Karpuz (Citrullus lanatus (Thunb.) Matsum. & Nakai) Üretimini Etkileyen Faktörlerin Veri Madenciliği ile Tahmini

Year 2023, Volume: 13 Issue: 2, 1323 - 1334, 01.06.2023

Köksal Karadaş , İbrahim Hakkı Kadirhanoğulları , Meryem Konu Kadirhanoğulları

https://doi.org/10.21597/jist.1177194

Abstract

Bu çalışmanın amacı Diyarbakır ilinde karpuz verimini etkileyen faktörleri belirlemektir. Veri Madenciliği Regresyon Ağacı yönteminden Ki-kare otomatik etkileşim detektörü (CHAID) algoritması kullanılan çalışmanın verileri Basit Tesadüfi Örnekleme Yöntemi’ne göre belirlenen 80 adet karpuz üreticisinden elde edilmiştir. Oluşturulan model de Bağımlı değişken WY (karpuz verimi), bağımsız değişkenler R (bölge), AF (çiftçinin yaşı), EL (eğitim düzeyi), CA (ekim alanı), FD (gübreleme zamanı), FA (gübre miktarı), DS (ilaçlama zamanı), AS (ilaç miktarı), NI (sulama sayısı), IT (sulama süresi), AN (çapa sayısı) ve HT (hasat zamanı) olarak belirlenmiştir. Dekar başına ortalama 4488.9 kg karpuz verimi elde edilmiş ve çapa sayısı karpuz verimini en çok etkileyen değişken olmuştur. Sonuç olarak birim alandan daha yüksek verim alabilmek için karpuz üreticilerinin 4 defadan fazla çapa, 2 saatten az olmak üzere 5-6 defa sulama yapmaları ve Mayıs ayında gübre uygulamaları yapmaları gerekmektedir. Ayrıca Çermik, Eğil, Yenişehir ve Bismil karpuz üretimi için daha uygun bölgeler olarak belirlenmiştir.

Keywords

Veri madenciliği, Regresyon ağacı, Karpuz, Diyarbakır

Prediction of The Factors Affecting Watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) Yield Using Data Mining

Abstract

The aim of this study was to evaluate the factors of affecting watermelon yield in Diyarbakır province. The data was obtained from surveying of 80 watermelon farmers in Diyarbakır province, Turkey by Simple Random Sampling Method using the Chi-square automatic interaction detector (EXHAUSTIVE CHAID) algorithm of the Data Mining Regression Tree methods. In the model created, the dependent variable was WY (watermelon yield), and the independent variables were determined as R (region), AF (age of farmer), EL (education level), CA (cultivation are), FD (fertilization date), FA (amount of fertilization), DS (date of spraying), AS (amount of spraying), NI (number of irrigation), IT (irrigation time), AN (anchor number), HT (harvest time). As a result of the study, the factors that significantly affect the yield of watermelon; AN, NI, HT, CA, R has been determined. An average of 4488.9 kg watermelon yield per decare was obtained and the number of hoes was the variable that most affected the watermelon yield. As a result in order to get a higher yield per unit area, watermelon producers should anchor number more than 4 times, irrigate 5 to 6 times at less than 2 hours, and apply fertilizer in May. In addition, Çermik, Eğil, Yenişehir and Bismil were determined as more suitable regions for watermelon production.

