Bivariate spatial statistics applied to precipitation and off-season corn yield in the state of Paraná, Brazil

References

• Adhikari, P., Shukla, M.K., and Mexal, J.G., 2012. Spatial variability of soil properties in an arid ecosystem irrigated with treated municipal and industrial wastewater. Soil Science, 177 (7), 458–469. doi:10.1097/SS.0B013E318257C331
   Web of Science ®Google Scholar
• Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716–723. doi:10.1109/TAC.1974.1100705
   Web of Science ®Google Scholar
• ANA - Agência Nacional das Águas (Brazil), 2017. Data from: precipitation stations [dataset]. HidroWeb: sistemas de informações hidrológicas. Brasília: ANA. Available from: <https://www.snirh.gov.br/hidroweb/>. Access in 06 Nov 2017.
   Google Scholar
• Anselin, L., 2020. GeoDa: an introduction to spatial data science: contiguity-based spatial weights [online]. Available from: <https://geodacenter.github.io/workbook/4a_contig_weights/lab4a.html>. Acessed in: 2022.
   Google Scholar
• Anselin, L. and Arribas-Bel, D., 2013. Spatial fixed effects and spatial dependence in a single cross-section. Papers in Regional Science, 92 (1), 3–17. doi:10.1111/J.1435-5957.2012.00480.X
   Web of Science ®Google Scholar
• Anselin, L. and Li, X., 2019. Operational local join count statistics for cluster detection. Journal of Geographical Systems, 21 (2), 189–210. doi:10.1007/S10109-019-00299-X
   PubMed Web of Science ®Google Scholar
• Anselin, L., Syabri, I., and Kho, Y., 2006. GeoDa: an introduction to spatial data analysis. Geographical Analysis, 38 (1), 5–22. doi:10.1111/J.0016-7363.2005.00671.X
   Web of Science ®Google Scholar
• Anselin, L., Syabri, I., and Smirnov, O., 2002. Visualizing multivariate spatial correlation with dynamically linked windows. In: New tools for spatial data analysis. Santa Barbara: University of California, 1–20. CD-ROM http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.7163
   Google Scholar
• Araújo, E.C., Uribe-Opazo, M.A., and Johann, J.A., 2014. Modelo de regressão espacial para estimativa da produtividade da soja associada a variáveis agrometeorológicas na região oeste do estado do Paraná. Engenharia Agrícola, 34 (2), 286–299. doi:10.1590/S0100-69162014000200010
   Google Scholar
• Bone, C., et al., 2013. A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran’s I statistic. Applied Geography, 40, 161–170. doi:10.1016/J.APGEOG.2013.02.011
   Web of Science ®Google Scholar
• Borssoi, J.A., Uribe-Opazo, M.A., and Galea, M., 2011. Técnicas de diagnóstico de influência local na análise espacial da produtividade da soja. Engenharia Agrícola, 31 (2), 376–387. doi:10.1590/S0100-69162011000200018
   Google Scholar
• Chen, W. and Genton, M.G., 2019. Parametric variogram matrices incorporating both bounded and unbounded functions. Stochastic Environmental Research and Risk Assessment 2019, 33 (10), 1669–1679. doi:10.1007/S00477-019-01710-1
   Web of Science ®Google Scholar
• Cima, E.G. et al., 2018. Analysis of spatial autocorrelation of grain production and agricultural storage in Paraná. Engenharia Agrícola, 38 (3), 395–402. doi:10.1590/1809-4430-ENG.AGRIC.V38N3P395-402/2018
   Web of Science ®Google Scholar
• CONAB: Companhia Nacional de Abastecimento (Brazil), 2016. Acompanhamento da Safra Brasileira: grãos. Brasília: CONAB. 3 (12), 1–184. Available from: <http://www.conab.gov.br>. Acessed in: 2021
   Google Scholar
• CONAB: Companhia Nacional de Abastecimento (Brazil), 2020. Acompanhamento da Safra Brasileira: grãos. Brasília: CONAB. 7 (5), 1–113. Available from: <http://www.conab.gov.br>
   Google Scholar
• Cressie, N.A.C., 2015. Statistics for spatial data. Canada: Wiley.
