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.     

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.     

Factors affecting the health of residents in China: A perspective based on the living environment

Residents’ health is an important factor affecting social development and harmony. Based on 2010 China Family Panel Studies data of the Institute of Social Science Survey, Peking University and using a multi-classification logit regression model, we analyze the factors that affect the health status of residents in China. These factors include environmental pollution, which is a particularly important factor. Our study found that the impacts of residents’ characteristic variables, external living environment, and living habits vary. As residents age, their health status deteriorates. For the General, Less healthy, and Unhealthy groups, an income of less than CNY 10,000 significantly affects health status; however, when their income is greater than CNY 10,000, it no longer has a significant effect. For the Very unhealthy group, this particular threshold value is CNY 3000. At least one of urban–rural classification and residence registration status is significant, indicating that the urban–rural dual structure as well as the household registration system significantly affects residents’ health status. However, the direction of this effect is uncertain. Cooking water significantly affects the Less healthy and Unhealthy groups, and tap water is more conducive to health. Polluting enterprises within a radius of five kilometers mainly affect the Unhealthy group, but the direction of its impact is contrary to expectations. Smoking and drinking significantly affect the health status of the General, Less healthy, and Unhealthy groups. However, the direction of their impact was contrary to expectations. For the Very unhealthy group, drinking has a significant impact on residents’ health status, but the direction of the impact was again the opposite of what we expected. Smoking has no significant impact on the health status of this group. Exercise significantly affects the Less healthy and Unhealthy groups, but its influence has no obvious trend. Our study shows that living habits have a smaller influence on residents’ health status.