Evaluation of the dairy cow biotype through milk composition, nutrition and grazing management

An evaluation of the production and milk composition under different production biotypes, and nutrition methods in three commercial systems for a single production cycle was made. The effects of biotype, grazing management and different methods of feeding on milk composition were assessed.

The results indicated that cows with a small biotype seem more appropriate for milk production based on pasture. The difference in body condition of grazing Holstein cows indicated an insufficient intake of herbage dry matter. Differences in milk production and composition as fat, protein, non-fat solids, casein fraction, and insoluble fractions in particular, can be attributed to the small biotype and to herd management.

Introduction

Milk production systems are markedly influenced by nutritional handling and also by differences in milk cattle biotypes (Holmes et al 2002). Options for farmers to modify milk characteristics include management (e.g. calving spread, replacement policies), genetic selection and feeding (Holmes et al 2002, Lopez-Villalobos et al 2001, O'Brien, 2000). The use of pasture for dairy cows results in lower-cost feedingsystems because grazed forage is the cheapest source of nutrients(Peyraud and Delaby 2001; Lopez-Villalobo et al 2001). During the last 20 years the genetics of cattle in Argentina has been dominated by large Holstein-Friesian biotypes from North America. These animals were selected for high milk production in a predominantly confinement environment, and in a market that rewarded milk volume rather than milk solid content. A new biotype of cows from a crossbreed of Jersey-Holstein has been included in many crossbreeding programs in dairy systems. The reason for this is to include a change into multiple components pricing of milk, and the desire of some processors to move to yield pricing of milk; including a potential for improving herd fertility and health through heterosis or hybrid vigor through the effects of the crossbreeding (Lopez-Villalobos and Garrick 2002), and also emphasize an improvement in feed efficiency (Baudracco et al 2010).

Information is available from experimental assessments, but these assessments do not exist on commercial farms under grazing conditions. The objective of this study was to compare productive indices and feeding in three commercial systems during a cycle of production, regarding the effect of biotype and different grazing feeding managements on milk composition.

Material and methods

During the two previous years of this experiment three systems of milk production selected by milk fat and protein (% V/V), from farms of the region placed in a temperate zone (Tandil; latitude 37°45' South, longitude 58°18' West) were studied. These dairy systems were identified as JxHF with cows of biotype Jersey-Holstein, HF2 and HF3 with biotype Holstein Friesian cows, the experiment was carried out during one year, on the basis of calculation of animal-unit-equivalents (AUE) for different classes of dairy cattle, controlling: 1.) Productive parameters: average of milk production per cow (M), milk yield (l/d), live weight (LW, kg), body condition score (BCS, scale 1 to 5) at reproduction service (L) and at drying-off (H). 2) Nutritional records: grazing (GI/cow), concentrate (CI/cow), maize silage (kg MS/cow) and hay intake (GI/cow) were estimated, all of them expressed per day. 3.) Stocking variables: Stocking density (SD) as quantity of animal-unit on the land area (hectare) at any instant in time, (SD am/d and SD pm/d) (Scarnecchia and Kothmann, 1982). Pre-grazing mass (PeGM) and post-grazing mass (PoGM) in each grazing paddock at am. and pm. milking time were measured: using an adapted method of the rising plate meter (Rubio, 2000) in the perennial and annual ryegrass/clover pasture by cutting of Lucerne and it was estimated for sorghum and soy. 4.) Management: cows were supplemented once a day (silage) and managed under a strip grazing system on pasture (intensive paddock grazing). Concentrate was offered individually in the milking parlor in two equal feeds daily. 5.) Sampling: homogenized and frozen equal aliquots of bulk milk samples from morning and evening milking were taken from each farm. Milk samples from individual cows were collected of each herd in four opportunities for HF2, in three for JxHF and in two for HF3. Each single milk sample, identified by cow, was obtained by a mixture of milk aliquots, from the afternoon of the day before, refrigerated at 5ºC, and from the morning of the day after. 6.) Analysis: milk composition was analyzed by an automatized method (Bentley®). The composition of milk (% V/V) was analyzed for fat, total protein and lactose. The total solids (TS) and non fat solids (SNF) were calculated as percentage and weight (g). The electrophoresis of milk proteins were performed with milk of individual cows collected as described previously for each herd, respectively JxHF, n=59; HF2, n=24 and HF3, n=23 samples. Aliquots of 30 ml of milk were frozen and analyzed later. Skim milk was prepared for the determination of the fractions of proteins, using a one-dimensional electrophoresis with polyacrilamide 12% (SDS-PAGE) in miniature vertical gels (Mini Protean II, Bio-Rad®) using a pH 8.3 buffer. Laemmli technique (1970) was used, running gels with 200 V for 60 min and stained with Coomasie blue R 0.1%. Gels were distained in methanol-acetic-water solution overnight and dried with a suction-heat pump (Gel-dryer, Bio-Rad®). Images of gels were scanned (resolution 1500 x 1200) and processed for analysis (GelPro®). In order to quantify the optical density of each protein fraction, percentage results of each band of electrophoresis was calculated based on the total proteins in lane. A standard molecular weight marker to identify protein fractions was used. The identified bands of milk proteins obtained by densitometry were: lactoferrin (Lf), bovine serum albumin (BSA), serum immunoglobulin heavy chain (IgM), casein fractions: alpha S2 (aS2), alpha S1 (aS1), beta (b), kappa (k), proteins beta-lactoglobulin (LG) and alpha-lactoalbumin (LA). A new variable called as “insoluble fractions” was calculated as a sum of the casein fractions: aS2, aS1, b and k attending to their influence on producing cheese.Dairy systems were compared by ANOVA, Lsmeans (GLM, SAS 1989) with productive parameters , nutritional records, stocking variables and milk composition.

