Reference Standard Process Model for Agriculture: Introduction and Steps for Further Development

• Autorzy: Rupnik Rok

Identyfikatory

DOI

10.53502/WHVY4483

• Języki publikacji: EN

Abstrakty

EN

The use of digitalisation in agriculture can improve process efficiency on farms. Experience from a large-scale EU-funded project involving a software consortium shows that software companies have different knowledge and understanding of agricultural processes and the use of digitalisation technologies in agricultural processes. This finding, combined with expertise in the standard process model for IT governance (COBIT), triggered the idea of a reference standard process model for agriculture (RSPMA), which is presented in this paper. RSPMA is presented on a conceptual level, where concepts and the relations between them are presented and explained through conceptual sub-models, and some of the RSPMA processes are listed. A Delphi technique survey showed that RSPMA has the potential for implementation and further development. Based on this finding, the steps for further development are also defined and presented here.

Słowa kluczowe

EN

process model; process framework; processes in agriculture

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Journal of Research and Applications in Agricultural Engineering
• Rocznik: 2024
• Tom: Vol. 69, nr 1
• Strony: 49--56
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Rupnik Rok

• University of Ljubljana, Ljubljana, Slovenia

• https://orcid.org/0000-0001-9572-0016

Bibliografia

• [1] A. Kaloxylos et al., “A cloud-based Farm Management System: Architecture and implementation,” Comput. Electron. Agric., 2014, 100, 168–179.
• [2] Y. Ampatzidis, L. Tan, R. Haley, and M. D. Whiting, “Cloud-based harvest management information system for hand-harvested specialty crops,” Comput. Electron. Agric., 2016, 122, 161–167.
• [3] R. Rupnik, M. Kukar, P. Vračar, D. Košir, D. Pevec, and Z. Bosnić, “AgroDSS: A decision support system for agriculture and farming,” Comput. Electron. Agric., 2018, 161, 260-271.
• [4] S. Fountas et al., “Farm management information systems: Current situation and future perspectives,” Comput. Electron. Agric., 2015, 115, 40–50.
• [5] A. Kaloxylos et al., “Farm management systems and the Future Internet era,” Comput. Electron. Agric., 2012, 89, 130–144.
• [6] C. N. Verdouw, R. M. Robbemond, and J. Wolfert, “ERP in agriculture: Lessons learned from the Dutch horticulture,” Comput. Electron. Agric., 2015, 114, 125–133.
• [7] U. Lele and S. Goswami, “The fourth industrial revolution, agricultural and rural innovation, and implications for public policy and investments: a case of India,” Agric. Econ. (United Kingdom), 2017, 48, 87–100.
• [8] R. R. B. Khataza, A. Hailu, G. J. Doole, M. E. Kragt, and A. D. Alene, “Examining the relationship between farm size and productive efficiency: a Bayesian directional distance function approach,” Agric. Econ. (United Kingdom), 2019, 50(2), 237–246.
• [9] J. Santa, M. A. Zamora-Izquierdo, A. J. Jara, and A. F. Gómez-Skarmeta, “Telematic platform for integral management of agricultural/perishable goods in terrestrial logistics,” Comput. Electron. Agric., 2012, 80, 31–40.
• [10] C. Eastwood, M. Ayre, R. Nettle, and B. Dela Rue, “Making sense in the cloud: Farm advisory services in a smart farming future,” NJAS - Wageningen J. Life Sci., 2019.
• [11] B. Žvanut, M. Burnik, T. Š. Kolnik, and P. Pucer, “The applicability of COBIT processes representation structure for quality improvement in healthcare: a Delphi study,” Int. J. Qual. Heal. Care, vol. 32, 2020, 9, 577–584.
• [12] J. Tummers, A. Kassahun, and B. Tekinerdogan, “Obstacles and features of Farm Management Information Systems: A systematic literature review,” Computers and Electronics in Agriculture, 2019, 157, 189–204.
• [13] M. Vavpotic, D. and Bajec, “An approach for concurrent evaluation of technical and social aspects of software develop-ment methodologies,” Inf. Softw. Technol., 2009, 51(2), 528–545.
• [14] C. G. Sørensen et al., “Conceptual model of a future farm management information system,” Comput. Electron. Agric., 2010, 72(1), 37–47.
• [15] S. Fountas et al. 2015. “Farm management information systems: Current situation and future perspectives,” Comput. Electron. Agric., 115, 40–50.
• [16] ICASA, COBIT 2019. ISACA, 2019.
• [17] ISACA, COBIT 4.1. 2007.
• [18] D. Steuperaert, “COBIT 2019: A Significant Update,” EDP Audit. Control. Secur. Newsl., 2019, 59(1), 14–18.
• [19] O.B. Adeboye, B. Schultz, A.P. Adeboye, K.O. Adekalu, J.A. Osinbitan: Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans. Information processing in agriculture, 2021, Vol. 8, 419 - 436.
• [20] I. Drobotǎ, T. Robu, B. Drobotǎ, and A. D. Robu, “Risk management in modern cereal farms,” Metal. Int., vol. 18, no. 8, 196–199, 2013.
• [21] E. Gindu, A. Chiran, B. Drobota, and A.-F. Jitareanu, “Risk Management Methodology of Investment Projects With Environmental Impact,” J. Eng. Stud. Res., vol. 21, no. 1, 2018.
• [22] M. M. Grime and G. Wright, “Delphi Method,” Wiley StatsRef: Statistics Reference Online. 1–6, 2016.
• [23] C. Hsu and T. Ohio, “The Delphi Technique: Making Sense Of Consensus,” Pract. Assess. Res. Eval., vol. 12, no. 10, 2007.
• [24] M. O’Grady, D. Langton, F. Salinari, P. Daly, G. O’Hare: Service design for climate-smart agriculture. Computers and electronic in agriculture, 2021, Vol. 8, 328 - 340.