material was lower in PROD HM than in DIV HM (−10 pp, p < 0.01), and it increased markedly in DIV HM over the 3 periods (+79 and +72% respectively,

p < 0.05). PROD HM was composed of more grasses (+19 pp, p < 0.001) and less forbs (−10 pp, p < 0.05) than DIV HM over the season, with no system x period effect. Nutritive value of HM showed a similar pattern. PROD HM showed significantly higher OMD and CP than DIV HM (+8.2 pp and +58 g kg−1 DM, p < 0.001) over the three periods regardless of year but with a strong period effect (p < 0.001). Treatment × period interactions were found significant due to the fact that herbage quality remained stable in PROD HM but gradually declined over the grazing season in DIV HM. The digestibility of PROD HM was higher than DIV HM in the three periods (Fig. 1c). It was stable in PROD HM whereas it declined in DIV HM between P1 and P3 (−7.6 pp). The chemical and morphological composition of the herbage offered was not affected by plant community, and there was no plant community x period interaction. 3.3. Animal behaviour CP content was significantly higher in PROD cow faeces than DIV cow faeces (+32 g kg−1 DM, p < 0.001; Table 3). At the start of the season corresponding to P1, DIV and PROD cows had identical CP levels whereas at mid-July (P2) and early September (P3), there was a significant difference of +41 and +50 g kg−1 DM respectively in favour of PROD (p < 0.001; Fig. 1d). CP content decreased markedly in DIV cow faeces between P1 and P2 (−19%, p < 0.05) and again between P2 and P3 (−16%, p < 0.05) while remained steady in PROD cow faeces. In summer 2009, an average of 32.0 different species were identified with DNA barcoding technique in the faeces of DIV cows against 14.8 in PROD ones (p < 0.001). The number of dicotyledon species was 2.5 times higher in DIV cow faeces than PROD cow faeces (forbs: 16.9 vs. 6.8, p < 0.001 and legumes: 4.8 vs. 1.8, p < 0.001). The grazed area in the DIV plot was clearly different between the three grazing periods in 2009. At the beginning of June in P1, animals walked and grazed on the fertile area close to the entrance and the drinking troughs in the lower part of the plot (Fig. 2a). They extended their grazing to the fertile area in the upper plot at the beginning of July in P2 (Fig. 2b) before grazing the whole plot at the beginning of September in P3 (Fig. 2c). 3.4. Animal performances Over the three experimental periods, milk production per ha was 1.8 times lower in the DIV system than the PROD system (1906 vs. 3366 kg ha−1 ). Milk production per cow tended to be lower in DIV than PROD cows over the two years (−0.7 kg d−1 , p < 0.10; Table 5), without significant year effect but with strong period effect (p < 0.001) and system × period interaction (p < 0.001). At turn-out to pasture, milk production was higher in DIV cows than PROD cows (+2.3 kg d−1 , p < 0.01; Fig. 3) but decreased rapidly between P1 and P2 and even more rapidly between P2 and P3 (−20 and −38%, p < 0.05), to ultimately finish below PROD cow levels (−3.5 kg d−1 , p < 0.001). The milk production of PROD cows held steady between P1 and P2 before decreasing moderately in P3 (−22%; p < 0.05). The DIV cows had lower milk protein content over the season (p < 0.01), with a significant effect of system × period interaction (p < 0.001). A significant decrease was observed in P2 for the DIV cows, followed by an increase for the two groups of cows between P2 and P3. Fat content was similar between DIV and PROD milks, increasing significantly in P3 (+3.1 g kg−1 on average). The fat content/protein content ratio was always higher in DIV milk (p < 0.001), with a significant system × period interaction (p < 0.001) as the ratio increased significantly over the season, whereas PROD milk ratio remained stable. PROD and DIV cows showed a similar LW evolution between the beginning and the end of the grazing season. Strong period effect (p < 0.001) and system × period interaction (p < 0.01) were however observed. In P1, DIV cows lost less LW than PROD

Table 3 Effect of system on cumulated biomass, herbage mass per ha, pasture allowance, morphological composition, botanical composition and chemical composition of the herbage mass and crude protein in feaces, in the DIV and PROD plots, and within-plot evolution between periods.

