LOGICEB Operational Group: Automated individual identification and logistic regression for reducing mortality in lamb fattening farms

The feedlots will be equipped with individualized identification and tracking systems, enabling data collection with less effort. Software tools, based on epidemiological data, will be developed to determine whether the emergence of pathological processes originates in the feedlots or in previous production phases.

These tools will allow for the determination of the responsibility of the parties involved. Specific health protocols will be designed, including etiological, nutritional, management, facility, or origin factors that have been shown to be associated with morbidity and mortality caused by various agents, with special attention to respiratory diseases. Training programs will be established for feedlot staff to adapt them to the new technologies and management protocols, emphasizing their understanding of the rationale and benefits of the proposed actions. The aim is to improve the profitability of sheep fattening in Andalusia by increasing process efficiency and reducing losses due to health issues.

All of this will ultimately result in a substantial improvement in the health and economics of feedlots, with fewer cases, lower mortality, and without requiring additional effort from staff. Degree of innovation of the proposal: Innovation in identification: identification, which has traditionally been carried out at the collective level, according to the farms of origin, will now be done individually, with all the aforementioned advantages. In addition, there is the technological element, based on the use of barcode readers instead of the usual method of physically recording information on paper. This method saves labor and allows for the immediate transfer of scanned data to the database, which can be accessed remotely.

Furthermore, the fact that each reader is linked to a specific event (entry into the feedlot, entry into the infirmary, animal death) allows for the immediate modification of dependent variables, resulting in dynamic models. 2) Innovation in the methodology for analyzing the data obtained: through the proposed system, the routine use of a powerful statistical tool, logistic regression, commonly employed in research, will be implemented for decision-making at the feedlot level. This tool allows for the identification of factors associated with morbidity and mortality, enabling the design and implementation of tailored control plans. It also allows for determining whether the factors are acting at the feedlot level or in earlier stages of the production system, which is very useful for assigning responsibility.