A DP model of a dairy replacement herd was developed to analyze the impact of different dairy and replacement herd variables on the cost of rearing replacements for a representative dairy herd of 100 cows. A model was developed with Pennsylvania and US average information as the basis for the parameters. We used age at first calving of 25 mo, calving interval of 13 mo, herd-culling rate of 25%, and preweaned calf death of 10% as the base for comparison. We examined the impact of factors including age at first calving, calving interval, PDR, and the number of replacements required. From the base model, the total cost of rearing sufficient replacements for a 100-cow herd was $32,344. A reduction in culling rate to 20%, holding all other factors fixed, caused the net costs of raising replacements for the dairy herd to fall by $7968 or 24.6%. Increasing the culling rate above 25% led to a deficit in replacements for maintaining constant herd size, assuming a closed herd. The average age at first calving also affected the net costs of raising replacement heifers; reducing the age at first calving by 1 mo lowered the cost of a replacement program by $1400 or 4.3%. Changes in the length of the calving interval or in the PDR had marginal impacts on the net costs of replacement programs when compared with either herd-culling rate or average age at first calving.

The objective was to determine whether daily walking activity and milk yields could be used as predictors of metabolic and digestive disorders early in lactation. Data were collected from 1996 through 1999 from 1445 dairy cows in 3 Florida herds. Walking activity, milk yield, and other measures were collected from a computerized dairy management system. Mixed models analysis was used for data on cows before their first detected estrus, as identified by difference in activity. Healthy cows were defined as those without any metabolic or digestive disorder during the prebreeding stage, whereas a sick cow had an occurrence of those disorders at any time during the prebreeding stage. Metabolic disorders were ketosis, retained placenta, and milk fever. Digestive disorders included displaced abomasum, indigestion, reduced feed intake, traumatic gastritis, acidosis, and bloat. Data from cows with known cases of ketosis, left displaced abomasum, and digestive disorders were analyzed to determine changes in activity and milk yield before those specific disorders were clinically diagnosed. Although walking activity was generally lower among sick cows, cows with ketosis, left displaced abomasum, and digestive disorders had higher than average activity 8, 9, and 8 d, respectively, before each diagnosed disorder. Daily milk yields of sick cows were approximately 15 kg/d less than milk yields of healthy cows. Milk yields were lower by 6, 7, and 5 d, respectively, before diagnoses of ketosis, left displaced abomasum, and digestive disorders. Cows with ketosis, left displaced abomasum, and general digestive disorders could possibly be detected about 5 to 6 d earlier than clinical diagnoses based on changes in daily walking activity and milk yield.

A procedure for rumen tissue sampling was developed to determine treatment effects on rumen development and papillae growth in young calves and to improve repeatability in rumen tissue sampling techniques. Rumens were collected from 42 male Holstein calves from 4 separate experiments. Rumen sampling areas (n = 9) included the caudal dorsal blind sac, cranial dorsal sac, cranial ventral sac, and the caudal and ventral portions of the caudal ventral blind sac. Right and left sides of the rumen were sampled. Five 1-cm2 sections were removed from each area and measured for papillae length (n = 20/area), papillae width (n = 20/area), rumen wall thickness (n = 5/area), and number of papillae per cm2 (n = 5/area). Correlations between areas, samples, and measurements were obtained, and comparisons between experiments, areas, samples, and measurements were performed for all variables. In addition, power analyses were conducted for all variables to determine the efficacy of the procedure in detecting treatment differences. Results indicate that samples should be taken from the caudal and cranial sacs of the dorsal and ventral rumen to sufficiently represent papillae growth and development throughout the entire rumen. The procedure is capable of detecting treatment differences for papillae length and papillae width, has a decreased but acceptable capability of detecting treatment differences for rumen wall thickness, but appears limited in ability to detect treatment differences for papillae per square centimeter. In conclusion, rumen tissue sampling to determine extent of rumen development in calves can be performed in a nonbiased and repeatable manner utilizing a limited number of calves.

Dairy operations have a variety of resources and objectives, such that the most economical method of obtaining replacement heifers is only determined by individual analysis of costs. The objective of this study was the development of a cost analysis spreadsheet and validation of that spreadsheet on milking and custom heifer operations throughout Pennsylvania. A cost analysis spreadsheet was developed with an Excel '97 Microsoft file. The spreadsheet estimated the costs to raise a replacement heifer by specific age classes for feed, labor, health, reproduction, bedding, facilities, equipment, mortality, and interest costs. The simplistic and broad-based nature of the spreadsheet was a key component in the spreadsheet's flexibility to estimate costs for a variety of operational objectives, feeding management, housing systems, and labor management. A convenience sample of 16 milking operations and 14 custom heifer operations was evaluated to validate the cost analysis spreadsheet. Results from the validation are discussed to highlight the success and performance of the cost analysis spreadsheet. The average total cost to raise a replacement heifer for this data set was $1124.06 and $1019.20 for milking and custom heifer operations, respectively. Feed costs contributed 60.3 and 64.0% of the average total cost for milking and custom heifer operations, respectively. While no two operations are alike, individual operations possessing the ability to address costs to raise a replacement heifer can utilize critical information that can be used to improve operation profitability.

The objective of this research was to calculate the efficiency of a group of Pennsylvania dairy farms to determine factors that contributed to efficiency in production and business management. Data envelopment analysis (DEA) was used to estimate the efficiency. Two models were developed to measure the efficient use of physical (land, cows, and labor) inputs to produce physical (milk and butterfat) outputs, and the use of physical and economic (debt capital) inputs to produce physical and economic (income) outputs. The results showed that about 29% of the producers in our sample were DEA-efficient and demonstrated that there was no combination of inputs used by efficient producers that was best. In addition, the method of analysis illustrated the benefits of DEA in that it is possible to identify the set of efficient producers that inefficient producers can benchmark to in an effort to achieve similar levels of efficiency. Finally, the analysis demonstrated that producers should not benchmark to the highest level of production, but rather should combine resources in land, labor, cows, and debt capital to achieve an efficient level of production, which indeed may be less than the maximum production level of the group.