Abstract
The use of key performance indicators (KPIs) to assist on-farm decision making has long been seen as a promising strategy to improve operational efficiency of agriculture. The potential benefit of KPIs, however, is heavily dependent on the economic relevance of the metrics used, and an overabundance of ambiguously defined KPIs in the livestock industry has disincentivised many farmers to collect information beyond a minimum requirement. Using high-resolution sheep production data from the North Wyke Farm Platform, a system-scale grazing trial in southwest United Kingdom, this paper proposes a novel framework to quantify the information values of industry recommended KPIs, with the ultimate aim of compiling a list of variables to measure and not to measure. The results demonstrated a substantial financial benefit associated with a careful selection of metrics, with top-ranked variables exhibiting up to 3.5 times the information value of those randomly chosen. When individual metrics were used in isolation, ewe weight at lambing had the greatest ability to predict the subsequent lamb value at slaughter, surpassing all mid-season measures representing the lamb’s own performance. When information from multiple metrics was combined to inform on-farm decisions, the peak benefit was observed under four metrics, with inclusion of variables beyond this point shown to be detrimental to farm profitability regardless of the combination selected. The framework developed herein is readily extendable to other livestock species, and with minimal modifications to arable and mixed agriculture as well.
Highlights
The use of key performance indicators (KPIs) to assist on-farm decision making has long been seen as a promising strategy to improve operational efficiency of agriculture
Another study in New Zealand quantified the level of resilience embedded into dairy farms through variables strongly associated with inter-farm variability, and from this information produced a list of target KPIs for low-performing farms to measure and improve[28]
In a study designed to determine KPIs for the income of Australian wool producers, the technical efficiency of farms was first estimated and the data analysed through a principal component analysis (PCA) to identify production factors associated with maximum technical efficiency[29]
Summary
The use of key performance indicators (KPIs) to assist on-farm decision making has long been seen as a promising strategy to improve operational efficiency of agriculture. The presence of a substantial variability in production efficiency is widely recognised across the livestock industry[14], even within systems operating under comparable climatic, biophysical and socioeconomic conditions[15] This is the case at both the farm scale[16] and the animal scale[17], with economic and environmental performances often positively correlated with one another regardless of the spatial resolution[18,19]. In a study designed to determine KPIs for the income of Australian wool producers, the technical efficiency of farms was first estimated and the data analysed through a PCA to identify production factors associated with maximum technical efficiency[29] These farm-scale studies were explicitly designed to explore precision agriculture solutions for efficiency-related issues currently present within each flock/herd, thereby increasing the overall competitiveness of the local livestock industry
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