Genetic diversity in modern T. Aman Rice varieties of Bangladesh (Oryza sativa L.)

A field experiment was conducted from June to December, 2013 to study the genetic diversity of 15 modern T. Aman rice varieties of Bangladesh (Oryza sativa L.) with a view to assess the superior genotype in future hybridization program for developing new rice varieties that is suitable for the target environment. Analysis of variance for each trait showed significant differences among the varieties. High heritability associated with high genetic advance in percent of mean was observed for plant height and thousand seed weight which indicated that selection for these characters would be effective. Hence, thrust has to be given for these characters in future breeding program to improve the yield trait in rice. Multivariate analysis based on 10 agronomic characters indicated that the 15 varieties were grouped into four distant clusters. The inter cluster distance was maximum between cluster II and cluster IV. The highest intra-cluster distance was found in cluster IV. Based on positive value of vector 1 and vector 2, plant height and 1000-seed weight had maximum contribution towards genetic divergence. From the results, it can be concluded that the varieties BRRI dhan40, BRRI dhan44, BRRI dhan46, BRRI dhan49 and BINA dhan7 may be selected for future hybridization program

Forecasting of boro rice production in Bangladesh: An ARIMA approach

The study was undertaken to examine the best fitted ARIMA model that could be used to make efficient forecast boro rice production in Bangladesh from 2008-09 to 2012-13. It appeared from the study that local, modern and total boro time series are 1st order homogenous stationary. It is found from the study that the ARIMA (0,1,0) ARIMA (0,1,3) and ARIMA (0,1,2) are the best for local, modern and total boro rice production respectively. It is observed from the analysis that short term forecasts are more efficient for ARIMA models. The production uncertainty of boro rice can be minimizing if production can be forecasted well and necessary steps can be taken against losses. The government and producer as well use ARIMA methods to forecast future production more accurately in the short run