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Rice is a staple food for more than 50% of the world population, therefore a sustainable sufficient rice production is a key strategic issue to ensure their food security. In line with this issue, many studies have been and are being carried out to improve the rice production. In this report, we describe the results of our study to apply a sustainable and clean technology to increase the total grain weight per plant of rice variety of MR 219. In this study, a 2-D clinostat was utilised to treat the rice seeds (variety MR 219) to enhance its total grain weight per plant. A 23 full factorial design was employed to investigate the effects of three different factors (rotation period, rotation speed and distance of the seeds from the center of clinostat) on the total grain weight per plant. The treated seeds were planted in an open area and the total grain weight per plant was measured after harvesting the plants. Analysis of variance, the F-test and the Student’s t-test at 95% of confidence level shows that the distance of seeds from the center of clinostat affects the total grain weight per plant significantly.

Extreme rainfall can have severe negative impacts possibly resulting in loss of life and property, hence the need for means of predicting the occurrence of extreme rainfall. One method that addresses extreme phenomena is the extreme value theory. In bivariate data, extreme value analysis can be approximated by the distribution of maximum likelihood estimation. This paper discusses the role of the copula in modeling the structure of dependencies between two rainfall variables, namely duration, and severity. Goodness-of-fit approaches are used to resolve whether or not to reject a parametric copula which then determines the best copula to utilise for the data set. In this paper the Akaike information criterion (AIC) is investigated for its ability to choose an appropriate copula model from Archimedean copula models. The analysis shows that the Frank copula is the best model to explain the dependency structure of the two variables discussed.

Adsorption of Zn(II) ions from aqueous solutions by thermally treated rice husk was investigated using factorial experimental design to study effects of heating temperature and period of rice husk, pH, initial Zn(II) ion concentration, adsorption temperature and contact time, and adsorbent dosage. Main and interaction effects of these factors were analyzed using statistical techniques and the results were analyzed statistically using the Student’s t-analysis and analysis of variance which were used to determine significant factors that affect the percentage removal of Zn(II). These significant factors were heating temperature of rice husk, pH, initial metal concentration, contact time, and adsorbent dosage. The interaction between two different effects also affects the percentage removal of Zn(II) ions. These include the interactions between heating temperature of rice husk and initial metal concentration, pH and initial metal concentration, and pH and the adsorbent dosage.