https://doi.org/10.4081/jae.2021.1117
Comparative analysis of soil-sampling methods used in precision agriculture
Published: 25 January 2021
Abstract Views: 4209
PDF: 1375
HTML: 265
HTML: 265
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The aim of this study was to compare three different soil-sampling methods used in Precision Agriculture and their environmental impact on agricultural production. The sampling methods used were: zone management by elevation, grid sampling (GS) and sampling oriented by apparent soil electrical conductivity (OS). Three different fields were tested. When the recommendations were compared, a significant difference among the suggested doses was observed. This indicated the need to improve the soil-sampling techniques, since there were doubts about input deficits or overdoses, regardless of the technology studied. The GS method was the most environmentally viable alternative for phosphorus (P) compared to other methods and the OS presented as the better option for potassium (K) and nitrogen (N). However, the use of soil sensors appeared to be a viable technology that needs further improvement in order to improve productivity and, hence, economic and environmental benefits.
Artuzo F.D. 2015. Análise da eficiência técnica e econômica da agricultura de precisão a taxa variável de fertilizantes na cultura da soja no RJ. Master’s Thesis, Federal University of Rio Grande do Sul, Brazil.
Artuzo F.D., Jandrey W.F., Silva L.X. 2014. Impacto da agricultura de precisão a taxa variável na cultura da soja: questões produtivas e ambientais. pp 146-161 in Proc. 2º Simpósio da Ciência do Agronegócio, Porto Alegre, RS, Brazil.
Assad E.D., Martins S.C., Pinto H.S. 2012. Sustentabilidade no Agronegócio Brasileiro: Coleção de Estudos sobre Diretrizes para uma Economia Verde no Brasil. 1st ed. Fundação Brasileira para o Desenvolvimento Sustentável, Rio de Janeiro, RJ, Brazil.
Beras G. 2014. Economic viability analysis of the soybean crop seeding using fertilizer variable rate in the furrow. Master’s Thesis, Federal University of Santa Maria, Brazil.
Biesdorf E.M., Teixeira M.F.F., Dietrich O.H., Pimentel L.D., Araujo C.D. 2016. Nitrogen application methods on the maize crop in cerrado soil. Rev. Agric. Neotrop. 3:44-50.
DOI: https://doi.org/10.32404/rean.v3i1.805
Bobato A. 2006. Ãndice nutricional do Nitrogênio: uma ferramenta para o diagnóstico do estado nutricional da cultura do milho. Master’s Thesis, Federal University of Paraná, Brazil.
Catania P., Comparetti A., Febo P., Morello G., Orlando S., Roma E., Vallone M. 2020. Positioning accuracy comparison of GNSS receivers used for mapping and guidance of agricultural machines. Agronomy 10(7):doi:10.3390/agronomy10070924.
DOI: https://doi.org/10.3390/agronomy10070924
Celinski V.G. 2008. Evaluation of an electric measure sensor using correlation with soil attributes. Dissertation. São Paulo State University, Brazil.
Cherubin M.R., Santi A.L., Eitelwein M.T., Amado T.J.C., Simon D.H., Damian J.M. 2015. Sampling grid size for characterization of the spatial variability of phosphorus and potassium in an Oxisol. Pesqui. Agropecu. Bras. 50:168-77.
DOI: https://doi.org/10.1590/S0100-204X2015000200009
Costa C.C., Guilhoto J.J.M. 2013. Impactos potenciais da agricultura de precisão sobre a economia brasileira. Rev. Eco. Agro. 10:177-204.
Crookston R.K. 2006. A top 10 list of developments and issues impacting crop management and ecology during the past 50 years. Crop Sci. 46:2253-62.
DOI: https://doi.org/10.2135/cropsci2005.11.0416gas
Empresa Brasileira de Pesquisa Agropecuária, 1997. Manual de métodos de análise de solos. 2nd ed. Centro Nacional de Pesquisa de Solos, Rio de Janeiro, RJ, Brazil.
Ferraz G.A.S., Barbosa B.D.S., Reynaldo E.F., Santos S.A., Goncalves J.R.M.R., Ferraz P.F.P. 2019. Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation. Dyna 86:289-97.
DOI: https://doi.org/10.15446/dyna.v86n209.70897
Fontoura S.M.V., Vieira R.C.B., Bayer C., Viero F., Anghinoni I., Moraes R.P., 2015. Fertilidade do solo e seu manejo em sistema plantio direto no Centro-Sul do Paraná. 1st ed. Fundação Agrária de Pesquisa Agropecuária, Guarapuava, PR, Brazil.
