Fusarium species are the most destructive phytopathogenic and toxin-producing fungi, causing serious diseases in almost all economically important plants. Sporulation is an essential part of the life cycle of Fusarium. Fusarium most frequently produces three different types of asexual spores, i.e., macroconidia, chlamydospores, and microconidia. It also produces meiotic spores, but fewer than 20% of Fusaria have a known sexual cycle. Therefore, the asexual spores of the Fusarium species play an important role in their propagation and infection. This review places special emphasis on current developments in artificial anti-sporulation techniques as well as features of Fusarium’s asexual sporulation regulation, such as temperature, light, pH, host tissue, and nutrients. This description of sporulation regulation aspects and artificial anti-sporulation strategies will help to shed light on the ways to effectively control Fusarium diseases by inhibiting the production of spores, which eventually improves the production of food plants.

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This article deals with the objective Bayesian analysis of random censorship model with informative censoring using Weibull distribution. The objective Bayesian analysis has a long history from Bayes and Laplace through Jeffreys and is reaching the level of sophistication gradually. The reference prior method of Bernardo is a nice attempt in this direction. The reference prior method is based on the Kullback-Leibler divergence between the prior and the corresponding posterior distribution and easy to implement when the information matrix exists in closed-form. We apply this method to Weibull random censorship model and compare it with Jeffreys and maximum likelihood methods. It is observed that the closed-form expressions for the Bayes estimators are not possible; we use importance sampling technique to obtain the approximate Bayes estimates. The behaviour of maximum likelihood and Bayes estimators is observed via extensive numerical simulation. The proposed methodology is used for the analysis of a real-life data for illustration and appropriateness of the model is tested by Henze goodness-of-fit test.