−1

)

PROD

Mean

Mean

s.e.m

Effect Syst

Yr

Per

Syst × Per

Syst × Yr

Syst.× Yr × Per

DIV P1

P2

P3

P1

P2

P3

2182 29.1 2721 2834 0.31 0.29 0.44 0.22 67.0 136 163

3260 23.8 2340 311 0.46 0.19 0.63 0.12 75.2 194 195

186 7.60 89.8 113 0.012 0.014 0.016 0.012 0.50 3.6 2.8

* *** ns *** ** ** *** * *** *** ***

** ***

*** *** *** *** *** *** *** ns *** *** ***

ns ** * ** *** ** ns ns *** * ***

ns ns *** *** * * ns *

ns ns ns ns ns ns ns * * ns ***

1229 26.5 1737a 1809a 0.43a 0.14a 0.55 0.23 71.5a 165a 197a

3294 31.7 3059b 3187b 0.30b 0.25b 0.47 0.21 66.8b 129b 159b

– – 3285b 3422b 0.25b 0.43c 0.32 0.23 63.9c 124b 133c

2245 18.2 2065 177 0.47 0.10a 0.75 0.11 75.9 205 202a

4612 30.4 2333 339 0.41 0.19ab 0.62 0.10 74.9 182 200ab

– – 2624 412 0.50 0.24b 0.56 0.15 75.1 199 183b

†

** ns *** ** ns ns ns ns

†

* ***

PROD

ns (non significant): p ≥ 0.10; syst: system (DIV or PROD); per: period (P1, P2 or P3); Yr: year (2008 or 2009); HM: herbage mass; PA: pasture allowance; OMD: organic matter digestibility; CP: crude protein; s.e.m: standard error of the mean. *** p < 0.001. ** p < 0.01. * p < 0.05. † p < 0.10. a,b,c In a same system (DIV or PROD) different superscripts in the same row indicate significant differences (p < 0.05).

237

Fig. 1. Evolution over the three periods of (a) pasture allowance per cow, (b) proportion of green grass leaves in herbage biomass, (c) organic matter digestibility (OMD) of the herbage biomass, and (d) crude protein (CP) content of faeces in the DIV (in black) and PROD (in white) systems. *** : p < 0.001; ** : p < 0.01; * : p < 0.05; t: p < 0.10; ns (non significant): p ≥ 0.10 between the two systems (DIV or PROD) at each period.

A. Farruggia et al. / Agriculture, Ecosystems and Environment 185 (2014) 231–244

Cumulated biomass (kg DM ha DM of cumulated biomass (%) HM (kg DM ha−1 ) PA (kg DM cow−1 ) Green grass leaves proportion Dead material proportion Grass proportion Forbs proportion OMD (%) CP (g kg− 1 DM) CP faeces (g kg− 1 DM)

DIV

238

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Fig. 2. Paths walked by cows in the DIV plot in 2009 over the three periods: (a) in P1: 3–4 June; (b) in P2: 6–7 July; (c) in P3: 3–4 September. Each dotted line represents the path of one cow over the course of the two measurement days.

cows (p < 0.05), but their LW then increased only moderately over P2 and P3 (+14 kg; Fig. 4) whereas PROD LW increased much more sharply (+36 kg). BCS remained stable in PROD cows between P1 and P3 but steadily and significantly decreased in DIV cows (−0.27; p < 0.05). 3.5. Milk and cheese characteristics The average contents of SFA, OBCFA, MUFA, sum of trans-C18:1 isomers (without C18:1 trans11) and PUFA were similar in 2008 between the DIV and PROD milks over the 3 experimental periods (Table 6). However, compared with DIV milk, PROD milk provided significantly higher individual contents of five SFA: caprylic, capric, myristic, lauric and palmitic acids, one MUFA (vaccenic acid) and one PUFA (rumenic acid). On the other hand, DIV milk was richer than PROD milk in individual contents of two SFA (butyric and stearic acids), one MUFA (oleic acid) and two PUFA (linoleic and linolenic acids). Linoleic/linolenic acid ratio was below 5 in both systems. There was a strong period effect on all groups of FA and most individual FA. Several interactions between system and period were also found that resulted from different trends observed between PROD and DIV milk for many FA during the season. Capric and lauric acid contents were stable during the season for the PROD milk but declined steadily in DIV milk from P1 to P3 (−13% for both, p < 0.05). Vaccenic acid content was similar in DIV and PROD milk in P1 but then strongly decreased in DIV milk, becoming significantly lower than PROD milk in P2 (p < 0.05) and P3 (p < 0.01; Fig. 5a). PUFA content in PROD milk was constant over the season, whereas it declined in DIV milk between P1 and P2 (−15%, p < 0.05). Specifically, rumenic acid content was similar in PROD and DIV milk in P1 but clearly decreased in DIV milk between P1 and P2 (−38%, p < 0.05) whereas it remained unchanged in PROD milk (Fig. 5b and Table 6). PROD milk presented lower linolenic acid contents in P1 and P2 than the DIV milk (−0.3, p < 0.001 and −0.27 g 100 g−1 FA, p < 0.01, respectively) and similar content in P3 (Fig. 5c). An opposite trend was observed in DIV milk, where oleic acid increased from P2 to P3 (+14%) and linoleic acid increased from P1 to P3 (+10%; Fig. 5d). During the sensory triangular test on cheese in 2009, only 35.5% of the assessors (22 correct answers out of 62) were able to distinguish PROD from DIV cheese at 12

