Evidence map›Paper›PMID 42662554›Full record

ArticleArchives animal breeding2025

Predicting body weight of male Kuroiler chickens from linear body measurements using MARS and CART data-mining algorithms.

Simushi Liswaniso, Ruth Kasonso, Lubabalo Bila, Madumetja Cyril Mathapo, Oswin Chibinga, Thobela Louis Tyasi, Xue Sun, Rifu Xu, Ning Qin

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Article in Archives animal breeding, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Simushi LiswanisoSchool of Agricultural Sciences, Department of Animal Sciences, University of Zambia, P.O. Box 32379, Lusaka, Zambia.
Ruth KasonsoSchool of Agricultural Sciences, Department of Animal Sciences, University of Zambia, P.O. Box 32379, Lusaka, Zambia.
Lubabalo BilaPotchefstroom College of Agriculture, Department of Animal Production, Private Bag X1292, Potchefstroom, 2520, South Africa.
Madumetja Cyril MathapoSchool of Agricultural and Environmental Sciences, Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X1106, Sovenga 0727, Limpopo, South Africa.
Oswin ChibingaSchool of Agricultural Sciences, Department of Animal Sciences, University of Zambia, P.O. Box 32379, Lusaka, Zambia.
Thobela Louis TyasiSchool of Agricultural and Environmental Sciences, Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X1106, Sovenga 0727, Limpopo, South Africa.
Xue SunDepartment of Animal Breeding, Genetics and Reproduction, College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, Jilin, China.
Rifu XuDepartment of Animal Breeding, Genetics and Reproduction, College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, Jilin, China.
Ning QinDepartment of Animal Breeding, Genetics and Reproduction, College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, Jilin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Body weight is an essential trait in chickens, especially in markets where chickens are priced based on body weight and where medicine dosages depend on the animal's weight. However, not all farmers can afford scales to measure body weight, and they sometimes lack technical know-how during breakdowns, which is a challenge for small-scale farmers. Lately, data-mining algorithms have been used to help predict live body weight in livestock as they perform better than traditional prediction methods like linear regression. This study, therefore, aimed to develop models to predict the live body weight of the Kuroiler chicken breed from linear body measurements using classification and regression tree (CART) and multivariate adaptive regression spline (MARS) data-mining algorithms and to assess which of the two data-mining algorithms has a superior predictive performance. Linear body measurements were taken using a tailor's tape, and the body weight was taken using an electronic scale from 100 male Kuroiler chickens aged 23 weeks. The linear body measurements taken were corpus length (CL), chest circumference (CC), thigh length (TL), thigh circumference (TC), shank circumference (SC), shank length (SL), keel length (KL), and body length (BL). Results showed that the Kuroiler chickens used had an average live body weight of 2.01 kg, which correlated positively with all measured linear body measurements. Both MARS and CART developed models that included chest circumference, shank length, thigh circumference, and keel length. Furthermore, the MARS and CART models isolated the keel length and chest circumference as the most crucial traits in predicting the body weight of male Kuroiler chickens. The predictive performance showed that CART was the best model, with the highest

Identifiers

PMID42662554
PMCPMC13521114

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