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dc.contributor.authorRodrigues, Hugo Machado
dc.date.accessioned2023-12-22T01:39:54Z-
dc.date.available2023-12-22T01:39:54Z-
dc.date.issued2020-02-28
dc.identifier.citationRODRIGUES, Hugo Machado. Mapas rápidos e precisos de CEa do solo para pesquisa com EM38-MK2: otimização do espaçamento entre linhas, densidade amostral e resolução espacial de saída. 2020. 50 f. Dissertação (Mestrado em Agronomia, Ciência do Solo) - Instituto de Agronomia, Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, 2020.por
dc.identifier.urihttps://rima.ufrrj.br/jspui/handle/20.500.14407/10596-
dc.description.abstractEstabelecer um plano de delineamento amostral é um dos mais importantes estágios para o reconhecimento detalhado do solo e é um desafio para que interpoladores geoestatísticos, como krigagem, sejam utilizados em sistemas informatizados de suporte e apoio à decisão quando o tempo de processamento dos dados é alto. Este estudo teve o objetivo de avaliar quatro níveis de espaçamentos (40, 80, 150 e 300 m) entre linhas de coleta de dados de condutividade elétrica aparente (CEa) com o sensor Geonics EM38-MK2, em uma área de 72 ha sob pivô de irrigação, localizada no município de Itaí, São Paulo. Para avaliar o efeito do distanciamento entre as linhas nas incertezas dos mapas de CEa produzidos por krigagem ordinária, utilizou-se o índice de raiz do erro quadrado médio (REQM) e associaram-se os níveis de espaçamentos a sete resoluções espaciais para mapeamento (100, 80, 50, 30, 20, 10 e 5 m), a fim de identificar o menor tempo de processamento para produção de cada mapa via krigagem. Todas as análises foram realizadas no software R. Os mapas que utilizaram as resoluções 100, 80 e 50 m para os quatro níveis de espaçamento não demonstraram compatibilidade com a extensão da área de estudo, visto que inviabilizaram a interpretação da variação espacial da CEa. O mapa com resolução de 5 m com dados espaçados de 40 m demandou 25 minutos para execução e apresentou o menor REQM entre todos os mapas avaliados, 0,67 mS/m. O alto tempo de processamento associado ao excessivo detalhamento para operações em agricultura de precisão tornam a resolução de 5 m uma escolha inadequada. As resoluções restantes à escolha foram 30, 20 e 10 m. Observando-se que os valores de REQM aumentam conforme espaçam-se as linhas de dados e à medida que a resolução diminui, o REQM de 1,09 mS/m foi o menor valor encontrado nessas resoluções, a partir do espaçamento entre linhas de 80 m e resolução de 10 m. Para reduzir o tempo de 77 s necessários para a confecção do mapa, utilizando-se a combinação do conjunto de dados com 80 m de espaçamento e a resolução de 10 m, removeram-se, aleatoriamente, 25, 50, 75 e 95% dos pontos. O valor de REQM para o mapa com remoção de 25% foi 1,09 mS/m, e o tempo para a produção do mapa reduziu-se para 42 s. Os mapas com os dados removidos em 50 e 75% apresentaram REQM 1,21 mS/m, e tempos de processamento 20 e 4,9 s, respectivamente. Ao produzir o mapa com os dados removidos em 95% dos pontos, o tempo não ultrapassou 1 s, contudo, o REQM de 1,54 mS/m indica incertezas superiores ao se utilizar espaçamento entre linhas de 150 m. Portanto, o delineamento amostral contendo 512 pontos, referente ao conjunto com 75% de dados removidos a partir do espaçamento de 80 m e a resolução espacial de 10 m permitiram a caraterização detalhada da variação espacial da CEa do solo na área de estudo, com REQM 1,21 mS/m e tempo de processamento inferior a 10s.por