weeks of ripening (p > 0.05), whereas 44.5% of the assessors (25 correct answers out of 56) correctly classified PROD and DIV cheeses at 24 weeks (p < 0.05). 3.6. Linkages between the components within the two grazing systems The PCA, performed on the systems in 2008 and 2009 over the three periods gives an overview of the main correlations observed between herbage characteristics, animal behaviour and animal performances (Fig. 6). On the first principal component (PC) (57.6% of variance), nutritive value, diet digestibility and proportion of herbage grass and green grass leaves were associated to milk yield and opposed to herbage mass, percentage of herbage dead material and milk fat content. Forbs also contributed significantly to this PC1. On PC2 (18.5% of variance), milk protein content and LW evolution were opposed to milk production and forbs. The PCA confirms that the PROD and DIV systems were very close at P1 and were both associated with high nutritive value, abundance of grass and grass leaves in the plots, high diet digestibility and milk production. The separation between the two systems grew bigger over the season on PC1 and PC2 for DIV but only on PC2 for PROD. In P3, the DIV system was associated with high biomass in the plot and a higher proportion of dead material, high milk fat content, and a positive evolution of LW. No difference emerged between years for the DIV system within the periods whereas the PROD system was slightly separated on PC2 within periods. However, on PROD, the between-year separation was smaller than the between-system separation. The time-course evolution of the PROD system is related to PC2, whereas the time-course evolution of the DIV system is a combination of PC1 and PC2 in both years. Indeed, PC1 is mainly related to the phenological characteristics and nutritive value of the grass, whereas PC2 is mainly associated with the evolution of the stage

Table 4 Evolution of the grass height over the three periods in 2009 within the two plant communities in the DIV plot. More fertile

P1 P2 P3

Less fertile

Mean

s.e.m

Mean

s.e.m

9.3 8.7 6.4

3.5 3.7 3.2

8.2 8.4 5.8

3.2 3.6 2.9

s.e.m: Standard error of the mean.

Fig. 3. Evolution of milk production per cow in the DIV and PROD systems over the weeks within the three periods (P1, P2, P3). The solid black line indicates the DIV system and the dotted line the PROD system.

A. Farruggia et al. / Agriculture, Ecosystems and Environment 185 (2014) 231–244

239

Fig. 4. Evolution of live weight of cows in the DIV and PROD systems over the weeks within the three periods (P1, P2, P3). The solid black line indicates the DIV system and the dotted line the PROD system.

of lactation of the cows during the season (from mid to late lactation) responsible for a decreasing milk yield and increasing milk protein content and liveweight.

4. Discussion 4.1. The results on animal performances are not as “black and white” as expected Over the experimental grazing season, we found unexpected differences in animal performances between the two systems. The PROD system yields much more milk per ha than the DIV system, thanks to the smaller area of the rotational system. However, contrary to expectations, we did not observe higher performances per animal in the DIV system. Furthermore, we also found contrasted evolutions in milk production per cow, LW and BCS between the two systems which were closely linked to the parallel evolutions in the quantity and particularly the quality of herbage biomass in each experimental plot. During the first experimental period, the higher initial dairy performance of the DIV cows can be explained by the high quality of the herbage offered to the cows combined with abundant grass quantity due to the higher surface area offered. The reversal of dairy performance between the two systems, which started in July and deepened through September, did not result from grass allowance, which increased between P1 and P2 in the DIV plot. It was linked to a marked decline in herbage quality, as shown by the decrease in nutritive value of herbage mass and faecal CP content from P2. This decrease in quality due to maturing herbage in the DIV plot is evidenced by the decrease in percentage of green leaves and the strong increase in proportion of dead material in herbage biomass as it has been already reported in literature (Andueza et al., 2010). The fact that herbage OMD on offer in HM was higher in PROD than in DIV plots in P1, contrary to the results of the faeces analysis which showed a similar digestibility of PROD than DIV herbage can be explained both by sampling method and by animal grazing behaviour. Herbage was analysed from a cut below 5 cm in fixed quadrats where all grass is collected, whereas the faeces analysis integrated the feeding behaviour of the cows. It demonstrates the ability of DIV cows freely grazing a large plot area to select the best grasses. Cows grazing behaviour measurements in the DIV plot add lighting to these results. The monitoring of animals’ location have highlighted that cows increased their explored and grazed area over the three periods, starting by the most fertile area in P1. The decrease in herbage height observed in both areas over all three periods was consistent with these movements: herbage height decreased in the most fertile area from P1 to P3 but only started to

