Olfactory attractiveness of mixtures of some host plant and conspecific semiochemicals to the banana weevil Cosmopolites sordidus (Germar, 1824)

A simple "Y" shaped olfactometer was used in laboratory studies on the olfactory attractiveness of mixtures in various proportions of industrial analogues of some host plant and conspecific-based semiochemicals, or their combinations with banana rhizome, to the banana weevil. The aim was to identify factors that influence their attractiveness to the weevil, and consider the possibility for their use as lures for trapping the weevil in the field. Cosmopolites sordidus was attracted to the mixtures at specific concentrations and proportions of constituent chemicals. 6-methylhept-5-en-2-one was only attractive on its own at 1 µl/100 ml and in mixture with 4- mercaptophenol, but not at 10 µl, 0.01 µl, or in combination with banana rhizome. 4-mercaptohpenol and 2-n-butylfuran, which were compatible with most host plant-based chemicals and were attractive as a mixture, were perceived to be key elements in the composition of attractants to the weevil. It was concluded that in addition to the composition, other factors that may determine the attractiveness or otherwise of a mixture to C. sordidus are the proportions and concentrations of the constituent chemicals.

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.