Effects of Calfhood Pneumonia and Scours on Milk Production in Holstein Dairy Cattle

Kevin Gavin

Fall 2015

Lawrence K. Fox MS, PhD

Veterinary Clinical Sciences

Washington State University College of Veterinary Medicine

1

As thesis advisor for Kevin Gavin, I have read this paper and find it satisfactory.

_______________________________________ _________________ Lawrence K. Fox MS, PhD Date

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Précis

Milk production is an essential function for the 9.2 million dairy cows in the United

States. Many factors such as nutrition, genetics, weather, and health contribute to a cow’s ability

to produce milk. As management practices advance, dairy producers constantly search for new

ways to more efficiently produce milk. It has long been understood that healthy and well-

nourished calves will grow up to be healthy and productive adults, but the actual impact of

calfhood diseases is relatively unclear. The purpose of this study is to investigate the relationship

between calfhood diseases and first lactation milk production in Holstein dairy cattle. Pneumonia

and scours (diarrhea) were the two calfhood diseases studied.

A large dairy herd (n=8,000) in Eastern Washington was used for this study. First

lactation cows were classified into groups based on the number of scours and pneumonia events

they experienced as a calf. This health history and the cows’ first lactation milk production were

obtained through the computerized record management system that the dairy uses. Averages of

milk production were calculated for each group and t-tests were performed to determine

significance. Additional information about the cows, such as season of birth, season of calving,

and genetic information was also obtained for construction of a general linear model to explain

the variation in milk production.

The initial analysis and t-tests of first lactation milk production by categorical group

indicated that one or more calfhood scours events were associated with significantly reduced first

lactation milk production. Calves that had greater than three calfhood pneumonia events were

associated with significantly reduced milk production. No significant difference in first lactation

production for calves with no as compared to up to 3 pneumonia events was observed. The linear

model indicated that the number of calfhood scours events had no significant impact on first

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lactation milk production. A statistical trend was observed between the number of calfhood

pneumonia events and milk production.

Further research similar to ours could be conducted using more herds in other geographic

locations. Additionally, other variables that impact milk production and further analysis on the

interactions between variables in the model could be conducted.

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Table of Contents

Introduction………………………………………………………………………………………..6

Thesis Activity…………………………………………………………………………………….7

Materials and Methods…………………………………………………………………………….7

Results……………………………………………………………………………………………10

Discussion………………………………………………………………………………………..11

Conclusion……………………………………………………………………………………….15

References Cited…………………………………………………………………………………16

Appendix…………………………………………………………………………………………19

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I. Introduction

Studies over the past several decades have indicated strong correlations between dairy

calf care and milk production as lactating cows. The relationship between calfhood rearing

practices and future milk production may be a function of rate of gain. Milk production is

extremely important for dairy cows, as milk sales comprise the largest source of revenue on most

U.S. dairies (Penn State Extension, 2015).

Research has shown a strong correlation between preweaning nutrition and milk

production. A 2005 study indicated that calves fed two pounds of milk replacer per day produced

1,543 pounds more milk in the first 200 days of their first lactation than calves fed one pound of

milk replacer per day (Ballard et al., 2005). Studies have also shown a correlation between the

amount of colostrum fed to calves and their subsequent milk production. A study compared

Brown Swiss calves fed either two or four quarts of colostrum at birth and the effect on milk

production at the end of their second lactation. The calves fed four quarts of colostrum produced

2,263 more pounds of milk by the end of their second lactation than the calves fed two quarts

(Faber et al., 2005).

Calfhood disease incidence has been negatively correlated with body size later in life. A

study of female Holstein calves in Florida indicated calves diagnosed with pneumonia or scours

(diarrhea) during the first six months of life had reduced body weight at six months of age, 10.6

and 9.1 kg less weight than non-diseased cohorts (Donovan et al., 1998). A similar study found

that calves treated for bovine respiratory disease (BRD) in the first 60 days of the trial had

average daily gains 0.17 kg lower and were 1.7 cm shorter in height than calves that were not

treated for BRD at 13 months of age (Stanton et al., 2012).

