Abstract
In the last decades, geostatistics has been widely used for precision agriculture (PA) producing quite exciting results. Research on this topic is important for sustainable agriculture growth in Brazil. The objective of the review is an attempt to outline the current state of using geostatistical tools for PA applications in Brazil in the last 20 years (2002–2022), but not to provide an exhaustive review of models. We analyzed the scientific literature on this field in Brazil to identify their merits and weaknesses in the present, and to conjecture on future developments. We analyzed 151 proceeding papers and 144 peer-reviewed journal articles regarding applications of geostatistics in PA in Brazil from 2002 to 2022 using bibliometric techniques to reveal current research trends and hotspots. We detected using geostatistics for PA has been limited, mostly for univariate interpolation purposes. The co-citation analysis reveals four broad research clusters in the literature: (i) spatial variability, semivariogram, soil management, (ii) soil fertility, ordinary kriging, spatial dependence, (iii) coffee plant, coffee, Coffea arabica, and (iv) glycine max, zea mays, management zones. The presented review is a springboard to future modeling developments useful for geostatistics applications to PA in Brazil. We suggest expanding the use of geostatistics for smart agricultural technology by adding new potential approaches in new research. Combined with other approaches, such as machine learning, uncertainty modeling, efforts for more geostatistical training, and data fusion from multi-sensor and multi-source are a new frontier to be explored more often by the Brazilian PA community.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data and material are available from the corresponding author under reasonable request.
Code availability
The code is available from the corresponding author under reasonable request.
References
Adhikary, P. P., Dash, C., Bej, R., & Chandrasekharan, H. (2011). Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environmental Monitoring and Assessment, 176(1), 663–676. https://doi.org/10.1007/s10661-010-1611-4
Aggelopooulou, K., Castrignanò, A., Gemtos, T., & Benedetto, D. (2013). Delineation of management zones in an apple orchard in Greece using a multivariate approach. Computers and Electronics in Agriculture, 90, 119–130. https://doi.org/10.1016/j.compag.2012.09.009
Aguillo, I. F. (2012). Is google scholar useful for bibliometrics? A webometric analysis. Scientometrics, 91(2), 343–351. https://doi.org/10.1007/s11192-011-0582-8
Al-Anazi, A., & Gates, I. (2010). Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study. Computers & Geosciences, 36(12), 1494–1503. https://doi.org/10.1016/j.cageo.2010.03.022
Alves, M. C., Silva, F. M., Pozza, E. A., & Oliveira, M. S. (2009). Modeling spatial variability and pattern of rust and brown eye spot in coffee agroecosystem. Journal of Pest Science, 82(2), 137–148. https://doi.org/10.1007/s10340-008-0232-y
Amado, T. J. C., Pes, L. Z., Lemainski, C. L., & Schenato, R. B. (2009). Atributos químicos e físicos de latossolos e sua relação com os rendimentos de milho e feijão irrigados. Revista Brasileira de Ciência do Solo, 33, 831–843. https://doi.org/10.1590/s0100-06832009000400008
Amaral, L. R., & Justina, D. D. D. (2019). Spatial dependence degree and sampling neighborhood influence on interpolation process for fertilizer prescription maps. Engenharia Agrícola, 39, 85–95. https://doi.org/10.1590/1809-4430-Eng.Agric.v39nep85-95/2019
Amaro Filho, J., Negreiros, R. F. D., Assis Júnior, R. N., & Mota, J. C. A. (2007). Sampling size and spatial variability of physical attributes of an arenic kandiustults in Mossoró, Rio Grande do Norte State. Revista Brasileira de Ciência do Solo, 31, 415–422. https://doi.org/10.1590/S0100-06832007000300001
Anastasiou, E., Castrignanò, A., Arvanitis, K., & Fountas, S. (2019). A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece. Science of the Total Environment, 684, 155–163. https://doi.org/10.1016/j.scitotenv.2019.05.324
Andrade, A. D., de Oliveira Faria, R., Alonso, D. J. C., Araújo, G., Ferraz, S., Herrera, M. A. D., & da Silva, F. M. (2018). Spatial variability of soil penetration resistance in coffee growing. Coffee Science, 13(3), 341–348. https://doi.org/10.25186/cs.v13i3.1456
Araújo, G., Ferraz, S., da Silva, F. M., de Oliveira, M. S., da Silva, F. C., & Carvalho, L. C. C. (2017). Comparativo entre os atributos químicos do solo amostrados de forma convencional e em malha. Coffee Science, 12(1), 17–29. https://doi.org/10.25186/cs.v12i1.1188
Araújo, G., Ferraz, S., de Oliveira, M. S., da Silva, F. M., Sales, R. S., & Carvalho, L. C. C. (2018). Plant sampling grid determination in precision agriculture in coffee field. Coffee Science, 13(1), 112–121. https://doi.org/10.25186/cs.v13i1.1391
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An r-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Bachmaier, M., & Backes, M. (2008). Variogram or semivariogram? understanding the variances in a variogram. Precision Agriculture, 9, 173–175. https://doi.org/10.1007/s11119-008-9056-2
Baglaeva, E., Sergeev, A., Shichkin, A., & Buevich, A. (2020). The effect of splitting of raw data into training and test subsets on the accuracy of predicting spatial distribution by a multilayerperceptron. Mathematical Geosciences, 52, 111–121. https://doi.org/10.1007/s11004-019-09813-9
Barbosa, D. P., Bottega, E. L., Valente, D. S. M., Santos, N. T., & Guimarães, W. D. (2019). Delineamento de zonas homogêneas por geoestatística baseada em modelos robusta à outliers. Revista Caatinga, 32(2), 472–481. https://doi.org/10.1590/1983-21252019v32n220rc
Barbosa, D. P., Bottega, E. L., Valente, D. S. M., Santos, N. T., & Guimarães, W. D. (2019). Delineation of homogeneous zones based on geostatistical models robust to outliers. Revista Caatinga, 32(2), 472–481. https://doi.org/10.1590/1983-21252019v32n220rc
Barros, L. S., Silva, E. R. R., Maciel, M. N. M., & Melo, V.S.d., Cicerelli, R.E. & Almeida, T.D. (2022). Dispersão espacial de atributos químicos do solo de um açaizeiro na região amazônica. Anu. Inst. Geocienc., 45, 5–25. https://doi.org/10.11137/1982-3908_2022_45_40848
Basso, B., Ritchie, J., Pierce, F., Braga, R., & Jones, J. (2001). Spatial validation of crop models for precision agriculture. Agricultural Systems, 68(2), 97–112. https://doi.org/10.1016/S0308-521X(00)00063-9
Bazzi, C. L., Souza, E. G., Khosla, R., Opazo, M. A. U., & Schenatto, K. (2015). Profit maps for precision agriculture. Ciencia e investigación agraria: revista latinoamericana de ciencias de la agricultura, 42(3), 305–315. https://doi.org/10.4067/S0718-16202015000300007
