Predicting colostrum quality from performance in the previous lactation ...

J. Dairy Sci. 99:4048–4055 http://dx.doi.org/10.3168/jds.2015-9868 © American Dairy Science Association®, 2016.

Predicting colostrum quality from performance in the previous lactation and environmental changes R. G. Cabral,1 C. E. Chapman, K. M. Aragona, E. Clark, M. Lunak, and P. S. Erickson2 Department of Biological Sciences, University of New Hampshire, Durham 03824

ABSTRACT

Nine New Hampshire Holstein dairies contributed to a study to investigate if colostrum quality could be predicted by cow performance in the previous lactation and by environmental factors during the 21-d prepartum period. The numbers of days below 5°C (D), and days between 5 and 23°C (D) were used in the development of the regression equation. Between 2011 and 2014, 111 colostrum samples were obtained and analyzed for IgG. Producers recorded cow identification number, calf date of birth, sex of the calf, colostrum yield, hours from parturition to colostrum harvest, and weeks on pasture during the dry period (if any). Dairy Herd Improvement data from each cow and weather data were compiled for analysis. Information accessed was predicted transmitting abilities for milk, fat (PTAF), protein (PTAP), and dollars; previous lactation: milk yield, fat yield, fat percent, protein percent, protein yield, somatic cell score, days open, days dry, days in milk, and previous parity (PAR). Colostrum yield was negatively correlated with IgG concentration (r = −0.42) and D (r = −0.2). It was positively correlated with D> (r = 0.30), predicted transmitting ability for milk (r = 0.26), PTAF (r = 0.21), and PTAP (r = 0.22). Immunoglobulin G concentration (g/L) was positively correlated with days in milk (r = 0.21), milk yield (r = 0.30), fat yield (r = 0.34), protein yield (r = 0.26), days open (r = 0.21), PAR (r = 0.22), and tended to be positively correlated with DD (r = 0.17). Immunoglobulin G concentration (g/L) was negatively correlated with D> (r = −0.24) and PTAF (r = −0.21) and tended to be negatively correlated with PTAP (r = −0.18). To determine the best fit, values >0 were transformed to natural logarithm. All nontransformed variables were also used to develop the model. A variance inflation factor analysis was conducted, followed by a backward elimination procedure. The resulting regression model indicated that changes in Ln fat yield

Received May 26, 2015. Accepted January 12, 2016. 1 Present address: Famo Feeds Inc., Freeport, MN 56331. 2 Corresponding author: [email protected]

(β = 2.29), Ln fat percent (β = 2.15), Ln protein yield (β = −2.25), and Ln protein percent (β = 2.1) had largest effect on LnIgG. This model was validated using 27 colostrum samples from 9 different farms not used in the model. The difference between means for actual and predicted colostrum quality (IgG, g/L) was 13.6 g/L. Previous lactation DHI data and weather data can be used to predict the IgG concentration of colostrum. Key words: colostrum, immunoglobulin G, prediction equation INTRODUCTION

Colostrum is designed to be a concentrated source of nutrients, which includes fats, proteins, including immunoglobulins such as IgG, carbohydrates, vitamins, and minerals. It is key in supporting the health of the young dairy animal. Inadequate feeding of quality colostrum to the neonatal calf can result in reduced growth rates, increased risk of disease and death, increased risk of being culled, and decreased milk production in her first lactation (Smith and Foster, 2007). The long-term effects determine the success of the cow and therefore special care should be taken to ensure colostrum of the highest quality is provided to the newborn calf. Currently, colostrum can be tested on farm by either colostrometer (Fleenor and Stott, 1980) or refractometer (Quigley et al., 2013). These methods are effective in estimating IgG concentration (Bartier et al., 2015). Many producers do not have access to these tools or do not take the time to test their colostrum before feeding. Only 5.7% of US dairy producers evaluated colostrum quality using a colostrometer (NAHMS, 2007). Providing a method of predicting colostrum quality would provide a means of evaluating colostrum quality before the calf is born and colostrum is collected. Colostrum quality has been previously investigated with factors such as parity and quantity being the main contributors to quality variations. Several parties have observed enhanced colostrum quality with increasing numbers of lactations (Devery-Pocius and Larson, 1983; Tyler et al., 1999; Moore et al., 2005; Gulliksen et al., 2008). Quantity of colostrum produced at the first milking has been negatively correlated with IgG concentration,