Fig. 5. Evolution over the three periods of the (a) vaccenic acid, (b) rumenic acid, (c) linolenic acid and (d) linoleic acid, in the DIV (in black) and PROD (in white) milk of the two systems. *** : p < 0.001; ** : p < 0.01; * : p < 0.05; ns (non significant): p ≥ 0.10 between the two systems (DIV or PROD) at each period.

14.5b 39.4b 34.1b 1.16 11b 1.70 *** ns

†

†

ns ** *** ns

ns (non significant): p ≥ 0.10; syst: system (DIV or PROD); per: period (P1, P2 or P3); Yr: year (2008 or 2009); LW: liveweight; BCS: body condition score; s.e.m: standard error of the mean. ***p < 0.001. ** p < 0.01. *p < 0.05. † p < 0.10. a,b,c In a same system (DIV or PROD) different superscripts in the same row indicate significant differences (p < 0.05).

P2

18.6a 37.4a 32.8a 1.14 25b 1.65 19.9a 37.9a 32.6a 1.16 −37a 1.66

P1

17.8b 38.5a 31.1b 1.24a 13b 1.53ab 22.2a 39.0a 32.4a 1.21a −22a 1.67a *** ns **

* ns ns ns ns ns *** ** *** *** ** *** ns ** *** ** ** ns *** *** *** *** *** ** †

0.40 0.30 0.20 0.009 6.27 0.024 17.7 38.2 33.2 1.15 −1 1.67

Mean Mean

17.0 40.0 32.0 1.25 −9 1.53 Milk production (kg d−1 ) Fat content (g kg−1 ) Protein content (g kg−1 ) Ratio (fat cont./protein cont.) LW evolution (kg) BCS (0–5 scale)

PROD

P3 P2 DIV

P1 Syst × Per × Yr Syst × Yr Syst × Per Yr Per Syst

PROD DIV

s.e.m

Effect

11.0c 42.6b 32.7a 1.31b 1b 1.40b

P3

A. Farruggia et al. / Agriculture, Ecosystems and Environment 185 (2014) 231–244 Table 5 Effect of system on milk production per cow, milk fat content, milk protein content and milk fat/milk protein ratio, liveweight evolution and body condition score of the cows in the DIV and PROD systems, and within-system evolution between periods.

240

Fig. 6. (a) Representation of the relationships between herbage characteristics, animal behaviour and animal performances derived from a principal component analysis (data from 2008 and 2009) with variables distribution projected on the two principal components (PC1 and PC2). HM ha: herbage biomass per ha; OMD HM: organic matter digestibility of the herbage biomass, CP HM: crude protein content of the herbage biomass, grass: proportion of grass in herbage biomass, forbs: proportion of forbs in herbage biomass, grass leaves: proportion of green grass leaves in herbage biomass, dead mat: proportion of dead material in herbage biomass, CP faeces: crude protein content in faeces; milk yield: milk production per cow per day, LWe: evolution of live weight of cow between periods; M ProtCont: milk protein content; M FatCont: milk fat content. (b) Distribution of the two grazing systems at the three periods and the two years on the PC1 and PC2. Each triangle represents DIV system and each diamond represents PROD system. The small size of markers indicates period 1; the medium size, the period 2 and the large size indicates period 3. White colour is for 2008 and black colour is for 2009.

decrease in P2 in the less fertile area. Several hypotheses can be put forwards to understand the foraging behaviour of the DIV cows. The early preference for the most fertile area can be explained by the proximity to the drinking trough and to the plot entrance enabling cows to reduce their travelling distance and to economize energy when grazing, as shown by Putfarken et al. (2008). In addition, this area offers a richer abundance of grasses than the less fertile area that probably contributes to its preference as suggested by results from studies on permanent grasslands (Saether et al., 2006; Hessle et al., 2008; Coppa et al., 2011b). Finally, cows increase gradually their grazed area likely to compensate for the decreasing of sward height of the most fertile area and to satisfy their short term intake rate by grazing older patches of higher biomass of the ungrazed area, confirming the findings reported by several authors (Dumont et al., 1995; Distel et al., 1995). This behaviour probably contributes to accentuate the negative energy balance of the DIV cows in P3. In contrast, rotational grazing on fertile grassland allows stably high quality of herbage. The PROD HM was characterized by a high OMD value and CP content allowed cows to feed herbage with a

