Geomatics and Environmental Engineering, vol. 16, no. 3

Issue

Vol. 16 No. 3 (2022): Geomatics and Environmental Engineering

Date Log

Submitted
Feb 16, 2022
Accepted
Apr 6, 2022
Published
Jun 12, 2022
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Small Farms as “Data Producers” for the Needs of Agricultural Management Information System

https://doi.org/10.7494/geom.2022.16.3.79
Elżbieta Zysk
University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering, Institute of Spatial Management and Geography, Department of Spatial Analysis and Real Estate, Olsztyn, Poland
https://orcid.org/0000-0002-2793-6154
Tomasz Mroczkowski
Kogod School of Business American University, Fulbright Scholar, Department of Management, Washington DC, USA
Agnieszka Dawidowicz
University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering, Institute of Spatial Management and Geography, Department of Land Management and Geographic Information Systems, Olsztyn
https://orcid.org/0000-0002-8445-3095

Corresponding Author(s) : Elżbieta Zysk

elzbieta.zysk@uwm.edu.pl

Geomatics and Environmental Engineering, Vol. 16 No. 3 (2022): Geomatics and Environmental Engineering

Abstract

In the face of current global threats, including the COVID-19 Pandemic, new technological solutions are needed. Globalization, progressing urbanization, the decreasing availability of cultivable land for food production, water contamination, flood risk and climate change, can all be viewed as potential threats to food safety. According to forecasts and trends, the future of both agricultural policy and agricultural innovation will be based on big data, data analytics and machine learning. Therefore, it is and will continue to be important to develop information systems dedicated to agricultural innovation and the management of food security challenges. The main aim of the study is a classification of data for a uniform AMIS from data from IREIS, GC and AIIS based on survey and expert interview data obtained. We propose to expand the range of data produced by small farmers while keeping in mind the protection of farmers and their rights and the possible benefits of the data provided. The literature recognizes the value of such data but it has not yet been legally regulated, protected, managed and, above all, properly used for agricultural and food security policy purposes. Therefore, we develop the idea of extended farmers’ participation in the production of agricultural activity data. The research used a survey questionnaire and expert interviews. A viable AIIS needs current data that farmers already produce as well as additional data needs which we identify in our research. We propose an architecture of databases and describe their flow in the Agriculture Management Information System (AMIS).

