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.

Use of ordinal logistic regression in crop yield forecasting

Abstract: The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.

Nonlinear Support Vector Regression Model Selection Using Particle Swarm Optimization Algorithm

Abstract: Nonlinear support vector regression (SVR) model is thoroughly studied. Particle swarm optimization, which is a very efficient population-based stochastic optimization algorithm, is employed to estimate the optimal hyper-parameters. A computer program is written in MATLAB function (m file) and the same is appended as an “Appendix”. STATISTICA and MATLAB software packages are used for data analysis and superiority of Nonlinear SVR model over artificial neural network and multiple linear regression models is shown for the data under consideration.

De novo transcriptome sequencing assisted identification of terpene synthases from black pepper (Piper nigrum) berry.

Abstract: Though the volatile profiles of black pepper have been reported already, the information on terpene synthase family genes is not known. In this study, using a combinatorial approach, the berry hybrid transcriptome assembly of llumina and nanopore sequencing, the entire terpene synthase family responsible for the biosynthesis of the flavor-imparting volatiles in black pepper berries was profiled. The profile shows 98 terpene synthases from various terpene synthesis pathways. Three important monoterpene synthases were also validated by targeted amplification, sequencing and homology modeling. This study provides the first of its kind information on the terpene synthase family profile in Piper nigrum, which is potentially a major step for further characterization of the functional terpene synthase genes in black pepper.

Balanced and Unbalanced Ranked Set Sampling

Abstract: In many studies, it is observed that the measurement of the units depends upon a host of extraneous factors like difficulty of reaching the sampling unit, costs involved, destruction of the sampling unit (especially when repeated measurements are required in the sample), etc. McIntyre (1952) proposed a sampling method to provide the precise estimates for the grass and pasture yield in Australia.