Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

Modelling systems to describe maternal productivity, with the aim of improving beef production efficiency by eliciting practice change

B. J. Walmsley A B C and V. H. Oddy A B
+ Author Affiliations
- Author Affiliations

A Cooperative Research Centre for Beef Genetic Technologies.

B NSW Department of Primary Industries, Beef Industry Centre of Excellence, Trevenna Road, Armidale, NSW 2351, Australia.

C Corresponding author. Email: brad.walmsley@dpi.nsw.gov.au

Animal Production Science 58(1) 193-205 https://doi.org/10.1071/AN14874
Submitted: 14 October 2014  Accepted: 5 January 2015   Published: 7 September 2016

Abstract

The overall efficiency of beef production is considered more highly correlated with cow–calf efficiency, viz. maternal productivity (MP), than the efficiency of other segments of the beef production chain. Recently, concerns have been raised that improvements in feedlot and carcass performance have led to a decline in MP due to the uncertainty that surrounds the relationships between production and MP traits. The Beef Cooperative Research Centre ‘Maternal Productivity’ Project examined the impact of cow genotype and nutrient intake on breeding herd productivity. This experiment demonstrated that cow body composition is influenced by genetic differences in rib fat and residual feed intake, as well as nutrient availability. Genetic differences in rib fat were shown to influence heifer pregnancy rates, observed days to calving, MP when nutrient intake is restricted and ME intake by the cow–calf unit. Weaning rate was found to account for a large portion of the variation in MP, while cow genetic background and pre-weaning nutrient availability influenced the postweaning and carcass performance of progeny. These findings demonstrate that although balancing the requirements of MP with those of other traits is not straight forward, it is of critical importance. Incorporating modelling systems into decision-support systems (DSS) offers the opportunity to integrate fragmented knowledge into decision making. Unfortunately, previous DSS have gained little traction and limited adoption due to their perceived complexity, large input-data requirements, and mismatches between outputs and the decision-making styles of producers. The development of the BeefSpecs fat calculator provides an example of how producer-measurable inputs and simple user interactions can be combined using modelling systems to develop DSS to improve MP. No single model that addressed all issues affecting MP was found in the literature. Thus, it was concluded that previous modelling systems would need to be combined to develop a suite of DSS that target-specific components of MP, such as heifer pregnancy rates and interactions between the cow herd and the nutritional environment.

Additional keywords: biological hierarchy, genetic composition, heifer reproduction, morass systems, weaning rate.


References

Accioly JM, Copping KJ, Deland MPB, Hebart ML, Herd RM, Lee SJ, Jones FM, Laurence M, Speijers EJ, Walmsley BJ, Pitchford WS (2016) Divergent breeding values for fatness or residual feed intake in Angus cattle. 4. Fat EBVs’ influence on fatness fluctuation and supplementary feeding requirements. Animal Production Science
Divergent breeding values for fatness or residual feed intake in Angus cattle. 4. Fat EBVs’ influence on fatness fluctuation and supplementary feeding requirements.CrossRef |

Agriculture and Food Policy Reference Group (2006) ‘Creating our future: agriculture and food policy for the next generation.’ (Australian Bureau of Agricultural and Resource Economics: Canberra)

Angus Australia (2013) ‘Angus: percentile bands for 2011 born calves.’ Available at http://abri.une.edu.au/online/cgi-bin/i4.dll?1=22342A3D&2=2323&3=56&5=2B3C2B3C3A [Verified 27 October 2013]

ARC (1980) ‘The nutrient requirements of ruminant livestock.’ (CAB International: Wallingford, UK)

Archer JA, Richardson EC, Herd RM, Arthur PF (1999) Potential for selection to improve efficiency of feed use in beef cattle: a review. Australian Journal of Agricultural Research 50, 147–161.
Potential for selection to improve efficiency of feed use in beef cattle: a review.CrossRef |

Arthur PF, Herd RM, Wilkins JF, Archer JA (2005) Maternal productivity of Angus cows divergently selected for post-weaning residual feed intake. Australian Journal of Experimental Agriculture 45, 985–993.

