Comparison of the Emanox and Sulfacox coccidiostats in broiler rabbit farming

DOI: 10.15414/afz.2015.18.01.10–14Received 18. November 2014 ǀ Accepted 16. March 2015 ǀ Available online 31. March 2015The aim of the study was to analyze the effect of two different preparations against rabbit’s coccidiosis - naturally based preparation Emanox and conventional preparation Sulfacox - on selected production indicators. Preparations against coccidiosis were administered in the form of a beverage from weaning (42 days old) until the 60 days old. In the period of 84 days of rabbit’s age average body weight in the Emanox group reached 2673.40 g, while in the Sulfacox group live weight was 2704.73 g. In the Emanox group 4 cases of death was recorded during the fattening, which has occurred within 51 to 66 days of age. In the Sulfacox group only two cases of death were registered at age 53 and 57 days, both caused by rabbit’s constipation. Total feed consumption over the fattening period in the Emanox group was 121.99 kg and the average consumption of the complete feed mixture per 1 kg of body weight gain was 3.52 kg. In the Sulfacox group the total consumption of 122.56 kg of feed during fattening period was recorded and average consumption of complete feed mixture per 1 kg of body weight gain was 3.21 kg. Preparation Emanox PMX is a suitable alternative to conventional chemical preparations. Keywords: Emanox PMX, rabbit’s coccidiosis, coccidiostats, rabbits fattenin

The antimicrobial activity of honey, bee pollen loads and beeswax from Slovakia

The aim of this study was to test the antimicrobial activity of propolis, bee pollen loads and beeswax samples collected in the year 2009 from two locations in Slovakia to pathogenic bacteria, microscopic fungi and yeasts. The antimicrobial effect of the bee product samples were tested using the agar well diffusion method. For extraction, 99.9% and 70% methanol (aqueous, v/v) and 96% and 70% ethanol (aqueous, v/v) were used. Five different strains of bacteria, i.e. Listeria monocytogenes CC M 4699, Pseudomonas aeruginosa CC M 1960; Staphylococcus aureus CC M 3953; Salmonella enterica CC M 4420, Escherichia coli CC M 3988, three different strains of microscopic fungi, Aspergillus fumigatus, Aspergillus flavus, Aspergillus niger, and seven different strains of yeasts Candida krusei, Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Geotrichum candidum, Rhodotorula mucilaginosa, were tested. After 48 hours S. aureus was the bacterium most sensitive to the 70% ethanol extract of pollen, A. fumigatus was the most sensitive microscopic fungus (70% ethanol) and C. glabrata the most sensitive yeast (70% methanol). Microorganisms most sensitive to propolis extracts were L. monocytogenes, A. fumigatus (70% ethanol) and G. candidum (70% methanol). Most sensitive to beeswax extracts were E. coli, A. niger and C. tropicalis

Results of international standardised beekeeper surveys of colony losses for winter 2012-2013 : analysis of winter loss rates and mixed effects modelling of risk factors for winter loss.

This article presents results of an analysis of winter losses of honey bee colonies from 19 mainly European countries, most of which implemented the standardised 2013 COLOSS questionnaire. Generalised linear mixed effects models (GLMMs) were used to investigate the effects of several factors on the risk of colony loss, including different treatments for Varroa destructor, allowing for random effects of beekeeper and region. Both winter and summer treatments were considered, and the most common combinations of treatment and timing were used to define treatment factor levels. Overall and within country colony loss rates are presented. Significant factors in the model were found to be: percentage of young queens in the colonies before winter, extent of queen problems in summer, treatment of the varroa mite, and access by foraging honey bees to oilseed rape and maize. Spatial variation at the beekeeper level is shown across geographical regions using random effects from the fitted models, both before and after allowing for the effect of the significant terms in the model. This spatial variation is considerable

A critical analysis of the potential for EU Common Agricultural Policy measures to support wild pollinators on farmland

1. Agricultural intensification and associated loss of high‐quality habitats are key drivers of insect pollinator declines. With the aim of decreasing the environmental impact of agriculture, the 2014 EU Common Agricultural Policy (CAP) defined a set of habitat and landscape features (Ecological Focus Areas: EFAs) farmers could select from as a requirement to receive basic farm payments. To inform the post‐2020 CAP, we performed a European‐scale evaluation to determine how different EFA options vary in their potential to support insect pollinators under standard and pollinator‐friendly management, as well as the extent of farmer uptake. 2. A structured Delphi elicitation process engaged 22 experts from 18 European countries to evaluate EFAs options. By considering life cycle requirements of key pollinating taxa (i.e. bumble bees, solitary bees and hoverflies), each option was evaluated for its potential to provide forage, bee nesting sites and hoverfly larval resources. 3. EFA options varied substantially in the resources they were perceived to provide and their effectiveness varied geographically and temporally. For example, field margins provide relatively good forage throughout the season in Southern and Eastern Europe but lacked early‐season forage in Northern and Western Europe. Under standard management, no single EFA option achieved high scores across resource categories and a scarcity of late season forage was perceived. 4. Experts identified substantial opportunities to improve habitat quality by adopting pollinator‐friendly management. Improving management alone was, however, unlikely to ensure that all pollinator resource requirements were met. Our analyses suggest that a combination of poor management, differences in the inherent pollinator habitat quality and uptake bias towards catch crops and nitrogen‐fixing crops severely limit the potential of EFAs to support pollinators in European agricultural landscapes. 5. Policy Implications. To conserve pollinators and help protect pollination services, our expert elicitation highlights the need to create a variety of interconnected, well‐managed habitats that complement each other in the resources they offer. To achieve this the Common Agricultural Policy post‐2020 should take a holistic view to implementation that integrates the different delivery vehicles aimed at protecting biodiversity (e.g. enhanced conditionality, eco‐schemes and agri‐environment and climate measures). To improve habitat quality we recommend an effective monitoring framework with target‐orientated indicators and to facilitate the spatial targeting of options collaboration between land managers should be incentivised

