آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 642 |
| تعداد مقالات | 6,700 |
| تعداد مشاهده مقاله | 9,213,502 |
| تعداد دریافت فایل اصل مقاله | 8,640,732 |
Investigation of Egg Quality Characteristics Affecting Egg Weight of Lohmann Brown Hen with Data Mining Methods | ||
| Poultry Science Journal | ||
| دوره 12، شماره 1، خرداد 2024، صفحه 107-117 اصل مقاله (1.12 M) | ||
| نوع مقاله: Original Paper | ||
| شناسه دیجیتال (DOI): 10.22069/psj.2024.21337.1934 | ||
| نویسندگان | ||
| Thobela Louis Tyasi* 1؛ Şenol Çelik2 | ||
| 1School of Agricultural and Environmental Sciences, Department of Agricultural Economics and Animal Production, University of Limpopo, South Africa | ||
| 2Department of Animal Science, Biometry Genetics Unit, Agricultural Faculty, Bingöl University, Bingöl, Türkey | ||
| چکیده | ||
| Hen egg weight is one of the most important traits in the egg production industry; however, the egg traits influencing it are poorly understood. Random forest (RF), multivariate adaptive regression spline (MARS), classification and regression trees (CART), bagging MARS, chi-square automatic interaction detector (CHAID), and exhaustive CHAID were used in egg weight (EW) prediction from selected egg quality characteristics in chicken. A total of 400 egg weight (EW), egg length (EL), egg width (EWD), shell weight (SW), yolk weight (YW), and albumen weight (AW) predictors were turned into account. The goodness-of-fit criteria were used to select the best model to estimate Lohman Brown hen egg weight. The data was separated into train and test datasets for validation using a 10-fold cross-validation. The most significant EW predictors were albumen weight, egg width, and egg length. The correlation coefficient (r) value ranged from 0.957 (CHAID) to 0.99999 (MARS and Bagging MARS). The lowest RMSE (0.001) was found for MARS and bagging MARS algorithms and the highest (2.154) was obtained for CHAID. In general, the implemented algorithms excellently predicted the EW of hens. The ascertainment of the egg quality characteristics associated with EW using data mining algorithms can be considered an indirect selection criterion for further chicken breeding programs. | ||
| کلیدواژهها | ||
| Egg؛ MARS؛ Chicken؛ Bagging MARS؛ Random forest | ||
| مراجع | ||
|
Ahmadi F & Rahimi F. 2011. Factors affecting quality and quantity of egg production in layers: a review. World Applied Sciences Journal, 12(3): 372–384.
Alabi OJ, Ng’ambi JW, Norris D & Egena SSA. 2012. Comparative study of three indigenous chicken breeds of South Africa: body weight and linear body measurements. Agricultural Journal, 7(3): 220–225. DOI: 10.3923/aj.2012.220.225
Alapatt A, Chaudhary JK, Shyamsana N, Tolenkhomba TC, Kalita G & Jagan Mohanarao G. 2022. Prediction of egg weight from egg quality characteristics by using regression analysis methods in White Leghorn chicken. International Journal of Livestock Research, 12(2): 40–48. DOI: 10.5455/ijlr.20210929030220
Biggs D, De Ville B & Suen B. 1991. A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18: 49–62. DOI: 10.1080/02664769100000005
Breiman L, Friedman JH, Olshen R & Stone CJ. 1984. Classification and regression tree, Wadsworth Brooks/Cole Advanced Books and Software, Pacific California.
Breiman L. 2001. Random forest, Mach. Learn., 45: 5–32.
Canga D & Boga M. 2019. Hayvancılıkta Mars Kullanımı Ve Bır Uygulama. III. International Scientific and Vocational Studies Congress – Science and Health 27–30 June 2019, Ürgüp, Nevşehir / Turkiye.
Canga D, Yavuz E & Efe E. 2021. Prediction of egg weight using MARS data mining algorithm through R. KSÜ Tarım ve Doğa Derg, 24(1): 242–251.
Chen JL & Li GS. 2014. Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables. Theoretical and Applied Climatology, 115: 627–638. DOI: 10.1007/s00704-013-0924-y
Chen T, Guestrin C & Boost XG. 2016. A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM; 785–794.
Eyduran E. 2020. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0. URL: https://CRAN.Rproject.org/package=ehaGoF. (access date: September 27, 2022).
Friedman JH. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19: 1-67.
Goh ATC, Zhang W, Zhang Y, Xiao Y & Xiang Y. 2016. Determination of earth396pressure balance tunnel-related maximum surface settlement: a multivariate adaptive regression splines approach. Bull. Bulletin of Engineering Geology and the Environment, 77: 489–500.
