The application and benefits of digital technologies for agri-food value chain: Evidence from an emerging country

Mikhailov, Andrei; Camboim, Guilherme F.; Reichert, Fernanda M.; Zawislak, Paulo A.

doi:10.1590/1678-6971/eRAMR220114.en

Andrei Mikhailov Correspondence concerning this article should be addressed to Andrei Mikhailov, Avenida Doutor Nilo Peçanha, 1600, sala 214, Boa Vista, Porto Alegre, Rio Grande do Sul, Brazil, ZIP code 91330-002. Email: andreimikh@gmail.com

University of Vale do Rio dos Sinos, Porto Alegre, RS, Brazil

Author notes Andrei Mikhailov is now a researcher at the Graduate Program in Management of University of Vale do Rio dos Sinos (Universidade do Vale do Rio dos Sinos – Unisinos); Guilherme F. Camboim is now a researcher at the Department of Management of Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul – UFRGS); Fernanda M. Reichert is now an associate professor at the Department of Management of UFRGS; Paulo A. Zawislak is now a full professor and research coordinator at the Department of Management of UFRGS.

https://orcid.org/0000-0003-1465-1958

Guilherme F. Camboim

Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

Author notes Andrei Mikhailov is now a researcher at the Graduate Program in Management of University of Vale do Rio dos Sinos (Universidade do Vale do Rio dos Sinos – Unisinos); Guilherme F. Camboim is now a researcher at the Department of Management of Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul – UFRGS); Fernanda M. Reichert is now an associate professor at the Department of Management of UFRGS; Paulo A. Zawislak is now a full professor and research coordinator at the Department of Management of UFRGS.

https://orcid.org/0000-0001-8786-3930

Fernanda M. Reichert

Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

Author notes Andrei Mikhailov is now a researcher at the Graduate Program in Management of University of Vale do Rio dos Sinos (Universidade do Vale do Rio dos Sinos – Unisinos); Guilherme F. Camboim is now a researcher at the Department of Management of Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul – UFRGS); Fernanda M. Reichert is now an associate professor at the Department of Management of UFRGS; Paulo A. Zawislak is now a full professor and research coordinator at the Department of Management of UFRGS.

https://orcid.org/0000-0002-5639-5879

Paulo A. Zawislak

Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

Author notes Andrei Mikhailov is now a researcher at the Graduate Program in Management of University of Vale do Rio dos Sinos (Universidade do Vale do Rio dos Sinos – Unisinos); Guilherme F. Camboim is now a researcher at the Department of Management of Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul – UFRGS); Fernanda M. Reichert is now an associate professor at the Department of Management of UFRGS; Paulo A. Zawislak is now a full professor and research coordinator at the Department of Management of UFRGS.

https://orcid.org/0000-0002-0487-9177

Correspondence concerning this article should be addressed to Andrei Mikhailov, Avenida Doutor Nilo Peçanha, 1600, sala 214, Boa Vista, Porto Alegre, Rio Grande do Sul, Brazil, ZIP code 91330-002. Email: andreimikh@gmail.com

Author notes

Andrei Mikhailov is now a researcher at the Graduate Program in Management of University of Vale do Rio dos Sinos (Universidade do Vale do Rio dos Sinos – Unisinos); Guilherme F. Camboim is now a researcher at the Department of Management of Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul – UFRGS); Fernanda M. Reichert is now an associate professor at the Department of Management of UFRGS; Paulo A. Zawislak is now a full professor and research coordinator at the Department of Management of UFRGS.

