Determination of Factors Affecting Net Profit in Buffalo Milk Production by Different Data Mining Algorithms: A Case Study of Iğdır Province

Abstract

Buffalo milk is an important animal product because it has more protein and fat content compared to other types of milk. This study aimed to identify the key factors influencing profitability in buffalo milk production using advanced data mining algorithms. Data were collected from 92 buffalo farms in Iğdır Province, Türkiye, in 2016 by using the Simple Random Sampling Method. Among the 4 models developed in the R program, the Multivariate Adaptive Regression Splines (MARS) model demonstrated superior predictive performance based on cross-validation and goodness-of-fit criteria. The results revealed that lactation year (LY) and lactation period (LP) were the most significant variables affecting net profit. Profitability was highest in the seventh lactation year, while extending the lactation period beyond 175 days contributed to linear profit increases. The findings suggest that buffalo producers should adopt management strategies focused on culling buffaloes after the seventh lactation and extending lactation periods to improve economic outcomes. This research highlights the effectiveness of data mining techniques in determining profitability factors and provides recommendations to optimize production efficiency in livestock systems. In future research, more comprehensive models can be developed using larger datasets and additional variables.

Keywords

• Buffalo milk
• Net profit
• Data Mining Algorithms
• Iğdır province

References

1. Akin, M., Hand, C., Eyduran, E., & Reed, B. M. (2018). Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees. Plant Cell, Tissue and Organ Culture, 132(3), 545–559. https://doi.org/10.1007/s11240-017-1353-x.
2. Anonymous. (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
3. Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2020). Multivariate adaptive regression splines (MARS) approach to blast-induced ground vibration prediction. International Journal of Mining, Reclamation and Environment, 34(3), 198–222.
4. Atasever, S., & Erdem, H. (2008). Manda yetiştiriciliği ve türkiye’deki geleceği. Journal of the Faculty of Agriculture, 23(1), 59–64.
5. Aydoğdu, M. A., & Şahin, Z. (2022). Analysis of the recent periods of changes in water buffalo presence and milk production quantities in Turkey. International Journal of Social, Humanities and Administrative Sciences, 8(51), 612–616.
6. Balhara, S., Singh, R. P., & Ruhil, A. P. (2021). Data mining and decision support systems for efficient dairy production. Veterinary World, 14(5), 1258–1262.
7. Becskei, Z., Savić, M., Ćirković, D., Rašeta, M., Puvača, N., Pajić, M., Đorđević, S., & Paskaš, S. (2020). Assessment of water buffalo milk and traditional milk products in a sustainable production system. Sustainability, 12(16), 1–13.
8. Biecek, P. (2018). DALEX: Explainers for Complex Predictive Models in R. Journal of Machine Learning Research, 19(84), 1–5.

9. Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With Examples in R and Python. New York: Chapman and Hall. https://pbiecek.github.io/ema/.
10. Boehmke, B., & Greenwell, B. (2020). Hands-on Machine Learning with R. Chapman and Hall/CRC.
11. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. New York: Chapman & Hall.
12. Canbolat, Ö. (2012). Buffalo breeding and current situation in Turkey. Journal of Tarım Türk, 30, 176–180.
13. Çelik, Ş. (2019). Comparing predictive performances of tree-based data mining algorithms and mars algorithm in the prediction of live body weight from body traits in pakistan goats. Pakistan Journal of Zoology, 51(4), 1447–1456. https://doi.org/10.17582/journal.pjz/2019.51.4.1447.1456.
14. Çelik, Ş., & Yilmaz O. (2018) Prediction of Body Weight of Turkish Tazi Dogs using Data Mining Techniques: Classiication and Regression Tree (CART) and Multivariate Adaptive Regression Splines (MARS). Pakistan Journal of Zoology, 50(2),575-583.
15. Eyduran, E., Zaborski, D., Waheed, A., Çelik, Ş., Karadaş, 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 indigenous beetal goat of pakistan. Pakistan Journal of Zoology, 49(1).
16. FAO. (2022). Food and Agriculture Organization of the United Nations. Crops and Livestock Products. https://www.fao.org/faostat/en/#data/QCL
17. Faraz, A., Tirink, C., Eyduran, E., Waheed, A., Tauqir, N. A., Nabeel, M. S., & Tariq, M. M. (2021). Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using CART and MARS. Tropical Animal Health and Production, 53(2), 301. https://doi.org/10.1007/s11250-021-02748-6.
18. Fox, J., & Weisberg, S. (2019). An R Companion to Applied Regression. London: SAGE.
19. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 1–67.
20. Galsar, N. S., Shah, R. R., Gupta, J. P., Pandey, D. P., & Patel, K. B. (2016). Analysis of first production and reproduction traits of Mehsana buffaloes maintained at tropical and semi-arid region of Gujarat, India. *Life Sciences Leaflets*, 4297(77), 65–75.
21. Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: Concepts, tools, and techniques to build intelligent systems (2nd ed.). O'Reilly Media.
22. Guo, M., & Hendricks, G. (2010). Improving buffalo milk. In M. Griffiths (Ed.), Improving the safety and quality of milk (Vol. 2, pp. 402–416). Woodhead Publishing.
23. Işik, M., & Gül, M. (2016). Economic and social structures of water buffalo farming in Muş Province of Türkiye. i]Revista Brasileira de Zootecnia, 45(7), 400–408.
24. Kaygısız, A. (1999). Lactation curve traits of native buffaloes. Tarım Bilimleri Dergisi, 5(1), 1–8.
25. Khedkar, C. D., Kalyankar, S. D., & Deosarkar, S. S. (2016). Buffalo milk. In B. Caballero, P. Finglas, & F. Toldrá (Eds.), The encyclopedia of food and health 1, 522–528. Academic Press.
26. Konca, Y., & Yılmaz Adkinson, A. (2021). Manda eti üretimi ve kalite özellikleri Water buffalo meat production and quality characteristics]. European Journal of Science and Technology, 31(1), 420–428.
27. Kuhn, M., & Falbel, D. (2022). Brulee: High-level modeling functions with 'Torch' [Computer software]. https://CRAN.R-project.org/package=brulee
28. Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. https://doi.org/10.1007/978-1-4614-6849-3
29. Kuhn, M., & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles [Computer software]. https://www.tidymodels.org
30. Luna Palomera, C., Dominguez-Viveros, J., Aguilar-Palma, G. N., Castillo-Rangel, F., Sanchez-Dávila, F., & Macias-Cruz, U. (2021). Analysis of the lactation curve of Murrah buffaloes with mixed non-linear models. Chilean Journal of Agricultural & Animal Sciences (ex Agro-Ciencia), 37(1), 200–208.
31. Maksymiuk, S., Gosiewska, A., & Biecek, P. (2020). Landscape of R packages for explainable artificial intelligence. arXiv. https://arxiv.org/abs/2009.13248
32. Mane, B. G., & Chatli, M. K. (2015). Buffalo milk: Saviour of farmers and consumers for livelihood and providing nutrition. Agricultural and Rural Development, 2, 5–11.
33. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115–133. https://doi.org/10.1007/BF02478259
34. Milborrow, S. (2011). Earth: Multivariate adaptive regression splines [Computer software]. http://CRAN.R-project.org/package=earth
35. Nwanganga, F., & Chapple, M. (2020). Practical machine learning in R. Wiley. https://doi.org/[DOI if available]
