Dietary factors associated with milk somatic cell counts in dairy cows in Brittany, France

PREVENTIVE VETERINARY MEDICINE Preventive Veterinary Medicine 2 1 ( 1995 ) 299-3 11

Dietary factors associated with milk somatic cell counts in dairy cows in Brittany, France J. Barnouin*, M. Chassagne, I. Aimo Laboratoire d’Ecopathologie, INRA, Centre de Recherches de Clermont-Ferrand-Their, 63122 Saint GenPs Champanelle, France

Accepted 17 April 1994

Abstract A survey (EnquCte Ecopathologique Bretagne) was conducted in France for 4 years ( 1986-1.990) in 48 commercial dairy herds (25-80 cows per herd) located in Brittany. Three groups of herd-years were made up to study peripartum dietary factors associated with milk somatic cell counts in the early lactation period (ECC). The first group included 20 herd-years with high ECC (over 400 000 cells ml-‘). The second group included herdyears with medium ECC (n = 20, median ECC 222 000) and the last group herd-years with low ECC (n= 20, median ECC 95 000). Herd data (components of the diet, milk yield and reproduction parameters, clinical diseases, biochemical and hematological indicators, body and dirtiness scores) were analyzed using discrimination by barycentric analysis. Two separate analysis were conducted according to study period (LG period, last 60 days of gestation; EL period, first 60 days of lactation). The high ECC group had the following characteristics: ( 1) higher plasma gamma glutamy1 tran.sferase levels in the LG period; (2) higher quantities of cereal-based concentrates in the diet in the EL period, (3) shorter durations of feeding with Italian rye grass silage in the EL period. The relevance of the risk factors for high ECC are discussed with reference to dietary linolenic acid/linoleic acid ratio (through the synthesis of leukotrienes as chemotactic agents for the polymorphonuclear leukocytes), dietary energy supplies and liver fluke infestation in the herds. We speculate that nutritional factors could modulate the leukocyte activity in the milk and that supplementations with certain polyunsaturated fatty aci’ds would be able to prevent the development of inflammation in the udder.

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1. Introduction

Milk somatic cell count (SCC) is used worldwide to monitor udder health in the dairy cow (Leslie et al., 1983 ). As high SCC are linked with decreased milk, fat, lactose and casein production (Schukken et al., 1992)) they cause important financial losses and decrease profitability at the farm level. Numerous studies have centered on the relationships between climate, housing system and hygiene, milking and prophylactic practices, and herd or cow SCC (Kennedy et al., 1982; Bamouin et al., 1986; Hueston et al., 1990; Hutton et al., 1991; Bartlett et al., 1992). Although the relationship between dietary vitamin A, vitamin E, selenium and mastitis occurrence has been studied (Chew, 1993; Hogan et al., 1993)) dietary factors for SCC level have rarely been investigated, especially in the peripartum period, when nutrition plays an important role in the development of disease in the dairy cow (Morrow et al., 1979; Curtis et al., 1985; Erb and Grohn, 1988; Bamouin and Chassagne, 1990; Bamouin and Chacomac, 1992). The aim of the present work was to investigate dietary factors linked with SCC levels in the peripartum period from an ecopathological survey conducted in dairy farms under French management conditions. The study design used a form of discriminant analysis which determines the variables discriminating between groups without requiring assumptions regarding distribution. 2. Material and methods 2.1. Survey and information system This study is part of an overall epidemiological prospective survey called ‘Enq&e Ecopathologique Bretagne’ or EEPB (Faye and Bamouin, 1987). The survey aimed to study herd and cow risk factors for the clinical and subclinical diseases and losses of milk quality. It was carried out during four l-year periods ( 1 February 1986-31 January 1990) in 48 dairy herds (25-80 cows per herd) located in Brittany. The farmers were members of Dairy Herd Improvement Associations (DHIA) and volunteered to participate in the survey. They were selected according to their ability to detect and record diseases efficiently, as appreciated by their veterinarians and confirmed through a pre-study period. In the selected herds, 98.5% of the cows were Black-Pied. All the females were loosehoused (with or without cubicles) and brucellosis and tuberculosis free. A total of 8938 lactations from 4123 cows were surveyed. The average milk yield per lactation was 7413 kg (range 1653-l 247 1 kg) and the average SCC per lactation was 235x lo3 cells ml-’ (range 15x lo3 to 3755x 103) for the Black-Pied females more than 300 days in milk. Pre-study training and discussions about data collection and study design were used to standardize the survey process among the collaborators. Veterinarians and farmers were given a glossary including definitions of the main clinical diseases in the dairy cow. Every 6 weeks nine specialized surveyors from the State Veterinary Services visited the farms: (a) to col-