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which is most likely caused by a dilution effect (Pritchett et al., 1991). Approximately 22% of the US dairy herd is enrolled in DHI; the goal of this study was to evaluate DHI data and environmental data to provide producers with a means of predicting colostrum quality on the date the cow calves. The objective of this study was to evaluate the possible correlations between previous lactation, predicted transmitting abilities, and environmental conditions on the subsequent colostrum produced and develop a regression equation that could be incorporated into management software programs as an aid in predicting IgG concentration in colostrum.

(PY), previous lactation SCS, previous lactation days open (DO), previous lactation days dry (DD), previous lactation DIM, and previous parity (PAR). Individual farm was added to the model. Colostrum Analysis

Samples of colostrum were analyzed for IgG using radial immunoassay (Triple J Farms, Bellingham, WA). Statistical Analyses

Colostrum samples from 9 dairy farms from across New Hampshire were sampled over the years of 2011 through 2014. In total, 108 samples were obtained. Number of samples collected per farm ranged from 2 to 46. Two farms comprising the majority of the samples provided 77 samples. Of the other 6 farms, 2 farms provided 3 samples, 1 farm provided 4 samples, 1 farm provided 5 samples, and 2 farms provided 8 samples. For farms to participate, they had to be enrolled in DHI. Only Holstein cows with >1 lactation were used. Producers were asked to record calf birth date, time (h) of colostrum harvest after parturition, cow identification, yield of colostrum (COL), and sex of the calf (1 for heifer; 2 for bull). Producers were asked if cows grazed during the dry period and to provide the number of weeks (PASWK). From calf birth date, ordinal day was determined.

Based upon observation of these data it was determined that conversion of the model components into a natural logarithm to estimate IgG was appropriate. To determine the best-fit values >0 were transformed to natural logarithm. All nontransformed variables were also used to develop the model. Means, standard deviations, and Pearson correlation coefficients for all variables were calculated. (SAS Version 9.4, SAS Institute Inc., Cary, NC). The variance inflation factor procedure (VIF) of SAS was used to determine any relation between the model parameters. This procedure calculates a VIF for each variable. For each iteration, the highest valued parameter is removed from the model until all VIF values are ≤10. As a final step, the backward elimination procedure of SAS was conducted, Ln IgG was used as a dependent variable, and farm PASWK, time, SCS, sex, PTAM, PTAF, D>, Ln FY, Ln FP, Ln PY, Ln PP, Ln DD, Ln OD, Ln DO, Ln PAR were used as independent variables. Variables that were least significant, the ones with the largest P-value, were removed and the model refitted until all remaining variables had individual Pvalues ≤0.10.

Weather Data

Model Validation

Utilizing the website www.wunderground.com and entering the location of the farm and date of parturition, the number of days during the 21 d before parturition with temperatures ) were recorded.

Colostrum samples (n = 27) from 9 other New Hampshire farms (3 samples/farm) were used to validate the model. Samples were harvested during early fall of 2014. Any sample that was 2.5 SD from the mean was removed from the validation data set.