Table 6 Effect of system on the fatty acids composition of milk from the DIV and PROD cows, and within-system evolution between periods. DIV

PROD

DIV mean

PROD Mean

s.e.m

Syst

Per

Syst × Per

P1

P2

P3

P1

P2

P3

SFA (saturated FA) Odd- and branched-chain FA Butyric acid (C4:0) Caproic acid (C6:0) Caprylic acid (C8:0) Capric acid (C10:0) Lauric acid (C12:0) Myristic acid (C14:0) Palmitic acid (C16:0) Stearic acid (C18:0) MUFA (monounsaturated FA) Oleic acid (cis9-C18:1) Total trans C18:1 isomers except trans 11C18:1 Vaccenic acid (trans11C18:1) PUFA (polyunsaturated FA) Linoleic acid (C18:2 n-6, omega-6) Linolenic acid (C18:3 n-3, omega-3) Linoleic acid/linolenic acid ratio Rumenic acid (cis9trans11-CLA)

62.3 4.48 22.40 1.71 1.09 2.51 3.00 10.9 24.4 11.5 29.6 20.5 1.61 3.80 5.33 1.19 0.94 1.15 1.64

62.4 4.38 2.32 1.72 1.15 2.75 3.39 11.6 25.5 9.45 29.6 19.8 1.52 4.33 5.32 0.83 0.73 1.04 2.22

0.398 0.059 0.163 0.057 0.018 0.049 0.066 0.124 0.326 0.294 0.341 0.333 0.047 0.264 0.111 0.047 0.033 0.027 0.117

ns ns

***

†

†

ns ** *** *** ** * *** ns

ns ns ns ns ** ** ns *** * ns *** ** *** *** * **

ns * * * *** *** ***

60.4 4.40 1.70 1.54 1.11 2.68a 3.19a 10.9 23.0 11.6 30.6a 19.6a

63.9 4.33 2.64 1.78 1.12 2.53ab 3.01ab 11.2 25.8 11.3 28.2b 19.8a 1.85 3.24b 5.18b 1.24b 1.01b 1.13ab 1.42b

62.5 4.70 2.85 1.82 1.06 2.32b 2.79b 10.7 24.4 11.6 29.9ab 22.3b 1.66 2.48b 4.73b 1.21b 0.88a 1.25b 1.23b

60.3 4.55 1.67 1.48 1.08 2.65 3.27 11.0 24.4 10.0 31.7a 21.4a 1.33 4.86a 5.30 0.80 0.62a 1.19a 2.36

63.4 4.49 2.35 1.72 1.12 2.68 3.31 11.9 26.5 9.10 28.5b 18.9b 1.60 4.21ab 5.35 0.80 0.74b 0.97b 2.21

63.6 4.10 1.95 1.95 1.23 2.91 3.60 11.8 25.7 9.23 28.6b 19.2ab 1.53 3.91b 5.32 0.87 0.82c 0.96b 2.10

†

** ns *** *** *** ***

†

ns ns * ** ** *** ** *** *** *** *

ns (non significant): p ≥ 0.10; syst: system (DIV or PROD); per: period (P1, P2 or P3); FA: fatty acid; s.e.m: standard error of the mean. ***p < 0.001. **p < 0.01. *p < 0.05. † p < 0.10. a,b,c In a same system (DIV or PROD), different superscripts in the same row indicate significant differences (p < 0.05).

