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<p>This study investigates the relationship between soil physical properties and the efficiency of combine harvesters, focusing on the development of a composite model that links these factors. Agricultural productivity is increasingly dependent on mechanized equipment, with combine harvesters playing a vital role in the harvesting process. However, their performance is significantly influenced by soil conditions such as texture, moisture content, bulk density, and bearing capacity. Using data from various fields in Sylhet, Bangladesh, the study employs multiple linear regression to create a model predicting harvester efficiency based on these soil properties. The findings reveal that sandy loam soils provide optimal conditions for harvesting efficiency, while loamy sands lead to reduced performance due to lower bearing capacity and increased slippage. The developed composite model accounts for 82.2% of the variability in field efficiency, offering a predictive tool for improving mechanized farming practices. This research contributes to precision agriculture by offering strategies for optimizing machinery operations and reducing operational costs. It also provides insights for agronomists, machinery manufacturers, and farmers to enhance sustainability, profitability, and overall efficiency in agricultural operations. </p>

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The impact of soil physical properties on combine harvester efficiency: a composite model from correlation trends

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月11日

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  • Mohd. Saifur Rahman,
  • Nafis Shahid Fahim,
  • Bodruzzaman Khan,
  • Md. Fahad Jubayer,
  • Tariqul Islam,
  • Md Altaf Hossain

摘要

This study investigates the relationship between soil physical properties and the efficiency of combine harvesters, focusing on the development of a composite model that links these factors. Agricultural productivity is increasingly dependent on mechanized equipment, with combine harvesters playing a vital role in the harvesting process. However, their performance is significantly influenced by soil conditions such as texture, moisture content, bulk density, and bearing capacity. Using data from various fields in Sylhet, Bangladesh, the study employs multiple linear regression to create a model predicting harvester efficiency based on these soil properties. The findings reveal that sandy loam soils provide optimal conditions for harvesting efficiency, while loamy sands lead to reduced performance due to lower bearing capacity and increased slippage. The developed composite model accounts for 82.2% of the variability in field efficiency, offering a predictive tool for improving mechanized farming practices. This research contributes to precision agriculture by offering strategies for optimizing machinery operations and reducing operational costs. It also provides insights for agronomists, machinery manufacturers, and farmers to enhance sustainability, profitability, and overall efficiency in agricultural operations.