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Modelling of Agricultural Machinery Trends for Power, Mass, Working Width and Price

  • Francesco MarinelloEmail author
  • Tatevik Yezekyan
  • Giannantonio Armentano
  • Luigi Sartori
Conference paper
  • 27 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 67)

Abstract

Rural mechanisation and fleet organisation have an essential impact on agricultural production and sustainable development of farm institutions. Machine functional parameters define the fleet composition and management and, thus, play an important role in economic and environmental performance of a farm. Programming methods and decision support systems are available in the market, however, there is still a lack of applicative tools which allow modelling and forecasting of technical parameters as well as costs to complete the decision tasks. Availability of such models in relation to dimensions, mass, power or working capacity, is then particularly necessary not only to support decisions at the different applied management levels (farmer, stakeholder, policy makers), but also to study the impact of farm machine on the environment and in general to understand trends in agricultural mechanization. The present research is aimed at identifying the most relevant parameters (including working width, overall dimensions, mass and power) for different groups of agricultural machines, modelled and characterised through the application of linear regression analyses. The study is defined on the basis of a database populated on purpose with more than 5000 agricultural machines models (30 machine groups) available in the market. Extracted equations give evidence of high correlations (R2 > 0.75) in particular between prices, mass and needed power, supporting the possibility of analyses on mechanisation trends, from both economical, management and environmental point of view.

Keywords

Functional parameter Operation research Modelling Linear programming Fleet management 

References

  1. Al-mansour, F., & Jejcic, V. (2017). A model calculation of the carbon footprint of agricultural products: The case of Slovenia. Energy, 136, 7–15.  https://doi.org/10.1016/j.energy.2016.10.099.CrossRefGoogle Scholar
  2. Bochtis, D. D., Sørensen, C. G. C., & Busato, P. (2014). Advances in agricultural machinery management: A review. Biosystems Engineering, 126, 69–81.  https://doi.org/10.1016/j.biosystemseng.2014.07.012.CrossRefGoogle Scholar
  3. Borsato, E., Tarolli, P., & Marinello, F. (2018). Sustainable patterns of main agricultural products combining different footprint parameters. Journal of Cleaner Production, 179, 357–367.  https://doi.org/10.1016/j.jclepro.2018.01.044.CrossRefGoogle Scholar
  4. Bulgakov, V., Adamchuk, V., Arak, M., & Olt, J. (2015). Mathematical Modelling of the Process of Renewal of the Fleet of Combine Harvesters. Agriculture and Agricultural Science Procedia, 7, 35–39.  https://doi.org/10.1016/j.aaspro.2015.12.027.CrossRefGoogle Scholar
  5. Camarena, E. A., Gracia, C., & Sixto, J. M. C. (2004). A Mixed Integer Linear Programming Machinery Selection Model for Multifarm Systems., 87, 145–147.  https://doi.org/10.1016/j.biosystemseng.2003.10.003.CrossRefGoogle Scholar
  6. Cavallo, E., Ferrari, E., Bollani, L., & Coccia, M. (2014). Attitudes and behaviour of adopters of technological innovations in agricultural tractors: A case study in Italian agricultural system. Agricultural Systems, 130, 44–54.  https://doi.org/10.1016/j.agsy.2014.05.012.CrossRefGoogle Scholar
  7. Chamen, T. (2015). Controlled traffic farming–From worldwide research to adoption in Europe and its future prospects. Acta Technologica Agriculturae, 18(3), 64–73.  https://doi.org/10.1515/ata-2015-0014.CrossRefGoogle Scholar
  8. Edwards, W. (2015). Estimating farm machinery repair costs. Iowa State University Extension and Outreach, Ag Decision Maker, File A3-29.Google Scholar
  9. Filippi, C., Mansini, R., & Stevanato, E. (2017). Mixed integer linear programming models for optimal crop selection. Computers & Operations Research, 81, 26–39.  https://doi.org/10.1016/j.cor.2016.12.004.MathSciNetCrossRefzbMATHGoogle Scholar
  10. Lazzari, M., & Mazzetto, F. (1996). A PC model for selecting multicropping farm machinery systems. Computers and Electronics in Agriculture, 14(1), 43–59.  https://doi.org/10.1016/0168-1699(95)00036-4.CrossRefGoogle Scholar
  11. Nikkilä, R., Seilonen, I., & Koskinen, K. (2010). Software architecture for farm management information systems in precision agriculture. Computers and Electronics in Agriculture, 70(2), 328–336.  https://doi.org/10.1016/j.compag.2009.08.013.CrossRefGoogle Scholar
  12. Peart, R., & Shown, V. (2006). Agricultural systems management. optimizing efficiency and performance. In Agricultural Systems Management. New York: Marcel Dekker, INC.Google Scholar
  13. Reidsma, P., Janssen, S., Jansen, J., & Van Ittersum, M. K. (2018). On the development and use of farm models for policy impact assessment in the European Union. Agricultural Systems, 159, 111–125.  https://doi.org/10.1016/j.agsy.2017.10.012.CrossRefGoogle Scholar
  14. Sims, B., & Heney, J. (2017). Promoting Smallholder Adoption of Conservation Agriculture through Mechanization Services. Agriculture, 7(64).  https://doi.org/10.3390/agriculture7080064.CrossRefGoogle Scholar
  15. Søgaard, H. T., & Sørensen, C. 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.CrossRefGoogle Scholar
  16. Sopegno, A., Busato, P., Berruto, R., & Romanelli, T. L. (2016). A cost prediction model for machine operation in multi-field production systems. Scientia Agricola, 73(5), 397–405.  https://doi.org/10.1590/0103-9016-2015-0304.CrossRefGoogle Scholar
  17. Sørensen, C. G., & Bochtis, D. D. (2010). Conceptual model of fleet management in agriculture. Biosystems Engineering, 105(1), 41–50.  https://doi.org/10.1016/j.biosystemseng.2009.09.009.CrossRefGoogle Scholar
  18. Taechatanasat, P., & Armstrong, L. (2014). Decision support system data for farmer decision making. In Proceedings of Asian Federation for Information Technology in Agriculture, 472–486.Google Scholar
  19. Tieppo, R. C., Romanelli, T. L., Milan, M., Sørensen, C. A. G., & Bochtis, D. (2019). Modeling cost and energy demand in agricultural machinery fleets for soybean and maize cultivated using a no-tillage system. Computers and Electronics in Agriculture, 156, 282–292.  https://doi.org/10.1016/j.compag.2018.11.032.CrossRefGoogle Scholar
  20. Yezekyan, T., Marinello, F., Armentano, G., & Sartori, L. (2018a). Analysis of cost and performances of agricultural machinery: Reference model for sprayers. Agronomy Research, 16(2), 604–614.  https://doi.org/10.15159/AR.18.049.
  21. Yezekyan, T., Marinello, F., Armentano, G., Trestini, S., & Sartori, L. (2018b). Definition of Reference Models for Power, Weight, Working Width, and Price for Seeding Machines. Agriculture, 8(12), 186.  https://doi.org/10.3390/agriculture8120186.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Francesco Marinello
    • 1
    Email author
  • Tatevik Yezekyan
    • 1
  • Giannantonio Armentano
    • 2
  • Luigi Sartori
    • 1
  1. 1.Department of Land, Environment, Agriculture and ForestryUniversity of PadovaLegnaroItaly
  2. 2.Edizioni L’Informatore Agrario SrlVeronaItaly

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