The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in dairy cows was 0.88 (20.0MJ), 0.78 (18.6MJ), and 0.63 (22.0MJ), respectively, based on cross-validation. These studies, however, are limited to results from one research group based on data from 2 contrasting production systems in the United Kingdom and Ireland and would need to be replicated, especially in a range of production systems because the prediction equations are not accurate when the variability used in validation is not represented in the calibration data set. Heritable genetic variation exists for all predicted traits. Phenotypic differences in energy intake also exists among animals stratified based on genetic merit for energy intake predicted from milk FTIRS, substantiating the usefulness of such FTIR-predicted phenotypes not only for day-to-day herd management, but also as part of a breeding strategy to improve cow performance.

Developing breeding schemes to assist mitigation of greenhouse gas emissions

Genetic improvement of livestock is a particularly effective technology, producing permanent and cumulative changes in performance. This paper highlights some of the options for including mitigation in livestock breeding schemes, focusing on ruminant species, and details three routes through which genetic improvement can help to reduce emissions per kg product via: (i) improving productivity and efficiency, (ii) reducing wastage in the farming system and (iii) directly selecting on emissions, if or when these are measurable. Selecting on traits that improve the efficiency of the system (e.g. residual feed intake, longevity) will have a favourable effect on the overall emissions from the system. Specific examples of how genetic selection will have a favourable effect on emissions for UK dairy systems are described. The development of breeding schemes that incorporate environmental concerns is both desirable and possible. An example of how economic valuation of public good outcomes can be incorporated into UK dairy selection indices is given. This paper focuses on genetic selection tools using, on the whole, currently available traits and tools. However, new direct and indirect measurement techniques for emissions will improve the potential to reduce emissions by genetic selection. The complexities of global forces on defining selection objectives are also highlighted.