Robust Statistical Approaches for Evaluating Dairy Cow Performance & Health in Field Study Using JMP (2026-EU-PO-2786)

Accurately assessing milk yield and health outcomes in dairy herds is essential to develop effective nutritional and management strategies, yet variability remains a major challenge. JMP 19 enables thorough analysis of farm intervention data with advanced statistical comparisons, providing robust interpretation of continuous and categorical variables in an intuitive way.

Continuous variables (e.g., milk yield, rumination time) are summarized using descriptive statistics (mean, standard deviation, standard error, and 95% confidence interval) and visualized through box plots, histograms, and time series plots to characterize their distributions and temporal trends. Group comparisons are performed using t-tests for two-level factors and ANOVA for multi-level treatments, followed by Tukey or Bonferroni post-hoc tests.

Regarding categorical variables (e.g., mastitis incidence), JMP 19 offers chi-square tests, Fisher’s exact tests for small samples, and logistic regression to estimate odds ratios. Statistical assumptions, as normality (Shapiro–Wilk test), independence (residual diagnostics), and homogeneity of variance (Levene’s test) are rigorously verified.

This analytical approach provides a robust basis for accurate evaluation of significant treatment effects and herd decision making.