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Several studies have indicated that average daily gain pre-parturition is highly correlated

with milk production. A 2012 Cornell study found that for every 1 kg of preweaning average

daily gain, heifers produced an average of 850 kg more milk during the first lactation (Soberon et

al., 2012). Another study found every additional pound of average daily gain resulted in 1,000

pounds more milk during the first lactation (Van Amburgh et al., 2009). A third study found that

heifers with average daily gains less than 0.85 kg per day during months 5-14 of life produced

approximately 450 pounds less milk during the first lactation than heifers with average daily

gains greater than or equal to 0.85 kg (Krpálková et al., 2014).

Given that calfhood diseases can affect average daily gains and thus body development

during the pre-parturient period, and with a significant relationship between body weight during

calf development and first lactation milk production, it would be expected that calfhood diseases

would have a negative impact on future milk production. The primary independent variables of

interest in this study are: calfhood pneumonia and scours, two common diseases of calves. Other

variables known to influence the dependent variable, first lactation milk production, will also be

considered as independent variables.

II. Thesis Activity

This project studied the relationship between calfhood diseases and first lactation milk

production in Holstein dairy cattle. The calfhood diseases studied were pneumonia and scours.

III. Materials and Methods

Study Herd

A large dairy herd in Eastern Washington (n=8,000 cows) with Good Health Records was

used for this study. According to the Good Health Records Program, Good Health Records are

defined as those that are accurate, consistent, and informative (WSU Extension, 2015). Good

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Health Records are achieved by recording all disease episodes, using a single, specific event to

record each disease, and recording the same information, in the same order with the same

abbreviations for all disease episodes (WSU Extension, 2015; Wenz and Giebel, 2012). The

study herd was selected because of its previous participation in the Good Health Records

Program and an existing relationship with the university and research team.

The study population was first lactation Holsteins cows (“study cows”) with a listed

305ME (305 day mature equivalent milk production) of greater than zero. Complete records

were available for 2,858 study cows.

305ME vs Calfhood Scours and Pneumonia

Disease events in the DairyComp 305 database management software system (Valley Ag

Software, Tulare, CA) recorded prior to the cow’s current lactation are not included in the cow’s

current cow card; rather, they are stored in archive files. Calfhood pneumonia and scours events

for the study cows were retrieved from archive files by using the command EVENTS ID BDAT

XCOMB XSCOU\2I and were exported into an Excel (Microsoft Corp., Redmond, WA)

spreadsheet. The number of calfhood disease events were tallied.

Initially study cows were classified into groups based on their medical history for

calfhood pneumonia and scours. For scours, study cows were sorted into a group for zero

calfhood scours events, “Zero Scours”, or a group for greater than zero scours events, “Greater

than Zero Scours” (Table 1). Study cows were sorted into three groups for calfhood pneumonia

history: zero; one to three; or greater than three pneumonia events (Table 2). The DairyComp

Commands used to classify these cows are listed in Table 3.

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Reports containing the ID number and 305ME of each study cow were generated using

DairyComp 305 and exported to Excel. Initial analysis included calculations of the 305ME mean

and standard deviation for each group.

An unpaired t-test from www.graphpad.com (GraphPad Software, La Jolla, CA) was

used to compare the average 305ME between each group (Table 1 and 2) of the same disease.

Testing Multiple Variable Effects on 305ME

Using DairyComp 305, the command SHOW ID 305ME XCOMB XSCOU BDAT

FRAGE FDAT XMAST XLAME XPNEU XMETR PAMLK SID FOR LACT=1 CBRD=H

305ME>0 was used to generate a report including ID number, first lactation milk production,

pneumonia and scours, birth month, birth year, age at first calving, calving month, calving year,

first lactation diseases (mastitis, lameness, pneumonia, metritis), and parent average PTA milk

for each first lactation Holstein cow with a 305ME greater than zero. This data was exported to

Excel where birth date and fresh date were converted to birth month, birth year, fresh date, and

fresh year, respectively, using the Find and Replace function on Excel. Fresh age was converted

from years-months (2-0 indicates 24 months) to months (24 indicates 24 months) also using the

Find and Replace function. One hundred fifty five cows were removed from the study because

they did not meet the breed criteria or not all data was recorded.

