Effect of pea bacterial blight (Pseudomonas syringae pv. pisi) on the yield of spring sown combining peas (Pisum sativum)

Field trials to examine the effect of pea bacterial blight (Pseudomonas syringae pv. pisi) (Psp) on the yield of combining peas were carried out at five sites (HRI Wellesbourne, ADAS Rosemaund, ADAS Terrington, PGRO, SASA East Craigs) in the UK in 1990, 1991 and 1992. Healthy seed, cv. Solara, and seed naturally infected with Psp Race 2 was sown in large plots (c. 200 m²) in the open or under nets to prevent bird damage by pigeons. Despite relatively low disease severity levels (< 15% leaf area) and separation by at least 12 m of cv. Consort (resistant to Race 2) between plots there was considerable spread of disease into plots sown with healthy seed. Regression analysis demonstrated a significant relationship between yield and disease. Of the disease measurements examined, disease severity on the leaves (stipules) at growth stage 208 was found to be the best predictor of yield. A model containing environmental and bird damage terms, in addition to disease, suggested that a yield loss of 0.98 t/ha would be expected for a disease severity score of 1, equivalent to 5% leaf area affected, at growth stage 208.     

The use of novel phenotyping methods for validation of equine conformation scoring results

In this experiment, which is based on a cohort of 44 Lipizzan mares from the Austrian state stud farm of Piber, we present new statistical techniques for the analysis of shape and equine conformation using image data. In addition, we examined which strategies and procedures of image processing techniques led to a successful interpretation of the traits implemented in horse breeding programs. A total of 246 two-dimensional anatomical and somatometric landmarks were digitized from standardized photographs, and the variation of shape has been analyzed by the use of generalized orthogonal least-squares Procrustes (generalized Procrustes analysis (GPA)) procedures. The resulting shape variables have been regressed on the results from linear type trait classifications. In addition, the rating scores of six conformation classifiers were tested for agreement, yielding an inter-rater correlation (inter-class correlation) ranging from 0.41 to 0.68, respectively, a κ coefficient ranging from 0.16 to 0.53. From the 12 linear type traits assessed on a valuating scale, only the type-related traits (type, breed-type and harmony) revealed significant (P<0.05) results in the regression analysis of shape variables on linear type traits. The other nine traits were characterized by a lower agreement between classifiers and did not result in a significant ‘shape regression’. Finally, the ‘horse shape space’ defined by shape variables resulting from GPA procedures offered the possibility to assist in trait definition and in the evaluation of ratings, and it is an adequate biological and objective scale to human perception of conformation, which is expressed in numerical data only.     

Analysis of Rates Using a Generalized Poisson Regression Model

In this paper a generalization of the Poisson regression model indexed by a shape parameter is proposed for the analysis of life table and follow-up data with concomitant variables. The model is suitable for analysis of extra-Poisson variation data. The model is used to fit the survival data given in Holford (1980). The model parameters, the hazard and survival functions are estimated by the method of maximum likelihood. The results obtained from this study seem to be comparable to those obtained by Chen (1988). Approximate tests of the dispersion and goodness-of-fit of the data to the model are also discussed.     

Seemingly Unrelated Generalized Linear Models

By using deviance standardized residuals, the seemingly unrelated regression estimation procedure is extended to generalized linear models, and fitted by an iterative procedure. The matrix of cross products of standardized residuals is asymptotically multivariate normal, and can be used for further multivariate analyses and for hypothesis testing.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Unravelling the coordination between leaf and stem economics spectra through local and global scale approaches

The existence of a coordination between leaf and stem economic spectra in woody species has been postulated repeatedly in the literature, with contrasting results. Here, we postulated that this coordination is conditioned by climate factors, being stronger in stressful environments. To test this hypothesis we explored the coordination between leaf and stem economic spectra in a seasonally dry forest in central Argentina and at the global scale, we analysed if the outcome of their coordination varies along a climatic gradient. At the local scale, we characterized leaf and stem economic spectra in 37 woody species by measuring six leaf and stem functional traits related to resource acquisition and use, and two functional traits used as proxies of water transport and use capacities. At the global scale, a meta‐regression was performed to analyse if the outcome of the coordination among leaf and stem traits varies along gradients of the mean precipitation of the driest quarter and of the minimum temperature of the coldest month. At the local scale, we observed a high integration among the measured leaf and stem traits, and this coordination seemed to be linked to hydraulic properties. At the global scale, we found not only that the overall weighted mean effect size of the correlation between specific leaf area and wood density was significant and negative but also that the coordination between leaf and stem traits seemed to be shaped by climate and tends to become stronger under harsh climate conditions. Furthermore, although our results seem to suggest that their coordination is context‐dependent, alternative strategies could be observed under stressful conditions.