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Statistical Association between Temperature-Rainfall and Tea Yield at Sylhet Malnicherra Tea Estate: An Empirical Analysis

Authors: Rahman, Mizanur; Md. Nazrul Islam; Md. Rakib Hossain; Ali, Mohammad Ahmmed;

Statistical Association between Temperature-Rainfall and Tea Yield at Sylhet Malnicherra Tea Estate: An Empirical Analysis

Abstract

Tea is one of the most important cash crops in Bangladesh, playing a significant role in food security, poverty alleviation, employment, and the economy. As a rain-fed crop, tea cultivation depends on micro-climatic conditions for optimal growth. Bangladesh observed extreme weather and climate events over the last couple of decades. Bangladesh is experiencing the frequency of climate variations and extreme weather events, like prolonged drought, heavy rainfall, and late winter spells. The study area was carried out at Malnicherra tea estate in Sylhet district to correlate the micro-climatic variables to tea productivity. The study also examines the trend of micro-climatic variables like maximum temperature, minimum temperature, and rainfall by using the time series data from 2012 to 2017. The simple linear regressions reveal that temperature and rainfall influence tea production. Tea production was negatively impacted by drought and excessive rainfall. Heavy rains eroded the top fertile soil and washed away soil nutrients. The yield of tea leaf was declining at the rate of 110.8 kg/ha/year. There was a high negative relationship between mean maximum temperature and yield of tea. The study recommends an integrated adaptive measure to minimize adverse effects.

Keywords

Rainfall, Temperature, Relationship, Adaptation, Influence

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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