Supplementary Material for Pook et al. 2025

Figure S1: Obtained prediction accuracies based on the PLSR model with pre-processed MIR spectra input based on the number of principle components included in the prediction model.

Figure S2: Average prediction accuracy across the 12 considered fatty acids depending on the pre-processing and the number of principle components included in the prediction model.

Figure S3: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on lactate concentration in milk.

Figure S4: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-4:0 concentration in milk.

Figure S5: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-6:0 concentration in milk.

Figure S6: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-8:0 concentration in milk.

Figure S7: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-10:0 concentration in milk.

Figure S8: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-12:0 concentration in milk.

Figure S9: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-14:0 concentration in milk.

Figure S10: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-16:0 concentration in milk.

Figure S11: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-18:0 concentration in milk.

Figure S12: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-18:1 cis11 concentration in milk.

Figure S13: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-18:2 cis9,12 concentration in milk.

Figure S14: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on C-18:3 cis9,12,15 concentration in milk.

Figure S15: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on CLA cis9,trans11 concentration in milk.

Figure S16: Estimated effects of THI (A), season (C), and DIM (D) on fat percentage in milk based on separate datasets, split by parity.

Figure S17: Estimated THI x DIM effect using all data (A), records from first (B), second (C), third (D), forth or higher (E) parity cows.

Figure S18: Estimated effects of season (A), and DIM (B) on milk yield in percent relative to expectation based on the lactation curve.

Figure S19: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on relative yield of fat that was calculated based on fat percentage and relative milk yield.

Figure S20: Estimated effects of THI x parity (A), THI x DIM (B), season (C), and DIM (D) on relative yield of protein that was calculated based on protein percentage and relative milk yield.

Figure S21: Estimated genetic correlations between heat tolerance traits (slope) and commercial traits.

Figure S22: R^2 values for the linear regression models to derive heat tolerance trait for H_F% (A), H_P% (B), H_MY (C).

Table S1: Estimated variance components for different heat tolerance traits and their genetic variation compared to population-wise effects, when requiring at high number of records per cows to call a phenotype. *The genetic variation is expressed as the genetic standard deviation multiplied by 20 to express the differences between a ”standard” cow and 1 gSD superior cow in THI = 50 and THI = 70 conditions

File S1: Overview of the estimation procedure for fatty acid concentration via partial least squares regression.