Emerging Beef Producer Organisations (POs) in the Irish Beef Sector: An Analysis of Media Coverage in the Context of Nationwide Beef Producer Protests

International literature acknowledges benefits of the legally recognised Producer Organisations (POs). Successful leveraging of these benefits depends on two forms of cooperation: horizontal integration among the producers for more effective functioning of the POs; and vertical integration of POs with other actors in the production chain to facilitate processes of co-creation and interactive innovation. In 2016 PO legislation was first introduced in Ireland, and in 2019 Ireland’s first two beef POs emerged at a time when primary producers in the beef sector mobilised en masse, protesting against poor prices and seeking changes in supply chain relationships. Throughout this period, significant and detailed media reporting of the beef sector surrounded the protests, which takes the focus of our analysis. Building on an existing but limited literature on institutional conditions in the Irish beef industry and international accounts of factors influencing the success of POs, we analyse media coverage in order to shed light on the nature of emerging new forms of horizontal and vertical cooperation. In this regard, we focus on horizontal integration of producers into PO and associations of POs (APOs); and vertical integration of POs into Inter Branch Organisations (IBOs) and value-based supply chains (VBSCs). Our analysis shows that the media representations of the Irish beef sector evidence significant challenges to the establishment and successful operation of POs, in any form. The analysis suggests that current constellation of relations in the Irish beef sector represents an environment that is partially resistant to horizontal co-operation and significantly hostile to vertical co-operation. Interactive innovation involving different chain actors seems not to be imminent, at least in the short term, unless there are strategic public and/or private interventions introduced to support it.

Effect of welfare standards and biosecurity practices on antimicrobial use in beef cattle

Antimicrobial use (AMU) in livestock species and the associated antimicrobial resistance are a global concern, thus strategies for their reduction and a more judicious use are needed. Previous research has revealed a link between improved animal welfare, biosecurity and AMU reduction in pig and dairy sectors, however, little is known about the beef sector. This study aimed to investigate the impact of welfare standards and biosecurity on AMU in beef cattle. Data on performance traits and AMU were collected over a 3.5 year time from 27 specialised beef farms and a treatment incidence was calculated using the defined daily dose for animals. An on-farm assessment was carried out by assigning a score from 0 (very poor) to 100% (very good) to 3 sections: welfare, biosecurity and emergency management. The highest average score was obtained for the welfare section (76%) followed by emergency management (39%) and biosecurity (24%). This suggests that major focus on strategies for the implementation of biosecurity measures and emergency management is needed, due to the low scores reported. A statistically significant lower AMU was observed with improved level of welfare. These results may be helpful for farm benchmarking and highlight the importance of improved animal welfare for an efficient antimicrobial stewardship.

International price volatility transmission and structural change: a market connectivity analysis in the beef sector

Despite the abundance of literature on agricultural price transmissions and unexpectedly disrupted value chains from infectious disease outbreaks such as bovine spongiform encephalopathy and COVID-19, the importance of research on price connectivity in the international beef markets has largely been ignored. To assess agricultural price transmission issues, error correction-type models (ECMs) have been predominantly employed. These models, however, suffer a deficiency in that the method is incapable of depicting time-variant linkages between prices. This article examines the connections between global and local prices, as well as price volatility in the beef sector. Our analysis uses a generalised autoregressive conditional heteroscedasticity (GARCH) model with the dynamic conditional correlation (DCC) specification that enables us to identify market connection intensity dynamics. We pay assiduous attention to structural changes in the overall research processes to enhance the reliability of estimation. For the first time in meat or grain price transmission research, our autoregressive models have been developed with structural break dummy variables for DCC. The principal findings are that (1) local retail prices for Azerbaijan, Georgia, Japan, Kazakhstan, Kyrgyzstan, Tajikistan and the UK showed a structural change in mean or variance, all of which were identified after the global food crisis from 2007–2009, (2) international prices unidirectionally Granger-cause regional prices in Georgia, Tajikistan and the United States in both mean and volatility (accordingly, no country exhibited price or price-volatility transmission from regional to international markets), and (3) volatility liaisons between global and local beef markets are generally weak, but price volatility exhibited closer synchronisation around the 2008 global food crisis, which created structural changes during the period. This finding implies that national governments should shield domestic from global markets by implementing trade restrictions such as quotas or taxes in a global emergency.

Canadian Regional Agriculture Model: PMP Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use by a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves were then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Aggregation of Omic Data and Secretome Prediction Enable the Discovery of Candidate Plasma Biomarkers for Beef Tenderness

Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.