Date of Award

Fall 2023

Project Type

Dissertation

Program or Major

Animal and Nutritional Sciences

Degree Name

Doctor of Philosophy

First Advisor

Peter S Erickson

Second Advisor

Nancy Whitehouse

Third Advisor

David Marcinkowski

Abstract

Two experiments were conducted. The objectives of the first experiment were to evaluate colostrum quantity, quality, nutrient composition, and bioactive compounds from Jersey cows fed two levels of dietary cation anion difference (DCAD; –40 or –80 mEq/kg) with or without 23 g/d unprotected nicotinic acid (NA) and its effect on cow and calf performance. The hypothesis is that both DCAD and nicotinic acid could improve colostrum quality, increase quantity and bioactive compound concentration, and improve calf intestinal development. Previous research indicated the benefits of supplementing NA in prepartum Holsteins towards improved colostrum quality, as well as enhanced feed efficiency (FE) in their respective calves in the first 3 wk of life. Exact mechanisms for these results are not clear. The effects of NA supplementation to prepartum Holstein cows on growth and performance of their calves has been studied, but this has not been investigated in multiparous Jersey cows. Forty multiparous Jersey cows housed in a compost bedded pack barn were blocked by expected calving date and randomly assigned to treatments at 4 wk prepartum. Blood samples were collected every Friday until calving for analysis of nonesterified fatty acids (NEFA), ketones, and immunoglobulin G (IgG). Urine samples were collected 3 times weekly for analysis of pH, creatinine, and purine derivatives. Colostrum was collected within 90 min after parturition. Calves were removed from their dams before suckling, weighed and had blood sampled (for 0-h IgG) within 30 min of birth, and were enrolled if their dams made ≥ 2 L colostrum. If enrolled, calves received a minimum of 2 L and maximum of 3.78 L. The 31 enrolled calves were blocked based on treatments of dams and fed from d 1 to 6 wk: 3.78 L milk (4.9% fat, 3.7% protein, and 14.3% solids) and a coarse starter (34.61% starch, DM basis), and ad libitum water. For cows, there were no differences observed in grain intake, hay intake, total DMI, weekly BW, ADG, NEFA, weekly ketones, weekly urine pH, final urine pH, and urine volume. There was a tendency for cows supplemented the –40 DCAD to have lower final BW as compared to cows supplemented the –80 DCAD. There was a trend for an interaction with the –80/+ having increased final ketones. We observed a tendency to decrease allantoin and total PD with the addition of NA, as well as a tendency to decrease uric acid with the –80 DCAD. For colostrum, there were no differences observed in colostrum, IgG, and protein yield, and concentrations and yields for insulin, IGF-1, lactoferrin, TGFβ-1, and TGFβ-2. We observed a tendency to have an interaction effect with TGFβ-2, with the lowest concentration in –40/+. There was a tendency to have an interaction effect with IgG concentration, where –40/+ resulted in the highest IgG and –80/+ resulted in the lowest IgG. We observed an interaction effect with colostrum protein percentage, with the lowest in –40/- and the highest in –40/+. For DCAD, we observed no difference in protein content in colostrum. With NA supplementation, we observed a decrease in colostrum fat percentage, and a tendency to decrease fat yield. We observed an interaction effect with colostrum protein percentage, with the lowest in –40/-. With DCAD, we observed no effect on fat content in colostrum. Any result seen with bioactive compounds and colostral fatty acids do not have any studies to support or contradict the data seen in the current experiment. This is the only experiment to investigate DCAD concentration and NA supplementation and its effects on colostral bioactive compounds and fatty acids. To our knowledge, there have been no experiments evaluating colostral long chain fatty acids effects on calf development. We observed no differences in colostrum fed to calves, and intakes of insulin, TGFβ-1, and TGFβ-2. For IgG intake, we observed a lower average IgG intake in calves that came from cows supplemented the –80 DCAD as compared to –40 DCAD. For IGF-1 intake, we observed an interaction effect, with the highest intake of IGF-1 in calves that came from cows fed –40/+. For lactoferrin, we observed higher average lactoferrin intake in calves that came from cows supplemented NA. We can use the results seen with IGF-1 and lactoferrin intake to explain the results seen in previous studies with feed efficiency in the first three weeks of life for Holstein calves, because of the impact these bioactive compounds have on intestinal development. This is then confirmed by the xylose challenge data on the current experiment, where calves that came from NA supplemented cows had greater circulating xylose on d 5 during the challenge. We observed no differences in 24 h IgG, apparent efficiency of absorption (AEA), starter intake, milk solids intake, free water intake, total DMI, weekly BW, final BW, and all skeletal measurements and daily gains of the skeletal measurements, weekly glucose, final glucose, and weekly ketones. We observed an interaction effect for final