For data analysis, a General Linear Model was constructed using SAS (9.4) (SAS

Institute, Cary, NC). The dependent variable, 305ME was regressed against the independent

variables: the number of pneumonia and scours events as a calf, birth month, birth year, age at

first calving, calving month, calving year, first lactation diseases (number of mastitis, lameness,

pneumonia, and metritis events), and parent average PTA milk. Thus in this model the number of

pneumonia and scours disease events were considered continuous variables. Interactions between

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independent variables were not considered. A mathematical representation of the model is listed

below.

305ME=XCOMB+XSCOU+BMO+BYR+FRAGE+FMO+FYR+XMAST+XLAME+XPNEU+

XMETR+PAMLK

IV. Results

305ME vs. Calfhood Scours

The mean 305ME was 26,399.82 pounds for study cows with zero calfhood scours events

(n=899) and 24,956.47 pounds for study cows with greater than zero scours events (n=1959)

(Table 5). These values are statistically significantly different (unpaired t-test, P<0.0001).

305ME vs. Calfhood Pneumonia

The mean 305ME for study cows with zero calfhood pneumonia events (n=1847) was

25,511.69 pounds; 25,602.19 pounds for study cows with 1-3 calfhood pneumonia events

(n=853); and 23,192.34 pounds for study cows with greater than 3 calfhood pneumonia events

(n=158). The 305ME for study cows with zero and 1-3 calfhood pneumonia events was not

significantly different (unpaired t-test, P=0.6609) (Table 6). The 305ME for study cows with

greater than 3 calfhood pneumonia events was significantly lower when compared to milk

production in the other pneumonia groups (unpaired t-test, P<0.0001).

Testing Multiple Variable Effects on 305ME

The model indicated that independent variables birth month, fresh month, number of

mastitis events during the first lactation, number of metritis events during the first lactation, and

parent average Predicted Transmitting Ability for milk significantly affected 305ME (GLM,

P<0.05). There was a statistical trend between the number of pneumonia events as a calf and the

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305ME of the study cows (GLM, P=0.0669). All other independent variables did not indicate

statistical significance (Table 7).

Trends for the effect of birth month and fresh month on first lactation milk yield appeared

to be a function of season. Nadirs in first lactation milk production were seen in the summer.

Calves born in June through September had the lowest first lactation 305ME (Figure 1) and

heifers that that had their first parturition April through July had the lowest first lactation milk

production (Figure 2). Metritis during the first lactation had a significant effect on milk

production. It appeared that milk production decreased with increasing number of metritis cases

(Figure 3).

V. Discussion

The incidence of calfhood pneumonia and scours as independent variables were expected

to have a significant effect on the model predicting future milk production. The results of the

study indicate that the hypothesis was not well supported. Overall these diseases did not have a

significant impact on first lactation milk production in this study (Table 7). The initial analysis

where means of milk production by categorical group type (no calfhood scour event vs. at least

one scour event; or, no pneumonia event vs. 1-3 pneumonia events, vs. more than 3 pneumonia

events) indicated that calfhood health, as measured, influenced milk production during the first

lactation. These means were contrasted using an unpaired t-test to determine if this difference

was significant. One or more calfhood scours events were associated with significantly reduced

first lactation milk production (Table 5). Calves that had greater than three calfhood pneumonia

events were associated with significantly reduced milk production (Table 6). No significant

difference in first lactation production for calves with no as compared to up to 3 pneumonia

events was observed. These observations from the t-test comparisons suggested that pneumonia

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and scours during calfhood influences future milk production. To explore this relationship in

detail, a linear model was constructed to further analyze the impact of calfhood diseases on milk

production. The linear model allowed for analysis of the impact of number of disease events on a

continuum, as opposed to analyzing categorical groups of number of disease events. The model

indicated that the number of calfhood scours events had no significant impact on first lactation

milk production (Table 7). A statistical trend was observed between the number of calfhood

pneumonia events and milk production (Table 7). The results from the model suggest that the

number of calfhood scours events has very little impact on first lactation milk production, and

the number of calfhood pneumonia events potentially has an effect on milk production (P< 0.07).

