ASAS-NANP symposium: mathematical modeling in animal nutrition: agent-based modeling for livestock systems: the mechanics of development and application.

Over the last three decades, agent-based modeling/model (ABM) has been one of the most powerful and valuable simulation-based decision modeling techniques used to study the complex dynamic interactions between animals and their environment. ABM is a relatively new modeling technique in the animal research arena, with immense potential for routine decision-making in livestock systems. We describe ABM's fundamental characteristics for developing intelligent modeling systems, exemplify its use for livestock production, and describe commonly used software for designing and developing ABM. After that, we discuss several aspects of the developmental mechanics of an ABM, including (1) how livestock researchers can conceptualize and design a model, (2) the main components of an ABM, (3) different statistical methods of analyzing the outputs, and (4) verification, validation, and replication of an ABM. Then, we perform an overall analysis of the utilities of ABM in different subsystems of the livestock systems ranging from epidemiological prediction to nutritional management to livestock market dynamics. Finally, we discuss the concept of hybrid intelligent models (i.e., merging real-time data streams with intelligent ABM), which have applications in artificial intelligence-based decision-making for precision livestock farming. ABM captures individual agents' characteristics, interactions, and the emergent properties that arise from these interactions; thus, animal scientists can benefit from ABM in multiple ways, including understanding system-level outcomes, analyzing agent behaviors, exploring different scenarios, and evaluating policy interventions. Several platforms for building ABM exist (e.g., NetLogo, Repast J, and AnyLogic), but they have unique features making one more suitable for solving specific problems. The strengths of ABM can be combined with other modeling approaches, including artificial intelligence, allowing researchers to advance our understanding further and contribute to sustainable livestock management practices. There are many ways to develop and apply mathematical models in livestock production that might assist with sustainable development. However, users must be experienced when choosing the appropriate modeling technique and computer platform (i.e., modeling development tool) that will facilitate the adoption of mathematical models by certifying that the model is field-ready and versatile enough for untrained users.

Agent-based modeling (ABM) is a well-known simulation technique that decision-makers of livestock systems can use to develop holistic, long-term, and well-informed decisions. This modeling technique facilitates the investigation of complex systems of different individuals, given its capability to simulate individual agents, their specific characteristics, and their inherent capacity to memorize individuals' past behaviors. Livestock systems are complex systems involving multiple stakeholders with collaborative and sometimes competing interests; thus, ABM might aid in achieving sustainability goals of interest to livestock systems. The modeling processes involved in developing a generic ABM and its utilities are described, so that livestock researchers can build multiple models customized for their research needs. We discuss numerous software platforms that livestock systems modelers can utilize towards this goal. A brief overview of the state-of-the-art ABM developed by different domain experts researching livestock systems was done so that decision modelers working in the field can use those models to conceptualize and design their models for their specific research needs. We also made a case for hybridizing the ABM with real-time data streaming technology to support precision livestock sensor initiatives to enhance the utility of agent-based models for real-time decision-making.

System Economic Costs of Antibiotic Use Elimination in the US Beef Supply Chain.

There is consumer pressure on the US beef cattle industry to minimize antibiotic use (ABU) in order to aid in the global antimicrobial resistance mitigation efforts. Our objective was to estimate the economic costs of ABU constraints in a conceptual US integrated beef supply chain (IBSC) to aid the beef industry in mitigating the ever-increasing risk of antimicrobial resistance, by reducing their ABU. An IBSC network model was developed and differentiated into 37 different nodes of production. Each node could only raise a specific type of animals, differentiated based on the production technique and animal health status. The cost, as well as the weight gain coefficient, was estimated for each node, using an IBSC cost of production model. Linear programming solutions to this network model provided the least cost path of beef supply through the system, under various ABU constraints. The cost as well as weight gain coefficient of the 37 nodes, initial supply of 28.5 million calves weighing 0.65 million metric tons, and final demand of 16.14 million metric tons of slaughter-ready fed cattle were used as inputs/constraints to the three different linear programming scenarios, with different ABU constraints. Our first scenario, which placed no constraint on ABU, estimated that the minimum total economic cost to meet the final beef demand was $38.6 billion. The optimal solution was to use only the high health status calves for beef production. Because low health calves occur in the beef system, our second scenario required all the calves irrespective of their health status to be used, which increased the system cost to $41.5 billion. Thus, the value of only producing high health status calves is $2.9 billion. Our third scenario, which restricted feedlots from using antibiotics even for low health calves, incurred a total cost of $41.9 billion for antibiotic-free beef production. We concluded that the additional cost of $367 million for implementing antibiotic-free beef production is relatively low, ~0.90% of the minimum cost incurred for the conventional beef supply chain (model 2 cost of $41.5 billion). However, a much higher cost savings is obtained by producing only high health status calves.

