Adaptation and evaluation of the GrazeIn model of grass dry matter intake and milk yield prediction for grazing dairy cows
| Autor: | Remy Delagarde, Luc Delaby, Laurence Shalloo, T. M. Boland, Michael O'Donovan, Finbar Mulligan, B. F. O'Neill, Eva Lewis, E. Ruelle |
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| Přispěvatelé: | Animal and Grassland Research and Innovation Centre, Irish Agriculture and Food Development Authority, School of agriculture and food science, University College Dublin [Dublin] (UCD), School of veterinary medicine, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) |
| Rok vydání: | 2014 |
| Předmět: |
modèle de prédiction
Basal rate pâturage [SDV]Life Sciences [q-bio] Ice calving adaptation Poaceae SF1-1100 Models Biological milk yield 03 medical and health sciences Animal science Milk yield Lactation Grazing medicine Animals Dry matter Animal Husbandry production de lait 030304 developmental biology Mathematics 0303 health sciences model ingestion de matière sèche business.industry dairy cow 0402 animal and dairy science Feeding Behavior 04 agricultural and veterinary sciences Milk production Adaptation Physiological 040201 dairy & animal science Animal culture Biotechnology grass dry matter intake Dairying Milk medicine.anatomical_structure vache laitière herbe Animal Nutritional Physiological Phenomena Cattle Female GrazeIn Animal Science and Zoology business Secretory cell |
| Zdroj: | animal animal, Cambridge University Press (CUP), 2014, 8 (4), pp.596-609. ⟨10.1017/S1751731113002486⟩ Animal Animal, Published by Elsevier (since 2021) / Cambridge University Press (until 2020), 2014, 8 (4), pp.596-609. ⟨10.1017/S1751731113002486⟩ animal, Published by Elsevier (since 2021) / Cambridge University Press (until 2020), 2014, 8 (4), pp.596-609. ⟨10.1017/S1751731113002486⟩ Animal, Vol 8, Iss 4, Pp 596-609 (2014) Animal 4 (8), 596-609. (2014) |
| ISSN: | 1751-7311 1751-732X |
| DOI: | 10.1017/s1751731113002486 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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