Hay quality and intake by dairy cows. 2. Predicting feed intake with consumer-demand models
R. J. Sadler A B , D. B. Purser C D F and S. K. Baker C EA Trading as Bush Futures, 4 James Street, Guildford, WA 6055, Australia.
B School of Agricultural and Resource Economics, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.
C HAEN Pty Ltd, PO Box 524, Northam, WA 6401, Australia.
D Gilmac (Mackie Hay), 3 Ord Street, West Perth, WA 6005, Australia.
E Deceased.
F Corresponding author. Email: b.purser@bigpond.com
Animal Production Science 58(4) 730-743 https://doi.org/10.1071/AN15726
Submitted: 20 October 2015 Accepted: 9 May 2016 Published: 12 July 2016
Abstract
Daily food intake is the single most important factor affecting milk production by dairy cows. However, an animal’s choice of food depends not only on the nutritional characteristics of the food in question, but also on the nutritional characteristics of other available foods. Any prediction of intake should be based on the nutritional characteristics of all foods on offer. However, when the initial food-preference experiment possesses a control-specific design (i.e. experiments that include only a limited number of control foods for comparison) it is apparent that the prediction of future food choices must include the same controls as the initial experiment underpinning the prediction model. This requirement is clearly impractical. By drawing an analogy between animal food preference and economic choice, the total and relative dry matter intake of two oaten hays was modelled on their nutritive characteristics by estimating a consumer-demand model (here a generalised additive model representation of a direct bundle good model) from experimental data offering hays to lactating cows (adj-R2 > 80%; where adj-R2 is the value adjusted for the number of predictor terms in the model). To negate the problem of control-specificity, a simplex interpolation was developed to construct and test predictions of hay intake for a second food-preference experiment (adj-R2 > 53%; correlation between predictions and actual intakes = 76%). To improve prediction accuracy and avoid control-specificity, it is recommended that future preference experiments be designed to exclude control-specificity by mimicking fractional factorial designs, supported by a two-stage approach to select a cost-effective number of comparisons. Our approach to predicting food intake may be extended to a choice between more than two foods, and to combinations of foods other than oaten hays.
Additional keywords: experimental design, feeding behaviour, generalised additive models, modelling: cattle, ruminant nutrition.
References
Briceno JV, Van Horn HH, Harris B, Wilcox CJ (1987) Effects of neutral detergent fibre and roughage source on dry matter intake and milk yield and composition of dairy cows. Journal of Dairy Science 70, 298–308.| Effects of neutral detergent fibre and roughage source on dry matter intake and milk yield and composition of dairy cows.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL2s7psFKrtQ%3D%3D&md5=a719a5ff52cd96aa4c438a220aa7aa01CAS | 3033038PubMed |
Campion DP, Leek BF (1997) Investigation of a ‘fibre appetite’ in sheep fed a ‘long fibre-free’ diet. Applied Animal Behaviour Science 52, 79–86.
| Investigation of a ‘fibre appetite’ in sheep fed a ‘long fibre-free’ diet.Crossref | GoogleScholarGoogle Scholar |
Cannas A, Van Soest PJ, Pell AN (2003) Use of animal and dietary information to predict rumen turnover. Animal Feed Science and Technology 106, 95–117.
| Use of animal and dietary information to predict rumen turnover.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXjtlSmtrY%3D&md5=7999f6e01d1b8758265f4e6aad6e334bCAS |
Dharma S, Dahl A (2013) Australian dairy: financial performance of dairy producing farmers, 2010–11 to 2012–13. ABARES research report 13.9. ABARES, Canberra.
Diggle PJ (2003) ‘Statistical analysis of spatial point patterns.’ 2nd edn. (Arnold: London)
Dynes RA, Purser DB, Baker SK (2016) Hay quality and intake by dairy cows 1. Preference for oaten hays. Animal Production Science
Efron B, Tibshirani RJ (1994) ‘An introduction to the bootstrap.’ (CRC Press: Boca Raton, FL)
Everitt B (1974) ‘Cluster analysis.’ (Heinemann Educational Books: London)
Faverdin P, Baratte C, Delagarde R, 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.
| 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.Crossref | GoogleScholarGoogle Scholar |
Forbes JM (Ed.) (2007) ‘Voluntary food intake and diet selection in farm animals.’ (CABI: Wallingsford, UK)
Forbes JM (2010) Palatability: principles, methodology and practice for farm animals. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 5, 1–15.
