Evaluation of Equations Based on Animal Factors to Predict Intake of Lactating Holstein Cows

Fuentes-Pila, Joaquín; DeLorenzo, M.A.; Beede, D.K.; Staples, C.R.; Holter, J.B.

doi:10.3168/jds.s0022-0302(96)76518-9

Cited by 84 publications

(68 citation statements)

References 21 publications

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“…Accuracy was greatest at the intermediate feeding levels. The threshold of 0.10 error as suggested by Fuentes-Pila et al (1996) where there is a poor fit between actual and predicted values was not exceeded for any animals in the data set.…”

Section: Resultsmentioning

confidence: 81%

“…Fitness was defined as the size of the mean prediction error (MPE) of actual compared with predicted milk yields, the smaller the better as outlined by Fuentes-Pila et al (1996). Mayer et al (2001) recommends a population size of approximately twice the dimensionality of the problem, but not too small to ensure genetic diversity.…”

Section: Evolutionary Algorithm Analysesmentioning

confidence: 99%

See 1 more Smart Citation

The effect of level of feeding, genetic merit, body condition score and age on biological parameters of a mammary gland model

Bryant

López‐Villalobos

Holmes

et al. 2007

Animal

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An evolutionary algorithm was applied to a mechanistic model of the mammary gland to find the parameter values that minimised the difference between predicted and actual lactation curves of milk yields in New Zealand Jersey cattle managed at different feeding levels. The effect of feeding level, genetic merit, body condition score at parturition and age on total lactation yields of milk, fat and protein, days in milk, live weight and evolutionary algorithm derived mammary gland parameters was then determined using a multiple regression model. The mechanistic model of the mammary gland was able to fit lactation curves that corresponded to actual lactation curves with a high degree of accuracy. The senescence rate of quiescent (inactive) alveoli was highest at the very low feeding level. The active alveoli population at peak lactation was highest at very low feeding levels, but lower nutritional status at this feeding level prevented high milk yields from being achieved. Genetic merit had a significant linear effect on the active alveoli population at peak and mid to late lactation, with higher values in animals, which had higher breeding values for milk yields. A type of genetic merit £ feeding level scaling effect was observed for total yields of milk and fat, and total number of alveoli produced from conception until the end of lactation with the benefits of increases in genetic merit being greater at high feeding levels. A genetic merit £ age scaling effect was observed for total lactation protein yields. Initial rates of differentiation of progenitor cells declined with age. Production levels of alveoli from conception to the end of lactation were lowest in 5-to 8-year-old animals; however, in these older animals, quiescent alveoli were reactivated more frequently. The active alveoli population at peak lactation and rates of active alveoli proceeding to quiescence were highest in animals of intermediate body condition scores of 4.0 to 5.0. The results illustrate the potential uses of a mechanistic model of the mammary gland to fit a lactation curve and to quantify the effects of feeding level, genetic merit, body condition score, and age on mammary gland dynamics throughout lactation.

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Section: Resultsmentioning

confidence: 81%

Section: Evolutionary Algorithm Analysesmentioning

confidence: 99%

The effect of level of feeding, genetic merit, body condition score and age on biological parameters of a mammary gland model

Bryant

López‐Villalobos

Holmes

et al. 2007

Animal

View full text Add to dashboard Cite

show abstract

“…The RPE is an expression of the RMSE as a percentage of the actual data. According to Fuentes-Pila et al (1996), a RPE <10% indicates a satisfactory prediction, between 10% and 20% a relatively acceptable prediction, and an RPE >20% suggests a poor model prediction. In this study, the RMSE and RPE were used on the comparison of the different sub cattle at the lactation week scale.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Development and evaluation of the herd dynamic milk model with focus on the individual cow component

