Dynamic Model for Combined Simulation of Dairy Management Strategies

Congleton, W.R.

doi:10.3168/jds.s0022-0302(84)81350-8

Cited by 31 publications

(7 citation statements)

References 26 publications

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“…This was not the case with previous models that focused on reproduction and ignored feeding (e.g. Oltenacu et al, 1980;Cournut and Dedieu, 2004) or represented animal performance with a pull approach (Congleton, 1984). In addition, the dynamic representation of energy partitioning (depending on the goat's physiological state and body reserve level) makes it possible to express individual variability, which is not the case with models based on fixed rules of energy partitioning (e.g.…”

Section: Simulation Resultsmentioning

confidence: 82%

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An individual-based model simulating goat response variability and long-term herd performance

Puillet

Martin²,

Sauvant³

et al. 2010

Animal

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Finding ways of increasing the efficiency of production systems is a key issue of sustainability. System efficiency is based on long-term individual efficiency, which is highly variable and management driven. To study the effects of management on herd and individual efficiency, we developed the model simulation of goat herd management (SIGHMA). This dynamic model is individual-based and represents the interactions between technical operations (relative to replacement, reproduction and feeding) and individual biological processes (performance dynamics based on energy partitioning and production potential). It simulates outputs at both herd and goat levels over 20 years. A farmer's production project (i.e. a targeted milk production pattern) is represented by configuring the herd into female groups reflecting the organisation of kidding periods. Each group is managed by discrete events applying decision rules to simulate the carrying out of technical operations. The animal level is represented by a set of individual goat models. Each model simulates a goat's biological dynamics through its productive life. It integrates the variability of biological responses driven by genetic scaling parameters (milk production potential and mature body weight), by the regulations of energy partitioning among physiological functions and by responses to diet energy defined by the feeding strategy. A sensitivity analysis shows that herd efficiency was mainly affected by feeding management and to a lesser extent by the herd production potential. The same effects were observed on herd milk feed costs with an even lower difference between production potential and feeding management. SIGHMA was used in a virtual experiment to observe the effects of feeding strategies on herd and individual performances. We found that overfeeding led to a herd production increase and a feed cost decrease. However, this apparent increase in efficiency at the herd level (as feed cost decreased) was related to goats that had directed energy towards body reserves. Such a process is not efficient as far as feed conversion is concerned. The underfeeding strategy led to production decrease and to a slight feed cost decrease. This apparent increase in efficiency was related to goats that had mobilised their reserves to sustain production. Our results highlight the interest of using SIGHMA to study the underlying processes affecting herd performance and analyse the role of individual variability regarding herd response to management. It opens perspectives to further quantify the link between individual variability, herd performance and management and thus further our understanding of livestock farming systems.Keywords: simulation, herd, management, individual variability, dairy goat ImplicationsThe evaluation and design of sustainable livestock farming systems require predicting herd responses to management. This study describes a dairy goat herd model, representing dynamic interactions between management and individual biological processes. The model provides a...

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Section: Simulation Resultsmentioning

confidence: 82%

“…Individual-based models have overcome this limitation as they take into account each individual whose state changes over time independently from others according to management events (Congleton, 1984;Sorensen et al, 1992;Tess and Kolstad, 2000). Nutrient partitioning is, however, still based on a fixed hierarchy among functions.…”

Section: Introductionmentioning

confidence: 99%

An individual-based model simulating goat response variability and long-term herd performance

Puillet

Martin²,

Sauvant³

et al. 2010

Animal

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“…Various simulation models with different purposes have been developed for beef cattle (Long et al, 1975;Lamb et al, 1992;Hirooka et al, 1998), dairy cattle (Congleton, 1984;Groen, 1988;Bryant et al, 2008), swine (Tess et al, 1983;McLaren et al, 1987), and sheep Wang and Dickerson, 1991;Cannas et al, 2004). For goats, the first prodnction model was described by Blackbnrn et al (1987) as an extended version of a sheep model.…”

Section: Discussionmentioning

confidence: 98%

Development and application of a crossbreeding simulation model for goat production systems in tropical regions1