MATERIALS AND METHODS On-Farm Information

RESULTS Dairy Herd Improvement Data

PC Dart (Dairy Records Management Systems, Raleigh, NC) was used to access information for predicted transmitting ability for milk (PTAM), fat (PTAF), protein (PTAP), dollars (PTAD), previous lactation milk yield (PROD), previous lactation fat yield (FY), previous lactation fat percent (FP), previous lactation protein percent (PP), previous lactation protein yield

Descriptive characteristics of each farm are in Table 1. Means and standard deviations of variables used in the development of the model are in Table 2. Results indicate relatively large variability for COL, IgG, and DO. The range in IgG concentration was 21.4 (poor) to 141.4 (excellent) g/L. The wide range of ordinal day (from 26 to 338 d) indicates that date of calving data represented the entire year. Journal of Dairy Science Vol. 99 No. 5, 2016

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Table 1. Descriptive statistics for farms used in the development of the colostrum prediction equation (average ± SD)1 Farm Cows, n Time, h SD COL, kg SD IgG, g/L SD DIM SD PROD, kg SD FY, kg SD FP, % SD PY, kg SD PP, % SD SCS SD DD, d SD OD, d SD DO, d SD PAR SD PTAD SD PTAM, kg SD PTAF, kg SD PTAP, kg SD D, d SD Sex SD PASWK SD

1

2

3

4

5

6

7

8

9

2 2.8 2.5 5.7 2.3 84 3.4 309 10 11,724 1,710 383 114 3.4 1.5 346 42 3.0 0.1 4.0 1.2 61 14 36 0.7 87 17 3.0 1.4 −65 7 51 3.5 −4.8 0.3 0 2.6 16.5 0.7 4.5 0.7 0 0 2.0 0 0 0

5 6.2 1.9 5.6 2.5 81 13.8 315 18 11,408 1,724 441 55 3.9 0.3 336 29 3.0 0.2 1.9 1.0 51 5 36 1.8 83 16 2.4 1.1 107 125 80 236 1.5 1.1 2.3 3.1 16.4 0.5 4.6 0.5 0 0 1.4 0.6 0 0

8 8.6 2.3 10.6 4.9 78 12.1 344 63 11,125 4,367 407 180 3.6 0.6 334 144 3.0 0.3 1.8 0.8 80 28 30 2.4 144 85 1.9 0.8 136 120 −0.1 178 2.0 10.4 1.1 5.5 14.5 0.8 6.5 0.8 0 0 1.5 0.5 0 0

3 7.0 4.1 7.7 1.9 73 4.4 326 48 14,674 1,239 484 48 3.3 0.2 416 31 2.9 0.1 1.2 0.3 54 2 33 4.2 101 51 2.3 0.5 152 96 160 113 1.1 2.6 4.8 2.4 13.7 2.5 7.3 2.5 0 0 1.7 0.5 0 0

8 8.8 4.8 4.0 2.2 83 13 365 40 13,649 1,312 472 85 3.4 0.4 435 58 3.2 0.2 2.2 1.7 50 15 38 4.5 136 47 2.8 1.6 128 141 74 232 3.2 6.1 3.2 5.0 17.8 1.6 3.2 1.6 0 0 1.0 0 0 0

4 6.8 4.8 20 14 64 11 352 20 11,628 2,467 414 90 3.6 0.8 352 63 3.1 0.2 2.5 0.8 55 11 38 6.4 126 24 1.8 0.9 91 91 216 487 3.6 3.2 5.3 11.9 15.0 2.7 6 2.7 0 0 2.0 0 0 0

3 4 3.5 5.9 3.9 68 2.4 325 33 12,110 2,673 418 57 3.5 0.4 348 64 2.9 0.2 1.4 0.6 54 3 41 1.5 96 32 3.0 1.7 161 98 169 116 6.0 5.5 3.8 1.9 18.7 1.5 2.3 1.5 0 0 1.3 0.6 0 0

45 1.1 0.2 6.2 6.3 89 26 319 40 11,971 2,136 468 91 3.9 0.3 366 71 3.1 0.2 1.9 1.1 64 18 160 84 103 48 2.0 1.2 259 123 337 214 9.9 9.0 8.7 6.8 2.2 4.2 14.5 5.8 3.8 5.4 1.4 5.0 0 0

