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Simple Summary: Following the growth of a fattening animal is an important aspect since farmers aim to maximize their profits by improving their slaughter weight. Growth models can fit the age–weight data into biologically interpretable parameters that provide a growth curve for monitoring animal growth, thus identifying the growth declining point during the fattening period. In this study, we aimed to define the best fitting growth model for describing Kivircik lamb growth. While the Gompertz model was identified as the best fitting growth model, it was determined that male lambs, twin lambs, and winter-born lambs had a higher mature weight. Even though they may be lighter at birth, the proportion of their prospective weight gain was found to be higher compared to female, single-born, and spring-born lambs. Our results showed that the estimation error of the Gompertz model ranged between −0.43 kg to 0.60 kg. In conclusion, according to the Gompertz model's estimation, Kivircik lambs reached 40.7% of their slaughter weight at weaning and had a 66.3% degree of maturity at the slaughter age, which indicated inappropriate management. The Kivircik is an indigenous sheep breed from Turkey, and it has superior meat quality compared to other indigenous breeds. Therefore, farmers prioritize Kivircik lamb fattening instead of milk production. Here, we aimed to determine the best nonlinear growth model, i.e., Gompertz, Logistic, Von Bertalanffy, and Brody, to describe the growth curve of Kivircik lambs. The body weight data from birth until 150 days of age belonging to 612 lambs were used as the material of this study. The best fitting model was selected by considering the adjusted coefficient of determination (R2adj), residual mean square, and Akaike's (AIC) and Bayesian information criteria (BIC). Even though the Brody model had a better statistical fit, considering its biological interpretation, the Gompertz model was identified as an appropriate model for describing Kivircik lamb growth. Male lambs, twin lambs, and lambs born in winter had higher mature live weights (44.2 kg, 71.2 kg, and 38.5 kg, respectively) and rate of weight gain (2.1, 2.6, and 2.0, respectively). However, our subgroups revealed a similar rate of maturity (0.01). Growth models are important tools for deciding the optimal slaughter age and they provide valuable information on the management practices of both sexes, birth types, and birth seasons. These results can be applied to breeding programs for early selection, enabling intervention strategies when needed. [ABSTRACT FROM AUTHOR]

Simple Summary: In the small–medium scale farm management systems, farmers make rational decisions by combining available inputs to optimize or maximize their production outcome. As the availability of inputs differs in each location, variability in farm typology is becoming more evident, resulting in different economic performances. We aimed to determine buffalo farms' heterogeneity and differences in productivity and profitability in Marmara Region, Turkey. Six components were identified as the best predictors of farms' efficiency: "Milk productivity", "Economic efficiency", "Roughage management", "Dual purpose farming", "Concentrate supply", and "Fodder production", each of which aggregate indexes that characterize feeding management and economics, in addition to location, investment in buildings and equipment, and grazing type. These factors' scores allow for farms' characterization and identifying management inefficiency areas to prompt early and effective interventions by farmers. This study aimed to determine the heterogeneity that exists in water buffalo husbandry systems in Marmara Region, Turkey. A questionnaire containing a total of 60 indicators was submitted to 52 farmers. A Principal Component Analysis was performed to reduce original variables into a simplified and latent structure, which was characterized by six orthogonal components: milk productivity, economic efficiency, roughage management, dual-purpose farming, concentrate supply, and fodder production. An ANOVA model was applied to the six components to investigate the effects of the province, investment levels, grazing type, milk production, and profitability. Differences in milk productivity, roughage management, concentrate supply, and fodder management were significant according to the province and grazing type, which indicated a difference in intensification levels among the cities. Economic efficiency and dual-purpose farming differed significantly for milk production levels as well as milk productivity, and economic efficiency differed for profitability levels. We found a tendency regarding the impact of roughage management on profitability. The results conclude that profitability was associated with improving the milking traits of buffaloes and roughage management of the farms. [ABSTRACT FROM AUTHOR]

• Characterization of dairy goat farms was carried out. • Four clusters were obtained based on farm socio-economic structure. • Among clusters, main differences were education level and farm marketing. Our objective was to classify and characterize dairy goat farms on the basis of some socio-economic aspects in two Mediterranean countries, Italy and Turkey. Random samples of 11 farms from the north-eastern part of Italy and of 18 farms from Çanakkale province in the west region of Turkey have been used. Farmers were interviewed using 44 indicators related to general farm characteristics based on socio-economic structure. For the classification of farms, Principal Component Analysis and cluster analysis have been performed. Farms have been classified into 4 clusters. Cluster 1 (6 farms, 20.7 %) included "Intensive/Semi-intensive, medium-sized with high owned land area, using technology Italian farms". Cluster 2 (5 farms, 17.2 %) included "Intensive/Semi-intensive, large-sized with high amount of concentrate feed supply, using technology and with highly cheese production Italian farms". Cluster 3 (14 farms, 48.3 %) included "Semi-intensive, medium-sized, mostly member of cooperatives, directly raw milk seller Turkish farms". Cluster 4 (4 farms, 13.8 %) included "Intensive/semi-intensive, large-sized with high land area, member of cooperatives, work with highly hired labour, directly raw milk seller Turkish farms". Results of this study point out the main differences between Italian and Turkish farms, mostly related to the farmer education level, the use of technology, the marketing and organization structure, and the milk yield. However, the older age of the farmers, their membership of union breeders and their commitment to a second economic activity to provide extra income were the more common features. In conclusion, taking into account this socio-economic variety between countries, outcomes of this study might help to identify some possible requirements for developing new strategies and policies on the dairy goat sector during the EU-harmonization process in Turkey. [ABSTRACT FROM AUTHOR]