Development and evaluation of a pastoral simulation model that predicts dairy cattle performance based on animal genotype and environmental sensitivity information

Bryant, Jeremy; López‐Villalobos, N.; Holmes, C. W.; Pryce, J.E.; Rossi, José Luis; Macdonald, Kevin

doi:10.1016/j.agsy.2007.10.007

Cited by 21 publications

(18 citation statements)

References 52 publications

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“…The models of Petruzzi and Danfaer (2004), Bryant et al (2008), Baudracco et al (2012) and Brun-Lafleur (2011) all explicitly recognize genetically driven changes in body energy reserves. It can also be argued that the notion has become implicit in recent versions of the Cornell Net Carbohydrate and Protein System (Tylutki et al, 2008) and the INRA Fill Unit System (Faverdin et al, 2007), as both recognize the value of adjusting intake predictions for a lactational trajectory of body reserve usage, as well as a lactation curve of milk production.…”

Section: Introductionmentioning

confidence: 99%

“…With respect to milk production, there are a number of models that were conceived to achieve this (Neal and Thornley, 1983;Østergaard et al, 2000;Vetharaniam et al, 2003b), and McNamara and Baldwin (2000) showed in principle how Molly could accommodate different genotypes by adjusting milk fat and lactose synthesis rates. However, there are far fewer models that explicitly incorporate genotype effects on multiple functions or nutrient partitioning (Bryant et al, 2008;Martin and Sauvant, 2010a). Although it is relatively easy to incorporate genotype at the conceptual level, this does not necessarily facilitate the task of obtaining operational descriptions of genotype with respect to nutrient partitioning.…”

Section: Introductionmentioning

confidence: 99%

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Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Friggens

Brun-Lafleur

Faverdin

et al. 2013

Animal

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In recent years, it has become increasingly clear that understanding nutrient partitioning is central to a much broader range of issues than just being able to predict productive outputs. The extent to which nutrients are partitioned to other functions such as health and reproduction is clearly important, as are the efficiency consequences of nutrient partitioning. Further, with increasing environmental variability, there is a greater need to be able to predict the ability of an animal to respond to the nutritional limitations that arise from the environment in which it is placed. How the animal partitions its nutrients when resources are limited, or imbalanced, is a major component of its ability to cope, that is, its robustness. There is mounting evidence that reliance on body reserves is increased and that robustness of dairy cows is reduced by selection for increased milk production. A key element for predicting the partition of nutrients in this wider context is to incorporate the priorities of the animal, that is, an explicit recognition of the role of both the cow's genotype (genetic make-up), and the expression of this genotype through time on nutrient partitioning. Accordingly, there has been a growing recognition of the need to incorporate in nutritional models these innate driving forces that alter nutrient partitioning according to physiological state, the genetically driven trajectories. This paper summarizes some of the work carried out to extend nutritional models to incorporate these trajectories, the genetic effects on them, as well as how these factors affect the homeostatic capacity of the animal. At present, there are models capable of predicting the partition of nutrients throughout lactation for cows of differing milk production potentials. Information concerning genotype and stage of lactation effects on homeostatic capacity has not yet been explicitly included in metabolic models that predict nutrient partition, although recent results suggest that this is achievable. These developments have greatly extended the generality of nutrient partitioning models with respect to the type of animal and its physiological state. However, these models remain very largely focussed on predicting partition between productive outputs and body reserves and, for the most part, remain research models, although substantial progress has been made towards developing models that can be applied in the field. The challenge of linking prediction of nutrient partitioning to its consequences on health, reproduction and longevity, although widely recognized, is only now beginning to be addressed. This is an important perspective for future work on nutrient partitioning.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Friggens

