Issue 4 (204)/2025

ALSE and ACS Style
Rotimi, E.A. Correlation and path analysis of body weight and linear body measurements in Marshall broiler chickens. Journal of Applied Life Sciences and Environment 2025, 58 (4), 541-548.
https://doi.org/10.46909/alse-584191

AMA Style
Rotimi E.A. Correlation and path analysis of body weight and linear body measurements in Marshall broiler chickens. Journal of Applied Life Sciences and Environment. 2025; 58 (4): 541-548.
https://doi.org/10.46909/alse-584191

Chicago/Turabian Style
Rotimi, Emmanuel Abayomi. 2025. “Correlation and path analysis of body weight and linear body measurements in Marshall broiler chickens.” Journal of Applied Life Sciences and Environment 58, no. 4: 541-548.
https://doi.org/10.46909/alse-584191

Correlation and path analysis of body weight and linear body measurements in Marshall broiler chickens

Emmanuel Abayomi ROTIMI

Department of Animal Science, Federal University Dutsin-Ma [FUDMA], Katsina, Nigeria

*Correspondence: earotimi@gmail.com 

Received: Oct. 14, 2025. Revised: Nov. 05, 2025. Accepted: Nov. 18, 2025. Published online: Dec. 15, 2025

ABSTRACT. Understanding the association between body weight (BWT) and linear body measurements (LBMs) is crucial for animal breeders, as it helps identify the optimal traits that can be prioritised to improve BWT through selective breeding programmes. This study was undertaken to investigate the relationships between BWT and LBM in Marshall broiler chickens. A total of 100 Marshall broilers at 7 weeks of age were used. Data were collected on BWT and seven LBM: body length (BL); chest girth; thigh length TL; shank length; shank girth; wing length; and keel length. Data were analysed using the statistical procedure of IBM SPSS (23.0.0) statistical package. Descriptive statistics, phenotypic correlations, path coefficient analysis, and stepwise regression were employed to determine the predictive power of the LBM on BWT. A significant positive correlation between BWT and BL (r = 0.764) was revealed. Path analysis indicated that BL had the greatest direct effect on BWT, suggesting its utility as a selection criterion in breeding programmes aimed at enhancing broiler performance. The findings provide valuable insights for optimising breeding strategies for improved productivity in broiler production. Farmers can use BL at the 7^th week of age to select chickens that will be expected to grow larger and faster.

Keywords: body weight and linear body measurements; body weight prediction; Marshall broiler chickens; path analysis.

INTRODUCTION

Broiler chicken production plays a critical role in the poultry industry due to their fast growth rate and high feed conversion efficiency (Sanda et al., 2015). Marshall broiler chickens are particularly popular in many developing countries for their adaptability and robust performance (Liswaniso et al., 2024). Accurate body weight estimation is essential for assessing growth performance, optimising feeding regimens, and selecting superior breeds for breeding programmes. Identifying reliable proxy traits that can be easily measured and are highly correlated with body weight is crucial for enhancing poultry production.

Body weight (BWT) is one of the most economically important traits in livestock for selection (Bila et al., 2021; Dekhili and Aggoun, 2013). Nosike et al. (2017) reported that linear body measurements (LBM) are important traits in estimating BWT in animal production. The relationship between BWT and LBM provides vital information on the carcass value in livestock (Dzungwe et al., 2018). Understanding the relationship between BWT and LBM facilitates the development of effective selection strategies that leverage easily measurable traits to predict BWT accurately, thereby improving the overall productivity and profitability of broiler production systems.

The ability to predict BWT from LBM offers a cost-effective and practical approach for assessing broiler growth performance. This is particularly beneficial in resource-constrained settings where weighing scales may be unavailable or impractical. Identifying key LBM that significantly correlate with BWT can serve as a foundation for genetic selection, leading to faster genetic gains and improved broiler performance.

Moreover, such information can be pivotal for developing breeding programmes that prioritise growth-related traits, thereby enhancing meat yield and feed efficiency.

