FORECASTING LEAFY VEGETABLE PRODUCTION IN BANGLADESH: UNRAVELLING THE NEXUS AMONG AGRO-METEOROLOGICAL FACTORS
DOI:
https://doi.org/10.29121/granthaalayah.v12.i10.2024.5825Keywords:
Climate Change, Dynamic Regression, Leafy Vegetable, Scenario AnalysisAbstract [English]
Leafy vegetables in the food basket provide vital nutrients required for human health, but Bangladesh faces challenges of sustainable production potential due to the vulnerability of various agrometeorological factors. This study has addressed a holistic approach to forecasting leafy vegetable production through the nexus of agricultural responses and climatic factors. The secondary yearly data of seasonal leafy vegetable production and area wise daily key climatic predictors such as rainfall, humidity, sunshine, and temperature for the period 2000-2001 to 2022-2023 have been collected from Bangladesh Bureau of Statistics (BBS) and Bangladesh Meteorological Division (BMD), respectively. Scenario analysis and stepwise dynamic regression model was applied to diagnose the pattern and also forecast winter and summer leafy vegetable production for the years 2023-2024 to 2030-2031 addressing meteorological predictors. Results show that winter production is significantly influenced by the previous year’s output (lag-1), sunshine hour, and maximum temperature. In contrast, summer leafy vegetable production is influenced solely by previous year production. Using growth projection of sunshine hours and maximum temperature of Bangladesh Meteorological Department (BMD) in the fitted dynamic regression model, winter leafy vegetable production is forecasted to rise from 167,558 tons in 2022-2023 to 211,929 tons by 2030-2031. Summer production is expected to increase from 176,234 tons in 2022-2023 to 285,085 tons by 2030-2031. These findings provide critical insights for policymakers aiming to enhance food security and stabilize leafy vegetable markets amidst climate change for achieving sustainable development goals (SDG) 12.
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Copyright (c) 2024 Md. Fakhrul Hasan, Sheikh Mohammad Sayem, Md. Bayazid Hossen, Md. Bazlur Rashid
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