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals

Summary of winter honey bee colony losses in Slovakia between the years 2009 and 2015

Between the seasons 2009/2010 and 2014/2015 was evaluated 1305 questionnaires in total, received from Slovak beekeepers. Standard questionnaires of COST working group COLOSS were used with sets of questions related to overwintering of bee colonies and possible reasons of its losses. In season 2009/2010 winter losses in Slovakia reached 7.10 %, subsequently in 2010/2011 - 5.96 %, 2011/2012 - 9.70 %, 2012/2013 - 9.50 %, 2013/2014 - 8.84 %, 2014/2015 - 10.00 %. Expected causes of winter mortality (starvation, poor queen´s quality, parasitism, robbery) were evaluated in the study to detect the presence of depopulation syndrome of bee colonies - CCD (colony collapse disorder) reported from some North American and European areas. As acceptable level of winter losses is generally considered level 10 %, which was not exceeded in any season, thereby Slovakia ranks among countries with the lowest winter mortality of bee colonies worldwide. Possible reason of this situation is most probably multiple Varroa treatment throughout the year, but other reasons are discussed as well in the study

Effect of GnRH (Lecirelinum) on some quality parameters of rabbit ejaculate

The aim of this study was to evaluate the effect of two concentrations of GnRH in insemination doses on selected quality parameters of rabbits ejaculate in vitro. Insemination doses (ID) were diluted to a concentration of 50 x 106 spermatozoa in ID (0.5 ml). Subsequently ID was divided into 3 samples (control - C, experiment 1, experiment 2). Implementor GnRH (Lecirelinum – commercial product Supergestran, Ferring Pharmaceuticals, the Czech Republic) was added to experimental insemination dose samples at concentrations as follows: experiment 1 to 0.2 ml (5 mg) GnRH / ID and experiment 2 to 0.3 ml (7.5 mg) GnRH / ID. Experimental samples were compared with the control sample. For the assessment of spermatozoa motility the CASA (Computer-Assistend Sperm Analysis) system SpermVision (MiniTüb, Tiefenbach, FRG) with a microscope Olympus BX 51 (Olympus, Japan) was used. Monitored spermatozoa parameters were motility (%), progressive motility (%), velocity (μm/s), curvilinear velocity of motility (μm/s) and beat cross frequency. In experimental samples (experiment 1, 2) increase of the spermatozoa motility values was detected in time periods of 1 and 3 hours (1 hour – C: 47.30 ± 7.99%, experiment 1: 86.39 ± 5.60%, experiment 2: 72.48 ± 3.80%, 3 hours – C: 57.09 ± 23.36%, experiment 1: 89.42 ± 2.41%, experiment 2: 63.92 ± 12.65%) and decrease over a period of 6 hours (C: 64.65 ± 8.60%, experiment 1: 35.26 ± 5.22%, experiment 2: 50.08 ± 8.27%). Progressive spermatozoa motility within time periods of 1 and 3 hours showed a similar trend as spermatozoa motility (1 hour – C: 30.50 ± 7.35%, experiment 1: 79.18 ± 6.58%, experiment 2: 59.85 ± 6.03%; 3 hours – C: 42.06 ± 22.69%, experiment 1: 82.31 ± 3.64%, experiment 2: 44.45 ± 12.01%) and decreased over a period of 6 hours (C: 56.34 ± 8.88%, experiment 1: 23.36 ± 5.95%, experiment 2: 39.07 ± 11.17%). Spermatozoa curvilinear velocity in experiment 1 reached after 1 hour 82.26 ± 4.47 μm/s, after 3 hours 68.40 ± 3.20 μm/s, after 6 hours 58.21 ± 3.89 μm/s; in experiment 2 was after 1 hour 62.00 ± 4.33 μm/s, after 3 hours 44.37 ± 9.19 μm/s and after 6 hours 52.73 ± 9.10 μm/s, in control group after 1 hour 71.86 ± 8.19 μm/s, after 3 hours 62.35 ± 7.89 μm/s and after 6 hours 73.93 ± 8.18 μm/s. Lower concentration of the implementor (1 to 0.2 ml GnRH / ID) ​​increased level of motility, progressive motility, velocity and curvilinear velocity of motility in the time period 1 and 3 hours after GnRH implementor application compared with the control sample. In 6 hours after application only lower changes of monitored parameters has occurred. The effect of GnRH under in vivo conditions may vary significantly comparing with results obtained in vivo