Suppression of ovarian progesterone production in dairy cows using an implant of GnRH-agonist (deslorelin) for the purpose of evaluating progesterone metabolism

OBJECTIVE: To evaluate the potential of an implant of a GnRH-agonist (deslorelin) to create a progesterone free animal suitable for studying progesterone (P4) metabolism in intact cows by measuring blood P4 and faecal P4 metabolites. METHODS: Experiment 1: Eighteen non-lactating cycling Holstein-Friesian cows, 4 to 7 years old, were allocated to one of three groups to study plasma P4 concentrations preceding an intravaginal insert. These groups comprised: i) a deslorelin group (GnRH-agonist implanted); ii) a PGF group receiving two injections of prostaglandin (PGF2alpha) 12 days apart; and, iii) an ovariectomised (OVX) group. An intravaginal device (CIDR) was inserted into the vagina of each animal and left in place for 11 days. Plasma P4 concentrations were measured during the study period. Experiment 2: Twelve non-lactating cycling Holstein-Friesian cows, 4 to 7 years old, were allocated to two groups: i) a deslorelin group (GnRH-agonist implanted); and ii) an ovariectomised group. Plasma P4 and faecal P4 metabolites (20-oxo-pregnanes, 20alpha-OH and 20beta-OH) were monitored for a period of 5 weeks. RESULTS: Experiment 1: Average plasma P4 concentration did not differ between the three groups (1.28, 1.43 and 1.55 ng/mL for deslorelin, OVX and PGF cows, respectively, P = 0.8) during the period of supplementation. Experiment 2: There was no difference in plasma P4 (mean plasma P4 < 0.02 ng/mL, P = 0.9) and faecal P4 metabolites between deslorelin and OVX cows 2 weeks after the implantation (P = 0.7). CONCLUSIONS: These data showed that a GnRH-agonist (deslorelin) implant may be used as an alternative to ovariectomy to create a progesterone free animal suitable for studying the metabolism of administered P4.     

Genetic relationship, inbreeding and generation interval in registered Gir cattle in Brazil

SUMMARY: Two-hundred random pedigrees of cows and fifty of sires born in 1987 from the Gir herdbook of the Brazilian Association of Zebu Breeders were analized. The bull Chave de Ouro DGR no. 2851 was the most influential animal with a 7% direct relationship to the breed. Total average inbreeding was 3.62 and 3.25 for the female and male sample, respectively. The subdivision of total inbreeding into current and non-current components resulted in values of 1.62, 1.25, 2.00 and 2.00 for the female and male sample, respectively. Long-term inbreeding was the principal component of non-current inbreeding, with values of 1.52% and 1.26%, respectively. Inbreeding due to strain formation strain due formation was less important (0.48% and 0.74%, respectively). The average generation interval was 8.02 years. The breed appears not yet to be subdivided into strains but this process may be starting. The average generation interval for the pedigree population was very high. Culling of old bulls could decrease this interval. ZUSAMMENFASSUNG: Verwandtschaft, Inzucht und Generationsintervall bei Gir Herdbuchrindern in Brasilien 200 zuf?llige Stammb?ume von Kühen und 50 von 1987 geborenen Stieren wurden vom Gir Herdbuch des brasilianischen Zebu Zuchtverbandes genommen. Der Stier Chave de Ouro DGR Nr. 2851 war das einflu?reichste Tier mit einer 7%igen direkten Verwandtschaft zur Rasse. Der gesamte Inzuchtkoeffizient war 3.6 und 3.25% für die weibliche und m?nnliche Stichprobe. Die Unterteilung der gesamten Inzucht in laufende und nicht laufende Komponenten resultierte in Werten von 1.62, 1.25, 2.0, 2.0 für die weibliche und die m?nnliche Stichprobe. Langfristige Inzucht war die Hauptursache für Nicht-Rezente Inzucht mit Werten von 1.5 und 1.26. Inzucht aufgrund von Linienbildung war weniger wichtig (0.48% und 0.74%). Das durchschnittliche Generationsintervall war 8.02 Jahre. Die Rasse scheint noch nicht in Linien unterteilt zu sein, aber dieser Proze? k?nnte begonnen haben. Das durchschnittliche Generationsintervall ist mit 8.02 Jahren sehr hoch. Nichtverwendung alter Stiere k?nnte dieses Intervall verkürzen.     

