Pedology in Precision Agriculture from a Brazilian context
DOI:
https://doi.org/10.22267/rcia.20234003.216Keywords:
Soils, electric conductivity, relief, remote sensing, productivity, precision farmingAbstract
Precision agriculture (PA) is advancing in Brazil concerning several crops, mainly for medium to large-sized farms, occasionally evolving towards automation and digital agriculture. Soil knowledge is fundamental in this process, requiring studies on a detailed scale greater than 1:5,000, and demanding overcoming soil taxonomy concepts. This review article presents and discusses the most effective methods of PA from a soil management perspective, inducing advances for farming and producers in Brazil. Topography, electrical conductivity, proximal and remote sensing, and productivity work outlined soil mapping and their relationship to soil. The modeling of topographic variables through artificial intelligence offers new perspectives. The soil's apparent electrical conductivity can work at various depths, providing information about several pedological parameters. Finally, proximal, and remote sensing techniques could simulate different soil attributes, potentially integrating productivity data on studying plant attributes. Despite some differences, the four themes are complementary, and integrating data through geographic information systems results in a consistent option for defining management zones.
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Adamchuk, V.I.; Hummel, J.W.; Morgan, M.T.; Upadhyaya, S.K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronic in Agriculture. 44: 71-91. https://doi.org/10.1016/j.compag.2004.03.002
Amado, T.J.C.; Santi, A.L. (2011). Using precision farming to overcome tield-limitating factors in southern Brazil oxisols: case study. In: Clay, D.E.; Shanahan, J.F. GIS applications in agriculture. pp. 31-60. 1st Edition. Boca Raton, U.S: CRC Press.
Amatulli, G; Domisch, S.; Tuanmu, M.; Parmentier, B.; Ranipeta, A.; Malczyk, J.; Jetz, W. (2018). Data Descriptor: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data. 5: 180040. https://doi.org/10.1038/sdata.2018.40
Anderson-Cook, C.M.; Alley, M.M.; Roygard, J.K.F.; Khosla, R.; Noble, R.B.; Doolittle, J.A. (2002).
Differentiating Soil Types Using Electromagnetic Conductivity and Crop Yield Maps. Soil Science Society American Journal. 66: 1562-1570. https://doi.org/10.2136/sssaj2002.1562
Baize, D.; Girard, M.C. (2009) Référentiel pédologique 2008. Versaille, France: Editions Quae.
Bernardi, A.C.C.; Naime, J.M.; Resende, A.V.; Bassoi, L.H.; Inamasu, R.Y. (2014). Agricultura de Precisão: resultados de um novo olhar. Brasília: Embrapa.
Bernardi, A.C.C.; Tupy, O.; Santos, K.E.L.; Mazzuco, G.G.; Bettiol, G.M.; Rabello, L.M.; Inamasu, R.Y.
(2018). Mapping of yield, economic return, soil electrical conductivity, and management zones of irrigated corn for silage. Pesquisa Agropecuária Brasileira. 53 (12): 1289-1298. https://doi.org/10.1590/S0100-204X2018001200001