Keywords

Data mining, Regression tree, Watermelon, Diyarbakır

References

• Altaş, S, 2015. Investigation of In Vivo and In Vitro Antioxidant Activities of Diyarbakır Watermelon. (Doctoral Thesis). Dicle University Institute of Science, Diyarbakır. (In Turkish).
• Anonymous, 2005.Watermelon cultivation, T.R. Ministry of Agriculture and Forestry, Samsun Provincial Directorate of Agriculture and Forestry https://samsun.tarimorman.gov.tr/Belgeler/Yayinlar/Lifletlerimiz/s-13.pdf
• Anonymous, 2019a. Diyarbakır Metropolitan Municipality, Geography, Climate, Population Data. https://www.diyarbakir.bel.tr/diyarbakir/genel-bilgiler/cografi-bilgiler.html (In Turkish). (Date of access: 10 May 2022).
• Anonymous, 2019b. T.R. Ministry of Agriculture and Forestry, Diyarbakır Provincial Directorate of Agriculture and Forestry Crop Production Records. https://diyarbakir.tarimorman.gov.tr/ (In Turkish). (Date of access: 10 May 2022).
• Anonymous, 2021. World Watermelon Production by Countries. https://www.atlasbig.com/tr/ulkelerin-karpuz-uretimi
• Aytekin, İ., Eyduran, E., Karadas, K., Akşahan, R., Keskin, İ, 2018. Prediction of Fattening Final Live Weight from some Body Measurements and Fattening Period in Young Bulls of Crossbred and Exotic Breeds using MARS Data Mining Algorithm. R. Bras. Zootec., 50(1):189-195.
• Ban, D., Ban, SG., Oplanic, M., Horvat, J., Novak, B., Zanic, K., Znidarcic, D, 2011. Growth and Yield Response of Watermelon to In-Row Plant Spacing and Mycorrhiza. Chilean Journal of Agricultural Research 71(4):497-502.
• Bostancı, B., Eren Atay, C, 2018. Decision Support Tools for Barley Yield: The Case of Menemen – Turkey. Dokuz Eylul University Faculty of Engineering Science and Engineering Journal, 20(60): 1057-1067.
• Büyükkalay, H., 2019. Watermelon Production and Marketing Structure in Antalya Province. Antalya University, Institute of Science, Master's Thesis, page, 71.
• Çat, A., Yardımcı, N., Kılıç, HÇ, 2016. Determination of Viral Factors in Greenhouse Cucumber (Cucumis sativus L.) and Cabbage (Cucurbita pepo L.) Production Areas in Antalya Province and Districts. Süleyman Demirel University Journal of the Institute of Science. 1, 129-132. (In Turkish)
• Celik, S., Eyduran, E., Karadas, K., Tariq, MM., 2017. Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan. Revista Brasileira de Zootecnia, 46(11): 863-872.
• Celik, S., Eyduran, E., Tatliyer, A., Karadas, K., Kara, MK., Waheed, A, 2018. Comparing Predictive Performances of some Nonlinear Functions and Multivariate Adaptive Regression Splines (MARS) for Describing the Growth of Daera Dın Panah (DDP) Goat in Pakistan. Pakistan J. Zool., 50(3):1-4.
• Duru, M., Duru, A., Karadas, K., Eyduran, E., Cinli, H., Tariq, MM, 2017. Effect of carrot (Daucus carota) leaf powder on external and ınternal egg characteristics of hy-line white laying hens. Pakistan Journal of zoology, 49: 125-132. https://doi. org/10.17582/journal.pjz/2017.49.1.125.132.
• Eyduran, E., Zaborski, D., Waheed, A., Celik, S., Karadas, K., Grzesiak, W, 2017. Comparison of the predictive capabilities of several data mining algorithms and multiple linear regression in the prediction of body weight by means of body measurements in the ındigenous beetal goat of Pakistan. Pakistan Journal of zoology, 49: 257-265. https:// doi.org/10.17582/journal.pjz/2017.49.1.257.265.
• FAO, 2019. Food and Agricultural Organization of the United Nations. Crops and Livestock Products. https://www.fao.org/faostat/en/#data/QCL (Date of access: 16 May 2022).
• Filho, FSO., Pereira, FHF., Brito, MEB., Medeiros, JE., Lacerda, F. H. D., Junior, JE C, 2019. Yield, quality and nutrient accumulation in watermelon as a function of organo-mineral fertilization. Comunicata Scientiae 10(1): 141-149.