Keywords

Agriculture Management Information System agriculture information technology big data precision agriculture
Zysk, E., Mroczkowski, T., & Dawidowicz, A. (2022). Small Farms as “Data Producers” for the Needs of Agricultural Management Information System. Geomatics and Environmental Engineering, 16(3), 79–109. https://doi.org/10.7494/geom.2022.16.3.79
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. World Health Organization (WHO): WHO Coronavirus (COVID-19) Dashboard. 2020. https://who.sprinklr.com/ [access: 7.04.2021].
  2. Beltrami S.: How to minimize the impact of Coronavirus on food security. World Food Programme (WFP), 16 March 2020. https://insight.wfp.org/how-to-minimize-the-impact-of-coronavirus-on-food-security-be2fa7885d7e [access: 7.04.2020].
  3. Kalnay E., Cai M.: Impact of urbanization and land-use change on climate. Nature, vol. 423, 2003, pp. 528-531. https://doi.org/10.1038/nature01675.
  4. Antrop M.: Landscape change and the urbanization process in Europe. Landscape and Urban Planning, vol. 67(1–4), 2004, pp. 9–26. https://doi.org/10.1016/S0169-2046(03)00026-4.
  5. Cohen B.: Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society, vol. 28(1–2), 2006, pp. 63–80. https://doi.org/10.1016/j.techsoc.2005.10.005.
  6. EU Science Hub: Many popular packaged foods in the EU contain too much fat, sugar, salt and too little fibre. 23 October 2019. https://ec.europa.eu/jrc/en/news/many-popular-packaged-foods-eu-contain-too-much-fat-sugar-salt-and-too-little-fibre [access: 15.03.2021].
  7. Stilgoe J., Owen R., Macnaghten P.: Developing a framework for responsible innovation. Research Policy, vol. 42(9), 2013, pp. 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008.
  8. FAO, IFAD, UNICEF, WFP and WHO: The State of Food Security and Nutrition in the World 2019: Safeguarding against Economic Slowdowns and Downturns. FAO, Rome 2019. https://www.fao.org/3/ca5162en/ca5162en.pdf [access: 1.02.2022].
  9. European Parliamentary Research Service (EPRS): Precision agriculture and the future of farming in Europe: Scientific Foresight Study. December 2016. https://www.europarl.europa.eu/RegData/etudes/STUD/2016/581892/EPRS_STU(2016)581892_EN.pdf [access: 15.03.2021].
  10. Intergovernmental Panel on Climate Change (IPCC): Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri R.K., Meyer L.A. (eds.)]. IPCC, Geneva 2014. https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf [access: 15.03.2021].
  11. Abson D.J., Termansen M., Pascual U., Aslam U., Fezzi C., Bateman I.: Valuing climate change effects upon UK agricultural GHG emissions: spatial analysis of a regulating ecosystem service. Environmental and Resource Economics, vol. 57(2), 2014, pp. 215-231. https://doi.org/10.1007/s10640-013-9661-z.
  12. Lal R.: Managing soils for feeding a global population of 10 billion. Journal of the Science of Food and Agriculture, vol. 86(14), 2006, pp. 2273–2284. https://doi.org/10.1002/jsfa.2626.
  13. Chase T.N., Pielke R.A., Kittel T.G.F., Zhao M., Pitman A.J., Running S.W., Nemani R.R.: Relative climatic effects of landcover change and elevated carbon dioxide combined with aerosols: a comparison of model results and observations. Journal of Geophysical Research: Atmospheres, vol. 106(D23), 2001, pp. 31685–31691. https://doi.org/10.1029/2000JD000129.
  14. FAO, IFAD, UNICEF, WFP and WHO: The State of Food Security and Nutrition in the World 2020: Transforming Food Systems for Affordable Healthy Diets. FAO, Rome 2020. https://doi.org/10.4060/ca9692en [access: 15.05.2022].
  15. UN General Assembly: United Nations Millennium Declaration: resolution / adopted by the General Assembly. A/RES/55/2, 18 September 2000. https://documents-dds-ny.un.org/doc/UNDOC/GEN/N00/559/51/PDF/N0055951.pdf [access: 5.04.2016].
