Evaluation of 12-month interval methods for estimating animal-times at risk in a traditional African livestock farming system
MetadataShow full item record
Preventive Veterinary Medicine;85 (1-2): 9-16
Permanent link to cite or share this item: http://hdl.handle.net/10568/33127
Demographic parameters are useful for assessing productivity and dynamics of tropical livestock populations. Common parameters are the annual instantaneous hazard rates, which can be estimated by m/T (where m represents the number of the considered demographic events occurred during the year and T the cumulated animal-time at risk). Different approaches are encountered in the literature for computing T from on-farm survey data. One crude approach (";the 12-month interval approach";) only uses estimations of herds'; sizes at beginning and end of the year and aggregated counts of demographic events over the year. I evaluated the potential biases in using four 12-month interval methods (M1-;M4) to estimate T. Biases were evaluated by comparing the 12-month estimates to gold-standard values of T. Data came from long-term herd monitoring on cattle and small ruminants in extensive agro-pastoral systems. Animal-times at risks were correctly estimated in average by methods M1, M2 and M4 (average relative biases <=6% in absolute values), except for adult-male small ruminants. For young animals, M2 and M4 showed equivalent biases. M2 is simple to implement and has the advantage of being applicable for any age-group, although M4 is only applicable for young animals. M3 was highly biased and I do not recommend it. Although accurate in average, 12-month interval methods showed highly variable biases. This variability results from interactions between the dates delimiting the 12-month interval and the distributions of the demographic events over time. This phenomenon is particularly important for the adult-male small ruminants. Based on the bias variability observed in the study, the user of 12-month interval methods has to remember that they only provide approximate results and that they cannot completely replace the gold-standard approaches.