Forecasting Seasonal Milk Production Using MARS Algorithm for Multiple Continuous Responses in Holstein Dairy Cattles
Öz
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, Turkey. 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), lactation number (LD), 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 LD, 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 LD, 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.
Anahtar Kelimeler
Destekleyen Kurum
This research did not receive any specific grant from funding or financial support.
Kaynakça
- 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.
Öz
Kaynakça
- 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.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Hayvansal Üretim (Diğer) |
| Bölüm | Research Articles |
| Yazarlar | |
| Erken Görünüm Tarihi | 9 Şubat 2024 |
| Yayımlanma Tarihi | |
| Gönderilme Tarihi | 31 Ekim 2023 |
| Kabul Tarihi | 30 Ocak 2024 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 7 Sayı: 3 |