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpor
dc.formatapplication/pdf*
dc.languageporpor
dc.publisherUniversidade Federal Rural do Rio de Janeiropor
dc.rightsAcesso Abertopor
dc.subjectSensoriamento proximal do solopor
dc.subjectGeoestatísticapor
dc.subjectKrigagempor
dc.subjectIncertezapor
dc.subjectProximal soil sensingeng
dc.subjectGeostatisticseng
dc.subjectKrigingeng
dc.subjectUncertaintyeng
dc.titleMapas rápidos e precisos de CEa do solo para pesquisa com EM38-MK2: otimização do espaçamento entre linhas, densidade amostral e resolução espacial de saídapor
dc.title.alternativeFast and accurate soil ECa maps for EM38-MK2 survey: optimizing transect spacing, sample density and output spatial resolutioneng
dc.typeDissertaçãopor
dc.description.abstractOtherChoosing a sampling design is one of the most important stages for detailed soil recognition and is a challenge for geostatistical interpolators, such as kriging, to be used in automated decision support systems when the data processing time is large. This study aimed to evaluate four levels of spacing (40, 80, 150 and 300 m) between data transects of apparent electrical conductivity (CEa) measured by the Geonics EM38-MK2 sensor, in an area of 72 ha under irrigation pivot, located in the municipality of Itaí, São Paulo, Brazil. To assess the effect of the distance between the transects in the uncertainty of the CEa maps produced by ordinary kriging, the root mean square error index (RMSE) was used, and the spacing levels were associated with seven spatial resolutions for the output map (100 , 80, 50, 30, 20, 10 and 5 m), in order to identify the shortest processing time for producing each map by kriging. The maps at resolutions 100, 80 and 50 m for the four spacing levels did not demonstrate compatibility with the extension of the study area since they made the interpretation of the spatial variation of CEa unfeasible. The 5 m resolution map made from data with 40 m spacing took 25 minutes to execute and had the lowest RMSE among all the evaluated maps, namely 0.67 mS/m. The large processing time associated with excessively detailed maps for precision agriculture operations makes the 5 m resolution a suboptimal choice. Thus, the remaining resolutions to choose from were 30, 20 and 10 m. Observing that the RMSE values increase as the transect spacing increases and resolutions degrade, the RMSE reached 1.09 mS/m as the lowest value among these resolution options, from data taken at 80 m spacing using 10 m resolution. To reduce the time of 77 s required for making the map, using the combination of the data set with 80 m spacing and resolution of 10 m, 25, 50, 75 and 95% of the points were randomly removed from this set. The RMSE value for the map with 25% removal was 1.09 mS/m, and the time for producing the map was reduced to 42 s. The maps with 50 and 75% of the data removed presented RMSE of 1.21 mS/m, and the processing times were 20 and 4.9 seconds, respectively. When producing the map with 95% of the data removed, the time did not exceed 1 s, however, the RMSE of 1.54 mS/m indicates greater uncertainty when using spacing between transects of 150 m. Therefore, the sample design containing 512 points, referring to the set with 75% of the data removed from the 80 m spacing data, and a 10 m spatial resolution, allowed assessing in detail the spatial variation of soil CEa in the study area, with a RMSE of 1.21 mS/m and processing time lower than 10 s.eng
