Utilisation De l'ENERGIE Des Aliments Par Les Animaux

Leroy, A. M.

doi:10.1051/animres:19540402

Cited by 9 publications

(3 citation statements)

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“…AFRC = Agricultural and Food Research Council; ARC = Agricultural Research Council; CNCPS = Cornell Net Carbohydrate and Protein System; LRNS = Large Ruminant Nutrition System; CSIRO = Commonwealth Scientific and Industrial Research Organization; INRA = Institut National de la Recherche Agronomique; NASEM = National Academies of Sciences, Engineering, and Medicine; NRC = National Research Council; RNS = Ruminant Nutrition System; SRNS = Small Ruminant Nutrition System; TPS = Tropical Pasture Simulator. Key references (empty blue squircles) are (A1) NRC (1945a, b), (A2) Leroy (1954), (B1) (Blaxter, 1962), (B2) Van Soest (1963a) and Van Soest (1963b), (C1) Nehring et al (1966), (C2) Lofgreen and Garrett (1968), (C3) Moe et al (1970), (D1) Schiemann et al (1971), (D2) Waldo et al (1972), (D3) Hoffmann et al (1974), (D4) Ministry of Agriculture, Fisheries and Food (1975), (D5) Van Es (1975), (E1) Baldwin et al (1977), (E2) Baldwin et al (1980), (E3) Loewer et al (1980), (F1) France et al (1982), (F2) Gill et al (1984), (F3) Fox and Black (1984), (F4) Conrad et al (1984), (F5) Loewer et al (1981), (F6) Loewer et al (1983), (G1) Danfær (1990), (G2) Mertens (1985; 1987), (G3) Bridges et al (1986), (H1) Illius and Gordon (1991), (H2) France et al (1992), (H3) Russell et al (1992), Sniffen et al (1992), and Fox et al (1992), (H4) Dijkstra et al (1992), Neal et al (1992), and Dijkstra (1993), (H5) Tamminga et al (1994), (I1) Riedo et al (1998) based on the Hurley Pasture Model (Thornley, 1998), (I2) Loewer (1998), (I3) Freer et al (1997), (J1) Nagorcka et al (2000), (J2) Mills et al (2001), (J3) Fox et al (2004), (J4) Cannas et al (2004) and Tedeschi et al (2010), (J5) Vazquez and Smith (2001), (J6) Heard et al (2004), (J7) Baudracco et al (2010), (J8) Herrero et al (2000b; 2000a), (J9) Vetharaniam et al (2003), (K1) Bannink et al (2006), (K2) Bannink et al (2008), (K3) Jouven et al (2006a, b), (L1) Gr...…”

Section: Extant Mathematical Models In Ruminant Productionmentioning

confidence: 99%

“…Together, their data and results formed the common base of our scientific knowledge. Experimental results were published in scientific papers (Leroy, 1954; Blaxter and Graham, 1955; Blaxter and Wainman, 1961), reports (National Research Council, 1944a, 1944b, 1945a, 1945b, 1945c, 1949), and books (Brody, 1945; Kleiber, 1961; Blaxter, 1962). The publication of these experiment results raised more questions, which prompted the formation and establishment of public, governmental research entities to investigate further the recent findings by the scientific community and to promote discoveries.…”

Section: Introductionmentioning

confidence: 99%

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ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Tedeschi

2019

Journal of Animal Science

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This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models ( MM ) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler’s ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence ( AI ) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

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Section: Extant Mathematical Models In Ruminant Productionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Tedeschi

2019

Journal of Animal Science

View full text Add to dashboard Cite

show abstract

“…The amount of the diets to be given was computed from the results of previous determinations of the fasting energy metabolism of the birds and an estimate of the energy value of the diet (2-908 kcal/kg dry matter). This net energy value was computed on the basis of previously determined, nutritional values (Burlacu, 1960;Burlacu et al 1967), and by employing the net energy computation method suggested by Leroy (1961).…”

Section: Methodsmentioning

confidence: 99%