IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8676-d431363.html
   My bibliography  Save this article

Mechanisms of Grazing Management in Heterogeneous Swards

Author

Listed:
  • Arthur Pontes-Prates

    (Department of Plant Sciences, UC Davis, Davis, CA 95616, USA
    Grazing Ecology Research Group, UFRGS, Porto Alegre, RS 90040-060, Brazil)

  • Paulo César de Faccio Carvalho

    (Grazing Ecology Research Group, UFRGS, Porto Alegre, RS 90040-060, Brazil)

  • Emilio Andrés Laca

    (Department of Plant Sciences, UC Davis, Davis, CA 95616, USA)

Abstract

We explored the effects of heterogeneity of sward height on the functioning of grazing systems through a spatially implicit mechanistic model of grazing and sward growth. The model uses a population dynamic approach where a sward is spatially structured by height, which changes as a function of defoliation, trampling, and growth. The grazing component incorporates mechanisms of bite formation, intake, and digestion rates, but excludes sward quality effects. Sward height selection is determined by maximization of the instantaneous intake rate of forage dry mass. For any given average sward height, intake rate increased with increasing spatial heterogeneity. Spatio-temporal distribution of animal density over paddocks did not markedly affect animal performance but it modified the balance of vegetation heterogeneity within and between paddocks. Herbage allowance was a weak predictor of animal performance because the same value can result from multiples combinations of herbage mass per unit area, number of animals, animal liveweight, and paddock area, which are the proximate determinants of intake rate. Our results differ from models that assume homogeneity and provide strong evidence of how heterogeneity influences the dynamic of grazing systems. Thus, we argue that grazing management and research need to incorporate the concept of heterogeneity into the design of future grazing systems.

