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    Weather forecasts to enhance an Irish grass growth model


    McDonnell, J. and Brophy, Caroline and Ruelle, E. and Shalloo, Laurence and Lambkin, Keith and Hennessy, Deirdre (2019) Weather forecasts to enhance an Irish grass growth model. European Journal of Agronomy, 105. pp. 168-175. ISSN 1161-0301

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    Abstract

    However, to predict future grass growth to aid farm management, weather forecasts are necessary inputs. The Moorepark St. Gilles grass growth model (MoSt GGM) is mechanistic and was developed to predict perennial ryegrass growth on any Irish farm. To date, it has used local farm information, (retrospective) weather data and management factors to predict daily paddock-level grass growth. Here, we include weather forecasts in the MoSt GGM and assess its performance through two studies: daily grass growth predictions at four nitrogen fertiliser application levels using weather forecasts up to ten days in advance were compared with those using weather observations; and the GGM predictions for an Irish dairy farm using observed and forecast weather were compared with on-farm grass growth observations from 2013 to 2016. In the first study, all weather inputs captured the rise in grass growth predictions with higher fertiliser application. Based on the Root Mean Squared Error (RMSE), European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts outperformed a forecast based on climatological averages as GGM inputs up to six days in advance, and up to ten days in advance after bias correction. In the second study, ECMWF forecasts were the best weather forecast to predict grass growth since they captured weather variability well and did not require the local weather observations necessary for bias corrections. Weather forecasts are useful inputs to the MoSt GGM, and yield accurate weekly predictions that could aid management decisions.

    Item Type: Article
    Keywords: Grass growth model; On-farm decision tools; Grassland management; Weather forecasts; Lolium perenneL;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 13519
    Identification Number: https://doi.org/10.1016/j.eja.2019.02.013
    Depositing User: Jack McDonnell
    Date Deposited: 10 Nov 2020 15:24
    Journal or Publication Title: European Journal of Agronomy
    Publisher: Elsevier
    Refereed: Yes
    URI:

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