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

Feedlots will be equipped with individualized identification and monitoring systems, enabling data collection with reduced effort. Computer tools based on epidemiological data will be developed to determine whether the onset of pathological processes originates in feedlots or in previous production phases.

These tools will allow the responsibility of the parties involved to be determined. Specific health protocols will be designed, including etiological, nutritional, management, facility, and source factors that have been shown to be associated with morbidity and mortality caused by various agents, with special attention to respiratory disease. Training programs will be established for feedlot personnel to adapt them to the new technology and management protocols, emphasizing their understanding of the rationale and benefits of the proposed actions. Improve the profitability of sheep fattening in Andalusia by increasing process efficiency and reducing losses due to health problems.

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 a greater effort from staff. Degree of innovation of the proposal - Innovation in identification: Identification, which has usually been carried out at the collective level, according to the farms of origin, will now be carried out individually, with all the advantages mentioned. To this must be added the element of technicalization, based on the use of a barcode reader to replace the usual method of physically writing it down on paper. This method saves work and enables the immediate arrival of the reading data to the database, which can be processed 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 immediate modification of the 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, such as logistic regression, which is commonly used in research, will be implemented for decision-making at the feedlot level. This tool makes it possible to identify factors associated with morbidity and mortality, enabling the design and implementation of tailored control plans. It also makes it possible to determine whether the factors are acting at the feedlot level or in previous phases of the production system, which is very useful for determining responsibility.