Abstract
The main characteristic of what is called Digital Agriculture, or Agriculture 4.0, is the intensive use of data. It can be said that Digital Agriculture is data-driven. In other words, data, which are becoming increasingly available with spatial and temporal attributes, at high frequencies and on an unprecedented scale, have become essential inputs for the processes that culminate in decision-making.
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The Economist. Available at: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data
References
Aggateway (2020a) Precision Ag Irrigation Language (PAIL). Available at https://www.aggateway.org/eConnectivityActivities/Implementation/PrecisionAgIrrigationLanguage(PAIL).aspx. Accessed 12 Mar 2020
Aggateway (2020b) Specific Initiatives. Available at https://www.aggateway.org/GetConnected/SpecificInitiatives.aspx. Accessed 13 Mar 2020
Boyd D, Crawford K (2011) Six provocations for Big Data. In: Symposium on the dynamics of the internet and society: a decade in internet time, September. Available at https://ssrn.com/abstract=1926431 or https://doi.org/10.2139/ssrn.1926431. Accessed 13 Mar 2020
Cox SWR (1997) Measurement and control in agriculture. Blackwell Science, London. ISBN 0-632-04114-5
Curry E (2016) The big data value chain: definitions, concepts, and theoretical approaches. In: Cavanillas J, Curry E, Wahlster W (eds) New horizons for a data-driven economy. Springer, Chain
Di LP, Charvatdim F, Sakuda LO (org) (2020) Radar AgTech Brasil 2019: mapeamento das startups do setor agro brasileiro. Brasília: Embrapa, SP Ventures e Homo Ludens, K. Agriculture DWG. Available at https://www.ogc.org/projects/groups/agriculturedwg. Accessed 13 Mar 2020
EC – European Commission (2015) Towards a thriving data-driven economy. Off J Eur Union 58:61–65
Ferreyra R (2017) ADAPT Public Space. 15 set. Available at https://www.aggateway.org/GetConnected/AgGateway%E2%80%99sADAPT.aspx. Accessed 14 Mar 2020
Ferreyra, R (2019) A AgGateway Post-Image Collection Specification (PICS). Available at https://aggateway.atlassian.net/wiki/x/XABrDw. Accessed 13 Mar 2020
Krishnan N (2017) Cultivating Ag Tech: 5 trends shaping the future of agriculture. Available at https://www.cbinsights.com/research/agtech-startup-investor-funding-trends. Accessed 14 Mar 2020
Malaverri JEG, Medeiros CB (2012) Data quality in agriculture applications. In: GEOINFO, 13 November 25–27 de 2012, Campos do Jordão, SP, Brazil. Proceedings of the Brazilian Symposium on GeoInformatics. Campos do Jordão, SP, pp 128–139
Mckee L (2020) OGC history (detailed). Available at https://www.ogc.org/ogc/historylong. Accessed 13 Mar 2020
Miller HG, Mork P (2013) From data to decisions: a value chain for big data. In It Profess 15:57–59. https://doi.org/10.1109/MITP.2013.11
Morando F, Iemma R, Raitieri E (2014) Privacy evaluation what empirical research on users’ valuation of personal data tells us. Internet Pol Rev 3(2). https://doi.org/10.14763/2014.2.283
Redman T (1998) The impact of poor data quality on the typical enterprise. Communications of the ACM. https://doi.org/10.1145/269012.269025
Silva DL (2017) Estratégia computacional para apoiar a reprodutibilidade e reuso de dados científicos baseado em metadados de proveniência. PhD thesis – Escola Politécnica, Universidade de São Paulo, São Paulo. https://doi.org/10.11606/T.3.2017.tde-05092017-095907. Available at https://www.teses.usp.br. Accessed 3 Jan 2020. (English title: Computational strategy to support reproducibility and reuse of scientific data based on provenance metadata)
Veiga AK, Saraiva AM, Chapman AD, Morris PJ, Gendreau C, Schiegel D, Robertson TJ (2017) A conceptual framework for quality assessment and management of biodiversity data. PLOS One 12:e0178731. Available at https://doi.org/10.1371/journal.pone.0178731. Accessed 3 Jan 2020
Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst (Published by M.E. Sharpe, Inc.) 12:5–33. Available at http://www.jstor.org/stable/40398176. Accessed 3 Jan 2020
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, Hoen Peter AC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, Lei J, Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018. Available at https://doi.org/10.1038/sdata.2016.18. Accessed 3 Jan 2020
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Saraiva, A.M. et al. (2022). Digital Data: Cycle, Standardization, Quality, Sharing, and Security. In: Marçal de Queiroz, D., M. Valente, D.S., de Assis de Carvalho Pinto, F., Borém, A., Schueller, J.K. (eds) Digital Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-14533-9_16
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