The conflicting results of the t-tests and linear model are an interesting finding that merits

discussion. While the t-tests found a significant difference between groups (P< 0.05) of study

cows based on health history, the major differences were between animals at the extremes. For

example, according to the t-test, the only significant difference in milk production for calfhood

pneumonia was associated with animals that had greater than three pneumonia events as a calf as

compared to calves with no pneumonia events. The model, which evaluated number of disease

events on a continuum, suggested that the impact of calfhood pneumonia on milk production

approached significance. This could indicate that chronic calfhood respiratory diseases do have

an impact on milk production, but one or two disease events do not.

Warnick et al. (1995) also reported a similar lack of association between calfhood

diseases and milk production. The Warnick study evaluated the impact of three different

calfhood diseases; respiratory illness, scours, and dullness, in 24 New York State dairy herds.

While milk production was not found to be affected by calfhood diseases, heifers that

experienced a case of respiratory disease or illness were at much greater risk to die or be culled

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from the herd prior to calving. Despite the best efforts of the researchers, many variables went

uncontrolled in Warnick’s study as well as ours. At least 24 different people were in charge of

noticing, diagnosing, and recording calfhood diseases in Warnick’s study, and the study herd for

our investigation employed multiple people for this job. The potential exists for lack of

agreement between evaluators of disease which could diminish precision. Since our study only

analyzed one herd and the Warnick study considered the herd to be an independent variable, the

variation from management differences between farms is accounted for. Our study did not

consider the culling or death risk for the calves, but given the cost to raise a heifer this is an

important factor to consider. The fact that diseased heifers were more likely to leave the herd

could help explain the lack of association between calfhood diseases and milk production.

Animals that do not make it to adulthood will not produce milk and therefore the effects of their

diseases are unknown. Yet given the study design, to simply examine first lactation milk

production after a disease event, the data from animals that died were censored and thus any

negative effects of a calfhood disease event they experienced was excluded from the data set.

Also, heifers that are purchased to replace culled or died heifers likely will not come with good

health records. During retrospective analysis, these heifers would likely be categorized as having

no disease history, which could further skew the results.

Statistical analysis is an extremely valuable tool for science, yet it is imperfect and must

be used differently depending on the situation. Ronald Fisher was a pioneer of statistical analysis

in the early 20th century when he suggested a P value of 0.05 as a reasonable cutoff for the

rejection of the null hypothesis. He later indicated that he did not intend for the P value to be

fixed at 0.05 for all experiments, rather this was a general guideline that should be altered

depending on the precision of the tests (Fisher, 1955). When using P values, one must remember

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that 0.051 is really not hugely different than 0.049, even though one may be considered

significant and the other is not. It is suggested to begin preliminary trials with a larger P value

(such as 0.1) for initial testing in order to reduce the risk of false negatives, and then slowly

decrease the P value to detect fewer false positives (McDonald, 2014). Values that are just

outside of the significant range can be considered a trend.

The results of the linear model suggested that parental genetic merit, birth month, month

of the heifers’ first parturition, number of mastitis and metritis cases the test animals had in their

first lactation, had a significant impacts on first lactation milk production (Table 7). Genetics was

assumed to be a significant driver of milk production, as multiple studies have indicated that

heritability for milk yield is at minimum 0.3 and could be as high as 0.5 (Van Tassell et al.,

1999; Eaglen et al., 2013). The seasonal trends for the effect of birth month and calving month

were in accordance with a study by Barash et al., (1996) which indicated that births and calvings

in the late spring and summer resulted in lower milk yields. The proposed explanation for this

trend is the impact of increased photoperiod and temperature during the summer months, which

is positively correlated with circulating levels of prolactin (Barash et al., 1996). It is postulated

that increased levels of circulating prolactin during the second and third trimesters of pregnancy

contribute to greater mammary gland development and increased milk production (Barash et al.,

1996). Cows exposed to shorter photoperiods and lower temperatures during the second and third

trimesters of pregnancy (spring/early summer calving) will have lower levels of prolactin and

lower milk production. Cases of clinical mastitis were found to significantly impact milk

production, which is supported by Hagnestam et al. (2007). Sepúlveda-Varas et al. (2014) found

that cows experiencing clinical mastitis had depressed dry matter intakes up to several weeks

before and after the disease event, thus decreasing milk production. Significant permanent

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damage to mammary tissue resulting from a clinical mastitis event has been observed by Zhao

and Lacasse (2008), which limits the cow’s milk producing ability for the rest of the lactation.

