Effect of data collection methods on the availability of calving ease, fertility and herd health data for evaluating Australian dairy cattle
M. Haile-Mariam A D , E. Schelfhorst C and M. E. Goddard A BA Primary Industries Research Victoria, Department of Primary Industries, Attwood Centre, 475 Mickleham Road, Vic. 3049, Australia.
B Faculty of Land and Food Resources, University of Melbourne, Parkville, Vic. 3052, Australia.
C Australian Dairy Herds Improvement Scheme, Level 6, 84 William St Melbourne, Vic. 3000, Australia.
D Corresponding author. Email: Mekonnen.HaileMariam@dpi.vic.gov.au
Australian Journal of Experimental Agriculture 47(6) 664-671 https://doi.org/10.1071/EA05267
Submitted: 31 October 2005 Accepted: 25 October 2006 Published: 17 May 2007
Abstract
There is concern in the Australian dairy industry that the fertility, calving ease and disease resistance of cows is declining and that this decline is, at least in part, a genetic change. Improvement in these traits might be achieved through better herd management and genetic selection. Both these strategies are dependant on the availability of suitable data. The Australian Dairy Herd Improvement Scheme publishes estimated breeding values for fertility, calving ease and somatic cell count. However, the accuracy of the estimated breeding values is limited by the amount and quality of data collected. This paper reports on a project conducted to identify a more efficient system for collecting non-production data, with the hypothesis that quantity and quality of data collected would improve if farmers used electronic data collection methods instead of ‘traditional’ methods, such as writing in a notebook. Of 78 farmers involved in the trial, 51 used a PALM handheld (PALM group), 18 wrote data on paper and later entered it in their farm computer (PC group) and nine submitted a paper record to their data processing centres for entry into the centres’ computers (PAPER group). Data collected from these 78 trial herds during the trial period (2002–04) were compared to data collected from 88 similar non-trial farms, which kept records on PC or paper. The ratio of number of events (health, calving ease or fertility) recorded to number of calvings was considered as a measure of level of recording.
The results showed that, after adjusting for location and level of recording before the trial started, the PALM group collected significantly more calving ease, pregnancy test and other fertility data per calving than farmers who were not involved in the trial and PAPER and PC groups. The number of records collected by the PALM group increased from 0.13 pregnancy tests in 2001 to 0.36 per calving in 2004, whereas there was little change in the amount of data collected by the other groups. Similarly, the number of calving ease records increased from 0.26 in 2001 to 0.33 in 2004 and the number of heats recorded increased from 0.02 in 2001 to 0.12 in 2004. This increase in data capture among farmers using the PALM was partly due to an increase in the number of farmers who submitted any data at all. For instance, of the PALM group, 86% sent data on calving ease and 61% on pregnancy, as compared to those from the PC and PAPER groups (below 57%) or those who were not involved in the trial (below 44%). When farmers who at least submitted one record of each type of data are considered, farmers in the PALM group still submitted significantly more fertility event data than those who were not involved in the trial and those in the PAPER group. The quality of the data did not appear to be affected by the data collection methods, though the completeness of the mating data was better in PALM and PC users. The use of electronic data entry on farms would increase the amount of data available for the calculation of estimated breeding values and hence the accuracy of these values for fertility, calving ease and health traits.
Acknowledgements
Dairy Australia funded this project. We acknowledge the dairy farmers involved in the project for their effort to collect and provide their data. The assistance in various forms from Gippsland and Western Herd Improvement, ADHIS, Genetics Australia, Countdown Downunder, InCalf and University Melbourne and D&K Technologies is acknowledged. Dr Kevin Beard of ADHIS is acknowledged for extracting the data from the ADHIS database.
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