Despite considerable effort to understand and represent decision making by farmers, there has been little attempt to integrate socio‐economic, psychological and farming variables within a comprehensive framework. This paper attempts to do this in the context of two types of farming behaviour ‐ business and environmental ‐for a sample of over 200 Scottish farmers. Using variables covering their attitudes, objectives and behaviours relevant to many aspects of farming, we proposed mediating variables models of business‐oriented and environmentally‐oriented farming behaviours. Structural equation modelling was used to test the adequacy of the proposed models. The results of the study emphasise the importance of psychological factors in the decision making of farmers.

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Strategies for controlling plant epidemics are investigated by fitting continuous time spatiotemporal stochastic models to data consisting of maps of disease incidence observed at discrete times. Markov chain Monte Carlo methods are used for fitting two such models to data describing the spread of citrus tristeza virus (CTV) in an orchard. The approach overcomes some of the difficulties encountered when fitting stochastic models to infrequent observations of a continuous process. The results of the analysis cast doubt on the effectiveness of a strategy identified from a previous spatial analysis of the CTV data. Extensions of the approaches to more general models and other problems are also considered.

Abstract-Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyze the response from a histogram of either photon counts or integrated intensities to assess the number, positions, and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterization of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework, which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far-range depth imaging.

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The spread of Huanglongbing through citrus groves is used as a case study for modeling an emerging epidemic in the presence of a control. Specifically, the spread of the disease is modeled as a susceptible-exposed-infectious-detected-removed epidemic, where the exposure and infectious times are not observed, detection times are censored, removal times are known, and the disease is spreading through a heterogeneous host population with trees of different age and susceptibility. We show that it is possible to characterize the disease transmission process under these conditions. Two innovations in our work are (i) accounting for control measures via time dependence of the infectious process and (ii) including seasonal and host age effects in the model of the latent period. By estimating parameters in different subregions of a large commercially cultivated orchard, we establish a temporal pattern of invasion, host age dependence of the dispersal parameters, and a close to linear relationship between primary and secondary infectious rates. The model can be used to simulate Huanglongbing epidemics to assess economic costs and potential benefits of putative control scenarios.