Mathematical modeling in animal production

Tedeschi, L. O.; Menendez, Hector Manuel

doi:10.1016/b978-0-12-817052-6.00025-2

Cited by 8 publications

(12 citation statements)

References 107 publications

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“…Many models to estimate cattle growth (kg/day) exist and take into consideration different factors such as breed type, climate, environmental conditions (e.g. mud or humidity) and utilize a wide array of empirical, probabilistic, deterministic and mechanistic equations (Oltjen et al ., 1986; Leon-Velarde and Quiroz, 1999; Tedeschi et al ., 2019; 2004; Tedeschi, 2019 a , b ; Tedeschi and Menendez, 2020). Baudracco et al .…”

Section: Discussionmentioning

confidence: 99%

The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability

Menendez

Tedeschi

2020

J. Agric. Sci.

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Perception of freshwater use varies between nations and has led to concerns of how to evaluate water use for sustainable food production. The water footprint of beef cattle (WFB) is an important metric to determine current levels of freshwater use and to set sustainability goals. However, current WFB publications provide broad WF values with inconsistent units preventing direct comparison of WFB models. The water footprint assessment (WFA) methodologies use static physio-enviro-managerial equations, rather than dynamic, which limits their ability to estimate cattle water use. This study aimed to advance current WFA methods for WFB estimation by formulating the WFA into a system dynamics methodology to adequately characterize the major phases of the beef cattle industry and provide a tool to identify high-leverage solutions for complex water use systems. Texas is one of the largest cattle producing areas in the United States, a significant water user. This geolocation is an ideal template for WFB estimation in other regions due to its diverse geography, management-cultures, climate and natural resources. The Texas Beef Water Footprint model comprised seven submodels (cattle population, growth, nutrition, forage, WFB, supply chain and regional water use; 1432 state variables). Calibration of our model replicated initial WFB values from an independent study by Chapagain and Hoekstra in 2003 (CH2003). This CH2003 v. Texas production scenarios evaluated model parameters and assumptions and estimated a 41–66% WFB variability. The current model provides an insightful tool to improve complex, unsustainable and inefficient water use systems.

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Section: Discussionmentioning

confidence: 99%

The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability

Menendez

Tedeschi

2020

J. Agric. Sci.

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“…For instance, Alves et al (2019) compared multiple linear regression with ML (i.e., support vector ML, Bayesian network) to predict carcass traits and commercial meat cuts in lambs and reported that both could be used to pre-select input variables for an ML approach. Perhaps, hybrid models (mechanistic and AI) might provide better forecasting, interpretation, and comprehension of the predictions as it combines the conceptual features of MM with the speedy AI’s data handling attributes ( Tedeschi, 2020 ). The missing link to foster the development of the next generation of computer modeling ( Tedeschi and Menendez, 2020 ) that will spur an innovative technological wave in predictive analysis ( Tedeschi, 2019 ) might be the combination of AI (a data-driven approach) with MM (a concept-driven approach).…”

Section: Hybrid Intelligent Mechanistic Modelsmentioning

confidence: 99%

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

Tedeschi

2022

Journal of Animal Science

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A renewed interest in data analytics and decision support systems in developing automated computer systems is facilitating the emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling (HIMM) and agent-based models (iABM). Data analytics have evolved remarkably, and the scientific community may not yet fully grasp the power and limitations of some tools. Existing statistical assumptions might need to be re-assessed to provide a more thorough competitive advantage in animal production systems towards sustainability. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing HIMM. The progress of analytical tools was divided into three stages: collect and respond, predict and prescribe, and smart learning and policy-making, depending on the level of their sophistication (simple to complicated analysis). The collect and respond stage is responsible for ensuring the data is correct and free of influential data points, and it represents the data and information phases for which data is cataloged and organized. The predict and prescribe stage results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. This stage represents the pinnacle of acquired knowledge that leads to wisdom, and AI technology is intrinsic. Although still incipient, HIMM and iABM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community still has some issues to be resolved, including the lack of transparency and reporting of AI that might limit code reproducibility. It might be prudent for the scientific community to avoid the shiny object syndrome (i.e., AI) and look beyond the current knowledge to understand the mechanisms that might improve productivity and efficiency to lead agriculture towards sustainable and responsible achievements.

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“…More advanced algorithms have been used with GPS and accelerometer sensors to accurately predict livestock behavior and calculate metrics such as individual daily time spent grazing ( Brennan et al, 2021 ). Variability in movements and behavior associated with GPS-tracked livestock can be an effective way to monitor livestock welfare concerns such as water failure, disease detection, or changes in behavior linked to distress or parturition ( Tedeschi and Menendez, 2020 ; Tobin et al, 2020 , 2021 ; Fogarty et al, 2021 ).…”

Section: Extensive Precision Operationsmentioning

confidence: 99%

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production

Menendez

Brennan

Gaillard

et al. 2022

Journal of Animal Science

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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 (PSM). 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 PSM, ultimately strengthening the benefits from precision technology to achieve short-and long-term goals.

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Mathematical modeling in animal production

Cited by 8 publications

References 107 publications

The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability

The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production

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