Bernardi, A.C.C.; Pitrat, T.; Rabello, L.M.; Pezzopane, J.R.M.; Bosi, C.; Mazzuco, G.G.; Bettiol, G.M. (2019). Differences in soil electrical resistivity tomography due to soil water contents in an integrated agricultural system. Pesquisa Agropecuária Brasileira. 54: 00774. 1-5. https://doi.org/10.1590/S1678-3921.pab2019.v54.00774
Bolfe, E.L.; Jorge, L.A.C.; Sanches, I.D.; Luchiari Júnior, A.; Costa, C.C. ; Victoria, D.C.; 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. 10: 653-668. https://doi.org/10.3390/agriculture10120653
Bramley, R.; Trengove, S. (2013). Precision agriculture in Australia: present status and recent developments. Engenharia Agrícola. 33(3): 575-588. https://doi.org/10.1590/S0100-69162013000300014
Bünemann, E. K.; Bongiorno, G.; Bai, Z.; Creamer, R. E.; De Deyn, G.; de Goede, R.; Fleskens, L.; Geissen, V.; Kuyper, T.W.; Mäder, P.; Pulleman, M.M.; Sukkel, W.; van Groenigen; J.W.; Brussaard, L. (2018). Soil quality - A critical review. Soil Biology Biochemistry. 120: 105–125. https://doi.org/10.1016/j.soilbio.2018.01.030
Coipel, J.; Lovelle, R.B.; Sipp, C.; van Leeuwen, C. (2006). Terroir effect, as a result of enviromental stess, depends more on soil depth than on soil type (Vitis vinifera L. cv. Grenache Noir, Côtes du Rhône, France, 2000). Journal International des Sciences de la Vigne et du Vin. 40(4): 177-185. https://doi.org/10.20870/oeno-one.2006.40.4.867
Cambardella, C.A.; Karlen, D.L. (1999). Spatial analysis of soil fertility parameters. Precision Agriculture. 1: 5-14. https://doi.org/10.1023/A:1009925919134
Corwin, D.; Lesch, S. (2005). Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture. 46: 11-43. https://doi:10.1016/j.compag.2004.10.005
Demattê, J.A.M.; Dotto, A.C.; Paiva, A.F.S.; Sato, M.V.; Dalmolin, R.S.D.; Araújo, M.D.S.B.; Silva, E.B.; Nanni, M.R.; ten Caten, A.; Noronha, N.C.; Lacerda, M.P.C.; Araújo Filho, J.; Rizzo, R.; Bellinaso, H.; Francelino, M.R.; Schaefer, C.E.G.R., Vicente, L.E.; Santos, U.J.; Sampaio, E.V. S.B.; Menezes, R.S.C.; Souza, J.J.L.L.; Abrahão, W.A.P.; Coelho, R.M.; Grego, C.R.; Lani, J.L.; Fernandes, A.R.; Gonçalves, D.A.M.; Silva, S.H.G.; Menezes, M.D.; Curi, N.; Couto, E.G.; Anjos, L.H.C. ; Ceddia, M.B.; Pinheiro, E.F.M.; Grunwald, S.; Vasques, G.M.; Marques Júnior, J.; Silva, A.J. ; Barreto, M.C.D.V.; Nóbrega, G.N.; Silva, M.Z. ; Souza, S.F. ; Valladares, G.S.; Viana J.H.M.; Silva Terra, F. ; Horák-Terra, I.; Fiorio, P.R.; Silva, R.C. ; Frade Júnior, E.F.; Lima, R.H.C.; Filippini-Alba, J.M.; Souza Junior, V.S.; Brefin, M.D.L.M.S.; Ruivo, M.D.L.P.; Ferreira, T.O.; Brait, M.A.; Caetano, N.R.; Bringhenti, I.; Mendes, W.S.; Safanelli, J.L.; Guimarães, C.C.B.; Poppiel, R.R.; Souza, A.B.; Quesada, C.A.; Couto, H.T.Z. (2019). The Brazilian Soil Spectral Library (BSSL): a general view, application and challenges. Geoderma. 354: 1-21. http://dx.doi.org/10.1016/j.geoderma.2019.05.043
Dobermann, A.; Ping, J.L. (2004). Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agronomy Journal. 96(1): 285-297. https://doi.org/10.2134/agronj2004.0285
Embrapa. (2018). Visão 2030: o futuro da agricultura brasileira. https://www.embrapa.br/visao/o-futuro-da-agricultura-brasileira
FAO - Food and Agriculture Organization. (1996). The Digitized Soil Map of the World Including Derived Soil Properties. CD-ROM. Rome: FAO.
Filippini-Alba, J.M.; Cruz, L.E.C.; Ducati, J.R.; Cunha, H.N.; Domingues, J.M.M. (2019). Processing spectroradiometry data for the simulation of soil physicochemical parameters. International Journal of Development Research. 09(08): 28875-28880. https://doi.org/10.37118
Filippini-Alba, J.M.; Cruz, L.E.; Cunha, E.N.; Pillon, C.N; Silva, L.F. (2015). Modelagem de parâmetros pedológicos por meio de sensoriamento remoto: uma estratégia para agricultura de precisão. http://marte2.sid.inpe.br/col/sid.inpe.br/marte2/2015/06.15.14.12.50/doc/p0132.pdf
Filippini-Alba, J.M.; Flores, C.A.; Miele, A. (2017). Geotechnologies and Soil Mapping for Delimitation of Management Zones as an Approach to Precision Viticulture. Applied and Environmental Soil Science. 2017: 4180965. https://doi.org/10.1155/2017/4180965
Filippini-Alba, J.M.; Flores, C.A.; Miele, A. (2021). Relationships between A and B/A2 horizons of three soils in the context of viticulture. Journal of Agricultural Studies. 9(1): 440-454. https://doi.org/10.5296/jas.v9il.18320
Filippini-Alba, J.M.; Zanella, M. (2016). Revisão e Processamento de Informações sobre Agricultura de Precisão no Brasil. https://acortar.link/S4cFUy
Filippini-Alba J.M.; Flores, C.A.; Wrege, M.S. (2013). Zoneamento edafoclimático da olivicultura para o Rio Grande do Sul. https://www.agricultura.rs.gov.br/upload/arquivos/202107/28143415-zoneamento-edafoclimatico-oliveira-2013.pdf
Filippini-Alba, J.M.; Wrege M.S.; Flores, C.A.; Garrastazu, M.C. (2011). Zoning based on climate and soil for planting eucalyptus in Southern region of Rio Grande do Sul State, Brazil. In: Prado, H.A.; Luiz, A.J.B.; Filho, H. (Eds.). Computational methods for agricultural research: advances and applications. pp.127-143. United States of America: IGI Global. 72p.
Filippini-Alba, J.M.; Wrege, M.S.; Almeida, I.R.; Martins, C.R.; Zemnicahak, S.; Souza, T.G. (2020). Zoneamento edafoclimático da nogueira-pecã para o Sul do Brasil. https://acortar.link/gJljas
Flores, C.A.; Garrastazu, M.C.; Filippini-Alba, J.M. (2009). Metodologia de zoneamento edáfico de culturas para o estado do Rio Grande do Sul. https://acortar.link/f7ymQP
Flores, C.A.; Filippini-Alba, J.M.; Levien, H.F.; Zarnott, D.H.; Miele, A.; Pavan, C. (2011) Levantamento detalhado dos solos e a viticultura de precisão. https://ainfo.cnptia.embrapa.br/digital/bitstream/item/42718/1/1315-1.pdf
Fritzsons, E.; Mantovani, L.E.; Aguiar, A.V. (2008). Relação entre altitude e temperatura: uma contribuição ao zoneamento climático no Estado do Paraná. Revista de Estudos Ambientais. 10(1): p.49-64. http://dx.doi.org/10.7867/1983-1501.2008v10n1p49-64
Ge, Y.; Thomasson, A.; Sui, R. (2011). Remote sensing of soil properties in precision agriculture: A review. Frontier Earth Sciences. 5(3): 229-238. http://dx.doi.org/10.1007/s11707-011-0175-0
Hole, F.D.; Campbell, J.B. (1985). Soil landform analysis. Totowa: Rowman&Allanheld pub.
Houborg, R.; McCabe, M.F. (2016). High-Resolution NDVI from Planet’s Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture. Remote Sensing. 8: 768-785. https://doi.org/10.3390/rs8090768
IBGE - Instituto Brasileiro de Geografia e Estatística. (2021). Pedology. https://acortar.link/mscUjq
IBGE - Instituto Brasileiro de Geografia e Estatística. (2018). Mapeamento de recursos naturais do Brasil. https://geoftp.ibge.gov.br/informacoes_ambientais/pedologia/vetores/escala_250_mil/DOCUMENTACAO_TECNICA_MRN.pdf
Inamasu, R.Y.; Naime, J.M.; Resende, A.V.; Bassol, L.H.; Bernardi, A.C.C. (2011). Agricultura de Precisão: um novo olhar. São Carlos: Embrapa. 334p.
