Plant analysis as a tool to determine crop nitrogen status

An effective plant nutrient management strategy optimises nitrogen (N) use efficiency for minimised environmental impact, while ensuring an optimum N status of the crop for good product quality and maximum growth. Soil or plant analysis can be used to evaluate the strategy; however the use of plant analysis for this purpose has been limited. One reason is lack of reliable reference values for the critical concentration needed for optimal growth. This study builds on theories that relate ontogenetic changes in the critical N concentration to changes in the relation between mass and surface area of the entire plant and of individual leaves. Through the establishment of critical N concentrations on the basis of these theories, some of the drawbacks hitherto experienced with plant analysis, such as difficulties in defining growth stage or plant part to sample, can be avoided. The aim of this thesis was to establish critical N concentrations for white cabbage (Brassica oleracea L. var. capitata L. f. alba D.C.) on the basis of these theories. Multi-N-rate and multi-harvest experiments were conducted in the field and in a climate chamber. The results showed that the critical N concentration declined at the same rate (-0.33) as the plant's leaf area ratio (leaf area divided by plant mass), which is in agreement with the 2/3-Power rule or "skin-core" hypothesis. The critical N concentration (% of DM) on a whole plant basis was estimated to 4.5 (W1.5 t ha-1), where W is weight per unit area of plant dry matter exclusive of roots. Moreover, it was concluded that the unshaded horizontally orientated leaves of cabbage can be used for leaf area based plant analysis of individual leaves. The critical N concentration of these leaves expressed on an area basis was found to be 3.7 g N m-2, while that for the whole plant N on a leaf area basis was 4.7 g N m-2. The ratio of these two critical concentrations, 0.8, was similar to the leaf N ratio (leaf N/whole plant N) of young plants before self shading occurs

Modelling the spatial behaviour of a tropical tuna purse seine fleet.

Industrial tuna fisheries operate in the Indian, Atlantic and Pacific Oceans, but concerns over sustainability and environmental impacts of these fisheries have resulted in increased scrutiny of how they are managed. An important but often overlooked factor in the success or failure of tuna fisheries management is the behaviour of fishers and fishing fleets. Uncertainty in how a fishing fleet will respond to management or other influences can be reduced by anticipating fleet behaviour, although to date there has been little research directed at understanding and anticipating the human dimension of tuna fisheries. The aim of this study was to address gaps in knowledge of the behaviour of tuna fleets, using the Indian Ocean tropical tuna purse seine fishery as a case study. We use statistical modelling to examine the factors that influence the spatial behaviour of the purse seine fleet at broad spatiotemporal scales. This analysis reveals very high consistency between years in the use of seasonal fishing grounds by the fleet, as well as a forcing influence of biophysical ocean conditions on the distribution of fishing effort. These findings suggest strong inertia in the spatial behaviour of the fleet, which has important implications for predicting the response of the fleet to natural events or management measures (e.g., spatial closures)

Specification-Driven Predictive Business Process Monitoring

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.Comment: This article significantly extends the previous work in https://doi.org/10.1007/978-3-319-91704-7_7 which has a technical report in arXiv:1804.00617. This article and the previous work have a coauthor in commo

Exploring disparities in acute myocardial infarction events between Aboriginal and non-Aboriginal Australians: roles of age, gender, geography and area-level disadvantage

We investigated disparities in rates of acute myocardial infarction (AMI) between Aboriginal and non-Aboriginal people in the 199 Statistical Local Areas (SLAs) in New South Wales, Australia. Using routinely collected and linked hospital and mortality data from 2002 to 2007, we developed multilevel Poisson regression models to estimate the relative rates of first AMI events in the study period accounting for area of residence. Rates of AMI in Aboriginal people were more than two times that in non-Aboriginal people, with the disparity greatest in more disadvantaged and remote areas. AMI rates in Aboriginal people varied significantly by SLA, as did the Aboriginal to non-Aboriginal rate ratio. We identified almost 30 priority areas for universal and targeted preventive interventions that had both high rates of AMI for Aboriginal people and large disparities in rates