Modelling_herbage_intake.pdf - European Grassland Federation
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Modelling_herbage_intake.pdf - European Grassland Federation

Meeting of the EGF-working group grazing« Research methodology of grazing »Kiel, August 29Modelling herbage intake for predictingperformance: the example of INRAtionsoftwareRémy DELAGARDE, Philippe FAVERDIN,Christine BARATTE, Jean-Louis PEYRAUDINRA, UMR Production du Lait, Saint-Gilles, France

History• From 30 years:INRA Fill and Feed Unit systemsAll ruminants, all feedsOnly indoors feedingINRAtion software• 2000-2003:Grazemore European project (Mayne et al.)Development of GrazeIn model (Peyraud et al.)• 2007: Inclusion of GrazeIn model in INRAtion 4.0• 2011: Full model published in Grass and ForageScience (Faverdin et al., Delagarde et al.)

The principles of INRA Fill Unit system(voluntary intake prediction)Cows characteristicsIntake Capacity =( Pasture Intake Fill Value P )Chemicalcomposition+ ( Forage Intake Fill Value F )+ ( Concentrate Intake Fill Value C )InputsSubstitution rate(energy balance)

Adaptation to grazing situations• Principle: grazing intake = relative to voluntaryintake• 2 grazing systems: rotational and continuous• For each grazing system, 2 sources of limitations:- Pasture availability (allowance, mass)- Time availability (access time)

General framework of the INRAtionpasture intake modelCow intakecapacity(potential MY, LW,BCS, parity ...)Pastureingestibility(fill value)SupplementationConcentrateForages(amount, feedvalues)Grazing conditions(Pasture and time availability)IndoorsvoluntaryintakesubstitutionrateEnergybalanceIntake atgrazing(relative to VI)

Prediction of grazing conditions effects onherbage intake by grazing dairy cowsRelative herbage intake (% indoors voluntary herbage intake) 1 2 30 4 8 12 16 202 4 6 8 10 12Relative HA > 2 cm(% VHI)Daily access time(h)Sward surface height(cm stick)RotationalRotational+Set-stockingSet-stocking

Factors affecting herbage intake at grazingAnimalSwardGrazingmanagementSupplementsAgeParityLWBCSGrowthPeak milkDIMBreedStrainBotanical compSpeciesOMDFibreCPDMSward heightHerbage massBulk density% dead% laminaGrazing systemStocking rateH. AllowanceResidence timeTime of accessForagesConcentrates(amount,nutritivevalues)WeatherSeason, Temperature,Rain, Wind

Other INRA grazing intake modelsfor dairy cowsINRA Feed Tables 2007:Simplified equations from INRAtion modelbased on pre- and post-grazing sward height(english version in 2010-2011)Pâtur’In (Delaby et al.):Software to assist grazingmanagement of dairy cows(herd, paddocks, pasture growth,decision rules)

Prediction of dairy cows pasture intakeaccording to pre- and post-grazing sward heightIC 39,7 631 2,1 PrH 459PI = (38,78 + 16,83 FV + + - - )17 PoH ² PrH ² PoH PrH PoHPostgrazingswardheight(cm)876Kg DMI:1817161514548 10 12 14 16 18 20Pre-grazing sward height (cm)INRA Feed Tables

Discussion: Validation• Objectives: Precision, robustnessTime-consuming, but necessary (improvements)• Internal validationSimulations (virtual experiments)Response laws to inputs and interactions• External validationPredicted vs. actual values (independent data)Global precision(INRAtion : 304 herds, TEAGASC and INRA,actual DMI 10-22 kgMPE: 11% MPE, 1.6 kg DM)

Discussion:What we need to predict ?• Objective of the model:Grazing processes, grazing managementScale of approach (possible integration ?)minute day yearNeed for different input variables(research model, practical tool ?)• Milk production:Responses law from energy and protein supply(relative to potential production)Not yet included in INRAtion

Thank you for your attention

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