Ruelle

Delaby

Wallace

et al. 2016

animal

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The herd dynamic milk (HDM) model is a dynamic model capable of simulating the performance of individual dairy animals (from birth to death), with a daily time step. Within this study, the HDM model is described and evaluated in relation to milk production, body condition score (BCS) and BCS change throughout lactation by comparing model simulations against data from published experimental studies. The model's response to variation in genetic potential, herbage allowance and concentrate supplementation was tested in a sensitivity analysis. Data from experiments in Ireland and France over a 3-year period (2009-11) were used to complete the evaluation. The aim of the Irish experiment was to determine the impact of different stocking rates (SRs) (SR1: 3.28 cow/ha, SR2: 2.51 cow/ha) on key physical, biological and economic performance. The aim of the French experiment was to evaluate over a prolonged time period, the ability of two breeds of dairy cows (Holstein and Normande) to produce and to reproduce under two feeding strategies (high level and low level) in the context of compact calving. The model evaluation was conducted at the herd level with separate evaluations for the primiparous and multiparous cows. The evaluation included the two extreme SRs for the Irish experiment, and an evaluation at the overall herd and individual animal level for the different breeds and feeding levels for the French data. The comparison of simulation and experimental data for all scenarios resulted in a relative prediction error, which was consistently <15% across experiments for weekly milk production and BCS. In relation to BCS, the highest root mean square error was 0.27 points of BCS, which arose for Holstein cows in the low feeding group in late lactation. The model responded in a realistic fashion to variation in genetic potential for milk production, herbage allowance and concentrate supplementation.

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“…Ideally, models are considered robust for practical use if they have a satisfactory level of accuracy for most data sets (RPE ⩽ 10%) rather than high accuracy for some data sets and poor accuracy for others (Fuentes-Pila et al, 1996;Keady et al, 2004). An RPE of ⩽10% is achievable for models predicting DMI of dairy cows in confined systems of milk production (Zom et al, 2012) but is very difficult to achieve for models predicting GDMI of grazing dairy cows (Delagarde and O' Donovan, 2005).…”

Section: Adaptation Of Grazein Model For Grazing Dairy Cowsmentioning

confidence: 99%

Adaptation and evaluation of the GrazeIn model of grass dry matter intake and milk yield prediction for grazing dairy cows

O’Neill

Ruelle

O’Donovan

et al. 2014

Animal

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The prediction of grass dry matter intake (GDMI) and milk yield (MY) are important to aid sward and grazing management decision making. Previous evaluations of the GrazeIn model identified weaknesses in the prediction of GDMI and MY for grazing dairy cows. To increase the accuracy of GDMI and MY prediction, GrazeIn was adapted, and then re-evaluated, using a data set of 3960 individual cow measurements. The adaptation process was completed in four additive steps with different components of the model reparameterised or altered. These components were: (1) intake capacity (IC) that was increased by 5% to reduce a general GDMI underprediction. This resulted in a correction of the GDMI mean and a lower relative prediction error (RPE) for the total data set, and at all stages of lactation, compared with the original model; (2) body fat reserve (BFR) deposition from 84 days in milk to next calving that was included in the model. This partitioned some energy to BFR deposition after body condition score nadir had been reached. This reduced total energy available for milk production, reducing the overprediction of MY and reducing RPE for MY in mid and late lactation, compared with the previous step. There was no effect on predicted GDMI; (3) The potential milk curve was reparameterised by optimising the rate of decrease in the theoretical hormone related to secretory cell differentiation and the basal rate of secretory cell death to achieve the lowest possible mean prediction error (MPE) for MY. This resulted in a reduction in the RPE for MY and an increase in the RPE for GDMI in all stages of lactation compared with the previous step; and (4) finally, IC was optimised, for GDMI, to achieve the lowest possible MPE. This resulted in an IC correction coefficient of 1.11. This increased the RPE for MY but decreased the RPE for GDMI compared with the previous step. Compared with the original model, modifying this combination of four model components improved the prediction accuracy of MY, particularly in late lactation with a decrease in RPE from 27.8% in the original model to 22.1% in the adapted model. However, testing of the adapted model using an independent data set would be beneficial and necessary to make definitive conclusions on improved predictions.

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Evaluation of Equations Based on Animal Factors to Predict Intake of Lactating Holstein Cows

Cited by 84 publications

References 21 publications

The effect of level of feeding, genetic merit, body condition score and age on biological parameters of a mammary gland model

The effect of level of feeding, genetic merit, body condition score and age on biological parameters of a mammary gland model

Development and evaluation of the herd dynamic milk model with focus on the individual cow component

Adaptation and evaluation of the GrazeIn model of grass dry matter intake and milk yield prediction for grazing dairy cows

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