Clinical metritis cases were found to have a significant impact on milk production, which

supports findings of Mahnani et al. (2015). Wittrock et al. (2012) suggests decreased dry matter

intake during and surrounding the disease episode contributes to this decrease in milk yield. By

adding the variables listed in Table 7, in addition to pneumonia and scours events, to the model

lead to a more precise estimate of factors affecting future milk production. This added precision

may have accounted for the discrepancy seen between the statistical analysis of the un-paired t

test and the general linear model; as in the simple contrast of the means the effects of the

alternative variables were not specifically estimated. The alternative variables may have been

associated and possibly may have had confounding effects with the dependent variables of

greatest interest.

VI. Conclusion

The results of this study provide perspective of the impact of calfhood diseases on first

lactation of milk production. Unpaired t-tests indicated that calfhood scours events and greater

than three calfhood pneumonia events are correlated with decreased milk production. While

there were discrepancies between the t-tests and the linear model, calfhood pneumonia appears to

potentially have an impact on milk yield. The value of the 2,300 pounds of lost milk production

in the greater than three pneumonia events group is approximately $460. Given that 5.5% of

calves experienced greater than three calfhood pneumonia events, approximately $25 per calf

could be invested to decrease the incidence of greater than three pneumonia events. This study

could also help producers make culling decisions for chronically ill (more than 3 pneumonia

events for example) animals, although other factors such as size, appearance, and physical

15

examination results should also be important factors in these decisions. Despite our efforts to

include most major measurable variables, the model was only able to explain 27.5% of the

variation between milk production of first lactation cows. Further studies similar to ours could be

conducted using more herds in different parts of the county. Additionally, other variables that

impact milk production and further analysis on the interactions between variables in the model

could be conducted.

VII. References Cited

Ballard, C., H. Wolford, T. Sato, K., Uchida, M. Suekawa, Y. Yabuuchi, and K. Kobayashi. 2005. The effect of feeding three milk replacer regimens preweaning on first lactation performance of Holstein cattle. J. Dairy Sci. 88:22.

Barash, H., N. Silanikove, and J. I. Weller. 1996. Effect of Season of Birth on Milk, Fat and Protein Production of Israeli Holsteins. J. Dairy Sci. 79:1016-1020.

Donovan, G., I. Dohoo, D. Montgomery, and F. Bennett. 1998. Calf disease factors affecting growth in female Holstein calves in Florida, USA. Preventative Veterinary Medicine 33:1-10.

Eaglen, S. A. E., M. P. Coffey, J. A. Woolliams, and E. Wall. 2013. Direct and maternal genetic relationships between calving ease, gestation length, milk production, fertility, type, and lifespan of Holstein-Friesian primiparous cows. J. Dairy Sci. 96:4015-4025.

Faber, S. N., N. E. Faber, T. C. McCauley, and R. L. Ax. 2005. Case Study: Effects of colostrum ingestion on lactational performance. Prof. Anim. Scientist 21:420.

Fisher, Ronald. 1955. Statistical Methods and Scientific Induction. Department of Genetics. University of Cambridge.

Hagnestam, C., U. Emanuelson, and B. Berglund. 2007. Yield Losses Associated with Clinical Mastitis Occurring in Different Weeks of Lactation. J. Dairy Sci. 90:2260-2270.

Krpálková, K., V. E. Cabrera, M. Vacek, M. Stípková, L. Stádník, and P. Crump. 2014. Effect of prepubertal and postpubertal growth and age at first calving on production and reproduction traits during the first three lactations in Holstein dairy cattle. J. Dairy Sci. 97: 3017-3027.

Mahnani, A., A. Sadeghi-Sefidmazgi, and V. E. Cabrera. 2015. Consequences and economics of metritis in Iranian Holstein dairy farms. J. Dairy Sci. 98:1-10.

McDonald, J.H. 2014. Handbook of Biological Statistics. 3rd ed. Sparky House Publishing, Baltimore, Maryland.