Brun-Lafleur

Faverdin

et al. 2013

Animal

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“…This was also reported for other simulation models, and is possibly the result of greater experimental error in the measurements of LW than on other variables. Two other models, the animal model MOOSIM (Bryant et al, 2008) and the whole-farm model (WFM; Beukes et al, 2006 and were also validated with the same dataset as the e-Cow model, but in all three cases, validation points were different. The WFM was validated for each HF strain separately and the validation points were annual values averaged per herd (3 years), the MOOSIM model was validated including both NA and NZ strain in the same dataset and the validation points were the daily values of individual cows in early lactation (1 year), while the e-Cow model was validated more intensively, with validation for each HF strain and for each parity separately, and the validation points were weekly values for the whole lactation (3 years).…”

Section: Model Validationmentioning

confidence: 99%

“…Bryant et al (2008) developed a model that predicts the performance of dairy cows at grazing and accounts for genetic differences between cows. This model was validated for cows in early lactation, with acceptable accuracy of prediction for milk yield, but with low accuracy of prediction for herbage DM intake and live weight (LW) change.…”

Section: Introductionmentioning

confidence: 99%

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

Baudracco

López‐Villalobos

Holmes

et al. 2012

Animal

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This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel R . The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.Keywords: dairy cow, grazing, milk yield, body lipid reserves, model ImplicationsThe e-Cow model predicts the performance of dairy cows of different genetic merit, grazing either ryegrass or lucernebased pastures, with or without supplementary feeding, thus being useful for conditions in different countries. The model is sensitive to genotype 3 environment interactions.The e-Cow model is useful for applied research, teaching and extension purposes, allowing a quick and practical understanding of the effects of feeding level, that is, pasture and supplements offered, and cow's genetic merit on herbage dry matter intake, milk yield and changes in body condition score and live weight...

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“…data) indicates that perennial ryegrass with these lipid attributes may enable farmers to have more efficient pasture utilisation, leading to increased farm profitability. A dairy cow model (Bryant et al 2008) was used to estimate the change in feed intake with an increase in lipid content from 3.9% to 8%. Results suggested a reduction in feed intake of 30%, with no change in the production of milk solids.…”

Section: High-energy Foragesmentioning

confidence: 99%

Breaking through the feed barrier: options for improving forage genetics

et al. 2015

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Abstract. Pasture based on perennial ryegrass (Lolium perenne L.) and white clover (Trifolium repens L.) is the foundation for production and profit in the Australasian pastoral sectors. The improvement of these species offers direct opportunities to enhance sector performance, provided there is good alignment with industry priorities as quantified by means such as the forage value index. However, the rate of forage genetic improvement must increase to sustain industry competitiveness. New forage technologies and breeding strategies that can complement and enhance traditional approaches are required to achieve this. We highlight current and future research in plant breeding, including genomic and gene technology approaches to improve rate of genetic gain. Genomic diversity is the basis of breeding and improvement. Recent advances in the range and focus of introgression from wild Trifolium species have created additional specific options to improve production and resource-use-efficiency traits. Symbiont genetic resources, especially advances in grass fungal endophytes, make a critical contribution to forage, supporting pastoral productivity, with benefits to both pastures and animals in some dairy regions. Genomic selection, now widely used in animal breeding, offers an opportunity to lift the rate of genetic gain in forages as well. Accuracy and relevance of trait data are paramount, it is essential that genomic breeding approaches be linked with robust field evaluation strategies including advanced phenotyping technologies. This requires excellent data management and integration with decision-support systems to deliver improved effectiveness from forage breeding. Novel traits being developed through genetic modification include increased energy content and potential increased biomass in ryegrass, and expression of condensed tannins in forage legumes. These examples from the wider set of research emphasise forage adaptation, yield and energy content, while covering the spectrum from exotic germplasm and symbionts through to advanced breeding strategies and gene technologies. To ensure that these opportunities are realised on farm, continuity of industry-relevant delivery of forage-improvement research is essential, as is sustained research input from the supporting pasture and plant sciences.

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Development and evaluation of a pastoral simulation model that predicts dairy cattle performance based on animal genotype and environmental sensitivity information

Cited by 21 publications

References 52 publications

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

Breaking through the feed barrier: options for improving forage genetics

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