5.67a 6.07a 1.10a 0.93a 1.08a 2.28a

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Effect g 100 g−1 FA

241

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nutritional value quite constant over the three grazing periods. In spring, the lower milk production of the PROD cows could result from a more limited quantity of available herbage biomass, leading to lower intakes than the DIV cows even though good grass quality was offered. The increase in milk fat content in P3 under both systems is consistent with the decrease of milk production in this period (concentration effect) with a larger increase in DIV milk due to the more significant decline of milk production. The increase in protein content in PROD milk in P3 tends to indicate that their energy balance was positive, as confirmed by their stable BCS and increased LW. In contrast, the slight increase in milk protein content and LW of DIV cows between P2 and P3, associated with the decrease of their BCS clearly indicates that their energy balance was negative, probably due to the declining quality of the pasture, as highlighted by the association in the PCA between the DIV system in P3 over the two years and the proportion of dead material in herbage biomass. The higher protein content associated with the lower and more stable fat content/protein content ratio observed in the PROD system over the grazing season can be seen as an advantage for the manufacture of cheeses (Verdier-Metz et al., 2001). There is over 30 years of research comparing different grazing managements (continuous, rotational, and restricted) but always on the same vegetation type at identical stocking rates (Hoden et al., 1986; Arriaga-Jordan and Holmes, 1986; Pulido and Leaver, 2003). Studies generally conclude that there is little or no difference between systems in terms of dairy performance. The originality of this paper is that we demonstrate that each system offers its own benefits in terms of grassland management and animal performances. Rotational grazing on fertile grassland allows stable high milk production per cow, high milk production per ha, high hay stocks and good BCS at the end of the grazing season, but it is more complex to manage and can lead to grass shortages in summer, as was the case here in 2008 and 2009. This type of grazing management also needs more inputs for fertilization. Conversely, continuous grazing gives good milk performance per cow on turn out to pasture but there is a subsequent sharp fall in milk production and a decline in animal BCS that farmers may find unacceptable. Moreover, it requires higher hectares per animal, which weighs heavily as a factor in a context of increasing human land demand. 4.2. The key role of herbage stage in milk FA profiles also nuances our assumptions Unexpectedly, the grazing system has only slight effects on average milk FA contents. As suggested by Coppa et al. (2011a), the higher concentration of linolenic acid in DIV milk in P1 is supported by the results of several authors (Collomb et al., 2002; Chilliard et al., 2007). In upland regions, where cows graze meadows with high floristic diversity as it is the case of DIV plot, plant secondary metabolites, which are abundant in dicotyledonous species, might partially inhibit the biohydrogenation of dietary PUFA (Leiber et al., 2005; Chilliard et al., 2007; Ferlay et al., 2008), resulting in a milk richer in linolenic acid and PUFA. The higher concentration of oleic acid in P3 DIV milk may be explained by a higher intake of this FA, first because diversified grass pastures are richer in oleic acid (Tornambé et al., 2007), and second because mature-stage grass has a higher oleic acid content (Elgersma et al., 2006). It could also stem from a mobilization of body fat reserves as a consequence of the negative energy balance (Chilliard et al., 2008). However, our results clearly demonstrated that important period changes in milk FA composition were observed in DIV milk to the contrary of PROD milk. This can be explained by a combination of the quality of available herbage, related to its phenologic stage, and the selection of herbage by cows during grazing. In the PROD system, cows grazed paddocks at a vegetative stage throughout the

season offering fat- and linoleic acid-rich herbage (Elgersma et al., 2006), thus giving a stable FA profile. In contrast, continuous grazing allowed herbage phenology to develop and consequently lose content of lipids and PUFA. In P1, the DIV cows could select preferentially species at a vegetative stage with a higher quality and palatability, thus producing milk richer in PUFA. Continuous grazing at lenient stocking rate on very diverse grassland therefore appears a promising strategy for FA concentrations in milk, but especially at the beginning of the grazing season. Its global nutritional value decreases indeed over time as the herbage matures. On the contrary, rotational grazing allows obtaining milk with a more stable profile FA and thus a stable nutritional value over the grazing season. This study highlights interactions between grazing management, cow grazing selection, vegetation phenology and species richness on milk FA concentrations throughout the whole grazing season, revealing complexity in the relationships between grazing management and the nutritional characteristics of dairy products. In contrast to most studies that give snapshot results, our results emerge the underlying dynamics of the system components and provide original conclusions on the milk properties ascribed to a given management system. 4.3. The differences on the sensory properties of the cheeses produced by the systems are showed but expressed only after a long ripening The assessors were unable to detect differences between 12week-ripened DIV and PROD cheeses. This absence of difference at the early stage of ripening has already been reported in studies by Agabriel et al. (2004) and Cornu et al. (2009) who considered that this ripening time was too short to reveal diet effects on the sensory properties of Cantal cheeses. Cheeses aged until 24 weeks have developed the sensory properties needed for differences between DIV and PROD cheeses become perceptible. The modifications in cheese sensory properties that we observed in our study between DIV and PROD cheese, have been already observed on-farm studies on different varieties of farmhouse cheeses when cows successively graze several plots presenting different botanical compositions (Buchin et al., 1999; Bugaud et al., 2002; Martin et al., 2005). The various hypotheses listed in the previously cited literature to explain the effect of pasture botanical composition on cheese flavour are directly or indirectly linked to the secondary compounds of the dicotyledons. It fits ours observations as the DIV cows ate more dicotyledonous species than PROD cows in summer, as shown by our DNA extraction results. Among these secondary compounds, the odour-active compounds such as terpenes, have often been mentioned. Coppa et al. (2011d) showed that the DIV milk was richer in mono- and sesqui-terpens, however in the case of Cantal cheeses; Tornambé et al. (2008) concluded that terpenes have only a negligible effect on the development of the flavour. Other odour active compounds like skatole could also be involved as they have been shown to be higher in PROD milks (Coppa et al., 2011d). Advanced cheese analyses, not planned in this experiment, would be necessary to ascertain the origin of the sensory differences evidenced here. Finally, we have also to consider that the sensory properties of the cheeses may vary during the grazing period as shown by Coppa et al. (2011a) on cheese texture and appearance. 4.4. The better ecological performances of the lenient grazing system are confirmed The better ecological performance of the DIV plot compared to the PROD plot was fully expected under our experimental strategy. Indeed, the DIV system was designed to offer a high level of biodiversity while the PROD system was oriented primarily towards

