Intervention decision model to prevent spiking mortality of turkeys.
Based on the daily records on turkeys' mortalities for the series of flocks placed on different farms in a relatively compact geographical area for the period of approximately 2 yr and other relevant explanatory variables, the goal of the research was to design a decision model to determine whether or not to use the fluorquinolone antibiotic, sarafloxacin, to prevent spiking mortality of turkeys. The core of the designed decision model is the forecasting model that attempts to ex-ante predict the cumulative flock mortality for the period between 8 and 28 d of age. Forecasts were generated with the parameters of the linear regression model where continuous values of daily mortalities served as a dependent variable. The decision variable is a binary (yes/no) choice variable, where "yes" means "go ahead with treatment" and "no" means "do nothing". If the predicted cumulative mortality for the period between 8 and 28 d of age exceeds 9% of the total initial placement, the model generates a "yes" signal. If the predicted cumulative mortality for the same period is below 9% of the total initial placement, the model generates a "no" signal. The results indicate a reasonable accuracy of the prediction model where the number of correct prediction increases and the number of incorrect predictions falls very fast as the forecasting window shortens. The intervention decision model could help veterinarians in making decisions on whether or not to treat the suspect flocks.
Vukina
T
T
Department of Agricultural and Resource Economics, College of Agriculture and Life Sciences, North Carolina State University, Raleigh 27695, USA. tom_vukina@ncsu.edu
Barnes
H J
HJ
Solakoglu
M N
MN
D000375:Aging; D000818:Animals; D003661:Decision Support Techniques; D016014:Linear Models; D011201:Poultry Diseases / Q000401:mortality* / Q000517:prevention & control*; D012621:Seasons; D013696:Temperature; D014422:Turkeys