31 4.9 2.9 9.6 8.1 62 18.7 310 46 10,803 2,250 390 95 3.7 0.4 333 60 3.0 0.2 2.4 1.6 59 7 164 27 90 46 2.3 1.6 211 119 183 272 7.7 9.0 5.5 6.4 0 0 14.5 5.1 6.5 5.1 1.5 0.5 4.2 3.2

1

Cows = number of cows sampled from each farm. Time = time of colostrum harvest after parturition. COL = colostrum yield, kg. IgG = immunoglobulin G concentration. DIM = DIM of previous lactation. PROD = previous lactation milk yield. FY = previous lactation fat yield. FP = previous lactation milkfat percentage. PY = previous lactation protein yield. PP = previous lactation milk protein percentage. DD = days dry. OD = ordinal day is the day of the year when a calf was born for each respective cow. DO = days open. PAR = parity of previous lactation. PTAD = predicted transmitting ability of dollars. PTAM = predicted transmitting ability of milk. PTAF = predicted transmitting ability of fat. PTAP = predicted transmitting ability of protein. D< = number of d 21 d prepartum where the minimum environmental temperature was = number of d 21 d prepartum where the maximum environmental temperature was >23°C. Sex = sex of calf, 1 = heifer, 2 = bull. PASWK = number of weeks during the dry period when a cow was on pasture.

Pearson’s correlation coefficients are presented in Table 3. Colostrum yield was negatively correlated with IgG concentration with the highest correlation of −0.42 and D (r = −0.20), and positively correlated with D>, PTAM, PTAF, PTAP, and PASWK. The correlation coefficients for these variables ranged from r = 0.21 to 0.30 (Table 3). Journal of Dairy Science Vol. 99 No. 5, 2016

Immunoglobulin G concentration (g/L) was positively correlated with most of the variables. The highest positive correlation was noted for FY (r = 0.34) and PROD (r = 0.30). The correlation for DIM, PY, DO, and PAR ranged from r = 0.21 to 0.26. Immunoglobulin G concentration (g/L) was negatively correlated with COL (r = −0.42), D> (r = −0.24), PTAF (r = −0.21),

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Table 2. Means, standard deviations, minimums, and maximums of variables used in regression analysis (n = 111)1 Item Colostrum characteristics Time, h COL, kg IgG, g/L DHI information DIM PROD, kg FY, kg FP, % PY, kg PP, % SCS DD, d OD, d DO, d PAR Predicted transmitting abilities PTAD PTAM, kg PTAF, kg PTAP, kg Environmental temperature D, d Sex PASWK

Mean 4.75 7.87 77.4 323 11,729 436 3.74 358 3.03 2.08 61.2 124 104 2.16 202.7 99.2 3.2 2.7 5.70 11.68 3.44 1.45 1.19

SD 3.8 7.2 23.4

Minimum

Maximum

1.0 0.5 21.4

14.5 39.7 141.4

44 2,430 102 0.4 75 0.19 1.29 16.4 80.1 51 1.3

245 6,530 191 2.3 180 2.6 0.2 39 26 37 1

442 18,132 807 4.8 574 3.6 6.2 147 338 257 7

133.9 117.3 4.0 3.1

−90.0 −240 −7.20 −4.32

7.44 6.48 5.05 0.50 2.55

551 428 13.78 11.93

0 0 0 1 0

20 21 20 2 9

1

Time = time of colostrum harvest after parturition. COL = colostrum yield, kg. DIM = DIM of previous lactation. PROD = previous lactation milk yield. FY = previous lactation fat yield. FP = previous lactation milkfat percentage. PY = previous lactation protein yield. PP = previous lactation milk protein percentage. DD = days dry. OD = ordinal day is the day of the year when a calf was born for each respective cow. DO = days open. PAR = parity of previous lactation. PTAD = predicted transmitting ability of dollars. PTAM = predicted transmitting ability of milk. PTAF = predicted transmitting ability of fat. PTAP = predicted transmitting ability of protein. D< = number of d 21 d prepartum where the minimum environmental temperature was = number of d 21 d prepartum where the maximum environmental temperature was >23°C. Sex = sex of calf, 1 = heifer, 2 = bull. PASWK = number of weeks during the dry period when a cow was on pasture.