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lect ob:servational and management data (calvings, clinical diseases, preventive practices, diets, reproduction status, characteristics of the housing system); (b) to measure individual body and dirtiness scores around calving (c) to obtain blood samples from the dry cows for biochemical and hematological analysis; (d) to collect silage, drinking water and milk samples for chemical, biochemical and bacteriological analysis. Every month, individual milk samples were collected from all the Ilactating cows by DHIA technicians to determine production parameters. The data were transferred to a database (Lescourret et al., 1993) (approximately 150 basic variables per cow-lactation and 350 per herd-year). The consistency o’fthe data was checked with a set of logical constraints before and after the data wlere stored. The database was managed by a relational database management system, ORACLE 6.0 (ORACLE Corp., Redwood Shores, CA) designed with the MERISE method (Tardieu et al., 1983). Data were retrieved with a standard query language (SQL) and a SUN 4/390 computer (SUN Microsystems Inc., Mountain View, CA) operating on the UNIX system (UNIX Systems Laboratories, Summit, NJ). 2.2. Observational periods The herd-year ( 1 July-30 June) was the study unit (two to three 1-year observation periods per herd; total number of herd-years 139). It was defined to describe homogeneous management periods in the herds and disregard initial and final observation periods, and therefore strengthen the information quality. For each herd-year, two observational periods were considered: (a) the last 60 days of gestation (LG period); (b) the first 60 days of lactation (EL period). LG (n = 49 ) and EL (n = 5 1) variables were calculated according to definition of the observational periods (Tables 1 and 2). In each herd-year, early lactation SCC (ECC) was calculated from the sum of all individual SCC values determined from milk samples collected within the first 60 days of lactation, divided by the number of observations. 2.3. Selection of cows To standardize the study population, cows were selected for each herd-year on the following criteria: (a) Black-pie breed; (b) date of the drying-off for the previous lactation recorded, (c) a blood sample collected within the last 6 weeks of gestation preceding the current lactation; (d) one calving between the first and the lasi. day of the l-year period, (e) at least two milk records in the current lactation; ( f ) presence in the herd from previous drying-off until at least day 60 of the current lactation. All the study variables were calculated from the selected cows. As the pregnant heifers, which were managed in the farms under feeding, housing and hygiene practices different to those under which cows were kept, were not included in the study, the first lactation animals were only considered in the LG period preceding their second calving.

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Table 1 Production, management, disease and blood herd variables for late gestation (last 60 days of gestation) and early lactation (first 60 days of lactation) periods Late gestation period

Early lactation period

Production/management Body score (O-5 points) Dry period length (days) Dirtiness score (O-8 points)

Production/management Autumn calving (I of calvings) Body score variation (gest. minus lact.) Calving to lactation peak interval (days) Calving to milk record interval (days) Dirtiness score variation (gest. minus lact.) Milk protein content (g l- ’ ) Lactation range Loose housing with cubicles (% of cows) Milk fat content (g l- ’ ) Milk yield (kg per cow day-’ ) Number of milking cows Services per conception Spring calving (% of calvings) Summer calving (I of calvings ) Winter calving (% of calvings)

Blood indicator 3-hydroxybutyrate (mmol I- ’ ) Albumin (g 1-l ) Basophils ( 10’ mm-‘) Blood toll. to calving interval (days) Calcium (mg per 100 ml) Ceruloplasmin (oxydasic unit ) Eosinophils ( lo3 mm-‘) Free amino acids ( mmol l- ’ ) Free fatty acids (mmol I-’ ) Gamma-ghttamyl transferase (U 1-l ) Glucose (g l- 1) Glutamate dehydrogenase (U I-‘) IgGl (g 1-l) Lymphocytes ( lo3 mm-‘) Magnesium ( mg per 100 ml) Monocytes ( 1O3mm-‘) Neutrophils ( lo3 mm-‘) Plasma coloring score ( l-4 points) Red blood cells ( lo6 mm-‘) Urea (mg per 100 ml)

Clinical disease (% of calvings) Abortion Difftcult calving Lameness Mastitis Met&is Milk fever Perinatal calf mortality Retained placenta

