Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-10-30T13:24:29.211Z Has data issue: false hasContentIssue false

e-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems

Published online by Cambridge University Press:  20 December 2012

J. Baudracco*
Affiliation:
Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11-222, Palmerston North 5301, New Zealand Departamento de Produccion Animal, Facultad de Ciencias Agrarias, Universidad Nacional del Litoral, Kreder 2805, (3080) Esperanza, Argentina
N. Lopez-Villalobos
Affiliation:
Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11-222, Palmerston North 5301, New Zealand
C. W. Holmes
Affiliation:
Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11-222, Palmerston North 5301, New Zealand
E. A. Comeron
Affiliation:
Instituto Nacional de Tecnología Agropecuaria (INTA), AIPA, Ruta 34 Km 227, (2300) Rafaela, Santa Fe, Argentina
K. A. Macdonald
Affiliation:
DairyNZ, Private Bag 3221, Hamilton 3240, New Zealand
T. N. Barry
Affiliation:
Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11-222, Palmerston North 5301, New Zealand
*
Get access

Abstract

A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype × environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were <10% for all variables, and concordance correlation coefficients over 0.80 for annual pasture utilisation, yields of milk and milk solids (MS; fat plus protein), and of 0.69 and 0.48 for LW and BCS, respectively. A simulation of two contrasting dairy systems is presented to show the practical use of the model. The model can be used to explore the effects of feeding level and genetic merit and their interactions for grazing dairy systems, evaluating the trade-offs between profit and the associated risk.

Type
Farming systems and environment
Copyright
Copyright © The Animal Consortium 2012