Gebler L., Bertol I., Biasi L.R.D., Ramos R.R., Louzada J.A.S. 2014. Superficial loss of reactive phosphorus potentially contaminant by intense rainfall simulation. Eng. Sanit. Ambient. 19:393-399.
DOI: https://doi.org/10.1590/S1413-41522014019000000564
Gomes M.A.F., Barizon R.R.M. 2014. Panorama da contaminação Ambiental por Agrotóxicos e nitrato de origem agrÃcola no Brasil: Cenário 1992/201. 1st ed. Embrapa Meio Ambiente, Jaguariúna, SP, Brazil.
Hauschild F.G. 2013. Precision farming techiniques for definiton management zones of soil. Master’s Thesis. Federal University of Santa Maria, Brazil.
Kaiser D.R., Reinert D.J., Reichert J.M., Streck C.A., Pellegrini A. 2010. Nitrate and ammonium in soil solution in tobacco management systems. Rev. Bras. Cienc. Solo 34:379-88.
DOI: https://doi.org/10.1590/S0100-06832010000200011
Keskin M., Sekerli Y.E. 2016. Awareness and adoption of precision agriculture in the Cukurova region of Turkey. Agron. Res. 14:1307-20.
Klein C., Agne S.A.A. 2012. Phosphorus: from the nutrient to pollutant! Rev. Eletrônica Gest. Educ. Tecnol. Ambient. 8:1713-21.
Machado P.L.O.A., 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. Electrical conductivity mapping in relation to clay of a Ferralsol under no tillage system. Pesq. Agropec. Bras. 41:1023-31.
Mallarino A.P., Wittry D.J. 2004. Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precis. Agric. 5:131-44.
DOI: https://doi.org/10.1023/B:PRAG.0000022358.24102.1b
Miqueloni D.P., Gianello E.M., Bueno C.R.P. 2015. Variabilidade espacial de atributos e perda de solo na definição de zonas de manejo. Pesq. Agropec. Trop. 45:18-28.
DOI: https://doi.org/10.1590/1983-40632015v4528029
Mulla D.J. 2013. Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 114:358-71.
DOI: https://doi.org/10.1016/j.biosystemseng.2012.08.009
Oliveira R.B.D., Lima J.S.D.S., Xavier A.C., Passos R.R., Silva D.A.S., Silva A.F.D. 2008. Comparison between soil sampling methods for conilon coffee liming and fertilization recommendation. Eng. AgrÃc. 28:176-86.
DOI: https://doi.org/10.1590/S0100-69162008000100018
Pierce F.J., Nowak P. 1999. Aspects of precision agriculture. Adv. Agron. 67:1-86.
DOI: https://doi.org/10.1016/S0065-2113(08)60513-1
Pierpaoli E., Carli G., Pignatti E., Canavari M. 2013. Drivers of precision agriculture technologies adoption: a literature review. Proc. Technol. 8:61-9.
DOI: https://doi.org/10.1016/j.protcy.2013.11.010
Prado E.V., Machado T.A., Prado F.M.T. 2015. Geração e correlação de zonas de manejo usando sensor spad e condutividade elétrica aparente do solo para a cafeicultura irrigada na zona da mata mineira. Rev. Cient. Eletr. Agron. 1:1-7.
Ragagnin V.A., Sena Júnior D.G., Silveira Neto N.A. 2010. Recommendation of liming at variable rates under different sampling intensities. Rev. Bras. Eng. Agr. Amb. 14:600-7.
DOI: https://doi.org/10.1590/S1415-43662010000600006
Rambo L., Silva P.R.F., Argenta G., Sangoi L. 2004. Plant parameters to refine the management of nitrogen side-dress application in maize. Cienc. Rural 34:1637-45.
DOI: https://doi.org/10.1590/S0103-84782004000500052
Sanchez R.B., Marques Júnior J., Pereira G.T., Baracat Neto J., Siqueira D.S., Souza Z.M.D. 2012. Mapping the relief forms to estimate the fertilization expenses in sugarcane. Eng. AgrÃc. 32:280-92.
DOI: https://doi.org/10.1590/S0100-69162012000200008
Sapkota T.B., Majumdar K., Jat M.L., Kumar A., Bishnoi D.K., McDonald A.J., Pampolino M. 2014. Precision nutrient management in conservation agriculture based wheat production of Northwest India: Profitability, nutrient use efficiency and environmental footprint. Field Crops Res. 155:233-44.
DOI: https://doi.org/10.1016/j.fcr.2013.09.001
Silva C.B. 2009. Innovation in the sugarcane industry of São Paulo state: determiners to adopt technologies of precision agriculture. Dissertation. University of São Paulo, Brazil.
