Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems

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 over...

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Autores principales:	Andy Jones, Taro Takahashi, Hannah Fleming, Bruce Griffith, Paul Harris, Michael Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/03a275d2bb8c40a19f7b6df8215a64d2
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spelling	oai:doaj.org-article:03a275d2bb8c40a19f7b6df8215a64d22021-12-02T17:08:35ZQuantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems10.1038/s41598-021-96336-12045-2322https://doaj.org/article/03a275d2bb8c40a19f7b6df8215a64d22021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96336-1https://doaj.org/toc/2045-2322Abstract 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.Andy JonesTaro TakahashiHannah FlemingBruce GriffithPaul HarrisMichael LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andy Jones Taro Takahashi Hannah Fleming Bruce Griffith Paul Harris Michael Lee Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
description	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.
format	article
author	Andy Jones Taro Takahashi Hannah Fleming Bruce Griffith Paul Harris Michael Lee
author_facet	Andy Jones Taro Takahashi Hannah Fleming Bruce Griffith Paul Harris Michael Lee
author_sort	Andy Jones
title	Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
title_short	Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
title_full	Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
title_fullStr	Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
title_full_unstemmed	Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
title_sort	quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/03a275d2bb8c40a19f7b6df8215a64d2
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Quantifying the value of on-farm measurements to inform the selection of key performance indicators for livestock production systems

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