Bazzi, C. L., Souza, E. G., Opazo, M. A. U., Nóbrega, L. H., & Pinheiro Neto, R. (2008). Influence of distance between combines equipped with yield monitors on the precision of yield maps for corn crops. Engenharia Agrícola, 28, 355–363. https://doi.org/10.1590/S0100-69162008000200016
Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., & MacMillan, R. A. (2018). Spatial modelling with euclidean distance fields and machine learning. European journal of soil science, 69(5), 757–770. https://doi.org/10.1111/ejss.12687
Benhossi, G., Reynaldo, É. F., & Machado, T. M. (2021). Differences between laboratory and sensor analyses for soil attributes. Pesquisa Agropecuária Tropical,. https://doi.org/10.1590/1983-40632021v5165491
Bernardi, A. C. C., Grego, C. R., Andrade, R. G., Rabello, L. M., & Inamasu, R. Y. (2017). Variabilidade espacial de índices de vegetação e propriedades do solo em sistema de integração lavoura-pecuária. Revista Brasileira de Engenharia Agrícola e Ambiental, 21(8), 513–518. https://doi.org/10.1590/1807-1929/agriambi.v21n8p513-518
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
Betzek, N. M., Souza, E. G., Bazzi, C. L., Schenatto, K., Gavioli, A., & Magalhães, P. S. G. (2019). Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps. Computers and Electronics in Agriculture, 157, 49–62. https://doi.org/10.1016/j.compag.2018.12.004
Betzek, N. M., Souza, E. G., Bazzi, C. L., Sobjak, R., Bier, V. A., & Mercante, E. (2017). Interpolation methods for thematic maps of soybean yield and soil chemical attributes. Semina: Ciências Agrárias, 38(2), 1059. https://doi.org/10.5433/1679-0359.2017v38n2p1059
Bevilacqua, M., Gaetan, C., Mateu, J., & Porcu, E. (2012). Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach. Journal of the American Statistical Association, 107(497), 268–280. https://doi.org/10.1080/01621459.2011.646928
Biffi, L. J., & Rafaeli Neto, S. L. (2008). Spatial behavior of the agronomic variables of the ‘Fuji’ apple during two years in the planalto serrano of Santa Catarina state. Revista Brasileira de Fruticultura, 30, 975–980. https://doi.org/10.1590/S0100-29452008000400023
Bivand, R. S., Pebesma, E. J., Gomez-Rubio, V., & Pebesma, E. J. (2008). Applied spatial data analysis with r (Vol. 747248717). Springer.
Bocchi, S., Castrignano, A., Fornaro, F., & Maggiore, T. (2000). Application of factorial kriging for mapping soil variation at field scale. European Journal of Agronomy, 13(4), 295–308. https://doi.org/10.1016/S1161-0301(00)00061-7
Bogunovic, I., Trevisani, S., Pereira, P., & Vukadinovic, V. (2018). Mapping soil organic matter in the baranja region (croatia): Geological and anthropic forcing parameters. Science of the total environment, 643, 335–345. https://doi.org/10.1016/j.scitotenv.2018.06.193
Borém, A., Marçal de Queiroz, D., Valente, D. S. M., & Assis de Carvalho Pinto, F. A. (2021). Agricultura digital. Oficina de Textos.
Bottega, E. L., Queiroz, D. M., Pinto, F. A. C., Neto, A. M. O., Vilar, C. C., & Souza, C. M. A. (2014). Sampling grid density and lime recommendation in an oxisol. Revista Brasileira de Engenharia Agrícola e Ambiental, 18(11), 1142–1148. https://doi.org/10.1590/1807-1929/agriambi.v18n11p1142-1148
Bottega, E. L., Queiroz, D. M., Santos, N. T., Souza, C. M. A., & Pinto, F. A. C. (2014). Estimativa de valores granulométricos do solo em locais não amostrados utilizando-se cokrigagem. Rev. Bras. Cienc. Agrar./Braz. J. Agric. Sci., 9(2), 244–250. https://doi.org/10.5039/agraria.v9i2a3093
Bressan, T. S., de Souza, M. K., Girelli, T. J., & Junior, F. C. (2020). Evaluation of machine learning methods for lithology classification using geophysical data. Computers & Geosciences, 139, 104475. https://doi.org/10.1016/j.cageo.2020.104475
Burgess, T., & Webster, R. (1980). Optimal interpolation and isarithmic mapping of soil properties. II. Block kriging. Journal of Soil Science, 31(2), 333–341. https://doi.org/10.1111/j.1365-2389.1980.tb02085.x
Buttafuoco, G., Castrignanò, A., Colecchia, A. S., & Ricca, N. (2010). Delineation of management zones using soil properties and a multivariate geostatistical approach. Italian Journal of Agronomy, 5(4), 323–332. https://doi.org/10.4081/ija.2010.323
Buttafuoco, G., Castrignanò, A., Cucci, G., Lacolla, G., & Lucà, F. (2017). Geostatistical modelling of within-field soil and yield variability for management zones delineation: A case study in a Durum wheat field. Precision Agriculture, 18, 37–58. https://doi.org/10.1007/s11119-016-9462-9
Buttafuoco, G. , Castrignanò, A. , Cucci, G. , Rinaldi, M., & Ruggieri, S. (2015). An approach to delineate management zones in a durum wheat field: validation using remote sensing and yield mapping. Precision Agriculture, 15, 330. https://doi.org/10.3920/978-90-8686-814-8_29
Buttafuoco, G., Quarto, R., Quarto, F., Conforti, M., Venezia, A., Vitti, C., & Castrignanò, A. (2021). Taking into account change of support when merging heterogeneous spatial data for field partition. Precision Agriculture, 22, 586–607. https://doi.org/10.1007/s11119-020-09781-9
Butts, C. T. (2023). network: A package for managing relational data. R package version 1.18.1. https://CRAN.R-project.org/package=network
Camicia, R. G. M., Maggi, M. F., Souza, E. G., Bazzi, C. L., Konopatzki, E. A., Michelon, G. K., & Pinheiro, J. B. S. (2018). Productivity of soybean in management zones with application of different sowing densities. Ciência Rural, 48, 12. https://doi.org/10.1590/0103-8478cr20180532
Cao, G., Yoo, E. H., & Wang, S. (2014). A statistical framework of data fusion for spatial prediction of categorical variables. Stochastic Environmental Research and Risk Assessment, 28, 1785–1799. https://doi.org/10.1007/s00477-013-0842-7
Caon, D., & Genú, A. M. (2013). Mapeamento de atributos químicos em diferentes densidades amostrais e influência na adubação e calagem. Revista Brasileira de Engenharia Agricola e Ambiental/The Brazilian Journal of Agricultural and Environmental Engineering, 17(6), 629–639. https://doi.org/10.1590/S1415-43662013000600009
Carneiro, J. S., Faria, Á., Fidelis, R., Silva Neto, S., Santos, A., & Silva, R. (2016). Diagnosis and management of spatial variability of soil fertility in the Cerrado. Scientia Agraria, 17(3), 38–49. https://doi.org/10.1590/01047760202026012683
Carr, J. R. (1994). Order relation correction experiments for probability kriging. Mathematical Geology, 26(5), 605–621. https://doi.org/10.1007/BF02089244
Carvalho, P. S. M., Franco, L. B., Silva, S. A., Sodré, G. A., Queiroz, D. M., & Lima, J. S. S. (2016). Cacao crop management zones determination based on soil properties and crop yield. Revista Brasileira de Ciência do Solo, 40, e0150520.