and PASWK (r = −0.34) and tended to be negatively correlated with PTAP (r = −0.18; Table 3). Likewise, the degree of relationship between these variables was also rather weak, and the significance indicates that IgG concentration is affected by these variables, particularly COL, FY, PTAF, D>, and PASWK. Opposite, significant correlation values for IgG and COL were for PTAF, D>, and PASWK, supporting the contention that cows that produced more colostrum resulted in poorer quality as expressed by IgG concentration. Sex of the calf was not correlated with IgG concentration or COL. There were 48 heifer calves, 54 bull calves, and for 7 calves, sex was not listed by the producer. One set of freemartin twins was present. Farm was not correlated with IgG concentration or COL. Of the 15 variables used in the backward elimination regression, 8 variables were retained in the final model. The model developed was Ln IgG = 4.03864 – 0.05018 × PASWK + 2.28887 × Ln FY − 2.15129 × Ln FP

− 2.25429 × Ln PY + 2.10609 × Ln PP + 0.14457 × Ln PAR – 0.00025683 × PTAM + 0.01553 × D>; r2 = 0.56 (Figure 1). Validation of the model used 28 samples; 1 sample was removed due to very high IgG concentration (>2.5 SD from the predicted mean value; Table 4). The model predicted IgG concentration of colostrum to be 13.6 g/L lower than the actual measured IgG concentration (Table 5). The residuals (predicted IgG, g/L – actual IgG, g/L) are depicted in Figure 2, indicating that the predicted colostrum concentrations were lower than the actual values. DISCUSSION

Colostrum quality is primarily evaluated by concentration of IgG. Colostrum is considered good quality when IgG is ≥50 g/L (Fleenor and Stott, 1980; NAHMS, 2007). Previous work has suggested that IgG content of colostrum can be affected by quantity produced Journal of Dairy Science Vol. 99 No. 5, 2016

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Table 3. Pearson correlation coefficients (r) of components used to develop the regression model (n = 111)1

Table 3 (Continued). Pearson correlation coefficients (r) of components used to develop the regression model (n = 111)1

Item

Item

COL r P Farm r P DIM r P PROD r P FY r P FP r P PY r P PP r P SCS r P DD r P OD r P DO r P Time r P PAR r P PTAD r P PTAM r P PTAF r P PTAP r P D< r P D r P D> r P Sex r P

IgG

COL

−0.42 – 0.05018 × PASWK; R2 = 0.56. FY = previous lactation fat yield. FP = previous lactation milkfat percentage. PY = previous lactation protein yield. PP = previous lactation milk protein percentage. PAR = parity of previous lactation. PTAM = predicted transmitting ability of milk. D> = number of d 21 d prepartum where the maximum environmental temperature was >23°C. PASWK = number of weeks during the dry period when a cow was on pasture. Color version available online.

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Table 4. Mean, standard deviation, minimum, and maximum of variables used to validate the regression equation to calculate IgG concentration in colostrum (n = 27)1 Item

Mean

SD

Minimum

Maximum

IgG, g/L FY, kg FP, % PY, kg PP, % PAR PTAM D> PASWK

88.3 459 3.86 362 3.11 2.44 59.1 3.70 1.17

19.7 129 0.45 88 0.24 1.42 174 2.51 3.03

27.7 207 3.20 203 2.80 1 −366 0 0

116.3 727 5.0 553 3.60 7 438 9 9

1 IgG (g/L) in colostrum. FY = previous lactation fat yield. FP = previous lactation milkfat percentage. PY = previous lactation protein yield. PP = previous lactation milk protein percentage. PAR = parity of previous lactation. PTAM = predicted transmitting ability of milk. D> = number of d 21 d prepartum where the maximum environmental temperature was >23°C. PASWK = number of weeks during the dry period when a cow was on pasture.