2.4. Selection of herd-years

To identify herd factors which were associated with high vs. medium and low SCC, 20 herd-years were first selected (ECC+ group), i.e. those with a cell count at least equal to 400 x 10’ cells ml- ’ (the regulatory limit in the European Community). Two other groups of herd-years were chosen: a medium group (centered around the median of ECC; ECCO group, n = 20 ) and a low group (ECC group, n = 20) with low cell counts. In the three groups, there were the same number of herds for each l-year period, i.e. nine for 1986-1987, two for 1987-1988 and nine for 1988-1989. A single herd could appear in a group either once or twice, but not in 2 consecutive years ( 18 separate herds in ECC- and ECCO groups, and 17 in the ECC + group ) . Finally, the selection process aimed to analyze a significant proportion of the surveyed herd-years, to eliminate the period effect, to ensure a satisfactory independence between data and to take into account via the three groups the SCC variation in the herd-years.

.I. Barnouin et al. /Preventive VeterinaryMedicine 21 (1995) 299-311 Table 2 Herd feeding variables for late gestation (last 60 days of gestation) of lactation) periods

and early lactation (first 60 days

Late gestation period

Early lactation period

Feeding days (O-60) Alfalfa/clover: fresh Alfalfa/clover: hay Alfalfa/clover: silage Barley: grain Beet: fresh Beet: pulp Cabbage: fresh Corn: fresh Corn: silage English rfe grass: fresh English tye grass: hay English tye grass: silage Italian rye grass: fresh Italian rye grass: hay Italian rye grass: silage Minerals: Ca, P, Mg Natural grassland: fresh Natural grassland: hay Natural grassland: silage Rape: fresh Straw Urea Wheat: grain

Alfalfa/clover: fresh Alfalfa/clover: hay Alfalfa/clover: silage Barley: grain Beet: fresh Beet: pulp Cabbage: fresh Corn: silage Corn: fresh English rye grass: fresh English rye grass: hay English rye grass: silage Italian rye grass: fresh Italian rye grass: hay Italian rye grass: silage Minerals: Ca, P, Mg Natural grassland: fresh Natural grassland: hay Natural grassland: silage Rape: fresh Straw Urea Wheat: grain

Quantity (kgper cow) Cereal concentrate: total quantity Soybean meal: total quantity

Supplementation (% of cows) Vitamins ADE

303

Cereal concentrate: max. daily quantity Cereal concentrate: max. daily quantity minus calving day quantity Soybean meal: max. daily quantity Soybean meal: max. daily quantity minus calving day quantity

Vitamins ADE

2.5. Variables Cumulative incidences of eight clinical diseases in the herd-years were determined from cases observed by the veterinarians and the farmers. Incidences were calculated as the number of cows developing a disease divided by the number of calvings. Nutritional herd data included the major components of the diet given to dry cows (forages, energy/protein supplements, minerals, vitamins). Since accurate measures of quantity were rarely available for all the diet components, the mean number of days each foodstuff was fed during the last 60 days before calv-

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ing and the first 60 days after calving were calculated from the selected cows with an automated procedure using C-language software (Lescourret et al., 1994) to estimate the importance of each component. Cereal concentrate and soybean meal quantities were collected from the farmers. The milk yield variables (milk yield, milk fat and protein contents) were calculated, as for ECC, as herd means for the first two milk records in the lactation. Fluoro-opto-electronic cell counting (Schmidt Madsen, 1975 ) was used to measure individual milk cell count. Calving seasons were classified according to Huffman et al. ( 1984) as Spring (March, April, May), Summer (June, July, August), Autumn (September, October, November) and Winter (December, January, February). Individual body scores were evaluated both in the last 6 weeks before calving and in the first 6 weeks after calving using the method of Bazin ( 1984) based on a five point scale graduated in 0.5 intervals from 0 (thin) to 5 (fat). Dirtiness scores were evaluated as body scores by the method of Faye and Bamouin ( 1985) based on an eight point scale graduated in 0.5 intervals from 0 (clean) to 8 (dirty). A blood sample was collected from each dry cow in the last 6 weeks before calving between 09:OOand 1O:OOh to control for diurnal fluctuaTable 3 Variables subjected to barycentric analysis that explained variation between the low (ECC -, n = 20), medium (ECCO, n = 20) and high (ECC + , n = 20) milk leukocyte count groups of herd-years in the late gestation period (discrimination by barycentric analysis) (French dairy cows) Variable

Factor score

PEV”

