Differential response to stocking rates and feeding by two genotypes of Holstein-Friesian cows in a pasture-based automatic milking system

AbstractThe aim of the present study was to evaluate the changes in selected production and functional traits of Polish Holstein-Friesian cows after switching from a conventional (CMS) to an automatic milking system (AMS). The study consisted of 3398 Polish Holstein- Friesian dairy cows, from 16 herds in which CMS was changed to AMS. Cows were in their 1st (L1) or 2nd lactation (L2). The data consisted of milk yield [MY, kg], fat content [FC, %], protein content [PC, %], dry matter [DM, %], lactose content [LC, %], urea content [MU, mg/l], somatic cell count [SCC, thous./ml] and score [SCS, log]. The milking system had a significant impact on milk yield, fat, lactose, dry matter and urea contents. Regardless of lactation number, milk derived from CMS was characterised by higher values for FC, PC, DM SCC and SCS, while milk from AMS had higher MY, LC and MU. Multifactor analysis of variance also confirmed significant effect of herd, season, herd × milking system interaction on SCS in milk of cows in L1. In the studied herds change from CMS to AMS was evaluated separately for cows in L1 and L2. The transitioning from CMS to AMS resulted in the decrease of fat content in 6 L1 and 7 L2 herds, dry matter in 8 L1 and 5 L2 herds. SCS in milk also decreased in 4 L1 and 5 L2 herds. The change caused the increase of MY in 11 L1 and 9 L2 herds, lactose content in 6 L1 and 4 L2 herds and urea content in 9 L1 and 10 L2 herds. AMS may positively affect milk yield and health status, however, the change of milking system should be also accompanied by the change in herd management.

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Stall and feed bunk stocking rates impact cows' diurnal behavior and activity in automatic milking system farms

Journal of Veterinary Behavior ◽

10.1016/j.jveb.2018.01.004 ◽

2018 ◽

Vol 24 ◽

pp. 48-55 ◽

Cited By ~ 4

Author(s):

Ali A. Witaifi ◽

Ahmed B.A. Ali ◽

Janice M. Siegford

Keyword(s):

Diurnal Behavior ◽

Automatic Milking System ◽

Automatic Milking ◽

Stocking Rates

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Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems

Journal of Dairy Science ◽

10.3168/jds.2017-13441 ◽

2018 ◽

Vol 101 (3) ◽

pp. 2226-2234 ◽

Cited By ~ 15

Author(s):

S. van Engelen ◽

H. Bovenhuis ◽

P.P.J. van der Tol ◽

M.H.P.W. Visker

Keyword(s):

Methane Emission ◽

Genetic Background ◽

Infrared Sensors ◽

Holstein Friesian ◽

Automatic Milking ◽

Automatic Milking Systems ◽

Friesian Cows

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The milking frequency of primiparous cows in their early stage of lactation and its impact on milking performance

Animal Production Science ◽

10.1071/an18409 ◽

2020 ◽

Vol 60 (3) ◽

pp. 436

Author(s):

Beata Sitkowska ◽

Dariusz Piwczyński ◽

Magdalena Kolenda ◽

Jolanta Różańska-Zawieja

Keyword(s):

Early Stage ◽

Stage Of Lactation ◽

Holstein Friesian ◽

Full Production ◽

Milk Performance ◽

Milking Frequency ◽

Automatic Milking System ◽

Automatic Milking ◽

The Relationship ◽

Potential Use

An automatic milking system allows cows to present their full production capability by not limiting them to a specific time when the milking occurs or a fix number of milkings per day. The beginning of the first lactation is a key point in terms of subsequent milk production. The aim of the present study was to indicate the relationship between the milking frequency of primiparous cows during the first month of lactation and their subsequent milk performance. Material of the study consisted of 25 Polish herds of Holstein–Friesian dairy cattle. All cows were milked with the use of an automatic milking system. Animals were divided into five groups, depending on the milking frequency in the first month after calving (MFF). The collected data were statistically processed using the multifactorial ANOVA. The best milk and milking parameters characterised primiparous cows, for which the average number of milkings per day was at the level of 3–3.5 or above, this group did not have a preferred time for their milking. This group of cows milked more frequently during the first month of lactation (MFF5) and had the highest milk yield (MY) and milking duration. The highest culling percentage (57.77%) was noted within the group of primiparous cows with the lowest milking frequency during the first month of lactation (MFF1). MFF5 animals maintained better milk and milking parameters in all months of lactation than did those in the other groups. Older animals, that calved after the 28th month of life, and those that calved during warmer seasons, showed the tendency to have a lower milking frequency and poorer milk and milking parameters. The findings obtained in the present study are interesting in terms of their potential use, because they show that frequent milking during the first month after calving corresponds to a better overall MY during that lactation. Hopefully, by promoting frequent milkings at the beginning of lactation, farmer may increase the overall lactation MY.