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, Comeron, EA, Macdonald, KA, Barry, TN, Friggens, NC 2012. e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding. Animal 6, 980993.Google Scholar
Baudracco, J, Lopez-Villalobos, N, Romero, LA, Scandolo, D, Maciel, MG, Comeron, EA, Holmes, CW, Barry, TN 2011. Effects of stocking rate on pasture production, milk production and reproduction of supplemented crossbred Holstein–Jersey dairy cows grazing lucerne pasture. Animal Feed Science and Technology 168, 131143.Google Scholar
Beukes, PC, Palliser, CC, Macdonald, KA, Lancaster, JAS, Levy, G, Thorrold, BS, Wastney, ME 2008. Evaluation of a whole-farm model for pasture-based dairy systems. Journal of Dairy Science 91, 23532360.Google Scholar
Bircham, JS, Hodgson, J 1983. The influence of sward condition on rates of herbage growth and senescence in mixed swards under continuous stocking management. Grass and Forage Science 38, 323331.Google Scholar
Bryant, J, Ogle, G, Marshall, P, Glassey, C, Lancaster, J, Garcia, SC, Holmes, CW 2010. Description and evaluation of the Farmax Dairy Pro decision support model. New Zealand Journal of Agricultural Research 53, 1328.Google Scholar
DairyNZ 2009. Dairy Operating Profit. Retrieved February 25, 2011, from www.dairynz.co.nz/file/fileid/28974.Google Scholar
DairyNZ 2010. Economic Survey 2008/2009. Retrieved February 16, 2011, from www.dairynz.co.nz/file/fileid/31376.Google Scholar
Delagarde, R, Valk, H, Mayne, CS, Rook, AJ, Gonzalez-Rodrıguez, A, Baratte, C, Faverdin, P, Peyraud, JL 2011. GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 3. Simulations and external validation of the model. Grass and Forage Science 66, 6177.Google Scholar
Fox, DG, Van Amburgh, ME, Tylutki, TP 1999. Predicting requirements for growth, maturity, and body reserves in dairy cattle. Journal of Dairy Science 82, 19681977.Google Scholar
Freer, M, Moore, AD, Donnelly, JR 1997. GRAZPLAN: decision support systems for Australian grazing enterprises-II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77126.Google Scholar
Friggens, NC, Newbold, JR 2007. Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example. Animal 1, 8797.Google Scholar
Friggens, NC, Ingvartsen, KL, Emmans, GC 2004. Prediction of body lipid change in pregnancy and lactation. Journal of Dairy Science 87, 9881000.Google Scholar
Friggens, NC, Brun-Lafleur, L, Faverdin, P, Sauvant, D, Martin, O 2011. Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression. Animal, published online doi:10.1017/S1751731111001820.Google Scholar
Fuentes-Pila, J, Ibanez, M, De Miguel, J, Beede, DK 2003. Predicting average feed intake of lactating Holstein cows fed totally mixed rations. Journal of Dairy Science 86, 309323.Google Scholar
Fuentes Pila, J, DeLorenzo, MA, Beede, DK, Staples, CR, Holter, JB 1996. Evaluation of equations based on animal factors to predict intake of lactating Holstein cows. Journal of Dairy Science 79, 15621571.Google Scholar
Garcia, SC 2000. Systems, component, and modelling studies of pasture-based dairy systems in which the cows calve at different times of the year. PhD, Massey University, New Zealand.Google Scholar
Gartner, JA 1981. Replacement policy in dairy herds on farms where heifers compete with the cows for Grassland – part 1: model construction and validation. Agricultural Systems 7, 289318.Google Scholar
Holmes, CW, Roche, JF 2007. Pasture and supplements in New Zealand dairy production systems. In Pastures and supplements for grazing animals. Occ. Pub. No 14., pp. 221242. New Zealand Society of Animal Production, Hamilton, New Zealand.Google Scholar
Landis, JR, Koch, GG 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159174.Google Scholar
Larcombe, M 1990. UDDER: a desktop dairyfarm for extension and research. In Proceedings of the 7th Seminar of the Dairy Cattle Society of the New Zealand Veterinary Association, Hamilton, New Zealand, 22–25 May 1990, 99, 151–152.Google Scholar
Lin, LIK 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255268.Google Scholar
Livestock Improvement Corporation 2010. Dairy Statistics 2008–2009. Livestock Improvement Corp. Ltd. Hamilton, New Zealand. Retrieved February 18, 2011, from http://www.lic.co.nz/pdf/DAIRY%20STATISTICS%2009-10-WEB.pdf.Google Scholar
Lopez-Villalobos, N, Garrick, DJ, Holmes, CW, Blair, HT, Spelman, RJ 2000. Profitabilities of some mating systems for dairy herds in New Zealand. Journal of Dairy Science 83, 144153.Google Scholar
Macdonald, KA, Penno, JW, Lancaster, JAS, Roche, JR 2008a. Effect of stocking rate on pasture production, milk production, and reproduction of dairy cows in pasture-based systems. Journal of Dairy Science 91, 21512163.Google Scholar
Macdonald, KA, Verkerk, GA, Thorrold, BS, Pryce, JE, Penno, JW, McNaughton, LR, Burton, LJ, Lancaster, JAS, Williamson, JH, Holmes, CW 2008b. A comparison of three strains of Holstein–Friesian grazed on pasture and managed under different feed allowances. Journal of Dairy Science 91, 16931707.Google Scholar
Marshall, KR 1989. The origin and history of the A + B − C payment system. In Milk payment and quality (ed. GK Barrell), pp. 911. Animal Industries Workshop, Lincoln College, New Zealand.Google Scholar
Martin, O, Sauvant, D 2010. A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 2. Voluntary intake and energy partitioning. Animal 4, 20482056.Google Scholar
Roche, JF, Berry, DP, 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, 35323543.Google Scholar
Sanderson, MA, Karnezos, TP, Matches, AG 1994. Morphological development of alfalfa as a function of growing degree-days. Journal of Production Agriculture 7, 239242.Google Scholar
Schils, RLM, De Haan, MHA, Hemmer, JGA, Van den Pol-Van Dasselaar, A, De Boer, JA, Evers, GA, Holshof, G, van Middelkoop, JC, Zom, RLG 2007. Dairy wise, a whole-farm dairy model. Journal of Dairy Science 90, 53345346.Google Scholar
Shalloo, L, Dillon, P, Rath, M, Wallace, M 2004. Description and validation of the Moorepark dairy system model. Journal of Dairy Science 87, 19451959.Google Scholar
Vayssières, J, Guerrin, F, Paillat, J, Lecomte, P 2009. GAMEDE: a global activity model for evaluating the sustainability of dairy enterprises. Part I – whole-farm dynamic model. Agricultural Systems 101, 128138.Google Scholar
Vetharaniam, I, Davis, SR, Upsdell, M, Kolver, ES, Pleasants, AB 2003. Modeling the effect of energy status on mammary gland growth and lactation. Journal of Dairy Science 86, 31483156.Google Scholar
Wilmink, JBM 1987. Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation. Livestock Production Science 16, 335348.Google Scholar
Woodward, SJR, Romera, AJ, Beskow, WB, Lovatt, SJ 2008. Better simulation modelling to support farming systems innovation: review and synthesis. New Zealand Journal of Agricultural Research 51, 235252.Google Scholar
Supplementary material: File

Baudracco Supplementary Material

Appendix

Download Baudracco Supplementary Material(File)
File 1.3 MB