Usefulness of MARS and Bagging MARS Algorithms in Prediction of Honey Production in Beekeeping Enterprises from Elazig Province of Turkey

The present survey was conducted on beekeeping enterprises in Elazig province of Turkey with the purpose of predicting honey yield per beehive via Multivariate Adaptive Regression Splines (MARS) and Bootstrap Aggregating Multivariate Adaptive Regression Splines (Bagging MARS) algorithms. To realize this purpose, a questionnaire form including several questions i.e. honey yield per beehive of enterprise, age of enterprise, educational level of enterprise, migratory status of enterprise (yes and no), other working area except for beekeeping (yes and no), number of full beehives and bee race (Caucasian and others), were elaborated. In MARS algorithm, no over-fitting problem was observed under a set of explanatory variables consisting of number of full beehives and total honey production, whereas Bagging MARS captured the interaction effects of number of full beehives with educational degree and other working areas. The constructed MARS and Bagging MARS models produced a marvelous fit in the prediction of honey yield per beehive. It was concluded that both algorithms were a good statistical tool to reveal tendency of beekeepers in the observed location and produced some considerable hints in increasing honey yield.