Classification and evaluation of uncertain influence factors for farm machinery service
Keywords:
uncertainty, uncertain influence factor (UIF), classification, uncertainty composite index (UCI), machinery cooperativesAbstract
Uncertainty extremely interferes with the execution of farm machinery operation. Treating uncertainties is especially important for machinery cooperatives providing social service since they face more uncertain influence factors (UIFs) than family farms. Under social service circumstance, uncertainties may arise from participants and environments. Classification and evaluation of UIFs were studied in this research. According to the production system, 32 UIFs are defined and classified into six categories, which include supply, demand, interactivity, nature, society and others. Uncertainty composite index (UCI) is defined to evaluate the importance of UIFs, which is the square root of the product of occurrence frequency (OF) and impact degree (ID) calculated from the well-designed questionnaire responded by farm machinery operators. UCI is divided into five ranks based on normalization distribution test to illustrate the level of importance. Results from questionnaire showed that natural UIFs have an extreme impact on farm operation, UIFs of the demand and the supply have a serious influence on farm operation, UIFs of interactivity cannot be ignored, and social UIFs have a weak impact on farm operations. This study discovered the uncertainty problems under the specific circumstance of farm machinery service, which may provide a theoretical basis and potential methods for risk management of machinery cooperatives. Keywords: uncertainty, uncertain influence factor (UIF), classification, uncertainty composite index (UCI), machinery cooperatives DOI: 10.25165/j.ijabe.20171006.3045 Citation: Wu C C, Cai Y P, Hu B B, Wang J. Classification and evaluation of uncertain influence factors for farm machinery service. Int J Agric & Biol Eng, 2017;10(6):164–174.References
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[2] Dreyer B, Grønhaug K. Uncertainty, flexibility, and sustained competitive advantage. Journal of Business Research, 2004; 57(5):484–494.
[3] Lodwick W A. An overview of flexibility and generalized uncertainty in optimization. Computational & Applied Mathematics, 2012; 31(3):569–589.
[4] Merschmann U, Thonemann U W. Supply chain flexibility, uncertainty and firm performance: An empirical analysis of german manufacturing firms. International Journal of Production Economics, 2011; 130(1):43–53.
[5] Pivoto D, Becker J M, Bremm C, Albano F D M. Uncertainty measurement in the homogenization and sample reduction in the physical classification of rice and beans. Ciencia Rural, 2016; 46(4):599–603.
[6] Sun M, Zhang X, Huo Z, Feng S, Huang G, Mao X. Uncertainty and sensitivity assessments of an agricultural-hydrological model (rzwqm2) using the glue method. Journal of Hydrology, 2016; 534:19–30.
[7] Nandakumar M, Jharkharia S, Nair A. Environmental uncertainty and flexibility. Global Journal of Flexible Systems Management, 2012; 13(3):121–122.
[8] Borodin V, Bourtembourg J, Hnaien F, Labadie N. Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 2016; 254(2):348–359.
[9] Cowan L, Kaine G, Wright V. The role of strategic and tactical flexibility in managing input variability on farms. Systems Research and Behavioral Science, 2013; 30(4):470–494.
[10] Milne A E, Glendining M J, Bellamy P, Misselbrook T, Gilhespy S, Casado M R, et al. Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the uk's greenhouse gas inventory for agriculture. Atmospheric Environment, 2014; 82:94–105.
[11] Houska T, Multsch S, Kraft P, Frede H G, Breuer L. Monte carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model. Biogeosciences, 2014; 11(7):2069–2082.
[12] Vermeulen S J, Challinor A J, Thornton P K, Campbell B M, Eriyagama N, Vervoort J M, et al. Addressing uncertainty in adaptation planning for agriculture. Proceedings of the National Academy of Sciences, 2013; 110(21):8357–8362.
[13] Jain A, Jain P K, Chan F T S, Singh S. A review on manufacturing flexibility. International Journal of Production Research, 2013; 51(19):5946–5970.
[14] Adams M L, Cook S, Corner R. Managing uncertainty in site-specific management: What is the best model? Precision Agriculture, 2000; 2(1):39–54.
[15] Nie J, Sun R, Deng X, Yang H. Uncertain complex event processing in precision agriculture based on data provenance management. Transactions of the CSAM, 2016; 47(5):245–253. (in Chinese)
[16] Bochtis D D. Concepts and methods for scheduling field machinery operations, In: Hussein M Khodr (Ed.), Computer Science, Technology and Applications. Nova Science Publishers, Incorporated, New York, 2012, pp.179–191.