dc.contributor.advisor1Ceddia, Marcos Bacis
dc.contributor.advisor1ID141.571.218-21por
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2115137917689655por
dc.contributor.advisor-co1Vasques, Gustavo de Mattos
dc.contributor.advisor-co1ID084.272.437-07por
dc.contributor.referee1Ceddia, Marcos Bacis
dc.contributor.referee2Pinheiro, Helena Saraiva Koenow
dc.contributor.referee3Oliveira, Ronaldo Pereira de
dc.creator.ID124.541.897-12por
dc.creator.IDhttps://orcid.org/0000-0002-8070-8126por
dc.creator.Latteshttp://lattes.cnpq.br/1594791643571293por
dc.publisher.countryBrasilpor
dc.publisher.departmentInstituto de Agronomiapor
dc.publisher.initialsUFRRJpor
dc.publisher.programPrograma de Pós-Graduação em Agronomia - Ciência do Solopor
dc.relation.referencesADAMCHUK, V.; JI, W.; VISCARRA ROSSEL, R.; GEBBERS, R.; TREMBLAY, N. Proximal Soil and Plant Sensing. In: SHANNON, D. K.; CLAY, D. E.; KITCHEN, N. R. (Eds.). Precision Agriculture Basics. Hoboken: John Wiley & Sons, 2018. p. 119-140. AKRAMKHANOV, A.; BRUS, D. J.; WALVOORT, D. J. J. Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys. Geoderma, v. 21, p. 600-607, 2014. AKRAMKHANOV, A.; SOMMER, R.; MARTIUS, C.; HENDRICKX, J. M. H.; VLEK, P. L. G. Comparison and sensitivity of measurement techniques for spatial distribution of soil salinity. Irrigation & Drainage Systems Engineering, v. 22, p. 115-126, 2008. ALLRED, B. J.; ADAMCHUK, V. I.; ROSSEL, VISCARRA, R. A.; DOOLITTLE, J.; FREELAND, R. S.; GROTE, K. R.; CORWIN, D. L. Geophysical Methods. In: LAL, R. (Eds.). Encyclopedia of Soil Science. 3. ed. Boca Raton: Taylor & Francis, 2016. ALMASI, A.; JALALIAN, A.; TOOMANIAN, N. Using OK and IDW methods for prediction the spatial variability of A horizon depth and OM in soils of Shahrekord, Iran. Journal of Environment and Earth Science, v. 4, p. 17-28, 2014. ANDERSON-COOK, C. M.; ALLEY, M. M.; ROYGARD, J. K. F. F.; KHOSLA, R.; NOBLE, R. B.; DOOLITTLE, J. A. Differentiating soil types using electromagnetic conductivity and crop yield maps. Soil Science Society of America Journal, v. 66, p. 1562- 1570, 2002. BECEGATO, V. A.; FERREIRA, F. J. F. Gamaespectrometria, resistividade elétrica e susceptibilidade magnética de solos agrícolas no noroeste do estado do Paraná. Revista Brasileira de Geofísica, v. 23, p. 371-405, 2005. BENNETT, D. L.; GEORGE, R.; RYDER, A. Soil Salinity Assessment Using the EM38: Field Operating Instructions and Data Interpretation. Report 4/95. Perth: Department of Agriculture and Food, Western Australia, 1995. BISWAS, A.; ZHANG, Y. Sampling designs for validating digital soil maps: A review. Pedosphere, v. 28, p. 1-15, 2018. BREVIK, E. C.; CALZOLARI, C.; MILLER, B. A.; PEREIRA, P.; KABALA, C.; BAUMGARTEN, A.; JORDÁN, A. Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma, v. 264, p. 256-274, 2016. BRUS, D. J. Sampling for digital soil mapping: A tutorial supported by R scripts. Geoderma, v. 338, p. 464-480, 2018. BURGESS, T. M.; WEBSTER, R. Optimal interpolation and isarithmic mapping of soil properties. Journal of Soil Science, v. 31, p. 333–341, 1980. CAMERON, D. R.; READ, D. W. L.; JONG, E. DE; OOSTERVELD, M. Mapping Salinity Using Resistivity and Electromagnetic Inductive Techniques. Canadian Journal of Soil Science, v. 61, n. 1, p. 67-78, 1981. CEDDIA, M. B.; VILLELA, A. L. O.; PINHEIRO, É. F. M.; WENDROTH, O. Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil. Science of the Total Environment, v. 526, p. 58-69, 2015. COCKX, L.; VAN MEIRVENNE, M.; DE VOS, B. Using the EM38DD soil sensor to delineate clay lenses in a sandy forest soil. Soil Science Society of America Journal, v. 71, p. 1314-1322, 2007. CORWIN, D. L.; LESCH, S. M. Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, v. 46, p. 11-43, 2005. DE BENEDETTO, D.; CASTRIGNANÒ, A.; DIACONO, M.; RINALDI, M.; RUGGIERI, S.; TAMBORRINO, R. Field partition by proximal and remote sensing data fusion. Biosystems Engineering, v. 114, p. 372-383, 2013. DE CARVALHO JÚNIOR, W.; CHAGAS, C. S.; MUSELLI, A.; PINHEIRO, H. S. K.; PEREIRA, N. R.; BHERING, S. B. Método do hipercubo latino condicionado para a amostragem de solos na presença de covariáveis ambientais visando o mapeamento digital de solos. Revista Brasileira de Ciência do Solo, v. 38, p. 386-396, 2014. DE GRUIJTER, J. J.; BRUS, D. J.; BIERKENS, M. F. P.; KNOTTERS, M. Sampling for Natural Resource Monitoring. Berlim: Springer, 2006. DE WIJS, H. J. Statistics of ore distribution. Part I: Frequency distribution of assay values. Netherlands Journal of Geosciences, v. 13, p. 365-375, 1951. DE WIJS, H. J. Statistics of ore distribution. Part II: Theory of binomial distribution applied to sampling and engineering problems. Netherlands Journal of Geosciences, v. 15, p. 12-24, 1953. DOOLITTLE, J. A.; BREVIK, E. C. The use of electromagnetic induction techniques in soils studies. Geoderma, v. 223-225, p. 33-45, 2014. FISHER, R. A. Statistical Methods and Statistical Iinference. Edimburgo: Oliver and Boyd, 1956. FULTON, A.; SCHWANKL, L.; LYNN, K.; LAMPINEN, B.; EDSTROM, J.; PRICHARD, T. Using EM and VERIS technology to assess land suitability for orchard and vineyard development. Irrigation Science, v. 29, p. 497-512, 2011. GHOLIZADEH, A.; AMIN, M. S. M.; ANUAR, A. R.; AIMRUN, W. Apparent electrical conductivity in correspondence to soil chemical properties and plant nutrients in soil. Communications in Soil Science and Plant Analysis, v. 42, p. 1447-1461, 2011. GOMEZ, C.; VISCARRA ROSSEL, R. A.; MCBRATNEY, A. B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, v. 146, p. 403-411, 2008. GREENHOUSE, J. P.; SLAINE, D. D. The use of reconnaissance electromagnetic methods to map contaminant migration. Groundwater Monitoring & Remediation, v. 3, p. 47-59, 1983. GUO, Y.; HUANG, J.; SHI, Z.; LI, H. Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors. PLoS ONE, v. 10, p. 1-12, 2015. HARRIS J. A. Practical universality of field heterogeneity as a factor influencing plot yields. Journal of Agricultural Research, v. 19, p. 279-314, 1920. HARTEMINK, A. E.; MINASNY, B. Towards digital soil morphometrics. Geoderma, v. 230–231, p. 305-317, 2014. HEIL, K.; SCHMIDHALTER, U. The application of EM38: Determination of soil parameters, selection of soil sampling points and use in agriculture and archaeology. Sensors, v. 17, p. 2540, 2017. HENGL, T. Finding the right pixel size. Computers & Geosciences, v. 32, p. 1283–1298, 2006. HUANG, J.; LARK, R. M.; ROBINSON, D. A.; LEBRON, I.; KEITH, A. M.; RAWLINS, B.; TYE, A.; KURAS, O.; RAINES, M.; TRIANTAFILIS, J. Scope