Silva M.A.S.D., Griebeler N.P., Borges L.C. 2007. Use of stillage and its impact on soil properties and groundwater. Rev. Bras. Eng. AgrÃc. Ambient. 11:108-14.
SimplÃcio N. 2015. Fertilizers ecotoxicity: A comparative analysis of nitrogen, phosphorus and potassium-based products and its active ingredients separately. Master’s Thesis. University of BrasÃlia, Brazil.
DOI: https://doi.org/10.3390/toxics5010002
Vargas G.R. 2012. Potassium fertilization effect on soybean yield. Publ. UEPG Ci. Exatas Terra, Ci. Agr. Eng. 18:79-84.
Walton J.C., Roberts R.K., Lambert D.M., Larson J.A., English B.C., Larkin S.L., Martin S.W., Marra M.C., Paxton K.W., Reeves J.M. 2010. Grid soil sampling adoption and abandonment in cotton production. Precis. Agric. 11:135-47.
DOI: https://doi.org/10.1007/s11119-009-9144-y
Wang J., Lu X., Feng Y., Yang R. 2018. Integrating multi-fractal theory and geo-statistics method to characterize the spatial variability of particle size distribution of minesoils. Geoderma 317:39-46.
DOI: https://doi.org/10.1016/j.geoderma.2017.12.027
Zhang Q., Yang Z., Li Y., Chen D., Zhang J., Chen M. 2010. Spatial variability of soil nutrients and GIS-based nutrient management in Yongji County, China. Int. J. Geogr. Inf. Sci. 24:965-81.
DOI: https://doi.org/10.1080/13658810903257954
ZinkeviÄius R. 2018. Influence of soil sampling for precision fertilizing. Agron. Res. 6:423-9.
How to Cite
Moreira Ribeiro Gonçalves, J. R. (2021) “Comparative analysis of soil-sampling methods used in precision agriculture”, Journal of Agricultural Engineering, 52(1). doi: 10.4081/jae.2021.1117.
Copyright (c) 2021 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Enrica Santolini, Marco Bovo, Alberto Barbaresi, Daniele Torreggiani, Patrizia Tassinari, Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Zanbin Zhu, Yubin Li, Shoufu Gong, Design and experiment of Internet-of-Things cooling system in glass greenhouse based on computational fluid dynamics simulation , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Simone Pascuzzi, Ileana Blanco, Alexandros Sotirios Anifantis, Giacomo Scarascia-Mugnozza, Hazards assessment and technical actions due to the production of pressured hydrogen within a pilot photovoltaic-electrolyser-fuel cell power system for agricultural equipment , Journal of Agricultural Engineering: Vol. 47 No. 2 (2016)
- Kamalesh Kanna S, Kumaraperumal Ramalingam, Pazhanivelan P, Jagadeeswaran R, Prabu P.C., YOLO deep learning algorithm for object detection in agriculture: a review , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Carlo Bibbiani, Carlo A. Campiotti, Luca Incrocci, Alberto Pardossi, Determination of the water diffusivity of horticultural substrates: comparison of different approaches for the one-step outflow data analysis , Journal of Agricultural Engineering: Vol. 44 No. 4 (2013)
- Evelia Schettini, Giovanni Puglisi, Fabiana Convertino, Fernando Antonio Cancellara, Giuliano Vox, Greenhouse localized heating powered by a polygeneration system , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Giuseppe Modica, Francesco Solano, Angelo Merlino, Salvatore Di Fazio, Francesco Barreca, Luigi Laudari, Carmelo Riccardo Fichera, Using Landsat 8 imagery in detecting cork oak (Quercus suber L.) woodlands: a case study in Calabria (Italy) , Journal of Agricultural Engineering: Vol. 47 No. 4 (2016)
- Alessio Tugnolo, Roberto Beghi, Valentina Giovenzana, Alessia Pampuri, Simone Virginio Marai, Andrea Casson, Enrico Ferrari, Riccardo Guidetti, Testing of ultrasonic vibration to speed up the remuage operation in sparkling wine production , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Fan Cui, Guoqi Dong, Baiping Chen, Penglin Yong, Suping Peng, Application of ground penetrating radar technology in moisture content detection of stored grain , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Saddam Hussain, Decong Zheng , Haiyan Song, Muhammad Usman Farid, Abdul Ghafoor, Xinyi Ba, Hao Wang, Wenjun Wang, Alam Sher, Salah Jumaa Alshamali, Computational fluid dynamics simulation and optimisation of the threshing unit of buckwheat thresher for effective cleaning of the cleaning chamber , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
<< < 8 9 10 11 12 13 14 15 16 17 > >>
You may also start an advanced similarity search for this article.