IBM Corp. 2019. Released. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. DOI: 10.3923/aj.2012.220.225
James G, Witten D, Hastie T, Tibshirani R. 2013. An introduction to statistical learning: with applications in R. New York: Springer-Verlag.
Kornacki J & Cwik J. 2005. Statistical learning systems (in Polish). WNT, Warsaw.
Kunn M & Johnson K. 2013. Applied predictive modeling. NY. DOI: 10.1007/978-1-4614-6849-3.
Kutu FR & Asiwe JAN. 2010. Assessment of maize and dry bean productivity under different intercrop systems and fertilization regimes. African Journal of Agricultural Research, 5: 1627–1631.
Liddle AR. 2007. Information criteria for astrophysical model selection. Monthly Notices of the Royal Astronomical Society: Letters, 377: L74-L78.
Liswaniso S, Qin N, Tyasi TL & Chimbaka IM. 2021. Use of data mining algorithms CHAID and CART in predicting egg weight from egg quality traits of indigenous free-range chickens in Zambia. Advanced Animal and Veterinary Science, 9(2): 215–220. DOI: 10.17582/journal.aavs/2021/9.2.215.220
Mathapo MC, Mugwabana TJ & Tyasi TL. 2022. Prediction of body weight from morphological traits of South African non-descript indigenous goats of Lepelle Nkumbi Local Municipality using different data mining algorithm. Tropical Animal Health and Production, 54: 102. DOI: 10.1007/s11250-022-03096-9.
Olaswumi SO & Ogunlade JT. 2008. Phenotypic correlation between some external and internal egg quality traits in the Exotic Isa Layer Breeds. Asian Journal of Poultry Science, 2(1): 30–35.
Olaswumi SO & Ogunlade JT. 2009. The effect of genotype and age of layer breeders on egg quality traits. Nigerian Journal of Animal Production, 36(2): 228–236. DOI: 10.51791/njap.v36i2.1339
Pires PGS, Baveresco C, Prato BS, Wirth ML & Moraes PO. 2021. The relationship between egg quality and hen housing systems - A systematic review. Livestock Science, 250: 104597. DOI: 10.1016/j.livsci.2021.104597
R Core Team. 2021. R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01).
Ramadhan MM, Sitanggang IS, Nasution FR & Ghifari. 2017. Parameter Tuning in Random Forest Based on Grid Search Method for Gender Classification Based on Voice Frequency. International Conference on Computer, Electronics and Communication Engineering (CECE 2017) ISBN: 978-1-60595-476-9
Škrbić Z, Lukić M, Petričević V, Bogosavljević-Bošković S, Rakonjac S, Dosković V & Tolimir N. 2020. Quality of eggs from pasture rearing layers of different genotypes. Biotechnology in Animal Husbandry, 36(2): 181–190. DOI: 10.2298/BAH2202125S
Stadelman WJ. 1977. Quality identification of shell eggs in egg science and technology. Ed. W.J. Stadelman, D.J. Cotterill, AVI Publishing Company Inc. Wesport, Connecticut 2nd Edition, pg. 33.
Takma C, Atil H & Aksakal V. 2012. Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields, Kafkas Univ Vet Fak Derg. 18: 941–944.
Troncoso A, Salcedo-Sanz S, Casanova-Mateo C, Riquelme JC & Prieto L. 2015. Local models-based regression trees for very short-term wind speed prediction. Renewable, Energy, 81: 589–598. DOI: 10.1016/j.renene.2015.03.071
Tso GKF & Yau KKW. 2007. Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks. Energy, 32(9): 1761–1768. DOI: 10.1016/j.energy.2006.11.010
Tutkun M, Denli M & Demirel R. 2018. Productivity and egg quality of two-layer hybrids kept in free-range system. Turkish Journal of Agriculture - Food Science and Technology, 6(10): 1444–1447. DOI: 10.24925/turjaf.v6i10.1444-1447.2070
Ukwu HO, Ezihe CO, Asaa SK & Anyogo ME. 2017. Effect of egg weight on external and internal egg quality traits of Isa Brown egg layer chickens in Nigeria. Veterinary and Animal Science, 2: 126–132. https://doi. org/10.31248/JASVM2017.051
Willmott A & 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: 79–82. DOI: 10.3354/cr030079
Yang HM, Yang Z, Wang W, Wang ZY, Sun HN, Ju XJ & Qi XM. 2014. Effects of different housing systems on visceral organs, serum biochemical proportions, immune performance and egg quality of laying hens. European Poultry Science, 78: 1–9. DOI: 10.1399/eps.2014.48
Zhang W & Goh ATC. 2016. Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers, 7: 45–52. DOI: 10.1016/ j.gsf.2014.10.003 | ||
|
آمار تعداد مشاهده مقاله: 464 تعداد دریافت فایل اصل مقاله: 517 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب