A Multi-Method Approach to Assess the Adoption of Precision Agriculture Technology in Brazil

DOI 10.7160/aol.2024.160304
No 3/2024, September
pp. 45-58

Ivale, A. H., de Alencar Nããs, I. and de Camargo Jani, M. (2024) "A Multi-Method Approach to Assess the Adoptionof Precision Agriculture Technology in Brazil", AGRIS on-line Papers in Economics and Informatics, Vol. 16, No. 3, pp. 45-58. ISSN 1804-1930. DOI 10.7160/aol.2024.160304.

Abstract

Precision Agriculture (PA) application aims to increase crop productivity while minimizing environmental impacts. We analyzed the topics most studied in the advancement of crop production in Brazil by applying the concepts of PA using the systematic literature review (SLR). A multi-method approach combined an SLR applying the PRISMA method and secondary data analysis. We found five clusters of technologies using the PA concept related to hardware development and four clusters related to applying technologies to software development in the PA concept. Most topics focused on using sensors to control water (soil and environment), soil electrical conductivity, and data communication. The focus on sustainability led researchers to reduce chemical products related to fertilizers and pesticides using Variable Rate Fertilizers (VRT) and reducing the environmental loading. According to the research results, it was evident that PA technology might help farmers make more accurate decisions about cultivation, production, harvest, and soil management. The availability of decision support systems powered by big data and artificial intelligence to select the best crop for a given season and soil might assist Brazil's sustainable growth of food production.

Keywords

Agricultural production, crop production, food production, hardware, software, sustainability.