Suggested Citation

  • Arthur Pontes-Prates & Paulo César de Faccio Carvalho & Emilio Andrés Laca, 2020. "Mechanisms of Grazing Management in Heterogeneous Swards," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8676-:d:431363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8676/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/20/8676/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Piñeiro, Gervasio & Perelman, Susana & Guerschman, Juan P. & Paruelo, José M., 2008. "How to evaluate models: Observed vs. predicted or predicted vs. observed?," Ecological Modelling, Elsevier, vol. 216(3), pages 316-322.
    2. Gregorini, Pablo & Beukes, Pierre C. & Romera, Alvaro J. & Levy, Gil & Hanigan, Mark D., 2013. "A model of diurnal grazing patterns and herbage intake of a dairy cow, MINDY: Model description," Ecological Modelling, Elsevier, vol. 270(C), pages 11-29.
    3. Noy-Meir, I., 1976. "Rotational grazing in a continuously growing pasture: A simple model," Agricultural Systems, Elsevier, vol. 1(2), pages 87-112, April.
    4. Ungar, Eugene David, 2019. "Perspectives on the concept of rangeland carrying capacity, and their exploration by means of Noy-Meir's two-function model," Agricultural Systems, Elsevier, vol. 173(C), pages 403-413.
    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. Booth, Shawn & Walters, William J & Steenbeek, Jeroen & Christensen, Villy & Charmasson, Sabine, 2020. "An Ecopath with Ecosim model for the Pacific coast of eastern Japan: Describing the marine environment and its fisheries prior to the Great East Japan earthquake," Ecological Modelling, Elsevier, vol. 428(C).
    2. Luca Piciullo & Vittoria Capobianco & Håkon Heyerdahl, 2022. "A first step towards a IoT-based local early warning system for an unsaturated slope in Norway," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3377-3407, December.
    3. Majid Majzoubi & Eric Yanfei Zhao, 2023. "Going beyond optimal distinctiveness: Strategic positioning for gaining an audience composition premium," Strategic Management Journal, Wiley Blackwell, vol. 44(3), pages 737-777, March.
    4. Dowson, Oscar & Philpott, Andy & Mason, Andrew & Downward, Anthony, 2019. "A multi-stage stochastic optimization model of a pastoral dairy farm," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1077-1089.
    5. Shuang Liu & David I Stern, 2008. "A Meta-Analysis of Contingent Valuation Studies in Coastal and Near-Shore Marine Ecosystems," Socio-Economics and the Environment in Discussion (SEED) Working Paper Series 2008-15, CSIRO Sustainable Ecosystems.
    6. Sepaskhah, Ali Reza & Fahandezh-Saadi, Saghar & Zand-Parsa, Shahrokh, 2011. "Logistic model application for prediction of maize yield under water and nitrogen management," Agricultural Water Management, Elsevier, vol. 99(1), pages 51-57.
    7. Michael Gbenga Ogungbuyi & Juan P. Guerschman & Andrew M. Fischer & Richard Azu Crabbe & Caroline Mohammed & Peter Scarth & Phil Tickle & Jason Whitehead & Matthew Tom Harrison, 2023. "Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning," Land, MDPI, vol. 12(6), pages 1-25, May.
    8. Sileshi, Gudeta & Hailu, Girma & Nyadzi, Gerson I., 2009. "Traditional occupancy–abundance models are inadequate for zero-inflated ecological count data," Ecological Modelling, Elsevier, vol. 220(15), pages 1764-1775.
    9. Gergs, André & Ratte, Hans Toni, 2009. "Predicting functional response and size selectivity of juvenile Notonecta maculata foraging on Daphnia magna," Ecological Modelling, Elsevier, vol. 220(23), pages 3331-3341.
    10. Blal, Mohamed & Benatiallah, Ali & NeÇaibia, Ammar & Lachtar, Salah & Sahouane, Nordine & Belasri, Ahmed, 2019. "Contribution and investigation to compare models parameters of (PEMFC), comprehensives review of fuel cell models and their degradation," Energy, Elsevier, vol. 168(C), pages 182-199.
    11. Ungar, Eugene David, 2019. "Perspectives on the concept of rangeland carrying capacity, and their exploration by means of Noy-Meir's two-function model," Agricultural Systems, Elsevier, vol. 173(C), pages 403-413.
    12. Della Nave, Facundo N. & Ojeda, Jonathan J. & Irisarri, J. Gonzalo N. & Pembleton, Keith & Oyarzabal, Mariano & Oesterheld, Martín, 2022. "Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions," Agricultural Systems, Elsevier, vol. 201(C).
    13. Kaitaniemi, Pekka & Lintunen, Anna & Sievänen, Risto, 2020. "Power-law estimation of branch growth," Ecological Modelling, Elsevier, vol. 416(C).
    14. Wang, Tong & Seong, Park & Teague, W. Richard & Bevers, Stan, "undated". "Evaluate long-term economic consequences of continuous and multi-paddock grazing in southern tallgrass prairie," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170406, Agricultural and Applied Economics Association.
    15. Cameron J. Marshall & Pablo Gregorini, 2021. "Animal as the Solution: Searching for Environmentally Friendly Dairy Cows," Sustainability, MDPI, vol. 13(18), pages 1-14, September.
    16. Tozer, Peter R. & Huffaker, Ray G., 1999. "Dairy Deregulation And Low-Input Dairy Production: A Bioeconomic Evaluation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(1), pages 1-18, July.
    17. 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.
    18. 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).
    19. M. D. Petrie & J. B. Bradford & W. K. Lauenroth & D. R. Schlaepfer & C. M. Andrews & D. M. Bell, 2020. "Non-analog increases to air, surface, and belowground temperature extreme events due to climate change," Climatic Change, Springer, vol. 163(4), pages 2233-2256, December.
    20. Steingruber, Sandra Martina, 2020. "Improved empirical models for predicting nitrogen retention in lakes and reservoirs," Ecological Modelling, Elsevier, vol. 416(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:gam:jsusta:v:12:y:2020:i:20:p:8676-:d:431363. 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.