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.

Study of Growth Pattern of Cattle Under Different Climatic Conditions

Abstract: Different sigmoidal nonlinear growth models were fitted in the growth data of triple cross (Friesian×Sahiwal×Hariana) breed and double cross (Friesian×Sahiwal) breed at Agra and Dehradun farms which have different climatic conditions. On the basis of minimum RMSE, the Gompertz model was found to be the best fit. The estimates of growth rates and maturity weights of Friesian×Sahiwal×Hariana breed were considered better under homoscedastic and heteroscedastic error conditions at the Agra farm. Similarly, the growth rates and maturity weights of Friesian×Sahiwal breed were better at the Agra farm, as compared to the Dehradun farm. Therefore, it may be concluded that the climate of Agra (plain area) was more suitable for both the breeds for attaining the maturity weight, as compared to Dehradun (hilly area).

Combinatorial Equivalence of Fractional Factorial Designs

Abstract: Two symmetrical fractional factorial designs are said to be combinatorially equivalent if one design can be obtained from another by reordering the runs, relabeling the factors and relabeling the levels of one or more factors. This article presents concepts of ordered distance frequency matrix, distance frequency vector, and reduced distance frequency vector for a design. Necessary conditions for two designs to be combinatorial equivalent based on these concepts are presented. A new algorithm based on the results obtained is proposed to check combinatorial non-equivalence of two factorial designs and several illustrating examples are provided.

Multi-Sample Slippage Test for Ordered Observations

Abstract: A multi-sample slippage test based on ordered observations has been given. The test statistic is based on the sum of ranks of the sample. The probability distribution of the test statistic has been worked out for small sample and it turns out to be chi-square distribution for large sample. The analytical procedure has been explained by a numerical example.

Distinctness, Uniformity and Stability Testing in Maize Inbreds

Abstract: A set of 21 maize inbreds were evaluated for 29 traits for studying the Distinctness, Uniformity and Stability parameters as per the guidelines issued by the Protection of Plant Varieties and Farmers Rights Authority (PPV&FRA), India. Out of 29 traits, five traits were monomorphic, 18 traits were dimorphic, while six traits were polymorphic. The studied quantitative traits were independent and their interaction with environment as well as themselves were negligible as revealed by modified joint regression analysis. The combine over year analysis revealed that five inbreds were completely uniform, for all the MS or MG traits, while rest of the inbreds were non-uniform for 1–4 traits, but within the acceptable limit of uniformity test as per PPV&FRA guideline. Combine over year distinct analysis revealed that all the studied inbreds were found distinct to each other except VQL 1 and CM 212, which showed non-distinctiveness to each other but showed distinctiveness to other 19 inbreds. The present data can be used to develop database for inbreds and also safeguard the ownership issue.