Australian Bureau of Agricultural and Resource Economics (2010) ‘Australian commodities: March quarter 2010.’ (Australian Bureau of Agricultural and Resource Economics and Sciences: Canberra)

Azzam SM, Kinder JE, Nielsen MK (1990) Modelling reproductive management systems for beef cattle. Agricultural Systems 34, 103–122.
Modelling reproductive management systems for beef cattle.CrossRef |

Ball AJ, Oddy VH, Thompson JM (1997) Nutritional manipulation of body composition and efficiency in ruminants. In ‘Recent advances in animal nutrition in Australia’. (Eds JL Corbett, M Choct, JV Nolan, JB Rowe) pp. 192–208. (University of New England: Armidale, NSW)

Barwick SA, Swan AA, Hermesch S, Graser H-U (2011) Experience in breeding objectives for beef cattle, sheep and pigs, new developments and future needs. In ‘Proceedings of the Association for the Advancement of Animal Breeding and Genetics’. Vol. 19. (Ed. PE Vercoe) pp. 23–30. (The Association for the Advancement of Animal Breeding and Genetics: Perth)

Basarab JA, McCartney D, Okine EK, Baron VS (2007) Relationships between progeny residual feed intake and dam productivity. Canadian Journal of Animal Science 87, 489–502.
Relationships between progeny residual feed intake and dam productivity.CrossRef | 1:CAS:528:DC%2BD1cXivFyqsLg%3D&md5=b763a4ff0ec68238912b7b23932393c9CAS |

Bennett GL, Leymaster KA (1989) Integration of ovulation rate, potential embryonic viability and uterine capacity into a model of litter size in swine. Journal of Animal Science 67, 1230–1241.

Bindon BM, Burrow HM, Kinghorn BP (2001) Communication, education and training strategies to deliver CRC outcomes to beef industry stakeholders. Australian Journal of Experimental Agriculture 41, 1073–1087.
Communication, education and training strategies to deliver CRC outcomes to beef industry stakeholders.CrossRef |

Blackburn HD, Cartwright TC (1987a) Simulated genotype, environment and interaction effects on performance characters of sheep. Journal of Animal Science 65, 387–398.

Blackburn HD, Cartwright TC (1987b) Simulated production and biological efficiency of sheep flocks in a shifting environment. Journal of Animal Science 65, 399–408.

Blanc F, Agabriel J (2008) Modelling the reproductive efficiency in a beef cow herd: effect of calving date, bull exposure and body composition at calving on the calving–conception interval and calving distribution. The Journal of Agricultural Science 146, 143–161.
Modelling the reproductive efficiency in a beef cow herd: effect of calving date, bull exposure and body composition at calving on the calving–conception interval and calving distribution.CrossRef |

Blanc F, Martin GB, Bocquier F (2001) Modelling reproduction in farm animals: a review. Reproduction, Fertility and Development 13, 337–353.
Modelling reproduction in farm animals: a review.CrossRef | 1:STN:280:DC%2BD387gt1Cgsw%3D%3D&md5=13fd19612fa20a908a778143739b0d47CAS |

Bourdon RM (1998) Shortcomings of current genetic evaluation systems. Journal of Animal Science 76, 2308–2323.

Bourdon RM, Brinks JS (1987) Simulated efficiency of range beef production 1. Growth and milk production. Journal of Animal Science 65, 943–955.

Bourdon RM, Enns RM (1998) Physiological breeding values: Rethinking the way we express genetic values for improving production systems. In ‘Proceedings of the 6th world congress on genetics applied to livestock production’. Vol. 25. Armidale, NSW. pp. 227–234.

Brody S (1945) ‘Bioenergetics and growth.’ (Reinhold Publishing Corporation: New York, NY)

Camproux A-C, Thalabard J-C, Thomas G (1994) Stochastic modelling of the hypothalamic pulse generator activity. The American Journal of Physiology 267, E795–E800.

Cartwright M, Husain M (1986) A model for the control of testosterone secretion. Journal of Theoretical Biology 123, 239–250.
A model for the control of testosterone secretion.CrossRef | 1:CAS:528:DyaL2sXisl2gtw%3D%3D&md5=f3e8799afbce1a680033d81161a9d82dCAS | 3306160PubMed |

Christian KR, Freer M, Donnelly JR, Davidson JL, Armstrong JS (1978) ‘Simulation of grazing systems.’ (Centre for Agricultural Publishing and Documentation: Wageningen, The Netherlands)

Clément F, Gruet MA, Monget P, Jolivet E, Monniaux D (1997) Growth kinetics of the granulosa cell population in ovarian follicles: an approach by mathematical modelling. Cell Proliferation 30, 255–270.
Growth kinetics of the granulosa cell population in ovarian follicles: an approach by mathematical modelling.CrossRef | 9451417PubMed |

Copping KJ, Accioly JM, Deland MPB, Edwards NJ, Graham JF, Hebart ML, Herd RM, Jones FM, Laurence M, Lee SJ, Speijers EJ, Pitchford WS (2016) Divergent genotypes for fatness or residual feed intake in Angus cattle. 3. Performance of mature cows. Animal Production Science
Divergent genotypes for fatness or residual feed intake in Angus cattle. 3. Performance of mature cows.CrossRef |

