Time series and ensemble models for forecasting Tanzanian banana crop yield under various effects of Climate change

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.