36. Okut, H., Wu, X.-L., Rosa, G. J. M., Bauck, S., Woodward, B. W., Schnabel, R. D., Taylor, J. F., & Gianola, D. (2013). Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. Genetics Selection Evolution, 45(1), 34. https://doi.org/10.1186/1297-9686-45-34
37. Park, W. Y., & Haenlein, G. F. W. (2008). Buffalo milk: Utilization for dairy products. In Y. W. Park & G. F. W. Haenlein (Eds.), Handbook of milk of non-bovine mammals (pp. 195–274). Wiley-Blackwell.
38. Pudja, P., Djerovski, J., & Radovanović, M. (2008). An autochthonous Serbian product—Kajmak characteristics and production procedures. Dairy Science and Technology, 88, 163–172.
39. Sabia, E., Napolitano, F., Claps, S., Braghieri, A., Piazzolla, N., & Pacelli, C. (2015). Feeding, nutrition and sustainability in dairy enterprises: The case of Mediterranean buffaloes. In A. Vastola (Ed.), The sustainability of agro-food and natural resource systems in the Mediterranean basin (pp. 57–64).
40. Şahin, A., Aksoy, Y., Ulutaş, Z., Yıldırım, A., & Sarıkaya, Ö. (2024). Anadolu mandalarının ilk üç laktasyonlarına ait laktasyon eğrisi parametrelerinin ve eğri şeklinin belirlenmesi [Determination of lactation curve parameters and curve shape for the first three lactations of Anatolian buffaloes]. Journal of Animal Sciences and Products, 7(1), 12–18.
41. Sahraei, M. A., Duman, H. Muhammed Y., & Eyduran, E. (2021). Prediction Of Transportation Energy Demand: Multivariate Adaptive Regression Splines Energy, 224.
42. Sarıözkan, S. (2011). Türkiye’de manda yetiştiriciliği’nin önemi [The importance of buffalo breeding in Türkiye]. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 17(1), 163–166.
43. Soysal, M. İ., Genç, S., Aksel, M., Ünal, E. Ö., & Gürcan, E. K. (2018). Effect of environmental factors on lactation milk yield, lactation length and calving interval of anatolian buffalo in Istanbul. Journal of Animal Science and Products, 1(1), 93–97.
44. Sweers, W., Möhring, T., & Müller, J. (2014). The Economics Of Water Buffalo (Bubalus Bubalis) Breeding, Rearing And Direct Marketing. Archiv Tierzucht, 57(22), 1–11.
45. Therneau, T., & Atkinson, B. (2022). Rpart: Recursive Partitioning And Regression Trees [R package]. https://CRAN.R-project.org/package=rpart
46. Titterington, M. (2010). Neural networks. WIREs Computational Statistics, 2(1), 1–8. https://doi.org/10.1002/wics.50
47. Toparslan, E., & Mercan, L. (2018). Türkiye yerli manda popülasyonlarında yapılan moleküler genetik çalışmalar [Molecular genetic studies on native water buffalo populations in Türkiye]. Academia Journal of Engineering and Applied Sciences, Special Issue.
48. Turkish Statistical Institute (TÜİK). (2022). Data portal for statistics: Consumer price index. https://data.tuik.gov.tr/Bulten/Index?p=Tuketici-Fiyat-Endeksi-Aralik-2022-49651
49. Turkish Statistical Institute (TÜİK). (2023). Data portal for statistics: Livestock statistics. https://biruni.tuik.gov.tr/medas/?kn=79&locale=tr
50. Weber, G.-W., Batmaz, I., Köksal, G., Taylan, P., & Yerlikaya-Özkurt, F. (2012). CMARS: A new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Problems in Science and Engineering, 20(3), 371–400.
51. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Research, 30(1), 79–82.
52. Yenimez, K., Doğa, H., & Özbaşer, F. T. (2022). environmental factors influencing milk yield and lactation length in italian mediterranean buffaloes in Türkiye. Journal of the Hellenic Veterinary Medical Society, 73(3), 4296–4302.
53. Yeo, I.-K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. biometrika, 87(4), 954–959.
54. Yılmaz Adkinson, A., & Konca, Y. (2021). Sütçü manda ırklarının performans ve verimliliğini etkileyen faktörler ve Türkiye’deki geleceği [Factors affecting the performance and productivity of dairy buffalo breeds and their future in Türkiye]. European Journal of Science and Technology, 25, 498–508.
55. Yılmaz, O., & Ertuğrul, M. (2011). Domestic livestock resources of Türkiye: water buffalo. Tropical Animal Health and Production, 44,707-714. https://doi.org/10.1007/s11250-011-9957-3
56. Zhang, J., Liu, Z., Shi, Z., Jiang, L., & Ding, T. (2023). Milk yield prediction and economic analysis of optimized rearing environment in a cold region using neural network model. Agriculture, 13. https://doi.org/10.3390/agriculture13122206