Cox PG (1996) Some issues in the design of agricultural decision support systems. Agricultural Systems 52, 355–381.
Some issues in the design of agricultural decision support systems.CrossRef |

CSIRO (2007) ‘Nutrient requirements of domesticated ruminants.’ (Eds M Freer, H Dove, JV Nolan) (CSIRO Publishing: Melbourne)

Davis ME, Rutledge JJ, Cundiff LV, Hauser ER (1983) Life cycle efficiency of beef production 1. Cow efficiency ratios for progeny weaned. Journal of Animal Science 57, 832–851.

de Mol RM, Keen A, Kroeze GH, Achten JMFH (1999) Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter. Computers and Electronics in Agriculture 22, 171–185.
Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter.CrossRef |

Deland MPB, Newman S (1991) Lifetime productivity of crossbred cows 1. Experimental design, growth and carcass characteristics of progeny. Australian Journal of Experimental Agriculture 31, 285–292.
Lifetime productivity of crossbred cows 1. Experimental design, growth and carcass characteristics of progeny.CrossRef |

Deland MPB, Accioly JM, Copping KJ, Graham JF, Lee SJ, McGilchrist P, Pitchford WS (2016) Divergent breeding values for fatness or residual feed intake in Angus cattle. 6. Dam-line impacts on steer carcass compliance. Animal Production Science
Divergent breeding values for fatness or residual feed intake in Angus cattle. 6. Dam-line impacts on steer carcass compliance.CrossRef |

Denham SC, Larsen RE, Boucher J, Adams EL (1991) Structure and behavior of a deterministic model of reproductive performance in beef cattle. Agricultural Systems 35, 21–36.
Structure and behavior of a deterministic model of reproductive performance in beef cattle.CrossRef |

DiCostanzo A, Meiske JC, Plegge SD (1991) Characterization of energetically efficient and inefficient beef cows. Journal of Animal Science 69, 1337–1348.

Dijkstra J, Forbes JM, France J (2005) Introduction. In ‘Quantitative aspects of ruminant digestion and metabolism’. (Eds J Dijkstra, JM Forbes, J France) pp. 1–10. (CAB International: Wallingford, UK)

Dobos RC, Carberry PC, Vleeskens S, Sangsari E, Johnston BD, Oddy VH (1997) An age and herd structure model for beef breeding enterprises. In ‘MODSIM 1997 international congress on modelling and simulation’, University of Tasmania, Hobart, 8–11 December 1997. (Eds A Zerger, RM Argent) pp. 1080–1085. (Modelling and Simulation Sopciety of Australia and New Zealand: Perth)

Doeschl-Wilson AB, Knap PW, Kinghorn BP (2006) Evaluating animal genotypes through model inversion. In ‘Mechanistic modelling in pig and poultry production’. (Eds C Fisher, R Gous, T Morris) pp. 163–187. (CABI Publishing: Wallingford, UK)

Doeschl-Wilson AB, Knap PW, Kinghorn BP, van der Steen HAM (2007) Using mechanistic animal growth models to estimate genetic parameters of biological traits. Animal 1, 489–499.
Using mechanistic animal growth models to estimate genetic parameters of biological traits.CrossRef | 1:STN:280:DC%2BC38vptFKktA%3D%3D&md5=a052edb41d0c302a973ddc35ddee791bCAS | 22444406PubMed |

Doeschl-Wilson AB, Vagenas D, Kyriazakis I, Bishop SC (2008) Exploring the assumptions underlying genetic variation in host nematode resistance. Genetics, Selection, Evolution 40, 241–264.

Donoghue KA, Arthur PF, Wilkins JF, Herd RM (2011) Onset of puberty and early-life reproduction in Angus females divergently selected for post-weaning residual feed intake. Animal Production Science 51, 183–190.
Onset of puberty and early-life reproduction in Angus females divergently selected for post-weaning residual feed intake.CrossRef |

Donoghue KA, Lee SJ, Parnell PF, Pitchford WS (2016) Maternal body composition in seedstock herds. 2. Genetic parameters in Angus and Hereford cows. Animal Production Science
Maternal body composition in seedstock herds. 2. Genetic parameters in Angus and Hereford cows.CrossRef |

Egan AF, Ferguson DM, Thompson JM (2001) Consumer sensory requirements for beef and their implications for the Australian beef industry. Australian Journal of Experimental Agriculture 41, 855–859.
Consumer sensory requirements for beef and their implications for the Australian beef industry.CrossRef |

