Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-26T15:20:07.225Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  17 January 2014

B. F. O’Neill ,
E. Ruelle ,
M. O’Donovan ,
L. Shalloo ,
F. J. Mulligan ,
T. M. Boland ,
L. Delaby ,
R. Delagarde  and
E. Lewis
B. F. O’Neill
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy Co., Cork, Ireland School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
E. Ruelle
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy Co., Cork, Ireland School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
M. O’Donovan
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy Co., Cork, Ireland
L. Shalloo
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy Co., Cork, Ireland
F. J. Mulligan
Affiliation:
School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
T. M. Boland
Affiliation:
School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
L. Delaby
Affiliation:
INRA, AgroCampus Ouest, UMR 1348, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage, F-35590 Saint-Gilles, France
R. Delagarde
Affiliation:
INRA, AgroCampus Ouest, UMR 1348, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage, F-35590 Saint-Gilles, France
E. Lewis*
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy Co., Cork, Ireland
*
†E-mail: eva.lewis@teagasc.ie
Get access

Abstract

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.

Keywords

dairy cowgrass dry matter intakemilk yieldmodeladaptation
Type
Full Paper
Information
animal , Volume 8 , Issue 4 , April 2014 , pp. 596 - 609
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baudracco, J, Lopez-Villalobos, N, Holmes, CW and MacDonald, KA 2010. Prediction of herbage dry matter intake for dairy cows grazing ryegrass-based pastures. Proceedings of the New Zealand Society of Animal Production 70, 80–85.Google Scholar
Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. Wiley, London, UK.Google Scholar
Coleman, J, Pierce, KM, Berry, DP, Brennan, A and Horan, B 2010. Increasing milk solids production across lactation through genetic selection and intensive pasture based feed system. Journal of Dairy Science 93, 4302–4317.Google Scholar
Corbett, JL, Langlands, JP and Reid, GW 1963. Effects of season of growth and digestibility of herbage on intake by grazing dairy cows. Animal Production 5, 119–129.Google Scholar
Coulon, JB, Hoden, A, Faverdin, P and Journet, M 1989. Dairy cows. In Ruminant nutrition: recommended allowances and feed tables (ed. R Jarrige), pp. 73–92. INRA and John Libbey, Paris.Google Scholar
Cros, MJ, Duru, M, Garcia, F and Martin-Clouaire, R 2003. A biophysical dairy farm model to evaluate rotational grazing management strategies. Agronomie 23, 105–122.Google Scholar
Curran, J, Delaby, L, Kennedy, E, Murphy, JP, Boland, TM and O'Donovan, M 2010. Sward characteristics, grass dry matter intake and milk production performance are affected by pre-grazing herbage mass and pasture allowance. Livestock Science 127, 144–154.Google Scholar
Delaby, L, Peyraud, JP and Faverdin, P 2001. Pâtur’IN: computer-assisted grazing of dairy cows (in French). Fourrages 167, 385–398.Google Scholar
Delagarde, R and O’ Donovan, M 2005. Modelling of herbage intake and milk yield by grazing dairy cows. In Utilisation of grazed grass in temperate animal systems (ed. JJ Murphy), pp. 89–104. Wageningen Academic Publishers, Wageningen, the Netherlands.Google Scholar
Delagarde, R, Faverdin, P, Baratte, C and Peyraud, JL 2011a. GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 2. Prediction of intake under rotational and continuously-stocked grazing management. Grass and Forage Science 66, 45–60.Google Scholar
Delagarde, R, Valk, H, Mayne, CS, Rook, AJ, Gonzalez-Rodriguez, A, Baratte, C, Faverdin, P and Peyraud, JL 2011b. GrazeIn: a model of herbage intake and milk yield for grazing dairy cows. 3. Simulations and external validation of the model. Grass and Forage Science 66, 61–77.CrossRefGoogle Scholar
Dillon, P, Roche, JR, Shalloo, L and Horan, B 2005. Optimising financial return from grazing in temperate pastures. In Utilisation of grazed grass in temperate animal systems (ed. JJ Murphy), pp. 131–147. Wageningen Academic Publishers, Wageningen, the Netherlands.Google Scholar