Type of technology	Description
Digital platforms	Digital platforms collect data and provide broader access to information and services. These platforms enable commercial and non-commercial transactions in B2B, B2C, and C2C markets.
Sensors	Sensors allow us to transform the properties of physical world into data. The use of sensors allows for better soil and plant analysis and for gathering valuable data that will be used to predict yields (Basnet & Bang, 2018Basnet, B., & Bang, J. (2018). The state-of-the-art of knowledge-intensive agriculture: A review on applied sensing systems and data analytics. Journal of Sensors, 2018. https://doi.org/10.1155/2018/3528296 https://doi.org/10.1155/2018/3528296... ).
IoT	The IoT allows us to connect different digital and physical devices into a unique information network. Within the farm, IoT helps monitor the location of animals, humans, and production processes.
Robotics and drones	Robots are small-sized automatic machines that can substitute traditional agricultural machinery in different farm activities. Drones, also known as unmanned aerial vehicles (UAV), can support the application of precision agriculture techniques. UAV allows to obtain images of large agricultural areas and gather information about soil quality and plant diseases (Gašparović et al., 2020Gašparović, M., Zrinjski, M., Barković, D., & Radočaj, D. (2020). An automatic method for weed mapping in oat fields based on UAV imagery. Computers and Electronics in Agriculture, 173. https://doi.org/10.1016/j.compag.2020.105385 https://doi.org/10.1016/j.compag.2020.10... ; Zhang & Kovacs, 2012Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision agriculture, 13 (6), 693–712. https://doi.org/10.1016/j.jclepro.2020.122194 https://doi.org/10.1016/j.jclepro.2020.1... ).
Big data	big data is formed by large quantities of information collected from sensors, agricultural equipment, agricultural machinery and by monitoring dairy farming activities. It includes a wide range of information, such as the incidence of pests, crop management, production results, and information on agricultural commodities prices (Kamilaris et al., 2017Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... ). When analyzed through data analytics, it supports the farmer’s decision-making process ( Newton et al., 2020Newton, J. E., Nettle, R., & Pryce, J. E. (2020). Farming smarter with big data: Insights from the case of Australia’s national dairy herd milk recording scheme. Agricultural Systems, 181. https://doi.org/10.1016/j.agsy.2020.102811 https://doi.org/10.1016/j.agsy.2020.1028... ; Sarker et al., 2020Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225.).
Cloud computing	Cloud computing offers the capacity required for data storage and data integration. In this way, cloud computing supports big data analytics (OECD, 2018Organization for Economic Cooperation and Development – OECD (2018). Digital technologies in food agriculture: Reaping the benefits [Global Forum on Agriculture, Paris, France]).
Artificial intelligence	Artificial intelligence is defined as the ability to acquire and apply knowledge and carry out the so-called “intelligent” behavior (OECD, 2018Organization for Economic Cooperation and Development – OECD (2018). Digital technologies in food agriculture: Reaping the benefits [Global Forum on Agriculture, Paris, France]).
Blockchain	Refers to the distributed database operated jointly by the users. In the agri-food sector, it serves to execute programs such as smart contracts (Mukherjee et al., 2021Mukherjee, A. A., Singh, R. K., Mishra, R., & Bag, S. (2021). Application of blockchain technology for sustainability development in agricultural supply chain: Justification framework. Operations Management Research. https://doi.org/10.1007/S12063-021-00180-5 https://doi.org/10.1007/S12063-021-00180... ; Zhang, 2020Zhang, D. (2020). The innovation research of contract farming financing mode under the block chain technology. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122194 https://doi.org/10.1016/j.jclepro.2020.1... ).

Type of solution	Before the farm	Inside the farm	After the farm	Total
Farm management		30		30
UAV		22		22
Meteorology and irrigation		10		10
IoT		9		9
E-grocery			8	8
Precision farming		7		7
Telemetry and automation		7		7
Scouting		6		6
Financial services	5			5
Food safety and traceability			5	5
Distribution management		5		5
Sharing economy	5			5
Restaurant marketplace			3	3
Content and education		3		3
Image diagnosis		2		2
Industry 4.0			2	2
Loyalty program	2			2
Total	12(9%)	101 (77%)	18 (14%)	131

