Non-parametric analysis of the effects of nongenetic factors on milk yield, fat, protein, lactose, dry matter content and somatic cell count in Murciano-Granadina goats

Abstract

The objective of this work was to evaluate the influence of non-genetic factors on milk yield, its composition (fat, protein, dry matter and lactose) and the count of somatic cells in Murciano-Granadina goat breed, through the application of alternative nonparametric tests to routine parametric tests to explain the variability found in the population with respect the aforementioned traits. 2594 milk yield and composition records belonging to 159 goats from the selection nucleus were analysed. Predictors evaluated were farm, type of birth and parity order, live kids, parturition month, season and year, control number, control type, control month, season and year, days in lactation, days from first control, days from last control to drying, drying month, season and year. All of them presented a significant influence (p < .0001) on all the variables studied with the exception of the number of stillborn kids which did not significantly influence the percentage of each component and the year of drying which seemed not to significantly influence dry matter percentage. Conclusively, nongenetic factors affect milk yield and its compositions in the Murciano-Granadina goat breed. Additionally, the inclusion of the type of milk control and information related to the drying period in models predicting for milk yield and content may provide interesting information, which must be included in genetic evaluations to promote higher and better-quality milk production improving the profitability of autochthonous breeds. HIGHLIGHTSNon-genetic factors may affect milk components more than milk yield. Including factors related to lactation cycle can help identifying critical points. Drying-off period information promotes milk yield and quality. Studying nongenetic factors helps maximising milk predictive model potential. Factor combinations studied explain up to 41.8% of variability of milk yield.

Lactation Curve and Milk Production Traits of Syrian Damascus Goats

“…Similar findings were reported by Abraham et al (2017) who found that the highest DMY was recorded in the fourth parity and the lowest one was in the first parity in Begait goats in Ethiopia. Likewise, Pizarro et al (2020) found that, lowest DMY was observed at the first parity and increased with increasing parity till the fourth, then decreased of Murciano-Granadina goats in Spain. Khandoker et al (2018) reported that, the highest DMY in Saanen goats was recorded at 2-3 years of age in Malaysia.…”

“…Likewise, Zamuner et al (2020) found the highest DMY was 2.4 litres in February and the lowest was 1.8 litres in June in dairy goats in Australia. Pizarro et al (2020) found that goats kidded in January and March had DMY of 2.815 kg to 2.820 kg which is higher than those kidded in June to October (2.717 kg and 1.699 kg, respectively of Murciano-Granadina goats in Spain.…”

Conditioning Factors of Linearized Wood’s Function Lactation Curve Shape Parameters, Milk Yield, Fat and Protein Content in Murciano-Granadina Primiparous Does

“…Many of these variations are related to pasture curve and food availability during each season, which will influence not only milk production but also lactation length and the shapes which lactation curve describes [32]. Pizarro et al [33] found similar results to those in the present study on Murciano-Granadina goats and ascribed the lack of significance in literature found for protein and fat contents to studies only recording the information from animals with single and twin-parities, hence the influence of higher numbers of kids may be disregarded and responsible for our findings.…”

Changes in the goat’s milk composition due to heat stress at the farm of the Ukrainian steppe zone

“…Наразі існує можливість використання метеорологічних даних, доступних широкому колу фахівців з метою виявлення взаємозв'язку між станом навколишнього середовища та продуктивними якості молочних тварин, зокрема корів. Використання формули ТВІ = 1,8 × T -(1 -RH / 100) × (T -14,3) + 32, згідно якої значення нижче 68 відповідають комфортним умовам для корів, 68-71 -стан незначного, 72-79 -помірного, 80-89 -сильного стресу (Mylostyvyi & Chernenko, 2019).…”

“…Обсяг загального надою молока підвищується при збільшенні кількості козенят, що пояснюють гормональною стимуляцією від більшого утворення плацентарного гонадотропіну, прогестерону і пролактину при багатоплідній вагітності. Кози з двома козенятами давали майже на 15% більше молока, жиру та білка за лактацію, ніж при наявності лише одного козеняти (Hamzaoui et al, 2013;Bermejo et al, 2020;Pizarro et al, 2020).…”