# Evaluation of empirical and mechanistic models and sampling intervals to model the lactation curves of F1 dairy sheep

## TL;DR

This study compares different models and sampling intervals to predict milk production patterns in dairy sheep, finding that some models work better for specific curve shapes and sampling frequencies.

## Contribution

The study evaluates the performance of empirical and mechanistic models across different sampling intervals for modeling lactation curves in F1 dairy sheep.

## Key findings

- Estimation of total milk yield was not affected by sampling interval.
- Peak yield and day at peak yield were better estimated with 7D and 14D intervals.
- Dijkstra model fit typical curves best, while Pollott model fit atypical curves best.

## Abstract

Recently Latin American countries have developed a dairy sheep industry with an increasing number of specialized dairy-sheep flocks. The objective of this study was to evaluate the goodness of fit of empirical and mechanistic models and sampling interval to model the lactation curve of F1 dairy sheep with different shape of lactation curves in an intensively managed flock of the central highlands of Mexico. A total of 4,494 weekly (7D) test day records (TDR) from 156 lactations were analyzed. Three datasets were generated from the original 7D data set, setting TDR at different sampling intervals: 14 (14D), 21 (21D) and 28 (28D) days. Lactation curves were fitted using two empirical (Wood and Wilmink) and two mechanistic models (Dijkstra and Pollott). The parameters of the empirical and mechanistic models were estimated using the iterative non-linear curve fitting procedure in R. The results showed that the estimation of TMY was not affected by the sampling interval. However, the estimation of peak yield (PY) and day at peak yield (TPY) was affected by sampling interval (P < 0.05), with better estimates for 7D and 14D. Estimates of PY and TPY differed between the empirical and mechanistic models with both methods failing to estimate PY and TPY in atypical curves. The Dijkstra model showed the best fit for typical curves and the Pollott model for atypical curves in all the sampling intervals evaluated.