Background: Lumpy Skin Disease (LSD) is an infectious disease induced by the Capripoxvirus, causing epidemics in Turkey and several countries worldwide and inducing significant economic losses. Although this disease occurs in Turkish cattle every year, it is a notifiable disease. In this study, LSD in Turkey was modelled using the Susceptible, Exposed, Infectious, and Recovered (SEIR) epidemiological model, and production losses were estimated with predictions of the course of the disease. The animal population was categorized into four groups: Susceptible, Exposed, Infectious, and Recovered, and model parameters were obtained. The SEIR model was formulated with an outbreak calculator simulator applied for demonstration purposes. Results: Production losses caused by the LSD epidemic and the SEIR model’s predictions on the disease’s course were evaluated. Although 1282 cases were identified in Turkey during the study period, the prevalence of LSD was calculated as 4.51%, and the mortality rate was 1.09%. The relationship between the disease duration and incubation period was emphasized in the simulated SEIR model to understand the dynamics of LSD. Early detection of the disease during the incubation period significantly affected the peak time of the disease. According to the model, if the disease was detected during the incubation period, the sick animal's time could transmit the disease (Tinf) was calculated as 2.66 days. Production loss from LSD infection was estimated at US $ 886.34 for dairy cattle and the US $ 1,066.61 for beef cattle per animal. Conclusion: Detection of LSD infection during the incubation period changes the course of the disease and may reduce the resulting economic loss.

This study aimed to determine common pregnancy rates and effect sizes with meta‐analysis of studies investigating the effect of different synchronization protocols applied to sheep during breeding and non‐breeding seasons on pregnancy rates. Common pregnancy rates were estimated by coalescing pregnancy rates of studies performed independently, and heterogeneity between the studies was investigated. The meta‐analysis included 24 studies that determined pregnancy rates in 78 different groups consisting of 1934 sheep with five different synchronization protocols in Turkey between 2001 and 2020. Among the different synchronization methods, the P4+PMSG group (90.37%) during the breeding season and P4+PGF2α (69.77%) and P4 (68.75%) groups during the non‐breeding season showed the highest pregnancy rate. Also, the effect size of P4+PMSG application during the breeding season was calculated as 0.934 (95% confidence interval: 0.901–0.967), and the effect size of P4+PGF2α application during the non‐breeding season was calculated as 0.709 (95% confidence interval: 0.406–1.013). To conclude, the combination of P4+PMSG during the breeding season and progestogen and other hormone applications during the non‐breeding season are the most effective methods for estrus synchronization and for achieving the desired pregnancy rates.

The aim of this study were to detect the gyrA , parC and marR mutations and qnr genes ( qnrA , qnrB and qnrS ) in 120 strains of Escherichia coli isolated from animals. European Committee on Antimicrobial Susceptibility Testing and Clinical Laboratory Standards Institute disc diffusion and minimum inhibitory concentration (MIC) tests, respectively, were used to determine fluoroquinolone (FQ) resistance, and molecular methods were used to detect the mutations and the genes. E coli isolates with an MIC of ≥8 mg/l had mutation at Ser‐80 in parC in addition to mutations at Ser‐83, Asp‐87 or both in gyrA. The nucleotide change was detected in marR (Ser‐3 → Asn, Ala‐53 → Glu, Gly‐103 → Ser, Tyr‐137 → His). Only four E coli isolates (3.3 per cent) contained qnrA and qnrS , and qnrB was not detected. Two E coli isolates from healthy calves also contained qnrA and qnrS . The MICs of enrofloxacin and danofloxacin for qnr ‐containing E coli isolates ranged from 32 mg/l to 256 mg/l. The results of this study indicated that the FQ‐resistant E coli isolates presented an alteration in gyrA (Ser‐83 → Leu, Asp‐87 → Asn) and parC (Ser‐80 → Ile) with high MICs (8–256 mg/l), and there was a low prevalence of qnr genes among E coli isolated from animals.

It is interesting to identify the reasons and the direction of the correlation between the input/output prices and the macro/micro parameters in animal production processes. In the present study, the time series of the monthly data between the years 2014 and 2019 were analyzed to examine the factors that affected the consumer price of carcass meat in Turkey. An attempt was made to identify the relationship between the consumer price of carcass meat and the prices of cattle fattening feed, the exchange rate of the dollar, producer price index (PPI), and the agricultural PPI, which were anticipated to affect the consumer price of carcass meat as determined by the Granger causality analysis. According to econometric analysis results, when there is a change in carcass producer price, cattle fattening feed and PPI in the short term, the consumer price of carcass meat is affected by this. The producer price of carcass and PPI variables are determined to be the cause of each other’s Granger. At the same time, the PPI variable and the consumer price of carcass meat and dollar rate variables were found to be the cause of each other’s Granger. If Turkey is to prevent the excessive fluctuations in the consumer- and producer-prices of carcass meat caused by macro variables, an effective price control mechanism should be put into practice. It seems that this change would be possible only by developing and implementing policies to lower the input prices and production costs.

Recently, Türkiye has seen significant increases in animal products and input prices. These recent increases in prices have made it even more important to examine the prices of animal products and their associated factors. This study aimed to reveal the pattern network structure and characteristics of the prices of animal products and related variables for the 2010-2020 period in Türkiye. For this aim, a network analysis covered the prices of animal products, input prices used in producing these foods, and some economic variables. The study results revealed that Producer Price Index (PPI) and Agricultural PPI are key variables that are highly active in the network and act as a bridge between other unconnected variables. In addition, the results of the analysis suggest that the overall network consists of highly correlated variables and that the PPI and Agricultural PPI are the two most important variables. As a result, the pattern network structure shows that economic variables have a high impact on the prices of animal products. The network structure visualizes Türkiye's import-substitution model in animal production and foreign dependency on feed inputs, and the market structure affected by high exchange rates. In addition, as can be understood from the positive connections between feed inputs, the pattern network of the variables affecting the prices of animal products is largely shaped by feed prices and the internal dynamics of economic variables.