Internal and external quality characters of poultry eggs are quitely important to determine egg weight. Also, the quality of eggs is important for both hatching and egg production. The purpose of this study was modelling of egg weight with the MARS (Multivariate Adaptive Regression Splines) method using inner and outsider quality characters of egg in Lohmann LSL Classic white hybrit flock. For this purpose, the eggs of the Lohmann LSL Classic white hybrid flock (n=60) were used. Weekly egg yields were evaluated from the 22nd week to the 62nd week. In the research, for the prediction of dependent and continuous variable egg weight; shape index (SI), shell breaking resistance (SBS), shell weight (SW), shell thickness (ST), yolk diameter (YD), yolk width (YW), yolk height (YH), color (YC ), albumen length (AW), albumen height (AL) and albumen height (AH) were used. In order to obtain perfect goodness of fit, in the “earth” package of the R program, the definitions of penalty -1, degree = 2, nprune = 10 and nk = 60. The research, the mars prediction model was determined such as EW = 63.1-0.906 * max (0,75-SI)-0.32 * max (0, SI-75) -62.4 * max (0,0.57-ST) -354 * max (0, ST-0.57) + 1.13 * Groupa2 * max (0, 75-SI) + 1.49 * (0.0.57-ST) max * YD + 8.2*max(0, ST 0.57) * YD-0.02*(0 YD-38.5)max* YC-0.0366*YH * max(0,13-YC). As a result, some quality variables were found to be important in determining egg weight. Variables such as group a2, SI, YC, ST, YD, YH to estimate the weight of the egg determined as the dependent variable were used. Other variables are not included in this equation. In the poultry, the MARS prediction model may be a better alternative to classical nonlinear models in predicting egg weight since that it is easier and has higher accuracy.

The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.

Çalışmada, BAGGING ile MARS birleştirilerek elde edilen hibrid bir yaklaşımın yumurta tavuklarından elde edilen bir veri setinde sıcaklık ve nemin yumurta verimi, kırık/çatlak yumurta, ölen hayvan sayısı ve yem tüketimi üzerine etkisinin olup olmadığı test edilmiştir. Çalışmada Adana ili Çukurova Bölgesinde bulunan özel bir hayvancılık işletmesinden 2018 yılına ait yumurta üretim işletmesinden elde edilen veriler kullanılmıştır. Araştırmada, ortalama 60 haftalık yaşta olan Lohman ırkı tavuklardan elde edilen veri seti kullanılmıştır. Oluşan MARS ve Bagging MARS algoritmasında regresyon problemlerinin daha güçlü bir çözümünü sunmak için R STUDIO programındaki earth (enhanced adaptive regression through hinges) ve caret (classification and regression training), mda (Mixture Discriminant Analysis) paketleri kullanılmıştır. Bagging MARS tekniğinin tahmin performansı, bootstrap örnek sayısı olan B değeri 3 alınarak uyum iyiliği kriterleri ile değerlendirilmiştir. Çalışmada, MARS ve bagging MARS analizi kullanarak sıcaklık ve nemin yumurta verimi, kırık/çatlak yumurta, ölen hayvan sayısı ve yem tüketimi üzerine etkisi araştırılmıştır. Yumurta verimi üzerine, akşam sıcaklığının(t3) etkisinin olduğu fakat sabah(t1) ve öğle(t2) sıcaklıklarının ise etkisinin olmadığı belirtilmiştir. Kırık/çatlak yumurta ve ölen hayvan hayvan sayısı 5 den az/ hafta olmasından dolayı, MARS ve Bagging MARS modellerinde bu değişkenlere tahmin denkleminde yer verilmemiştir. Yem tüketiminin ise, her iki modelde de pozitif bir katkısının olduğu görülmüştür.

The aim of this study was to define the growth by using nonlinear functions in intensive feedlot system with XGBoost algorithm. To achieve this aim, five nonlinear functions were implemented. To implementation of the study, Brown Swiss (n=41) and Simental (n=95) breed were used. Each nonlinear functions were examined for each breed. According to the results of the nonlinear functions, logistic model was the best prediction model for defining the growth of each breed. In this study, the parameters in the best prediction model were calculated individually and the relationship of these parameters with body weight was evaluated with the XGBoost algorithm. Model comparison criteria such as standard deviation ratio (SDratio), Pearson’s correlation coefficient (PC), determination of coefficient (R2) and Akaike’s information criteria (AIC) were used to evaluate the XGBoost algorithm. In conclusion, the XGBoost algorithm can be an effective and optional approach that allows breeders to estimate live weight from growth parameters. This algorithm can operate on large data sets with high accuracy and speed, leading to significant improvements in agricultural productivity and animal health management. XGBoost enables more accurate predictions by analyzing the effects of various characteristics (e.g., nutritional level, breed, age). Therefore, this method can be used to determine critical parameters such as body weight in animal breeding practices, serving as a powerful support tool for operational decisions.

Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.

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.