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Penn State Extension. 2015. Farm Income Statement Sample. http://extension.psu.edu/courses/ meat-goat/financial-information/farm-business-analysis/farm-income-statement-sample. (Accessed 18 March 2015.)

Progressive Dairyman. 2015. July 20, 2015 Marketwatch- Cattle. http://www.progressivedairy .com/images/downloads/2015/07/24/1315pd_cattle.pdf. (Accessed 11 August 2015.)

Sepúlveda-Varas, Pilar, Kathryn L. Proudfoot, Daniel M. Weary, and Marina A.G. von Keyserlingk. 2014. Changes in behaviour of dairy cows with clinical mastitis. Applied Animal Behaviour Science. http://dx.doi.org/10.1016/j.applanim.2014.09.022.

Soberon, F., E. Raffrenato, R. W. Everett, and M. E. Van Amburgh. 2012. Preweaning milk replacer intake and effects on long-term productivity of dairy calves. J. Dairy Sci. 95: 783-793.

Stanton, A. L., D. F. Kelton, S. J. LeBlanc, J. Wormuth, and K. E. Leslie. 2012. The effect of respiratory disease and a preventative antibiotic treatment on growth, survival, age at first calving, and milk production of dairy heifers. J. Dairy Sci. 95:4950-4960.

University of Wisconsin-Madison. 2015. Understanding Dairy Markets- Milk, All – Price. http://future.aae.wisc.edu/data/annual_values/by_area/10?tab=prices. (Accessed 11 August 2015.)

Van Amburgh, M. E., E. Raffrenato, F. Soberon and R. W. Everett. 2009. Early Life Management and Long-Term Productivity of Dairy Calves. http://dairy.ifas.ufl.edu/rns/2009/VanAmburgh.pdf. (Accessed 28 February 2015.)

Van Tassell, C. P., G. R. Wiggans, and H. D. Norman. 1999. Method R Estimates of Heritability for Milk, Fat, and Protein Yields of United States Dairy Cattle. J. Dairy Sci. 82: 2231-2237.

Virtala, A. M. K., G. D. Mechor, Y. T. Gröhn, and H. N. Erb. 1996. The effect of calfhood diseases on growth of female dairy calves during the first three months of life in New York State. J. Dairy Sci. 79:1040-1049.

Warnick, L. D., H. N. Erb, and M. E. White. 1995. Lack of Association Between Calf Morbidity and Subsequent First Lactation Milk Production in 25 New York Holstein Herds. J. Dairy Sci. 78: 2819-2830.

Washington State University Extension. 2015. What are “Good Health Records?” http://extension.wsu.edu/gdhr/information/what/Pages/default.aspx. (Accessed 28 March 2015.)

Wenz, J. R., and S. K. Giebel. 2012. Retrospective evaluation of health event data recording on 50 dairies using Dairy Comp 305. J. Dairy Sci. 95: 4699-4706.

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Wittrock, Julie, and J. Huzzey. 2012. Effect of Metritis on Intake, Milk Yield, and Culling Risk. http://dairycentre.landfood.ubc.ca/2012/01/01/effect-of-metritis-on-intake-milk-yield-and-culling-risk/. (Accessed 8 August 2015.)

Zanton, G. I., and A. J. Heinrichs. 2005. Meta-Analysis to assess effect of prepubertal average daily gain of Holstein heifers on first-lactation production. J. Dairy Sci. 88:3860-3867.

Zhao, X. and P. Lacasse. 2008. Mammary tissue damage during bovine mastitis: Causes and control. J. Anim. Sci. 86(Suppl. 1):57–65.

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VIII. Appendix

Figure 1. Average 305ME milk production (pounds) for study cows by month of birth

Jan Feb Mar Apr May

Jun Jul Aug Sep Oct Nov Dec 0

5000

10000

15000

20000

25000

30000

305ME vs. Birth Month

Birth Month

305M

E (lb

)

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Figure 2. Average 305ME milk production (pounds) for study cows by month of calving

Jan Feb Mar

Apr May

Jun Jul Aug Sep Oct Nov Dec 0

5000

10000

15000

20000

25000

30000

305ME vs. Fresh Month

Fresh Month

305M

E (lb

)

20

Figure 3. Average 305ME milk production (pounds) for study cows by number of metritis events