[17] Guan S, Nakamura M, Shikanai T, Okazaki T. Resource assignment and scheduling based on a two-phase metaheuristic for cropping system. Computers and Electronics in Agriculture, 2009; 66(2):181–190.
[18] Bochtis D D, Dogoulis P, Busato P, Sørensen C G, Berruto R, Gemtos T. A flow-shop problem formulation of biomass handling operations scheduling. Computers and Electronics in Agriculture, 2013; 91:49–56.
[19] David A L. Activity network techniques applied to a farm machinery selection problem. Transactions of the ASAE, 1967; 10(3): 0310–0317.
[20] Bochtis D D, Sørensen C G, Green O, Bartzanas T, Fountas S. Feasibility of a modelling suite for the optimised biomass harvest scheduling. Biosystems Engineering, 2010; 107(4):283–293.
[21] Wu C C, Cai Y P, Luo M J, Su H H, Ding L J. Time-windows based temporal and spatial scheduling model for agricultural machinery resources. Transactions of the CSAM, 2013; 44(5):237–241, 231. (in Chinese)
[22] Fountas S, Carli G, Sørensen C G, Tsiropoulos Z, Cavalaris C, Vatsanidou A, et al. Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 2015; 115:40–50.
[23] Wu C C, Zhou L, Wang J, Cai Y P. Smartphone based precise monitoring method for farm operation. International Journal of Agricultural and Biological Engineering, 2016; 9(3):111–121.
[24] Zhang Y, Huang Z H. Identifying risks inherent in farmer cooperatives in China. China Agricultural Economic Review, 2014; 6(2):335–354.
[25] AhmadA, NabiSTM, FahimeY. Factors affecting pistachio production uncertainty in sirjan. Journal of Agricultural Economics Researches, 2014; 6(3):175–190.
[26] Yu J, Zhu Q. Agriculture production planning under supply uncertainty and demand uncertainty. 12th International Conference on Service Systems and Service Management, ICSSSM 2015, June 22-24, 2015. Institute of Electrical and Electronics Engineers Inc., Guangzhou, China.
[27] Li E, Yang M, Cook M L. Agricultural machinery cooperatives in china: Origin, development, and innovation. ASABE Annual International Meeting 2009, June 21-24. Reno, NV, United states, pp.5835–5853.
[28] Wu C C, Zhou L, Zhao J, Wang J. Uncertainty of influence factors for farm machinery operation scheduling. ASABE Annual International Meeting 2015, July 26-29. New Orleans, LA, United states, pp.4054–4061.
[29] Bochtis D D, Sørensen C G C, Busato P. Advances in agricultural machinery management: A review. Biosystems Engineering, 2014; 126:69–81.
[30] Harwood T D, Al Said F A, Pearson S, Houghton S J, Hadley P. Modelling uncertainty in field grown iceberg lettuce production for decision support. Computers and Electronics in Agriculture, 2010; 71(1):57–63.
[31] Nikkila R, Seilonen I, Koskinen K. Software architecture for farm management information systems in precision agriculture. Computers And Electronics In Agriculture, 2010; 70(2):328–336.
[32] Fountas S, Sorensen C G, Tsiropoulos Z, Cavalaris C, Liakos V, Gemtos T. Farm machinery management information system. Computers and Electronics in Agriculture, 2015; 110:131–138.
[33] Orfanou A, Busato P, Bochtis D D, Edwards G, Pavlou D, Sørensen C G, et al. Scheduling for machinery fleets in biomass multiple-field operations. Computers and Electronics in Agriculture, 2013; 94:12–19.
[34] Srensen C G, Bochtis D D. Conceptual model of fleet management in agriculture. Biosystems Engineering, 2010; 105(1):41–50.
[35] Foy A S, Carstensen L W, Prisley S P, Campbell J B, Dymond R L. A review and evaluation of uncertainty classification and the error-band geometry model. Transactions in GIS, 2015; 19(4):604–618.
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Published
2017-11-30
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Caicong, W., Yaping, C., Bingbing, H., & Jie, W. (2017). Classification and evaluation of uncertain influence factors for farm machinery service. International Journal of Agricultural and Biological Engineering, 10(6), 164–174. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3045
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Information Technology, Sensors and Control Systems
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