Time series and ensemble models for forecasting Tanzanian banana crop yield under various effects of Climate change
Loading...
Date
2024-07
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
NM-AIST
Abstract
Amid escalating global worries about climate change’s impact on agriculture, this study thor
oughly explores how climate shifts might affect Tanzania’s vital bananas. The study employed
a multiple regression model to analyze the correlation between bananas and key climate vari
ables in Tanzania, the results showed gradual decrease in bananas. Additionally, the study
utilized two powerful global sensitivity analysis methods, Sobol’ Sensitivity Indices and Re
sponse Surface Methodology, to comprehensively explore the sensitivity of bananas to climate
variables. So, these methods showed that minimum temperature, precipitation and soil moisture
have the most impact on bananas and affect the crop’s production variability. Furthermore, un
certainty quantification was performed using Monte Carlo simulation, estimating uncertainties
in regression model parameters to enhance the reliability of findings, this indicated substantial
variability in the predictions. Conversely, the study configured time series models such as Sea
sonal ARIMAwithExogenousVariables (SARIMAX),State Space (SS), and Long Short-Term
Memory (LSTM) to forecast bananas in Tanzania under the effects of climate change. Hence,
the study builds predictive frameworks capturing temporal variations and offering glimpses of
future trends. Leveraging historical bananas data and relevant climate variables, an ensemble
model was formulated using a weighted average approach, revealing a future decrease in ba
nanas. This study combines data analysis and advanced models to explore how climate change
affects bananas. Its insights reach beyond farming, impacting stakeholders, policymakers, and
farmers alike. By understanding sensitivities, vulnerabilities, and future trends, this research
informs decisions for sustainable banana production, enhances food security, and encourages
adaptable strategies amidst changing climates.