STATISTICAL AND PREDICTIVE ANALYSIS OF BROILER CHICKEN PRODUCTION IN THE WORLD’S MAJOR PRODUCING COUNTRIES: A TIME-SERIES APPROACH
DOI:
https://doi.org/10.56238/revgeov17n3-162Keywords:
Poultry Production, Time Series, Inferential Statistics, ARIMA Modeling, Agroeconomic AnalysisAbstract
Chicken meat is the most consumed source of animal protein worldwide. United States, Brazil, and China account for more than 40% of global production. This study examines whether their production trajectories exhibit long-term growth, temporal association, and statistical predictability. The objective was to conduct a statistical and predictive analysis of chicken meat production from 1961 to 2022. The methodology integrated descriptive statistics, inferential analysis, and univariate time-series modeling based on ARIMA, supported by moving averages and seasonal decomposition for trend assessment. The results revealed strong temporal association between Brazil and China (r ≈ 0.983) and between Brazil and the United States (r ≈ 0.991). Inferential analysis showed no statistically significant difference between the mean production levels of Brazil and China (p = 0.618), while a highly significant difference was observed between Brazil and the United States (p < 0.001). Descriptive indicators demonstrated higher relative variability for Brazil (CV ≈ 97.0%) and China (CV ≈ 92.4%) compared with the United States (CV ≈ 57.4%). ARIMA-based forecasts indicate continued production growth in all three countries in the short to medium term. The findings confirm long-term growth, strong temporal association, and statistical predictability, highlighting structural differences in scale and variability.
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