ArticlePloS one2026
Growth, instability and future outlook of livestock population and products in Bangladesh.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the changing agri-food system, livestock play an important role, providing not only protein and micronutrients but also organic fertilizers, employment, and women's empowerment in many developing countries including Bangladesh. Reliable forecasting of livestock populations and products is necessary to inform policy decisions related to food security, supply, demand, and imports and exports, as well as self-sufficiency goals and associated inter- and intra-regional trade opportunities. However, robust long-term projections for livestock systems remain limited in the literature. Therefore, this study employs growth modeling and time series forecasting techniques to analyze historical trends (1961-2020) in livestock populations and products, assess their instability using the Cuddy Della instability index, and generate forecasts up to 2041 using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) framework. Model selection was based on standard statistical criteria, including goodness of fit measures and diagnostic checks of residual behaviour. The forecasting framework assumes that historical patterns and structural relations observed in the data remain stable over the projection period and that no major structural breaks or unforeseen external shocks significantly alter underlying dynamics. The results reveal that the cubic growth model best captures long-term trends in livestock populations and products, with generally low growth rates and low instability across categories. The highest annual growth rate was observed for chicken (3.71%) among the livestock population and eggs (7.40%) among the livestock products. The analysis further indicates a persistent deficit in milk and meat production, while egg production shows comparatively stronger performance. Forecasts generated using the selected ARIMA models were ARIMA (1,2,1), ARIMA (0,2,1), and ARIMA (1,2,5), for milk, meat, and egg, respectively, suggest a continuing upward trend in production up to 2041, consistent with historical patterns and growth model estimates. Under model assumptions, milk, meat, and egg projected production levels in 2041 are estimated at yearly 4,449,394, 9,77,042, and 1,289,863 metric tons (MT), respectively, reflecting substantial increases of around 24.34%, 35.24%, and 61.51% over 2020. Although these estimates represent point forecasts and are subject to various assumptions, findings of this study will serve as invaluable evidence-based insights for livestock sector stakeholders, including researchers, policymakers, academia, livestock producers, and the feed industry.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.