Modelling systems to describe maternal productivity, with the aim of improving beef production efficiency by eliciting practice change
B. J. Walmsley A B C and V. H. Oddy A B
+ Author Affiliations
- Author Affiliations
A Cooperative Research Centre for Beef Genetic Technologies.
B NSW Department of Primary Industries, Beef Industry Centre of Excellence, Trevenna Road, Armidale, NSW 2351, Australia.
C Corresponding author. Email: brad.walmsley@dpi.nsw.gov.au
Animal Production Science 58(1) 193-205 https://doi.org/10.1071/AN14874
Submitted: 14 October 2014 Accepted: 5 January 2015 Published: 7 September 2016
Abstract
The overall efficiency of beef production is considered more highly correlated with cow–calf efficiency, viz. maternal productivity (MP), than the efficiency of other segments of the beef production chain. Recently, concerns have been raised that improvements in feedlot and carcass performance have led to a decline in MP due to the uncertainty that surrounds the relationships between production and MP traits. The Beef Cooperative Research Centre ‘Maternal Productivity’ Project examined the impact of cow genotype and nutrient intake on breeding herd productivity. This experiment demonstrated that cow body composition is influenced by genetic differences in rib fat and residual feed intake, as well as nutrient availability. Genetic differences in rib fat were shown to influence heifer pregnancy rates, observed days to calving, MP when nutrient intake is restricted and ME intake by the cow–calf unit. Weaning rate was found to account for a large portion of the variation in MP, while cow genetic background and pre-weaning nutrient availability influenced the postweaning and carcass performance of progeny. These findings demonstrate that although balancing the requirements of MP with those of other traits is not straight forward, it is of critical importance. Incorporating modelling systems into decision-support systems (DSS) offers the opportunity to integrate fragmented knowledge into decision making. Unfortunately, previous DSS have gained little traction and limited adoption due to their perceived complexity, large input-data requirements, and mismatches between outputs and the decision-making styles of producers. The development of the BeefSpecs fat calculator provides an example of how producer-measurable inputs and simple user interactions can be combined using modelling systems to develop DSS to improve MP. No single model that addressed all issues affecting MP was found in the literature. Thus, it was concluded that previous modelling systems would need to be combined to develop a suite of DSS that target-specific components of MP, such as heifer pregnancy rates and interactions between the cow herd and the nutritional environment.
Additional keywords: biological hierarchy, genetic composition, heifer reproduction, morass systems, weaning rate.
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