Quantity of colostrum produced at the initial milking was negatively correlated with IgG concentration, which is in agreement with Pritchett et al. (1991) and Kehoe et al. (2011). This can be attributed to the dilution effect from increased colostrum volume. Colostrum produced ranged from 0.5 to 39.7 kg. Overall individual cow PROD in the previous lactation was tested as a variable due to the possibility that high-producing cows may produce more colostrum. However, COL was not affected by PROD. Milk production, FY, and PY were all positively correlated with IgG concentration. Fat yield had a positive effect on predicting IgG concentration. For every kilogram of FY and after conversion to Ln, the Ln IgG was increased by approximately 2.29 units. Conversely, for every kilogram of PY produced after conversion to Ln, Ln IgG was decreased by approximately 2.25 units. Based on these data, higher PROD, FY, and PY within a herd indicate better management and nutrition, which would positively affect colostrum quality. Natural logarithm converted FP reduced Ln IgG by 2.15 units, while Ln converted PP increased Ln IgG by approximately 2.11 units. Previous lactation length and DO were positively correlated with IgG concentration, and DD tended to be positively correlated. A longer lactation with more DO may allow for the cow to become more metabolically stable before taking on the stress of a pregnancy. Being in better metabolic condition may help the cow

transition out of the dry period more easily. Days dry tended to be positively correlated with IgG. This supports the work of Caja et al. (2006) who observed improved colostrum quality in doe goats given a dry period compared with doe goats given no dry period. However, doe goats given either a 27- or 56-d dry period produced colostrum of similar quality. A similar effect was observed with cows given no dry period or a shorter and longer dry period. Primiparous cows given no dry period produced lower quality colostrum compared with primiparous cows given a dry period (2.5 vs. 7.25 g/L). Multiparous cows produced similar quality colostrum regardless of treatment (Annen et al., 2004). All cows in that study produced poor quality colostrum (mean (number of D>23°C during the 21 d prepartum period). During heat stress, blood vessels dilate, which can cause them to be more permeable. Increased permeability of the blood vessels may lead to an increase in COL. Immunoglobulin G concentration was negatively affected by D>. Days above the thermoneutral zone (TNZ) had a small, but positive effect on Ln IgG, for every °C above the TNZ, Ln IgG was increased by approximately 0.015 units. In Florida, Monteiro et al. (2014) observed no difference in COL in heat-stressed versus cooled cows. Nardone et al. (1997) in Italy also reported no change in COL between primiparous heifers under heat-stressed or thermally neutral conditions; however, IgG and IgA were decreased in the heatstressed group. This agrees with our data showing a negative correlation with temperature outside of the TNZ. In Norway, Gulliksen et al. (2008) observed the Journal of Dairy Science Vol. 99 No. 5, 2016

