Evidence map›Paper›PMID 36242599›Full record

ArticleTropical animal health and production2022

The estimation and interpretation of ordered logit models for assessing the factors connected with the productivity of Holstein-Friesian dairy cows in Egypt.

Sherif A Moawed, Ayman H Abd El-Aziz

Abstract read
In one paragraph

Article in Tropical animal health and production, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Sherif A MoawedDepartment of Animal Wealth Development (Biostatistics Division), Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522, Egypt. sherifmoawed@vet.suez.edu.eg.ORCID http://orcid.org/0000-0002-1460-6789
Ayman H Abd El-AzizAnimal Husbandry and Animal Wealth Development Department, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incorporation of novel technologies such as artificial intelligence, data mining, and advanced statistical methodologies have received wide responses from researchers. This study was designed to model the factors impacting the actual milk yield of Holstein-Friesian cows using the proportional odds ordered logit model (OLM). A total of 8300 lactation records were collected for cows calved between 2005 and 2019. The actual milk yield, the outcome variable, was categorized into three levels: low (< 4500 kg), medium (4500-7500 kg), and high (> 7500 kg). The studied predictor variables were age at first calving (AFC), lactation order (LO), days open (DO), lactation period (LP), peak milk yield (PMY), and dry period (DP). The proportionality assumption of odds using the logit link function was verified for the current datasets. The goodness-of-fit measures revealed the suitability of the ordered logit models to datasets structure. Results showed that cows with younger ages at first calving produce two times higher milk quantities. Also, longer days open were associated with higher milk yield. The highest amount of milk yield was denoted by higher lactation periods (> 250 days). The peak yield per kg was significantly related to the actual yield (P < 0.05). Moreover, shorter dry periods showed about 1.5 times higher milk yield. The greatest yield was observed in the 2nd and 4th parities, with an odds ratio (OR) equal to 1.75, on average. In conclusion, OLM can be used for analyzing dairy cows' data, denoting fruitful information as compared to the other classical regression models. In addition, the current study showed the possibility and applicability of OLM in understanding and analyzing livestock datasets suited for planning effective breeding programs.

Indexed as

Artificial IntelligencePlant BreedingAnimalsCattleEgyptFemaleLactationLogistic ModelsMilkHolstein–Friesian dairy cowsMilk productionOdds ratioOrdered logit modelsPredicted probabilityProportional odds

Identifiers

PMID36242599
PMCPMC9569299

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.