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production. At the implementation of our experiment, the DIV plot was selected based on its broad floristic diversity while the PROD plot was chosen for its high level of productivity associated to a low number of plant species in the pasture. The observed differences in plant diversity between the DIV and PROD plots clearly result from their contrasted historical management. Many studies have widely proved that lenient management intensity and a low level of fertilization will benefit plant diversity (Klimek et al., 2007; Scimone et al., 2007; Dumont et al., 2009). Thus, the DIV plot had been grazed at a low stocking rate and left unfertilized for the last 20 years whereas the PROD plot was formerly temporary hay grassland that annually received relatively high levels of mineral and organic fertilizer. The two response traits that we estimated at plot level consistently responded to this previous management history, and confirmed the results of Lavorel et al. (2011) who demonstrated that a high level of fertility increased the VH and decreased the LDMC of plant communities. The DIV plot also provided a much higher flowering intensity in July than the PROD plot, thus yielding not just a more colourful landscape but also more foraging opportunities for flower-visiting insects, as evidenced by the greater abundance of Lepidoptera in the DIV plot. These results are in accordance with studies showing the pivotal role of abundant nectar plants in Lepidoptera diversity (Farruggia et al., 2012; Scohier et al., 2013). We also highlighted positive effects of the DIV system on the abundance of Orthoptera, Heteroptera, Arachnida and Coleoptera. Many species in these taxa benefited from the greater herbage biomass offered by the DIV plot and its higher structural diversity, as revealed by the high standard deviation of herbage height found in this plot over the grazing season and within the two plant communities. These vegetation characteristics provide more food for herbivorous taxa (Orthoptera and Heteroptera) and more shelter and more complex vegetation structures benefiting webbuilding spider species (Scohier and Dumont, 2012). The beneficial effect on Coleoptera is more difficult to analyse, since literature reports on the impact of grazing point to different trends due to the diversity of their feeding strategies, which range from herbivorous to coprophagous and predatory (Scohier and Dumont, 2012). Conversely, the fact that the cows consumed most of the vegetation in the PROD paddocks, as shown by plot height after grazing, helps explain the lower richness of arthropods as a result of fewer shelters and the lower live plant biomass for herbivorous insects. Interestingly, we found equivalent numbers of arthropods between the two systems. Similar relationships between stocking rate and abundance of arthropods were reported by McMahon et al. (2010), who demonstrated that higher nutrient input levels associated with intensive practices may also be beneficial for the availability of invertebrate foods. Our findings thus confirmed the higher ecological potential of the DIV plot, especially since DIV management allows to upkeep nearly twice as many hectares as the PROD system. However, maintaining this ecosystem area still hinges on using mechanical intervention to control invasive species such as brooms in extensive grasslands.

5. Conclusion This experiment explored linkages between management intensity, animal performances, dairy product quality, and biodiversity over the grazing season in two contrasted upland grazing systems. Our conclusions nuance previous assumptions on grazing systems orientations, since we demonstrated that the benefits of each system are period-dependent and vary over the grazing season, especially those linked to milk production and milk nutritional quality. Our findings underline the crucial role played by vegetation stage in combination with sward botanical composition on

