Browsing by Author "Giannitsopoulos, Michail L."

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    Modelling the effects of soil organic content and pH on the yield responses of tea to nitrogen fertilizer
    (Elsevier, 2023-09-08) Giannitsopoulos, Michail L.; Burgess, Paul J.; Sakrabani, Ruben; Holden, Ann; Saini, Helen; Kirui, Charles
    CONTEXT: Sustained high yields of tea rely on the supply of nitrogen (N) from soil reserves, typically maintained by N fertilisation from inorganic or organic sources. OBJECTIVE: This paper describes how soil N levels, including the effects of soil organic content and pH, were developed and incorporated into a crop yield simulation model called CUPPA-Tea. METHODS: The nitrogen dynamics are presented in terms of i) the initial nitrogen stocks, ii) the addition of nitrogen to the system, iii) the uptake, use and loss of nitrogen by tea plants, and iv) nitrogen flows within the soil. CUPPA-Tea was then calibrated and validated using measured tea yields from Tanzania and Kenya. RESULTS AND CONCLUSIONS: After integrating a wide range of nitrogen algorithms, the model explained 79% of the variation in annual yields within a nitrogen and irrigation experiment in Tanzania and a fertilizer experiment in Kenya. The slope of the relationship was 0.84 and 0.73 respectively, the root mean square error was 660 kg ha−1 and 507 kg ha−1, and the modelling efficiency was 0.77 and 0.75 respectively. The model predicted that in the absence of N application, tea yields would be higher from a site with a high rather than a low soil organic content. By contrast, at high levels of mineral N application, the yield response in the model was not sensitive to the soil organic content. Hence within the model, a site in Tanzania with a low soil organic content of 1.6% showed a greater yield response to applied mineral N than a site in Kenya where the soil organic carbon was 4.0%. The model also predicted small losses of N from the cropping system through denitrification and leaching due to the acidic soil conditions (pH < 4.5) and an assumed tea rooting depth between 300 and 500 cm. In Tanzania, irrigation was predicted to result in around 10% higher nitrogen uptake than under unirrigated conditions. SIGNIFICANCE: The use of the CUPPA-Tea model can be useful in supporting decision making and improving the accuracy of tea yield estimates, as well as predictions of N fate within the soil-plant-atmosphere continuum.
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    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.
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    Towards the coordinated and fit-for-purpose deployment of Unmanned Aerial Systems (UASs) for flood risk management in England
    (IWA Publishing, 2022-07-18) Giannitsopoulos, Michail L.; Leinster, Paul; Butler, David; Smith, Mike; Rivas Casado, Monica
    Preparedness for flood emergency response is crucial for effective flood management. The need for advanced flood decision support tools that aid flood management has been recognized by several authors. This work examines the variability that currently exists across England with regard to the Unmanned Aerial System (UAS) data collection and processing strategy in flood emergency events. Expert elicitation was carried out using a tailored questionnaire about UAS deployment in three flood emergency scenarios. The survey highlighted that reduced equipment assembly time, a national network of appropriately qualified UAS pilots and the effective UAS deployment when on-site, can reduce the response time to flood emergency. For improved comparability and reduced bias in data collection and interpretation, clear guidelines on which data products are most beneficial for particular purposes, processing time required, platform and sensor selection may also be necessary. We consider that releasing a comprehensive documentation pack, which includes guidelines, standards and protocols that detail the methods, tools, technology, quantity and quality of data, to UAS pilots on a flood emergency call, will enhance the timely response.
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    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.
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    Whole system valuation of arable, agroforestry and tree-only systems at three case study sites in Europe
    (Elsevier, 2020-05-24) Giannitsopoulos, Michail L.; Graves, Anil R.; Burgess, Paul J.; Crous-Duran, Josep; Moreno, Gerardo; Herzog, Felix; Palma, João H. N.; Kay, Sonja; Garcíade Jalóne, Silvestre
    There is an increasing demand to study the long-term effects of land use from both local farm and wider societal and environmental perspectives. This study applied an approach to evaluate both the financial profitability of arable, agroforestry, and tree-only systems and the wider societal benefits over a period of 30-60 years. The biophysical inputs and yields from the three systems were modelled for three case study sites in the United Kingdom, Spain, and Switzerland, using a tree and crop simulation model called Yield-SAFE. A bio-economic model called Farm-SAFE was then used to compare the financial (EAVF) and economic (or societal) equivalent annual values (EAVE) by including monetary values for five environmental externalities: carbon dioxide emissions, carbon sequestration, soil erosion by water, and nitrogen and phosphorus balances. Across the three case studies, arable farming generated higher farm incomes than the agroforestry or tree-only systems, but the arable systems also created the greatest environmental costs. By comparison the agroforestry and tree-only systems generated lower CO2 emissions and sequestered more carbon. Applying monetary values to the environmental externalities meant that the EAVE of the agroforestry and tree-only systems were greater or similar to that for the arable system in the UK case study. In Spain, the slow predicted growth of the trees meant that, even after including the environmental externalities, the arable system created greater societal benefit than the agroforestry and tree-only systems. In Switzerland, including the environmental externalities increased the attraction of the tree-only system, but the high subsidies for arable and agroforestry systems meant that the EAVE for the agroforestry and arable systems were the most attractive from a farmer’s perspective. A breakeven analysis was used to determine the environmental externality values at which the agroforestry and tree-only systems produced the same societal return as the arable system in each case study. In the UK, a carbon price of ₠16 (t CO2)-1 allowed the EAVE of the agroforestry system to attain parity with the arable EAVE. In both the UK and Spain, an environmental nitrogen cost of ₠3-6 (kg N)-1 was sufficient for the EAVE of the agroforestry and tree-only systems to match those of arable farming. Because trees on farms provide ‘‘economies of multifunction’’ for environmental benefits, the breakeven values will be less if environmental benefits are considered together as packages. The described approach provides a method for governments and others to examine the cost effectiveness of new agri-environment measures