A review of simulation analyses of economics and genetics for the use of in-vitro produced embryos and artificial insemination in dairy herds.

The use of in-vitro produced (IVP) embryo transfer (ET) in dairy herds is growing fast. Much of this growth is on dairy farms where the focus is on milk production and not on selling breeding stock. The value of implementing IVP-ET in a dairy herd arises from a higher genetic merit of the IVP-embryo, but the cost to produce a pregnancy with an IVP embryo is greater than the cost of artificial insemination (AI). The first objective of this study was to review estimates of the net benefit of using IVP-ET over AI in dairy herds using existing literature. Another objective was to show how much IVP-ET use in a herd is optimal. Most of the literature is based on simulation modeling, including our own work that focuses on the dairy industry in the USA. We found that the most profitable use of AI and IVP-ET is often a combination of the two. More IVP-ET should be used when the value of surplus calves is greater and the cost of IVP-ET is lower, among many other factors. In the future, use of IVP-ET will be further improved by more accurately identifying superior donors and recipients, reducing the generation interval, and achieving greater efficiency in embryo production.

Corrigendum: Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining.

[This corrects the article DOI: 10.3389/fmicb.2019.00687.].

Antimicrobial Policies in United States Beef Production: Choosing the Right Instruments to Reduce Antimicrobial Use and Resistance Under Structural and Market Constraints.

Antimicrobial use (AMU) in animal agriculture contributes to the selection of resistant bacteria, potentially constituting a public health threat. To address antimicrobial resistance, public policies set by governments, as well as intra-sectoral approaches, can be implemented. In this paper, we explore how common policy instruments such as regulations, economic incentives, and voluntary agreements could help reduce AMU in beef production. We first describe the structure of the beef supply chain which directly influences the choice of policy instruments. We describe how externalities and imperfect information affect this system. We then discuss how five policy instruments would each perform to achieve a reduction in AMU. Bovine respiratory disease complex (BRD) represents the major driver of AMU in beef production; consequently, reducing its incidence would decrease significantly the amounts of antimicrobials administered. We consider control options for BRD at different stages of the beef supply chain.

Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining.

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 21,243 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.

An agent-based model evaluation of economic control strategies for paratuberculosis in a dairy herd.

This paper uses an agent-based simulation model to estimate the costs associated with Mycobacterium avium ssp. paratuberculosis (MAP), or Johne's disease, in a milking herd, and to determine the net benefits of implementing various control strategies. The net present value (NPV) of a 1,000-cow milking herd is calculated over 20 yr, parametrized to a representative US commercial herd. The revenues of the herd are generated from sales of milk and culled animals. The costs include all variable and fixed costs necessary to operate a representative 1,000-cow milking herd. We estimate the NPV of the herd with no MAP infection, under an expected endemic infection distribution with no controls, and under an expected endemic infection distribution with various controls. The initial number of cows in a herd with an endemic MAP infection is distributed as 75% susceptible, 13% latent, 9% low MAP shedding, and 3% high MAP shedding. Control strategies include testing using ELISA and fecal culture tests and culling of cows that test positive, and culling based on observable milk production decrease. Results show that culling cows based on test results does not increase the herd's NPV and in most cases decreases NPV due to test costs as well as false positives and negatives with their associated costs (e.g., culling healthy cows and keeping infected cows). Culling consistently low producing cows when MAP is believed to be present in the herd produces higher NPV over the strategy of testing and culling MAP infected animals, and over the case of no MAP control.