Solution	Mean	St. Dev.	Minimum	Maximum
Distribution management	20.25	10.84	5	28
Industry 4.0	11	2.83	9	13
Telemetry and automation	10.57	7.59	2	20
Food safety and traceability	8.80	5.80	2	14
Farm management	8.50	6.23	1	24
Scouting	8.00	7.92	2	23
Precision farming	7.50	5.21	2	14
Image diagnosis	7.00	0.00	7	7
UAV	6.11	3.49	2	8
Meteorology and irrigation	6.10	4.63	2	14
IoT	5.38	2.97	2	10
Financial services	4.80	2.78	2	9
Restaurant marketplace	4.33	3.22	2	8
E-grocery	3.88	1.81	2	7
Content and education	3.00	0.00	3	3
Loyalty program	3.00	0.00	3	3
Shared economy	2.33	1.16	1	3
Overall	7.28	5.86	1	28

Type of technology	Before the farm	Inside the farm	After the farm	Total
Digital platforms	8	81	16	105
Big data	9	69	15	93
Cloud computing	6	29	5	40
IoT	4	21	3	28
Sensors	2	21	4	27
Robotics and drones	4	20	0	24
Artificial intelligence	7	12	1	20
Blockchain	1	2	0	3

Benefits and advantages	References
Data analysis and decision making	Basnet and Bang (2018)Basnet, B., & Bang, J. (2018). The state-of-the-art of knowledge-intensive agriculture: A review on applied sensing systems and data analytics. Journal of Sensors, 2018. https://doi.org/10.1155/2018/3528296 https://doi.org/10.1155/2018/3528296... , Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kernecker et al. (2020)Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., & Borges, F. (2020). Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe. Precision Agriculture, 21 (1), 34–50. https://doi.org/10.1007/s11119-019-09651-z https://doi.org/10.1007/s11119-019-09651... , Kittipanya-ngam and Tan (2019)Kittipanya-ngam, P., & Tan, K. H. (2019). A framework for food supply chain digitalization: lessons from Thailand. Production Planning & Control, 31 (2-3), 158–172. https://doi.org/10.1080/09537287.2019.1631462 https://doi.org/10.1080/09537287.2019.16... , Newton et al. (2020)Newton, J. E., Nettle, R., & Pryce, J. E. (2020). Farming smarter with big data: Insights from the case of Australia’s national dairy herd milk recording scheme. Agricultural Systems, 181. https://doi.org/10.1016/j.agsy.2020.102811 https://doi.org/10.1016/j.agsy.2020.1028... , Sarker et al. (2020)Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225., Wolfert et al. (2017)Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023 https://doi.org/10.1016/j.agsy.2017.01.0... .
Costs reduction	Basnet and Bang (2018)Basnet, B., & Bang, J. (2018). The state-of-the-art of knowledge-intensive agriculture: A review on applied sensing systems and data analytics. Journal of Sensors, 2018. https://doi.org/10.1155/2018/3528296 https://doi.org/10.1155/2018/3528296... , Jayashankar et al. (2019)Jayashankar, P., Johnston, W. J., Nilakanta, S., & Burres, R. (2019). Co-creation of value-in-use through big data technology-a B2B agricultural perspective. Journal of Business & Industrial Marketing, 35 (3), 508–523. https://doi.org/10.1108/jbim-12-2018-0411 https://doi.org/10.1108/jbim-12-2018-041... , Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kernecker et al. (2020)Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., & Borges, F. (2020). Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe. Precision Agriculture, 21 (1), 34–50. https://doi.org/10.1007/s11119-019-09651-z https://doi.org/10.1007/s11119-019-09651... , Sarker et al. (2020)Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225., Weersink et al. (2018)Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics, 10, 19–37. https://doi.org/10.1146/annurev-resource-100516-053654 https://doi.org/10.1146/annurev-resource... , Zhang (2020)Zhang, D. (2020). The innovation research of contract farming financing mode under the block chain technology. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122194 https://doi.org/10.1016/j.jclepro.2020.1... .