   Google Scholar
• Cressie, N. and Wikle, C.K., 1998. The variance-based cross-variogram: you can add apples and oranges1. Mathematical Geology, 30 (7), 789–799. doi:10.1023/A:1021770324434
   Google Scholar
• De Bastiani, F., et al., 2015. Global influence diagnostics in gaussian spatial linear model with multiple repetitions. Procedia Environmental Sciences, 26, 74–77. doi:10.1016/J.PROENV.2015.05.002
   Google Scholar
• de Carvalho, J.R.P. and Assad, E.D., 2002. Comparação de interpoladores espaciais univariados para precipitação pluvial anual no Estado de São Paulo. - Portal Embrapa. EMBRAPA - EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA. Available from: https://www.embrapa.br/busca-de-publicacoes/-/publicacao/8635/comparacao-de-interpoladores-espaciais-univariados-para-precipitacao-pluvial-anual-no-estado-de-sao-paulo.
   Google Scholar
• de Carvalho, J.R.P.D. and Assad, E.D., 2005. Análise espacial da precipitação pluviométrica no Estado de São Paulo: comparação de métodos de interpolação. Engenharia Agrícola, 25 (2), 377–384. doi:10.1590/S0100-69162005000200011
   Google Scholar
• DERAL - Departamento de Economia Rural, 2022. Data from: Levantamento da Produção Agropecuária. Secretaria Da Agricultura e Do Abastecimento - SEAB [dataset]. Available from: https://www.agricultura.pr.gov.br/deral/ProducaoAnual Acessed in: 2022.
   Google Scholar
• Diggle, P.J. and Ribeiro, J.R., 2007. Model-based geostatistics. New York: Springer.
   Google Scholar
• ECMWF – European Centre for Medium-Range Weather Forecasts, 2017. Data from: ERA interim, daily [dataset]. ERA5. Available from: <https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/>. Access in: 2017.
   Google Scholar
• Faraco, M.A., et al., 2008. Seleção de modelos de variabilidade espacial para elaboração de mapas temáticos de atributos físicos do solo e produtividade da soja. Revista Brasileira de Ciência Do Solo, 32 (2), 463–476. doi:10.1590/S0100-06832008000200001
   Google Scholar
• Gamero, P., et al., 2020. Variabilidade espacial da precipitação no cultivo de milho segunda safra no Paraná utilizando o modelo wave. Irriga, 25 (3), 521–536. doi:10.15809/IRRIGA.2020V25N3P521-536
   Google Scholar
• García-Pérez, A., 2021. New robust cross-variogram estimators and approximations of their distributions based on saddlepoint techniques. Mathematics, 9 (7), 762. doi:10.3390/MATH9070762
   Web of Science ®Google Scholar
• Gradshteyn, I.S. and Ryzhik, I.M., 2015. Table of contents for table of integrals, series, and products. 8th. D. Zwillinger and V. Moll, Eds. Amsterdam: Academic Press.
   Google Scholar
• Gräler, B., Pebesma, E., and Heuvelink, G., 2016. Spatio-temporal interpolation using gstat. The R Journal, 8 (1), 204–218. doi:10.32614/RJ-2016-014
   Google Scholar
• IBGE - Instituto Brasileiro de Geografia e Estatística (Brazil), 2018. Território e Ambiente: Área da unidade territorial [online]. Available from: <https://cidades.ibge.gov.br/brasil/pr/panorama>. Access in: 2018.
   Google Scholar
• Ingdal, M., Johnsen, R., and Harrington, D.A., 2019. The Akaike information criterion in weighted regression of immittance data. Electrochimica Acta, 317, 648–653. doi:10.1016/J.ELECTACTA.2019.06.030
   Web of Science ®Google Scholar
• Katsalirou, E., et al., 2010. Spatial structure of microbial biomass and activity in prairie soil ecosystems. European Journal of Soil Biology, 46 (3–4), 181–189. doi:10.1016/J.EJSOBI.2010.04.005
   Web of Science ®Google Scholar
• Kestring‎, F.B.F., et al., 2015. Comparação de mapas temáticos de diferentes grades amostrais para a produtividade da soja. Engenharia Agrícola, 35 (4), 733–743. doi:10.1590/1809-4430-ENG.AGRIC.V35N4P733-743/2015
   Google Scholar
• MAPA – Ministério da Agricultura, Pecuária e Abastecimento (Brazil). Programa Nacional de Zoneamento Agrícola de Risco Climático [online]. Available from: https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/programa-nacional-de-zoneamento-agricola-de-risco-climatico. Access in: 2022.
