IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v182y2020ics0308521x19303877.html
   My bibliography  Save this article

Development and evaluation of a dynamic simulation model of reproductive performance in pasture based suckler beef systems

Author

Listed:
  • Lynch, R.
  • Kelly, A.K.
  • Kenny, D.A.
  • Crosson, P.

Abstract

The reproductive performance of a beef cow herd is largely a factor of efficient animal management strategies and genetics. The complex and cumulative nature of individual management decisions on a farm system's performance over multiple years mean that robust evaluation is time consuming and costly. A dynamic deterministic simulation model (Grange Reproductive Management Model; GReMM) with the capacity to replicate herd inventory dynamics over multiple reproductive cycles was developed using the Stella Architect dynamic modelling platform. The model is representative of a pasture-based spring calving suckled beef cow herd and is initialised by specifying individual farm parameters with respect to reproductive management, such as the breeding season duration, nutrition and management of the cow and calf postpartum. The focus was on the key factors which affect the duration of the postpartum anoestrus interval (PPAI); body condition score of the cow at calving (BCSc), postpartum nutrition (PPN), access of the suckling calf to the dam and, exposure of the dam to a fertile male. Model output was displayed in the form of a shift in calving distribution, the number of calves produced per cow bred per year and, percentage of cows culled due to barrenness. Three management scenarios were investigated to represent a herd implementing current industry best practice (BASE), a herd implementing intensive levels of reproductive management (Intensive reproductive management; IRM) and a herd implementing poor levels of reproductive management (Poor reproductive management; PRM). When evaluated in terms of calving distribution over six production cycles, both PRM and IRM showed a shift in the calving spread with a higher proportion of animals calving earlier and later, respectively, in the calving season compared to the BASE. This resulted in a 5% increase and a 14% decrease in six-week calving rates relative to BASE by year six, for IRM and PRM, respectively. Correspondingly, culling rates due to barrenness reduced by 0.9% and increased by 3.3% relative to BASE for IRM and PRM, respectively. The model developed offers a realistic and intuitive dynamic simulation model capable of investigating practical on-farm management decisions on herd reproductive performance.

