Investigating the Impact of Climate Parameters on Honey Yield under Migratory Beekeeping Conditions through Decision Tree Analysis: The Case of İzmir Province

Author:

ŞENGÜL Zekiye1ORCID,YÜCEL Banu2ORCID,SANER Gamze3ORCID,TAKMA Çiğdem2ORCID

Affiliation:

1. SİİRT ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, TARIM EKONOMİSİ BÖLÜMÜ

2. EGE ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, ZOOTEKNİ BÖLÜMÜ

3. EGE ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, TARIM EKONOMİSİ BÖLÜMÜ

Abstract

This study has investigated how climatic parameters affect honey yield in İzmir Province under the conditions of migratory beekeeping. The climate parameters of the years 1990-2020 obtained from the Turkish Statistical Institute (TURKSTAT) and the General Directorate of Meteorology were used in this research. The data were analyzed considering the routes used by migratory beekeepers in İzmir province to transport their colonies, and the effects of climatic parameters in these regions on honey yield were determined using a decision tree algorithm. The average minimum temperature was identified as the first effective factor for honey yield. This was followed by average wind speed, average relative humidity, average maximum temperature, total precipitation and average temperature. Based on results the average honey yield per hive is predicted to be 26.29 kg in climatic conditions where the average minimum temperature is greater than 10.81°C, the relative humidity is more than 66.03% and the average temperature is more than 18.36°C.

Publisher

Anadolu Ege Tarimsal Arastirmalar Enstitusu Dergisi

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference52 articles.

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2. Aksoy, A., Y.E. Ertürk, S. Erdoğan, E. Eyduran, and M.M. Tariq. 2018. Estimation of honey production in beekeeping enterprises from eastern part of Turkey through some data mining algorithms. Pakistan Journal of Zoology 50(6): 2199-2207.

3. Aktürk, D., and B. Aydın. 2019. Structural characteristics of beekeeping enterprises and beekeeping activities in Çanakkale province. Turkish Journal of Agriculture-Food Science and Technology, 7(10):618-1628.

4. Almahdi, H. 2020. Predicting crops yield: Machine learning nano-degree capstone project data science-exercise. Available at: https://towardsdatascience.com/ predicting-crops-yield-machine-learning-nanodegree-capstone-project-e6ec9349f69 (Accessed on 17.08.2022).

5. Anonymous.2009. İzmir Metropolitan Municipality. 2010-2017 Stratejik Planı. Available at: http://www.izmir.bel.tr/ (Accessed: 17 June 2022)

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