Emmans GC (1988) Genetic components of potential and actual growth. In ‘Animal breeding opportunities’. Occasional Publication. (Eds RB Land, G Butterfield, WG Hill) pp. 153–181. (British Society of Animal Production: Penicuik, UK)

Emmans GC (1997) A method to predict the food intake of domestic animals from birth to maturity as a function of time. Journal of Theoretical Biology 186, 189–199.
A method to predict the food intake of domestic animals from birth to maturity as a function of time.CrossRef |

Emmans GC, Fischer C (1986) Problems in nutritional theory. In ‘Nutrient requirements of poultry and nutritional research’. (Eds C Fischer, KN Boorman) pp. 9–39. (Butterworths: London, UK)

Ferguson NS, Gous RM, Emmans GC (1997) Predicting the effects of animal variation on growth and food intake in growing pigs using simulation modelling. Animal Science 64, 513–522.
Predicting the effects of animal variation on growth and food intake in growing pigs using simulation modelling.CrossRef |

Ferrell CL, Garrett WN, Hinman N (1976) Growth, development and composition of the udder and gravid uterus of beef heifers during pregnancy. Journal of Animal Science 42, 1477–1489.

Fox DG, Tedeschi LO, Tylutki TP, Russell JB, van Amburgh ME, Chase LE, Pell AN, Overton TR (2004) The Cornell net carbohydrate and protein system model for evaluating herd nutrition and nutrient excretion. Animal Feed Science and Technology 112, 29–78.
The Cornell net carbohydrate and protein system model for evaluating herd nutrition and nutrient excretion.CrossRef | 1:CAS:528:DC%2BD2cXmvFymsQ%3D%3D&md5=146b04f26de9a677bb67d7a8c43e7af4CAS |

France J, Dijkstra J (2006) Scientific progress and mathematical modelling: different approaches to modelling animal systems. In ‘Mechanistic modelling in pig and poultry production’. (Eds R Gous, T Morris, C Fisher) pp. 6–21. (CAB International: Wallingford, UK)

France J, Kebreab E (2008) Introduction. In ‘Mathematical modelling in animal nutrition’. (Eds J France, E Kebreab) pp. 1–11. (CAB International: Wallingford, UK)

Freer M, Davidson JL, Armstrong JS, Donnelly JR (1970) Simulation of summer grazing. In ‘Proceedings of the 11th international grasslands congress’, Gold Coast, Qld, 13–23 April. (Ed. MJT Norman) pp. 913–917. (University of Queensland Press: Brisbane)

Freer M, Moore AD, Donnelly JR (1997) GRAZPLAN decision support systems for Australian grazing enterprises. 2. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77–126.
GRAZPLAN decision support systems for Australian grazing enterprises. 2. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS.CrossRef |

Friggens NC (2003) Body lipid reserves and the reproductive cycle: towards a better understanding. Livestock Production Science 83, 219–236.
Body lipid reserves and the reproductive cycle: towards a better understanding.CrossRef |

Friggens NC, Ingvartsen KL, Emmans GC (2004) Prediction of body lipid change in pregnancy and lactation. Journal of Dairy Science 87, 988–1000.
Prediction of body lipid change in pregnancy and lactation.CrossRef | 1:CAS:528:DC%2BD2cXivFyqsr4%3D&md5=3c395959f259524eb0a3c248ae3572e5CAS | 15259234PubMed |

Garcia F, Agabriel J (2008) CompoCow: a predictive model to estimate variations in body composition and the energy requirements of cull cows during finishing. The Journal of Agricultural Science 146, 251–265.
CompoCow: a predictive model to estimate variations in body composition and the energy requirements of cull cows during finishing.CrossRef |

Gompertz B (1825) On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society 115, 513–583.
On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies.CrossRef |

Graser H-U, Tier B, Johnston DJ, Barwick SA (2005) Genetic evaluation for the beef industry in Australia. Australian Journal of Experimental Agriculture 45, 913–921.
Genetic evaluation for the beef industry in Australia.CrossRef |

Green RD (2009) ASAS centennial paper: future needs in animal breeding and genetics. Journal of Animal Science 87, 793–800.
ASAS centennial paper: future needs in animal breeding and genetics.CrossRef | 1:CAS:528:DC%2BD1MXisFGnsLc%3D&md5=6701a5256a70035cc7ce75df98d4ea94CAS | 18952735PubMed |

Gregory KE (1972) Beef cattle type for maximum efficiency: putting it together Journal of Animal Science 34, 881–884.

