Browsing by Author "Patrick, Sabas"

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Sensitivity analysis and uncertainty quantification of climate change effects on Tanzanian banana crop yield
(Elsevier B. V., 205-01-09) Patrick, Sabas; Mirau, Silas; Mbalawata, Isambi; Leo, Judith
Concerns about the impact of climate change on agricultural systems have heightened, particularly in regions where crop cultivation is essential for economic stability and sustenance. This research addresses a critical gap in understanding by investigating how climate change influences Tanzania’s bananas, a vital component of the country’s agricultural sector. The study used a multiple regression model to analyze the correlation between bananas and key climate variables in Tanzania, the results showed gradual decrease in bananas. Specifically, the climate variables, including precipitation (𝑋1), soil moisture (𝑋2), minimum temperature (𝑋3), maximum temperature (𝑋4), and relative humidity (𝑋5) have coefficients 0.0206, −0.0085,4.8328, −1.6594, and −0.0991, respectively. In this case, a large positive coefficient and a negligible negative coefficient show that the independent variable greatly influences the yield of the bananas. Additionally, the study utilize two powerful global sensitivity analysis methods, Sobol’ Sensitivity Indices and Response Surface Methodology, to comprehensively explore the sensitivity of bananas to climate variables. So, these methods revealed that minimum temperature, precipitation and soil moisture have the most impact on bananas and affect the crop’s production variability. Uncertainty quantification was performed using Monte Carlo simulation, estimating uncertainties in model parameters to enhance the reliability of the findings. This research not only contributes to our broader understanding of how climate change impacts bananas but also offers practical policy suggestions tailored to Tanzania’s unique context, ensuring resilience and sustainability in the face of environmental changes. The outcomes of this study carry significance for policymakers, stakeholders, and farmers, providing actionable insights to shape adaptive agricultural strategies. By bridging the gap between climate change and bananas
Time series and ensemble models for forecasting Tanzanian banana crop yield under various effects of Climate change
(NM-AIST, 2024-07) Patrick, Sabas
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.
Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change
(Elsevier, 2023-12-01) Patrick, Sabas; Mirau, Silas; Mbalawata, Isambi; Leo, Judith
Banana cultivation plays a pivotal role in Tanzania’s agricultural landscape and food security. Precisely forecasting banana crop yield is essential for resource optimization, market stability, and informed policymaking, particularly in the face of climate change. This study employed time series and ensemble models to forecast banana crop yield in Tanzania, offering crucial insights into future production trends. We utilized Seasonal ARIMA with Exogenous Variables (SARIMAX), State Space (SS), and Long Short-Term Memory (LSTM) models, chosen based on regression analysis and data exploration. Leveraging historical banana yield data (1961–2020) and relevant climate variables, we formulated an ensemble model using a weighted average approach. Our findings underscore the potential of time series and ensemble models for accurate banana crop yield forecasting. Statistical evaluation metrics validate their effectiveness in capturing temporal variations and delivering reliable predictions. This research advances agricultural forecasting by demonstrating the successful application of these models in Tanzania. It emphasizes the importance of considering temporal dynamics and relevant factors for precise predictions. Policymakers, farmers, and stakeholders can leverage this study’s outcomes to make informed decisions on resource allocation, market planning, and agricultural policies. Ultimately, our research bolsters sustainable banana production and enhances food security in Tanzania.