scholarly journals The effect of reproductive performance on the dairy cattle herd value assessed by integrating a daily dynamic programming model with a daily Markov chain model

2012 ◽  
Vol 95 (10) ◽  
pp. 6160-6170 ◽  
Author(s):  
A.S. Kalantari ◽  
V.E. Cabrera
Keyword(s):  
Dynamic Programming ◽  
Markov Chain ◽  
Dairy Cattle ◽  
Reproductive Performance ◽  
Programming Model ◽  
Markov Chain Model ◽  
Cattle Herd ◽  
Chain Model ◽  
Dairy Cattle Herd ◽  
Dynamic Programming Model

scholarly journals Records of performance and sanitary status from a dairy cattle herd in southern Brazil

2011 ◽  
Vol 31 (1) ◽  
pp. 01-09 ◽  
Author(s):  
Cláudio E. F. Cruz ◽  
Djeison L. Raymundo ◽  
Cristine Cerva ◽  
Saulo P. Pavarini ◽  
André G. C. Dalto ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Reproductive Performance ◽  
Cattle Herd ◽  
Lactation Period ◽  
Mean Values ◽  
Cattle Diseases ◽  
Dairy Cattle Herd ◽  
The Mean ◽  
The Impact ◽  
Voluntary Waiting Period

Over the last decades, the emphasis on the health of dairy cows has changed from an individual to a herd level. In this scenario, the role played by the recording system and its interpretation by veterinarians has gained primordial importance. The records of productive and reproductive performance and of sanitary status from a southern Brazilian dairy cattle herd have been presented and discussed. The period of study was 2000-2009. Mean values per lactation period were 349D 8436M 290F 275P 201SCS (D: days in lactation, M: kg of milk yield, F: kg of fat, P: kg of protein and SCS: somatic cell score in 1000 cells/ml of milk). Major indexes of reproductive efficiency included age at first calving (31 months), services per conception (2.1), intercalving interval (428 days), calving to conception interval (146 days), mean annual rates of parturitions (76.2%), fetal losses (9.8-19.0%), and stillbirths (3.6%), apart of voluntary waiting period (94 days). Main information on sanitary status of the herd was associated with the mean prevalence of common disorders of dairy cattle such as anaplasmosis (29.8%), mastitis (27.8%), digital diseases (26.3%), ovarian cysts (21.3%), placental retention (19.7%), postpartum uterine infections (10.6%), and calf diarrhea (23.7%) and pneumonia (16.8%), among others. In addition, culling reasons (low reproductive performance [56.3%] and udder/mastitis problems [33.6%]), causes of cattle deaths (anaplasmosis [16.4%] and leukosis [11.4]), and the impact of cattle diseases such as tuberculosis, leukosis, and neosporosis on the herd have also been presented and succinctly discussed. Numbers between brackets represent rates accumulated in the 10-year period.

scholarly journals Understanding the reproductive performance of a dairy cattle herd by using both analytical and systemic approaches: a case study based on a system experiment

animal ◽  
2010 ◽  
Vol 4 (6) ◽  
pp. 827-841 ◽  
Author(s):  
L. Gouttenoire ◽  
J.L. Fiorelli ◽  
J.M. Trommenschlager ◽  
X. Coquil ◽  
S. Cournut
Keyword(s):  
Dairy Cattle ◽  
Reproductive Performance ◽  
Cattle Herd ◽  
System Experiment ◽  
Dairy Cattle Herd

Stochastic economic evaluation of dairy farm reproductive performance

2015 ◽  
Vol 95 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Afshin S. Kalantari ◽  
Victor E. Cabrera
Keyword(s):  
Markov Chain ◽  
Economic Evaluation ◽  
Reproductive Performance ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Dairy Farm ◽  
Chain Model ◽  
Baseline Scenario ◽  
State Variables ◽  
Net Return

Kalantari, A. S. and Cabrera, V. E. 2015. Stochastic economic evaluation of dairy farm reproductive performance. Can. J. Anim. Sci. 95: 59–70. The objective of this study was to assess the economic value of reproductive performance in dairy farms under uncertain and variable conditions. Consequently, the study developed methods to introduce stochasticity into transition probabilities of a Markov chain model. A robust Markov chain model with 21-d stage length and three state variables, parity, days in milk, and days in pregnancy, was developed. Uncertainty was added to all transition probabilities, milk production level, and reproductive costs. The model was run for 10 000 replications after introducing each random variable. The expected net return (US$ cow−1 yr−1±standard deviation) was $3192±75.0 for the baseline scenario that had 15% 21-d pregnancy rate (21-d PR). After verifying the model's behavior, it was run for 2000 replications to study the effect of changing 21-d PR from 10 to 30% with one-unit-percentage interval. The economic gain of changing 21-d PR from 10 to 30% resulted in a US$75 cow−1 yr−1, and this overall increase in the net return was observed mainly due to the lower reproductive and culling cost and higher calf value. The gain was even greater when milk price and milk cut-off threshold decreased.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

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