Lactation Curve Modelling and Genetic Parameters Estimation in Murciano-Granadina Goats.

Abstract

The present study aims to determine the best non-linear model for describing lactation curves and estimating genetic parameters for the lactation curve traits in the Murciano-Granadina goats in Iran. We compared five mathematical models including the Cappio-Borlino (CB), Cobby and Le Du (CD), Narushin-Takma (NT), Wilmink (WL), and Wood (WD) to characterise the lactation curve in the first and second lactations of Murciano-Granadina does. The dataset consisted of 36,958 and 23,319 milk yield test-day records from 4964 first-parity and 3335 s-parity Murciano-Granadina does, respectively. These records were collected from 2017 to 2024 in a private dairy farm, located in Ghale-Ganj city, Kerman province, southern area of Iran. In both lactation periods, the WD model showed the lowest values for root mean squares of prediction error (RMSE) and Akaike's information criterion (AIC), as well as the highest adjusted coefficient of determination ( ${R^2}_{\mathrm{adj}}$ ) among the evaluated models. Additionally, positive autocorrelations were observed among the residuals for all the models considered, with the lowest positive autocorrelation obtained under the WD model. Therefore, WD was identified as the best model to characterise the lactation curve of the Murciano-Granadina does in the first and second lactation periods. Consequently, we computed the individual lactation curve traits for does in the ith parity (where i = 1 for the first parity and i = 2 for the second parity), including peak time (PTi), peak milk yield (PYi), and lactation persistency (LPi), using the parameters derived from the WD model. A multivariate animal model utilising a Bayesian approach was employed to estimate the genetic parameters of the lactation curve traits. The posterior means for heritability estimates were 0.07, 0.13, 0.05, 0.05, 0.11, and 0.08 for PT1, PY1, LP1, PT2, PY2, and LP2, respectively. In the first parity, genetic correlations among the lactation curve traits were positive estimates of 0.28, 0.96, and 0.25 for PT1-PY1, PT1-LP1, and PY1-LP1, respectively. In the second parity, the corresponding genetic correlation estimates were 0.88, 0.89, and 0.59 for PT2-PY2, PT2-LP2 and PY2-LP2, respectively. It can be concluded that the low heritability estimates for the investigated lactation curve traits suggest these traits are mainly affected by non-additive genetic and environmental effects. Consequently, direct genetic selection may not effectively modify the shape of the Murciano-Granadina lactation curve. The positive genetic correlation estimates among the traits examined within each parity, as well as among the same traits across the parities, suggest that selecting one trait will also enhance the other traits.