RESUMO
Managing a milk zone in the dairy industry is demanding. Data necessary for efficient management are difficult to acquire because they usually must be collected in organized and standardized ways. On the other hand, software practices constantly provide new tools that can go beyond simple record-keeping practices and add value to the data. In this work, FarmDain is a novel web-based application for sheep and goat management. It aims to improve milk production and processing by digitizing the value chain in data acquisition, processing and visualization between dairy production businesses and their milk suppliers. FarmDain uses state-of-the-art software technologies to model the data collection process and provides a straightforward user interface to facilitate data processing and visualization. Using the app in a case study carried out for 12 months in a dairy sheep farm resulted in lower feeding cost per milked ewe by 5.5% when ewes were allocated into high and low milk production groups compared to the scenario of remaining in one single group. Furthermore, based on reports provided by the app, culling and genetic selection decisions were made to improve the overall farm performance. Similar practices were applied in all farms optimizing their productivity, which led to increased profitability for farms and the Dairy Factory.
RESUMO
The objective was to assess the effect of housing conditions during the summer months on the success rates of cervical artificial insemination (AI) with cooled semen, in intensively reared dairy ewes in Greece. The study involved 2083 Lacaune ewes from 23 flocks that were serviced during May to September. An estrous synchronization protocol with the insertion of progestogen sponges for 14 days and eCG administration at sponge removal, was used. All ewes were inseminated 54−57 h after sponge removal with cooled semen (15 °C) from 10 Lacaune rams. Pregnancy diagnosis was performed via trans-dermal ultrasonography at 35−40 days after AI. Data recording started the day after sponge placement (15 days prior to AI), and lasted up to 14 days after AI. Daily records included temperature, relative humidity, and Temperature-Humidity Index (THI) inside the shed. Available space and volume per animal, frequency of bedding renewal, access to a yard, and indoor light were also recorded in each farm. Binary logistic regression of data records showed that temperature and THI increases at days −15 to +4 around AI (day 0) had a negative effect on pregnancy rates (reducing the likelihood of pregnancy by 3−6% and 7%, respectively). The latter also decreased significantly (p < 0.05) in farms with high stocking density, non-frequent bedding renewal, and outdoor access by ewes (by 30%, 34%, and 44%, respectively). Overall, the results indicate that appropriate housing conditions are warranted to increase the success of AI in dairy ewes during the summer months.