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animal, ecological and quality performances of the grazing system. To improve our understanding of within-grazing-system interactions, this integrated system approach should be extended to other contexts, for instance alpine pastures, where management as well as ecological conditions differ strongly to those investigated in this paper. These results also provide evidence to support discussion on the complementarity of different types of grassland and management strategies in grassland-based farming systems. Most farmers, especially those located in upland areas, combine different types of grazing management in response to the different types of grasslands present on their farm. They can thus capture benefits from each management, and conceal production targets, biodiversity and quality products at their whole farm level, as demonstrated by Tichit et al. (2011) in a modelling approach. Acknowledgements This work was funded by the French Ministry of Agriculture and Fisheries under a CASDAR project managed by the Pôle Fromager AOP du Massif Central. The authors thank the staff of the INRA farm at Marcenat (INRA, UE1296 Monts d’Auvergne) for animal care, Francis Decuq for monitoring the animals’ location, René Lavigne (INRA, UR545 Fromagères) for making the cheeses, Isabelle Constant (INRA, UMR1213 Herbivores) for her valuable help with the analyses, Pierre Capitan (INRA, UMR1213 Herbivores) for running the milk FA analyses, and Sandrine Paglia and Helène Albouy (CFPPA–ENIVL Aurillac) for the sensory evaluation of the cheeses. References Agabriel, C., Martin, B., Sibra, C., Bonnefoy, J.B., Montel, M.C., Didienne, R., Hulin, S., 2004. Effect of dairy production systems on the sensory characteristics of Cantal cheeses: a plant-scale study. Anim. Res. 53, 221–234. Andueza, D., Cruz, P., Farruggia, A., Baumont, R., Picard, F., Michalet-Doreau, B., 2010. Nutritive value of two meadows and relationships with some vegetation traits. Grass Forage Sci. 65, 325–334. Arriaga-Jordan, C.M., Holmes, W., 1986. The effect of concentrate supplementation on high-yielding dairy cows under two systems of grazing. J. Agric. Sci. 107, 453–461. Aufrère, J., Demarquilly, C., 1989. Predicting organic matter digestibility of forage by two pepsin-cellulase methods. In: Proceedings of the XVI International Grassland Congress, Nice, France, pp. 877–878, Vol. 2. Buchin, S., Martin, B., Dupont, D., Bornard, A., Achilleos, C., 1999. Influence of the composition of Alpine highland pasture on the chemical, rheological and sensory properties of cheese. J. Dairy Res. 66, 579–588. Bugaud, C., Buchin, S., Hauwuy, A., Coulon, J.B., 2002. Flavour and texture of cheeses according to grazing type: the Abundance cheese. INRA Prod. Anim. 15, 31–36. Chilliard, Y., Glasser, F., Ferlay, A., Bernard, L., Rouel, J., Doreau, M., 2007. Diet, rumen biohydrogenation and nutritional quality of cow and goat milk fat. Eur. J. Lipid Sci. Technol. 109, 828–855. Chilliard, Y., Glasser, F., Enjalbert, F., Ferlay, A., Schmidely, P., 2008. Recent data on the effects of feeding factors on cow milk fatty acid composition. Sci. Aliment. 28, 156–167. Collomb, M., Butikofer, U., Sieber, R., Jeangros, B., Bosset, J.O., 2002. Correlation between fatty acids in cows’ milk fat produced in the lowlands, mountains and highlands of Switzerland and botanical composition of the fodder. Int. Dairy J. 12, 661–666. Coppa, M., Ferlay, A., Monsallier, F., Verdier-Metz, I., Pradel, P., Didienne, R., Farruggia, A., Montel, M.C., Martin, B., 2011a. Milk fatty acid composition and cheese texture and appearance from cows fed hay or different grazing systems on upland pastures. J. Dairy Sci. 94, 1132–1145. Coppa, M., Farruggia, A., Pradel, P., Lombardi, G., Martin, B., 2011b. An improved grazed class method to estimate species selection and dry matter intake by cows at pasture. Ital. J. Anim. Sci. 10, 58–65. Coppa, M., Verdier-Metz, I., Ferlay, A., Pradel, P., Didienne, R., Farruggia, A., Montel, M.C., Martin, B., 2011c. Effect of different grazing systems on upland pastures compared with hay diet on cheese sensory properties evaluated at different ripening times. Int. Dairy J. 21, 815–822. Coppa, M., Martin, B., Pradel, P., Leotta, B., Priolo, A., Vasta, V., 2011d. Effect of a haybased diet or different upland grazing systems on milk volatile compounds. J. Agric. Food Chem. 59, 4947–4954. Cornu, A., Rabiau, N., Kondjoyan, N., Verdier-Metz, I., Pradel, P., Tournayre, P., Berdague, J.L., Martin, B., 2009. Odour-active compound profiles in Cantal-type cheese: effect of cow diet, milk pasteurization and cheese ripening. Int. Dairy J. 19, 588–594.

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