Castrignanò, A., Belmonte, A., Antelmi, I., Quarto, R., Quarto, F., Shaddad, S., & Nigro, F. (2021). A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees. Science of the Total Environment, 752, 141814. https://doi.org/10.1016/j.scitotenv.2020.141814
Castrignano, A., & Buttafuoco, G. (2004). Geostatistical stochastic simulation of soil water content in a forested area of South Italy. Biosystems Engineering, 87(2), 257–266. https://doi.org/10.1016/j.biosystemseng.2003.11.002
Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., Langella, G., & Venezia, A. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in precision agriculture. Catena, 167, 293–304. https://doi.org/10.1016/j.catena.2018.05.011
Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., & Venezia, A. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors, 17(12), 2794. https://doi.org/10.3390/s17122794
Castrignanò, A., Khosla, R., Moshou, D., Buttafuoco, G., Mouazen, A. M., & Naud, O. (2020). Agricultural internet of things and decision support for precision smart farming. Academic Press.
Basilan, M. L. J. C. A., & Padilla, M. (2023). Assessment of teaching english language skills: Input to digitized activities for campus journalism advisers. International Multidisciplinary Research Journal. https://doi.org/10.54476/ioer-imrj/245694
Chang, N. B., & Bai, K. (2018). Multisensor data fusion and machine learning for environmental remote sensing. CRC Press.
Chen, L., Ren, C., Li, L., Wang, Y., Zhang, B., Wang, Z., & Li, L. (2019). A comparative assessment of geostatistical, machine learning, and hybrid approaches for mapping topsoil organic carbon content. ISPRS International Journal of Geo-Information, 8(4), 174. https://doi.org/10.3390/ijgi8040174
Chilés, J. P., & Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty (2nd ed.). Wiley.
Christensen, W. F. (2011). Filtered kriging for spatial data with heterogeneous measurement error variances. Biometrics, 67(3), 947–957. https://doi.org/10.1111/j.1541-0420.2011.01563.x
Coelho, E. C., Souza, E. G., Uribe-Opazo, M. A., & Pinheiro Neto, R. (2009). Influência da densidade amostral e do tipo de interpolador na elaboração de mapas temáticos. Acta Scientiarum. Agronomy, 31, 165–174. https://doi.org/10.4025/actasciagron.v31i1.6645
Comerio, N., & Strozzi, F. (2019). Tourism and its economic impact: A literature review using bibliometric tools. Tourism economics, 25(1), 109–131. https://doi.org/10.1177/1354816618793762
Corá, J., Araujo, A., Pereira, G., & Beraldo, J. (2004). Assessment of spatial variability of soil attributes as a basis for the adoption of precision agriculture in sugarcane plantations. Revista Brasileira de Ciência do Solo, 28, 1013–1021. https://doi.org/10.1590/S0100-06832004000600010
Coulston, J. W., Blinn, C. E., Thomas, V. A., & Wynne, R. H. (2016). Approximating prediction uncertainty for random forest regression models. Photogrammetric Engineering & Remote Sensing, 82(3), 189–197. https://doi.org/10.14358/PERS.82.3.189
Cressie, N. (2006). Block kriging for lognormal spatial processes. Mathematical Geology, 38, 413–443. https://doi.org/10.1007/s11004-005-9022-8
Cressie, N. (2015). Statistics for spatial data. New Jersey: Wiley.
Cressie, N. A. (1996). Change of support and the modifiable areal unit problem. https://ro.uow.edu.au/infopapers/2392/
Cruz, J. S., Assis Júnior, R. N., Matias, S. S. R., Camacho-Tamayo, J. H., & Tavares, R. C. (2010). Spatial analysis of physical attributes and organic carbon from yellow-red alfissol with sugarcane crop. Ciência e Agrotecnologia, 34, 271–278. https://doi.org/10.1590/S1413-70542010000200001
Dalchiavon, F. C., Carvalho, M. P., Andreotti, M., & Montanari, R. (2012). Variabilidade espacial de atributos da fertilidade de um latossolo vermelho distroférrico sob sistema plantio direto. Revista Ciência Agronômica, 43, 453–461. https://doi.org/10.1590/s1806-66902012000300006.
Dalchiavon, F. C., Carvalho, M. P., Andreotti, M., & Montanari, R. (2012). Spatial variability of the fertility attributes of dystropheric red latosol under a no-tillage system. Revista Ciência Agronômica, 43(3), 453. https://doi.org/10.1590/S1806-66902012000300006
Dalchiavon, F. C., Rodrigues, A. R., Lima, E. S., Lovera, L. H., & Montanari, R. (2017). Variabilidade espacial de atributos químicos do solo cultivado com soja sob plantio direto. Revista de Ciências Agroveterinárias, 16(2), 144–154. https://doi.org/10.1590/S0100-06832007000300019
Dall’Agnol, R. W., Michelon, G. K., Bazzi, C. L., Magalhães, P. S. G., Souza, E. G., Betzek, N. M., & Sobjak, R. (2020). Web applications for spatial analyses and thematic map generation. Computers and Electronics in Agriculture, 172, 105374. https://doi.org/10.1016/j.compag.2020.105374
Da Silva, A. F., Pereira, M. J., Carneiro, J. D., Zimback, C. R. L., Landim, P. M. B., & Soares, A. (2014). A new approach to soil classification mapping based on the spatial distribution of soil properties. Geoderma, 219, 106–116. https://doi.org/10.1016/j.geoderma.2013.12.011
da Silva Júnior, J. C., Medeiros, V., Garrozi, C., Montenegro, A., & Gonçalves, G. E. (2019). Random forest techniques for spatial interpolation of evapotranspiration data from brazilian’s northeast. Computers and Electronics in Agriculture, 166, 105017. https://doi.org/10.1016/j.compag.2019.105017
De Avila, Í. A. M., Hurtado, S. M. C., Jezus, G. C., Silva, G. C., & Rezende, M. M. (2019). Soil attributes and weed seedbank spatial correlation. Bioscience Journal, 35, 6. https://doi.org/10.14393/BJ-v35n6a2019-46995
de Carvalho, J. B. P., & Dassie, B. A. (2012). The history of mathematics education in Brazil. ZDM, 44, 499–511. https://doi.org/10.1007/s11858-012-0439-5
de Freitas Coelho, A. L., Queiroz, D. M., Valente, D. S. M., & Carvalho Pinto, F. A. (2018). An open-source spatial analysis system for embedded systems. Computers and Electronics in Agriculture, 154, 289–295. https://doi.org/10.1016/j.compag.2018.09.019
De Iaco, S., Hristopulos, D. T., & Lin, G. (2022). Geostatistics and machine learning. Mathematical Geosciences, 54(3), 459–465. https://doi.org/10.1007/s11004-022-09998-6
De Iaco, S., Myers, D., & Posa, D. (2002). Space–time variograms and a functional form for total air pollution measurements. Computational Statistics & Data Analysis, 41(2), 311–328. https://doi.org/10.1016/S0167-9473(02)00081-6
De Iaco, S., & Posa, D. (2016). Wind velocity prediction through complex kriging: Formalism and computational aspects. Environmental and Ecological Statistics, 23, 115–139. https://doi.org/10.1007/s10651-015-0331-x
de Lima, R. P., Duarte, D., Nicholson, C., Slatt, R., & Marfurt, K. J. (2020). Petrographic microfacies classification with deep convolutional neural networks. Computers & geosciences, 142, 104481. https://doi.org/10.1016/j.cageo.2020.104481
Deiss, L., Franzluebbers, A. J., & Moraes, A. (2017). Soil texture and organic carbon fractions predicted from near-infrared spectroscopy and geostatistics. Soil Science Society of America Journal, 81(5), 1222–1234. https://doi.org/10.2136/sssaj2016.10.0326
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Elbasiouny, H., Abowaly, M., Abu Alkheir, A., & Gad, A. (2014). Spatial variation of soil carbon and nitrogen pools by using ordinary kriging method in an area of North Nile Delta, Egypt. Catena, 113, 70–78. https://doi.org/10.1016/j.catena.2013.09.008
Emadi, M., Shahriari, A. R., Sadegh-Zadeh, F., Jalili Seh-Bardan, B., & Dindarlou, A. (2016). Geostatistics-based spatial distribution of soil moisture and temperature regime classes in Mazandaran Province, Northern Iran. Archives of Agronomy and Soil Science, 62(4), 502–522. https://doi.org/10.1080/03650340.2015.1065607
Emery, X. (2007). On some consistency conditions for geostatistical change-of-support models. Mathematical Geology, 39(2), 205–223. https://doi.org/10.1007/s11004-006-9073-5
ESRI. (2022). Arcgis pro advanced 28. Environmental Systems Research Institute.