Dulphy, JP, Faverdin, P and Jarrige, R 1989. Feed Intake: the fill unit system. In Ruminant nutrition: recommended allowances and feed tables (ed. R Jarrige), pp. 61–72. INRA and John Libbey, Paris.Google Scholar
Faverdin, P, Delaby, L and Delagarde, R 2007. The feed intake in dairy cows and its prediction during lactation (in French). INRA Productions Animales 20, 151–162.Google Scholar
Faverdin, P, Baratte, C, Delagarde, R and Peyraud, JL 2011. GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 1. Prediction of intake capacity, voluntary intake and milk production during lactation. Grass and Forage Science 66, 29–44.CrossRefGoogle Scholar
Finneran, E, Crosson, P, O’Kiely, P, Shalloo, L, Forristal, D and Wallace, M 2010. Simulation modelling of the cost of producing and utilising feeds for ruminants on Irish farms. Journal of Farm Management 14, 95–116.Google Scholar
Friggens, N, Ingvartsen, KL and Emmans, G 2004. Prediction of body lipid change in pregnancy and lactation. Journal of Dairy Science 87, 988–1000.CrossRefGoogle ScholarPubMed
Fuentes-Pila, J, Delorenzo, MA, Beede, DK, Staples, CR and Holter, JB 1996. Evaluation of equations based on animal factors to predict intake of lactating Holstein cows. Journal of Dairy Science 79, 1562–1571.Google Scholar
Greenhalgh, JFD and Reid, GW 1969. The effects of grazing intensity on herbage consumption and animal production. III. Dairy cows grazed at two intensities on clean or contaminated pastures. Journal of Agricultural Science 72, 223–228.CrossRefGoogle Scholar
Hayirli, A, Grummer, RR, Nordheim, EV and Crump, PM 2003. Models for predicting dry matter intake of Holsteins during the pre-fresh transition period. Journal of Dairy Science 86, 1771–1779.Google Scholar
Heard, JW, Cohen, DC, Doyle, PT, Wales, WJ and Stockdale, CR 2004. Diet-check – a tactical decision support tool for feeding decisions with grazing dairy cows. Animal Feed Science and Technology 112, 177–194.CrossRefGoogle Scholar
Ingvartsen, KL 1994. Models of voluntary food intake in cattle. Livestock Production Science 39, 19–38.CrossRefGoogle Scholar
Kaufmann, LD, Münger, A, Rérat, M, Junghans, P, Görs, S, Metges, CC and Dohme-Meier, F 2011. Energy expenditure of grazing cows and cows fed grass indoors as determined by the 13C bicarbonate dilution technique using an automatic blood sampling system. Journal of Dairy Science 94, 1989–2000.Google Scholar
Keady, TWJ, Mayne, CS and Kilpatrick, DJ 2004. An evaluation of five models commonly used to predict food intake of lactating dairy cattle. Livestock Production Science 89, 129–1138.Google Scholar
Kennedy, E, O'Donovan, M, Murphy, JP, O'Mara, FP and Delaby, L 2006. The effect of initial spring grazing date and subsequent stocking rate on the grazing management, grass dry matter intake and milk production of dairy cows in summer. Grass and Forage Science 61, 375–384.Google Scholar
Kennedy, E, O'Donovan, M, Delaby, L and O'Mara, FP 2008. Effect of herbage allowance and concentrate supplementation on dry matter intake, milk production and energy balance of early lactating dairy cows. Livestock Science 117, 275–286.Google Scholar
Kennedy, E, McEvoy, M, Murphy, JP and O'Donovan, M 2009. Effect of restricted access time to pasture on dairy cow milk production grazing behaviour, and dry matter intake. Journal of Dairy Science 92, 168–176.Google Scholar
Landis, JR and Koch, GG 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159–174.Google Scholar
Le Du, YLP, Baker, RD and Newberry, RD 1981. Herbage intake and milk production by grazing dairy cows. 3. The effect of grazing severity under continuous stocking. Grass and Forage Science 36, 307–318.Google Scholar
Lin, LIK 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.Google Scholar
Linnane, MI, Brereton, AJ and Giller, PS 2001. Seasonal changes in circadian grazing patterns of Kerry cows (Bos taurus) in semi-feral conditions in Killarney National Park Co. Kerry, Ireland. Applied Animal Behaviour Science 71, 277–292.CrossRefGoogle ScholarPubMed
Lowman, BG, Scott, N and Somerville, S 1976. Condition scoring of cattle. East of Scotland College of Agriculture. Revised (ed.) Edinburgh, UK.Google Scholar
McCarthy, S, Horan, B, Rath, M, Linnane, M, O'Conor, P and Dillon, P 2007. The influence of strain of Holstein-Friesian dairy cow and pasture-based feeding system on grazing behaviour, intake and milk production. Grass and Forage Science 62, 13–26.Google Scholar