Monocytes Italian ryegrass: silage Dry period length Natural grassland: fresh Beet: fresh Barley Blood collection to calving interval Wheat Plasma urea Beet: pulp Free amino acids Alfalfa/clover: hay Cereal concentrate Rape Straw Plasma coloring score Hemoglobin IgGl Glutamate dehydrogenase Red blood cells Dirtiness score Gamma-glutamyl transferase

-0.168 -0.148 -0.116 -0.112 -0.081 -0.082 -0.071 -0.066 -0.061 -0.039 -0.011 0.002 0.028 0.032 0.040 0.043 0.085 0.093 0.112 0.114 0.120 0.265

12.0 9.3 5.7 5.2 2.8 2.8 2.1 1.9 1.5 0.7 0.1 0.1 0.3 0.4 0.7 0.8 3.1 3.7 5.3 5.5 6.2 29.8b

“Percentage of explained variation. bPEV> mean PEV+ 1.96 PEV standard deviation.

J. Barnouin et al. /Preventive VeterinaryMedicine 21(1995) 299-311 Number

of herd-years

12 10 8 8

L

I

-.15

1

(KC-,

-.05

0

1.05 t .15 (ECCO,(KC+) Factor score

Fig. 1. Hilstogram of the barycentric analysis for the late gestation period: factor scores on the discriminant axis of 60 herd-years with low (ECC - , n = 20)) medium (ECCO, n = 20) and high (ECC+, n= 20) milk somatic cell count. Factor scores of the explanatory variables are mentioned in Table 3. Barycenters of the three groups of herd-years are displayed in parentheses (dairy herds in Brittany, France, 1986-1990).

tions in blood parameters. Heparinized plasmas were frozen at - 20 “C within 5 h after blood collection until analysis. Biochemical plasma indicators were determined using previously described automated methods (Chilliard et al., 1984; Bamouin et al., 1986; Chacomac et al., 1986; Aissaoui, 1989; Chacomac et al., 1993)) which were performed on a discrete biochemical analyzer (Isamat, ISABIOLOGIE, France). Hematological parameters (red and blood cell counts) were determined using standard methods (Coles, 1979; Jain, 1986). A four point scale from 1 l(clear) to 4 (dark) was used according to Friesecke ( 1978) to evaluate plasma coloring scores. 2.6. Statistical procedures The statistical analysis was performed by barycentric analysis (BA) using the ITCF Statistical Package 5.0 (Beaux et al., 1988). Because the values of numerous variables before and after calving were not independent (components of the diet, body and dirtiness scores), two separate analysis were conducted (analysis 1, LG period; analysis 2, EL period). BA alhows the determination of the variables which discriminate between groups (three in the present study) without making any assumption about type of data (discrete, continuous) and underlying distribution (Gaussian or not). This method :isa form of discriminant analysis (Benzecri, 1977; Kouadi, 1984) which uses correspondence analysis (Hill, 1974). The degree of discrimination between groups is estimated by calculating a percentage of the correct classification on the

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Table 4 Variables that explained variation between the low (ECC - , n = 20)) medium (ECCO, n = 20) and high (ECC+, n=20) milk leukocyte count groups of herd-years in the early lactation period (discrimination by barycentric analysis) (French dairy cows) Variable

Factor score

PEV”

Italian ryegrass: silage Soybean meal variation Autumn calving Difficult calving Minerals Dirtiness variation Barley Cabbage English ryegrass: hay Lameness Beet: fresh Beet: pulp Milk fat English ryegrass: silage Corn: fresh Alfalfa/clover: fresh Alfalfa/clover: hay Abortion Natural grassland: hay Italian raygrass: fresh straw Met&is Services per conception Milk protein Cubicles Rape Clinical mastitis Lactation range Perinatal mortality Maximum cereal concentrate

0.161 0.118 0.110 0.105 0.086 0.079 0.076 0.069 0.068 0.062 0.053 0.05 1 0.050 0.032 0.03 1 0.011 -0.028 -0.031 - 0.037 - 0.040 - 0.049 -0.070 -0.075 -0.095 -0.108 -0.118 -0.130 -0.130 -0.141 -0.191

10.4b 5.6 4.9 4.4 3.0 2.5 2.4 ‘9 1.9 1.5 1.1 1.0 1.0 0.4 0.4 0.1 0.3 0.4 0.6 0.7 1.0 2.0 2.3 3.6 4.7 5.6 6.8 6.8 8.0 14.7b

‘Percentage of explained variation. bPEV > mean PEVS 1.96 PEV standard deviation.