Ferraz, G. A. S., Da Silva, F. M., Alves, M. C., Bueno, R. L., & Costa, P. A. N. (2012). Geostatistical analysis of fruit yield and detachment force in coffee. Precision Agriculture, 13(1), 76–89. https://doi.org/10.1007/s11119-011-9223-8
Ferraz, G. A., Da Silva, F., De Oliveira, M., Custódio, A. A. P., & Ferraz, P. F. P. (2017). Spatial variability of plant attributes in a coffee plantation. Revista Ciência Agronômica, 48(1), 81–91. https://doi.org/10.5935/1806-6690.20170009
Ferraz, G. A. S., Da Silva, F. M., De Oliveira, M. S., Silva, F. C., & Bueno, R. L. (2014). Variabilidade espacial da força de desprendimento de frutos do cafeeiro. Engenharia Agrícola, 34(6), 1210–1223. https://doi.org/10.1590/S0100-69162014000600016
Ferraz, G. A. S., Silva, F. M., Carvalho, L. C., Alves, M. C., & Franco, B. C. (2012). Variabilidade espacial e temporal do fósforo, potássio e da produtividade de uma lavoura cafeeira. Engenharia Agrícola, 32, 140–150. https://doi.org/10.1590/S0100-69162012000100015
Ferraz, G. A. S., Souza Barbosa, B. D., Reynaldo, É. F., Santos, S. A., Moreira Ribeiro Gonçalves, J. R., & Ferreira Ponciano Ferraz, P. (2019). Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation. Dyna (Medellin), 86(209), 289–297. https://doi.org/10.15446/dyna.v86n209.70897
Ferreira Rodrigues, R. H., Silva, L. B., Silva, M. C. F., Lopes, J. W. B., Araujo Lima, E., Sobreira Barbosa, R., & Oliveira Almeida, L. F. (2022). Population fluctuation and distribution of Bemisia tabaci MEAM1 (hemiptera: Aleyrodidae) in soybean crops (p. 4). Front.
Foresti, L. , Pozdnoukhov, A. , Tuia, D. & Kanevski, M. (2010). Extreme precipitation modelling using geostatistics and machine learning algorithms. In P. M. Atkinson & C. D. Lloyd (Eds.), geoENV VII—Geostatistics for environmental applications (pp. 41–52). Springer. https://doi.org/10.1007/978-90-481-2322-3_4
Fouedjio, F., & Klump, J. (2019). Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches. Environmental Earth Sciences, 78(1), 38. https://doi.org/10.1007/s12665-018-8032-z
Franco, T. C. R., Ferraz, G. A. S., Carvalho, L. C. C., Silva, F. M., Alves, M. C., & Marin, D. B. (2022). Spatial variability of soil physical properties in longitudinal profiles. Anais da Academia Brasileira de Ciências, 94(2), e20200411. https://doi.org/10.1590/0001-3765202220200411
Gelfand, A. E., Zhu, L., & Carlin, B. P. (2001). On the change of support problem for spatio-temporal data. Biostatistics, 21, 31–45. https://doi.org/10.1093/biostatistics/2.1.31
Goovaerts, P. (1992). Factorial kriging analysis: A useful tool for exploring the structure of multivariate spatial soil information. Journal of Soil Science, 434, 597–619. https://doi.org/10.1111/j.1365-2389.1992.tb00163.x
Goovaerts, P. (1997). Geostatistics for natural reources evaluation. Oxford University Press.
Goovaerts, P. (1998). Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biology and Fertility of Soils, 27, 315–334. https://doi.org/10.1007/s003740050439
Goovaerts, P. (1999). Geostatistics in soil science: State-of-the-art and perspectives. Geoderma, 89(1–2), 1–45. https://doi.org/10.1016/S0016-7061(98)00078-0
Goovaerts, P. (2001). Geostatistical modelling of uncertainty in soil science. Geoderma, 103(1–2), 3–26. https://doi.org/10.1016/S0016-7061(01)00067-2
Goovaerts, P. (2021). From natural resources evaluation to spatial epidemiology: 25 years in the making. Mathematical Geosciences, 53(2), 239–266. https://doi.org/10.1007/s11004-020-09886-x
Gräler, B., Pebesma, E. J., & Heuvelink, G. B. (2016). Spatio-temporal interpolation using gstat. R J., 8(1), 204.
Guedes, L. P. C., Uribe-Opazo, M. A., Johann, J. A., & Souza, E. G. (2008). Anisotropia no estudo da variabilidade espacial de algumas variáveis químicas do solo. Revista Brasileira de Ciência do Solo, 32, 2217–2226. https://doi.org/10.1590/s0100-06832008000600001
Hall, D. L., & McMullen, S. A. H. (2004). Mathematical techniques in multisensor data fusion (2nd ed.). Artech House Publishers.
Halotel, J., Demyanov, V., & Gardiner, A. (2020). Value of geologically derived features in machine learning facies classification. Mathematical Geosciences, 52, 5–29. https://doi.org/10.1007/s11004-019-09838-0
Hamzehpour, N., Eghbal, M., Bogaert, P., Toomanian, N., & Sokouti, R. (2013). Spatial prediction of soil salinity using kriging with measurement errors and probabilistic soft data. Arid Land Research and Management, 27(2), 128–139. https://doi.org/10.1007/978-90-481-2322-3_26
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2). Springer.
Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., & Tamene, L. (2015). Mapping soil properties of africa at 250 m resolution: Random forests significantly improve current predictions. PloS one, 10(6), e0125814. https://doi.org/10.1371/journal.pone.0125814
Hengl, T., Heuvelink, G. B., Perčec Tadić, M., & Pebesma, E. J. (2012). Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theoretical and Applied Climatology, 107, 265–277. https://doi.org/10.1007/s00704-011-0464-2
Hengl, T., Heuvelink, G., & Rossiter, D. (2007). About regression-kriging: from theory to interpretation of results. Computers & Geosciences, 33(10), 1301–1315.
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. https://doi.org/10.7717/peerj.5518
Hiemstra, P. H., Pebesma, E. J., Twenhöfel, C. J., & Heuvelink, G. B. (2009). Real-time automatic interpolation of ambient gamma dose rates from the dutch radioactivity monitoring network. Computers & Geosciences, 35(8), 1711–1721. https://doi.org/10.1016/j.cageo.2008.10.011
Hurtado, S. M. C., Silva, C. A., Resende, Á. V., Von Pinho, R. G., Inácio, E. S. B., & Higashikawa, F. S. (2009). Spatial variability of soil acidity attributes and the spatialization of liming requirement for corn. Ciência e Agrotecnologia, 33, 1351–1359. https://doi.org/10.1590/S1413-70542009000500022
Isaaks, E. H., & Srivastava, M. R. (1989). Applied geostatistics (No. 551.72 ISA). Oxford University Press.
ISPAG. (2019). Precision ag definition. Retrieved April 21, 2022, from https://www.ispag.org/about/definition/
Johnson, L. M., Rezaee, R., Kadkhodaie, A., Smith, G., & Yu, H. (2018). Geochemical property modelling of a potential shale reservoir in the canning basin (western australia), using artificial neural networks and geostatistical tools. Computers & Geosciences, 120, 73–81. https://doi.org/10.1016/j.cageo.2018.08.004
Juang, K. W., & Lee, D. Y. (2000). Comparison of three nonparametric kriging methods for delineating heavy-metal contaminated soils. Journal of Environmental Quality, 21(1), 197–205. https://doi.org/10.2134/jeq2000.00472425002900010025x
Kanevski, M. (2009). Machine learning for spatial environmental data: theory, applications, and software. EPFL Press.
Kang, J., Jin, R., Li, X., & Zhang, Y. (2016). Block kriging with measurement errors: A case study of the spatial prediction of soil moisture in the middle reaches of heihe river basin. IEEE Geoscience and Remote Sensing Letters, 14(1), 87–91. https://doi.org/10.1109/LGRS.2016.2628767
Kaplan, U. E., & Topal, E. (2020). A new ore grade estimation using combine machine learning algorithms. Minerals, 10(10), 847. https://doi.org/10.3390/min10100847
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103
Klein, W. L., Souza, E. G., Uribe-Opazo, M. A., & Nóbrega, L. H. P. (2007). Altura do ipê-roxo (Tabebuia avellanedae) nos manejos convencional e de precisão, analisada pela geoestatística. Ciencia Florestal, 17(4), 299–309. https://doi.org/10.5902/198050981962
Klemmer, K. , Koshiyama, A., & Flennerhag, S. (2019). Augmenting correlation structures in spatial data using deep generative models. arXiv preprint. arXiv:1905.09796.
Krug, E. T. S., Gomes, G. J., Souza, EGd., Gebler, L., Sobjak, R., & Bazzi, C. L. (2022). Estimating soil loss by laminar erosion using precision agriculture computational tools. Revista Brasileira de Engenharia Agricola e Ambiental/The Brazilian Journal of Agricultural and Environmental Engineering, 26(12), 907–914. https://doi.org/10.1590/1807-1929/agriambi.v26n12p907-914
Lambert, D., Lowenberg-DeBoer, J., & Malzer, G. (2007). Understanding phosphorous in Minnesota soils. Agricultural Economics, 37(1), 43–53. https://doi.org/10.1111/j.1574-0862.2007.00221.x
Leão, M. G., Marques Júnior, J., Souza, Z. M., Siqueira, D. S., & Pereira, G. T. (2011). Terrain forms and spatial variability of soil properties in an area cultivated with citrus. Engenharia Agrícola, 31(4), 643–651. https://doi.org/10.1590/S0100-69162011000400003
Lee, J. J., Jang, C. S., Wang, S. W., & Liu, C. W. (2007). Evaluation of potential health risk of arsenic-affected groundwater using indicator kriging and dose response model. Science of the Total Environment, 384(1–3), 151–162. https://doi.org/10.1016/j.scitotenv.2007.06.021
Legendre, P., & Fortin, M. J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80(2), 107–138. https://doi.org/10.1007/BF00048036
Lima, J. S. S., Silva, S. A., de Oliveira, R. B. & de Fonseca, A. S. (2016). Estimativa da produtividade de café conilon utilizando técnicas de cokrigagem. Ceres, 63(1), 54–61. https://doi.org/10.1590/0034-737X201663010008
Lloyd, C., & Atkinson, P. M. (2001). Assessing uncertainty in estimates with ordinary and indicator kriging. Computers & Geosciences, 27(8), 929–937. https://doi.org/10.1016/S0098-3004(00)00132-1
Lowenberg-DeBoer, J., & Erickson, B. (2019). Setting the record straight on precision agriculture adoption. Agronomy Journal, 111(4), 1552–1569. https://doi.org/10.2134/agronj2018.12.0779
Lv, J., Liu, Y., Zhang, Z., & Dai, J. (2013). Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils. Journal of hazardous materials, 261, 387–397. https://doi.org/10.1016/j.jhazmat.2013.07.065
Ma, Y., Royer, J. J., Wang, H., Wang, Y., & Zhang, T. (2014). Factorial kriging for multiscale modelling. Journal of the Southern African Institute of Mining and Metallurgy, 114(8), 651–659.
Machado, L. O., Lana, Â. M. Q., Lana, R. M. Q., Guimarães, E. C., & Ferreira, C. V. (2007). Variabilidade espacial de atributos químicos do solo em áreas sob sistema plantio convencional. Revista Brasileira de Ciência do Solo, 31, 591–599. https://doi.org/10.1590/s0100-06832007000300019
Manzione, R. L., & Castrignanò, A. (2019). A geostatistical approach for multi-source data fusion to predict water table depth. Science of the Total Environment, 696, 133763. https://doi.org/10.1016/j.scitotenv.2019.133763
Manzione, R. L., Silva, C. O. F., & Castrignanò, A. (2020). A combined geostatistical approach of data fusion and stochastic simulation for probabilistic assessment of shallow water table depth risk. Science of the Total Environment, 765, 142743. https://doi.org/10.1016/j.scitotenv.2020.142743
Manzione, R., Takafuji, E. , De Iaco, S. , Cappello, C., & da Rocha, M. (2019). Spatio-temporal kriging to predict water table depths from monitoring data in a conservation area at São Paulo State, Brazil. Geoinfor Geostat: An Overview, 7, 1. https://doi.org/10.4172/2327-4581.1000205
Martins, R. N., Santos, F. F. L., Araújo, G. M., Viana, L. A., & Rosas, J. T. F. (2019). Accuracy assessments of stochastic and deterministic interpolation methods in estimating soil attributes spatial variability. Communications in Soil Science and Plant Analysis, 50(20), 2570–2578. https://doi.org/10.1080/00103624.2019.1670836
Martyn, J. (1964). Bibliographic coupling. Journal of Documentation, 20, 36.
McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7–23. https://doi.org/10.1007/s11119-005-0681-8
Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications-moving from data reproduction to spatial prediction. Ecological Modelling, 411, 108815. https://doi.org/10.1016/j.ecolmodel.2019.108815
Michelon, G. K., Bazzi, C. L., Upadhyaya, S., Souza, E. G., Magalhães, P. S. G., Borges, L. F., & Betzek, N. M. (2019). Software AgDataBox-Map to precision agriculture management. Software X, 10, 100320. https://doi.org/10.1016/j.softx.2019.100320
Molin, J. P., & Faulin, G. D. C. (2013). Spatial and temporal variability of soil electrical conductivity related to soil moisture. Scientia Agricola, 70, 01–05. https://doi.org/10.1590/s0103-90162013000100001
Molin, J. P., Motomiya, A. V. A., Frasson, F. R., Faulin, G. D. C., & Tosta, W. (2010). Método para avaliação de aplicação de fertilizantes em taxa variável em café. Acta Scientiarum. Agronomy, 32(4), 569–575. https://doi.org/10.4025/actasciagron.v32i4.5282.
Monquero, P., Amaral, L., Binha, D., Silva, P., Silva, A., & Martins, F. (2008). Weed infestation maps under different sugarcane harvest systems. Planta Daninha, 26, 47–55. https://doi.org/10.1590/S0100-83582008000100005
Monquero, P., Silva, P., Hirata, A., & Martins, F. (2011). Weed infestation maps under different sugarcane harvest systems. Planta Daninha, 29, 107–119. https://doi.org/10.1590/S0100-83582008000100005
Morari, F., Castrignanò, A., & Pagliarin, C. (2009). Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Computers and Electronics in Agriculture, 68(1), 97–107. https://doi.org/10.1016/j.compag.2009.05.003
Motomiya, A. V. A., Corá, J. E., & Pereira, G. T. (2006). Using indicator kriging for evaluating soil fertility indicators. Revista Brasileira de Ciência do Solo, 30, 485–496. https://doi.org/10.1590/S0100-06832006000300010
Mueller, E., Sandoval, J. O., Mudigonda, S., & Elliott, M. (2018). A cluster-based machine learning ensemble approach for geospatial data: Estimation of health insurance status in missouri. ISPRS International Journal of Geo-Information, 8(1), 13. https://doi.org/10.3390/ijgi8010013
Nanni, M. R., Povh, F. P., Demattê, J. A. M., Oliveira, R. B., Chicati, M. L., & Cezar, E. (2011). Tamanho ideal em grades de amostragem de solos para aplicação em taxa variável em manejo localizado. Scientia Agricola, 68(3), 386–392. https://doi.org/10.1590/S0103-90162011000300017
Nardi, P., Di Matteo, G., Palahi, M., & Scarascia Mugnozza, G. (2016). Structure and evolution of Mediterranean forest research: A science mapping approach. PLoS ONE, 115, e0155016. https://doi.org/10.1371/journal.pone.0155016
Nascimento, P. S., Silva, J. A., Costa, B. R. S., & Bassoi, L. H. (2014). Homogeneous zones of soil properties for irrigation management in a vineyard. Revista Brasileira de Ciência do Solo, 38, 1101–1113. https://doi.org/10.1590/S0100-06832014000400006
Negreiros, N., Santos, A. C., Guarnieri, A., Souza, D. A., Daronch, D. J., Dotto, M. A., & Araújo, A. S. (2014). Spatial variability of chemical and physical attributes of dystrophic red-yellow latosol in no tillage. Semina: Ciências Agrárias (Londrina), 35(1), 193–203. https://doi.org/10.5433/1679-0359.2014v35n1p193
Nguyen, H., Katzfuss, M., Cressie, N., & Braverman, A. (2014). Spatio-temporal data fusion for very large remote sensing datasets. Technometrics, 56(2), 174–185. https://doi.org/10.1080/00401706.2013.831774
Nogueira Martins, R., dos Santos, F. F. L., de Moura Araújo, G., De Arruda Viana, L., & Rosas, J. T. F. (2019). Accuracy assessments of stochastic and deterministic interpolation methods in estimating soil attributes spatial variability. Communications in Soil Science and Plant Analysis, 50(20), 2570–2578. https://doi.org/10.1080/00103624.2019.1670836
Oldoni, H., & Bassoi, L. H. (2016). Delineation of irrigation management zones in a quartzipsamment of the brazilian semiarid region. Pesquisa Agropecuária Brasileira, 51, 1283–1294. https://doi.org/10.1590/S0100-204X2016000900028
Oldoni, H., Terra, V. S. S., Timm, L. C., Júnior, C. R., & Monteiro, A. B. (2019). Delineation of management zones in a peach orchard using multivariate and geostatistical analyses. Soil and Tillage Research, 191, 1–10. https://doi.org/10.1016/j.still.2019.03.008
Olea, R. A. (1991). Geostatistical glossary and multilingual dictionary. Oxford University Press.
Oliveira, A. L. G., Lima, J. P., Brasco, T. L., & Amaral, L. R. (2022). The importance of modeling the effects of trend and anisotropy on soil fertility maps. Computers and Electronics in Agriculture, 196(106877), 106877. https://doi.org/10.1016/j.compag.2022.106877
Oliver, M. A., & Webster, R. (2015). Basic steps in geostatistics: The variogram and kriging. Springer.
Pallottino, F., Biocca, M., Nardi, P., Figorilli, S., Menesatti, P., & Costa, C. (2018). Science mapping approach to analyze the research evolution on precision agriculture: World, EU and Italian situation. Precision Agriculture, 196, 1011–1026. https://doi.org/10.1007/s11119-018-9569-2
Penteado, M. G., Marcondes, F. G. V., Nogueira, C. M. I., & Yokoyama, L. A. (2018). Difference, inclusion and mathematics education in Brazil. In Mathematics education in Brazil: Panorama of current research (pp. 265–278). Springer. https://doi.org/10.1007/978-3-319-93455-6_14
Pereira, G. T., Souza, Z. M., Teixeira, D. B., Montanari, R., & Marques Júnior, J. (2013). Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging. Revista Brasileira de Ciência do Solo, 37, 1128–1135. https://doi.org/10.1590/S0100-06832013000500002
Pereira, G. W., Valente, D. S. M., Queiroz, D. M., Coelho, A. L. F., Costa, M. M., & Grift, T. (2022). Smart-Map: An open-source QGIS plugin for digital mapping using machine learning techniques and ordinary kriging. Agronomy (Basel), 12(6), 1350. https://doi.org/10.3390/agronomy12061350
Pereira, G. W., Valente, D. S. M., Queiroz, D. M., Santos, N. T., & Fernandes-Filho, E. I. (2022). Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting. Precision Agriculture, 23(4), 1189–1204. https://doi.org/10.1007/s11119-022-09880-9
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
Pohjankukka, J., Pahikkala, T., Nevalainen, P., & Heikkonen, J. (2017). Estimating the prediction performance of spatial models via spatial k-fold cross validation. International Journal of Geographical Information Science, 31(10), 2001–2019. https://doi.org/10.1080/13658816.2017.1346255
Prado, N. V., Uribe-Opazo, M. A., Galea, M., & Assumpcao, R. A. B. (2013). Influência local em um modelo espacial linear da produtividade da soja utilizando distribuição t-student. Artigos Científicos 33(5), 1003–1016. https://doi.org/10.1590/S0100-69162013000500012
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, 181–199. https://doi.org/10.1007/s10021-005-0054-1
Pusch, M., Samuel-Rosa, A., Oliveira, A. L. G., Magalhães, P. S. G., & do Amaral, L.R. (2022). Improving soil property maps for precision agriculture in the presence of outliers using covariates. Precision Agriculture, 23(5), 1575–1603. https://doi.org/10.1007/s11119-022-09898-z
QGIS Development Team. (2023). QGIS geographic information system version 3.28.3. https://www.qgis.org/en/site/
Ragagnin, V. A., Júnior, D. G. S., & Neto, A. N. S. (2010). Recomendação de calagem a taxa variada sob diferentes intensidades de amostragem. Revista Brasileira de Engenharia Agrícola e Ambiental, 14(6), 600–607. https://doi.org/10.1590/S1415-43662010000600006
Ramos, F. T., Santos, R. T., & Júnior, J. H. C. & Maia, J. C. S. (2017). Defining management zones based on soil attributes and soybean productivity. Revista Caatinga, 30(2), 427–436. https://doi.org/10.1590/1983-21252017v30n218rc
Rasmussen, C. E., & Williams, C. K. (2006). Gaussian processes for machine learning (Vol. 1). Springer.
Riffel, C. T., Garcia, M. S., Santi, A. L., Basso, C. J., Della Flora, L. P., Cherubin, M. R., & Eitelwein, M. T. (2012). Densidade amostrai aplicada ao monitoramento georreferenciado de lagartas desfolhadoras na cultura da soja. Ciência Rural, 42(12), 2112–2120. https://doi.org/10.1590/S0103-84782012005000116
Rivoirard, J. (1994). Introduction to disjunctive kriging and non-linear geostatistics. Clarendon Press.
Robert, P. C. (2002). Precision agriculture: A challenge for crop nutrition management. In Progress in plant nutrition: Plenary lectures of the XIV international plant nutrition colloquium (pp. 143–149). https://doi.org/10.1007/978-94-017-2789-1_11
Rodrigues, M. S., Alves, D. C., Souza, V. C., Melo, A. C., & do Nascimento Lima, A. M. (2018). Spatial interpolation techniques for site-specific irrigation management in a mango orchard. Comunicata Scientiae, 9(1), 93–101. https://doi.org/10.14295/cs.v9i1.2645
Rodrigues, M. S., Castrignanò, A., Belmonte, A., Silva, K. A. D., & Lessa, B. F. T. (2021). Geostatistics and its potential in agriculture 4.0. Revista Ciência Agronômica, 51, 2. https://doi.org/10.5935/1806-6690.20200095
Rogova, G. L., & Nimier, V. (2004). Reliability in information fusion: literature survey. In Proceedings of the 7th international conference on information fusion (Vol. 2, pp. 1158–1165).
Rossel, R. V., Adamchuk, V., Sudduth, K., McKenzie, N., & Lobsey, C. (2011). Proximal soil sensing: An effective approach for soil measurements in space and time. Advances in Agronomy, 113, 243–291. https://doi.org/10.1016/B978-0-12-386473-4.00005-1
Samui, P., & Sitharam, T. (2010). Applicability of statistical learning algorithms for spatial variability of rock depth. Mathematical Geosciences, 42, 433–446. https://doi.org/10.1007/s11004-010-9268-7
Sanchez, M. G. B., Marques, J., Siqueira, D. S., Camargo, L. A., & Pereira, G. T. (2013). Delineation of specific management areas for coffee cultivation based on the soil–relief relationship and numerical classification. Precision Agriculture, 14, 201–214. https://doi.org/10.1007/s11119-012-9288-z
Sasiadek, J. Z. (2002). Sensor fusion. Annual Reviews in Control, 26(2), 203–228. https://doi.org/10.1016/S1474-6670(17)37896-5
Savelyeva, E. , Utkin, S. , Kazakov, S., & Demyanov, V. (2010). Modeling spatial uncertainty for locally uncertain data. In P. M. Atkinson & C. D. Lloyd (Eds.), geoENV VII—Geostatistics for environmental applications (pp. 295–306). Springer. https://doi.org/10.1007/978-90-481-2322-3_26
Schenatto, K., Souza, E., Bazzi, C., Bier, V., Betzek, N., & Gavioli, A. (2016). Interpolação de dados na definição de unidades de manejo. Acta Scientiarum-Technology, 38(1), 31–34.
Schossler, T. R., Mantovanelli, B. C., Almeida, B. G., Freire, F. J., Silva, M. M., Almeida, C. D. G. C., & Freire, M. B. G. S. (2019). Geospatial variation of physical attributes and sugarcane productivity in cohesive soils. Precision Agriculture, 20(6), 1274–1291. https://doi.org/10.1007/s11119-019-09652-y
Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, 14(02), 69–106. https://doi.org/10.1142/S0129065704001899
Sekulić, A., Kilibarda, M., Protić, D., Tadić, M. P., & Bajat, B. (2020). Spatio-temporal regression kriging model of mean daily temperature for Croatia. Theoretical and Applied Climatology, 140, 101–114. https://doi.org/10.1007/s00704-019-03077-3
SGEA. (2019). V sgea- simpòsio de geoestatística aplicada em ciências agrárias. Retrieved April 21, 2022, from https://www.fca.unesp.br/sgea/
Shaddad, S. M., Buttafuoco, G., & Castrignanò, A. (2020). Assessment and mapping of soil salinization risk in an Egyptian field using a probabilistic approach. Agronomy, 10(1), 85. https://doi.org/10.3390/agronomy10010085
Silva, F. M., Alves, M. C., Souza, J. C. S., & Oliveira, M.S.d. (2010). Effects of manual harvesting on coffee (Coffea arabica L.) crop biannuality in Ijaci, Minas Gerais. Ciência e Agrotecnologia, 34, 625–632. https://doi.org/10.1590/S1413-70542010000300014
Silva, F. M., Souza, Z. M., Figueiredo, C. A. P., Vieira, L. H. S., & de Oliveira, E. (2008). Variabilidade espacial de atributos químicos e produtividade da cultura do café em duas safras agrícolas. Ciência e Agrotecnologia, 32, 231–241. https://doi.org/10.1590/S1413-70542008000100034