Results and discussion

Herd JxHF produced more fat and protein (kg), than HF2 and HF3 (P<0.05). Biotype Holstein (HF2 and HF3) produced similar composition of milk (Table 1).

Table 1. Main characteristics of production of three systems of grazing dairy cows.
Herd	Number of cows/herd	Milk fat (kg/cow/d)	Milk protein (kg/cow/d)	Concentrate consumed (kg/liter/d)
JxHF	263±73^a	0.94±0.05^a	0.76±0.04^a	0.23±0.02^a
HF2	374±33^b	0.68±0.02^b	0.66±0.02^b	0.38±0.08^b
HF3	265±28^a	0.62±0.04^b	0.59±0.04^b	0.21±0.05^a
^ab means in the same column for each parameter with different superscripts are significantly different (P<0.05)

On a dry matter basis, in the three herds (JxHF, HF2 and HF3) the yearly average ration consisted of respectively 52±10.4^a, 46±20^a, 61±25.3^b %, grazed pasture 24±2,2^a, 22±2.0, 21±5.3 %, grass silage plus hay and 24±2.4^a, 32±2.0^b, 30±2.3^b% concentrates (Horan et al 2005, McCarthy et al 2007). Biotype Jersey-Holstein (JxHF) presented better BCS than Holstein (HF2 and HF3) between period’s lactation (Table 2). Jerseys had higher condition scores and lower body weights than Holsteins according with the research cited (Washburn et al 2002)

Table 2. Main characteristics of cows of different biotypes and nutrition
Herd	Live Weight	BCS Dry	BCS Lact	Concentrate ingestion (kg/cow/d)
JxHF	489±60^a	3.10±0.02	2.40±0.02^a	4.50±0.24^a
HF2	631±80^b	3.00±0.02	2.20±0.02^b	7.50±0.31^b
HF3	624±15^b	3.00±0.02	2.20±0.02^b	3.80±0.34^a
^ab means in the same column for each parameter with different superscripts are significantly different (P<0.05)

Milk production was different according to system and also to composition in fat and protein (%)(Table 3). A significantly higher content of non fat solids and total solids was determined in the herd with cows with the smaller biotype (JxHF>HF2>HF3), (P<0.05).

Table 3. Daily milk production (l/cow/d) and composition of fat and protein (%)
Herd	l/cow/d	fat %	protein %	SNF %
JxHF	19.8±4.6^a	4.64±0.10^a	3.75±0.06^a	9.48±0.05^a
HF2	19.5±2.1^a	3.59±0.06^b	3.36±0.05^b	8.95±0.08^b
HF3	17.9±2.5^b	3.53±0.07^b	3.15±0.02^c	8.74±0.04^c
^abcmeans in the same column for each parameter with different superscripts are significantly different (P<0.05)

A significantly higher stocking density was determined in herd with cows with the smaller biotype (Table 4).

Table 4. Stocking density and grazing paddock after morning and afternoon milking
Herd	Stocking density (cow/ha/d)		Grazing paddock
	After morning milking (1)	After afternoon milking (2)	Ha (1)	Ha (2)
JxHF	181±48^a	130±23^a	1.45±0.15^a	2.08±0.10^a
HF2	82±7^c	91±8^b	4.48±0.89^b	4.11±0.67^b
HF3	117±11^b	116±5^ab	2.28±0.57^a	2.29±0.34^a
^abc means in the same column for each parameter with different superscripts are significantly different (P<0.05)

