Research Article
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Year 2022, Volume: 28 Issue: 1, 25 - 32, 25.02.2022
https://doi.org/10.15832/ankutbd.739230

Abstract

References

  • Aksoy A, Ertürk Y E, Erdoğan S, Eyduran E.& Tariq M M (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.
  • Anigbogu T U, Agbasi O E & Okoli I M ( 2017). Socioeconomic determinants of farmers membership of cooperative societies in Anambra State, Nigeria. International Journal for Innovative Research in Multidisciplinary Field 3(12): 13-20.
  • Balgah R A (2019). Factors influencing coffee farmers’ decisions to join cooperatives. Sustainable Agriculture Research 8(1): 42-58.
  • Bernard T & Spielman D J (2009). Reaching the rural poor through rural producer organizations? A study of agricultural marketing cooperatives in Ethiopia. Food Policy 34: 60-69.
  • Bulut F (2017). Different mathematical models for entropy in information theory. Bilge International Journal of Science and Technology Research 1(2): 167-174.
  • Can B A, Engindeniz S & Can O (2017). The role and importance of cooperatives in rural development: the case of Çavuşlu agricultural development cooperative with limited liability. Third Sector Social Economic Review 52:120-139.
  • Cho Y J, Lee H & Jun C H (2011). Optimization of decision tree for classification using a particle swarm. Industrial Engineering and Management Systems 10(4): 272-278.
  • Debeb D & Haile M (2016). A study on factors affecting farmers’ cooperative membership increment in Bench Maji Zone, Southwestern Ethiopia. Developing Country Studies 6(2): 129-138.
  • Dorgi O & Gala G (2016). Assessment of factors affecting members’ participation in fishery cooperatives (the case of Gambella Region, Ethiopia). Journal of Business Management & Social Sciences Research 5(12): 347-363.
  • Edwards-Murphy F, Magno M, Whelan P M, O’Halloran J & Popovici E M (2016). b+WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring. Computers and Electronics in Agriculture 124: 211-219.
  • Eren B & Eyüpoğlu V (2011). Modelling of recovery efficiency of Ni (II) ion using artificial neural network. 6th International Advanced Technologies Symposium, 186-190p, 16-18 May 2011, Elazığ, Turkey.
  • Ertek N, Demir N & Aksoy A (2016). Analysis of the factors affecting the cooperative membership of the cattle enterprises: the case of TRA region. Alınteri 30(B): 38-45.
  • Gashaw B A & Kibret S M (2018). Factors influencing farmers’ membership preferences in agricultural cooperatives in Ethiopia. American Journal of Rural Development 6(3): 94-103.
  • Gutierrez J D (2014). Smallholders’ agricultural cooperatives in Colombia: ¿vehicles for rural development?. Revista Desarrollo y Sociedad 73: 219-271.
  • Karadas K & Kadirhanogullari I H (2017). Predicting honey production using data mining and artificial neural network algorithms in apiculture. Pakistan Journal of Zoology 49(5): 1611-1619.
  • Karlı B, Bilgiç A & Çelik Y (2006). Factors affecting farmers’ decision to enter agricultural cooperatives using random utility model in the South Eastern Anatolian Region of Turkey. Journal of Agriculture and Rural Development in the Tropics and Subtropics 107(2): 115-127.
  • Kızılaslan H & Doğan H G (2013). Importance and status of producer unions in the producer organizations of Turkey (a case study with fresh vegetable and fruit producers organization of Kazova region in Tokat province). Akademik Bakış Journal 38: 1-17.
  • Liu Y (2018). Determinants and impacts of marketing channel choice among cooperatives members: Evidence from agricultural cooperative in China. 10thInternational Conference of Agricultural Economists, July 28-Agust 2, Vancouver, Canada.
  • Mojo D, Fischer C & Degefa T (2015a). Who benefits from collective action? Determinants and economic impacts of coffee farmer cooperatives in Ethiopia. Agriculture in an Interconnected World, 8-14 August 2015, Milano, Italy.
  • Mojo D, Fischer C & Degefa T (2015b). Social and environmental impacts of agricultural cooperatives: evidence from Ethiopia. International Journal of Sustainable Development & World Ecology 22(5): 388-400.
  • Newbold P (1995). Statistics for Business and Economics, Prentice-Hall International, New Jersey, USA. 867p
  • Ogunleye A A, Oluwafemi Z O, Arowolo K O & Odegbile O S (2015). Analysis of socio economic factors affecting farmers participation in cooperative societies in Surulere Local Government Area of Oyo State. Journal of Agriculture and Veterinary Science 8(5): 40-44.
  • Omotesho K F, Ogunlade I, Lawal M A & Kehinde F B (2016). Determinants of level of participation of farmers in group activities in Kwara State, Nigeria. Journal of Agricultural Faculty of Gaziosmanpasa University 33(3): 21-27.
  • Özkan Y (2016). Data Mining Methods (Third Edition). Papatya Publishing, 236s.
  • Ramya Y, Kumar P, Mugilan D & Babykala M (2018). A review of different classification techniques in machine learning using weka for plant disease detection. International Research Journal of Engineering and Technology 5(5): 3818-3823.
  • Rondovic B, Djurickovic T. & Kascelan L (2019). Drivers of e-business diffusion in tourism: a decision tree approach. Journal of Theoretical and Applied Electronic Commerce Research 14(1): 30-50.
  • Seyrek İ H & Ata H A, (2010). Efficiency measurement in deposit banks using data envelopment analysis and data mining. Journal of BRSA Banking and Financial Markets 4(2): 67-84. (in Turkish)
  • Viera A J & Garrett J M (2005). Understanding inter observer agreement: the kappa statistic. Society of Teachers of Family Medicine 37(5): 360-363.
  • Wang T C & Lee D D (2006). Constructing a fuzzy decision tree by integrating fuzzy sets and entropy. Proceedings of the 5th WSEAS international conference on Applied computer science, Hangzhou, China.
  • Woldu T, Tadesse F & Waller M K (2013). Women’s Participation in Agricultural Cooperatives in Ethiopia. – ESSP Working Paper 57, 22p.