ketones, where the lowest two final ketones in calves were from –40/- and –80/+. We observed that –80 DCAD supplemented to prepartum cows resulted in a tendency to decrease calf ADG, and that NA supplementation to prepartum cows resulted in a tendency to decrease calf ADG. The results on the current study for ADG/DMI (feed efficiency, FE) indicate that –80 DCAD supplemented to prepartum cows resulted in a decrease for calf FE, and that NA supplementation to prepartum cows resulted in a decrease for calf FE. Reasons for both of these decreases in FE are not clear, especially considering the results with xylose concentration, lactoferrin, and IGF-1 fed to calves indicating towards intestinal development in calves that came from NA supplemented cows. We hypothesized that we would see an increase in FE from calves that came from NA supplemented cows. However, the opposite was true, with an interaction effect of the lowest FE in calves that came from cows fed –80/+. These data suggest that supplementing NA in a –80 DCAD diet is not recommended and providing Jersey cows with a lesser amount of NA may prove beneficial due to its effects on xylose absorption and lactoferrin.In the second experiment, the objective was to identify specific factors that could be affecting colostrum yield in Jersey cows. We aimed to see if a prediction model could be created to identify correlations between colostrum yield, IgG concentration, and IgG yield and independent variables, categorized as cow performance in the previous lactation, environmental and management conditions during the dry period, calving and individual cow information, and predicted transmitting abilities. Twenty-eight dairies with Jersey cows from across the United States enrolled 415 multiparous cows onto this study between 2021 and 2023. Producers collected colostrum weight and colostrum samples to be analyzed for IgG. Producers recorded cow identification number, calf date of birth, sex of the calf, colostrum yield, hours from parturition to colostrum harvest, number of feedings per day during the dry period, type of diet fed to dry cows (total mixed ration (TMR), partially mixed ration (PMR), or component), amount of time spent on pasture during the dry period, and number of hours a day of light exposure. Dairy Herd Information (DHI) data from each cow and weather data were compiled for analysis. Weather was recorded as the number of days below 5℃ (D<), days above 23℃ (D>), and days between 5 and 23℃ (D). Information acquired from DHI were predicted transmitting abilities for: milk (PTAM), fat (PTAF), protein (PTAP), and dollars (PTAD); previous lactation: milk yield (PLMY), fat percent (PLFP), fat yield (PLFY), protein percent (PLPP), protein yield (PLPY), somatic cell score (PLSC), days open (PLDO), days dry (PLDD), days in milk (PLDIM), and previous parity (PP); current lactation: parity, days dry, and calving type (single, twins, or stillborn). For each cow, we also recorded ordinal day (OD) for the calving date and latitude of the farm. In order to analyze all variables, values for colostrum yield, IgG concentration, and IgG yield had 1 added to correct for values = 0. Two points were added to colostrum harvest to correct for values ≤ 0. After addition, the values > 0 underwent transformation to ln or log10. All nontransformed variables were also used to develop the model. A variance inflation factor analysis (VIF) was conducted, followed by a backward elimination procedure. The resulting regression model for log10 colostrum yield (kg, r2 = 0.55) indicated that herd size (β = −0.0001), OD (β = −0.001), Ln OD (β = 0.07), latitude (β = −0.02), dry period length (β = 0.004), D< (β = −0.005), D (β = −0.003), harvest time (β = 0.05), Ln harvest time (β = −0.35), IgG concentration (β = −0.004), log10 IgG concentration (β = 0.46), feedings (β = 0.06), Ln pasture (β = −0.13), and Ln PLDO (β = 0.14) had the largest effect on log10 colostrum yield. The intercept of this model was 0.43035. The resulting regression model for IgG concentration (g/L, r2 = 0.21) indicated that herd size (β = 0.02), D> (β = 0.38), Ln harvest time (β = −19.42), colostrum yield (β = −4.29), Ln diet (β = 18.00), Ln PLFP (β = 74.43), and PP (β = 5.72) had the largest effect on nontransformed IgG concentration. The intercept for this model was 7.47170. The resulting regression model for log10 IgG yield (g, r2 = 0.79) indicated that Ln OD (β = 0.03), harvest time (β = −0.01), colostrum yield (β = −0.11), Ln colostrum yield (β = 1.20), Ln pasture (β = −0.09), Ln PLFP (β = 0.53), and PP (β = 0.02) had the largest effect on log10 IgG yield. The intercept for this model was 0.22045. All three models were validated using 39 colostrum samples from 22 of 28 farms. Data from these cows were not used in the creation of the models. The differences between means for actual and predicted colostrum yield (kg) was 0.89, IgG concentration (g/L) was −21.10, and IgG yield (g) was −65.15. These models indicate that it is possible to use previous lactation, environmental and management conditions during the dry period, and farm and individual cow information to predict colostrum yield, IgG concentration, and IgG yield of colostrum.

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