An early release system developed for Nesidiocoris tenuis Reuter (Heteroptera: Miridae) could provide a good control of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in tomato. Tuta absoluta and the whitefly Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) often appear simultaneously in tomato crops and this might affect control capacity. Therefore, the new approach needs to be tested in a situation with both pests present. In addition, Bacillus thuringiensis Berliner and Trichogramma achaeae Nagaraja & Nagarkatti (Hymenoptera: Trichogrammatidae) have been shown to be effective against T. absoluta and could be a supplement to N. tenuis. Two experiments were carried out to evaluate the potential of this approach and its combination with supplementary control agents against T. absoluta. In the first experiment four treatments were compared (T. absoluta, B. tabaci, T. absoluta + N. tenuis, and T. absoluta + B. tabaci + N. tenuis) and N. tenuis was able to control T. absoluta and B. tabaci either alone or together. In the second experiment, five treatments were compared: T. absoluta, T. absoluta + N. tenuis, T. absoluta + N. tenuis + T. achaeae, T. absoluta + N. tenuis + B. thuringiensis, and T. absoluta + N. tenuis + T. achaeae + B. thuringiensis. Nesidiocoris tenuis again proved capable of significantly reducing T. absoluta populations, and the implementation of additional agents did not increase its effectiveness.
The predatory mite Amblyseius swirskii quickly became one of the most successful biocontrol agents in protected cultivation after its introduction into the market in 2005 and is now released in more than 50 countries. There are several key factors contributing to this success: (1) it can control several major pests including the western flower thrips, Frankliniella occidentalis, the whiteflies Bemisia tabaci and Trialeurodes vaporariorum and the broad mite, Polyphagotarsonemus latus, simultaneously in vegetables and ornamental crops; (2) it can develop and reproduce feeding on non-prey food sources such as pollen, which allows populations of the predator to build up on plants before the pests are present and to persist in the crop during periods when prey is scarce or absent; and (3) it can be easily reared on factitious prey, which allows economic mass production. However, despite the fact that A. swirskii provides growers with a robust control method, external demands were initially a key factor in promoting the use of this predator, particularly in Spain. In 2006, when exports of fresh vegetables from Spain were stopped due to the presence of pesticide residues, growers were forced to look for alternatives to chemical control. This resulted in the massive adoption of biological control-based integrated pest management programmes based on the use of A. swirskii in sweet pepper. Biological control increased from 5 % in 2005, 1 year before A. swirskii was commercially released, to almost 100 % of a total 6,000 ha of protected sweet pepper in Spain within 3 years. Later, it was demonstrated that A. swirskii was equally effective in other crops and countries, resulting in extensive worldwide use of A. swirskii in greenhouses.
Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1,536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.
One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3,229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BN) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analyzed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.
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