to predict soil properties at within-field scale from small samples using proximally sensed γ-ray spectrometer and EM induction data. Geoderma, v. 232-34, p. 69-80, 2014. HUANG, J.; TAGHIZADEH-MEHRJARDI, R.; MINASNY, B.; TRIANTAFILIS, J. Modeling soil salinity along a hillslope in Iran by inversion of EM38 data. Soil Science Society of America Journal, v. 79, p. 1142-1153, 2015. HUANG, J.; MCBRATNEY, A. B.; MINASNY, B.; TRIANTAFILIS, J. 3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter. Journal of Hydrology, v. 549, p. 62-78, 2017. HUANG, J.; PROCHAZKA, M. J.; TRIANTAFILIS, J. Irrigation salinity hazard assessment and risk mapping in the lower Macintyre Valley, Australia. Science of the Total Environment, v. 551-552, p. 460-473, 2016. ISLAM, M. M.; MEERSCHMAN, E.; SAEY, T.; DE SMEDT, P.; VAN DE VIJVER, E.; VAN MEIRVENNE, M. Comparing apparent electrical conductivity measurements on a paddy field under flooded and drained conditions. Precision Agriculture, v. 13, p. 384-392, 2012. ISLAM, M. M.; SAEY, T.; MEERSCHMAN, E.; DE SMEDT, P.; MEEUWS, F.; VAN DE VIJVER, E.; VAN MEIRVENNE, M. Delineating water management zones in a paddy rice field using a floating soil sensing system. Agricultural Water Management, v. 102, p. 8-12, 2011. JAHKNWA, C. J.; RAY, H. H.; ZEMBA, A. A.; ADEBAYO, A. A.; WUYEP, S. Z. Spatial heterogeneity of salinity parameters in vertisols of Kerau, Guyuk area of Adamawa state, Nigeria. International Research Journal of Agriculture and Soil Science, v. 4, p. 5-12, 2014. JOURNEL, A.; HUIJBREGTS, C. J. Mining Geostatistics. Londres: Academic Press, 1978. KACHANOSKI, R. G.; GREGORICH, E. G.; VAN WESENBEECK, I. J. Estimating spatial variations of soil water content using noncontacting electromagnetic inductive methods. Canadian Journal of Soil Science, v. 68, p. 715-722, 1988. KESKIN, H.; GRUNWALD, S.; HARRIS, W. G. Digital mapping of soil carbon fractions with machine learning. Geoderma, v. 339, p. 40-58, 2019. KRIGE, D. G. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical Metallurgical & Mining Society of South Africa, v. 52, p. 119-139, 1951. KWON, T. J.; MURESAN, M.; FU, L.; USMAN, T. Development of zonal-specific semivariograms for a strategic RWIS network optimization: Case study. Journal of Infrastructure Systems, v. 25, p. 1-9, 2019. LANDRUM, C.; CASTRIGNANÒ, A.; MUELLER, T.; ZOURARAKIS, D.; ZHU, J.; DE BENEDETTO, D. An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics. Agricultural Water Management, v. 147, p. 144-153, 2015. LESCH, S. M.; RHOADES, J. D.; LUND, L. J.; CORWIN, D. L. Mapping soil salinity using calibrated electromagnetic measurements. Soil Science Society of America Journal, v. 56, p. 540–548, 1992. LI, H. Y.; SHI, Z.; WEBSTER, R.; TRIANTAFILIS, J. Mapping the three-dimensional variation of soil salinity in a rice-paddy soil. Geoderma, v. 195-196, p. 31-41, 2013. LOPES, I; MONTENEGRO, A. A. A. Spatialization of electrical conductivity and physical hydraulic parameters of soils under different uses in an alluvial valley. Revista Caatinga, v. 32, p. 222-233, 2019. MA, R.; MCBRATNEY, A.; WHELAN, B.; MINASNY, B.; SHORT, M. Comparing temperature correction models for soil electrical conductivity measurement. Precision Agriculture, v. 12, p. 55-66, 2011. 