Compost is beneficial for agriculture fields in many ways such as soil conditioner, fertilizer, and natural pesticide and above all it helps to manage organic wastes and adds vital humic acids to soil. Four indigenous composts prepared from readily available organic wastes viz. vermicompost, banana, NADEP, and Calotropis were used in the present investigation for growth and disease suppression in mung beans. The composts were amended with Trichoderma viride in the concentration of 0.1 and 0.2% to determine their influence on length and weight of roots and shoots, disease incidence, soil moisture, and soil microflora in plants. The best results were observed in the treatment with T. viride (0.2%), followed by T. viride (0.1%) in vermicompost, while the treatment T. viride (0.1%) with Calotropis compost showed little growth and suppression of disease. All composts enhanced the soil moisture content and microbial populations in amended soil resulting in the reduction of disease incidence. Among T. viride enriched composts, the counts of fungi, bacteria, and actinomycetes were higher in the vermicompost and banana compost‐amended soils. Thus, preparing these composts from readily available organic wastes and amending soil with T. viride enriched composts hold a great promise for improving soil fertility and suppressing the soil‐borne plant pathogens for sustainable agriculture.
This work proposes a statistical model for crossover trials with multiple skewed responses measured in each period. A 3 × 3 crossover trial data where different doses of a drug were administered to subjects with a history of seasonal asthma rhinitis to grass pollen is used for motivation. In each period, gene expression values for ten genes were measured from each subject. It considers a linear mixed effect model with skew normally distributed random effect or random error term to model the asymmetric responses in the crossover trials. The paper examines cases (i) when a random effect follows a skew-normal distribution, as well as (ii) when a random error follows a skew-normal distribution. The EM algorithm is used in both cases to compute maximum likelihood estimates of parameters. Simulations and crossover data from the gene expression study illustrate the proposed approach.
For gene expression data measured in a crossover trial, a multivariate mixed effects model seems to be most appropriate. Standard statistical inference fails to provide reliable results when some responses are missing. Particularly for crossover studies, missingness is a serious concern as the trial requires a small number of participants. A Monte Carlo EM (MCEM) based technique has been adopted to deal with this situation. Along with estimation, a MCEM likelihood ratio test (LRTs) is developed for testing the fixed effects in such a multivariate crossover model with missing data. Intensive simulation studies have been carried out prior to the analysis of the gene expression data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.