Publications by authors named "Joao Durr"
Article Synopsis
- Johne's disease (JD) is a major infectious disease affecting ruminants like cattle, leading to significant economic losses globally; this study focused on assessing the genetic traits related to JD susceptibility in Holstein cows in the U.S.* -
- Using extensive data from 365,980 Holstein cows, researchers analyzed milk samples to determine JD incidence, which averaged 4.72%, and estimated the heritability of susceptibility to JD with varying results depending on the models used.* -
- The findings revealed a negative genetic trend for JD susceptibility despite no previous genetic selection efforts, and the genetic correlations between JD susceptibility and important traits like milk yield were notably low.*
Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF).
View Article and Find Full Text PDFThis study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model.
View Article and Find Full Text PDFArticle Synopsis
- In the U.S., milk yields are calculated from test-day yields, which are derived from partial yields from single milkings, with various methods developed to address issues from uneven milking intervals since the 70s and 80s.
- The Wiggans model is a widely used method for estimating test-day yields, but it assumes a linear relationship that doesn't fit well with Holstein cows milked twice a day due to inconsistent milking intervals.
- Recent studies highlight nonlinear models as more accurate alternatives to conventional methods, offering improved estimation of test-day milk yields by avoiding inaccuracies from simplified interval corrections and utilizing flexible modeling approaches.
Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night.
View Article and Find Full Text PDFCost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows.
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- Genetic selection has been effective in improving calving traits like stillbirth in dairy cattle, with the study focusing on five non-Holstein breeds: Ayrshire, Guernsey, Milking Shorthorn, Brown Swiss, and Jersey.
- Data from the U.S. national calving ease database indicated that Jersey and Brown Swiss had a significant number of stillbirth records (about 486K and over 80K, respectively) while the other three breeds had too few records for reliable evaluations.
- Genetic evaluations showed varying heritability estimates for stillbirth in Jersey (6.0% for direct, 4.7% for maternal) and Brown Swiss (6.8% for direct, 1.1% for maternal
Article Synopsis
- There is growing interest in using residual feed intake (RFI) as a way to measure how efficiently dairy cattle convert feed into energy, considering factors like body tissue mobilization.
- The authors developed a method to interpret RFI through a rearranged linear regression model, highlighting how energy allocation impacts dry matter intake without simultaneous effects.
- They proposed a Bayesian recursive structural equation model to predict RFI and energy allocation, which allows for estimating genetic parameters and extends the analysis to consider multiple traits simultaneously.
Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago.
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