No abstract

The present paper deals with the estimation of population mean of the study variable using the linear combination of known population values of coefficient of skewness and quartile deviation of auxiliary variable. Two modified ratio estimators for estimation of population mean of the study variable involving the above linear combinations are being used. Mean squared errors and biases up to the first degree of approximation are derived and compared with the proposed modified ratio estimators. The proposed modified ratio estimators perform better than the existing ratio estimators. The empirical study has been carried out in support of the results.

Thirty maize in breds were evaluated for twelve traits and the experiment was laid out in a randomized block design with three replications. Analysis of variance showed significant difference for all the characters. Higher estimates of genotypic and phenotypic coefficient of variation were registered for anthesis-silking interval followed by cob height. All the characters showed marked differences between GCV and PCV estimates indicating that substantial variation for these characters was contributed by environmental components. The heritability estimates were found to be very high for plant height, 100 seed weight, cob length, days to tasseling, cob diameter, days to silking, grain yield hectare -1 and kernel rows cob -1 . High genetic advance was recorded for plant height. Grain yield per hectare exhibited high significant positive correlation with cob diameter, cob height, 100 seed weight, number of kernels row -1 , plant height and kernels cob -1 . The significant positive correlation of these traits indicates the importance of these traits in selection for yield. This study gave an insight into the variability pattern of the inbred lines which will be helpful in their further utilization.

Abstract:In this paper, we have proposed a class of modified ratio type variance estimator for estimation of population variance of the study variable, when Tri Mean and Quartile average of the auxiliary variable are known. The bias and mean square error (MSE) of the proposed estimator are obtained. From the numerical study it is observed that the proposed estimator performs better than the existing estimators in the literature.

No abstract

In the present study we have proposed an improved family of estimators for estimation of population mean using the auxiliary information of median, quartile deviation, Gini's mean difference, Downton's Method, Probability Weighted Moments and their linear combinations with correlation coefficient and coefficient of variation. The performance of the proposed family of estimators is analysed by mean square error and bias and compared with the existing estimators in the literature. By this comparison we conclude that our proposed family of estimators is more efficient than the existing estimators. To support the theoretical results, we also provide the empirical study.

Innovative techniques and technologies are being developed at different research stations for the enhancement of apple production. But due to poor extension contact with credible agencies, the same technology has not been fully utilised by the growers at their farms. The study was conducted in three districts of Kashmir division selected purposively with multistage sampling; having maximum area under apple cultivation. The study used focus group discussions, key informant interviews, and a household survey. The majority of the apple growers from district Shopian (55.44%) and district Baramulla (48.67%) were having medium level of extension contact with different extension agencies in contrast to district Budgam, where majority of the apple growers were having low level of extension contact. However, majority of the apple growers from district Shopian (61.39%) contacted private agencies for consultation, while high level of contact (48.81% of apple growers) with private agencies was found among the apple growers of district Budgam.

In this paper Rank set sampling (RSS) is introduced with a view of increasing the efficiency of estimates of Simple regression model. Regression model is considered with respect to samples taken from sampling techniques like Simple random sampling (SRS), Systematic sampling (SYS) and Rank set sampling (RSS). It is found that R 2 and Adj R 2 obtained from regression model based on Rank set sample is higher than rest of two sampling schemes. Similarly Root mean square error, p-values, coefficient of variation are much lower in Rank set based regression model, also under validation technique (Jackknifing) there is consistency in the measure of R 2 , Adj R 2 and RMSE in case of RSS as compared to SRS and SYS. Results are supported with an empirical study involving a real data set generated of Pinus Wallichiana taken from block Langate of district Kupwara.