Optimum Stratification for Estimation of Mango Production in Himachal Pradesh
Ajay Rattan
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India.
P. K. Mahajan
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India.
R. K. Gupta
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India.
Ashu Chandel
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India
Sarita Devi
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India.
Smriti Bansal *
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India.
T. V. Vinay
Department of Basic Sciences, Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh-173230, India.
*Author to whom correspondence should be addressed.
Abstract
Mango plays an important role in socio-economic transformation of rural masses in the state. Proper assessment of area and production estimate is a prerequisite for effective horticulture planning Optimum stratification brings gain in precision in estimation of a characteristic of the population with limited time, money and human power. The primary data of area and production of mango of 325 mango orchardists of Himachal Pradesh were collected through well designed survey. The area under mango, auxiliary variable, was then subject to stratification in order to stratify the mango production, study variable. Four stratification methods 1) Equalization of Strata Total 2) Equalization of cumulative \(\sqrt{f(y) 3}\) 3) Equalization of cumulative \(\sqrt[3]{f(y)}\) and 4) Equalization of cumulative1/2[r(y)+f(y)] were used for stratification of area under mango production into varying number of strata L = 3, 4, 5, 6. From each strata a SRSWOR sample was drawn of size ni which was allocated by using proportional and Neyman allocation. After that estimate of mean and variance were computed for varying number of strata and for varying number of sample sizes n = 60, 90 and 120 allocated by proportional and Neyman allocation under these four stratification rules. The gain in precisions was also computed and is presented. It was found that \(\sqrt[3]{f(y)}\) for L = 6 and n = 120 yield minimum variance and maximum gain in precision. This showed that optimum stratification will lead efficient estimates and can be used for estimation of production in the state.
Keywords: Stratification, stratified random sampling, estimation of mean and variance, mango production, optimum strata boundaries, neyman allocation, proportional allocation