ISPA-International Society for Precision Agriculture. (2018). Precision Agriculture Definition. https://www.ispag.org/about/definition.2
IUSS Working Group. (2022). World Reference Base for Soil Resources. www.fao.org/soils-portal/soil-survey/soil-classification/world-reference-base/en/
Jorge, L.A.C.; Inamasu, R.Y. (2014). Uso de veículos aéreos não tripulados (VANT) em agricultura de precisão. In: Bernardi, A.C.C.; Naime, J.M.; Resende, A. V.; Bassoi, L. H.; Inamasu, R. Y. (Eds.). Agricultura de precisão: resultados de um novo olhar. pp. 109-134. Brasília: Embrapa.
Lark, R.M.; Stafford, J.V. (1997). Exploratory analysis of yield maps of combine crops. https://eurekamag.com/research/003/136/003136297.php
Leeuwen,V.C.; Roby, J.P.; Pernet, D.; Bois, B. (2010). Methodology of soil based zoning for viticultural terroirs. Bulletin de l'OIV. 83(947): 948-949.
McBratney, A.; Whelan, B.; Ancev, T. (2005). Future directions of precision agriculture. Precision Agriculture. 6: 7-23. https://doi.org/10.1007/s11119-005-0681-8
McBratney, A.B.; Mendonça, M.L.; Minasmy, B. (2003). On digital soil mapping. Geoderma. 117: 3-52. http://dx.doi.org/10.1016/S0016-7061(03)00223-4
MacDonald, K.B.; Kloosterman, B. (1984). The Canada Soil Information System (CanSIS) General User’s User's Manual. Ottawa, Canada: Land Resource Research Centre, Research Branch, Agriculture Canada. 56p.
Machado, P.L.O.; Bernardi, A.C.C.; Valencia, L.I.O.; Molin, J.P.; Gimenez, L.M.; Silva, C.A.; Andrade, A.G.; Madari, B.E.; Meirelles, M.S.P. (2006). Mapeamento da condutividade elétrica e relação com a argila de latossolo sob plantio direto. Pesquisa Agropecuária Brasileira. 41: 1023-1031. https://doi.org/10.1590/S0100-204X2006000600019
MacMillan R.A.; Pettapieceb, W.W.; Nolanc, S.C.; Goddard, T.W. (2000). A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets Syst. 113: 81-109. https://doi.org/10.1016/S0165-0114(99)00014-7
Mermut, A.R.; Eswaran, H. (2001). Some major developments in soil science since the mid-1960s. Geoderma. 100: 403-426. https://doi.org/10.1016/S0016-7061(01)00030-1
Michot, Y.; Benderitter, A.; Dorigny, B.; Nicoullaud, D.; King, D.; Tabbagh, A. (2003). Spatial and temporal monitoring of soil water content with an irrigated corn crop cover using electrical resistivity tomography. Water Resource Research. 39: 1138-1156. https://doi.org/10.1029/2002WR001581
Molin, J.P.; Tavares, T.R. (2019). Sensor systems for mapping soil fertility attributes: Challenges, advances and perspectives in Brazilian tropical soils. Engenharia Agrícola. 39: 126-147. https://doi.org/10.1590/1809-4430-Eng.Agric.v39nep126-147/2019
Moral, F.; Terrón, J.; Marques da Silva, J. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil & Tillage Research. 106: 335-343. https://doi.org/10.1016/j.still.2009.12.002
Mulla, D.J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering. 114: 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Omuto, C.; Nachtergaele, F.; Vargas, R. (2023). State of the art report on global and regional soil information. Rome: FAO. 69p.
Pierce, F.J.; Nowak, P. (1999). Aspects of Precision Agriculture. Advances in Agronomy. 67: 1-85. https://doi.org/10.1016/S0065-2113(08)60513-1
Rabello, L.M.; Inamasu, R.Y. (2014). Conductividade elétrica aparente do solo. In: Bernardi, A.C.; Naime, J.M.; Resende, A.V.; Bassoi, L.H.; Inamasu, R.Y. (Eds.). Agricultura de Precisão: resultados de um novo olhar. pp. 48-57. Brasília: Embrapa.