References

  1. Čechura, L., Žáková Kroupová Z. and Hockmann, H. (2015) "Market Power in the European Dairy Industry", AGRIS on-line Papers in Economics and Informatics, Vol. 7, No. 4, pp. 39-47. ISSN 1804-1930. DOI 10.7160/aol.2015.070404.
  2. Acorsi, M. G., das Dores Abati Miranda, F., Martello, M., Smaniotto, D. A. and Sartor, L. R. (2019) "Estimating biomass of black oat using UAV-based RGB imaging", Agronomy, Vol. 9, No. 7, p. 344. ISSN 2073-4395. DOI 10.3390/agronomy9070344.
  3. Aitken, J., Esain, A. E. and Williams, S. (2021) "Management of complexity in the care ecosystem", Supply Chain Management: An International Journal, Vol. 26, No. 4, pp. 481-494. E-ISSN 2050-7399, ISSN 2051-3771. DOI 10.1108/SCM-05-2020-0207.
  4. Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998) "Crop evapotranspiration-Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56", FAO, Rome. ISBN 92-5-104219-5.
  5. Bassoi, L. H., Inamasu, R. Y., Bernardi, A. D. C., Vaz, C. M. P., Speranza, E. A. and Cruvinel, P. E. (2019) "Agricultura de precisão e agricultura digital", TECCOGS Revista Digital de Tecnologias Cognitivas, May 2020. (In Portuguese). DOI 10.23925/1984-3585.2019i20p17-36.
  6. Bassoi, L. H., Naime, J. D. M., Resende, A., Inamasu, R., Bernardi, A. D. C., Naime, J. N. and Inamasu, R. Y. (2014) "Agricultura de precisão: resultados de um novo olhar. In Bernardi, A. C. de C.; Naime, J. de M., Resende, A. V., Bassoi, L. H. and Inamasu, R. Y. (Eds.) "Agricultura de precisão: Resultados de um novo olhar", 1st ed., pp. 350-360. EMBRAPA. (In Portuguese).
  7. Bazame, H. C., Molin, J. P., Althoff, D. and Martello, M. (2021) "Detection, classification, and mapping of coffee fruits during harvest with computer vision", Computers and Electronics in Agriculture, Vol. 183, p. 106066. ISSN 0168-1699. DOI 10.1016/j.compag.2021.106066.
  8. Bernardi, A. de C. and Inamasu, R. Y. (2014) "Adoção da agricultura de precisão no Brasil". [Online]. Available: https://www.alice.cnptia.embrapa.br/bitstream/doc/1003522/1/CAP60.pdf [Accessed: April 7, 2023]. (In Portuguese).
  9. Bolfe, É. L, Jorge, de Castro Jorge, L. A., Del´Arco Sanches, L., Luchiari, Jr. A, da Costa, C. C, De Castro Victoria, D., Inamasu, R. Y., Grego , C. R., Ferreira, V. R., Ramirez, A. R. (2020) "Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers", Agriculture, Vol. 10, No. 12, p. 653. ISSN 2077-0472. DOI 10.3390/agriculture10120653.
  10. Bongiovanni, R. and Lowenberg-DeBoer, J. (2004) "Precision agriculture and sustainability", Precision Agriculture, Vol. 5, pp. 359-387. ISSN 1385-2256. DOI 10.1023/B:PRAG.0000040806.39604.aa.
  11. Botelho, L. L. R., de Almeida Cunha, C. C. and Macedo, M. (2011) "O método da revisão integrativa nos estudos organizacionais", Gestão e Sociedade, Vol. 5, No. 11, pp. 121-136. ISSN 1980-5756. (In Portuguese). DOI 10.21171/GES.V5I11.1220.
  12. Braunger, M. L., Shimizu, F. M., Jimenez, M. J., Amaral, L. R., Piazzetta, M. H. D. O., Gobbi, Â. L., Magalhães, P. S. G., Rodriguez, V., Oliveira, O. N. and Riul, A. (2017) "Microfluidic electronic tongue applied to soil analysis", Chemosensors, Vol. 5, No. 2, p. 14. ISSN 2227-9040. DOI 10.3390/chemosensors5020014.
  13. Broekhuizen, T. L., Emrich, O., Gijsenberg, M. J., Broekhuis, M., Donkers, B. and Sloot, L. M. (2021) "Digital platform openness: Drivers, dimensions and outcomes", Journal of Business Research, Vol. 122, pp. 902-914. ISSN 0148-2963. DOI 10.1016/j.jbusres.2019.07.001.
  14. Cardoso, L. A. S., Farias, P. R. S. and Soares, J. A. C. (2022) "Use of Unmanned Aerial Vehicle in Sugarcane Cultivation in Brazil: A Review", Sugar Tech, Vol. 24, No. 6, pp. 1636-1648. E-ISSN 0974-0740, ISSN 0972-1525. DOI 10.1007/s12355-022-01149-9.
  15. Chheda, J. P. and Boradak, V. K. (2020) "Control and remote sensing of an irrigation system using ZigBee wireless network", In Intelligent Computing Techniques for Smart Energy Systems: Proceedings of ICTSES 2018, pp. 989-998. Springer Singapore. DOI 10.1007/978-981-15-0214-9_105.
  16. CONAB - Companhia Nacional de Abastecimento (2022) "Acompanhamento da safra brasileira de grãos – Safra 2021/22, n.11 - Décimo primeiro levantamento", p. 1-85. [Online]. Available: https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos [Accessed: August 30, 2022]. (In Portuguese).
