Evidence map›Paper›PMID 42361003›Full record

ArticlePloS one2026

Growth, instability and future outlook of livestock population and products in Bangladesh.

Abu Hayat Md Saiful Islam, Md Mahfuzul Hasan, Mohammad Saiful Islam, Masayuki Sato

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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.

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1 · What the graph read from it

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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

4 authors.

Abu Hayat Md Saiful IslamDepartment of Agricultural Economics, Bangladesh Agricultural University (BAU), Mymensingh, Bangladesh.ORCID https://orcid.org/0000-0003-2368-5500
Md Mahfuzul HasanACASA Project, Bangladesh Agricultural Research Council, Farmgate, Dhaka, Bangladesh.ORCID https://orcid.org/0009-0001-0193-8549
Mohammad Saiful IslamGraduate School of Human Development and Environment, Kobe University, Hyogo, Japan.
Masayuki SatoGraduate School of Human Development and Environment, Kobe University, Hyogo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Food SupplyLivestockAgricultureAnimalsBangladeshForecastingHumans

Identifiers

PMID42361003
PMCPMC13308833

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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.