Accuracy of predicting milk yield from alternative milk recording schemes

Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

The effect of dairy heifer pre-breeding growth rate on first lactation milk yield in spring-calving, pasture-based herds

Optimising heifer growth rate may offer an opportunity to improve lifetime milk yield per cow, enhancing the environmental and economic efficiency of dairy farming operations. The effect of dairy heifer pre-breeding average daily weight gain (ADG_PB) on first lactation milk yield was investigated. This observational study employed a data set comprising 265 Holstein-Friesian, or Holstein-Friesian-cross-Jersey heifers from seven commercial, spring-calving, pasture-based dairy herds, where the major component of the diet was grazed grass. These were weighed at birth and prior to breeding and ADG_PB was calculated. Milk recordings were performed throughout the heifers' first lactation and 305-day yield figures calculated from these records. Yields were corrected to 4% fat and 3.1% protein to create standardised 305-day milk yield (SMY), which was the outcome of interest. Median ADG_PB was 0.72 kg/day. Median 305-day yield was 5 967 kg. Linear regression was used to investigate the effect of weight and genetic, age and first calving factors on SMY. Pre-breeding average daily weight gain, age at first calving and predicted transmitting abilities for milk protein production and calving interval were all significant in the final model, which also included the random effects of farm and month of calving within year. ADG_PB was quadratically related to first lactation SMY, with an ADG_PB of 0.82 kg/day corresponding to the maximum predicted SMY. The model predicted that a heifer growing at 0.82 kg/day would produce 1 120 kg more SMY than a heifer growing at 0.55 kg/day, 218 kg more than a heifer growing at 0.7 kg/day and 103 kg more than a heifer growing at 0.90 kg/day. Manipulation of heifer growth rate may offer a viable method of increasing first lactation milk yield.

Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations.