Analysis of growth trends in area, production and yield of tea in Bangladesh

Saha, Jiban Krishna; Adnan, K M Mehedi; Sarker, Swati Anindita; Bunerjee, Shefali

doi:10.1016/j.jafr.2021.100136

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…This carries more importance when tea is used for export purposes after satisfying the national demand. The analysis also suggested suitable locations where tea is not currently being produced, which essentially agreed with the findings of Saha et al [6] and could be used to increase tea cultivation.…”

Section: Discussionsupporting

confidence: 87%

“…Bangladesh annually earns around BDT 1.775 billion, which is 0.81% of the GDP (Gross Domestic Product) in foreign currency in the export of tea [4]. Despite the involvement of about 0.15 million people directly and many indirectly in the tea industry as employees, the average yield is 1529 kgha −1 , which is low compared to the other teaproducing countries [5,6]. This is due to the change in agroclimatic conditions that presents ecological stress [7].…”

Section: Introductionmentioning

confidence: 99%

“…Hossain and Abdulla [26] have conducted similar studies using the ARIMA model to predict tea production from 1972-2013; the data and adequacy of the fitted model were tested using the run test and the Jarque-Bera test criteria, followed by residual test analysis. On the other hand, Saha et al [6] compared the growth rates of the area, production, and yield of tea in Bangladesh before and after (1972-2018) independence, and found an increase in the average area, production, and productivity by 1.05, 1.89, and 0.98%, respectively, after independence, using a growth model and decomposition analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

et al. 2022

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Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

et al. 2022

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“…On the other hand, non-irrigation and average annual rainfall both indicated a favourable but statistically negligible impact on rice output during the course of the study. In the short run, all of the determinants had a negative and significant effect on the total rice production's convergence towards equilibrium, but in the long run, none of them [11][12][13][14]. It has been found that the region experiences a higher level of volatility in comparison to the production and yields of food grains.…”

Section: Review Of Literaturementioning

confidence: 99%

Growth and Trend in Area, Production, and Yield of Wheat Crops in Uttar Pradesh: A Johansen Co-integration Approach

Khan

Ansari

et al. 2023

AJAEES

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This study investigated wheat crop area, production, and yield in Uttar Pradesh. Wheat crop data from 1990-91 to 2019-20 were obtained. Compound growth rate and trend analysis show that wheat crop area has reduced due to shift of rabi crops. The growth in Uttar Pradesh's per-hectare yield boosted wheat production from 1990-91 to 2019-20. The methods were calculated indicate use of semi log trend function and Johansen cointegration results, which reveal that number of irrigations affects wheat production. The study founds that cropping intensity, fertilizer, and transportation positively affected wheat production.

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“…Generally, tea is grown in the northern and eastern part of the country include Sylhet, Maulvibazar, Habiganj, Brahmanbaria, Rangamati, Chattogram and Panchagar district [ 1 ]. These tea producing zones encompass with three agro-ecological zones and various tea cultivars are cultivated due to different climatic condition and geographical location [ 3 ]. Bangladeshi tea is cultivated in three distinct ecological zones, which are the Surma valley in greater Sylhet, the Halda valley in Chattogram, and the Karatoa valley in Panchagar district [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Tea (Camellia sinensis) cultivated in three agro-ecological regions of Bangladesh: Unveiling the variability of methylxanthine, bioactive phenolic compound, and antioxidant activity

Abdullah,

Sayka,

Rahman

et al. 2024

Heliyon

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Analysis of growth trends in area, production and yield of tea in Bangladesh

Cited by 14 publications

References 8 publications

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

Growth and Trend in Area, Production, and Yield of Wheat Crops in Uttar Pradesh: A Johansen Co-integration Approach

Tea (Camellia sinensis) cultivated in three agro-ecological regions of Bangladesh: Unveiling the variability of methylxanthine, bioactive phenolic compound, and antioxidant activity

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