Browsing by Author "Topp, Cairistiona F. E."
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Item Open Access Key challenges and priorities for modelling European grasslands under climate change(Elsevier, 2016-05-19) Kipling, Richard P.; Virkajarvi, Perttu; Breitsameter, Laura; Curnel, Yannick; De Swaef, Tom; Gustavsson, Anne-Maj; Hennart, Sylvain; Hoglind, Mats; Jarvenranta, Kirsi; Minet, Julien; Nendel, Claas; Persson, Tomas; Picon-Cochard, Catherine; Rolinski, Susanne; Sandars, Daniel L.; Scollan, Nigel D.; Sebek, Leon; Seddaiu, Giovanna; Topp, Cairistiona F. E.; Twardy, Stanislaw; van Middelkoop, Jantine; Wu, Lianhai; Bellocchi, GianniGrassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.Item Open 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, TaroThis 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.Item Open Access To what extent is climate change adaptation a novel challenge for agricultural modellers?(Elsevier, 2019-07-29) Kipling, Richard P.; Topp, Cairistiona F. E.; Bannink, André D.; Bartley, David J.; Blanco-Penedo, Isabel; Cortignani, Raffaele; del Prado, Agustín; Dono, Gabriele; Faverdin, Philippe; Graux, Anne Isabelle; Hutchings, Nicholas J.; Lauwers, Ludwig; Özkan Gülzari, Şeyda; Reidsma, Pytrik; Rolinski, Susanne; Ruiz-Ramos, Margarita; Sandars, Daniel L.; Sandor, Renata; Schönhart, Martin; Seddaiu, Giovanna; van Middelkoop, Jantine C.; Shrestha, Shailesh S.; Weindl, Isabelle; Eory, VeraModelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.Item Open 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, TaroEach 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.