Genetic parameters for worm resistance in Santa Inês sheep using the Bayesian animal model

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses.Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model.Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model).Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

Assessing the Pollution Level in the Kuban River Basin by Multivariate Cluster Analysis

Considering features of hydrological conditions for hydro-chemical system, this paper analyses the performance of the hydro-ecological status of the Kuban river basin.. The results of the study on water chemical composition depending on the distance from the source are presented. By comparing the results with the reference values of water quality, increased aluminium, zinc, and copper content was established. Respective dendrograms of hydro-ecological studies obtained according to performed analysis for the Kuban River and its tributaries are presented. The relevance of the findings received is p<0.0005 and the correlation coefficient corresponds to 0.935...1. The results of multivariate cluster analysis showed that the Kuban basin has an increased content of particular heavy metals such as aluminium, copper, and zinc.

Preventing interview falsifications during fieldwork in the Survey of Health, Ageing and Retirement in Europe (SHARE)

The fabrication of an entire interview, is a rare event in the Survey of Health, Ageing and Retirement in Europe (SHARE) but can nevertheless lead to negative consequences regarding the panel sample, such as a loss in sample size or the need for time-consuming data corrections of information collected in previous waves. The work presented in this article started with the discovery of a case of interviewer fabrication after fieldwork for the sixth wave of SHARE was completed. As a consequence, we developed a technical procedure to identify interview fabrication and deal with it during ongoing fieldwork in the seventh wave. Unlike previous work that often used small experimental datasets and/or only a few variables to identify fake interviews, we implemented a more complex approach with a multivariate cluster analysis using many indicators from the available CAPI data and paradata. Analyses with the known outcome (interview fabrication or not) in wave 6 revealed that we were able to correctly identify a large number of the truly faked interviews while keeping the rate of ‘false alarms’ rather low. With these promising results, we started using the same script during the fieldwork for wave 7. We provided the survey agencies with information for targeted (instead of random) back checks to increase the likelihood of confirming our initial suspicion. The results show that only a very small number of interview fabrications could be unequivocally identified.

Spatial Multivariate Cluster Analysis for Defining Target Population of Environments in West Africa for Yam Breeding

Yam (Dioscorea spp.) is a major staple crop with high agricultural and cultural significance for over 300 million people in West Africa. Despite its importance, productivity is miserably low. A better understanding of the environmental context in the region is essential to unlock the crop's potential for food security and wealth creation. The article aims to characterize the production environments into homologous mega-environments, having operational significance for breeding research. Principal component analysis (PCA) was performed separately on environmental data related to climate, soil, topography, and vegetation. Significant PCA layers were used in spatial multivariate cluster analysis. Seven clusters were identified for West Africa; four were country-specific; the rest were region-wide in extent. Clustering results are valuable inputs to optimize yam varietal selection and testing within and across the countries in West Africa. The impact of breeding research on poverty reduction and problems of market accessibility in yam production zones were highlighted.

How the calving order of cows affects the performance of Nellore calves

This study aimed to verify the effect of age of dam on the performance of male and female Nellore calves, using the following variables: average daily gain (ADG), adjusted weight for 205 days of age (W205), and number of days to reach 160 kg (D160). Information were collected from a commercial herd consisting of 1,122 calves and 1,009 heifers and their mothers. To classify animals with similar performance based on the cows’ calving orders (age of dam), the multivariate cluster analysis was adopted through the complete linkage hierarchical method. The best performance was observed in the calves of cows in their sixth calving at most; for heifers, the best performance was seen in those born to cows in their eighth calving at most. Cows in their eighth calving should be discarded.