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. Mapeamento da condutividade elétrica e relação com a argila de Latossolo sob plantio direto. Pesquisa Agropecuária Brasileira, v. 41, p. 1023-1031, 2006. MATHERON, G. Les Variables Régionalisées et Leur Estimation. Une Application de la Théorie des Fonctions Aléatoires aux Siences de la Nature. Paris: Masson et Cie, 1965. MATHERON, G. Principles of geostatistics. Economic Geology, v. 58, p. 1246-1266, 1963. MCBRATNEY, A. B.; MENDONÇA SANTOS, M. L.; MINASNY, B. On digital soil mapping. Geoderma, v. 117, p. 3-52, 2003. MCBRATNEY, A. B.; ODEH, I. O. A.; BISHOP, T. F. A.; DUNBAR, M. S.; SHATAR, T. M. An overview of pedometric techniques for use in soil survey. Geoderma, v. 97, p. 293- 327, 2000. MCKENZIE, R. C.; CHOMISTEK, W.; CLARK, N. F. Conversion of electromagnetic inductance readings to saturated past extract values in soils for different temperature, texture, and moisture conditions. Canadian Journal of Soil Science, v. 69, p. 25-32, 1989. MCNEILL, J. D. Electromagnetic Terrain Conductivity Measurement at Low Induction Numbers. Ontario: Geonics Limited, 1980. MERSMANN, O. microbenchmark: Accurate Timing Functions. R Package Version 1.4-7. 2019. MILLER, B. A.; SCHAETZL, R. J. The historical role of base maps in soil geography. Geoderma, v. 230-231, p. 329-339, 2014. MINASNY, B.; MCBRATNEY, A. B. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, v. 32, p. 1378-1388, 2006. MONTGOMERY, E. G. Experiments in Wheat Breeding: Experimental Error in the Nursery and Variation in Nitrogen and Yield. Washington: United States Department of Agriculture, 1913. MULDER, V. L.; DE BRUIN, S.; SCHAEPMAN, M. E.; MAYR, T. R. The use of remote sensing in soil and terrain mapping - A review. Geoderma, v. 162, p. 1-19, 2011. MULLINS, C. E. Magnetic susceptibility of the soil and its significance in soil science – A review. Journal of Soil Science, v. 28, p. 223-246, 1977. MYERS, D. B.; KITCHEN, N. R.; SUDDUTH, K. A.; GRUNWALD, S.; MILES, R. J.; SADLER, E. J.; UDAWATTA, R. P. Combining Proximal and Penetrating Soil Electrical Conductivity Sensors for High-resolution Digital Soil Mapping. In: VISCARRA ROSSEL, R. A.; MCBRATNEY, A. B.; MINASNY B. (Eds.). Proximal Soil Sensing. Dordrecht: Springer, 2010. p. 233-243. NOURI, H.; BORUJENI, S. C.; ALAGHMAND, S.; ANDERSON, S. J.; SUTTON, P. C.; PARVAZIAN, S.; BEECHAM, S. Soil salinity mapping of urban greenery using remote sensing and proximal sensing techniques; The case of Veale Gardens within the Adelaide Parklands. Sustainability, v. 10, p. 1-14, 2018. PEBESMA, E. J. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, v. 30, p. 683-691, 2004. PENDLETON, R. L. Are soils mapped under a given type name by the bureau of soils method closely similar to one another? Berkeley: University of California Press, 1919. PIIKKI, K.; SÖDERSTRÖM, M.; STENBERG, B. Sensor data fusion for topsoil clay mapping. Geoderma, v. 199, p. 106-116, 2013. R CORE TEAM. R: A Language and Environment for Statistical Computing. Viena: R Foundation for Statistical Computing, 2019. RAMIREZ-LOPEZ, L.; SCHMIDT, K.; BEHRENS, T.; VAN WESEMAEL, B.; DEMATTÊ, J. A. M.; SCHOLTEN, T. Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, v. 226-227, p. 140-150, 2014. RAMOS, A. M.; SANTOS, L. A. R.; FORTES, L. T. G. (Eds.). Normais Climatológicas do Brasil 1961-1990. Versão Revista e Ampliada. Brasília: Instituto Nacional de Meteorologia, 2009. RAWLINS, B. G.; MARCHANT, B. P.; SMYTH, D.; SCHEIB, C.; LARK, R. M.; JORDAN, C. Airborne radiometric survey data and a DTM as covariates for regional scale mapping of soil organic carbon across Northern Ireland. European Journal of Soil Science, v. 60, p. 44- 54, 2009. RHOADES, J. D.; CORWIN, D. L. Determining soil electrical conductivity-depth relations using an inductive electromagnetic soil conductivity meter. Soil Science Society of America Journal, v. 45, p. 255-260, 1981. ROBINSON, G. W.; LLOYD, W. E. On the probable error of sampling in soil surveys. The Journal of Agricultural Science, v. 7, p. 144-153, 1915. RODRIGUES, F. A.; BRAMLEY, R. G. V.; GOBBETT, D. L. Proximal soil sensing for Precision Agriculture: Simultaneous use of electromagnetic induction and gamma radiometrics in contrasting soils. Geoderma, v. 243-244, p. 183-195, 2015. ROSSI, M. Mapa Pedológico do Estado de São Paulo: Revisado e Ampliado. 1. ed. São Paulo: Instituto Florestal, 2017. ROSSITER, D. G. Past, present & future of information technology in pedometrics. Geoderma, v. 324, p. 131-137, 2018. ROUDIER, P. clhs: a R Package for Conditioned Latin Hypercube Sampling. Software R Package. 2011. SAMET, R.; ÇELIK, E.; TURAL, S.; ŞENGÖNÜL, E.; ÖZKAN, M.; DAMCI, E. Using interpolation techniques to determine the optimal profile interval in ground-penetrating radar applications. Journal of Applied Geophysics, v. 140, p. 154-167, 2017. SAMUEL-ROSA, A. spsann: Optimization of Sample Configurations Using Spatial Simulated Annealing. Software R Package Version 2.2.0. 2019. SHEETS, K. R.; HENDRICKX, J. M. H. Noninvasive soil water content measurement using electromagnetic induction. Water Resources Research, v. 31, p. 2401-2409, 1995. SLAVICH, P. G. Determining ECa-depth profiles from electromagnetic induction measurements. Australian Journal of Soil Research, v. 28, p. 443-452, 1990. SLAVICH, P. G.; READ B. J. Assessment of electromagnetic induction measurements using an inductive electromagnetic soil conductivity meter. Soil Science Society of America Journal. v. 45, p. 255-260, 1983. SMITH, L. H. Plot arrangement for variety experiments with corn. Agronomy Journal, v. 1, p. 84-89, 1909. SÖDERSTRÖM, M.; ERIKSSON, J.; ISENDAHL, C.; ARAÚJO, S. R.; REBELLATO, L.; PAHL SCHAAN, D.; STENBORG, P. Using proximal soil sensors and fuzzy classification for mapping Amazonian Dark Earths. Agricultural and Food Science, v. 22, p. 380-389, 2013. SUDDUTH, K. A.; KITCHEN, N. R.; WIEBOLD, W. J.; BATCHELOR, W. D.; BOLLERO, G. A.; BULLOCK, D. G.; CLAY, D. E.; PALM, H. L.; PIERCE, F. J.; SCHULER, R. T.; THELEN, K. D. Relating apparent electrical conductivity to soil properties across the northcentral USA. Computers and Electronics in Agriculture, v. 46, p. 263-283, 2005. SUDDUTH, K. A.; DRUMMOND, S. T.; KITCHEN, N. R. Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers and Electronics in Agriculture, v. 31, p. 239-264, 2001. SUDDUTH, K. A; KITCHEN, N. R.; MYERS, D. B.; DRUMMOND, S. T. Mapping depth to argillic soil horizons using apparent electrical conductivity. Journal of Environmental and Engineering Geophysics, v. 15, p. 135-146, 2010. SUDDUTH, K. A.; MYERS, D. B.; KITCHEN, N. R.; DRUMMOND, S. T. Modeling soil electrical conductivity-depth relationships with data from proximal and penetrating ECa sensors. Geoderma, v. 199, p. 12-21, 