Browsing by Author "Whitmore, Andrew P."
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Item Open Access Effect of different organic amendments on actual and achievable yields in a cereal-based cropping system(Springer, 2023-02-27) Albano, Xavier; Whitmore, Andrew P.; Sakrabani, Ruben; Thomas, Cathy L.; Sizmur, Tom; Ritz, Karl; Harris, Jim A.; Pawlett, Mark; Watts, Chris; Haefele, Stephan M.Soil fertility is at risk in intensive cropping systems when using an exclusive regime of inorganic fertilisers without returning sufficient organic matter to the soil. Our objective was to evaluate the long-term effects of commonly used organic amendments interacting with different rates of inorganic nitrogen fertiliser on crop yields of winter wheat. Yield data from winter wheat were collected for five seasons between 2013 and 2019 from a continuous field trial based at Rothamsted Research, SE England. Organic amendments (anaerobic digestate, compost, farmyard manure, and straw at a rate of 0 and 2.5 ton C per hectare) and five rates of inorganic nitrogen fertiliser (NH4NO3 at 0, 80, 150, 190, 220 kg N ha−1) were applied to winter wheat grown in an arable rotation. At the same inorganic N rate, grain yields for the different organic amendment treatments (excluding the straw treatment) were statistically similar but significantly greater than the unamended control treatment. The nitrogen rate required for optimum yields tended to be lower in plots receiving a combination of organic amendments and mineral fertiliser. Based on the observed and modelled response functions, organic amendments excluding straw increased maximum achievable yields compared to non-amended controls. The size of the effect varied between seasons and amendments (+4.6 to +19.0% of the control yield), increasing the mean maximum achievable yield by 8.8% across four seasons. We conclude that the application of organic amendments can increase the yield potential in winter wheat substantially over what is achievable with inorganic fertiliser only.Item Open Access Facilitating the elicitation of beliefs for use in Bayesian Belief modelling(Elsevier, 2019-10-01) Hassall, Kirsty L.; Dailey, Gordon; Zawadzka, Joanna; Milne, Alice E.; Harris, Jim A.; Corstanje, Ronald; Whitmore, Andrew P.Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE Hassall (2019)