In this paper, estimation of survey precision for nonlinear estimators is considered and an equation is presented based on sampling and nonsampling variances. Furthermore, by considering the response error model in surveys and the estimators of variance components, some relations are presented to computation of survey precision for nonlinear estimators. As an application case for the results, two typical data sets are considered and survey precisions of a nonlinear estimator are computed in the both data sets.

Ticks are important ectoparasites in equids which causes economic losses in animal husbandry of Iran and worldwide. This study was aimed to determine frequency and species diversity of hard ticks in equids in Turkman sahra, Iran. 240 equids were randomly selected and examined from 2018-2019. Ixodid ticks were collected from body surface of examined animals and identified. Of all examined equids, 37.91% horses, and 4.16% donkeys were infested with a total number of 357 ixodid ticks. Tick indices (tick number per animal) were 3.53.  The highest prevalence of hard ticks was found in ≥3 years-old equids in the region. There was significant difference between prevalence and different age groups of infested animals. Of all examined ticks (357), three genera including Hyalomma spp., Rhipicephalus spp. and Dermacentor spp. with four species. i.e. Hyalomma anatolicum (65.26%), H. marginatum (10.64%), Rhipicephalus bursa (22.12%) and Dermacentor marginatus (1.96%) were identified. Ixodid ticks’ infestations occured throughout the year with the highest prevalence in spring. There was significant difference between prevalence of hard ticks and seasons in this study. The results revealed that species diversity and ixodid ticks’ infestations were prevalent in the equids of Turkman sahra, Iran.

Survey implementation is one of the common methods to data collection. To determine quality of survey results, survey errors should be studied. Response error is an essential part of nonsampling errors and modeling of response error has many applications. One of the applications is to estimate precision of survey results. In this paper, two methods of variance components estimation of the response error model is studied and compared. In this purpose, estimators of variance components is computed by Maximum Likelihood and ANOVA methods. As an application of the presented maximum likelihood estimators, variance components is estimated for some data sets and optimization is applied to assess validity of the proposed estimators. These calculations show that nonlinear optimization confirms the presented maximum likelihood estimators, in all of the data sets. Furthermore, Mean Squares Error criterion is applied to compare the presented maximum likelihood estimators with ANOVA estimators of variance components of the response error model. In the other words, variance and bias of the estimators is calculated to compare two methods of estimation.