Exploring relationships between Dairy Herd Improvement monitors of performance and the Transition Cow Index in Wisconsin dairy herds

Abstract

Transition cow management has been tracked via the Transition Cow Index (TCI; AgSource Cooperative Services, Verona, WI) since 2006. Transition Cow Index was developed to measure the difference between actual and predicted milk yield at first test day to evaluate the relative success of the transition period program. This project aimed to assess TCI in relation to all commonly used Dairy Herd Improvement (DHI) metrics available through AgSource Cooperative Services. Regression analysis was used to isolate variables that were relevant to TCI, and then principal components analysis and network analysis were used to determine the relative strength and relatedness among variables. Finally, cluster analysis was used to segregate herds based on similarity of relevant variables. The DHI data were obtained from 2,131 Wisconsin dairy herds with test-day mean ≥30 cows, which were tested ≥10 times throughout the 2014 calendar year. The original list of 940 DHI variables was reduced through expert-driven selection and regression analysis to 23 variables. The K-means cluster analysis produced 5 distinct clusters. Descriptive statistics were calculated for the 23 variables per cluster grouping. Using principal components analysis, cluster analysis, and network analysis, 4 parameters were isolated as most relevant to TCI; these were energy-corrected milk, 3 measures of intramammary infection (dry cow cure rate, linear somatic cell count score in primiparous cows, and new infection rate), peak ratio, and days in milk at peak milk production. These variables together with cow and newborn calf survival measures form a group of metrics that can be used to assist in the evaluation of overall transition period performance.

Invited review: Big Data in precision dairy farming

“…(2016) combined classification and clustering, Schultz et al . (2016) combined regression and clustering and Nielen et al . (1995) combined classification and dimensionality reduction techniques.…”

Predicting daily milk yield for primiparous cows using data of within-herd relatives to capture genotype-by-environment interactions

“…Previous studies using traditional time-series models were unable to predict the performance of primiparous cows, because lactation data from the same cow in previous lactations is essential for model training (Murphy et al, 2014;Zhang et al, 2016;Nguyen et al, 2020). Likewise, predicting MY at first TD using a transition cow index model is also limited to multiparous cows and applicable only in the transition period (Schultz et al, 2016).…”

“…Considering other recent studies that predicted only first TD MY for heifers (Schultz et al, 2016;Dallago et al, 2019), the K-R-S hybrid approach presents several advantages, including predicting MY for the first TD with relativity high accuracy (0.2 kg/d lower than actual MY), predictions for the entire first-lactation, and generating predictions for primiparous cows with no prior MY data. Other studies that predicted MY at the herd or the individual cow level required prior production records of the animals on which the prediction was made.…”

Associations of reproductive indices with fertility outcomes, milk yield, and survival in Holstein cows

“…However, the ability of a cow to cope with critical events during the transition period could provide an indicator of future success of reproductive and milk yield performance. One metric that has been proposed as a measure of early-lactation health and performance is the Transition Cow Index (Nordlund and Cook, 2004;Nordlund, 2006;Nordlund et al, 2011;Schultz et al, 2016), which considers the difference between actual and predicted milk production at the first DHI test, expressed on a 305-d basis. Interestingly, high values for this index were significantly correlated with other predictors that are characteristic of well-managed herds, such as milk production and udder health (Brotzman et al, 2015).…”