Determining Factors Affecting Cooperative Membership of the Beekeepers Using Decision Tree Algorithms

Year 2022, Volume: 28 Issue: 1, 25 - 32, 25.02.2022
https://doi.org/10.15832/ankutbd.739230

Abstract

Agricultural cooperatives have important contributions to farmers. Thanks to cooperatives, agricultural products are sold at high prices, while agricultural inputs can be purchased at low prices. Cooperatives provide their partners with technical support in product processing, grading, standardization, storage and quality. On the other hand, cooperatives contribute to the sustainability of agricultural activities by providing credit support to their members. The current research was carried out Milas district of Muğla province, which is the center of pine honey production in Turkey. In the current research, a survey was conducted face to face with 62 farmers engaged in beekeeping, and the decision tree model, which is one of the data mining methods, was used in determining the factors that affect the beekeepers' membership in cooperatives. As a result of the statistical analyses conducted, it was concluded that on the cooperative membership of beekeepers, their status of using credit, education level and status of receiving beekeeping supports have a highly significant influence.

References

  • Aksoy A, Ertürk Y E, Erdoğan S, Eyduran E.& Tariq M M (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.
  • Anigbogu T U, Agbasi O E & Okoli I M ( 2017). Socioeconomic determinants of farmers membership of cooperative societies in Anambra State, Nigeria. International Journal for Innovative Research in Multidisciplinary Field 3(12): 13-20.
  • Balgah R A (2019). Factors influencing coffee farmers’ decisions to join cooperatives. Sustainable Agriculture Research 8(1): 42-58.
  • Bernard T & Spielman D J (2009). Reaching the rural poor through rural producer organizations? A study of agricultural marketing cooperatives in Ethiopia. Food Policy 34: 60-69.
  • Bulut F (2017). Different mathematical models for entropy in information theory. Bilge International Journal of Science and Technology Research 1(2): 167-174.
  • Can B A, Engindeniz S & Can O (2017). The role and importance of cooperatives in rural development: the case of Çavuşlu agricultural development cooperative with limited liability. Third Sector Social Economic Review 52:120-139.
  • Cho Y J, Lee H & Jun C H (2011). Optimization of decision tree for classification using a particle swarm. Industrial Engineering and Management Systems 10(4): 272-278.
  • Debeb D & Haile M (2016). A study on factors affecting farmers’ cooperative membership increment in Bench Maji Zone, Southwestern Ethiopia. Developing Country Studies 6(2): 129-138.
  • Dorgi O & Gala G (2016). Assessment of factors affecting members’ participation in fishery cooperatives (the case of Gambella Region, Ethiopia). Journal of Business Management & Social Sciences Research 5(12): 347-363.
  • Edwards-Murphy F, Magno M, Whelan P M, O’Halloran J & Popovici E M (2016). b+WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring. Computers and Electronics in Agriculture 124: 211-219.
  • Eren B & Eyüpoğlu V (2011). Modelling of recovery efficiency of Ni (II) ion using artificial neural network. 6th International Advanced Technologies Symposium, 186-190p, 16-18 May 2011, Elazığ, Turkey.
  • Ertek N, Demir N & Aksoy A (2016). Analysis of the factors affecting the cooperative membership of the cattle enterprises: the case of TRA region. Alınteri 30(B): 38-45.
  • Gashaw B A & Kibret S M (2018). Factors influencing farmers’ membership preferences in agricultural cooperatives in Ethiopia. American Journal of Rural Development 6(3): 94-103.
  • Gutierrez J D (2014). Smallholders’ agricultural cooperatives in Colombia: ¿vehicles for rural development?. Revista Desarrollo y Sociedad 73: 219-271.