2013. TEIXEIRA, P. C.; DONAGEMMA, G. K.; FONTANA, A.; TEIXEIRA, W. G. Manual de Métodos de Análise de Solo. 3. ed. rev ed. Brasília, DF: Embrapa, 2017. TOBLER, W. R. A Computer movie simulating urban growth in the Detroit region. Economic Geography, v. 46, p. 234-240, 1970. TRANGMAR, B. B.; YOST, R. S.; UEHARA, G. Application of geostatistics to spatial studies of soil properties. Advances in Agronomy, v. 36, p. 45-94, 1985. TRIANTAFILIS, J.; LESCH, S. M. Mapping clay content variation using electromagnetic induction techniques. Computers and Electronics in Agriculture, v. 46, p. 203237, 2005. TRIANTAFILIS, J.; SANTOS, F. A. M. Resolving the spatial distribution of the true electrical conductivity with depth using EM38 and EM31 signal data and a laterally constrained inversion model. Australian Journal of Soil Research, v. 48, p. 434-446, 2010. USDA (UNITED STATES DEPARTMENT OF AGRICULTURE). Soil Survey Manual. Washington, D.C.: United States Department of Agriculture, 2017. VAN DER LELIJ, A., Use of an electromagnetic induction instrument (type EM38) for mapping of soil salinity, Internal Report Research Branch, Water Resources Commission, NSW, Australia, 1983. VIEIRA, S. R.; MILLETE, J.; TOPP, G. Handbook for Geostatistical Analysis of Variability in Soil and Climate Data. In: ALVAREZ, V. H.; SCHAEFER, C. R.; BARROS, N. F.; MELLO, J. W. V.; COSTA, L. M. (Eds.). Tópicos em Ciência do Solo. Volume 2. Viçosa: Sociedade Brasileira de Ciência do Solo, 2002. p. 1-45. VISCARRA ROSSEL, R. A.; ADAMCHUK, V. I.; SUDDUTH, K. A.; MCKENZIE, N. J.; LOBSEY, C. Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time. In: SPARKS, D. (Ed.). Advances in Agronomy. Volume 113. Amsterdã: Elsevier, 2011. p. 243-291. VISCARRA ROSSEL, R. A.; BOUMA, J. Soil sensing: A new paradigm for agriculture. Agricultural Systems, v. 148, p. 71-74, 2016. WACKERNAGEL, H. Multivariate Geoestatistics: An Introduction with applications. 3. ed. Verlag Berlin Heidelberg: Springer, 2003. WALVOORT, D. J. J.; BRUS, D. J.; DE GRUIJTER, J. J. An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers & Geosciences, v. 36, p. 1261-1267, 2010. WAYNICK, D. D. Variability in soils and its significance to past and future soil investigations: I. A statistical study of nitrification in soils. University of California Publications in Agricultural Science, v. 3, p. 243-270, 1918. WAYNICK, D. D.; SHARP, L. T. Variability in soils and its significance to past and future soil investigations: II. Variability in nitrogen and carbon in field soils and their relation to the accuracy of field trials. University of California Publications in Agricultural Science, v. 4, p. 121-139, 1919. WEBSTER, R.; OLIVER, M. A. Geostatistics for Environmental Scientists. 2. ed. Chichester: John Wiley & Sons, 2007. WOLLENHAUPT, N. C.; RICHARDSON, J. L.; FOSS, J. E.; DOLL, E. C. A rapid method for estimating weighted soil salinity from apparent soil electrical conductivity measured with an aboveground electromagnetic induction meter. Canadian Journal of Soil Science, v. 66, p. 315-321, 1986. YAMAMOTO, J. K.; LANDIM, P. M. B. Geoestatística: Conceitos e Aplicações. São Carlos: Oficina de Textos, 2013. YAO, X.; FU, B.; LÜ, Y.; SUN, F.; WANG, S.; LIU, M. Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment. PLoS ONE, v. 8, p. 1-13, 2013.por
dc.subject.cnpqAgronomiapor
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