IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i7p1446-d1199945.html
   My bibliography  Save this article

Mathematical Models to Predict Dry Matter Intake and Milk Production by Dairy Cows Managed under Tropical Conditions

Author

Listed:
  • Antonio Leandro Chaves Gurgel

    (Campus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus 64900-000, Piauí, Brazil
    Department of Animal Science, State University of Maringá, Maringá 87020-900, Paraná, Brazil)

  • Geraldo Tadeu dos Santos

    (Department of Animal Science, State University of Maringá, Maringá 87020-900, Paraná, Brazil)

  • Luís Carlos Vinhas Ítavo

    (College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil)

  • Camila Celeste Brandão Ferreira Ítavo

    (College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil)

  • Gelson dos Santos Difante

    (College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil)

  • Alexandre Menezes Dias

    (College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil)

  • Vanessa Zirondi Longhini

    (College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil)

  • Tairon Pannunzio Dias-Silva

    (Campus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus 64900-000, Piauí, Brazil)

  • Marcos Jácome de Araújo

    (Campus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus 64900-000, Piauí, Brazil)

  • João Virgínio Emerenciano Neto

    (Academic Unit Specialized in Agricultural Sciences, Federal University of Rio Grande do Norte, Macaíba 59280-000, Rio Grande do Norte, Brazil)

  • Patrick Bezerra Fernandes

    (Goiás Federal Institute, Campus Rio Verde, Rio Verde 75901-970, Goiás, Brazil)

  • Alfonso Juventino Chay-Canul

    (División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa 86298, Tabasco, México)

Abstract

This study aimed to create an equation to predict dry matter intake (DMI) and milk production and N-ureic in the milk of dairy cows managed in tropical conditions in Brazil. We used 113 observations from three experiments using lactating Jersey, Girolando, and Holstein cows. The goodness of fit of the developed equations was evaluated using the coefficients of determination (r 2 ) and root mean square error (RMSE). There was a positive correlation between body weight and milk yield (MY) of r = 0.73. The equation considered DMI to be the most important variable to estimate the MY (r 2 = 0.65). Four equations were adjusted to estimate the DMI, where, by a stepwise procedure, the first variable included in the equation was the neutral detergent fibre intake, which explained 92% of the DMI of the cows. However, when the variables BW, MY, and milk fat were included in the equation, there was a reduction of 0.06 in RMSE and an increase in precision (r 2 = 0.94). The nutrient intake, milk production, and characteristics prediction equations present satisfactory precision and accuracy for dairy cows managed in tropical conditions in Brazil.

Suggested Citation

  • Antonio Leandro Chaves Gurgel & Geraldo Tadeu dos Santos & Luís Carlos Vinhas Ítavo & Camila Celeste Brandão Ferreira Ítavo & Gelson dos Santos Difante & Alexandre Menezes Dias & Vanessa Zirondi Longh, 2023. "Mathematical Models to Predict Dry Matter Intake and Milk Production by Dairy Cows Managed under Tropical Conditions," Agriculture, MDPI, vol. 13(7), pages 1-11, July.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1446-:d:1199945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/7/1446/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/13/7/1446/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tedeschi, Luis Orlindo, 2006. "Assessment of the adequacy of mathematical models," Agricultural Systems, Elsevier, vol. 89(2-3), pages 225-247, September.
    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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Stirling, Sofía & Fariña, Santiago & Pacheco, David & Vibart, Ronaldo, 2021. "Whole-farm modelling of grazing dairy systems in Uruguay," Agricultural Systems, Elsevier, vol. 193(C).
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. 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).
    15. Yury B. Melnikov & Yelena A. Onokhina & Sergey A. Shitikov, 2018. "Improving the Adequacy of Economic Models," Journal of New Economy, Ural State University of Economics, vol. 19(1), pages 94-106, February.
    16. Ségolène Maucourt & Frédéric Fortin & Claude Robert & Pierre Giovenazzo, 2021. "Genetic Progress Achieved during 10 Years of Selective Breeding for Honeybee Traits of Interest to the Beekeeping Industry," Agriculture, MDPI, vol. 11(6), pages 1-14, June.
    17. Mateus P Gionbelli & Marcio S Duarte & Sebastião C Valadares Filho & Edenio Detmann & Mario L Chizzotti & Felipe C Rodrigues & Diego Zanetti & Tathyane R S Gionbelli & Marcelo G Machado, 2015. "Achieving Body Weight Adjustments for Feeding Status and Pregnant or Non-Pregnant Condition in Beef Cows," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.
    18. repec:url:i20181:v:19:y:2018:i:1:p:94-106 is not listed on IDEAS
    19. Mollier, Alain & De Willigen, Peter & Heinen, Marius & Morel, Christian & Schneider, André & Pellerin, Sylvain, 2008. "A two-dimensional simulation model of phosphorus uptake including crop growth and P-response," Ecological Modelling, Elsevier, vol. 210(4), pages 453-464.
    20. 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.
    21. Ojeda, J.J. & Pembleton, K.G. & Islam, M.R. & Agnusdei, M.G. & Garcia, S.C., 2016. "Evaluation of the agricultural production systems simulator simulating Lucerne and annual ryegrass dry matter yield in the Argentine Pampas and south-eastern Australia," Agricultural Systems, Elsevier, vol. 143(C), pages 61-75.

    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:gam:jagris:v:13:y:2023:i:7:p:1446-:d:1199945. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.