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CO is known as a major attractant for many arthropod pests which can be exploited for pest control within novel attract-and-kill strategies. This study reports on the development of a slow-release system for CO based on calcium alginate beads containing granular corn starch, amyloglucosidase and Saccharomyces cerevisiae. Our aim was to evaluate the conditions which influence the CO release and to clarify the biochemical reactions taking place within the beads. The amyloglucosidase was immobilized with a high encapsulation efficiency of 87% in Ca-alginate beads supplemented with corn starch and S. cerevisiae biomass. The CO release from the beads was shown to be significantly affected by the concentration of amyloglucosidase and corn starch within the beads as well as by the incubation temperature. Beads prepared with 0.1 amyloglucosidase units/g matrix solution led to a long-lasting CO emission at temperatures between 6 and 25 °C. Starch degradation data correlated well with the CO release from beads during incubation and scanning electron microscopy micrographs visualized the degradation of corn starch granules by the co-encapsulated amyloglucosidase. By implementing MALDI-ToF mass spectrometry imaging for the analysis of Ca-alginate beads, we verified that the encapsulated amyloglucosidase converts starch into glucose which is immediately consumed by S. cerevisiae cells. When applied into the soil, the beads increased the CO concentration in soil significantly. Finally, we demonstrated that dried beads showed a CO production in soil comparable to the moist beads. The long-lasting CO-releasing beads will pave the way towards novel attract-and-kill strategies in pest control.

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The present work describes a new computer-assisted image analysis method for the rapid, simple, objective and reproducible quantification of actively discharged fungal spores which can serve as a manual for laboratories working in this context. The method can be used with conventional laboratory equipment by using bright field microscopes, standard scanners and the open-source software ImageJ. Compared to other conidia quantification methods by computer-assisted image analysis, the presented method bears a higher potential to be applied for large-scale sample quantities. The key to make quantification faster is the calculation of the linear relationship between the gray value and the automatically counted number of conidia that has only to be performed once in the beginning of analysis. Afterwards, the gray value is used as single parameter for quantification. The fast, easy and objective determination of sporulation capacity enables facilitated quality control of fungal formulations designed for biological pest control. Rapid, simple, objective and reproducible quantification of fungal sporulation suitable for large-scale sample quantities. Requires conventional laboratory equipment and open-source software without technical or computational expertise. The number of automatically counted conidia can be correlated with the gray value and after initial calculation of a linear fit, the gray value can be applied as single quantification parameter.