0 1 20

5,000

10,000

15,000

20,000

25,000

30,00025,681

23,624

19,031

305ME vs. Metritis Events

First Lactation Metritis Events

305M

E (lb

)

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Table 1. Classification groups for scours and number of study cows in each groupGroup Cases of Calfhood Scours Number of Study CowsZero Scours Events 0 899

Greater than Zero Scours Events

1 17092 2473 3Total 2858

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Table 2. Classification groups for pneumonia and number of study cows in each groupGroup Cases of Calfhood Pneumonia Number of Study CowsZero Pneumonia Events 0 1847

1-3 Pneumonia Events1 1622 6143 77

Greater than Three Pneumonia Events

4 1095 136 297 18 59 010 1Total 2858

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Table 3. Commands used to generate reports for preliminary data analysis. For abbreviation definitions see Table 4.

Group CommandZero scours events SHOW ID 305ME FOR XSCOU=0 LACT=1 CBRD=H

305ME>0Greater than zero scours events SHOW ID 305ME FOR XSCOU>0 LACT=1 CBRD=H

305ME>0Zero pneumonia events SHOW ID 305ME FOR XCOMB=0 LACT=1 CBRD=H

305ME>0One through three pneumonia events SHOW ID 305ME FOR XCOMB=1-3 LACT=1

CBRD=H 305ME>0Greater than three pneumonia events SHOW ID 305ME FOR XCOMB>3 LACT=1 CBRD=H

305ME>0

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Table 4. DairyComp 305 abbreviations and definitionsAbbreviation Definition305ME 305 day mature equivalent milk production (pounds)BDAT Birth dateBMO Birth monthBYR Birth yearCBRD Cattle breedFDAT Fresh (calving) dateFMO Fresh (calving) monthFRAGE Age at first calving (months)FYR Fresh (calving) yearID Cow ID numberLACT Lactation numberPAMLK Parent average Predicted Transmitting Ability for milkSID Sire ID codeXCOMB Number of pneumonia incidents as a calfXLAME Number of lame incidents in first lactationXMAST Number of mastitis incidents in first lactationXMETR Number of metritis incidents in first lactationXPNEU Number of pneumonia incidents in first lactationXSCOU Number of scours incidents as a calf

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Table 5. Mean 305ME milk production (pounds) for study cows with and without calfhood scours1

Zero Scours Events Greater Than Zero Scours Events305ME 26,399.82a 24,956.47b

Standard Deviation 4,852.49 4,987.61Number of calves 899 1959

1Means not sharing the same superscript were significantly different (P<0.05).

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Table 6. Mean 305ME for study cows based on number of calfhood pneumonia cases1

Zero Pneumonia Events 1-3 Pneumonia Events

Greater Than 3 Pneumonia Events

305ME 25,511.69a 25,602.19a 23,192.34b

S.D. 5,063.14 4,797.86 4,620.45Number of calves 1847 853 158

1Means not sharing the same superscript were significantly different (P<0.05).

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Table 7. General Linear Model ResultsSource DF Sum of Squares Mean Square F

ValuePr > F

Model 60 19603402028 326723367 17.72 <.0001

Error 2797

51581385706 18441682

Corrected Total 2857

71184787734

R-Square Coeff Var Root MSE _305ME Mean

0.275388 16.90003 4294.378 25410.48

Source DF Type III SS Mean Square

F Value Pr > F

Calfhood Pneumonia Events 9 295537807 32837534 1.78 0.0669

Calfhood Scours Events 3 44327555 14775852 0.80 0.4931

Birth Month 11 398874293 36261299 1.97 0.0278

Birth Year 2 68071951 34035976 1.85 0.1581

Fresh Age 8 183776474 22972059 1.25 0.2680

Fresh Month 11 1291081181 117371016 6.36 <0.0001

Fresh Year 2 27375230 13687615 0.74 0.4762

First Lactation Mastitis Events 2 163777002 81888501 4.44 0.0119

First Lactation Lameness Events

8 113328203 14166025 0.77 0.6310

First Lactation Metritis Events 2 722709064 361354532 19.59 <0.0001

Parent Average PTA for Milk 1 208550426 208550426 11.31 0.0008

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