Translating and applying a simulation model to enhance understanding of grassland management

Michail L. Giannitsopoulos, Paul J. Burgess, Matthew J. Bell, Goetz M. Richter, Cairistiona F. E. Topp, Julie Ingram, Taro Takahashi

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Abstract Each new generation of grassland managers could benefit from an improved understanding of how modification of nitrogen application and harvest dates in response to different weather and soil conditions will affect grass yields and quality. The purpose of this study was to develop a freely available grass yield simulation model, validated for England and Wales, and to examine its strengths and weaknesses as a teaching tool for improving grass management. The model, called LINGRA-N-Plus, was implemented in a Microsoft Excel spreadsheet and iteratively evaluated by students and practitioners (farmers, consultants, and researchers) in a series of workshops across the UK over 2?years. The iterative feedback led to the addition of new algorithms, an improved user interface, and the development of a teaching guide. The students and practitioners identified the ease of use and the capacity to understand, visualize and evaluate how decisions, such as variation of cutting intervals, affect grass yields as strengths of the model. We propose that an effective teaching tool must achieve an appropriate balance between being sufficiently detailed to demonstrate the major relationships (e.g., the effect of nitrogen on grass yields) whilst not becoming so complex that the relationships become incomprehensible. We observed that improving the user-interface allowed us to extend the scope of the model without reducing the level of comprehension. The students appeared to be interested in the explanatory nature of the model whilst the practitioners were more interested in the application of a validated model to enhance their decision making.
Original languageEnglish
Pages (from-to)50-63
Number of pages14
JournalGrass and Forage Science
Issue number1
Early online date27 Sept 2022
Publication statusPublished - Mar 2023


  • agronomy
  • decision support
  • education
  • grassland management


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