Analyzing the low country tea yield in southwestern region of Sri Lanka, with special reference to weather variables

dc.contributor.advisorEdirisinghe, PM
dc.contributor.authorPerera, SA
dc.date.accept2024
dc.date.accessioned2025-06-24T05:17:34Z
dc.date.issued2024
dc.description.abstractTea cultivation is a crucial economic activity in southwestern region of Sri Lanka, contributing significantly to the nation's economy. Understanding the impact of the weather on tea production is crucial for food security and economic stability in the region amid climate change. The research uses historical sample of weather records and tea yield data to assess the impact of weather conditions on daily tea crop yield in southwestern region in low country of Sri Lanka to develop a forecasting model which uses historical data for future tea yield predictions. Preliminary findings suggested that temperature fluctuations, particularly extreme heat conditions have a discernible effect on tea yield. Furthermore, the role of rainfall patterns, including seasonal distribution and intensity, was pivotal in shaping the success of tea cultivation. Additionally, the influence of humidity, wind speed and irradiance were explored on tea production, recognizing their potential as key drivers of yield variation. In this study, both conventional time series approaches and more recent machine learning methods were used to analyze the datasets for all variables. The success of the tea yield forecasting with the SARIMA model was observed in univariate time series. Additionally, when considering multivariate time series, it was found that other variables could be forecasted for our dependent variable, tea yield. The VAR model produced forecasts with a lag order of 1 in a stationary context. Machine learning forecasting techniques demonstrated higher precision and practical applicability, making their approach more essential for future studies. This study aims to enhance sustainable agriculture and climate resilience in Sri Lanka's low- country region by identifying critical weather variables and their impacts. It will inform local farmers, policymakers, and stakeholders, optimizing cultivation practices, resource allocation, and risk management strategies, ultimately enhancing tea farming's resilience and contributing to the global tea industry's stability midst climate change.
dc.identifier.accnoTH5637
dc.identifier.citationPerera, P.A. (2024). Analyzing the low country tea yield in southwestern region of Sri Lanka, with special reference to weather variables [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. https://dl.lib.uom.lk/handle/123/23712
dc.identifier.degreeMSc in Business Statistics
dc.identifier.departmentDepartment of Mathematics
dc.identifier.facultyEngineering
dc.identifier.urihttps://dl.lib.uom.lk/handle/123/23712
dc.language.isoen
dc.subjectTEA INDUSTRY-Yield-Forecasting
dc.subjectTEA INDUSTRY-Weather Conditions
dc.subjectWEATHER-Data
dc.subjectSEASONAL AUTO REGRESSIVE INTEGRATED MOVING AVERAGE
dc.subjectVECTOR AUTO REGRESSION
dc.subjectMATHEMATICS-Dissertation
dc.subjectMSc in Business Statistics
dc.titleAnalyzing the low country tea yield in southwestern region of Sri Lanka, with special reference to weather variables
dc.typeThesis-Abstract

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