Tsukahara

Oishi

Hirooka

2011

Journal of Animal Science

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A deterministic simulation model was developed to estimate biological production efficiency and to evaluate goat crossbreeding systems under tropical conditions. The model involves 5 production systems: pure indigenous, first filial generations (F1), backcross (BC), composite breeds of F1 (CMP(F1)), and BC (CMP(BC)). The model first simulates growth, reproduction, lactation, and energy intakes of a doe and a kid on a 1-d time step at the individual level and thereafter the outputs are integrated into the herd dynamics program. The ability of the model to simulate individual performances was tested under a base situation. The simulation results represented daily BW changes, ME requirements, and milk yield and the estimates were within the range of published data. Two conventional goat production scenarios (an intensive milk production scenario and an integrated goat and oil palm production scenario) in Malaysia were examined. The simulation results of the intensive milk production scenario showed the greater production efficiency of the CMP(BC) and CMP(F1) systems and decreased production efficiency of the F1 and BC systems. The results of the integrated goat and oil palm production scenario showed that the production efficiency and stocking rate were greater for the indigenous goats than for the crossbreeding systems.

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“…Le modèle permet ainsi évaluer les effets de la conduite de la reproduction et des politiques de réforme, basées sur des critères reproductifs et laitiers, sur les résultats du troupeau. Ce type de représentation est également adopté dans les modèles de Marsh et al (1987), Congleton (1984) et Dijhkuizen et al (1986). Le modèle d' Ezanno (2005) s'écarte de la production potentielle pour se diriger vers une représentation de la production permise par les ressources.…”

Section: / L'animal Un Ensemble De Variables Liées à La Production Punclassified

“…Du point de vue du renouvellement, le niveau individuel permet de prendre en compte des critères de sélection des animaux à réformer ou à conserver pour le renouvellement. Pour simuler la réforme d'un animal, les modèles intègrent une combinaison de critères individuels : infertilité, niveau de production laitière, âge (Oltenacu et al 1980, Congleton 1984, Dijhkuizen et al 1986. Pour simuler la sélection d'une femelle pour le renouvellement, les modèles intègrent principalement un critère laitier (Dijhkuizen et al 1986) et plus rarement un critère d'âge (Congleton 1984).…”

Section: B) Des Interactions Entre Les Paramètres Définis à L'échelleunclassified

Intérêts et limites de la prise en compte de la variabilité individuelle dans les modèles de fonctionnement du troupeau

2010

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La variabilité des réponses individuelles est un élément clé pour comprendre le fonctionnement du troupeau et évaluer ses performances. Cette variabilité repose sur une composante biologique (élaboration de réponses productives liées à l’expression des régulations physiologiques et du génotype) et sur une composante décisionnelle (ensemble de pratiques s’appuyant sur différents niveaux d’organisation du troupeau). Cette synthèse examine dans quelle mesure les modèles de fonctionnement du troupeau existants sont opérationnels pour étudier la variabilité individuelle et faire ainsi progresser notre capacité à prédire les réponses du troupeau aux pratiques. La première partie synthétise les types de réponses biologiques représentés dans les modèles et l’élaboration de ces réponses. La seconde passe en revue les types de pratiques représentés et les niveaux d’information qu’elles mobilisent. La conclusion présente les limites des modèles pour l’étude de la variabilité individuelle. Celles-ci résident dans une vision partielle de la composante biologique (un seul type de réponse biologique, absence des réponses à l’alimentation et des régulations physiologiques) et de la composante décisionnelle (absence d’informations individuelles, un seul type de pratiques représenté, pas de lien entre projet d’élevage, lots et pratiques). Par conséquent, simuler la construction de la variabilité individuelle générée par le fonctionnement du troupeau nécessite de développer un modèle individu-centré avec deux caractéristiques essentielles. La première concerne la formalisation de l’animal en tant qu’entité régulée répondant à son environnement nutritionnel. La seconde concerne la formalisation explicite du lot comme entité de gestion, support d’un système de pratiques d’alimentation, de reproduction et de renouvellement.

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Dynamic Model for Combined Simulation of Dairy Management Strategies

Cited by 31 publications

References 26 publications

An individual-based model simulating goat response variability and long-term herd performance

An individual-based model simulating goat response variability and long-term herd performance

Development and application of a crossbreeding simulation model for goat production systems in tropical regions1

Intérêts et limites de la prise en compte de la variabilité individuelle dans les modèles de fonctionnement du troupeau

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