  16. UN General Assembly: Transforming our world: the 2030 Agenda for Sustainable Development: resolution adopted by the General Assembly on 25 September 2015. A/RES/70/1, 21 October 2015. http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E [access: 1.07.2016].
  17. Food and Agriculture Organization of the United Nations (FAO): Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security. FAO, Rome 2012. http://www.fao.org/docrep/016/i2801e/i2801e.pdf [access: 2.01.2020].
  18. Regulation (EU) No 1306/2013 of the European Parliament and of the Council of 17 December 2013 on the financing, management and monitoring of the common agricultural policy and repealing Council Regulations (EEC) No 352/78, (EC) No 165/94, (EC) No 2799/98, (EC) No 814/2000, (EC) No 1290/2005 and (EC) No 485/2008. Official Journal of the European Union, L 347/549, 20.12.2013.
  19. Gilpin L.: How Big Data Is Going to Help Feed Nine Billion People by 2050. TechRepublic, 9 May 2014. http://www.techrepublic.com/article/how-big-data-is-going-to-help-feed-9-billion-people-by-2050/ [access: 16.05.2022].
  20. Poppe K.J., Wolfert J., Verdouw C.N., Renwick A.: European Perspective on the Economics of Big Data. Farm Policy Journal, vol. 12(1), 2015, pp. 11–19.
  21. World Bank: Agricultural Innovation Systems: An Investment Sourcebook. Agricultural and Rural Development, World Bank, 2012. https://openknowledge.worldbank.org/handle/10986/2247 [access: 16.05.2022].
  22. Food and Agriculture Organization of the United Nations (FAO): The Tropical Agriculture Platform (TAP). AIS: a new take on innovation. https://www.fao.org/in-action/tropical-agriculture-platform/background/ais-a-new-take-on-innovation/en/ [access: 15.03.2021].
  23. Copa Cogeca: Main principles underpinning the collection, use and exchange of agricultural data. QJ(16)2689:6, Copa Cogeca European Farmers European Agri-Cooperatives, Brussels 2017. https://ec.europa.eu/futurium/en/system/files/ged/main_principles_underpinning_the_collection_use_and_exchange_of_agricultural_data_.pdf [access: 27.09.2021].
  24. Bennett J.M.: Agricultural Big Data: utilisation to discover the unknown and instigate practice change. Farm Policy Journal, vol. 12(1), 2015, pp. 43–50.
  25. Lesser A.: Big Data and Big Agriculture. Gigaom Reports, 8 October 2014. https://research.gigaom.com/report/big-data-and-big-agriculture/ [access: 16.05.2022].
  26. Orts E., Spigonardo J.: Sustainability in the Age of Big Data: Special Report. IGEL, Wharton University of Pennsylvania, Pennsylvania 2014. http://d1c25a6gwz7q5e.cloudfront.net/reports/2014-09-12-Sustainability-in-the-Age-of-Big-Data.pdf [access: 16.05.2022].
  27. Fountas S., Wulfsohn D., Blackmore B.S., Jacobsen H.L., Pedersen S.M.: A model of decision-making and information flows for information-intensive agriculture. Agricultural Systems, vol. 87(2), 2003, pp. 192–210. https://doi.org/10.1016/j.agsy.2004.12.003.
  28. Piet L., Desjeux Y.: New perspectives on the distribution of farm incomes and the redistributive impact of CAP payments. European Review of Agricultural Economics, vol. 48(2), 2021, pp. 385–414. https://doi.org/10.1093/erae/jbab005.
  29. Finger R., El Benni N.: Farm income in European agriculture: new perspectives on measurement and implications for policy evaluation. European Review of Agricultural Economics, vol. 48(2), 2021, pp. 253–265. https://doi.org/10.1093/erae/jbab011.
  30. Sun Z., Du K., Zheng F., Yin S.: Perspectives of research and application of Big Data on smart agriculture. Journal of Agricultural Science and Technology, vol. 15(6), 2013, pp. 63–71.
  31. Li X., Chen S., Guo L.: Technological innovation of agricultural information service in the age of Big Data. Journal of Agricultural Science and Technology, vol. 16(4), 2014, pp. 10–15.
