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Tedeschi LO. Review: The prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production. Animal 2023; 17 Suppl 5:100813. [PMID: 37169649 DOI: 10.1016/j.animal.2023.100813] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/02/2023] [Accepted: 04/06/2023] [Indexed: 05/13/2023] Open
Abstract
Mathematical modeling is typically framed as the art of reductionism of scientific knowledge into an arithmetical layout. However, most untrained people get the art of modeling wrong and end up neglecting it because modeling is not simply about writing equations and generating numbers through simulations. Models tell not only about a story; they are spoken to by the circumstances under which they are envisioned. They guide apprentice and experienced modelers to build better models by preventing known pitfalls and invalid assumptions in the virtual world and, most importantly, learn from them through simulation and identify gaps in pushing scientific knowledge further. The power of the human mind is well-documented for idealizing concepts and creating virtual reality models, and as our hypotheses grow more complicated and more complex data become available, modeling earns more noticeable footing in biological sciences. The fundamental modeling paradigms include discrete-events, dynamic systems, agent-based (AB), and system dynamics (SD). The source of knowledge is the most critical step in the model-building process regardless of the paradigm, and the necessary expertise includes (a) clear and concise mental concepts acquired through different ways that provide the fundamental structure and expected behaviors of the model and (b) numerical data necessary for statistical analysis, not for building the model. The unreasonable effectiveness of models to grow scientific learning and knowledge in sciences arise because different researchers would model the same problem differently, given their knowledge and experiential background, leading to choosing different variables and model structures. Secondly, different researchers might use different paradigms and even unalike mathematics to resolve the same problem; thus, model needs are intrinsic to their perceived assumptions and structures. Thirdly, models evolve as the scientific community knowledge accumulates and matures over time, hopefully resulting in improved modeling efforts; thus, the perfect model is fictional. Some paradigms are most appropriate for macro, high abstraction with less detailed-oriented scenarios, while others are most suitable for micro, low abstraction with higher detailed-oriented strategies. Modern hybridization aggregating artificial intelligence (AI) to mathematical models can become the next technological wave in modeling. AI can be an integral part of the SD/AB models and, before long, write the model code by itself. Success and failures in model building are more related to the ability of the researcher to interpret the data and understand the underlying principles and mechanisms to formulate the correct relationship among variables rather than profound mathematical knowledge.
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Affiliation(s)
- L O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, United States.
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Stokka GL, Falkner TR. Systems Thinking Perspectives on Stewardship and Our Future. Vet Clin North Am Food Anim Pract 2022; 38:201-207. [PMID: 35691623 DOI: 10.1016/j.cvfa.2022.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The management and care of livestock can be integrated with a stewardship philosophy and a systems approach to health and wellness. Stewardship is the responsible management of things entrusted to one's care. This philosophic approach means that every resource in livestock production, the land, livestock, and people, must be considered in the care and feeding of livestock and the practice of veterinary medicine. The systems thinking discipline is a set of synergistic investigative skills, used to improve the capability of identifying and understanding complex adaptive systems, predicting their behaviors, and devising modifications to them to produce desired changes.
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Affiliation(s)
- Gerald L Stokka
- Department of Animal Science, College of Agriculture, Food Systems and Natural Resources, North Dakota State University, 207C Morrill Hall, NDSU Department 7630, PO Box 6050, Fargo, ND 58108-6050, USA.
| | - T Robin Falkner
- CattleFlow Consulting, Christiana, TN, USA; Elanco Animal Health, Greenfield, IN, USA
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Menendez HM, Brennan JR, Gaillard C, Ehlert K, Quintana J, Neethirajan S, Remus A, Jacobs M, Teixeira IAMA, Turner BL, Tedeschi LO. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production. J Anim Sci 2022; 100:6577180. [PMID: 35511692 PMCID: PMC9171331 DOI: 10.1093/jas/skac160] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.
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Affiliation(s)
- H M Menendez
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J R Brennan
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - C Gaillard
- Institut Agro, PEGASE, INRAE, 35590 Saint Gilles, France
| | - K Ehlert
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J Quintana
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - Suresh Neethirajan
- Farmworx, Adaptation Physiology, Animal Sciences Group, Wageningen University, 6700 AH, The Netherlands
| | - A Remus
- Sherbrooke Research and Development Centre, 2000 College Street, Sherbrooke, QC J1M 1Z3, Canada
| | - M Jacobs
- FR Analytics B.V., 7642 AP Wierden, The Netherlands
| | - I A M A Teixeira
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Twin Falls, ID 83301, USA
| | - B L Turner
- Department of Agriculture, Agribusiness, and Environmental Science, and King Ranch® Institute for Ranch Management, Texas A&M University-Kingsville, 700 University Blvd MSC 228, Kingsville, TX 78363, USA
| | - L O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
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Tedeschi LO, Bureau DP, Ferket PR, Trottier NL. ASAS-NANP SYMPOSIUM: Mathematical modeling in animal nutrition: training the future generation in data and predictive analytics for sustainable development. A Summary. J Anim Sci 2021; 99:6149203. [PMID: 33626148 PMCID: PMC7904039 DOI: 10.1093/jas/skab023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/21/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Luis O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX
| | - Dominique P Bureau
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Peter R Ferket
- Department of Poultry Science, North Carolina State University, Raleigh, NC
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