Suggested Citation

  • Lynch, R. & Kelly, A.K. & Kenny, D.A. & Crosson, P., 2020. "Development and evaluation of a dynamic simulation model of reproductive performance in pasture based suckler beef systems," Agricultural Systems, Elsevier, vol. 182(C).
  • Handle: RePEc:eee:agisys:v:182:y:2020:i:c:s0308521x19303877
    DOI: 10.1016/j.agsy.2020.102797
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X19303877
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agsy.2020.102797?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Azzam, Sara Melin & Kinder, J. E. & Nielsen, M. K., 1990. "Modelling reproductive management systems for beef cattle," Agricultural Systems, Elsevier, vol. 34(2), pages 103-122.
    2. Oltenacu, P. A. & Milligan, R. A. & Rounsaville, T. R. & Foote, R. H., 1980. "Modelling reproduction in a herd of dairy cattle," Agricultural Systems, Elsevier, vol. 5(3), pages 193-205, July.
    3. Tedeschi, Luis Orlindo, 2006. "Assessment of the adequacy of mathematical models," Agricultural Systems, Elsevier, vol. 89(2-3), pages 225-247, September.
    4. Turner, B.L. & Rhoades, R.D. & Tedeschi, L.O. & Hanagriff, R.D. & McCuistion, K.C. & Dunn, B.H., 2013. "Analyzing ranch profitability from varying cow sales and heifer replacement rates for beef cow-calf production using system dynamics," Agricultural Systems, Elsevier, vol. 114(C), pages 6-14.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin L. Turner & Vincent Tidwell & Alexander Fernald & José A. Rivera & Sylvia Rodriguez & Steven Guldan & Carlos Ochoa & Brian Hurd & Kenneth Boykin & Andres Cibils, 2016. "Modeling Acequia Irrigation Systems Using System Dynamics: Model Development, Evaluation, and Sensitivity Analyses to Investigate Effects of Socio-Economic and Biophysical Feedbacks," Sustainability, MDPI, vol. 8(10), pages 1-30, October.
    2. Turner, B.L. & Rhoades, R.D. & Tedeschi, L.O. & Hanagriff, R.D. & McCuistion, K.C. & Dunn, B.H., 2013. "Analyzing ranch profitability from varying cow sales and heifer replacement rates for beef cow-calf production using system dynamics," Agricultural Systems, Elsevier, vol. 114(C), pages 6-14.
    3. Percy, A. Jemila & Edwin, M., 2023. "Studies on the performance and emission characteristics of a dual fuel VCR engine using producer gas as secondary fuel: An optimization approach using response surface methodology," Energy, Elsevier, vol. 263(PA).
    4. Nasca, J.A. & Feldkamp, C.R. & Arroquy, J.I. & Colombatto, D., 2015. "Efficiency and stability in subtropical beef cattle grazing systems in the northwest of Argentina," Agricultural Systems, Elsevier, vol. 133(C), pages 85-96.
    5. Vosough Ahmadi, Bouda & Morgan, Colin A. & Stott, Alistair W., 2009. "Trade-offs between conflicting animal welfare concerns and cow replacement strategy in out-wintering Scottish suckler herds," Working Papers 61122, Scotland's Rural College (formerly Scottish Agricultural College), Land Economy & Environment Research Group.
    6. Amin, M.G. Mostofa & Šimůnek, Jirka & Lægdsmand, Mette, 2014. "Simulation of the redistribution and fate of contaminants from soil-injected animal slurry," Agricultural Water Management, Elsevier, vol. 131(C), pages 17-29.
    7. Confalonieri, Roberto & Acutis, Marco & Bellocchi, Gianni & Donatelli, Marcello, 2009. "Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice," Ecological Modelling, Elsevier, vol. 220(11), pages 1395-1410.
    8. María Gabriela Pizarro Inostroza & Francisco Javier Navas González & Vincenzo Landi & José Manuel León Jurado & Juan Vicente Delgado Bermejo & Javier Fernández Álvarez & María del Amparo Martínez Mart, 2020. "Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    9. Benjamin L. Turner & Melissa Wuellner & Erin Cortus & Steven Boot Chumbley, 2022. "A multi‐university cohort model for teaching complex and interdisciplinary problem‐solving using system dynamics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(2), pages 185-199, March.
    10. Steppe, Kathy & De Pauw, Dirk J.W. & Lemeur, Raoul, 2008. "Validation of a dynamic stem diameter variation model and the resulting seasonal changes in calibrated parameter values," Ecological Modelling, Elsevier, vol. 218(3), pages 247-259.
    11. Villalba, D. & Casasus, I. & Sanz, A. & Bernues, A. & Estany, J. & Revilla, R., 2006. "Stochastic simulation of mountain beef cattle systems," Agricultural Systems, Elsevier, vol. 89(2-3), pages 414-434, September.
    12. Kuo Jiang & Hong Zeng & Zefan Wu & Jianping Sun & Cai Chen & Bing Han, 2023. "Study on the Effect of Parameter Sensitivity on Engine Optimization Results," Energies, MDPI, vol. 16(23), pages 1-16, December.
    13. Stirling, Sofía & Fariña, Santiago & Pacheco, David & Vibart, Ronaldo, 2021. "Whole-farm modelling of grazing dairy systems in Uruguay," Agricultural Systems, Elsevier, vol. 193(C).
    14. Dieguez Cameroni, Francisco & Fort, Hugo, 2017. "Towards scientifically based management of extensive livestock farming in terms of ecological predator-prey modeling," Agricultural Systems, Elsevier, vol. 153(C), pages 127-137.
    15. Plaizier, J. C. B. & King, G. J. & Dekkers, J. C. M. & Lissemore, K., 1998. "Modeling the relationship between reproductive performance and net-revenue in dairy herds," Agricultural Systems, Elsevier, vol. 56(3), pages 305-322, March.
    16. Phelan, David C. & Harrison, Matthew T. & McLean, Greg & Cox, Howard & Pembleton, Kieth G. & Dean, Geoff J. & Parsons, David & do Amaral Richter, Maria E. & Pengilley, Georgie & Hinton, Sue J. & Moham, 2018. "Advancing a farmer decision support tool for agronomic decisions on rainfed and irrigated wheat cropping in Tasmania," Agricultural Systems, Elsevier, vol. 167(C), pages 113-124.
    17. Bryant, Jeremy & Lopez-Villalobos, Nicolas & Holmes, Colin & Pryce, Jennie & Rossi, Jose & Macdonald, Kevin, 2008. "Development and evaluation of a pastoral simulation model that predicts dairy cattle performance based on animal genotype and environmental sensitivity information," Agricultural Systems, Elsevier, vol. 97(1-2), pages 13-25, April.
    18. Ojeda, Jonathan J. & Volenec, Jeffrey J. & Brouder, Sylvie M. & Caviglia, Octavio P. & Agnusdei, Mónica G., 2018. "Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM," Agricultural Water Management, Elsevier, vol. 195(C), pages 154-171.
    19. Correndo, Adrian A. & Hefley, Trevor J. & Holzworth, Dean P. & Ciampitti, Ignacio A., 2021. "Revisiting linear regression to test agreement in continuous predicted-observed datasets," Agricultural Systems, Elsevier, vol. 192(C).
    20. Turner, Benjamin L., 2020. "Model laboratories: A quick-start guide for design of simulation experiments for dynamic systems models," Ecological Modelling, Elsevier, vol. 434(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agisys:v:182:y:2020:i:c:s0308521x19303877. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agsy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.