Optimization of tillage and sowing operations using discrete event simulation 

https://doi.org/10.17221/49/2017-RAECitation:Kosari Moghaddam A., Sadrnia H., Aghel H., Bannayan M. (2018): Optimization of tillage and sowing operations using discrete event simulation . Res. Agr. Eng., 64: 187-194.
download PDF

A simulation model was developed for secondary tillage and sowing operations in autumn, using discrete event simulation technique in Arena® simulation software (Version 14). Eight machinery sets were evaluated on a 50-hectare farm. Total costs including fixed-costs, variable costs and timeliness costs were calculated for each machinery set. Timeliness costs were estimated for 21-years period on daily basis (Daily Work method) and compared with another method (Average Work method) based on the equation proposed by ASAE Standards, EP 496.3FEB2006. The Inputs of the model were machinery sets, field size, machines performances and daily soil workability state. The optimization criteria were the lowest costs and lowest standard deviation in daily work method plus the lowest costs based on average work method. The validity of the model was evaluated by comparing the output of the model with field observed data collected from various farms. Results revealed that there was no significant difference (P > 0.01) between the observed and predicted finish day. 

Al-Hamed S. A., Al-Janobi A. A. (2001): An object-oriented program to predict tractor and machine system performance. Research Bulletin, King Saud University, 106: 5–24.
ASAE (2006): Agricultural Machinery Management ASAE EP496.3.
Bannayan M., Lakzian A., Gorbanzadeh N., Roshani A. (2011): Variability of growing season indices in northeast of Iran. Theoretical and Applied Climatology, 105, 485-494 https://doi.org/10.1007/s00704-011-0404-1
Camarena E.A., Gracia C., Cabrera Sixto J.M. (2004): A Mixed Integer Linear Programming Machinery Selection Model for Multifarm Systems. Biosystems Engineering, 87, 145-154 https://doi.org/10.1016/j.biosystemseng.2003.10.003
Dash R.C., Sirohi N.P.S. (2008): A computer model to select optimum size of farm power and machinery for paddy-wheat crop rotation in Northern India. Agricultural Engineering International: the CIGR Ejournal: 12.
Gunnarsson Carina, Hansson Per-Anders (2004): Optimisation of field machinery for an arable farm converting to organic farming. Agricultural Systems, 80, 85-103 https://doi.org/10.1016/j.agsy.2003.06.005
Haffar Imad, Khoury Ramzi (1992): A computer model for field machinery selection under multiple cropping. Computers and Electronics in Agriculture, 7, 219-229 https://doi.org/10.1016/S0168-1699(05)80021-3
Koocheki A., Shabahng J., Khorramdel S., Azimi R., Aghel H. (2011): Documentation of farming management with GIS and ArcView: A case study for agricultural Research Station of Faculty of Agriculture, Ferdowsi University of Mashhad, Iran. Journal of Iranian Field Crop Research, 6: 909–919. (in Farsi).
Kosari Moghaddam A. (2014): Optimization of tillage machinery selection in grain farms using discrete event simulation.[ Master’s Thesis.] Ferdowsi University of Mashhad (Iran) (in Farsi). Abstract available on http://thesis.um.ac.ir/moreinfo-56053-pg-1.html
Ministry of Agriculture (Iran) (2014): Agricultural Jihad Machinery Development Center. Estimation of Tariff of Mechanization Services in Iran.
Nazeri M.A., Abadi A. Z.F., Koohestani B., Mirak T.N. (2010): Investigation on the response of different wheat types in suitable and delayed sowing day in Mashhad climatic conditions. 11th Iranian Crop Science congress. Environmental Sciences Research Institute, Shahid Beheshti University, Tehran. (in Farsi).
Ogunlowo A.S. (1997): Machinery selection based on gross-margin costing analysis: a case Study of Abeokuta local government aeras in Nigeria. West Indian Journal of Engineering: 40–48.
C. A. Rotz , T. M. Harrigan (2005): PREDICTING SUITABLE DAYS FOR FIELD MACHINERY OPERATIONS IN A WHOLE FARM SIMULATION. Applied Engineering in Agriculture, 21, 563-571 https://doi.org/10.13031/2013.18563
C. Alan Rotz , Hannibal A. Muhtar , J. Roy Black (1983): A Multiple Crop Machinery Selection Algorithm. Transactions of the ASAE, 26, 1644-1649 https://doi.org/10.13031/2013.33817
Sahu R.K., Raheman H. (2008): A decision support system on matching and field performance prediction of tractor-implement system. Computers and Electronics in Agriculture, 60, 76-86 https://doi.org/10.1016/j.compag.2007.07.001
Søgaard Henning T., Sørensen Claus G. (2004): A Model for Optimal Selection of Machinery Sizes within the Farm Machinery System. Biosystems Engineering, 89, 13-28 https://doi.org/10.1016/j.biosystemseng.2004.05.004
Statistical Center of Iran. (2014): Agricultural services costs and products prices. Available at http://www.amar.org.ir/Portals/0/Files/reports/1393/g_ghmvhkhk_93.pdf (in Farsi).
de Toro A. (2005): Influences on Timeliness Costs and their Variability on Arable Farms. Biosystems Engineering, 92, 1-13 https://doi.org/10.1016/j.biosystemseng.2005.06.007
de Toro A., Hansson P.-A. (2004): Analysis of field machinery performance based on daily soil workability status using discrete event simulation or on average workday probability. Agricultural Systems, 79, 109-129 https://doi.org/10.1016/S0308-521X(03)00073-8
download PDF

© 2019 Czech Academy of Agricultural Sciences