A carbendazim resistant Trichoderma harzianum strain M1, inhibitory to the growth of the damping-off pathogen Pythium aphanidermatum was used for the development of new carrier formulations. Seven different formulations (talc, lignite, lignite + fly ash-based powder formulation, wettable powder, bentonite paste, polyethylene glycol-paste and gelatin-glycerin-gel) were developed for seed treatment. Shelf life of the formulations was evaluated under storage at 248C up to nine months. The population of propagules was optimum in all the formulations up to three months of storage. Seed treatment with Trichoderma formulations reduced the incidence of damping-off disease of tomato up to 74% and also enhanced the plant biomass under greenhouse and field conditions. Active colonization of T. harzianum in the rhizosphere of tomato plants was observed following seed treatment with the formulations.
Bacterial antagonists of Phytophthora capsici were isolated from underground shoot portions of rooted cuttings of black pepper. Initially isolates were screened by dual culture on potato dextrose agar and carrot agar. Further, a screening was done on black pepper shoots for supression of lesion caused by the pathogen. Most of the antagonists showed varying levels of antagonism in the dual culture and the shoot assay. Isolate PN-026, showing the highest suppression of lesion development in the shoot assay was found to be the most efficient antagonist in reducing Phytophthora capsici induced nursery wilt of black pepper. This screening involving the host, pathogen, and the antagonist, performed on black pepper shoot (the planting material for this vegetatively propagated crop), could be used as a rapid and reliable method for the isolation of efficient bacterial antagonists of P. capsici.
In this paper we use the concepts of information theory to analyze the time series obtained from complex systems. The procedure discussed here can be applied to quantify the regularity of chaotic time series, although it might not certify chaos. The main idea is to map the time series into a finite sequence of symbols using an efficient partitioning technique, and quantify the regularity of the resulting sequence by a chosen complexity measure. A proper partitioning technique is essential for any meaningful analysis of the resulting sequence. We have used a clustering technique to partition the time series into a finite sequence and the Lempel–Ziv complexity measure to quantify the regularity of this sequence.
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