Modification of lactation yield estimates for improved selection outcomes in developing dairy sectors

Animals are extremely important to the livelihood of smallholder dairy farmers in developing countries. However, due to limited resources, investment into long-term genetic improvement is rare and herd recording is minimal. Therefore, obtaining adequate performance records for genetic evaluation and selection is difficult and hence it is important to optimize the selection outcomes from any data that are collected. The aim of this study is to determine a robust and efficient method for estimating lactation yield for low producing dairy cattle and their subsequent genetic evaluation. Using Sahiwal cattle as an example, simulated data sets, based on lactation data from Pakistan, were used to compare different methods of lactation yield estimation (i.e., test-interval method, and three nonlinear models). Furthermore, these estimates were analysed to explore their implications on the subsequent estimated breeding value (EBV) ranking and selection outcomes. Utilising these results, different test-day sampling schedules were compared to investigate possible recording regimes involving few records that can accurately estimate lactation yield without significantly affecting selection. Results indicate that the lactation models proposed by Wood (1967) and Wilmink (1987) yield similar selection outcomes to the recommended test-interval method. These results provide opportunities for further research into test-day scheduling which could reduce the number of records required and have considerable implications on progeny testing systems of low producing dairy cattle and developing dairy sectors.