A Mechanistic dynamic simulation model of nutrient utilization, growth and body composition in pre-weaned lambs reared artificially

Anim-Jnr, S.; Morel, P. C. H.; Kenyon, P. R.; Blair, Hugh T.

doi:10.1016/j.anifeedsci.2020.114402

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The nutrition and metabolism domains, for instance, have benefited tremendously from predictive models ( Baldwin, 1995 ; Tedeschi and Fox, 2020 ), in part because of the worldwide respect of publications by the National Academies of Sciences, Engineering, and Medicine ( NASEM ) through their National Research Council’s ( NRC ) Nutrient Requirement series ( NASEM, 2016 ; NASEM, 2021 ; NRC, 2007a ; NRC, 2007b ; NRC, 2012 ), and in part because of the industry’s need to increase the standardization and quality of their animal products as well as profits for more competitive production scenarios. In that sense, knowing when feedlot animals will achieve their most profitable point, given their carcass composition and maturity degree ( Tedeschi et al, 2004 ), requires accurate predictive models for animal growth ( Anim-Jnr et al, 2020 ; Hoch and Agabriel, 2004 ; Pettigrew, 2018 ; Tedeschi et al, 2004 ). Epidemiological models have gained considerable interest given the increasing concerns of zoonotic diseases disrupting the animal production sector ( Manjoo-Docrat, 2022 ; Wisnieski et al, 2021 ) and their possible impact on humans, including the most recent concern about antimicrobial resistance ( Chantziaras et al, 2014 ; Spicknall et al, 2013 ).…”

Section: Competitive Advantage In Animal Production Systemsmentioning

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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Section: Competitive Advantage In Animal Production Systemsmentioning

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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Validation of a mechanistic dynamic pre-weaned lamb growth and body composition simulation model

Herath

Pain²,

Kenyon³

et al. 2022

Animal Feed Science and Technology

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Desarrollo de un modelo dinámico mecanicista para predecir el crecimiento de cuyes (Cavia porcellus) machos del genotipo Perúcir el crecimiento de cuyes (Cavia porcellus) machos del genotipo Perú

Tapie,

Posada-Ochoa,

Rosero-Noguera

et al. 2024

Rev. Acad. Colomb. Cienc. Ex. Fis. Nat.

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Los modelos dinámicos sirven para comprender y predecir el crecimiento y la composición corporal de los animales. El objetivo de nuestro estudio fue desarrollar un modelo dinámico mecanicista para predecir el crecimiento y la composición corporal de cuyes alimentados con una dieta granulada. El modelo integró la dinámica de crecimiento diario en términos de ganancia de peso (GPD), peso corporal vacío (PCV), peso vivo (PV), proteína corporal, grasa, agua y cenizas depositadas, así como los requerimientos de energía (EMm) y proteína para el mantenimiento (PCm). El modelo se desarrolló en el programa Scilab. Se utilizaron 21 ecuaciones algebraicas derivadas de un estudio de sacrificio comparativo. El modelo se evaluó utilizando la raíz del error cuadrático medio (RECM), el error relativo de predicción (ERP) y el coeficiente de correlación de concordancia (CCC) de los valores observados en 12 cuyes y los predichos por el modelo. Para el PV, el PCV, el agua, las cenizas, los EMm y la PCm el modelo mostró una alta concordancia (CCC>0,97) y un ERP menor al 5 %, lo que indicó que las predicciones eran satisfactorias. Las predicciones de la GPD y la proteína depositada tuvieron concordancias sustanciales (CCC de 0,742 y 0,809, respectivamente), con una predicción satisfactoria para la proteína (ERP<20 %). La predicción de la grasa depositada fue menos acertada (ERE=54,93 y CCC=0,031). El modelo representó satisfactoriamente la dinámica de crecimiento de los cuyes, excepto en la predicción de la grasa depositada.

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A Mechanistic dynamic simulation model of nutrient utilization, growth and body composition in pre-weaned lambs reared artificially

Cited by 3 publications

References 28 publications

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: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

Validation of a mechanistic dynamic pre-weaned lamb growth and body composition simulation model

Desarrollo de un modelo dinámico mecanicista para predecir el crecimiento de cuyes (Cavia porcellus) machos del genotipo Perúcir el crecimiento de cuyes (Cavia porcellus) machos del genotipo Perú

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