Rethinking efficiency: Growth curves as a proxy for inputs and impacts in finishing beef systems

Abstract

Quantifying and improving efficiency within beef systems is essential for economic and environmental sustainability. The industry standard for assessing efficiency is liveweight gain per day, however, this metric is limited in that it values each day of a growing animal's life as equally costly, despite the increasing maintenance requirements, inputs, and emissions associated with increasing liveweight. Quantifying the area under the growth curve (AUC) considers both time and liveweight as a cost and therefore may hold potential as a better estimate of cost, impact, and efficiency in beef systems. Liveweight data was taken from 439 finishing beef cattle split across three herds grazing on different pastures, known as ‘farmlets’. Analysis was conducted in three parts: [1] Validation of AUC as a proxy for costs using data from a sub-set of 87 animals that had been part of a previous life cycle assessment (LCA) study in which dry matter intake (DMI), methane emissions (CH4), and nitrous oxide emissions (N2O) were calculated. [2] Calculation of AUC relative to liveweight gain (LWG AUC−1) and comparison of that metric against the industry standard of liveweight gain per day (LWG day−1). [3] Assessment of how LWG AUC−1 varied with breed, sex, and management. When comparing to LCA results, AUC correlated significantly with DMI (r = 0.886), CH4 (r = 0.788) and N2O (r = 0.575) emissions. Over the full dataset, there was a negative non-linear relationship between LWG AUC−1 and slaughter age (r = −0.809). There was a significant difference in LWG AUC−1 between breeds (p = 0.046) and farmlets (p = 0.028), but not sex (p = 0.388). LWG AUC−1 has the potential to act as a proxy for feed intake and emissions. In that regard it is superior to LWG day−1, whilst requiring no additional data. Results highlighted the decreasing efficiency of beef cattle over time and the potential benefits of earlier slaughter. The use of LWG AUC−1 could allow farmers to improve their understanding of efficiency within their herds, aiding informed management decision making.