Forecasting Seasonal Milk Production Using MARS Algorithm for Multiple Continuous Responses in Holstein Dairy Cattles

Year 2024, , 203 - 214, 15.05.2024

Demet Çanga Boğa , Mustafa Boğa , Mutlu Bulut

https://doi.org/10.47115/bsagriculture.1383832

Abstract

In this study, seasonal milk yield estimation will be made using multivariate adaptive regression spline (MARS) algorithm for multiple continuous responses in dairy cattle (Holstein hybrid). For the research, milking records for the years 2020-2021 were collected from 157 dairy animals using Holstein hybrid dairy cattle from a research farm in Konya, Türkiye. The amount of feed given in this experiment was not changed and the effect of the season on the estimation of milk yield was investigated in the study. The analyzed independent variables used in the study were pregnancy status (PS), number of days milked (MDN), Lactation Number (LN), age of cows (months), average seven-day milk yield (7-Day Average Milk-SDMY), last lactation milk yield (last_MY), number of inseminations (IN), peak yield (Pik_Yield) and target variables were calculated as (YieldAutumn/winter/spring/summer (kg) = Mean milk mean of season. In this context, the ehaGoF package was used to measure the prediction performance of the simultaneous MARS model established with the earth package for MARS analysis. MARS estimation equations obtained simultaneously for four dependent variables (multiple responses) are given. By looking at the MARS equation, the MARS model estimation equation was determined for the optimum milk yield, the threshold values, the three threshold values determined in the model were determined as MDN, Age, Peak_Yield, and the corresponding values were respectively; 159 days, 39.6 (months) and 37.1 kg/day. Considering the estimation equation, it is seen that the independent variables MDN, SDMY and LN are the most important variables in determining the estimation equation. It is seen that the best fitting value for the estimation equation of the dependent variables is the YieldWinter variable.

Keywords

MARS, Multiple response, Dairy cattle, Milk yield

Supporting Institution

This research did not receive any specific grant from funding or financial support.

