Techniques for Development of Swine Performance Response Surfaces

Bridges, T. C.; Gates, Richard S.; Chao, Kun; Turner, Loren; Minagawa, H.

doi:10.13031/2013.27977

Cited by 12 publications

(7 citation statements)

References 1 publication

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“…Real-time or prospective: These procedures generate models that continuously modify their responses to inputs and outputs by estimating system parameters in real time. In PLF or precision livestock feeding applications, examples of these models are included in some research [33][34][35][36].…”

Section: Data Processingmentioning

confidence: 99%

Applying a Mathematical Model for Calculating the Ideal Nutrition for Sheep

Pavlova,

Trichkova-Kashamova,

Dimitrov

2024

Mathematics

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The principal economic sector devoted to the breeding, raising, and production of farm animals is known as the livestock industry. There are precise standards for making high-quality feed in animal husbandry. Precision livestock feeding is a crucial component, with the potential to significantly impact the profitability of livestock; it permits the provision of diets to animals that are precisely tailored to their specific daily nutritional needs. Through simulation modeling, a single model can be created for automated systems to determine daily rations for farm animals. For the purposes of this document, precision livestock feeding refers to the practice of tailoring feed to individual animals or groups of animals, taking into account their changing nutritional needs over time and individual differences in terms of nutritional requirements. The practice aims to optimize animal health and performance while reducing feed waste. This paper presents a formal model for determining the quantities of components needed to achieve a minimum cost mixture that satisfies compositional and quantitative criteria. The present research calculates the amount of hay and silage required to feed an animal per day at the most economical cost by applying an optimization approach that involves defining and solving an optimization problem. The problem is solved using a well-known software package, which is necessary for the practical application of the resulting model. Real data from livestock production in Bulgaria are used to numerically test the model.

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Section: Data Processingmentioning

confidence: 99%

Applying a Mathematical Model for Calculating the Ideal Nutrition for Sheep

Pavlova,

Trichkova-Kashamova,

Dimitrov

2024

Mathematics

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“…Model parameters are estimated on-line during the process, resulting in a model that continuously adapts its response to on-line process inputs and outputs. There are few examples in which these models have been used in PLF or precision livestock feeding applications (Korthals et al, 1994;Bridges et al, 1995;Aerts et al, 2000;Thomson and Smith, 2000). The limitation of using the recursive approach in precision livestock feeding is related to the fact that model parameters and model structure do not provide biological insight in the causal mechanisms implicated in animal responses, that animal response and input parameters may have unsymmetrical variation, and that the animal responses to input variation does not evolve in the same timeframe.…”

Section: Data Processingmentioning

confidence: 99%

“…The disadvantages associated with black-box models can be overcome by using an intermediate approach of grey-box models in which recursive technologies and mechanistic models are combined. This approach was suggested by Bridges et al (1995) who used a mechanistic swine growth model to generate physiological response data and this response data were then used to train and validate three backward propagation neural network models describing the effect of the environment on average daily gain, feed intake, heat production, and physiological status of the animal. The authors concluded that neural network models can be used to simplify data extraction from complex models and be used in instances where the use of the full model is difficult or impossible.…”

Section: Data Processingmentioning

confidence: 99%

18: Precision livestock feeding, principle and practice

Pomar

Milgen

Remus

2019

Poultry and Pig Nutrition

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Precision livestock farming (PLF) is proposed to the livestock industry as an essential tool to enhance sustainability and competitiveness • Precision livestock feeding is part of PLF and can have a great impact in livestock profitability due the ability of feeding pigs with diets tailored daily to their nutrient requirements. •Precision livestock feeding can decrease livestock environmental impacts by optimizing the use of dietary nutrients and animal nutrient utilization efficiency which results in less nutrient excretion. •Mathematical models developed for precision livestock feeding must be designed to operate in real-time using system measurements. These models are structurally different from traditional nutrition models. •The success of PLF is dependent on the precision livestock feeding integration into the system, as well, the adaptability and training of the farmers to use PLF systems.

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“…An important advantage of such mechanistic models is that they represent the state-of-the-art knowledge of the considered system [7, 90–93], and are particularly useful in the general scientific process of connecting biophysical findings to psychophysical phenomena, generating new hypotheses and developing new assertions [94], and improving reliability of drug development and drug dosing [13]. However, in terms of direct translational utility in terms of clinical decision-making (monitoring and/or controlling of systems), these models are either too unwieldy [95, 96] or contain too much uncertainty [94]. …”

Section: Applications Of Mechanistic Models To Acute Critical Illnessmentioning

confidence: 99%

From data patterns to mechanistic models in acute critical illness

Aerts

Haddad

et al. 2014

Journal of Critical Care

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The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches in order to address this unmet need. Two main paths of development have characterized the Society’s approach: i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiological signals or multivariate analyses of molecular and genetic data, and ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control, and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience, and the impact that merging these modeling approaches can have on general anesthesia.

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Techniques for Development of Swine Performance Response Surfaces

Cited by 12 publications

References 1 publication

Applying a Mathematical Model for Calculating the Ideal Nutrition for Sheep

Applying a Mathematical Model for Calculating the Ideal Nutrition for Sheep

18: Precision livestock feeding, principle and practice

From data patterns to mechanistic models in acute critical illness

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