  17. Demattê, J. A. M., Morgan, Ch. L. S., Chabrillat, S., Rizzo, R., Franceschini, M. H. D., da S.Terra, F. Vasques, G. M. and Wetterlind, J. (2015) "Spectral sensing from ground to space in soil science: state of the art, applications, potential, and perspectives". In Thenkabail, P. S. (Ed.) "Land resources monitoring, modeling, and mapping with remote sensing", 1st ed., CRC Press. E-ISBN 9780429089442.
  18. EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária (2023) "Projeções do Agronegócio". [Online]. Available: https://www.embrapa.br/agroindustria/projecoes-do-agronegocio/projecoes. [Accessed: April 7, 2023]. (In Portuguese).
  19. Faiçal, B. S., Freitas, H., Gomes, P. H., Mano, L. Y., Pessin, G., de Carvalho, A. C. P. L. F., Krishnamachari, B. and Ueyama, J. (2017) "An adaptive approach for UAV-based pesticide spraying in dynamic environments", Computers and Electronics in Agriculture, Vol. 138, pp. 210-223. ISSN 0168-1699. DOI 10.1016/j.compag.2017.04.011.
  20. Ferraz, G. A., da Silva, F. M., de Oliveira, M. S., Avelar, R. C. and Sales, R. S. (2015) "Spatial vatiability of the dosage of P2O5 and K2O to fertilize in variable rate and in a conventional way in a coffee field", Coffee Science, Vol. 10, No. 3, pp. 346-356. ISSN 1984-3909.
  21. Franceschini, M. H. D., Demattê, J. A. M., Kooistra, L., Bartholomeus, H., Rizzo, R., Fongaro, C. T. and Molin, J. P. (2018) "Effects of external factors on soil reflectance measured on-the-go and assessment of potential spectral correction through orthogonalisation and standardisation procedures", Soil and Tillage Research, Vol. 177, pp. 19-36. ISSN 1879-3444. DOI 10.1016/j.still.2017.10.004.
  22. Gavioli, A., de Souza, E. G., Bazzi, C. L., Guedes, L. P. C. and Schenatto, K. (2016) "Optimization of management zone delineation by using spatial principal components", Computers and Electronics in Agriculture, Vol. 127, pp. 302-310. ISSN 0168-1699. DOI 10.1016/j.compag.2016.06.029.
  23. Gil, M., Wróbel, K., Montewka, J. and Goerlandt, F. (2020) "A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident prevention", Safety Science, Vol. 128, p. 104717. ISSN 0925-7535. DOI 10.1016/j.ssci.2020.104717.
  24. Goldmeier, M. G. (2019) "Agricultura de precisão integrada ao manejo de lavouras de feijão visando alta produtividade", [Online]. Available: http://hdl.handle.net/10183/214356 [Accessed: April 10, 2023]. (In Portuguese).
  25. Harris, J., Quatman, C., Manring, M., Siston, R. and Flanigan, D. (2014) "How to Write a Systematic Review", The American Journal of Sports Medicine, Vol. 42, pp. 2761-2768. ISSN 0363-5465. DOI 10.1177/0363546513497567.
  26. Issad, H. A., Aoudjit, R. and Rodrigues, J. J. (2019) "A comprehensive review of Data Mining techniques in smart agriculture", Engineering in Agriculture, Environment and Food, Vol. 12, No. 4, pp. 511-525. E-ISSN 1881-8366. DOI 10.1016/j.eaef.2019.11.003.
  27. Jabro, J. D., Stevens, W. B., Iversen, W. M., Allen, B. L. and Sainju, U. M. (2020) "Irrigation scheduling based on wireless sensors output and soil-water characteristic curve in two soils", Sensors, Vol. 20, No. 5, p.1336. E-ISSN 1424-8220. DOI 10.3390/s20051336.
  28. Kamienski, C., Soininen, J. P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., Maia, R. F. and Torre Neto, A. (2019) "Smart water management platform: IoT-based precision irrigation for agriculture", Sensors, Vol. 19, No. 2, p. 276. E-ISSN 1424-8220. DOI 10.3390/s19020276.
  29. Keswani, B., Mohapatra, A. G., Mohanty, A., Khanna, A., Rodrigues, J. J., Gupta, D. and de Albuquerque, V. H. C. (2019) "Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms", Neural computing and applications, Vol. 31, pp. 277-292. ISSNM 1433-3058. DOI 10.1007/s00521-018-3737-1.
  30. Kipper, L. M., Furstenau, L. B., Hoppe, D., Frozza, R. and Iepsen, S. (2020) "Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis", International Journal of Production Research, Vol. 58, No. 6, pp. 1605-1627. E- ISSN 1366-588X. DOI 10.1080/00207543.2019.1671625.