  • Karadas K & Kadirhanogullari I H (2017). Predicting honey production using data mining and artificial neural network algorithms in apiculture. Pakistan Journal of Zoology 49(5): 1611-1619.
  • Karlı B, Bilgiç A & Çelik Y (2006). Factors affecting farmers’ decision to enter agricultural cooperatives using random utility model in the South Eastern Anatolian Region of Turkey. Journal of Agriculture and Rural Development in the Tropics and Subtropics 107(2): 115-127.
  • Kızılaslan H & Doğan H G (2013). Importance and status of producer unions in the producer organizations of Turkey (a case study with fresh vegetable and fruit producers organization of Kazova region in Tokat province). Akademik Bakış Journal 38: 1-17.
  • Liu Y (2018). Determinants and impacts of marketing channel choice among cooperatives members: Evidence from agricultural cooperative in China. 10thInternational Conference of Agricultural Economists, July 28-Agust 2, Vancouver, Canada.
  • Mojo D, Fischer C & Degefa T (2015a). Who benefits from collective action? Determinants and economic impacts of coffee farmer cooperatives in Ethiopia. Agriculture in an Interconnected World, 8-14 August 2015, Milano, Italy.
  • Mojo D, Fischer C & Degefa T (2015b). Social and environmental impacts of agricultural cooperatives: evidence from Ethiopia. International Journal of Sustainable Development & World Ecology 22(5): 388-400.
  • Newbold P (1995). Statistics for Business and Economics, Prentice-Hall International, New Jersey, USA. 867p
  • Ogunleye A A, Oluwafemi Z O, Arowolo K O & Odegbile O S (2015). Analysis of socio economic factors affecting farmers participation in cooperative societies in Surulere Local Government Area of Oyo State. Journal of Agriculture and Veterinary Science 8(5): 40-44.
  • Omotesho K F, Ogunlade I, Lawal M A & Kehinde F B (2016). Determinants of level of participation of farmers in group activities in Kwara State, Nigeria. Journal of Agricultural Faculty of Gaziosmanpasa University 33(3): 21-27.
  • Özkan Y (2016). Data Mining Methods (Third Edition). Papatya Publishing, 236s.
  • Ramya Y, Kumar P, Mugilan D & Babykala M (2018). A review of different classification techniques in machine learning using weka for plant disease detection. International Research Journal of Engineering and Technology 5(5): 3818-3823.
  • Rondovic B, Djurickovic T. & Kascelan L (2019). Drivers of e-business diffusion in tourism: a decision tree approach. Journal of Theoretical and Applied Electronic Commerce Research 14(1): 30-50.
  • Seyrek İ H & Ata H A, (2010). Efficiency measurement in deposit banks using data envelopment analysis and data mining. Journal of BRSA Banking and Financial Markets 4(2): 67-84. (in Turkish)
  • Viera A J & Garrett J M (2005). Understanding inter observer agreement: the kappa statistic. Society of Teachers of Family Medicine 37(5): 360-363.
  • Wang T C & Lee D D (2006). Constructing a fuzzy decision tree by integrating fuzzy sets and entropy. Proceedings of the 5th WSEAS international conference on Applied computer science, Hangzhou, China.
  • Woldu T, Tadesse F & Waller M K (2013). Women’s Participation in Agricultural Cooperatives in Ethiopia. – ESSP Working Paper 57, 22p.
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Tayfun Çukur 0000-0003-4273-6449

Figen Çukur 0000-0002-8788-0287

Publication Date February 25, 2022
Submission Date May 18, 2020
Acceptance Date September 29, 2020
Published in Issue Year 2022 Volume: 28 Issue: 1

Cite

APA Çukur, T., & Çukur, F. (2022). Determining Factors Affecting Cooperative Membership of the Beekeepers Using Decision Tree Algorithms. Journal of Agricultural Sciences, 28(1), 25-32. https://doi.org/10.15832/ankutbd.739230

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