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Survival of Polish Holstein-Friesian cows to second, third and fourth lactation in conventional and automatic milking systems

Annals of Animal Science ◽

10.2478/aoas-2021-0010 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Dariusz Piwczyński ◽

Beata Sitkowska ◽

Marcin Brzozowski ◽

Mariusz Bogucki ◽

Piotr Wójcik

Keyword(s):

Logistic Regression ◽

Milk Yield ◽

Low Fertility ◽

Holstein Friesian ◽

Chance Events ◽

Automatic Milking ◽

Calving Season ◽

Automatic Milking Systems ◽

Positive Effect ◽

Friesian Cows

Abstract The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility (infertility and reproductive disorders), locomotor diseases, low milk yield, other diseases (metabolic, digestive and respiratory diseases), accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

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External validation of the GrazeIn model of pasture dry matter intake and milk yield prediction for cows managed at different calving dates and stocking rates

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2017154-10380 ◽

2018 ◽

Vol 15 (4) ◽

pp. e0608

Author(s):

Ana I. Roca-Fernández ◽

Antonio González-Rodríguez

Keyword(s):

Milk Yield ◽

Dry Matter ◽

External Validation ◽

Dry Matter Intake ◽

Holstein Friesian ◽

Grazing System ◽

The Mean ◽

Using Data ◽

Stocking Rates ◽

Friesian Cows

The aim was to evaluate the prediction accuracy of pasture dry matter intake (PDMI) and milk yield (MY) predicted by the GrazeIn model using a database representing 124 PDMI measurements at paddock level and 2232 MY measurements at cow level. External validation of the model was conducted using data collected from a trial carried out with Holstein-Friesian cows (n=72) while grazed 28 paddocks and were managed in a 2×2 factorial design by considering two calving dates (CD), with different number of days in milk (DIM), early (E, 29 DIM) vs. middle (M, 167 DIM), and two stocking rates (SR), medium (M, 3.9 cows ha-1) vs. high (H, 4.8 cows ha-1), under a rotational grazing system. Cows were randomly assigned to four grazing scenarios (EM, EH, MM and MH). The mean observed PDMI of the total database was 14.2 kg DM cow-1 day-1 while GrazeIn predicted a mean PDMI for the database of 13.8 kg DM cow-1 day-1. The mean bias was −0.4 kg DM cow-1 day-1. GrazeIn predicted PDMI for the total database with a relative prediction error (RPE) of 10.0% at paddock level. The mean observed MY of the database was 23.2 kg cow-1 day-1 while GrazeIn predicted a MY for the database of 23.1 kg cow-1 day-1. The mean bias was –0.1 kg cow-1 day-1. GrazeIn predicted MY for the total database with a mean RPE of 17.3% at cow level. For the scenarios investigated, GrazeIn predicted PDMI and MY with a low level of error which made it a suitable tool for decision support systems.

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Nitrogen-fertilised grass in a subtropical dairy system 3. Effect of stocking rate on the response to nitrogen fertiliser

Australian Journal of Experimental Agriculture ◽

10.1071/ea9950145 ◽

1995 ◽

Vol 35 (2) ◽

pp. 145 ◽

Cited By ~ 2

Author(s):

RT Cowan ◽

KF Lowe ◽

PC Upton ◽

TM Bowdler

Keyword(s):

Milk Yield ◽

Ground Surface ◽

Yield Response ◽

Stocking Rate ◽

Rhodes Grass ◽

Holstein Friesian ◽

Chloris Gayana ◽

Low Efficiency ◽

Stocking Rates ◽

Friesian Cows

Two stocking rates, one as practised on farms (2 cows/ha) and the other 50% higher, were assessed for effect on pasture and milk yield response to applied nitrogen (N) fertiliser (0-600 kg N/ha. year) for Holstein-~Friesian cows grazing Rhodes grass (Chloris gayana) cv. Callide pastures. Pastures were grazed in combination with grazing oats for winter, with overall farm stocking rates of 1.17 and 1.37 cows/ha for ' the 2 treatments. Cows were maintained on these areas for 3 years. Cracked grain was given at 0.8 t/cow. year, and hay or silage supplements were given when green grass yield was <0.5 t dry matter (DM)/ha. The incremental response (P<0.05) in milk yield to each kg increase in level of applied N was 4.93 kg/ha at 1.17 cows/ha and 1.64 kg/ha at 1.37 cows/ha. The amount of conserved forage fed at the high stocking rate increased (530 and 970 kg/ha. year at 1.17 and 1.37 cows/ha), and financial margins over costs were reduced at the high stocking rate. The low milk response at the high stocking rate was associated with a low response in pasture growth. At <2 t pasture DM/ha on offer, incremental response to applied N declined, and there may have been an excessive loss of N through volatilisation in heavily grazed pastures. Milk yield per cow was closely related to total pasture yield on offer (P<0.01), and to leaf and stem yields (P<0.05). Relationships were stronger in summer and autumn than in spring. Over the full year, milk yield increased by 1.24 kg/kg leaf DM or 0.24 kg/kg total pasture DM on offer. At the higher stocking rate, surface soil (0-10 cm) concentrations of phosphorus and nitrate were higher than at the lower stocking rate. We conclude that in areas of moderate rainfall (<1000 mm/year) in the subtropics, high stocking rates resulting in low pasture yields and exposed ground surface will be associated with low efficiency of use of applied N.

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The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽

10.3390/ani11030721 ◽

2021 ◽

Vol 11 (3) ◽

pp. 721

Author(s):

Krzysztof Adamczyk ◽

Wilhelm Grzesiak ◽

Daniel Zaborski

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Discriminant Analysis ◽

Multilayer Perceptrons ◽

Correct Classification ◽

Correct Classification Rate ◽

Classification Rate ◽

Holstein Friesian ◽

Artificial Neural ◽

Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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