The correlation coefficient of an association only indicates the magnitude and direction of the relationship without establishing the cause-effect relationship among the variables (Tyasi et al., 2017). It is then necessary to estimate the direct and indirect contributions of LBM on BWT. Path analysis is a statistical tool used to evaluate the direct and indirect effects of independent variables on dependent variables. It involves partitioning the correlations among variables into direct and indirect effects, as demonstrated in Sahelian goat breeding (Rotimi et al., 2020a), nondescript rabbits (Rotimi et al., 2020b), and camel populations (Rotimi, 2024).

There is limited information regarding the use of path analysis to predict BWT using LBM in Marshall broiler chickens. Hence, this study aimed to determine the direct and indirect effects of LBM on BWT through path coefficient analysis and to develop predictive regression models for estimating BWT based on significant LBM parameters.

MATERIALS AND METHODS

Experimental animals

This study was conducted at the Prof Abdu Lawal Saulawa Livestock Teaching and Research Farm, Federal University Dutsin-Ma, Katsina, Nigeria. The location is described by Rotimi et al. (2020). A cohort of 100 1-day-old, unsexed Marshall broiler chicks was purchased from a reputable dealer. The chicks had free access to commercial diets during the experiment. The commercial diet was the standard starter–finisher ration (23 % crude protein starter; 20 % crude protein finisher) produced by Top Feeds Ltd., Nigeria.

Data collection

Data were collected from individual birds at 7 weeks of age. Measured traits were BWT and LBM (in cm) using the guidelines of FAO (2012) and adopted by Lamido et al. (2024). Seven LBMs were taken using a graduated tape: body length (BL); chest girth (CG); thigh length (TL); shank length (SL); shank girth (SG); wing length (WL); and keel length (KL).

Statistical analysis

Data were analysed using the IBM SPSS statistical package version 23.0.0 (IBM, 2020). Relationships between BWT and LBMs were evaluated through Pearson’s correlation analysis. Multicollinearity among independent variables were assessed by the Variance Inflation Factor (VIF) values.

Subsequently, regression analysis was performed, with path coefficients derived from partial regression coefficients (beta weights) to quantify the direct contributions of independent variables in explaining variance in the dependent variable (BWT). Path coefficients, representing the direct effect of an explanatory variable (X_i) on the dependent variable (Y), were calculated using the formula in Wright (1921) and adapted by Mendes et al. (2005), Rotimi (2024), and Rotimi et al. (2020a, 2020b) as follows (Equation 1):

where is the path coefficient from to Y (i = BL, CG, TL, SL, SG, WL, and KL), b_i is the partial regression coefficient, SX_i is the standard deviation (SD) of X_i, and SY is the SD of Y.

The regression model adopted was (Equation 2):

where Y is the criterion variable (BWT),  is the intercept, , , …,  are the regression coefficients, , , …,  are the explanatory variables (BL, CG, TL, SL, SG, WL, KL), and e is the errors. Indirect effects (IE) of X_i on Y via X_j variable was evaluated according to Rotimi (2024) (Equation 3).

where, IE(XYi) is the indirect effect of Xi via Xj on Y, rXiXj is the correlation coefficient (r) between the ith and jth xplanatory variables, and PY x Xj is the path coefficient indicating the direct contribution of the jth explanatory variable on the dependent variable.

RESULTS AND DISCUSSION

Descriptive statistics

Table 1 presents the least square means for BWT and the LBMs of the broiler chickens. The mean BWT was 1,942.50 ± 57.44 g, which aligns with the findings of Sanda et al. (2015) for the same breed. However, it exceeds those reported by Liswaniso et al. (2024) for Zambian chickens, Ikpeme et al. (2016) and Amao (2018) for Nigerian chickens, and Bila et al. (2021) for Ross 308 broilers (both sexes). In contrast, Rotimi (2025) documented higher BWT across three broiler strains. Such discrepancies may arise from differences in age, genetic background, and sex (Liswaniso et al., 2024; Patel et al., 2020).

Phenotypic correlations

Table 2 shows the Pearson’s correlation coefficients between BWT and each LBM. Phenotypic correlations showed strong positive and statistically significant associations between BWT and all LBM, with values ranging from 0.568 to 0.764. The strongest relationship was between BWT and BL (r = 0.764), indicating a very strong positive relationship between these two traits. The weakest correlation was between BWT and WL (r = 0.568).