Silva, S. A., & Lima, J. S. S. (2012). Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation. Revista Brasileira de Ciência do Solo, 36(2), 467–474. https://doi.org/10.1590/S0100-06832012000200016
Silva, S. A., & Lima, J. S. S. (2013). Relação espacial entre o estoque de nutrientes e a densidade de solo cultivado com cafeeiro. Pesquisa Agropecuária Tropical, 43(4), 377–384. https://doi.org/10.1590/S1983-40632013000400002
Silva Junior, J. F., Pereira, G. T., Camargo, L. A., & Marques Junior, J. (2013). Métodos geoestatísticos na modelagem espacial do diâmetro médio do cristal da goethita. Revista Brasileira de Engenharia Agricola e Ambiental/The Brazilian Journal of Agricultural and Environmental Engineering, 17(11), 1127–1134. https://doi.org/10.1590/S1415-43662013001100001
Smirnoff, A., Boisvert, E., & Paradis, S. J. (2008). Support vector machine for 3D modelling from sparse geological information of various origins. Computers & Geosciences, 34(2), 127–143. https://doi.org/10.1016/j.cageo.2006.12.008
Souza, E., Bazzi, C., Khosla, R., Uribe-Opazo, M., & Reich, R. M. (2016). Interpolation type and data computation of crop yield maps is important for precision crop production. Journal of Plant Nutrition, 39(4), 531–538. https://doi.org/10.1080/01904167.2015.1124893
Souza, W. J. O., Rozane, D. E., Souza, H. A., Natale, W., & dos Santos, P. A. F. (2018). Machine traffic and soil penetration resistance in guava tree orchards. Revista Caatinga, 31(4), 980–986. https://doi.org/10.1590/1983-21252018v31n421rc
Souza, Z. M., Barbieri, D. M., Marques Júnior, J., Pereira, G. T., & Campos, M. C. C. (2007). Influence of the spatial variability of latosol chemical attributes and input application for sugarcane culture. Ciência e Agrotecnologia, 31, 371–377. https://doi.org/10.1590/S1413-70542007000200016
Souza, Z. M., Souza, G. S., Marques Júnior, J., & Pereira, G. T. (2014). Número de amostras na análise geoestatística e na krigagem de mapas de atributos do solo. Ciência Rural, 44, 261–268. https://doi.org/10.12702/II-SGEA-A41
Spezia, G. R., Souza, E. G., Nóbrega, L. H. P., Uribe-Opazo, M. A., Milan, M., & Bazzi, C. L. (2012). Model to estimate the sampling density for establishment of yield mapping. Revista Brasileira de Engenharia Agrícola e Ambiental, 16(4), 449–457. https://doi.org/10.1590/S1415-43662012000400016
Stojanova, D., Ceci, M., Appice, A., Malerba, D., & Džeroski, S. (2013). Dealing with spatial autocorrelation when learning predictive clustering trees. Ecological Informatics, 13, 22–39. https://doi.org/10.1016/j.ecoinf.2012.10.006
Takafuji, E. H. M., da Rocha, M. M., & Manzione, R. L. (2020). Spatiotemporal forecast with local temporal drift applied to weather patterns in patagonia. SN Applied Sciences, 2(6), 1001. https://doi.org/10.1007/s42452-020-2814-0
Takafuji, E. H. M., da Rocha, M. M., & Manzione, R. L. (2019). Groundwater level prediction/forecasting and assessment of uncertainty using SGS and ARIMA models: A case study in the bauru aquifer system (Brazil). Natural Resources Research, 28(2), 487–503. https://doi.org/10.1007/s11053-018-9403-6
Thomas, G. W. (1970). Soil and climatic factors which affect nutrient mobility. In Nutrient mobility in soils: Accumulation and losses (Vol. 4, pp. 1–20). Wiley.
Tobler, W. R. (1970). A computer movie simulating urban growth in the detroit region. Economic Geography, 46(sup1), 234–240. https://doi.org/10.2307/143141
Valente, G. F., Ferraz, G. A. E. S., Santana, L. S., Ferraz, P. F. P., Mariano, D. C., Santos, C. M., & Rossi, G. (2022). Mapping soil and pasture attributes for buffalo management through remote sensing and geostatistics in amazon biome. Animals (Basel), 12(18), 2374. https://doi.org/10.3390/ani12182374
Varouchakis, E. A., Kamińska-Chuchmała, A., Kowalik, G., Spanoudaki, K., & Graña, M. (2021). Combining geostatistics and remote sensing data to improve spatiotemporal analysis of precipitation. Sensors, 21(9), 3132. https://doi.org/10.3390/s21093132
Vasques, G. M., Rodrigues, H. M., Coelho, M. R., Baca, J. F., Dart, R. O., Oliveira, R. P., & Ceddia, M. B. (2020). Field proximal soil sensor fusion for improving high-resolution soil property maps. Soil Systems, 4(3), 52. https://doi.org/10.3390/soilsystems4030052
Vaysse, K., & Lagacherie, P. (2017). Using quantile regression forest to estimate uncertainty of digital soil mapping products. Geoderma, 291, 55–64. https://doi.org/10.1016/j.geoderma.2016.12.017
Wackernagel, H. (2003). Multivariate geostatistics: an introduction with applications. Springer.
Watson, G. S. (1984). Smoothing and interpolation by kriging and with splines. Journal of the International Association for Mathematical Geology, 16, 601–615. https://doi.org/10.1007/BF01029320
Webster, R. (1991). Local disjunctive kriging of soil properties with change of support. Journal of Soil Science, 42(2), 301–318. https://doi.org/10.1111/j.1365-2389.1991.tb00411.x
Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Wiley.
Wickham, H. (2023). ggplot2: Create elegant data visualisations using the grammar of graphics. R package version 3.4.1. https://CRAN.R-project.org/package=ggplot2
Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine learning (Vol. 2). MIT.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
Xu, W. , Tran, T. T., Srivastava, R. M., & Journel, A. (1992). SPE annual technical conference and exhibition. In Proceedings of the IEEE (Vol. SPE-24742-MS, pp. 457–466). OnePetro.
Yost, R., Uehara, G., & Fox, R. (1982). Geostatistical analysis of soil chemical properties of large land areas. II. Kriging. Soil Science Society of America Journal, 46(5), 1033–1037. https://doi.org/10.2136/sssaj1982.03615995004600050029x
Zhang, Y. (2004). Understanding image fusion. Photogrammetric Engineering and Remote Sensing, 70(6), 657–661.
Zonta, J. H., Brandão, Z. N., Medeiros, J. C., Sana, R. S., & Sofiatti, V. (2014). Variabilidade espacial da fertilidade do solo em área cultivada com algodoeiro no cerrado do brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, 18, 595–602. https://doi.org/10.1590/S1415-43662014000600005
Acknowledgements
To the Coordination for the Improvement of Higher Education Personnel (in Portuguese: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES) (partially, under the Finance Code 001) for the funding. We also thank the reviewers of this article for the excellent observations that enriched this work.
Funding
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Author information
Authors and Affiliations
Contributions
COFS conducted the modeling research, including coding and data management, analyzed, and interpreted the data, designed the figures, and wrote the paper, with input and guidance from RLM, and SRMO. All co-authors worked on the discussion and agreed to the submitted version.
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Silva, C.O., Manzione, R.L. & Oliveira, S.R.M. Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what’s next?. Precision Agric 24, 2293–2326 (2023). https://doi.org/10.1007/s11119-023-10041-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-023-10041-9