  31. Lassalle, G. (2021) "Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review", Science of the Total Environment, Vol. 788, p. 147758. ISSN ISSN 0048-9697. DOI 10.1016/j.scitotenv.2021.147758.
  32. Liu, C., Jia, G. and Kong, J. (2020) "Requirement-oriented engineering characteristic identification for a sustainable product–service system: A multi-method approach", Sustainability, Vol. 12, No. 21, p. 8880. ISSN 2071-1050. DOI 10.3390/su12218880.
  33. Lost Filho, F. H., Heldens, W. B., Kong, Z. and de Lange, E. S. (2020) "Drones: innovative technology for use in precision pest management", Journal of Economic Entomology, Vol. 113, No. 1, pp. 1-25. E- ISSN 1938-291X. DOI 10.1093/jee/toz268.
  34. Maia, R. F., Netto, I. and Tran, A. L. H. (2017) "Precision agriculture using remote monitoring systems in Brazil", In 2017 IEEE global humanitarian technology conference (GHTC), pp. 1-6, IEEE. DOI 10.1109/GHTC.2017.8239290.
  35. Mapa Brasil. Ministério da Economia (2020) "Estatísticas de Comércio Exterior em Dados Abertos". [Online]. Available: https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/estatisticas/base-de-dados-bruta [Accessed: July 15, 2022]. (In Portuguese).
  36. Massruhá, S. M. F. S. and Leite, M. D. A. (2017) "Agro 4.0-rumo à agricultura digital". [Online]. Available: https://www.alice.cnptia.embrapa.br/bitstream/doc/1073150/1/PL-Agro4.0-JC-na-Escola.pdf [Accessed: April 14, 2023]. (In Portuguese).
  37. MAPA - Ministério Da Agricultura, Pecuária E Abastecimento (2020) "AGROSTAT. Estatísticas agropecuárias de comércio exterior do Brasil". [Online]. Available: http://indicadores.agricultura.gov. br/agrostat/index.htm. [Accessed: Nov. 13, 2020]. (In Portuguese).
  38. MAPA - Ministério da Agricultura da Agricultura, Pecuária e Abastecimento. (2019) "Estatísticas de Comércio Exterior do Agronegócio Brasileiro. Brasília: MAPA". [Online]. Available: https://indicadores.agricultura.gov.br/agrostat/index.htm [Accessed: Oct. 5, 2019]. (In Portuguese).
  39. Molin, J. P. and Tavares, T. R. (2019) "Sensor systems for mapping soil fertility attributes: Challenges, advances, and perspectives in brazilian tropical soils", Engenharia Agrícola, Vol. 39, pp. 126-147. E-ISSN 1809-4430, ISSN 0100-6916. DOI 10.1590/1809-4430-Eng.Agric.v39nep126-147/2019.
  40. Carneiro, F. M., Angeli Furlani, C. E. A., Zerbato, C., de Menezes, P. C., da Silva Gírio, L. A., and de Oliveira, M. F. (2020) "Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors", Precision Agriculture, Vol. 21, pp. 979-1007. ISSN 1385-2256. DOI 10.1007/s11119-019-09704-3.
  41. Oldoni, H. and Bassoi, L. H. (2016) "Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region", Pesquisa Agropecuária Brasileira, Vol. 51, pp. 1283-1294. E-ISSN ISSN 1678-3921. DOI 10.1590/S0100-204X2016000900028.
  42. Pathak, H. S., Brown, P. and Best, T. (2019) "A systematic literature review of the factors affecting the precision agriculture adoption process", Precision Agriculture, Vol. 20, pp. 1292-1316. ISSN 1573-1618. DOI 10.1007/s11119-019-09653-x.
  43. Patrício, D. I. and Rieder, R. (2018) "Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review", Computers and Electronics in Agriculture, Vol. 153, pp. 69-81. ISSN 0168-1699. DOI 10.1016/j.compag.2018.08.001.
  44. Pereira, A. S., Shitsuka, D. M., Parreira, F. J. and Shitsuka, R. (2018) "Metodologia da pesquisa científica". [Online]. Available: https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1&isAllowed=y [Accessed: April 13, 2023]. (In Portuguese).
  45. Raj, E. F. I., Appadurai, M. and Athiappan, K. (2022) "Precision farming in modern agriculture". In: Choudhury, A., Biswas, A., Singh, T. P., Ghosh, S. K. (eds) Smart Agriculture Automation Using Advanced Technologies. Transactions on Computer Systems and Networks. Springer, Singapore. ISBN 978-981-16-6123-5. DOI 10.1007/978-981-16-6124-2_4.
  46. Santos, T. T., de Souza, L. L., dos Santos, A. A. and Avila, S. (2020) "Grape detection, segmentation,and tracking using deep neural networks and three-dimensional association", Computers and Electronics in Agriculture, Vol. 170, p. 105247. ISSN 0168-1699. DOI 10.1016/j.compag.2020.105247.
