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<p>As sugarcane matures, its sucrose content increases, but delaying harvest too long can increase production costs and lead to financial losses. The metahara sugar factory currently relies on traditional harvesting methods based on field conditions. This study aims to develop a decision support tool to optimize the harvesting schedule of the factory. Historical data from 2010/11 to 2019/20 were analyzed, with a focus on crop variety, maturity, and soil fertility, to create predictive models via ordinary least squares. Most models showed statistical significance, with adjustments made for climatic factors. Forecasting was conducted for 1,199 fields over nine months and integrated into a Harvest Schedule Mixed Integer Linear Programming model. The optimized schedule recommended changing the harvest month for 804 fields (67.06% of the total), potentially increasing net returns by 15.28% while meeting all constraints. Additionally, the average theoretical recoverable sugar content was improved by 1.23%. </p>

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Maximizing economic returns from sugarcane production through the optimization of a harvesting schedule: a case study of the Metahara sugar factory, Ethiopia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月26日

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  • Yemane Gebrehiwot,
  • Solomon Alemu,
  • Getinet Alemayehu

摘要

As sugarcane matures, its sucrose content increases, but delaying harvest too long can increase production costs and lead to financial losses. The metahara sugar factory currently relies on traditional harvesting methods based on field conditions. This study aims to develop a decision support tool to optimize the harvesting schedule of the factory. Historical data from 2010/11 to 2019/20 were analyzed, with a focus on crop variety, maturity, and soil fertility, to create predictive models via ordinary least squares. Most models showed statistical significance, with adjustments made for climatic factors. Forecasting was conducted for 1,199 fields over nine months and integrated into a Harvest Schedule Mixed Integer Linear Programming model. The optimized schedule recommended changing the harvest month for 804 fields (67.06% of the total), potentially increasing net returns by 15.28% while meeting all constraints. Additionally, the average theoretical recoverable sugar content was improved by 1.23%.