  32. Plume K.: The Big Data Bounty: U.S. Startups Challenge Agribusiness Giants. Reuters, 8 October 2014. http://www.reuters.com/article/us-usa-farming-startups-idUSKCN0HX0C620141008 [access: 16.05.2022].
  33. Wolfert S., Ge L., Verdouw C., Bogaardt M.-J.: Big Data in Smart Farming – A review. Agricultural Systems, vol. 153, 2017, pp. 69–80. https://doi.org/10.1016/j.agsy.2017.01.023.
  34. Tong L., Hong T., Jinghua Z.: Research on the Big Data-based government decision and public information service model of food safety and nutrition industry. Journal of Food Safety and Quality, vol. 6(1), 2015, pp. 366–371.
  35. Sonka S.: Big Data: from hype to agricultural tool. Farm Policy Journal, vol. 12, 2015, pp. 1–9
  36. Verdouw C.N., Beulens A.J.M., Reijers H.A., van der Vorst J.G.A.J.: A control model for object virtualization in supply chain management. Computers in Industry, vol. 68, 2015, pp. 116–131. https://doi.org/10.1016/j.compind.2014.12.011.
  37. Maru A., Berne D., Beer J.D., Ballantyne P.G., Pesce V., Kalyesubula S., Fourie N. et al.: Digital and Data-driven Agriculture: Harnessing the Power of Data for Smallholders. Global Forum on Agricultural Research and Innovation, Rome 2018. https://doi.org/10.7490/f1000research.1115402.1.
  38. Nikkilä R., Seilonen I., Koskinen K.: Software architecture for farm management information systems in precision agriculture. Computers and Electronics in Agriculture, vol. 70(2), 2010, pp. 328–336. https://doi.org/10.1016/j.compag.2009.08.013.
  39. Řezník T., Kepka M., Charvát K., Charvát K. Jr, Horáková S., Lukas V.: Challenges of agricultural monitoring: integration of the Open Farm Management Information System into GEOSS and Digital Earth. IOP Conference Series: Earth and Environmental Science, vol. 34(1), 012031. https://doi.org/10.1088/1755-1315/34/1/012031.
  40. Dawidowicz A., Źróbek R.: Land Administration System for Sustainable Development – Case Study of Poland. Real Estate Management and Valuation, vol. 25, no. 1, 2017, pp. 112–122. https://doi.org/10.1515/remav-2017-0008.
  41. ISO 19152: Geographic information – Land Administration Domain Model (LADM). International Organization for Standardization, 2012. https://www.iso.org/standard/51206.html [access: 12.03.2020].
  42. Bydłosz J.: The application of the Land Administration Domain Model in building a country profile for the Polish cadastre. Land Use Policy, vol. 49, 2015, pp. 598–605. https://doi.org/10.1016/j.landusepol.2015.02.011.
  43. Kotsev A., Minghini M., Tomas R., Cetl V., Lutz M.: From Spatial Data Infrastructures to Data Spaces – A Technological Perspective on the Evolution of European SDIs. ISPRS International Journal of Geo-Information, vol. 9(3), 2020, 176. https://doi.org/10.3390/ijgi9030176.
  44. Główny Urząd Statystyczny (GUS): Rocznik Statystyczny Rolnictwa 2021 [Statistical Yearbook of Agriculture]. GUS, Warszawa 2021. https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/5515/6/15/1/rocznik_statystyczny_rolnictwa_2021_r.pdf [access: 23.09.2021].
  45. Kisielińska J.: Ranking województw ze względu na potencjał rolnictwa. Roczniki Naukowe Ekonomii Rolnictwa i Rozwoju Obszarów Wiejskich, t. 104, z. 1, 2017, s. 56–71.
  46. Ministerstwo Rolnictwa i Rozwoju Wsi (MRiRW): Polska wieś i rolnictwo 2019. PBS Sp. z o.o., 2019. https://www.gov.pl/attachment/e503584a-f259-462e-afeb-2dc1139527c7 [access: 23.03.2020].
  47. Regulation (EC) No 138/2004 of the European Parliament and of the Council of 5 December 2003 on the economic accounts for agriculture in the Community. Official Journal of the European Union, L 33/1, 5.02.2004.
  48. Delgado J., Short N. Jr., Roberts D., Vandenberg B.: Big Data Analysis for Sustainable Agriculture on a Geospatial Cloud Framework. Frontiers in Sustainable Food Systems, vol. 3, 2019, 54. https://doi.org/10.3389/fsufs.2019.00054.
