Statistical Analysis of Feed Intake and Chicken Weight Gain Using R
The relationship between feed consumption and body weight gain in poultry is a cornerstone of animal production science. This study explores this dynamic through a six-month dataset collected from a local poultry farm. The aim was to quantify how feed input influences chicken weight, using statistical techniques implemented in the R programming language.
Background and Justification
Poultry producers have long observed a direct correlation between the amount of feed provided and the resulting body mass of broiler chickens. According to Mottet & Tempio (2017), feed accounts for over 60% of total production costs, making feed efficiency a critical concern. Furthermore, research by Leeson & Summers (2010) confirms that optimizing feed intake significantly enhances growth performance and feed conversion ratios (FCR).
To inform sustainable poultry management, empirical analysis of feed-to-weight relationships is essential. This study contributes to that understanding using real-world farm data.
Data Collection and Preprocessing
The dataset included 120 observations of two variables: Feed (grams consumed per bird) and Chicken_Weight (grams gained). Data preprocessing involved:
- Removing missing or anomalous entries
- Checking for outliers using boxplots
- Ensuring consistent measurement units
Cleaned data was exported as a CSV file and analyzed using R (version 4.2.0), making use of packages such as ggplot2, dplyr, and stats.
Exploratory Data Analysis
Descriptive statistics showed:
- Mean Feed: 450g
- Mean Chicken Weight Gain: 320g
A scatter plot of the two variables suggested a positive linear relationship, which was further confirmed via correlation analysis (Pearson's r = 0.81).
Linear Regression Analysis
A simple linear regression model was fit to the data:
Chicken_Weight = Ξ²₀ + Ξ²₁ * Feed + Ξ΅
Results showed a statistically significant relationship (p < 0.001), with an R-squared of 0.66, indicating that 66% of the variation in chicken weight gain can be explained by feed consumption.
Diagnostic plots indicated no major violations of regression assumptions (normality, homoscedasticity, linearity).
Discussion
These findings support the theoretical framework established by earlier research. Ferket & Gernat (2006) note that increased nutrient density and feed intake typically result in improved body weight in broilers. However, they also caution that overfeeding can lead to inefficiencies and health issues.
The study recognizes that other factors—such as genetics, housing, and feed composition—also influence growth. Future research should consider multivariate models that incorporate these variables.
Conclusion
The statistical analysis confirms a strong positive correlation between feed quantity and weight gain in chickens. By leveraging tools such as R, poultry managers can optimize feeding strategies to enhance productivity while reducing waste.
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