Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Agricultural Price Forecasting using Decomposition-based Hybrid Model

Abstract: Agricultural price information needs for decision-making at all levels are increasing due to globalization and market integration. Due to its great reliance on biological processes, agricultural price forecasting is one of the most difficult fields of time series analysis. In this paper, a neural network model based on empirical mode decomposition is used to forecast potato prices. The monthly wholesale price series of potato from Chennai market was decomposed into five independent intrinsic modes (IMFs) and one residual with various frequencies. Then, to forecast these IMFs and residual components independently, an artificial neural network with a single hidden layer was built. Finally, the ensemble output for the original price series is formed by aggregating the forecast outcomes of all IMFs, including residuals. In terms of root mean square error and directional prediction statistics, empirical data show that the suggested ensemble model outperforms a single model.

Construction of resolvable incomplete block designs for estimating main effects with full efficiency

Abstract: Resolvable incomplete block designs are often used in many asymmetrical and symmetrical factorial experiments. In this article, two new methods of constructions are proposed to obtain resolvable in...

Randomized Response Techniques

Abstract: Generally, in surveys the randomization is done for the selection of the sample from a population for the estimation of some population parameters like population mean or total or proportion, ratios like birth or death rate, population correlation coefficient or regression coefficient, etc. The sampled units obtained through the randomization are approached, interviewed, and the required information is obtained. It is assumed that the reporting is truthful, without bias. The estimate of the population parameter of interest is obtained along with the sampling variance.

In silico Analysis of Synonymous Codon Usage Pattern of Rhizobium etli CFN 42

Abstract: Understanding the extent and pattern of codon bias and the forces affecting codon usage are the key steps towards elucidating choice of codons at the level of individual genes. In the present study, codon usage pattern among 3,703 genes of nitrogen fixing bacterium, Rhizobium etli CFN 42 was analyzed. The study aims to identify the factors responsible for codon usage bias and highly expressed genes of this bacterium. Multivariate statistical analysis revealed a single major explanatory axis accounting for codon usage variation among the genes. Correlation analysis suggested that the axis has strong significant positive correlation with GC3s i.e. GC content at the third codon position. A significant negative correlation between effective number of codons and GC3s content was observed suggesting that the codon usage was affected by gene nucleotide composition. These findings suggested mutational role as the major factor in shaping codon usage bias among the genes. Further, correspondence analysis of Relative Synonymous Codon Usage revealed cluster of highly expressed genes. Notably, 26 codons were determined as the ‘optimal codons’ that were significantly more frequent among the highly expressed genes tested by χ2 test (P < 0.01). Such results may add value to the efforts of developing bio-fertilizers based on symbiotic or non-symbiotic bacteria for improving soil fertility.