• Geçioğlu Erincik, B, 2015. The prevalence and intensity of watermelon fusarium wilt disease in Aydın, the breeds of the causative Fusarium oxysporum f. Sp. niveum (Fon), vegetative compatibility groups and the reactions of some watermelon varieties to the agent. (Doctoral Thesis). Adnan Menderes University Institute of Science, Aydın. (In Turkish).
• Grzesiak, W., Zaborski, D, 2012. Examples of the use of data mining methods in animal breeding. Data mining applications in engineering and medicine, 303-324.
• Güner, N. Wehner, T.C, 2004. The genes of watermelon. Hortscience, 39(6): 1175- 1182.
• Güneş, R Aşkın, B, 2016. Chemical properties and nutritional content of watermelon seed oil. Food, 41(1): 37-44. (In Turkish)
• Güngör, B, Balkaya, A., 2015. Mini Watermelon Cultivation. Journal of Turkish Seed Growers Association. 2: 26-29.
• Irmak, S., Ercan, U, 2017. Determination of Factors Affecting Vegetable Oil Consumption with Data Mining Methods. Kafkas University Journal of Economics and Administrative Sciences Faculty, 8(15): 57-79. (In Turkish). Karadas, K. and Kadirhanogullari, İH, 2017. Predicting Honey Production using Data Mining and Artificial Neural Network Algorithms in Apiculture. Pakistan Journal of zoology 49 (5):1611-1619.
• Karadas, K., Birinci, A, 2019. Determination of factors affecting dairy cattle: a case study of Ardahan province using data mining algorithms. Revista Brasileira de Zootecnia, 48:1-11.
• Karadas, K., Tariq, M., Tariq, MM, Eyduran, E. 2017. Measuring Predictive Performance of Data Mining and Artificial Neural Network Algorithms for Predicting Lactation Milk Yield in Indigenous Akkaraman Sheep. Pakistan Journal of zoology, 49(1):1-7.
• Karakaya, E., Çelik, Ş., ve Taysı, MR, 2018. Investigation of Factors Affecting Fish Meat Consumption with CHAID Algorithm. Gaziosmanpaşa University Journal of Agriculture Faculty, 35 (2), 85-93. (In Turkish).
• Kavut, YT., Geren, H., Simiã, A, 2014. Effect of Different Plant Densities on The Fruit Yield and Some Related Parameters and Storage Losses of Fodder Watermelon (Citrillus lanatus var. Cit). Turkish Journal of Field Crops, 19(2), 226-230.
• Koç, Y., Eyduran, E., Akbulut, O, 2017. Application of Regression Tree Method for Different Data from Animal Science. Pakistan Journal of zoology, 49(2): 599-607.
• Koçkaya, MA, 2019. Effects of Different Fertilizer Types on Yield and Quality in Diyarbakır Watermelon (Citrullus Lanatus). (Master Thesis). Dicle University Institute of Science, Diyarbakır. (In Turkish).
• Koleboshina, T. G., Varivoda, E. A. 2020. Melon growing industry analysis in modern economic conditions. In IOP Conference Series: Earth and Environmental Science (Vol. 459, No. 6, p. 062075). IOP Publishing. april
• Küçükönder, H., Vursavuş, KK., Üçkardeş, F, 2015. Determining the Effect of Some Mechanical Properties on Color Maturity of Tomatoes by K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms. Turkish Journal of Agriculture Food Science and Technology, 3(5): 300-306. (in Turkish).
• Kuşcu, H., Turan, A., Özmen, N., Aydınol, P., Demir, AO., 2015. Effects of different irrigation regimes on water use efficiency, yield and fruit quality of watermelon under Bursa ecological conditions. Journal of Akdeniz University Faculty of Agriculture, 28(1): 21-26.
• Malueva, S. V., Bocherova, I. N., & Kornilova, M. S. 2020. Use of the source material in the selection of watermelon and melon. News of FSVC, (2), 68-72. Oktay, A., Doran, I. 2005. The Effects of Nitrogen Fertilization on Fruit Yield and Quality of Turkey's Largest Watermelon Spreading Variety. Journal of Akdeniz University Faculty of Agriculture, 18(3), 305-311.
• Okumuş, V, 2010. Biotechnological Research on the Micropropagation of Diyarbakır Watermelon Genotypes (Citrullus Lanatus Cv. 'White Winter', 'Karakış' and 'Spread'). (Doctoral Thesis). Dicle University Institute of Science, Diyarbakır. (In Turkish).
