Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
Applied Economics & Management
Non Technical Summary
There are alternatives to extensive antimicrobial use to prevent or treat sick animals in cattle feedlots. One such alternative is preconditioning where calves are vaccinated and started on feedlot rations and facilities before movement to feedlots. This preconditioning of calves should reduce the incidence of illness and use of antimicrobials to treat these illnesses in feedlots. However, the use of preconditioning of calves is not universality used in the industry. One reason for non-use might be insufficient knowledge concerning the benefits of preconditioning and verification, and thus insufficient payment to preconditioners. We will estimate the net benefits of preconditioning and construct contracts (mechanisms) to make use of preconditioning attractive to the industry. The economic impact of additional alternatives to extensive antimicrobial will be evaluated.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
Develop and evaluate interventions (including alternatives to antibiotics) that reduce antimicrobial resistance in food production systems.
Quantify animal health, public health, social, economic, and environmental impacts of antimicrobial interventions in food production systems.
Project Methods
The objective is to measure the economic impact of strategies to reduce antimicrobial use (AMU) in the U.S. beef production economy. An econometric model will be constructed for the U.S. beef sector, and the potential impact and response to various antimicrobial use (AMU) techniques in the beef production sector and various components of that sector will be estimated. The individual components modeled with include calve production from beef-cow operations, backgrounding or stocking operations where weaned calves are further fed primarily on pasture, and feedlots where cattle are finished on rations. Each of these components and their interactions are complex given the myriad of sizes and location of production. The movement of a weaned calf further through the production system can follow many paths from weaning directly to the feedlot, weaning with first some preconditioning before the feedlot, or further grazing after weaning and then finishing in a feedlot, or harvested as grass fed beef. Ownership through each of these channels can also be complex with some beef cow operators owning the physical facilities of each channel or retaining ownership of the calve through the channels. Because decisions are made by the operators, this myriad of production system overlaid with ownership patterns will require modeling to replicate this structure.To model this system of heterogenous production units, we will use an agent model approach where the agents are the decision makers or owners (Schreinemachers and Berger, 2011). Thus beef-cow producers will be modeled as heterogenous agents that vary in size, location, and production characteristics. Other modeled agents will be backgrounders and feedlots, again modeled as heterogeneous agents. We have previously completed research using agent modeling approaches (Verteramo Chiu, et al 2018).The agents will be modeled as profit maximizing agents subject to the constraints on resources available, and conditional on constraints on production practices, such as AMU. Behavior other than profit maximization might be modeled, but profit maximization will allow determining the impact of simulations on social welfare (profit) to the sector and the distributions of welfare across the sectors. Much previous work on agent analysis has entailed behavior rules, but there are now computer algorithms and software available for agent optimization behavior. We propose using the public available software by Troost and Berger (2015) which has agent optimization, but if that software does prove satisfactory, we will use other commercial software or write our own routines using the software R. Our plan is to begin with a reduced agent space and then build in heterogeneity and complexity.The antimicrobial reductions that will be analyzed in this agent model will include increased use of preconditioning of weaned calves, and various restrictions on AMU, such as restricting AMU for metaphylaxis, treating only sick animals, or a complete ban on AMU. These various treatments scenarios may alter the movement of cattle through the various channels and impact producers of various sizes and locations differently. For instance, a ban on AMU may channel more cattle through complete grass feeding with no placement in feedlots. Discussion with the other participants on this project may provide additional alternative scenarios to evaluate. The data necessary to model the agents will be collected from published data from studies on the various components of the beef sector (Field, 2018). Other data sources will include U.S. Agricultural Census data, and data from the Agricultural Management System (ARMS). Data may also become available from research completed from participants in this regional project.The second part of this proposal will use Principal-Agent theory to design efficient contracts to encourage AMU reduction. One such practice to reduce AMU is greater use of preconditioning of weaned calves before placement into feedlots, where preconditioning has been shown to reduce AMU (Avent, Ward and Lalman, 2004). Although shown to be profitable for feedlots, precondition is not predominant in the industry (Bryant and Blasi, 2005; Dhuyvetter et al., 2005; Schumacher et al., 2012). The question is why incomplete adoption of this practice? It may be because of insufficient transfer of the benefits accruing to feedlots back to individuals preconditioning calves, often the beef-cow producers. Principle-agent theory allows the design of a contract that is written by the principle, in this case the feedlot, that is acceptable to the agent (the preconditioner) and which ensures the fulfillment of the contract (no shirking).The principal writes the contract that will optimize the gain to himself/herself, but that optimization is subject to constraints. One constraint is a participation constraint to ensure that the agent will participate in the contract. The second constraint is an incentive compatibility constraint which requires that the agent fulfils the specifications of the contract (no shirking or cheating).The issue with preconditioning is that the performance of the preconditioned animal is not known with certainty. That means that the gain to the principal is not a fixed value but instead a distribution of values. Modeling in a principle agent framework will required generating these distributions and optimizing in an expected utility setting. Published cost of production and return budgets of the cost and benefits of preconditioning will be used to simulate the distribution of returns (Dennis, et al., 2018) to construct optimal contracts. It is likely that given the uncertainty of the benefits of preconditioning, that the optimal contract will entail some sharing of benefits between the principal and agent rather than a fixed payment. This will require the monitoring of the performance of preconditioned calves through the feedlot stage to final harvest.We have completed economic research on the U.S. dairy sector, including AMU reduction in the dairy sector (Lhermie, Tauer, and Grohn, 2018). Participation in this regional project will give us access to the individuals who are working on the science and production aspects of AMU reduction.ReferencesAvent, R.K., C.E. Ward and D.L. Lalman (2004) Market Valuation of Preconditioning Feeder Calves. J. of Ag. and Applied Economis 36(01):173-183.Dennis, E.J., T.C. Schroeder, D.G. Renter and D.L. Pendell (2018) Value of Arrival Metaphylaxis in U.S. Cattle Industry. J. of Ag. and Resource Economics 43(2):233-250Dhuyvetter, K.C., A.M. Bryant and D.A. Blasi (2005) Case Study: Preconditioning Beef Calves: Are Expected Premiums Sufficient to Justify the Practice? The Professional Animal Scientist21(6):502-514.Field, T.G. (2018) Beef Production and Management Decisions. Pearson PublishingLhermie, G., L.W. Tauer, Y.T. Grohn (2018b) An assessment of the economic costs to the U.S. dairy market of antimicrobial use restrictions. Preventive Veterinary Medicine160:63-67.Schreinemachers, P. and T. Berger (2011) An Agent-based Simulation Model of Human-Environment Interactions in Agricultural Systems. Environmental Modelling & Software 26: 845-859Schumacher, T., T.C. Schroeder, and G.T. Tonsor (2012). Willingness-to-Pay for Calf Health Programs and Certification Agents. J.of Agri. and Applied Economics44(2):191-202Troost, C. and T. Berger (2015) Dealing with Uncertainty in Agent-based Simulation: Farm-level Modeling of Adaptation to Climate Change in Southwest Germany. Amer. J. Agri.Economics97: 833-854Verteramo C.L.J., L.W. Tauer, Mohammad A.A., K. Kaniyamattam, R.L. Smith, and Y.T. Grohn (2018) An agent-based model evaluation of economic control strategies for paratuberculosis in a dairy herd. Journal of Dairy Science101:6443-6454.