  47. Secex - Secretaria do Comércio Exterior (2022) "Estatística de Comércio Exterior do Agronegócio Brasileiro. Dados Exportação e Importação. 2020.", Ministério do Desenvolvimento, Indústria e Comércio Exterior. [Online]. Available: http://indicadores.agricultura.gov.br/agrostat/index.htm [Accessed: April 22, 2022]. (In Portuguese).
  48. Secex - Secretaria do Comércio Exterior (2022) "Estatísticas de Comércio Exterior, 2010." Ministério do Desenvolvimento, Indústria e Comércio Exterior. [Online]. Available: http://www.mdic.gov.br. [Accessed: Aug. 18, 2016]. (In Portuguese).
  49. Silva, S. A., Lima, J. S. S. and Bottega, E. L. (2013) "Yield mapping of arabic coffee and their relationship with plant nutritional status", Journal of soil science and plant nutrition, Vol. 13, No. 3, 556-564. DOI 10.4067/S0718-95162013005000044.
  50. Sott, M. K., Furstenau, L. B., Kipper, L. M., Giraldo, F. D., Lopez-Robles, J. R., Cobo, M. J., Zahid, A., Abbasi, Q. H. and Imran, M. A. (2020) "Precision techniques and agriculture 4.0 technologies to promote sustainability in the coffee sector: state of the art, challenges and future trends", IEEE Access, Vol. 8, pp. 149854-149867. E-ISSN I2169-3536. DOI 10.1109/ACCESS.2020.3016325.
  51. Souza, E. G., Bazzi, C. L., Khosla, R., Uribe-Opazo, M. A. and Reich, R. M. (2016) "Interpolation type and data computation of crop yield maps is important for precision crop production", Journal of Plant Nutrition, Vol. 39, No. 4, pp. 531-538. ISSN 0190-4167. DOI 10.1080/01904167.2015.1124893.
  52. Spekken, M. and de Bruin, S. (2013) "Optimized routing on agricultural fields by minimizing maneuvering and servicing time", Precision Agriculture, Vol. 14, pp. 224-244. ISSN 1385-2256. DOI 10.1007/s11119-012-9290-5.
  53. Tantalaki, N., Souravlas, S. and Roumeliotis, M. (2019) "Data-driven decision making in precision agriculture: The rise of big data in agricultural systems", Journal of Agricultural & Food Information, Vol. 20, No. 4, p. 344-380. E-ISSN 1540-4722. DOI 10.1080/10496505.2019.1638264.
  54. Tariq, A. and Mumtaz, F. (2023) "A series of spatio-temporal analyses and predicting modeling of land use and land cover changes using an integrated Markov chain and cellular automata models", Environmental Science and Pollution Research, Vol. 16, pp. 47470-47484. E-ISSN 1614-7499. DOI 10.1007/s11356-023-25722-1.
  55. Tavares, T. R., Molin, J. P., Javadi, S. H., Carvalho, H. W. P. D. and Mouazen, A. M. (2020). "Combined use of vis-NIR and XRF sensors for tropical soil fertility analysis: Assessing different data fusion approaches", Sensors, Vol. 21, No. 1, p. 148. ISSN 1424-8220. DOI 10.3390/s21010148.
  56. Tavares, T. R., Nunes, L. C., Alves, E. E. N., de Almeida, E., Maldaner, L. F., Krug, F. J., de Calvarho, H. W.ence and Molin, J. P. (2019) "Simplifying sample preparation for soil fertility analysis by X-ray fluoresce spectrometry", Sensors, Vol. 19, No. 23, p. 5066. ISSN 1424-8220. DOI 10.3390/s19235066.
  57. Valente, D. S., Queiroz, D. M. D., Pinto, F. D. A. D. C., Santos, F. L. and Santos, N. T. (2014) "Spatial variability of apparent electrical conductivity and soil properties in a coffee production field", Engenharia Agrícola, Vol. 34, pp. 1224-1233. E-ISSN 1809-4430, ISSN 0100-6916. DOI 10.1590/S0100-69162014000600017.
  58. Valente, D. S. M., Momin, A., Grift, T. and Hansen, A. (2020) "Accuracy and precision evaluation of two low-cost RTK global navigation satellite systems", Computers and Electronics in Agriculture, Vol. 168, p. 105142. ISSN 0168-1699. DOI 10.1016/j.compag.2019.105142.
  59. Van Eck, N. J. and Waltman, L. (2017) "VOSviewer Manual", [Online]. Available: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.6.pdf [Accessed: Oct. 10, 2022].
  60. Venkatalakshmi, B. and Devi, P. (2014) "Decision support system for precision agriculture", International Journal of Research in Engineering and Technology, Vol. 3, No. (Spec. Is.) 7, pp. 849-852. E-ISSN 2319-1163, ISSN 2321-7308.
  61. Walter, A., Finger, R., Huber, R. and Buchmann, N. (2017) "Smart farming is key to developing sustainable agriculture", Proceedings of the National Academy of Sciences, Vol. 114, No. 24, pp. 6148-6150. ISSN 0027-8424. DOI 10.1073/pnas.1707462114.

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