• Özçınar, S, 2020. A Characterization of Watermelon Mosaic Virus (Watermelon Mosaic Virus, Wmv) in Watermelon and Melon Fields of Calf and Mersin Provinces. (Master Thesis). Çukurova University Institute of Science, Adana. (In Turkish)
• Öztürk, N, 2018. Quantitative Real Time PCR Diagnosis and Detection of Watermelon Bacterial Fruit Spot Disease Acidovorax Citrulli and Investigation of Sensitivity Reactions of Watermelon and Melon Varieties. (Doctoral Thesis) Akdeniz University Institute of Science and Technology, Antalya. (In Turkish).
• Pejic, B., Mackic, k., Pavkovic, S., Lejevnaic-Masic, B., Aksic, M., Gvozdanovic-Varga, J, 2016. Water-Yield Relations of Drip Irrigated Watermelon in Temperate Climatic Conditions. Contemporary Agriculture 65(1):53-59.
• Rolbiecki, R., Rolbiecki, S., Piszczek, P., Figas, A., Jagosz, B., Ptach, W., Prus, P., Kazula, M. J, 2020. Impact of Nitrogen Fertigation on Watermelon Yield Grown on the Very Light Soil in Poland. Agronomy, 10:1-10.
• Şanlı, A., Kaya, M., Kara, B, 2009. Effects of Weed Control Times and Herbicide Applications on Yield and Some Yield Components in Chickpea (Cicer arietinum L.). Anatolian Journal of Agricultural Sciences, 24(1), 13-20. (In Turkish).
• Seçer, A., Çelik, F., Barut, H., 2020. The Factors Affecting the Producers’ Decision to Grow Fruit Tree and Their Expectations for the Future in Ağrı Province. Çukurova Journal of Agriculture and Food Science, 35(2): 77-88.
• Şimşek, A., Dinler, H., Duru, S., 2020. Determination of Approaches of the Stone Fruit Producers to the Phytopathological Problems of Uşak Province. International Journal of Life Sciences and Biotechnology, 3(2): 127- 147.
• Sun, J., Hui, LI, 2008. Data Mining Method for Listed Companies, Financial Distress Prediction. Knowledge-Based Systems, 21, No. 1.
• Sylvestre, H., Bosco, N. J., Emmanuel, N., Christine, U, 2014. Growth and yield of Watermelon as affected by different spacing and mulching types under Rubona conditions in Rwanda. Scholarly Journal of Agricultural Science, 4(10): 517-520.
• Tatlıyer, A, 2020. The Effect of Breeding Type on the Prediction Performance of Regression Tree Algorithms in Lambs. Kahramanmaraş Sütçü İmam University Journal of Agriculture and Nature,23(3), 772-780. (In Turkish).
• Tokgöz, H., Gölükcü, M., Toker, R., Turgut, DY, 2015. The Effects of Grafted Seedling Use and Harvest Time on Some Physical and Chemical Properties of Watermelon (Citrullus Lanatus), Gıda, 40(5), 263-270. (In Turkish).
• TUİK, 2021. Turkish Statistical Institute. https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr January 21, 2021. (In Turkish).
• Tuna, A. L., Ozer, O. 2005. Effect of Different Calcium Compounds on the Fruit Yield, Nutrition and some Quality Properties of Watermelon (Citrullus lanatus) Plant. Journal of The Faculty of Agriculture, 42(1), 203.
• Vural, Ç., Dağdelen, N, 2008. The Effects of Different Irrigation Programs on Yield and Some Agronomic Properties of Popcorn Irrigated by Drip Irrigation Method. Adnan Menderes University Journal of the Faculty of Agriculture, 5(2), 97-104. (In Turkish)
• Wehner, TC, 2010. Watermelon crop information. North Carolina State University. Raleigh, NC.
• Yamane, T, 2010. Basic Sampling Methods. Gazi University Faculty of Science and Letters, Department of Statistics, Literature Publications, No.53, 116 p., Istanbul (In Turkish)
• Yavuz, D., Seymen, M., Süheri, S., Yavuz, N., Türkmen, Ö., & Kurtar, E. S. 2020. How do rootstocks of citron watermelon (Citrullus lanatus var. citroides) affect the yield and quality of watermelon under deficit irrigation?. Agricultural Water Management, 241, 106351.
• Zaborski, D., Ali, M., Eyduran, E., Grzesiak, W., Tariq, M. M., Abbas, F., Waheed, A., Tirink, C, 2019. Prediction of Selected Reproductive Traits of Indigenous Harnai Sheep under the Farm Management System via various Data Mining Algorithms. Pakistan Journal of zoology, 51(2): 421-431.