Does the Use of Different Indicators to Benchmark Antimicrobial Use Affect Farm Ranking?

The need to reduce antimicrobial use (AMU) in livestock production has led to the establishment of national AMU data collection systems in several countries. However, there is currently no consensus on which AMU indicator should be used and many of the systems have defined their own indicators. This study sought to explore the effect of using different internationally recognized indicators on AMU data collected from Irish pig farms and to determine if they influenced the ranking of farms in a benchmarking system. AMU data for 2016 was collected from 67 pig farms (c. 35% of Irish pig production). Benchmarks were defined using seven AMU indicators: two based on weight of active ingredient; four based on the defined daily doses (DDD) used by the European Medicines Agency and the national monitoring systems of Denmark and the Netherlands; and one based on the treatment incidence (TI200) used in several published studies. An arbitrary "action zone," characterized by farms above an acceptable level of AMU, was set to the upper quartile (i.e., the top 25% of users, n = 17). Each pair of indicators was compared by calculating the Spearman rank correlation and assessing if farms above the threshold for one indicator were also above it for the comparison indicator. The action zone was broadly conserved across all indicators; even when using weight-based indicators. The lowest correlation between indicators was 0.94. Fifteen farms were above the action threshold for at least 6 of the 7 indicators while 10 farms were above the threshold for all indicators. However, there were important differences noted for individual farms between most pairs of indicators. The biggest discrepancies were seen when comparing the TI200 to the weight-based indicators and the TI200 to the DDDANED (as used by Dutch AMU monitoring system). Indicators using the same numerator were the most similar. All indicators used in this study identified the majority of high users. However, the discrepancies noted highlight the fact that different methods of measuring AMU can affect a benchmarking system. Therefore, careful consideration should be given to the limitations of any indicator chosen for use in an AMU monitoring system.

Quantification, description and international comparison of antimicrobial use on Irish pig farms.

BACKGROUND: There is concern that the use of antimicrobials in livestock production has a role in the emergence and dissemination of antimicrobial resistance in animals and humans. Consequently, there are increasing efforts to reduce antimicrobial use (AMU) in agriculture. As the largest consumer of veterinary antimicrobials in several countries, the pig sector is a particular focus of these efforts. Data on AMU in pig production in Ireland are lacking. This study aimed to quantify AMU on Irish pig farms, to identify the major patterns of use employed and to compare the results obtained to those from other published reports and studies. RESULTS: Antimicrobial use data for 2016 was collected from 67 Irish pig farms which represented c. 35% of national production. The combined sample population consumed 14.5 t of antimicrobial by weight of active ingredient suggesting that the pig sector accounted for approximately 40% of veterinary AMU in Ireland in 2016. At farm level, median AMU measured in milligram per population correction unit (mg/PCU) was 93.9 (range: 1.0-1196.0). When measured in terms of treatment incidence (TI200), median AMU was 15.4 (range: 0.2-169.2). Oral treatments accounted for 97.5% of all AMU by weight of active ingredient and were primarily administered via medicated feed to pigs in the post weaning stages of production. AMU in Irish pig production in 2016 was higher than results obtained from the national reports of Sweden, Denmark, the Netherlands and France but lower than the United Kingdom. CONCLUSIONS: Pig production in Ireland is an important consumer of veterinary antimicrobials. The quantities and patterns of AMU on Irish pig farms are comparable to pig production in other European countries but higher than some countries with more advanced AMU reduction strategies. This AMU is characterised by a high proportion of prophylactic use and is primarily administered to pigs post weaning via medicated feed. Further studies to better understand the reasons for AMU on Irish pig farms and strategies to improve health among weaner pigs will be of benefit in the effort to reduce AMU.

Description, evaluation, and validation of the Teagasc Pig Production Model1.

The Teagasc Pig Production Model (TPPM), a stochastic simulation model of a farrow-to-finish pig farm, was developed to investigate effects of changes in production systems on farm profitability. The model simulates, on a weekly basis, the annual production of a farm. Biological [e.g., herd size, number of litters/sow/year, and mortality rates (%)], physical (e.g., infrastructure), and technical (e.g., feeding practices) variables and their associated costs are included as components of the model. These inputs are used to calculate physical (e.g., feed usage and number of pigs slaughtered) and financial (e.g., annual cash flow, profit and loss account, and balance sheet) outputs. The model was validated using the Delphi method and by comparing the TPPM outputs to data recorded on 20 Irish pig farms through the Teagasc e-Profit monitor system and with complete receipts for the year 2016. Results showed that the TPPM closely simulates physical and financial performance of pig farms indicating that the TPPM can be used with confidence to study pig production systems under Irish conditions. Model applicability was demonstrated by investigating the impact of 2 changes in technical performance: 1) building of extra accommodation to increase body weight (BW) at sale by 15 kg (EXTRA ROOM) and 2) a change in feeding practices by providing finisher feed from 28 kg of BW (EARLY FINISHER) compared with over 38 kg of BW. In both scenarios, the same biological parameters were used. Mortality rates, feed ingredients costs, and price per kg of meat produced were included as stochastic variables with the input distributions derived based on historical data simulated using Monte Carlo sampling using the Microsoft Excel add-in @Risk. Annual mean net profit was €198,101 (90% confidence interval [CI]: €119,606-€275,539) for the TPPM base farm, €337,078 (90% CI: €246,320-€426,809) for the EXTRA ROOM, and €225,598 (90% CI: €146,685-€303,590) for the EARLY FINISHER. EXTRA ROOM was associated with higher costs and required higher income to cover the additional costs. The 90% CI of the EARLY FINISHER was similar to the TPPM base farm while the EXTRA ROOM scenario resulted in a wider confidence interval, suggesting that a change in feeding practices could be a better option for farmers looking to improve profit with minimum investment. Thus, the TPPM could be used to facilitate decision making in farrow-to-finish pig farms.

Using the Biocheck.UGent™ scoring tool in Irish farrow-to-finish pig farms: assessing biosecurity and its relation to productive performance.

BACKGROUND: Biosecurity is one of the main factors affecting disease occurrence and antimicrobial use, and it is associated with performance in pig production. However, the importance of specific measures could vary depending on the (national) context. The aim of this study was to describe the biosecurity status in a cohort of Irish pig farms, to investigate which of those biosecurity aspects are more relevant by using the Biocheck.UGent™ scoring system, and to study the impact of such aspects on farm performance. RESULTS: External biosecurity score was high compared to most countries due to the characteristics of the Irish pig sector (i.e. purchasing only semen and breeding gilts on farm). The internal biosecurity score was lower and had greater variability among farms than other EU countries. Using multivariable linear regression, the biosecurity practices explained 8, 23, and 16% of variability in piglet mortality, finisher mortality, and average daily gain, respectively. Three clusters of farms were defined based on their biosecurity scores (0 to 100) using principal components and hierarchical clustering analysis. Scores for clusters 1, 2 and 3 were (mean ± SD) 38 ± 7.6, 61 ± 7.0 and 66 ± 9.8 for internal and 73 ± 5.1, 74 ± 5.3 and 86 ± 4.5 for external biosecurity. Cluster 3 had lower piglet mortality (P = 0.022) and higher average daily gain (P = 0.037) when compared to cluster 2. CONCLUSIONS: Irish farms follow European tendencies with internal biosecurity posing as the biggest liability. Our results suggest that practices related to the environment and region, feed, water and equipment supply, and the management of the different stages, need to be addressed in lower performing farms to improve productive performance. Further studies on the economic impact of these biosecurity practices including complementary data on herd health, gilt rearing, piglet management, vaccination and feeding strategies are needed.