KeywordGrass growth model
On-farm decision tools
Lolium perenne L.
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CitationJ. McDonnell, C. Brophy, E. Ruelle, L. Shalloo, K. Lambkin, D. Hennessy, Weather forecasts to enhance an Irish grass growth model, European Journal of Agronomy, Volume 105, 2019, Pages 168-175, ISSN 1161-0301, https://doi.org/10.1016/j.eja.2019.02.013.
AbstractGrass growth models have retrospectively predicted grass growth in Ireland using weather observations. 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.
FunderIrish Department of Agriculture, Food and the Marine; Teagasc Walsh Fellowship
Grant NumberRSF 11/S/132
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