Association between bovine digital dermatitis and annual periods of lower pluviometric precipitation in Central Brazil

Tropical Animal Health and Production - We aimed to follow the epidemiologic evolution of bovine digital dermatitis (BDD) and other podal diseases grouped in a dairy farm in Central Brazil between...     

Detection and genetic diversity of porcine rotavirus A,B and C in eastern Australian piggeries

Rotaviruses (RV) have a high prevalence in piggeries worldwide and are one of the major pathogens causing severe diarrhoea in young pigs. RV species A, B, and C have been linked to piglet diarrhoea in Australian pig herds, but their genetic diversity has not been studied in detail. Based on sequencing of the structural viral protein 7 (VP7) RVA G genotypes G3, G4 and G5, and RVC types G1, G3, G5, and G6 have been identified in Australian piggeries in previous studies. Although occurrence of RVB was reported in Australia in 1988, no further genetic analysis has been conducted. To improve health management decisions in Australian pig herds, more information on RV prevalence and genetic diversity is needed. Here, 243 enteric samples collected from 20 pig farms within Eastern Australia were analysed for the presence of RV in different age groups using a novel PCR-based multiplex assay (Pork MultiPath™ enteric panel). RVA, RVB, and RVC were detected in 10, 14, and 14 farms, respectively. Further sequencing of VP7 in selected RV-positive samples revealed G genotypes G2, G5, G9 (RVA), G6, G8, G14, G16, G20 (RVB), and G1, G3, G5, G6 (RVC) present. RVA was only detected in young (<10 weeks old) pigs whereas RVB and RVC were also detected in older animals (>11 weeks old). Interestingly, RVB and RVC G-type occurrence differed between age groups. In conclusion, this study provides new insights on the prevalence and diversity of different RV species in pig herds of Eastern Australia whilst demonstrating the ability of the Pork MultiPath™ technology to accurately differentiate between these RV species.     

Isolation of Leptospira noguchii from sheep

The main goal of this study was to obtain new isolates of Leptospira spp. from sheep. A total of 10 kidney samples and 44 blood samples were collected from sheep slaughtered in Pelotas, Southern Brazil. One isolate was obtained which was identified by 16S rRNA gene sequencing and serogrouping to be Leptospira noguchii serogroup Autumnalis. Microscopic agglutination test (MAT) evaluation revealed that 4.5% of the sheep sera reacted against the Autumnalis serogroup. This is the first report of isolation of L. noguchii from sheep. Together these findings indicate that L. noguchii infections may be a potentially important veterinary problem in this domestic animal species.     

Contrasting edge and pasture matrix effects on ant diversity from fragmented landscapes across multiple spatial scales

Landscape Ecology - Harmful effects of habitat loss and fragmentation can be detected across multiple spatial scales, yet most studies that aim to characterize these effects take place at a single...     

Fall Armyworm Damaged Maize Plant Identification using Digital Images

The objectives of precision agriculture are profit maximisation, agricultural input rationalisation and environmental damage reduction, by adjusting the agricultural practices to the site demands. The fall armyworm (Spodoptera frugiperda) is one of the most important maize pests in Brazil and the use of insecticide is the main control method. It is believed that site-specific control can be implemented by using a machine vision system. The objective of this work was to develop and evaluate an algorithm at simplified lighting conditions for identifying damaged maize plants by the fall armyworm using digital colour images. Images of damaged and non-damaged maize plants were taken in eight different stages and in three different light intensities. The proposed algorithm had two stages: the processing and the image analysis. During the first stage, the images were processed to create binary images where the leaves were segmented from the other pixels. At the second stage, the images were subdivided into blocks and classified as ‘damaged’ or ‘non-damaged’ depending on the number of objects found in each block. The algorithm correctly classified 94·72% of 720 images.