Robinson, D.A.; Abdu, H.; Jones, S.B., Seyfried, M.; Lebron, I.; Knight, R. (2008). Eco-geophysical imaging of watershed-scale soil patterns links with plant community spatial patterns. Vadose Zone Journal. 7: 1132-1138. https://doi.org/10.2136/vzj2008.0101
Rosa, D.; Mayol, F.; Moreno, F.; Cabrera, F.; Diaz-Pereira, E.; Antoine, J.A. (2002) multilingual soil profile database (SDBm Plus) as an essential part of land resources information systems. Environmental Modelling & Software. 17: 721-730. https://doi.org/10.1016/S1364-8152(02)00031-2
Samuel-Rosa, A.; Dalmolin, R.S.D.; Moura-Bueno, J.M.; Teixeira, W.G.; Filippini-Alba, J.M. (2020). Open legacy soil survey data in Brazil: geospatial data quality and how to improve it. Scientia Agricola. 77: 1-11. https://doi.org/10.1590/1678-992X-2017-0430
Samouëlian, A.; Cousina, I.; Tabaghc, A.; Bruandd, A., RichardeI, G. (2005). Electrical resistivity survey in soil science: a review. Soil & Tillage Research. 83: 173-193.
Santos, H.G.; Jacomine, P.K.T.; Anjos, L.H.C.; Oliviera, V. A.; Lumbreras, J.F.; Coelho, M.R.; Almeida, J.A., Araujo Filho, J.C.; Oliveira, J.B.; Cunha, T.J.F. (2018). Sistema Brasileiro de Classificação de Solos. Rio de Janeiro: Embrapa. 26p.
Serrano, J.; Peça, J.; Silva, J.M.; Shahidian, S. (2014). Avaliação de tecnologias para aplicação diferenciada de fertilizantes: novos conceitos de gestão em pastagens permanentes. Revista de Ciências Agrárias. 37: 253-269. http://dx.doi.org/10.1007/s11119-012-9281-6
Shiratsuchi, L.S.; Brandao, Z.N.; VicenteI, L.E.; Victoria, D. Ducati, J.R.; Oliveira, R.P.; Vilela, M.F. (2014). Sensoriamento remoto: conceitos básicos e aplicações na agricultura de precisão. In: Bernardi, A.C.; Naime, J.M.; Resende, A.V.; Bassoi, L.H.; Inamasu, R.Y. (Eds.). Agricultura de Precisão: resultados de um novo olhar. Brasília: Embrapa. 58-73p.
Soil Survey Staff. (2022). Keys to Soil Taxonomy. Twelfth Edition. United States: United States Department of Agriculture National Resources Conservation Service.
USDA - Natural Resources Conservation Service. (2011). National soil information system (NASIS). https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/tools/?cid=nrcs142p2_053552
Sudduth, K.A.; Kitchena, N.R.; Wielbold, W.J.; Batchelorc, W.D.; Bollerod, G.A.; Bullockd, D.G.; Claye, D.E.; Palmb, H.L.; Piercef, F.J.; Schulerg, R.T.; Thelenh, K.D. (2005). Relating apparent electrical conductivity to soil properties across the north-central USA. Computers and Electronics in Agriculture. 46(1-3): 263-283. https://doi.org/10.1016/j.compag.2004.11.010
Thylén, L.; Jurschik, P.; Murphy, D.L.P. (1997). Precision Agriculture. https://archive.org/details/precisionagricul0000euro/page/n469/mode/2up
Viscarra-Rossel, R.A.; Bouma, J. (2016). Soil sensing: A new paradigm for agriculture. Agricultural Systems. 148: 71-74. https://doi.org/10.1016/j.agsy.2016.07.001
Young, E.; Hammer, R.D. (2000). Defining geograp hic soil bodies by landscape position, soil taxonomy and cluster analysis. Soil Science Society of Americam Journal 64(3): 989-998. https://doi.org/10.2136/SSSAJ2000.643989X
Zhang, F.S.; Yamasaki, S.; Kimura, K. (2002). Waste ashes for use in agricultural production: I. Liming effect, contents of plant nutrients and chemical characteristics of some metals. The Science of the Total Environment. 284: 215-225. https://doi.org/10.1016/S0048-9697(01)00887-7
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