Pre-grazing (PeG) and post-grazing (PoG) herbage mass (kgDM/ha) in each grazing paddock were: 2883 and 1963 soybean crop, 3780 and 2723 sorghum. PeG were 1930±366, 2601±310, 2040±290 and PoG 1206±83b, 1997±299a, 1145±230b of perennial and annual pasture in H1, H2 and H3 respectively. The PeG values were 1962±78, 2577 ±553 and PoG 951±266, 412±56 for Lucerne in H1 and H3 respectively. In this study the range of post-gazing residuals of perennial and annual ryegrass/clover pasture and Lucerne mass was lower than the target levels (Bargo et al 2003). Pre-grazing perennial and annual ryegrass/clover pasture and Lucerne mass should be too insufficient compared with its targets (Bargo et al 2002). These farms did not achieve these levels with comparatively high feed pre-grazing and post-grazing herbage mass targets, with adequate quantities of quality forage, silages and concentrates supplementation. Grazing animals will often prefer certain forages over others, and those preferred forages are said to be more palatable. The relative palatability of a plant species depends on factors such as the other species present, stage of growth of each species, and soil fertility level (Pulido and Leaver 2001). The dual objectives of adequate level of feeding per cow, and high herbage utilization per hectare can be achieved through the inclusion of supplements. The milk response to supplements depends mainly on the amount of relative energy deficit between potential energy demand and actual energy supply. Cows of different genotype differ in their potential for milk yield. Cows with high genetic potential for milk yield undergo higher relative energy deficits under grazing dairy systems, resulting in lower substitution rates, higher milk responses to supplements, but also lower body condition score, which in turn, leads to lower reproductive performance (Baudracco et al 2010). Daily grazing time can be maximized. Foraging decisions such as when to begin, at which frequency, and how to spread the grazing events through time might be more important within a smaller scale (i.e., paddock). Cattle have become adapted to modern husbandry methods, which can be used to stimulate the motivation to graze (Burns et al 2005). As such, the precise time of the grazing events can be modified, depending for instance upon events such as removal for milking or time of herbage allocations (Gregorini et al 2005, Gregorini et al 2006).

According to variations in protein fractions, a significant result was obtained in a General Linear Model using biotype and milk production/d as co-factors, with a R² explaining 67.4% of variance from insoluble fractions for all data (P<0.01). These variations are also observed for each individual herd. Values of total insoluble fractions in milk outstanding from cows of one herd composed by three different biotypes, revealed that biotype Holstein have significant lower content of insoluble fractions than Jersey½ or Jersey¾ with values 57.0±6.12^a, 65.24±2.60^b and 67.60±5.64^b % (mean±s.e., respectively).

Biotype and Sample Time analyzed as Least squares means for insoluble fractions of total casein (%) indicate higher values for small biotype (JxHF) than the larger biotype (Table 5). Same data analyzed in a GLM explains variance of insoluble fractions in a 65.30 % of total error. Insoluble fractions are also influenced by lactation period (R²= 0.65) showing a periodogram with a rise of fractions in mid lactation. Other individual fractions of proteins do not reveal same tendency.

Table 5. Least squares means for insoluble fractions of total casein (%) with 95.0 percent confidence intervals indicating biotype and sample time effects.
Level	n	Mean	Std. Error	Lower Limit	Upper Limit
Biotype	260	55.65^ab	0.85	53.96	57.34
JxHF	114	62.89^b	1.36	60.19	65.59
HF2	93	60.22^b	1.10	58.04	62.39
HF3	53	43.84^a	1.60	40.68	47.00
Sample time	n	Mean	Std. Error	Lower Limit	Upper Limit
1	100	45.02^a	1.14	42.77	47.26
2	96	72.02 ^c	1.15	69.76	74.29
3	43	48.56^ab	1.70	45.21	51.91
4	21	56.99 ^b	2.54	51.98	62.00
^abc means in the same row for each parameter with different superscripts are different at P<0.05

Conclusions

Acknowledgements

References

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Evaluation of the dairy cow biotype through milk composition, nutrition and grazing management

H Landi, L Barros* and C Micheo**

Department. Animal Production, Faculty of Veterinary Sciences, UNCPBA, Tandil, Argentinae.
* Department. Ruminant Clinics, Veterinary Faculty, Montevideo, Uruguay.
** Food Technology, Faculty of Veterinary Sciences, UNCPBA, Tandil, Argentinae.
landih@vet.unicen.edu.ar or luisb@adinet.com.uy

Abstract

Introduction

Material and methods

Results and discussion

Conclusions

Acknowledgements

References

Evaluation of the dairy cow biotype through milk composition, nutrition and grazing management

H Landi, L Barros* and C Micheo**

Department. Animal Production, Faculty of Veterinary Sciences, UNCPBA, Tandil, Argentinae. * Department. Ruminant Clinics, Veterinary Faculty, Montevideo, Uruguay. ** Food Technology, Faculty of Veterinary Sciences, UNCPBA, Tandil, Argentinae. landih@vet.unicen.edu.ar or luisb@adinet.com.uy

Abstract

Introduction

Material and methods

Results and discussion

Conclusions

Acknowledgements

References

Department. Animal Production, Faculty of Veterinary Sciences, UNCPBA, Tandil, Argentinae.
* Department. Ruminant Clinics, Veterinary Faculty, Montevideo, Uruguay.
** Food Technology, Faculty of Veterinary Sciences, UNCPBA, Tandil, Argentinae.
landih@vet.unicen.edu.ar or luisb@adinet.com.uy