GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 2. Prediction of intake under rotational and continuously stocked grazing management

Delagarde, Rémy; Faverdin, Philippe; Baratte, Christine; Peyraud, Jean‐Louis

doi:10.1111/j.1365-2494.2010.00770.x

Cited by 46 publications

(91 citation statements)

References 61 publications

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“…The INRA (Institut National de la Recherche Agronomique) feeding system (INRA, 2010) was selected to model the nutrient intake within the model. The intake when housed and at grazing is calculated based on the GrazeIn model as described in Delagarde et al (2011aand 2011b), Faverdin et al (2011. In simple terms, intake of the animal is the lesser of the intake permitted by the intake capacity and fill value (FV) of the feed, the HA and the intake needed to meet the requirements.…”

Section: Inputs and Outputs Of The Modelmentioning

confidence: 99%

“…The statistical analyses used were the concordance correlation coefficient (CCC) and the relative prediction error (RPE) to evaluate the daily herbage dry matter intake, the milk yield (MY) and live weight change. The milk production and herbage intake of the French model GrazIN (Delagarde et al, 2011a;Faverdin et al, 2011) was validated by comparing model outputs against experimental data using the mean square prediction error on 206 experimental herds (Delagarde et al, 2011b).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Inputs and Outputs Of The Modelmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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“…GrazeIn (Delagarde et al, 2011a;Faverdin et al, 2011) is a semi-mechanistic prediction model that simulates the GDMI and MY of grazing dairy cows using easily obtainable cow, sward, grazing management and supplementation variables. Cow variables include age, parity, week of lactation (WOL), potential peak milk yield (PMY peak ), milk fat concentration, milk protein concentration, BW, body condition score (BCS), week of conception, BCS at calving and calf birth weight.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Brief descriptions of the sub-models are provided below, and more detailed descriptions are provided by Delagarde et al (2011a) and Faverdin et al (2011).…”

Section: Model Descriptionmentioning

confidence: 99%

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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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Calculating herbage utilization and intake by dairy cows under subtropical conditions using conventional field measurement techniques or the HerbValo method

Wlodarski,

Delagarde,

Pozo

et al. 2023

Trop Anim Health Prod

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GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 2. Prediction of intake under rotational and continuously stocked grazing management

Cited by 46 publications

References 61 publications

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

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

Calculating herbage utilization and intake by dairy cows under subtropical conditions using conventional field measurement techniques or the HerbValo method

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