Bence Tarr

Milk quality has a growing importance for farmers as component-based pricing becomes more widespread. Food quality and precision manufacturing techniques demand consistent milk composition. Udder health, general cow condition, environmental factors, and especially feed composition all influence milk quality. The large volume of routinely collected milk data can be used to build prediction models that estimate valuable constituents from other measured parameters. In this study, casein was chosen as the target variable because of its high economic value. We developed a multiple linear-regression model and a feed-forward neural network model to estimate casein content from twelve commonly recorded milk traits. Evaluated on an independent test set, the regression model achieved R² = 0.86 and RMSE = 0.018%, with mean bias = +0.003% and slope bias = −0.10, whereas the neural network improved performance to R² = 0.924 and RMSE = 0.084%. In silico microgreen inclusion from 0% to 100% of dietary dry matter raised the predicted casein concentration from 2.662% to 3.398%, a relative increase of 27.6%. To extend practical applicability, a simulation module was created to explore how microgreen supplementation might modify milk casein levels, enabling virtual testing of dietary strategies before in vivo trials. Together, the predictive models and the microgreen simulation form a cost-effective, non-invasive decision-support tool that can accelerate diet optimization and improve casein management in precision dairy production.

Nowadays, public opinion is determined by economic and environmental considerations, so when selecting beef cattle for breeding, the aim is to achieve the largest possible body size while reducing feed costs and methane emissions. Computer-controlled automatic feeding systems can measure the exact amount of feed consumed by animals, enabling the RFI value to be determined. This value expresses the difference between the amount of feed actually consumed by the animal and the amount expected based on its live weight and body weight gain.In 2024, we studied 29 Blonde d'Aquitaine breeding bulls that were fed using the Vytille system and housed in two pens in Taliándörögd. The animals were divided into four groups based on their RFI values and average daily weight gain, and their production data were analysed using a multivariate generalised linear model (GLM) method.Based on our results, it was not necessary to take pen effect into account, as the husbandry technology was perfectly suited to the purpose. The four groups were sufficient for selection based on feed conversion and daily weight gain. Taking all the measured values into account, the young bulls in the group with favourable RFI and above-average daily weight gain are the most promising.

A testméretek elemzése fontos szerepet játszik mind az élősúly becslés mind pedig a tenyészállat kiválogatás és minősítés szempontjából. Minden olyan módszer mely segíti a korai tenyészállat kiválogatást, nem csak hatékony eszköz a tenyésztői munkában, de költségmegtakarítást is jelent. Jelen tanulmányban egy limousin törzstenyészetből származó összesen 311 (146 bika és 165 üsző) választott borjú testméreteit (marmagasság, farmagasság, háthosszúság, marszélesség, csípőszélesség, ülőgumótávolság) elemezzük (2021 és 2022 év között) döntési fa eljárással. Vizsgálataink során azt találtuk, hogy az életkor és a testméretek között a korrelációs értékek rrang = 0,02-től, rrang = 0,36 -ig változtak (n=311, P≤0,01). Ugyan akkor az élősúly vonatkozásban, ugyan ezek az értékek az alábbiak voltak: rrang = 0,32; rrang = 0,77 (n=311, P≤0,01). Megállapíthatjuk, hogy ebben az életkorban, a választott borjak esetében az egyes testméretek alakulására az élősúly nagyobb befolyással bírt, az életkorhoz képest. Az is megállapítható, hogy a CHAID elemzés alkalmas a testméretek és az élősúly közötti összefüggéseinek feltárására, melynek külön előnye, hogy megjeleníti a homogén csoportok jellemzőit.

Body weight and body measurements are important economic traits in beef cattle sector and can be used as selection factors. At young ages they give information on the maturity of the animals. Body measurements (withers height, tail height, width at shoulders, back length, width at hip bones, pin width) of Limousine heifers (n=313; age: 429.2 days) were taken on six Transdanubian farms in Hungary by skilled technicians with standardized equipment under appropriate conditions. Statistical analysis was made by SPSS 24.0. Normal distribution of data was confirmed by Shaphiro-Wilks test. The GLM model was focused on the intercept, the effect of herds (n=1 to 6) and covariant factors (age or live weight). The results showed that body measurement data of heifers in the same region can be evaluated by GLM which contains the effect of herd and age or weight as covariant. All components involved in the models were proven to have significant effects (P<0.001). Regarding their age, heifers of all herds were adequately developed both in live weight (69.6%) and height measurements (84-93%) compared to reference values of mature cows, ensuring a strong basis for successful breeding work in the future. The farm with the heaviest (505.7 kg) heifers had lower values for withers- (118.8 cm) and tail height (127.8 cm), and narrower shoulder (30.6 cm) which draws attention to the fact that large weight is not necessarily paired with a large frame.

Factors influencing diagonal body length of Charolais cows (n=311) were analysed by the CHAID (Chi-squared Automatic Interaction Detector) method and are presented in this paper. CHAID algorithm groups the dependent variables (Y) so that the variance within groups is as low as possible and the variance between groups is as high as possible. The hierarchy of explanatory variables in this study was shown by the order of live weight, height at withers and age of the cows. The 1st, 2nd and 3rd nodus were formed by the live weight, according to the following categories: ≤600 kg, 600-645 kg, >645 kg. Diagonal body length increased parallel with the live weight categories. The 158 cows forming the first nodus were divided into two separate groups by the program, based on height at withers: 4th nodus (n=50, ≤ 128 cm); 5th nodus (n=108, >128 cm). Finally, the 5th nodus was separated – according to the age – into two groups in the last phase of the analysis: 6th nodus (n=53, ≤ 5.35 age, 172 cm); 7th nodus (n=55, > 5.35 age, 177 cm). Grouping individuals within the population would help to identify cow types, therefore CHAID analysis may be a possible useful method in beef cattle breeding.