Information and data	Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kittipanya-ngam and Tan (2019)Kittipanya-ngam, P., & Tan, K. H. (2019). A framework for food supply chain digitalization: lessons from Thailand. Production Planning & Control, 31 (2-3), 158–172. https://doi.org/10.1080/09537287.2019.1631462 https://doi.org/10.1080/09537287.2019.16... , Sarker et al. (2020)Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225., Scuderi et al. (2019)Scuderi, A., Foti, V., & Timpanaro, G. (2019). The supply chain value of pod and pgi food products through the application of blockchain. Calitatea, 20 (S2), 580–587. https://www.proquest.com/scholarly-journals/supply-chain-value-pod-pgi-food-products-through/docview/2198417566/se-2?accountid=130632 https://www.proquest.com/scholarly-journ... , Jayashankar et al. (2019)Jayashankar, P., Johnston, W. J., Nilakanta, S., & Burres, R. (2019). Co-creation of value-in-use through big data technology-a B2B agricultural perspective. Journal of Business & Industrial Marketing, 35 (3), 508–523. https://doi.org/10.1108/jbim-12-2018-0411 https://doi.org/10.1108/jbim-12-2018-041... , Yadav et al. (2021)Yadav, S., Luthra, S., & Garg, D. (2021). Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environmental Science and Pollution Research, 28, 16633–16654. https://doi.org/10.1007/s11356-020-11676-1 https://doi.org/10.1007/s11356-020-11676... , Wolfert et al. (2017)Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023 https://doi.org/10.1016/j.agsy.2017.01.0... .
Planning and management	Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kumar et al. (2020)Kumar, A., Singh, R. K., & Modgil, S. (2020). Exploring the relationship between ICT, SCM practices and organizational performance in agri-food supply chain. Benchmarking: An International Journal, 27 (3), 1003–1041. https://doi.org/10.1108/BIJ-11-2019-0500 https://doi.org/10.1108/BIJ-11-2019-0500... , Sarker et al. (2020)Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225..
Control and monitoring	Gašparovic et al. (2020), Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Sarker et al. (2020)Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225., Scuderi et al. (2019)Scuderi, A., Foti, V., & Timpanaro, G. (2019). The supply chain value of pod and pgi food products through the application of blockchain. Calitatea, 20 (S2), 580–587. https://www.proquest.com/scholarly-journals/supply-chain-value-pod-pgi-food-products-through/docview/2198417566/se-2?accountid=130632 https://www.proquest.com/scholarly-journ... , Yadav et al. (2021)Yadav, S., Luthra, S., & Garg, D. (2021). Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environmental Science and Pollution Research, 28, 16633–16654. https://doi.org/10.1007/s11356-020-11676-1 https://doi.org/10.1007/s11356-020-11676... , Wolfert et al. (2017)Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023 https://doi.org/10.1016/j.agsy.2017.01.0... .
Quality improvement	Basnet and Bang (2018)Basnet, B., & Bang, J. (2018). The state-of-the-art of knowledge-intensive agriculture: A review on applied sensing systems and data analytics. Journal of Sensors, 2018. https://doi.org/10.1155/2018/3528296 https://doi.org/10.1155/2018/3528296... , Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kittipanya-ngam and Tan (2019)Kittipanya-ngam, P., & Tan, K. H. (2019). A framework for food supply chain digitalization: lessons from Thailand. Production Planning & Control, 31 (2-3), 158–172. https://doi.org/10.1080/09537287.2019.1631462 https://doi.org/10.1080/09537287.2019.16... , Sarker et al. (2020)Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. International Journal of Scientific & Technology Research, 9 (4), 1222–1225., Yadaev et al. (2021), Weersink et al. (2018)Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics, 10, 19–37. https://doi.org/10.1146/annurev-resource-100516-053654 https://doi.org/10.1146/annurev-resource... , Zhang (2020)Zhang, D. (2020). The innovation research of contract farming financing mode under the block chain technology. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122194 https://doi.org/10.1016/j.jclepro.2020.1... .