highest quality colostrum was produced during late summer and early fall. The poorest quality colostrum was produced during the winter. However, in Ireland, Conneely et al. (2013) observed that the highest quality colostrum was produced during the winter months and during late summer. It is apparent from these studies that environmental temperature or day length has an effect on colostrum quality and needs to be considered in future research. Time was not a factor in colostrum quality or yield in this study. The average time of harvest after calving was 4.75 h. This supports the results of Conneely et al. (2013) who did not observe any decrease in colostrum quality until 9 to 12 h or increase in COL for 15 to 18 h after parturition. It is recommended that colostrum be harvested and fed to the calf as soon as possible after parturition. Osaka et al. (2014) state that calves fed 3 L of fair to good quality colostrum (≥40 g/L) within 3 h of birth should avoid failure of passive transfer of IgG. Parity was a predictor for colostrum quality in the current study. Conneely et al. (2013) indicated that cows with greater parity produced higher quality colostrum. Gulliksen et al. (2008) reported an increase in IgG concentration between cows of first or second parity, in comparison to those with at least 4 parities, which corroborates with results of other studies (Tyler et al., 1999; Moore et al., 2005). Increased pathogen exposure of older dams compared with younger dams is believed to be the cause of differences in IgG concentration of colostrum. The presence of more specified circulating antibodies within older dams translates to an increased amount of antibodies available to be transferred to colostrum. Devery-Pocius and Larson (1983) indicated that total IgG1 reached peak concentration, at almost double the amount, in the third and fourth lactations. Concentrations of IgG2 increased in the fifth through eighth lactation. The IgM and IgA concentrations did not share this trend. Because IgG transport into the mammary gland occurs via a specialized transport mechanism, it can be hypothesized that this system may not be fully functional until later lactations. For every unit of parity converted to Ln, Ln IgG increased by approximately 0.14 units. Somatic cell score from the previous lactation did not contribute to predictions for colostrum quality. Mastitic infections during lactation do not appear to affect subsequent colostrum quality. Mastitis during the dry period may affect colostrum quality therefore teat sealents and antibiotics should be used at dry off. Quality may not only be affected in terms of IgG concentration but bacterial load as well. Total plate counts of bacteria should not exceed 100,000 cfu/mL (Morrill et al., 2012); however, infection would cause this number to increase dramatically.

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CONCLUSIONS

Colostrum quality can be predicted from previous lactation performance data. Producers will have the ability to estimate IgG content of the colostrum without having to handle the product. Testing colostrum quality using a refractometer or colostrometer is best; this equation will provide an alternative option for producers. Future research should focus on genetic markers that may affect colostrum quality. ACKNOWLEDGMENTS

The authors acknowledge the following New Hampshire dairy farms for providing colostrum for this experiment: Bodwell Dairy, Kensington; Bohannon Farm, Contoocook; Briarstone Farm, North Haverhill; Fernald Farm, Nottingham; Fitch Farm, Milford; Grafton County Farm, North Haverhill; Houston’s Pine Lane Farm, Contoocook; Jones Dairy Farm, Chichester; Morrill Farm Dairy, Penacook; Naughtaveel Farm, Conway; Perkins Dairy, Plymouth; Pomeroy Dairy, Mont Vernon; Stuart Farm, Stratham; Tullando Farm, Orford; University of New Hampshire Fairchild Dairy Teaching and Research Center, Durham; Wyndyhurst Holsteins, Westmoreland; and the Yeaton Farm, Epsom. This study was supported in part by the George Walker Research Fund. Partial funding was provided by the New Hampshire Agricultural Experiment Station. This is Scientific Contribution Number 2609. This work was supported by the USDA National Institute of Food and Agriculture Project (Hatch Multistate NC2042; accession number 1001283). REFERENCES Annen, E. L., R. J. Collier, M. A. McGuire, J. L. Vicini, J. M. Ballam, and M. J. Lormore. 2004. Effect of modified dry period lengths and bovine somatotropin on yield and composition of milk from dairy cows. J. Dairy Sci. 87:3746–3761. Bartier, A. L., M. C. Windeyer, and L. Doepel. 2015. Evaluation of on-farm tools for colostrum quality measurement. J. Dairy Sci. 98:1878–1884. Caja, G., A. A. K. Salama, and X. Such. 2006. Omitting the dry-off period negatively affects colostrum and milk yield in dairy goats. J. Dairy Sci. 89:4220–4228. Conneely, M., D. P. Berry, R. Sayers, J. P. Murphy, I. Lorenz, M. L. Doherty, and E. Kennedy. 2013. Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows. Animal 7:1824–1832.

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Journal of Dairy Science Vol. 99 No. 5, 2016