discriminant axis. On this axis, which is interpreted as a risk factor, the ECCO barycenter (the ‘center of gravity’ of the ECCO herd-years; median risk) is displayed between the ECC- (low risk) and the ECC+ (high risk) barycenter. If all the herd-years are correctly classified, all the ECC- herd-years are displayed on the side where the ECC - barycenter is displayed, 50% of the ECCO herd-years are displayed on each side of the discriminant axis and all the ECC+ herd-years are displayed on the side where the ECC + barycenter is displayed. From all the study variables (n = 100)) 22 LG and 30 EL variables were offered to BA, these selected variables were not highly correlated and their Spearman’s coefficient of correlation with ECC was significant at PC 0.5. In BA, the primary discriminant variables were those for which the percentage of explained variation

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Number of herd-years 14 12 10 8 8 4 2 O- -

-.15

-.05 1 (ECC-)

0f .05 (ECCO)

.15 , (WC+) Factor score

Fig. 2. Histogram of the barycentric analysis for the early lactation period: factor scores on the discriminant axis of 60 herd-years with low ( ECC - , n = 20)) medium ( ECCO, n = 20) and high ( ECC + , n=20) milk somatic cell count. Factor scores of the explanatory variables are mentioned in Table 4. Barycenters of the three groups of herd-years are displayed in parentheses (dairy herds in Brittany, France, 1986-1990). Table 5 Values of rhe discriminant variables in the low (ECC - , n = 20), medium (ECCO, n = 20) and high (ECC +, II = 20) milk somatic cell count groups of herd-years (the distributions of all the variables subjected to barycentric analysis are available on request) (French dairy cows) Discriminant

variable

Median (minimum-maximum) ECC-

ECCO

ECC+

LG period (last 60 days of gestation) Plasma gamma-glutamyl transferase (Ul-‘)

13.5 (10.6-42.2)

15.6 ( 13.8-25.2)

16.6 (12.5-79.1)

EL period first 60 days of lactation) Italian ryegrass silage (days) Maximum cereal concentrate (kg per cow day-’ )

8 (O-38) 3.94 (0.13-7.28)

0 (O-25) 4.96 (1.34-8.51)

0 (O-23) 5.71 (1.43-8.52)

(PEV) was at least equal to a discrimination threshold (mean PEV+ 1.96 PEV standard. deviation). Spearman’s coeffkient of correlation was used to assess the relationships between the primary discriminant variables. 3. Results ECC median values were respectively 95 x lo3 cells ml-’ (range (49157)x103)intheECC-group,222~103(range(187-284)x103)intheECCO group and 480x lo3 (range (400-755) x 103) in the ECC+ group.

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Table 6 Spearman’s coefficients of correlation between the discriminant variables determined by barycentric analysis from three groups of 20 herd-years with low, medium and high milk somatic cell count (GGT, plasma gamma-glutamyl transferase in the late gestation period; CON, maximum daily quantity of cereal concentrate in the early lactation period, IRG, number of feeding days with Italian ryegrass in the early lactation period) (French dairy cows) GGT GGT CON IRG

CON

IRG

0.209 -0.139

-0.182

In the LG period, from the variables offered to BA, 75% of the surveyed herdyears (45/60) were correctly classified on the discriminant axis. Fourteen ECC herd-years, seven ECCO and six ECC+ were displayed on the left side of the discriminant axis (Fig. 1)) vs. 6, 13 and 14 on the right side (x2 = 7.68; d.f. = 2; P=O.O2). The factor scores separated mainly the ECC- barycenter from the ECCO and the ECC + barycenters (Fig. 1). The plasma gamma-glutamyl transferase (GGT), which was the primary discriminating variable, explained 29.8% of the total variation (Table 3). ECC+ herd-years were characterized by relatively high GGT vs. ECCO and ECC- herd-years (Table 4). Next to GGT, circulating monocytes, displayed on the side of the risk (ECC + ), and number of feeding days with Italian ryegrass silage during the last 60 days of gestation, displayed on the side of the absence of risk (ECC - ), were the variables with the highest PEVs. In the EL period, from the variables offered to BA, 86.7% of the herd-years (52/60) were classified correctly on the discriminating axis. Nineteen ECCherd-years, seven ECCO and four ECC+ were displayed on the left side of the axis (Fig. 2), vs. 1, 13 and 16 on the right side ( x2= 25.20; d.f. =2; P< 0.001). The factor scores clearly separated the three barycenters (Fig. 2 ). The maximum daily quantity of cereal-based concentrate per cow (CON) and the number of feeding days with Italian ryegrass silage during the first 60 days of lactation (IRG), which were the primary discriminating variables, together explained 25.1% of the total variation of the discriminant axis. ECC - herd-years were characterized by relatively high IRG ( 15 ECC - herd-years supplied IRG to early lactating cows, vs. respectively nine and seven in the ECCO and ECC + groups), while ECC + herd-years were characterized by higher CON (Table 5). Next to the discriminant variables, perinatal calf mortality, lactation number and clinical mastitis, which were all displayed on the side of the risk (ECC + ), were the variables with the highest PEVs. The three discriminant variables were not correlated (Table 6). 4. Discussion