Processes optimization and time reduction	Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kernecker et al. (2020)Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., & Borges, F. (2020). Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe. Precision Agriculture, 21 (1), 34–50. https://doi.org/10.1007/s11119-019-09651-z https://doi.org/10.1007/s11119-019-09651... , Scuderi et al. (2019)Scuderi, A., Foti, V., & Timpanaro, G. (2019). The supply chain value of pod and pgi food products through the application of blockchain. Calitatea, 20 (S2), 580–587. https://www.proquest.com/scholarly-journals/supply-chain-value-pod-pgi-food-products-through/docview/2198417566/se-2?accountid=130632 https://www.proquest.com/scholarly-journ... , Yadaev et al. (2021), Zhang (2020)Zhang, D. (2020). The innovation research of contract farming financing mode under the block chain technology. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122194 https://doi.org/10.1016/j.jclepro.2020.1... .
Precision	Gašparović et al. (2020)Gašparović, M., Zrinjski, M., Barković, D., & Radočaj, D. (2020). An automatic method for weed mapping in oat fields based on UAV imagery. Computers and Electronics in Agriculture, 173. https://doi.org/10.1016/j.compag.2020.105385 https://doi.org/10.1016/j.compag.2020.10... , Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Yadaev et al. (2021), Weersink et al. (2018)Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics, 10, 19–37. https://doi.org/10.1146/annurev-resource-100516-053654 https://doi.org/10.1146/annurev-resource... .
Productivity	Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kernecker et al. (2020)Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., & Borges, F. (2020). Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe. Precision Agriculture, 21 (1), 34–50. https://doi.org/10.1007/s11119-019-09651-z https://doi.org/10.1007/s11119-019-09651... , Yadaev et al. (2021), Weersink et al. (2018)Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics, 10, 19–37. https://doi.org/10.1146/annurev-resource-100516-053654 https://doi.org/10.1146/annurev-resource... .
Speed and agility	Kamilaris et al. (2017)Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 https://doi.org/10.1016/j.compag.2017.09... , Kittipanya-ngam and Tan (2019)Kittipanya-ngam, P., & Tan, K. H. (2019). A framework for food supply chain digitalization: lessons from Thailand. Production Planning & Control, 31 (2-3), 158–172. https://doi.org/10.1080/09537287.2019.1631462 https://doi.org/10.1080/09537287.2019.16... , Mukherjee et al. (2021)Mukherjee, A. A., Singh, R. K., Mishra, R., & Bag, S. (2021). Application of blockchain technology for sustainability development in agricultural supply chain: Justification framework. Operations Management Research. https://doi.org/10.1007/S12063-021-00180-5 https://doi.org/10.1007/S12063-021-00180... , Scuderi et al. (2019)Scuderi, A., Foti, V., & Timpanaro, G. (2019). The supply chain value of pod and pgi food products through the application of blockchain. Calitatea, 20 (S2), 580–587. https://www.proquest.com/scholarly-journals/supply-chain-value-pod-pgi-food-products-through/docview/2198417566/se-2?accountid=130632 https://www.proquest.com/scholarly-journ... , Wolfert et al. (2017)Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023 https://doi.org/10.1016/j.agsy.2017.01.0... , Zhang (2020)Zhang, D. (2020). The innovation research of contract farming financing mode under the block chain technology. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122194 https://doi.org/10.1016/j.jclepro.2020.1... .
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[1] Correspondence concerning this article should be addressed to Andrei Mikhailov, Avenida Doutor Nilo Peçanha, 1600, sala 214, Boa Vista, Porto Alegre, Rio Grande do Sul, Brazil, ZIP code 91330-002. Email: andreimikh@gmail.com

Brasil

Brasil

The application and benefits of digital technologies for agri-food value chain: Evidence from an emerging country

Aplicação e benefícios das tecnologias digitais para as cadeias de valor agroalimentares: Evidências de um país emergente

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