Browsing by Author "Ingram, Julie"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Data underpinning: 'NERC Research Translation: Grassland Management' project
    (Cranfield University, 2022-09-08 13:38) Giannitsopoulos, Michail; Burgess, Paul; Richter, Goetz; Bell, Matthew; F. E. Topp, Cairistiona; Ingram, Julie; Takahashi, Taro
    LINGRA-N-Plus along with its Teaching Guide, as developed in the NERC Research Translation: Grassland Management Project, supported by the Sustainable Agriculture Research and Innovation Club (SARIC).
  • Thumbnail Image
    ItemOpen Access
    Modelling the interactions of soils, climate, and management for grass production in England and Wales
    (MDPI, 2021-04-02) Giannitsopoulos, Michail L.; Burgess, Paul J.; Richter, Goetz M.; Bell, Matt J.; Topp, Cairistiona F. E.; Ingram, Julie; Takahashi, Taro
    This study examines the effectiveness of a model called LINGRA-N-Plus to simulate the interaction of climate, soil and management on the green leaf and total dry matter yields of ryegrass in England and Wales. The LINGRA-N-Plus model includes modifications of the LINGRA-N model such as temperature- and moisture-dependent soil nitrogen mineralization and differential partitioning to leaves and stems with thermal time from the last harvest. The resulting model was calibrated against the green leaf and total grass yields from a harvest interval x nitrogen application experiment described by Wilman et al. (1976). When the LINGRA-N-Plus model was validated against total grass yields from nitrogen experiments at ten sites described by Morrison et al. (1980), its modelling efficiency improved greatly compared to the original LINGRA-N. High predicted yields, at zero nitrogen application, were related to soils with a high initial nitrogen content. The lowest predicted yields occurred at sites with low rainfall and shallow rooting depth; mitigating the effect of drought at such sites increased yields by up to 4 t ha−1. The results highlight the usefulness of grass models, such as LINGRA-N-Plus, to explore the combined effects of climate, soil, and management, like nitrogen application, and harvest intervals on grass productivity.
  • Thumbnail Image
    ItemOpen Access
    Translating and applying a simulation model to enhance understanding of grassland management
    (Wiley, 2022-09-27) Giannitsopoulos, Michail L.; Burgess, Paul J.; Bell, Matthew J.; Richter, Goetz M.; Topp, Cairistiona F. E.; Ingram, Julie; Takahashi, Taro
    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.