Grossi DA, Frizzas OG, Paz CCP, Bezerra LAF, Lôbo RB, Oliveria JA, Munari DP (2008) Genetic associations between accumulated productivity, and reproductive and growth traits in Nelore cattle. Livestock Science 117, 139–146.
Genetic associations between accumulated productivity, and reproductive and growth traits in Nelore cattle.CrossRef |

Hebart ML, Accioly JM, Copping KJ, Deland MPB, Herd RM, Jones FM, Laurence M, Lee SJ, Lines DS, Speijers EJ, Walmsley BJ, Pitchford WS (2016) Divergent breeding values for fatness or residual feed intake in Angus cattle 5. Cow genotype affects feed efficiency and maternal productivity. Animal Production Science
Divergent breeding values for fatness or residual feed intake in Angus cattle 5. Cow genotype affects feed efficiency and maternal productivity.CrossRef |

Heinze K, Keener RW, Midgley AR (1998) A mathematical model of luteinizing hormone release from ovine pituitary cells in perifusion. The American Journal of Physiology 275, E1061–E1071.

Herd RM, Arthur PF, Bottema CDK, Egarr AR, Geesink GH, Lines DS, Piper S, Siddell JP, Thompson JM, Pitchford WS (2014) Genetic divergence in residual feed intake affects growth, feed efficiency, carcass and meat quality characteristics of Angus steers in a large commercial feedlot. Animal Production Science
Genetic divergence in residual feed intake affects growth, feed efficiency, carcass and meat quality characteristics of Angus steers in a large commercial feedlot.CrossRef |

Howden SM, Crimp SJ, Stokes CJ (2008) Climate change and Australian livestock systems: Impacts, research and policy issues. Australian Journal of Experimental Agriculture 48, 780–788.
Climate change and Australian livestock systems: Impacts, research and policy issues.CrossRef |

Jenkins TG, Ferrell CL (1982) Lactation curves of mature crossbred cows: comparison of four estimating functions. Journal of Animal Science 54, 189

Jenkins TG, Ferrell CL (1984) A note on lactation curves of crossbred cows. Animal Production 39, 479–482.
A note on lactation curves of crossbred cows.CrossRef |

Jenkins TG, Williams CB (1998) DECI – Decision evaluator for the cattle industry. In ‘Proceedings of the 6th world congress on genetics applied to livestock production’. Vol. 27. Armidale, NSW. pp. 461–462.

Jones FM, Accioly JM, Copping KJ, Deland MPB, Graham JF, Hebart ML, Herd RM, Laurence M, Lee SJ, Speijers EJ, Pitchford WS (2016) Divergent breeding values for fatness or residual feed intake in Angus cattle. 1. Pregnancy rates of heifers differed between fat lines and were affected by weight and fat. Animal Production Science
Divergent breeding values for fatness or residual feed intake in Angus cattle. 1. Pregnancy rates of heifers differed between fat lines and were affected by weight and fat.CrossRef |

Keele JW, Williams CB, Bennett GL (1992) A computer model to predict the effects of level of nutrition on composition of empty body gain in beef cattle. 1. Theory and development. Journal of Animal Science 70, 841–857.

Keenan DM, Veldhuis JD (1997) Stochastic model of admixed basal and pulsatile hormone secretion as modulated by a deterministic oscillator. The American Journal of Physiology 273, R1182–R1192.

Keenan DM, Veldhuis JD (1998) A biomathematical model of time-delay feedback in the human male hypothalamic–pituitary–Leydig cell axis. The American Journal of Physiology 275, E157–E176.

Knap PW (1995) Aspects of stochasticity: variation between animals. In ‘Modelling growth in the pig’. (Eds PJ Moughan, MWA Verstegen, MI Visser-Reyneveld) pp. 165–172. (Wageningen Press: Wageningen, The Netherlands)

Knap PW, Roehe R, Kolstad K, Pomar C, Luting P (2003) Characterization of pig genotypes for growth modelling. Journal of Animal Science 81, E187–E195.

Lacker HM, Beers WH, Meuli LE, Akin E (1987a) A theory of follicle selection 1. Hypothesis and examples. Biology of Reproduction 37, 570–580.
A theory of follicle selection 1. Hypothesis and examples.CrossRef | 1:CAS:528:DyaL2sXmt12nur4%3D&md5=1ab44f7ab4259d894e4e07473e17cd6fCAS | 3118980PubMed |

Lacker HM, Beers WH, Meuli LE, Akin E (1987b) A theory of follicle selection 2. Computer simulation of estradiol administration in the primate. Biology of Reproduction 37, 581–588.
A theory of follicle selection 2. Computer simulation of estradiol administration in the primate.CrossRef | 1:CAS:528:DyaL2sXmt12nur8%3D&md5=ecae4a4f411ea5b3b88aebff5921e292CAS | 3676405PubMed |

Lamb MA, Tess MW, Robison OW (1992) Evaluation of mating systems involving five breeds for integrated beef production systems 1. Cow–calf segment. Journal of Animal Science 70, 689–699.

