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  1. Elsoragaby S, Yahya A, Mahadi MR, Nawi NM, Mairghany M
    Heliyon, 2019 Apr;5(4):e01427.
    PMID: 30984885 DOI: 10.1016/j.heliyon.2019.e01427
    In paddy cultivation, harvesting is the most important operation, which needs suitable machinery. Thus, this study was carried out to compare field performances and energy and environmental effect between the conventional 5 m cutting width NEW HOLLAND CLAYSON 8080, 82 kW@2500 rpm combine harvester running on a total net area of 42.78 ha of plots for two rice (Oryza sativa L.) cultivation seasons and the new mid-size 2.7 m cutting width WORLD STAR WS7.0, 76 kW@2600 rpm combine harvester running on a total net area of 16.95 ha of plots for two rice cultivation seasons. The conventional combine as compared to mid-size combine showed 14.4% greater mean fuel consumptions (21.13 versus 18.46 l/ha), 31.1% greater mean effective field capacity (0.69 versus 0.53 ha/h), 5.23% greater cornering time (turning time) percentage of total time (8.28% versus 3.05%) and 1.41% greater reversing time percentage of total time (7.2% versus 5.79%) but 20.90% lesser mean operational speed (3.24 versus 4.10 km/h), 11.69% lesser effective time percentage of total time (60.0%versus 71.69%h/ha), 10.8% lesser mean field efficiency (64.3% versus 72.1%). In terms of total energy use the conventional combine showed 24.64% greater mean total energy use in the harvesting operation (1445.81 versus 1160.00 MJ/ha), 14.46% greater mean fuel energy (1010.014 versus 882.39 MJ/ha), 56.47% greater mean machinery energy (431.32 versus 275.65 MJ/ha) and 59.25% greater mean human energy (3.48 and 2.18 MJ/ha), this cause 26.12% greater mean total Green House Gas emission (GHG) than the mid-size combine. The results revealed that the mid-size combine is more suitable in conducting the harvest operation in rice field in Malaysia than the conventional combine.
  2. Elsoragaby S, Yahya A, Nawi NM, Mahadi MR, Mairghany M, Muazu A, et al.
    Heliyon, 2020 Nov;6(11):e05332.
    PMID: 33294651 DOI: 10.1016/j.heliyon.2020.e05332
    Measurement of human energy expenditure during crop production helps in the optimization of production operations and costs by identifying steps which that can benefit from the use of appropriate mechanization technologies. This study measures human energy expenditure associated with all 6 major rice (Oryza sativa L.) cultivation operations using two measurement methods-i.e. conventional human energy expenditure method and direct measurement with a Garmin forerunner 35 body media. The aim of this study was to provide a detailed comparison of these two methods and document the human energy costs in a manner that will identify steps to be taken to help optimize agricultural practices. Results (mean + 95%CL) revealed that the total human energy expenditure obtained through the conventional method was 25.5% higher (33.3 ± 1 versus 26.6 ± 1.3) in transplanting and 26.1% higher (30.3 ± 1.9 versus 24.0 ± 2.1) than the human energy expenditure recorded using the Garmin method in broadcast seeding method. Similarly, during the harvesting operation, the conventional measurement and Garmin measurement methods differed significantly, with the conventional method the human energy expenditure was 89.9% higher (3.2 ± 0.4 versus 1.68 ± 0.2) in the fields using the transplanting and 88.7% higher (3.3 ± 0.5 versus 1.8 ± 0.3) in the fields using the broadcast seeding than the human energy expenditure recorded using the Garmin method. When using Garmin method, the human energy expenditure in the case of using the midsize combine harvester was 13.49% lesser (592.4 ± 67.2 versus 522.0 ± 75.1) than the case of using conventional one. Results based on heart rate also indicated that operations such as tillage were less intensive (72 ± 3.3 bpm) compared with operations such as chemicals spraying (135 ± 4 bpm). Although we did not have a criterion measure available to determine which method was the most accurate, the Garmin measurement gives an estimate of actual physical human energy expended in performing a specific task with consider all conditions and thus more information to aid in identifying critical operations that could be optimized and mechanized.
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