Sensitivity analysis and uncertainty quantification of climate change effects on Tanzanian banana crop yield

Q3 Mathematics
Sabas Patrick , Silas Mirau , Isambi Mbalawata , Judith Leo
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Abstract

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, this research offers valuable contributions to the broader field of agricultural sustainability.
气候变化对坦桑尼亚香蕉作物产量影响的敏感性分析和不确定性量化
对气候变化对农业系统影响的担忧有所加剧,特别是在作物种植对经济稳定和维持至关重要的地区。这项研究通过调查气候变化如何影响坦桑尼亚的香蕉(该国农业部门的重要组成部分),解决了理解上的一个关键空白。该研究使用多元回归模型分析了香蕉与坦桑尼亚关键气候变量之间的相关性,结果显示香蕉逐渐减少。其中,降水量(X1)、土壤湿度(X2)、最低温度(X3)、最高温度(X4)和相对湿度(X5)的系数分别为0.0206、- 0.0085、4.8328、- 1.6594和- 0.0991。在这种情况下,较大的正系数和可忽略的负系数表明自变量对香蕉产量的影响很大。此外,本研究还利用Sobol敏感性指数和响应面法两种强大的全局敏感性分析方法,全面探讨香蕉对气候变量的敏感性。因此,这些方法表明,最低温度、降水和土壤湿度对香蕉的影响最大,并影响作物的生产变异性。不确定性量化采用蒙特卡罗模拟,估计模型参数的不确定性,以提高结果的可靠性。这项研究不仅有助于我们更广泛地了解气候变化如何影响香蕉,而且还为坦桑尼亚的独特背景提供了切实可行的政策建议,以确保香蕉在面对环境变化时的适应力和可持续性。本研究的结果对政策制定者、利益相关者和农民具有重要意义,为制定适应性农业战略提供了可操作的见解。通过弥合气候变化和香蕉之间的差距,这项研究为更广泛的农业可持续性领域提供了宝贵的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Results in Control and Optimization
Results in Control and Optimization Mathematics-Control and Optimization
CiteScore
3.00
自引率
0.00%
发文量
51
审稿时长
91 days
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