Laurence M, Accioly JM, Copping KJ, Deland MPB, Graham JF, Hebart ML, Herd RM, Jones FM, Lee SJ, Speijers EJ, Pitchford WS (2016) Divergent genotypes for fatness or residual feed intake in Angus cattle. 2. Body composition but not reproduction was affected in first-parity cows on both low and high levels of nutrition. Animal Production Science
Divergent genotypes for fatness or residual feed intake in Angus cattle. 2. Body composition but not reproduction was affected in first-parity cows on both low and high levels of nutrition.CrossRef |

Lee SJ, Nuberg IK, Pitchford WS (2016a) Maternal body composition in Australian seedstock herds. 1. Grazing management strategy influences perspectives on optimal balance of production traits and maternal productivity. Animal Production Science
Maternal body composition in Australian seedstock herds. 1. Grazing management strategy influences perspectives on optimal balance of production traits and maternal productivity.CrossRef |

Lee SJ, Donoghue KA, Pitchford WS (2016b) Maternal body composition in seedstock herds. 2. Relationships between cow body composition and BREEDPLAN EBVs for Angus and Hereford cows. Animal Production Science
Maternal body composition in seedstock herds. 2. Relationships between cow body composition and BREEDPLAN EBVs for Angus and Hereford cows.CrossRef |

Li Y-X, Goldbeter A (1989) Frequency specificity in intercellular communication: influence of patterns of periodic signalling to target cell responsiveness. Biophysical Journal 55, 125–145.
Frequency specificity in intercellular communication: influence of patterns of periodic signalling to target cell responsiveness.CrossRef | 1:STN:280:DyaL1M7psVaqtw%3D%3D&md5=cf28c1733c1d2cc964618902d11be47eCAS |

Loewer OJ, Smith EM, Gay N, Fehr R (1983) Incorporation of environment and feed quality into a net energy model for beef cattle. Agricultural Systems 11, 67–94.
Incorporation of environment and feed quality into a net energy model for beef cattle.CrossRef |

Loewer OJ, Taul KL, Turner LW, Gay N, Muntifering R (1987) Graze: a model of selective grazing by beef animals. Agricultural Systems 25, 297–309.
Graze: a model of selective grazing by beef animals.CrossRef |

Lynch T, Gregor S, Midmore D (2000) Intelligent support systems in agriculture: how can we do better? Australian Journal of Experimental Agriculture 40, 609–620.
Intelligent support systems in agriculture: how can we do better?CrossRef |

MacNeil MD, Mott TB (2000) Using genetic evaluations for growth and maternal gain from birth to weaning to predict energy requirements of line 1 Hereford beef cows. Journal of Animal Science 78, 2299–2304.

Mariana JC, Corpet F, Chevalet C (1994) Lacker’s model: control of follicular growth and ovulation in domestic species. Acta Biotheoretica 42, 245–262.
Lacker’s model: control of follicular growth and ovulation in domestic species.CrossRef |

Martin GB, Thomas GB, Terqui M, Warner P (1987) Pulsatile LH secretion during the preovulatory surge in the ewe: experimental observations and theoretical considerations. Reproduction, Nutrition, Development 27, 1023–1040.
Pulsatile LH secretion during the preovulatory surge in the ewe: experimental observations and theoretical considerations.CrossRef | 1:CAS:528:DyaL1cXht1Crsrs%3D&md5=bd8fee897a9c4bace8ceb1ca5ed080e8CAS |

McKiernan W, Sundstrom B (2006) ‘Visual and manual assessment of fatness in cattle.’ Available at http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0004/95863/visual-and-manual-assessment-of-fatness-in-cattle.pdf [Verified 15 September 2009]

Michalewicz Z, Fogel DB (2004) ‘How to solve it: modern heuristics.’ (Springer: Berlin, Germany)

Newman S, Lynch T, Plummer AA (2000) Success and failure of decision support systems: learning as we go. Journal of Animal Science. E. 77, 1–12.