  49. Köksal Ö., Tekinerdogan B.: Architecture design approach for IoT-based farm management information systems. Precision Agriculture, vol. 20(5), 2019, pp. 926–958. https://doi.org/10.1007/s11119-018-09624-8.
  50. Lakshmisudha K., Hegde S., Kale N., Iyer S.: Smart precision based agriculture using sensors. International Journal of Computer Applications, vol. 146(11), 2016, pp. 36–38. https://doi.org/10.5120/ijca2016910916.
  51. Abubakar M.S., Ahmad A.B.: Development of Farm Records Software. Arid Zone Journal of Engineering, Technology and Environment, vol. 13(6), 2017, pp. 743–763.
  52. Kingsley J., Lawani S.O., Esther A.O., Ndiye K.M., Sunday O.J., Penížek V.: Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models. Modern Applied Science, vol. 13(10), 2019, pp. 60–77. https://doi.org/10.5539/mas.v13n10p60.
  53. Khanal S., Fulton J., Shearer S.: An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, vol. 139, 2017, pp. 22–32. https://doi.org/10.1016/j.compag.2017.05.001.
  54. Kruize J.W., Wolfert J., Scholten H., Verdouw C.N., Kassahun A., Beulens A.J.M.: A reference architecture for farm software ecosystems. Computers and Electronics in Agriculture, vol. 125, 2016, pp. 12–28. https://doi.org/10.1016/j.compag.2016.04.011.
  55. McCabe M.F., Houborg R., Lucieer A.: High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, vol. 9998, 2016. https://doi.org/10.1117/12.2241289.
  56. Fountas S., Carli G., Sørensen C.G., Tsiropoulos Z., Cavalaris C., Vatsanidou A., Liakos B. et al.: Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, vol. 115, 2015, pp. 40–50. https://doi.org/10.1016/j.compag.2015.05.011.
  57. United Nations Economic Commission for Europe (UNECE): Outcomes of the UNECE Project on Using Big Data for Official Statistics. 2013. https://statswiki.unece.org/display/bigdata/Big+Data+Projects?preview=/77170975/122454022/Outcomes%20of%20the%20UNECE%20Project%20on%20Using%20Big%20Data%20for%20Official%20Statistics.docx [access: 16.05.2022].
  58. Kaloxylos A., Eigenmann R., Teye F., Politopoulou Z., Wolfert S., Schrank C., Dillinger M. et al.: Farm management systems and the Future Internet era. Computers and Electronics in Agriculture, vol. 89, 2012, pp. 130–144. https://doi.org/10.1016/j.compag.2012.09.002.
  59. Pölling B., Sroka W., Mergenthaler M.: Success of urban farming’s city-adjust-ments and business models – Findings from a survey among farmers in Ruhr Metropolis, Germany. Land Use Policy, vol. 69, 2017, pp. 372–385. https://doi.org/10.1016/j.landusepol.2017.09.034.
  60. HORSCH: Talking Spraying: Advanced Spraying Technology. 2016. https://www.horsch.com/fileadmin/user_upload/news/en_english_UK/2016/Horsch_Talking_Spraying.pdf [access: 23.03.2021].
  61. Zysk E., Dawidowicz A., Nowak M., Figurska M., Źróbek S., Źróbek R., Burandt J.: Organizational aspects of the concept of a green cadastre for rural areas. Land Use Policy, vol. 91, 2020, 104373. https://doi.org/10.1016/j.landusepol.2019.104373.
  62. Dawidowicz A., Kulawiak M., Zysk E., Kocur-Bera K.: System architecture of an INSPIRE-compliant green cadastre system for the EU Member State of Poland. Remote Sensing Applications: Society and Environment, vol. 20, 2020, 100362. https://doi.org/10.1016/j.rsase.2020.100362.
  63. Zysk E.: Struktura użytkowania gruntów rolnych w Polsce na tle krajów Unii Europejskiej ze wskazania kierunków rozwoju polityki gospodarowania ziemia rolna. Przegląd Geodezyjny, R. 77, nr 3, 2005, pp. 17–21.
  64. Źróbek S., Manzhynski S., Zysk E., Rassokha Y.: Some aspects of local real estate taxes as an instrument of land use management. Real Estate Management and Valuation, vol. 24, 2016, pp. 93–105. https://doi.org/10.1515/remav-2016-0024.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 382 Download : 243

Downloads

Download data is not yet available.