Economic and genetic performance of various combinations of in vitro-produced embryo transfers and artificial insemination in a dairy herd.

The objective of this study was to find the optimal proportions of pregnancies from an in vitro-produced embryo transfer (IVP-ET) system and artificial insemination (AI) so that profitability is maximized over a range of prices for embryos and surplus dairy heifer calves. An existing stochastic, dynamic dairy model with genetic merits of 12 traits was adapted for scenarios where 0 to 100% of the eligible females in the herd were impregnated, in increments of 10%, using IVP-ET (ET0 to ET100, 11 scenarios). Oocytes were collected from the top donors selected for the trait lifetime net merit (NM$) and fertilized with sexed semen to produce IVP embryos. Due to their greater conception rates, first ranked were eligible heifer recipients based on lowest number of unsuccessful inseminations or embryo transfers, and then on age. Next, eligible cow recipients were ranked based on the greatest average estimated breeding values (EBV) of the traits cow conception rate and daughter pregnancy rate. Animals that were not recipients of IVP embryos received conventional semen through AI, except that the top 50% of heifers ranked for EBV of NM$ were inseminated with sexed semen for the first 2 AI. The economically optimal proportions of IVP-ET were determined using sensitivity analysis performed for 24 price sets involving 6 different selling prices of surplus dairy heifer calves at approximately 105 d of age and 4 different prices of IVP embryos. The model was run for 15 yr after the start of the IVP-ET program for each scenario. The mean ± standard error of true breeding values of NM$ of all cows in the herd in yr 15 was greater by $603 ± 2 per cow per year for ET100 when compared with ET0. The optimal proportion of IVP-ET ranged from ET100 (for surplus dairy heifer calves sold for ≥$300 along with an additional premium based on their EBV of NM$ and a ≤$100 embryo price) to as low as ET0 (surplus dairy heifer calves sold at $300 with a $200 embryo price). For the default assumptions, the profit/cow in yr 15 was greater by $337, $215, $116, and $69 compared with ET0 when embryo prices were $50, $100, $150, and $200. The optimal use of IVP-ET was 100, 100, 62, and 36% of all breedings for these embryo prices, respectively. At the input price of $165 for an IVP embryo, the difference in the net present value of yr 15 profit between ET40 (optimal scenario) and ET0 was $33 per cow. In conclusion, some use of IVP-ET was profitable for a wide range of IVP-ET prices and values of surplus dairy heifer calves.

Exposure to colony stimulating factor 2 during preimplantation development increases postnatal growth in cattle.

The microenvironment of a preimplantation embryo can influence changes in development that affect postnatal phenotypes. One of the potential mediators of this effect in many species is colony-stimulating factor (CSF2), which can increase an embryo's ability to establish pregnancy after its transfer into recipients. Exposure of embryos to CSF2 during early development can also affect the pattern of development later in pregnancy in a sex-dependent manner. We therefore hypothesized that treatment of in vitro-produced embryos with CSF2 in culture would alter birth weight and postnatal growth of the resultant calf. Body weight and withers height were measured for Holstein heifer calves produced in vitro with or without 10 ng/ml CSF2 and for calves produced by artificial insemination. There were no differences in birth weight between groups; thereafter, however, calves from the CSF2-treated group experienced greater increases in body weight through 13 months of age, with only small differences in withers height. These results support the model that an embryo's postnatal characteristics can be programmed during the preimplantation period, and that CSF2 is one of the embryokines through which programming is directed. Mol. Reprod. Dev. 82: 892-897, 2015. © 2015 Wiley Periodicals, Inc.