NRC (1996) ‘Nutrient requirements of beef cattle.’ (National Academy of Sciences: Washington, DC)

NRC (2000) ‘Nutrient requirements of beef cattle.’ (National Academy of Sciences: Washington, DC)

Oldham JD, Emmans GC (1988) Prediction of responses to protein and energy yielding nutrients. In ‘Nutrition and lactation in the dairy cow’. (Ed. PC Garnsworthy) pp. 76–96. (Buttersworth: Sydney)

Olney GR, Kirk GJ (1989) A management model that helps increase profit on Western Australian dairy farms. Agricultural Systems 31, 367–380.
A management model that helps increase profit on Western Australian dairy farms.CrossRef |

Oltenacu PA, Milligan RA, Rounsaville TR, Foote RH (1980) Modelling reproduction in a herd of dairy cattle. Agricultural Systems 5, 193–205.
Modelling reproduction in a herd of dairy cattle.CrossRef |

Pitchford WS, Accioly JM, Banks RG, Barnes AL, Barwick SA, Copping KJ, Deland MPB, Donoghue KA, Edwards N, Hebart ML, Herd RM, Jones FM, Laurence M, Lee SJ, McKiernan WA, Parnell PF, Speijers EJ, Tudor GD, Graham JF (2016) Genesis, design and methods of the Beef CRC Maternal Productivity Project. Animal Production Science
Genesis, design and methods of the Beef CRC Maternal Productivity Project.CrossRef |

Pleasants AB (1997) Use of a stochastic model of a calving distribution for beef cows for formulating optimal natural mating strategies. Animal Science 64, 413–421.
Use of a stochastic model of a calving distribution for beef cows for formulating optimal natural mating strategies.CrossRef |

Pomar C, Kyriazakis I, Emmans GC, Knap PW (2003) Modelling stochasticity: dealing with populations rather than individual pigs. Journal of Animal Science 81, E178–E186.

Ribeiro EL, Nielsen MK, Bennett GL, Leymaster KA (1997) A simulation model including ovulation rate, potential embryonic viability, and uterine capacity to explain litter size in mice 1. Model development and implementation. Journal of Animal Science 75, 641–651.

Richards MW, Spitzer JC, Warner MB (1986) Effect of varying levels of postpartum nutrition and body condition at calving on subsequent reproductive performance if beef cattle. Journal of Animal Science 62, 300–306.

Rickards PA, Passmore AL (1977) ‘Planning for profit in livestock grazing systems.’ (Agricultural Business Research Institute, University of New England: Armidale, NSW)

Robinson JJ, McDonald I (1979) Ovine prenatal growth, its mathematical description and the effects of maternal nutrition. Annales de Biologie Animale, Biochimie, Biophysique 19, 225–234.
Ovine prenatal growth, its mathematical description and the effects of maternal nutrition.CrossRef |

Romera AJ, Morris ST, Hodgson J, Stirling WD, Woodward SJR (2004) A model for simulating rule-based management of cow–calf systems. Computers and Electronics in Agriculture 42, 67–86.
A model for simulating rule-based management of cow–calf systems.CrossRef |

Sanders JO, Cartwright TC (1979a) A general cattle production systems model 1. Structure of the model. Agricultural Systems 4, 217–227.
A general cattle production systems model 1. Structure of the model.CrossRef |

Sanders JO, Cartwright TC (1979b) A general cattle production systems model. Part 2. Procedures used for simulating animal performance. Agricultural Systems 4, 289–309.
A general cattle production systems model. Part 2. Procedures used for simulating animal performance.CrossRef |

Scaramuzzi RJ, Adams NR, Baird DT, Campbell BK, Downing JA, Findlay JK, Henderson KM, Martin GB, McNatty KP, McNeilly AS, Tsonis CG (1993) A model for follicle selection and the determination of ovulation rate in the ewe. Reproduction, Fertility and Development 5, 459–478.
A model for follicle selection and the determination of ovulation rate in the ewe.CrossRef | 1:CAS:528:DyaK2cXivFSntr4%3D&md5=31db5e92955a45ab473e29c2b00781c8CAS |

Shafer WR, Enns RM, Baker BB, van Tassell LW, Golden BL, Snelling WM, Mallinckrodt CH, Anderson KJ, Comstock CR, Brinks JS, Johnson DE, Hanson JD, Bourdon RM (2005) ‘Bio-economic simulation of beef cattle production: the Colorado beef cattle production model.’ (Colorado State University, Department of Animal Science: Fort Collins, CO)

Short RE, Bellows RA, Staigmiller RB, Berardinelli JG, Custer EE (1990) Physiological mechanisms controlling anestrus and infertility in postpartum beef cattle. Journal of Animal Science 68, 799–816.

