The Role of Mechanized Peanut Shelling in Reducing Post-Harvest Losses and Labor Intensiveness

• Johann Heinrich P. Malongo

  Author

• Marilou S. Tomentos

  Author

• Grace T. Langcuyan

  Author

• Angelique C. Maquiso

  Author

• Charmaine U. Tilos

  Author

This study assesses the performance and user acceptability of a peanut sheller machine using Garvin's Eight Dimensions of Quality and the Technology Acceptance Model (TAM). A mixed-methods research design was employed, combining quantitative performance measurements with qualitative user perception assessments. Controlled shelling tests under three drying conditions (5–10 days, 10–15 days, and 15+ days) were conducted to evaluate efficiency, yield, and product quality. Expert judgment was used to rate the machine’s reliability, durability, and serviceability, while perceived usefulness and ease of use were evaluated through focus group discussions and structured questionnaires. Results revealed that the peanut sheller machine was rated "Very Acceptable" (mean = 3.82) under Garvin's model, with durability receiving the highest rating (4.00). Under TAM, it received a "Highly Acceptable" rating (mean = 4.63), highlighting its efficiency and ease of use. Shelling efficiency improved with longer drying time, with the 15+ day condition achieving the highest output (653.75g) and the lowest unshelled rate (0.45%). In contrast, shorter drying times (5–10 days) were more effective in preserving peanut quality and minimizing damage (0.9%). Furthermore, a comparative performance analysis between machine and manual shelling showed that mechanized shelling significantly outperformed manual methods in terms of throughput, efficiency, labor savings, and post-harvest loss reduction. Machine shelling reduced shelling time by over 80%, increased throughput by more than 500%, and lowered post-harvest losses by 43.5%. These findings validate the peanut sheller machine’s potential to improve post-harvest operations, reduce labor intensity, and enhance agricultural productivity in rural communities. Future research should focus on optimizing drying parameters, improving machine reliability, and developing user training and support systems to increase adoption.

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