A review of methods for improving resolution of milk production data and weather information for measuring heat stress in dairy cattle

Abstract

Genetic selection for heat tolerance is a sustainable strategy to support mitigation of the adverse effects of heat stress that may be achieved through housing and feeding modification in livestock. To identify and select heat tolerant animals, it is necessary to have high temporal resolution animal performance records and high spatio-temporal resolution weather information. The temporal resolution being the amount of time detail in terms of sampling frequency and the spatial resolution the amount of space area i.e. area covered and how detailed an observation is. This review highlights some approaches to improve the resolution of data necessary for evaluating heat stress in dairy cattle. Because heat stress occurs only periodically, mainly during summer, information from monthly test-day milk records only is limited in phenotypic data. The use of only test-day records over time captures a small fraction of the response due to heat stress. It is expected that daily milk production records would allow for better estimation of heat stress effects. In regions where data from weather stations are scarce or lacking, grid interpolated weather data may improve accuracy of prediction. This is because grid interpolation enables creation of reliable virtual weather stations that can provide complete and long-term weather data. However, studies are lacking to compare the agreement of measured weather data and grid interpolated data to assess the effects of heat stress in livestock. To evaluate the performance of animals under various climatic conditions, environmental parameters are applied either individually or augmented into an index and merged with production records. Although there are many indices defined in literature, most have not been applied in genetic evaluation studies and grid interpolated indices have not been developed for use in livestock studies. This review highlights approaches to contribute to improved temporal resolution of performance records as well as spatial and temporal resolution of weather data necessary for improvement of heat stress evaluation in dairy cattle.