Performance Evaluation of Half-Feed Rice Combine Harvester
DOI:
https://doi.org/10.53560/PPASA(61-1)858Keywords:
Combined Harvester, Effective Field Capacity, Field Efficiency, Fuel Consumption, Rice Header LossAbstract
Rice (Oryza sativa) is one of the most important cereal grains cultivated in an area of 165 million hectares with approximately 756.7 million metric tons of production in the world. In 2019, Pakistan’s area under rice cultivation was about 2.9 million hectares, with 7.5 million tons yield. Rice-wheat cropping system is the most famous, especially in Punjab, Pakistan. Harvesting is presently conducted through manual labor or with the utilization of outdated models of combined harvesters with huge grain quality and quantity losses. Imported half-feed rice combine harvester was introduced and an experiment was planned to evaluate their feasibility. The performance was evaluated at three levels of forward speed (3, 4, and 5 km/h) and cutter bar heights (12, 16, and 20 cm) during the harvesting season of 2021 in the district Sheikhupura, Punjab. The machine performance was based on header loss, effective field capacity, broken grains percentage, fuel consumption, and field efficiency. The collected data was analyzed at a 5% level of probability by randomized complete block design (RCBD). The statistical analysis revealed that the machine performed better at the speed S2 (4 km/h) and cutter bar height H2 (16 cm) with the maximum EFC (0.55 ha/h) and Field Efficiency (75.3 %) as well as minimum Grain Losses (24.7 kg/ha) and Grain Breakage (14.2 kg/ha) in standing crop condition. Therefore, this machine is recommended to farmers due to its higher EFC and Field Efficiency as well as lower Grain Losses and Grain Breakage as compared to the conventional methods and obsolete machinery.
References
J. Myszkowska-Ryciak, A.Je. Ishdorj, M. Zewska-Zychowicz, N.A. Mohidem, N. Hashim, R. Shamsudin, and H.C. Man. Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process, and Nutrient Content. Agriculture 12(6):741 (2022).
E. Elahi, Z. Khalid, and Z. Zhang. Understanding Farmers' Intention and Willingness to Install Renewable Energy Technology: A Solution to Reduce The Environmental Emissions of Agriculture. Applied Energy 309: 118459 (2022).
J.P. Sahoo, A.P. Mishra, and K.C. Samal. The Magical Low Glycaemic Index Rice Staple Truly For Diabetic People. Agriculture Letters II(6): 37 (2021).
R. Bhatt, P. Singh, A. Hossain, and J. Timsina. Rice-Wheat System in The Northwest Indo-Gangetic Plains of South Asia: Issues and Technological Interventions for Increasing Productivity and Sustainability. Paddy and Water Environment, 19(3): 345–365 (2021).
D.A. Zuniga-Vazquez, N. Fan, T. Teegerstrom, C. Seavert, H.M. Summers, E. Sproul, and J.C. Quinn. Optimal Production Planning and Machinery Scheduling for Semi-Arid Farms. Computers and Electronics in Agriculture 187: 106288 (2020).
M. Schmitt-Harsh, K. Waldman, L. Estes, and T. Evans. Understanding Social And Environmental Determinants of Piecework Labor in Smallholder Agricultural Systems. Applied Geography 121: 102243 ( 2020).
H. Rahaman, M.M. Rahman, A.K.M.S. Islam, M.D. Huda, and M. Kamruzzaman. Mechanical Rice Transplanting in Bangladesh: Current Situation, Technical Challenges, and Future Approach. Journal of Biosystems Engineering 47(4): 417–27 (2022).
J. Kershaw, R. Yu, Y.M. Zhang, and P. Wang. Hybrid Machine Learning-Enabled Adaptive Welding Speed Control. Journal of Manufacturing Processes 71: 374–383 (2021).
R.G. Trevisan. Advanced data analysis methods to optimize crop management decisions. Ph.D. Thesis. University of Illinois Urbana-Champaign, Urbana, Illinois, USA (2021).
V. Dwivedi, N. Parashar, and B. Srinivasan. Distributed Learning Machines for Solving Forward and Inverse Problems in Partial Differential Equations. Neurocomputing 420: 299–316 (2021).
R.K. Chaab, S.H. Karparvarfard, H. Rahmanian-Koushkaki, A. Mortezaei, and M. Mohammadi. Predicting Header Wheat Loss in a Combine Harvester, A New Approach. Journal of the Saudi Society of Agricultural Sciences 19(2): 179–184 (2020).
D. Savickas, D. Steponavičius, L. Špokas, L. Saldukaitė, and M. Semenišin. Impact of Combine Harvester Technological Operations on Global Warming Potential. Applied Sciences 11(18): 62-86 (2021).
R. Bawatharani, M.H. Bandara, and D.I.E. Senevirathne. Influence of Cutting Height and Forward Speed on Header Losses in Rice Harvesting. International Journal of Agriculture 4(2): 1-9 (2016).
M.K. Hasan, M.R. Ali, C.K. Saha, M.M. Alam, and M.E. Haque. Combine Harvester: Impact on Paddy Production in Bangladesh. Journal of the Bangladesh Agricultural University 17(4): 583–591 (2019).
S. Elsoragaby, A. Yahya, M.R. Mahadi, N.M. Nawi, and M. Mairghany. Comparative Field Performances Between a Conventional Combine and Mid-size Combine in Wetland Rice Cultivation. Heliyon 5(4): 14-27. (2019).
Q. Da, D. Li, X. Zhang, W. Guo, D. He, Y. Huang, and G. He. Research on Performance Evaluation Method of Rice Thresher Based on Neural Network. Actuators 11(9): 257 (2022).