   Google Scholar
• Matheron, G., 1963. Principles of geostatistics. Economic Geology, 58 (8), 1246–1266. doi:10.2113/gsecongeo.58.8.1246
   Google Scholar
• Matheron, G., 1965. Regionalized variables and their estimation. Paris: Masson et cie.
   Google Scholar
• Molin, J.P., et al., 2007. Variação espacial na produtividade de milho safrinha devido aos macronutrientes e à população de plantas. Revista Brasileira de Milho e Sorgo, 6 (3), 309–324. doi:10.18512/1980-6477/rbms.v6n3p309-324
   Google Scholar
• Montanari, R., et al., 2015. Variabilidade espacial da produtividade de sorgo e de atributos do solo na região do ecótono cerrado-pantanal, MS. Revista Brasileira de Ciência Do Solo, 39 (2), 385–396. doi:10.1590/01000683RBCS20140215
   Google Scholar
• Oldoni, H. and Bassoi, L.H., 2016. Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region. Pesquisa Agropecuária Brasileira, 51 (9), 1283–1294. doi:10.1590/S0100-204X2016000900028
   Web of Science ®Google Scholar
• Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30 (7), 683–691. doi:10.1016/J.CAGEO.2004.03.012
   Web of Science ®Google Scholar
• Profillidis, V.A. and Botzoris, G.N., 2019. Trend projection and time series methods. Modeling of Transport Demand, 225–270. doi:10.1016/B978-0-12-811513-8.00006-6
   Google Scholar
• R Development Core Team, 2021. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Resende, N.C., et al., 2019. Impacts of regional climate change on the runoff and root water uptake in corn crops in Parana, Brazil. Agricultural Water Management, 221, 556–565. doi:10.1016/J.AGWAT.2019.05.018
   Web of Science ®Google Scholar
• Santos, R.N., et al., 2019. Mapping of winter crops and second-crop corn in the Paraná State-Brazil, using multitemporal images from MODIS sensor. Journal of Agricultural Science, 11 (2), 477. doi:10.5539/JAS.V11N2P477
   Google Scholar
• Schwarz, G., 1978. Estimating the dimension of a model. Annals of statistics, 6 (2), 461–464. doi:10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Seffrin, R., Araújo, E.C., and Bazzi, C.L., 2018. Regression models for prediction of corn yield in the state of Paraná (Brazil) from 2012 to 2014. Acta Scientiarum. Agronomy, 40 (1), 36494. doi:10.4025/ACTASCIAGRON.V40I1.36494
   Google Scholar
• SIDRA – Sistema Ibge de Recuperação Automática (Brazil). Levantamento Sistemático da Produção Agrícola [online]. Available from: <https://sidra.ibge.gov.br/tabela/6588>. Access in: 2022.
   Google Scholar
• Uribe-Opazo, M.A., et al., 2021. Influence diagnostics on a reparameterized t-Student spatial linear model. Spatial Statistics, 41, 100481. doi:10.1016/J.SPASTA.2020.100481
   Web of Science ®Google Scholar
• Uribe-Opazo, M.A., Borssoi, J.A., and Galea, M., 2012. Influence diagnostics in Gaussian spatial linear models. Journal of Applied Statistics, 39 (3), 615–630. doi:10.1080/02664763.2011.607802
   Web of Science ®Google Scholar
• Varouchakis, E.A., 2019. Geostatistics: mathematical and statistical basis. Spatiotemporal Analysis of Extreme Hydrological Events, 1–38. doi:10.1016/B978-0-12-811689-0.00001-X
   Google Scholar
• Vauclin, M., et al., 1983. The use of cokriging with limited field soil observations. Soil Science Society of America Journal, 47 (2), 175–184. doi:10.2136/SSSAJ1983.03615995004700020001X
   Web of Science ®Google Scholar
• Xu, J., et al., 2020. Probabilistic estimation of cross-variogram based on Bayesian inference. Engineering Geology, 277, 105813. doi:10.1016/J.ENGGEO.2020.105813
   Web of Science ®Google Scholar