engleski

Significance and calculation of reference intervals for blood analytes in organically raised small ruminants

Introduction: Hematological and biochemical tests are a well-established tool for health status monitoring in organic farming. Interpretation of test results critically relies on accurate determination of reference intervals. Due to locally variable conditions specific to organic farming, such as breed, nutrition, climate and season, separate sets of reference intervals are often needed for specific situations. In this work, a machine learning approach was used to identify when separate reference intervals are justified. Novel methods based on robust statistics and bootstrap resampling were used to tackle the problem of reference interval calculation from small samples. Materials and methods: Nine flocks of approximately 30 female sheep each, of various (mostly Croatian indigenous) breeds, organically raised at different locations, were analyzed in different seasons for their key hematological and blood biochemical parameters. Machine learning algorithm RELIEF was used to assess the difference between groups and identify key parameters for their differentiation. Reference intervals were calculated using robust statistics and bootstrap resampling. Results: Location, a parameter closely linked to nutrition and environmental conditions, was the most important for differentiation between groups, even more than breed. No single hematological or biochemical parameter could be identified as critical for discrimination between groups. High values of the kappa statistic justified calculation of separate reference intervals for each group (flock), which was successfully done with small reference populations using robust statistics. Conclusions: Separate reference intervals are justified for narrow sets of conditions in organic farming, and can be successfully estimated from small samples using appropriate robust methods.

reference intervals; organic production

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