The ECC median in the ECC - group corresponds to a very satisfactory situation. It is definitely below 100 000 cells ml-’ and lower than the average of the

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‘low SCC herds’ defined by Hutton et al. ( 199 1). The ECCO group has a middleterm situation (median ECC 222 000 cells ml- ’ ) and the ECC+ group a poor situation, which is close to the average of the ‘elevated SCC herds’ in the study of Huttonetal. (1991). GGT in the LG period, and IRG and CON in the EL period, are the variables which discriminated between the three groups of herd-years according to the SCC status. GGT is a plasma indicator of liver fluke infestation in the dairy cow (Blackshaw, 1978). In a previous study conducted on the EEPB cows, Aissaoui ( 1989) found both lower SCC and plasma GGT values in cows from herds free of infestation with FuscioZu heputica than in cows from infested herds. This author also found a positive correlation between plasma levels of omithine carbamy1 tran sferase (a relevant indicator of liver necrosis ) before calving and SCC in the first milk record after calving. Moreover, Black and Froyd ( 1972) and Castagnetti et al. ( 1977) observed high SCC and low milk yield in cows infested with Fusciola heputicu. For Aissaoui ( 1989) some environmental conditions (rainfall, moisture, soil conditions) could both favor udder inflammation and liver fluke infestation in the cow. Two main nutritional characteristics could explain higher milk SCC in the herdyears with a low number of feeding days with IRG and a great quantity of CON: (a) a high level of energy intake; (b) a low linolenic acid/linoleic acid ratio. High frequencies of clinical mastitis, mammary edema or positive CMT were found in cows with a high energy ration or with biochemical signs of a low level of lipomobilization (Morrow et al., 1979; Johnsson and Otterby, 198 1; Bamouin et al., 1986). In a previous survey, Bamouin et al. ( 1986) found that dietary grass silage and fresh grass were associated with a low risk for mastitis, while corn silage and green crucifers increased the risk. Concerning polyunsaturated fatty acids ( PUFAs), a ration rich in IRG and poor in cereal-based concentrate, which characterized the ECC- herd-years, involves a high dietary linolenic acid/linoleic acid ratio, which varies from 2 to 5 in grass silage and is lower than 0.1 in concentrates (Bamouin and Chassagne, 199 1) . A high linolenic/linoleic ratio inhibits the enzymatic system leading to the synthesis of biologically active leukotrienes (LT) from arachidonic acid (Lewis and Austen, 1984). Thus such a high dietary ratio would be unfavorable for the synthesis of LTB4, which is a stimulator of polymorphonuclear neutrophil chemotaxis and adherence (Palmblad, 1990) and has an amti-inflammatory effect in the udder. Moreover, some studies suggest that supplementation with certain PUFAs can prevent and suppress the development of acute and chronic inflammation leading to tissue damage (B&in, 1990). Barycentric analysis is a suitable method to assess variables which discriminate between groups of herd-years without making any assumption about type and distribution of data. However, a discrimination in a more representative set of herd-years might be much lower. Moreover, the discriminant variables might only be descriptors of different farming systems in the selected groups, without causal relationship with SCC. Concerning the feeding variables, the number of days of exposure to a feedstuff is not a precise indicator for the estimation of actual con-

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sumption. In field studies including especially pasture periods, it is nearly impossible to define more accurate parameters to quantify the diets. Nevertheless, the present results suggest that future research on the causal factors for udder inflammation might usefully be focused on the relationships between dietary energy and PUFAs and mastitis susceptibility (Barnouin, 1992)) keeping in mind that when the variations of a health variable remain difficult to explain whatever the country and the farming system, precise and relevant new etiological hypotheses have to be tested to try new avenues for prevention.

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