| Palatability: principles, methodology and practice for farm animals.Crossref | GoogleScholarGoogle Scholar |
Horadagoda A, Fulkerson WJ, Nandra KS, Barchia IM (2009) Grazing preferences by dairy cows for 14 forage species. Animal Production Science 49, 586–594.
| Grazing preferences by dairy cows for 14 forage species.Crossref | GoogleScholarGoogle Scholar |
Illius AW, Jessop NS, Gill M (2000) Mathematical models of food intake and metabolism. In ‘Ruminant physiology: digestion, metabolism, growth and reproduction’. (Ed. PB Cronje) pp. 21–39. (CABI Publishing: Oxon, UK)
Kellems RO, Church DC (2009) ‘Livestock feeds and feeding.’ 6th edn. (Prentice Hall: Upper Saddle River, NJ)
Mertens DC (2010) NDF and DMI – has anything changed? In ‘Proceedings of the 2010 Cornell nutrition conference for feed manufacturers’, East Syracuse, NY. pp. 160–174. (Department of Animal Science, Cornell University: Ithaca, NY)
Niederreiter H (1995) ‘Random number generation and quasi-Monte Carlo methods.’ (Society for Industrial and Applied Mathematics: Philadelphia, PA)
Pain S, Revell D (2009) ‘Hay quality specifications: identifying predictors of preference between hays. RIRDC publication no 09/11.’ (RIRDC: Canberra)
Patterson DM, McGilloway DA, Cushnahan A, Mayne CS, Laidlaw AS (1998) Effect of duration of fasting period on short-term intake rates of lactating dairy cows. Animal Science 66, 299–305.
| Effect of duration of fasting period on short-term intake rates of lactating dairy cows.Crossref | GoogleScholarGoogle Scholar |
Phlips L (1990) Applied consumption analysis. In ‘Advanced textbooks in economics, vol. 5’. Revised edn. (North Holland Publishing Company: Amsterdam)
Provenza FD (1995) Postingestive feedback as an elementary determinant of food preference and intake in ruminants. Journal of Range Management 48, 2–17.
| Postingestive feedback as an elementary determinant of food preference and intake in ruminants.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2015) ‘R: a language and environment for statistical computing’ (R Foundation for Statistical Computing: Vienna) Available at https://www.R-project.org/ [Verified 30 September 2015]
Robinson PH (1989) Dynamic aspects of feeding management for dairy cows. Journal of Dairy Science 72, 1197–1209.
| Dynamic aspects of feeding management for dairy cows.Crossref | GoogleScholarGoogle Scholar |
Smit HJ, Tamminga S, Elgersma A (2006) Dairy cattle grazing preference among six cultivars of perennial ryegrass. Agronomy Journal 98, 1213–1220.
| Dairy cattle grazing preference among six cultivars of perennial ryegrass.Crossref | GoogleScholarGoogle Scholar |
Tedeschi LO, Cavalcanti LF, Fonseca MA, Herrero M, Thornton PK (2014) The evolution and evaluation of dairy cattle models for predicting milk production: an agricultural model intercomparison and improvement project for livestock. Animal Production Science 54, 2052–2067.
Van Soest PJ, Robertson JB, Lewis BA (1991) Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science 74, 3583–3597.
| Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK38%2FnvVCltA%3D%3D&md5=5ac74045d70ec4fdb1b477d24fb39c1dCAS | 1660498PubMed |
Villalba JJ, Provenza FD (2000) Roles of novelty, generalization, and postingestive feedback in the recognition of foods by lambs. Journal of Animal Science 78, 3060–3069.
Villalba JJ, Provenza FD, Bryant JP (2002) Consequences of the interaction between nutrients and plant secondary metabolites on herbivore selectivity: benefits or detriments for plants. Oikos 97, 282–292.
| Consequences of the interaction between nutrients and plant secondary metabolites on herbivore selectivity: benefits or detriments for plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XlvVKqsrc%3D&md5=f8d05903b5afbba15b376cfed9d7815fCAS |
Wood SN (2006) ‘Generalized additive models: an introduction with R.’ (Chapman and Hall/CRC Press: Boca Raton, FL)