References

• Akin M, Eyduran SP, Eyduran E. 2020a. R Yazılımı ile Mars (Multivariate Adaptive Regression Splines) Algoritması. Nobel Academic Publishing, Ankara, Türkiye, pp: 264.
• Akin M, Eyduran SP, Eyduran E, Reed BM. 2020b. Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. J Plant Biotechnol, 140(3): 661-670. https://doi.org/10.1007/S11240-019-01763-8.
• Altay Y, Aytekin İ, Eyduran E. 2022. Use of Multivariate Adaptive Regression Splines, Classification tree and Roc curve in diagnosis of subclinical mastitis in dairy cattle. J Hellenic Vet Med Soc, 73(1): 3817-3826. https://doi.org/10.12681/jhvms.25864.
• Bayril T, Yilmaz O. 2017. Holştayn sütçü ineklerde süt verim performanslarına buzağı cinsiyeti, servis periyodu, doğum sayısı ve buzağılama mevsiminin etkisi. Dicle Üniv Vet Fak Derg, 10(2): 89-94.
• Boğa M, Çevik KK, Burgut A. 2020. Classifying milk yield using deep neural network. Pakistan J Zool, 52(4): 1319-1325. https://doi.org/10.17582/journal.pjz/20190527090506.
• Bouallegue M, Haddad B, Aschi M, Ben H. 2013. Effect of environmental factors on lactation curves of milk production traits in Holstein – Friesian cows reared under North African condition. Livestock Res Rural Devel, 25(5): 37.
• Çanga D, Boga M. 2019. Use of MARS in livestock and an application. III. International Scientific and Vocational Studies Congress, December 20, Nevşehir, Türkiye, pp: 31-37.
• Çanga D, Boğa M. 2020. Determination of the effect of some properties on egg yield with regression analysis met-hod bagging Mars and R application. Turkish J Agri Food Sci Technol, 8(8): 1705-1712. https://doi.org/10.24925/turjaf.v8i8.1705-1712.3468.
• Çanga D. 2022. Use of Mars data mining algorithm based on training and test sets in determining carcass weight of cattle in different breeds. J Agr Sci, 28(2): 259-268. https://doi.org/10.15832/ankutbd.818397.
• Çelik Ş, Eyduran E, Şengül AY, Şengül T. 2021. Relationship among egg quality traits in Japanese quails and prediction of egg weight and color using data mining algorithms. Trop Anim Health Prod, 53(3): 382. https://doi.org/10.1007/s11250-021-02811-2.
• Çelik Ş, Yılmaz O. 2018. Prediction of body weight of Turkish tazi dogs using data mining techniques: Classification. Pakistan J Zool, 50(2): 575-583. https://doi.org/10.17582/journal.pjz/2018.50.2.575.583.
• Çelik Ş, Yilmaz O. 2021. The relationship between the coat colors of kars shepherd dog and its morphological characteristics using some data mining methods. IJLR, 11(1): 53-61. https://doi.org/10.5455/ijlr.20200604.
• Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T GH. 2015. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). J Hydrol, 529(3): 1590-1600
• Everingham YL, Sexton J. 2011. An introduction to Multivariate Adaptive Regression Splines for the cane industry. Proceedings of the 2011 Conference of the Australian Society of Sugar Cane Technologists, May 4-6, Palm Cove, Australia.
• Eyduran E, Akın M, Eyduran SP. 2019. Application of Multivariate Adaptive Regression Splines through R Software. Nobel Bilimsel Eserler, Ankara, Türkiye, pp: 112.
• Eyduran E, Yakubu A, Duman H, Aliyev P, Tırınk C. 2020. Predictive modeling of multivariate adaptive regression splines: An R Tutorial. In: Veri Madenciliği Yöntemleri: Tarım Alanında Uygulamaları. Rating Academy Ar-Ge Yazılım Yayıncılık Eğitim Danışmanlık ve Organizasyon Ticaret Limited Şirketi, Ankara, Türkiye, pp: 25-48.
• Eyduran E, Yilmaz I, Tariq MM, Kaygisiz A. 2013. Estimation of 305-d milk yield using regression tree method in brown Swiss cattle. J Anim Plant Sci, 23(3): 731-735.
• Eyduran E, Zaborski D. 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 J Zool, 49(1): 257-265. https://doi.org/10.17582/journal.pjz/2017.49.1.257.265.
• Eyduran E. 2020. Package ‘EhaGoF’. URL: https://cran.r-project.org/package=ehaGoF (accessed date: June 12, 2023).
• Faraz A, Tirink C, Eyduran E, Waheed A, Tauqir NA, Nabeel MS, Tariq MM. 2021. Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using cart and mars. Trop Anim Health Prod, 53(2): 301. https://doi.org/10.1007/s11250-021-02748-6.
• Fatih A, Çelik S, Eyduran E, Tirink C, Masood MT, Sheikh IS, Faraz A, Abdul Waheed A. 2021. Use of MARS algorithm for predicting mature weight of different camel (Camelus dromedarius) breeds reared in Pakistan and morphological characterization via cluster analysis. Trop Anim Health Prod, 53(1): 191. https://doi.org/10.1007/s11250-021-02633-2.
• Friedman JH. 1999. Multivariate adaptive regression splines. Annals Stat, 19: 67.
• Grzesiak W, Zaborski D. 2012. Examples of the use of data mining methods in animal breeding. In: Data Mining Applications in Engineering and Medicine. IntechOpen, pp: 304-321. https://doi.org/10.5772/50893.
• Javed K, Babar ME, Abdullah M. 2007. Within-herd phenotypic and genetic trend lines for milk yield in Holstein-Friesian dairy cows. J Cell Anim Biol, (4): 66-70.
• Küçükönder H, Boğa M, Burğut A ÜF.2015. Yapay sinir ağları ile laktasyon süt veriminin modellenmesi. Hayvansal Üretim, 56(2): 22-27.
• Küçükönder H, Üçkardeş F, Narinç D. 2014. A data mining application in animal breeding: Determination of some factors in Japanese quail eggs affecting fertility. Kafkas Univ Vet Fak Derg, 20(6): 903-908. https://doi.org/10.9775/kvfd.2014.11353.
• Milborrow S. 2019. Earth: Multivariate Adaptive Regression Splines (MARS). Annals Stat, 19(1): 1-67. https://doi.org/10.1214/aos/1176347963.
• Nayana BM, Kumar KR, Chesneau C. 2022. Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS). Agri Eng, 4(2): 461-474. https://doi.org/10.3390/AGRIENGINEERING4020030.
• Novak P, Vokralova J, Broucek J. 2009. Effects of the stage and number of lactation on milk yield of dairy cows kept in open barn during high temperatures in summer months. Archiv Tierzucht, 2: 574-586.
• Omar MY. 2022. Comparison of reproductive performance between holstein and simmental cows in-terms of milk production, milk yield persistence, first lactation peak point and it is duration. MSc Thesis, Bursa Uludağ University, Institute of Health Science, Veterinary Sciences, Bursa, Türkiye, pp: 61.
• Torshizi ME. 2016. Effects of season and age at first calving on genetic and phenotypic characteristics of lactation curve parameters in Holstein cows. J Anim Sci Technol, 58: 8. https://doi.org/10.1186/s40781-016-0089-1.
• Tyasi TL, Eyduran E, Çelik S. 2021. Comparison of tree-based regression tree methods for predicting live body weight from morphological traits in Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breeds raised in South Africa. Trop Anim Health Prod, 53(1): 7. https://doi.org/10.1007/s11250-020-02443-y.
• Vijayakumar M, Park JH, Ki KS, Lim DH, Kim SB, Park SM, Jeong HY, Park BY, Kim TI. 2017. The effect of lactation number, stage, length, and milking frequency on milk yield in Korean Holstein dairy cows using automatic milking system. Asian-Australas J Anim Sci, 30(8): 1093-1098. https://doi.org/10.5713/AJAS.16.0882.