Determination of Factors Affecting Gross Profit in Geographically Indicated Sugar Bean Production: Decision Tree Model




Sugar bean, production, gross profit, Decision Tree


The purpose of this research is to determine the effects of demographic and structural factors on gross profit per decare in farms that cultivate sugar beans in Kelkit, Şiran and Köse districts of Gümüşhane province. In this context, the relationships between gross profit and some demographic and structural factors were investigated, and the effects of these factors on profitability were analysed using the Decision Tree method. In the research region, geographical indication registration has been obtained for the Kelkit Sugar Bean, which is an local (ancestral) seed and has local characteristics compared to other sugar beans. However, in recent years, local farmers have been using sugar bean seeds obtained from surrounding regions instead of using ancestral seeds. The main question of this research is to reveal which demographic and structural factors are effective on the gross profit in the production of registered GI labelled Kelkit Sugar Beans and traditional sugar beans. According to the results of this study; the share of sugar beans in the total cultivated area, the total cultivated area, the production status of GI Kelkit Sugar Beans, the size of the property land, the number of person engaged in farming in the household, the share of sugar beans in the total agricultural production value and the age of the farmer were determined as effective factors on the gross profit obtained.


Anonim (2021). Gümüşhane İl Tarım ve Orman Müdürlüğü, 2021 Kurum Brifingi, Giriş Tarihi (05.05.2022):

Birleşmiş Milletler Gıda ve Tarım Örgütü (FAO) (2022). Crop Statistics Data. Access Address (20.12.2023):

Cholo, M., Marisennayya, S., Bojago, E., Leja, D., & Divya, R. K. (2023). Determinants of adoption and intensity of improved haricot bean: A socio-agronomic study from southern Ethiopia. Journal of Agriculture and Food Research, 13. Doi: 10.1016/j.jafr.2023.100656

Kalichkin, V.K., Alsova, O.K., & Maksimovich, K. Y. (2021). Application of the decision tree method for predicting the yield of spring wheat. AgriTech, 839. Doi: 10.1088/1755-1315/839/3/032042

Kelly, J.D., Kolkman, J.M., Doğan, H.H., & Schneider, K. (1998). Breeding for yield in dry bean. Euphytica, 102: 343-356.

McDermott, J & Wyatt, A. (2017). The role of pulses in sustainable and healthy food systems. Annals of the Newyork Academy of Sciences, 1392: 30-42. Doi: 10.1111/nyas.13319

Ma, J., Khan, N., Gong, J., Hao, X., Cheng, X., Chen, X., Chang, J., & Zhang, H. (2022). From an Introduced Pulse Variety to the Principal Local Agricultural Industry: A Case Study of Red Kidney Beans in Kelan, China. Agronomy, 12. Doi: 10.3390/agronomy12071717

Nasar, S., Shaheen, H., Murtaza, G., Tinghong, T., & Arfan, M. (2023). Socioeconomics evaluation of common bean cultivation in providing sustainable livelihood to the mountain populations of Kashmir Himalayas. Plants, 12. Doi: 10.390/plants12010213

Rasul, G., Saboor, A., Tiwari, P.C., Hussain, A., Ghosh, N., & Chettri, G. B. (2019). Food and Nutrition Security in the Hindu Kush Himalaya: Unique Challenges and Niche Opportunities. In Wester P, Mishra A, Mukherji A, Shrestha A (editors). In the Hindu Kush Himalaya Assessment. Springer: Cham, Switzerland, pp. 301-338. ISBN: 978-3-319-92287-4.

Steensels, M., Antler, A., Bahr, C., Berckmans, D., Maltz, E., & Halachmi, I. (2016). A decision-tree model to detect postcalving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot. Animal, 10: 1493-1500.

Tafesse, A., Gechere, G., Asale, A., Belay, A., Recha, J.W, Aynekulu, E., Berhane, Z., Osano, P.M., Demissie, T.D., & Solomon, D. (2023). Determinants of maize farmers’ market participation in southern Ethiopia: Emphasis on demographic, socio-economic and institutional factors. Cogent Food & Agriculture, 9. Doi: 10.1080/23311932.2023.2191850

Tarımsal Ekonomi ve Politika Geliştirme Enstitüsü (TEPGE) (2023). Kuru Fasulye Ürün Raporu. Giriş Tarihi (10.11.2023):

Tanaka, A., Diagne, M., & Saito, K. (2015). Causes of yield stagnation in irrigated lowland rice systems in the Senegal River Valley: Application of dichotomous decision tree analysis. Field Crops Research, 176: 99-107.

Türkiye Bankalar Birliği (TBB). (2023). Tarım Sektörü Raporu. Giriş Tarihi (31.10.2023): Upload/Dokuman/8960/Tarim_Sektor_Raporu_130723.pdf

Türkiye İstatistik Kurumu (TUİK). (2021). Adrese Dayalı Nüfus Kayıt Sistemi-Nüfus Artış. Giriş Tarihi (31.10.2022):

Türkiye İstatistik Kurumu (TUİK). (2023). Bitkisel Üretim Piyasaları. Giriş Tarihi (31.10.2023): Kategori/GetKategori?p=tarim-111&dil=1

Ueber, M.A., Cichy, K.A,, Gomez, F.E., Porch, T.G, Heitholt, J., Osorno, J. M., Kamfwa, K., Snapp, S.S., & Bales, S. (2022). Dry beans as a vital component of sustainable agriculture and food security- A review. Legume Science. Doi: 10.1002/leg3.155

Uçar, R. (2023). Dry Beans: An Overview. In Akgül Taş (editor). Advances in Plant Research Agriculture, İKSAD Yayınları, Ankara, sayfa 39-50. ISBN: 978-625-367-112-9.

Veenadhari, S., Mishra, B., & Singh, C. D. (2011). Soybean productivity modelling using Decision Tree Algorithms. International Journal of Computer Applications, 27: 11-15.

Witten, I.H & Frank, E. (2005). Data mining; practical machine learning tools and techniques, 2nd edition. Morgan Kaufmann, San Francisco, CA, USA.




How to Cite

Doğan, N., Adanacıoğlu, H., & Takma, Çiğdem. (2024). Determination of Factors Affecting Gross Profit in Geographically Indicated Sugar Bean Production: Decision Tree Model . Turkish Journal of Agriculture - Food Science and Technology, 12(3), 403–411.



Research Paper