These findings align with those of Liswaniso et al. (2024), who reported markedly high positive correlations between BWT and LBM in Sasso chickens. Since BL is highly correlated with BWT, it can serve as a practical proxy for selecting heavier broilers, particularly when direct BWT measurements are difficult or costly to obtain. Utilising BL as a selection criterion in breeding programmes can indirectly enhance BWT by accelerating genetic progress due to its ease of measurement and the resulting correlated genetic gains. High and positive correlations were also recorded among the LBM. The highest coefficient was between BL and CG (r = 0.839), whereas the lowest coefficient was between WL and TL (r = 0.391). The high and significant correlations obtained indicate that genetic gains can be achieved by selecting for these traits, which can lead to improved growth rates and more efficient production (Bello et al., 2021).

Path coefficient analysis

BL had the greatest direct effect on BWT (0.357), while its total indirect effect (0.407) was mostly realised via TL and SG) (Table 3). CG had a relatively small direct effect (–0.012) but a strong total indirect effect (0.726), mainly through BL.

Table 1
Least square means, standard deviation (SD), and coefficient of variation (CV) of body weight and linear body measurements of Marshall broiler chickens

Table 2
Pearson correlations between body weight (g) and six linear body measurements (cm) of Marshall broiler chickens

* = correlation at p < 0.05 level (2-tailed). ** =correlation at p < 0.01 level (2-tailed)

These findings suggest that BL exerts both direct and indirect influence on growth performance and can serve as a primary selection criterion for improving BWT in Marshall broiler chickens.

Liswaniso et al. (2024) found that corpus/keel length exerted the strongest direct effect on body weight in Zambian indigenous chickens, whereas Bila et al. (2021) reported that SG demonstrated the greatest direct effect on BWT in male Ross 308 broiler chickens. These variations highlight how BWT can be differentially affected by morphometric traits across poultry breeds and rearing conditions. BWT is influenced by various interacting factors such as age, sex, production system, genotype/breed, stocking rate, and dietary composition (Sanka et al., 2020).

Stepwise regression

Table 4 shows the stepwise regression analysis, with three models extracted. Adjusted R² values of the three models were significant (all P < 0.001). Adjusted R² for the number of predictors provides a more accurate estimate of model fit. The third model, which includes BL, SG, and TL, had the highest adjusted R².

This shows that about 69.3% of the variation in BWT of Marshall broiler chickens can be explained using the third model. The results imply that a significant proportion of the variation in BWT is influenced by genetic factors, making it a potential target for selective breeding. These models have moderate predictive power; hence, they can be used to develop breeding programme design.

Table 3
Path analysis of body weight (BWT) and linear body measurements of Marshall broiler chicken

** = significant at the 0.01 level (2-tailed); NS = not significant; BL = body length; CG = chest girth; TL = thigh length; SL = shank length; SG = shank girth; WL = wing length; KL = keel length

Table 4
Stepwise regression analysis of Marshall broiler chicken

VIF = variance inflation factor; BL = body length; SG = shank girth; TL = thigh length

CONCLUSIONS

This study revealed significant correlations between BWT and LBM in Marshall broiler chicken, emphasising the predictive power of BL, SG, and TL as critical biometric traits for estimating BWT.

The significant phenotypic correlations among other biometric traits further underscore the potential for indirect selection, thus enabling farmers to make more informed decisions regarding broiler selection and breeding. BL exhibited the strongest direct contribution on BWT, suggesting its utility as a primary selection criterion in breeding programmes aimed at enhancing BWT.

The stepwise regression analysis indicated that the combination of BL, SG, and TL contributed approximately 69% of the variation in BWT, signifying the reliability of these variables for predicting growth performance in Marshall broiler chickens.

Breeding programmes for Marshall broiler chickens should prioritise BL as a primary selection criterion, given its strong direct effect on BWT.

Funding: There was no form of external funding for this study.

Author contributions: Conceptualisation, investigation, verification, writing, editing: EAR. The author declares that he has read and approved the publication of the manuscript in this present form.

Data availability statement: The data presented in this study are available on request from the corresponding author.

Conflicts of interest: Author declares that there is no conflicts of interest.

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