Soboleva TK, Peterson AJ, Pleasants AB, McNatty KP (2000) A model of follicular development and ovulation in sheep and cattle. Animal Reproduction Science 58, 45–57.
A model of follicular development and ovulation in sheep and cattle.CrossRef | 1:CAS:528:DC%2BD3cXhsVWrtbY%3D&md5=08899d9d809271645082ce49bbc2a590CAS | 10700644PubMed |

Tedeschi LO, Fox DG, Chase LE, Wang SJ (2000) Whole-herd optimization with the Cornell net carbohydrate and protein system 1. Predicting feed biological values for diet optimization with linear programming. Journal of Dairy Science 83, 2139–2148.
Whole-herd optimization with the Cornell net carbohydrate and protein system 1. Predicting feed biological values for diet optimization with linear programming.CrossRef | 1:CAS:528:DC%2BD3cXmvFKitLg%3D&md5=3e2198c64b5747c66d9481dd5fac3870CAS | 11003249PubMed |

Tedeschi LO, Fox DG, Sainz RD, Barioni LG, de Medeiros SR, Boin C (2005) Mathematical models in ruminant nutrition. Scientia Agricola 62, 76–91.
Mathematical models in ruminant nutrition.CrossRef |

Tess MW, Kolstad K (2000) Simulation of cow–calf production systems in a range environment 1. Model development. Journal of Animal Science 78, 1159–1169.

Thornley JHM, France J (2007) Role of mathematical models. In ‘Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences’. (Eds JHM Thornley, J France) pp. 1–18. (CAB International: Wallingford, UK)

Tylutki TP, Fox DG (2000) An integrated cattle and crop production model to develop whole-farm nutrient management plans. In ‘Modelling nutrient utilisation in farm animals’. (Eds JP McNamara, J France, DE Beever) pp. 253–262. (CAB International: Wallingford, UK)

Veldhuis JD, Johnson ML (1988) A novel general biophysical model for simulating episodic endocrine gland signalling. The American Journal of Physiology 255, E749–E759.

Villalba D, Casasús I, Sanz A, Bernués A, Estany J, Revilla R (2006) Stochastic simulation of mountain beef cattle systems. Agricultural Systems 89, 414–434.
Stochastic simulation of mountain beef cattle systems.CrossRef |

Walmsley BJ, Kinghorn BP, Oddy VH, McPhee MJ, McKiernan WA (2013) A preliminary evaluation of a method for incorporating genetic information into phenotypic prediction models. In ‘Proceedings of the Association for the Advancement of Animal Breeding and Genetics’. Vol. 20. (Ed. N Lopez-Villalobos) Napier, New Zealand, 20–23 October 2013. pp. 527–530. (The Association for the Advancement of Animal Breeding and Genetics: Palmerston North, New Zealand)

Walmsley BJ, McPhee MJ, Oddy VH (2014) Development of the BeefSpecs fat calculator to assist decision making to increase compliance rates with beef carcass specifications. Animal Production Science 54, 2003–2010.
Development of the BeefSpecs fat calculator to assist decision making to increase compliance rates with beef carcass specifications.CrossRef |

Walmsley BJ, Lee SJ, Parnell PF, Pitchford WS (2016) A review of factors influencing key biological components of maternal productivity in temperate beef cattle. Animal Production Science
A review of factors influencing key biological components of maternal productivity in temperate beef cattle.CrossRef |

Wellock IJ, Emmans GC, Kyriazakis I (2004) Modelling the effects of stressors on the performance of populations of pigs. Journal of Animal Science 82, 2442–2450.

Wood PDP (1967) Algebraic model of the lactation curve in cattle. Nature 216, 164–165.
Algebraic model of the lactation curve in cattle.CrossRef |

Wood PDP (1976) Algebraic models of the lactation curves for milk, fat and protein production, with estimates of seasonal variation. Animal Production 22, 35–40.
Algebraic models of the lactation curves for milk, fat and protein production, with estimates of seasonal variation.CrossRef |

Yenikoye A, Mariana JC, Ley JP, Jolivet E, Terqui M, Lemon-Resplandy M (1981) Modèle mathématique de l’èvolution de progestérone chez la vache: application et mise en évidence de différence entre races. Reproduction, Nutrition, Development 21, 561–575.
Modèle mathématique de l’èvolution de progestérone chez la vache: application et mise en évidence de différence entre races.CrossRef | 1:CAS:528:DyaL3MXkslOks7o%3D&md5=2c05bd917fb7a736e6e206811c3380cbCAS |



Rent Article (via Deepdyve) Export Citation

View Altmetrics