McEvoy, M, Kennedy, E, Murphy, JP, Boland, TM, Delaby, L and O'Donovan, M 2008. The effect of herbage allowance and concentrate supplementation on milk production performance and dry matter intake of spring-calving dairy cows in early lactation. Journal of Dairy Science 91, 1258–1269.Google Scholar
McEvoy, M, O'Donovan, M, Kennedy, E, Murphy, JP, Delaby, L and Boland, TM 2009. Effect of pre grazing herbage mass and pasture allowance on the lactation performance of Holstein-Friesian dairy cows. Journal of Dairy Science 92, 414–422.CrossRefGoogle Scholar
Neal, HDSC and Thornley, JHM 1983. The lactation curve of cattle: a mathematical model of the mammary gland. The Journal of Agricultural Science 101, 389–400.Google Scholar
O'Neill, BF, Deighton, MH, O'Loughlin, BM, Mulligan, FJ, Boland, TM, O'Donovan, M and Lewis, E 2011. Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake and milk production. Journal of Dairy Science 94, 1941–1951.CrossRefGoogle ScholarPubMed
O’Neill, BF, Lewis, E, O’Donovan, M, Shalloo, L, Mulligan, FJ, Boland, TM and Delagarde, R 2013a. Evaluation of the GrazeIn model of grass dry-matter intake and milk production prediction for dairy cows in temperate grass-based production systems. 1-Sward characteristics and grazing management factors. Grass and Forage Science 68, 504–523.Google Scholar
O’Neill, BF, Lewis, E, O’Donovan, M, Shalloo, L, Mulligan, FJ, Boland, TM and Delagarde, R 2013b. Evaluation of the GrazeIn model of grass dry-matter intake and milk production prediction for dairy cows in temperate grass-based production systems 2 – animal characteristics. Grass and Forage Science 68, 524–536.Google Scholar
Paliser, CC, Wastney, ME, Bright, KP, MacDonald, KA and Penno, JW 2001. Modelling the lactation curves of New Zealand cows. In Proceedings of the International Conference on Modelling and Simulation, 10 to 13 December 2001, Canberra, Australia, pp. 1853–1858.Google Scholar
Prendiville, R, Lewis, E, Pierce, KM and Buckley, F 2010. Comparative grazing behaviour of lactating Holstein-Friesian, Jersey, and Jersey cross Holstein-Friesian dairy cows and its association with intake capacity and production efficiency. Journal of Dairy Science 93, 764–774.CrossRefGoogle Scholar
Roche, JR, Berry, DP and Kolver, ES 2006. Holstein-Friesian strain and feed effects on milk production, body weight, and body condition score profiles in grazing dairy cows. Journal of Dairy Science 89, 3532–3543.Google Scholar
Rook, AJ, Dhanoa, MS and Gill, M 1990. Prediction of the voluntary intake of grass silages by beef cattle 3. Accuracy of alternative prediction models. Animal Science 50, 455–466.Google Scholar
Shah, MA and Murphy, MR 2006. Development and evaluation of models to predict the feed intake of dairy cows in early lactation. Journal of Dairy Science 89, 294–306.CrossRefGoogle ScholarPubMed
Shalloo, L 2009. Pushing the barriers on milk costs/outputs. In Teagasc National Dairy Conference, 18 November 2009, Mullingar and Killarney, Ireland, pp. 19–39. Teagasc, Carlow, Ireland.Google Scholar
Shalloo, L, Creighton, P and O’Donovan, M 2011. The economics of reseeding on a dairy farm. Irish Journal of Agricultural and Food Research 50, 113–122.Google Scholar
Stanton, TL, Jones, LR, Everett, RW and Kachman, SD 1992. Estimating milk, fat, and protein lactation curves with a test day model. Journal of Dairy Science 75, 1691–1700.Google Scholar
Tedeschi, LO 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.Google Scholar
Volden, H, Nielsen, NI, Åkerlind, M, Larsen, M, Havrevoll, Ø and Rygh, AJ 2011. Prediction of voluntary feed intake. In NorFor – The Nordic feed evaluation system (ed. Volden, H), pp. 113–126. Wageningen Academic Publishers, Wageningen, the Netherlands.Google Scholar
Wims, CM, Deighton, MH, Lewis, E, O'Loughlin, B, Delaby, L, Boland, TM and O'Donovan, M 2010. The effect of pre grazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. Journal of Dairy Science 93, 4976–4985.Google Scholar
Zom, RLG, André, G and van Vuuren, AM 2012. Development of a model for the prediction of feed intake by dairy cows 2. Evaluation of prediction accuracy. Livestock Science 143, 58–69.Google Scholar