Comparison of inference methods for estimating semivariogram model parameters and their uncertainty: The case of small data sets

Pardo‐Igúzquiza, Eulogio; Dowd, P. A.

doi:10.1016/j.cageo.2012.06.002

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…Contudo, adotando-se métodos analíticos apropriados para entendimento do padrão espacial das variáveis que influenciam a produtividade, como a Geoestatística, que quantifica a variabilidade espacial e permite uma interpolação de qualidade, é possível predizer com maior confiabilidade os riscos envolvidos (DIGGLE E RIBEIRO JUNIOR, 2007;PARDO-IGÚZQUIZA E DOWD, 2013;KESTRING et al, 2015), visto que permite o entendimento da variabilidade espacial da precipitação no tempo (MOTA et al, 2008;BENOIT E MARIETHOZ, 2017;SANTOS et al, 2017). Dentre os modelos geoestatísticos existentes para representação espacial dos dados, o modelo wave, tem comportamento não monotônico e decrescente com o aumento da distância (MOTA et al, 2008), padrão conhecido como "hole effect", em que valores elevados podem sistematicamente estarem cercados por valores baixos, e vice versa, o que é uma caraterística de dados de precipitação (CHILÈS E DELFINER, 2012).…”

Section: Introductionunclassified

Variabilidade Espacial Da Precipitação No Cultivo De Milho Segunda Safra No Paraná Utilizando O Modelo Wave

Gamero

Uribe-Opazo

Bastiani

et al. 2020

R_I

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VARIABILIDADE ESPACIAL DA PRECIPITAÇÃO NO CULTIVO DE MILHO SEGUNDA SAFRA NO PARANÁ UTILIZANDO O MODELO WAVE PAULO GAMERO1; MIGUEL ANGEL URIBE-OPAZO2; FERNANDA DE BASTIANI3; JERRY ADRIANI JOHANN2 E LUCIANA PAGLIOSA CARVALHO GUEDES2 1 Doutorando no Programa de Pós-Graduação em Engenharia Agrícola – PGEAGRI, Centro de Ciências Exatas e Tecnológicas, Universidade Estadual do Oeste do Paraná - UNIOESTE, Rua Universitária, n° 2069, Jardim Universitário, CEP: 85819-110, Cascavel, PR, Brasil, e-mail: paulogamero94@gmail.com 2 Professor e Pesquisador do PGEAGRI, Centro de Ciências Exatas e Tecnológicas, Universidade Estadual do Oeste do Paraná - UNIOESTE, Rua Universitária, n° 2069, Jardim Universitário, CEP: 85819-110, Cascavel, PR, Brasil, e-mails: miguel.opazo@unioeste.br; jerry.johann@hotmail.com; luciana.guedes@unioeste.br 3 Professora e Pesquisadora do Programa de Pós-Graduação em Estatística – PPGE, Departamento de Estatística, Universidade Federal de Pernambuco - UFPE, Av. Prof. Moraes Rego, n° 1235, Cidade Universitária, CEP: 50670-901, Recife, PE, Brasil, e-mail: fernandadebastiani@gmail.com 1 RESUMO Compreender o padrão espaço-temporal da precipitação é fundamental para a agricultura, pois afeta a produtividade. O Paraná um dos maiores produtores de milho do país, realizando o cultivo em duas épocas (1ª e 2ª safra). Sendo assim, este trabalho avaliou o padrão espacial da precipitação, no período do cultivo de 2ª safra do milho, nos anos de 1996 a 2015. Utilizaram-se dados de estações pluviométricas acumulados decendialmente da Agência Nacional das Águas (ANA), e de estações virtuais do Europe Center for Medium-Range Weather Forecasts (ECMWF). O padrão espacial da precipitação no tempo, foi avaliado com o modelo geoestatístico wave. Na estimação de parâmetros usou-se o método de máxima verossimilhança e os mapas gerados por krigagem. A validação cruzada indicou bons resultados para o modelo wave quando comparado aos modelos exponencial, Gaussiano, Mátern k=1 e k=1,5. Em todos os anos analisados houve dependência espacial, com frequência de 0 a 100 mm no 3° decêndio de maio na maioria dos anos analisados. O período médio ideal para a semeadura o milho 2ª safra no Paraná é o 1° decêndio de março, visto que a cultura atingirá o máximo desenvolvimento vegetativo no 3° decêndio de maio, com uma precipitação ideal para o cultivo. Palavras-chave: Dependência Espacial, Geoestatística, Krigagem. GAMERO, P.; URIBE-OPAZO, M. A.; DE BASTIANI, F.; JOHANN, J. A.; GUEDES, L. P. C. SPATIAL VARIABILITY OF PRECIPITATIONIN SECOND CROP CORN CULTIVATION IN THE STATE OF PARANÁ USING THE WAVE MODEL 2 ABSTRACT Understanding the spatio-temporal behavior of rainfall is important for agriculture, because it affects productivity. Paraná is one of the largest corn producers in the country, and cultivates corn in two seasons (1st and 2nd crop). Thus, this work evaluated rainfall spatial pattern spatial during the 2nd corn crop period of cultivation in the years 1996 to 2015. Accumulated ten-year data from the National Water Agency (ANA) rainfall stations, and Europe Center for Medium-Range Weather Forecasts (ECMWF) virtual stations were used. The spatial behavior of rainfall over time was analyzed using the geostatistical wave model. In the estimate parameters the maximum likelihood method was used and maps were generated by kriging. Cross-validation indicated good results for the wave model compared to those of the exponential, Gaussian, Mátern k = 1, k = 1.5 models. For all years analyzed there was spatial dependence, with a frequency of 0 to 100 mm in the 3rd ten-day period of May in most of the years analyzed. The ideal average period for sowing the 2nd crop corn in Paraná is the 1st ten-day period in March, when the crop will reach its vegetative peak in the 3rd ten-day period in May, with space and time of ideal rainfall for cultivation. Keywords: Spatial Dependence, Geostatistic, Kriging.

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Section: Introductionunclassified

Variabilidade Espacial Da Precipitação No Cultivo De Milho Segunda Safra No Paraná Utilizando O Modelo Wave

Gamero

Uribe-Opazo

Bastiani

et al. 2020

R_I

View full text Add to dashboard Cite

show abstract

“…To achieve an optimal interpolation result, it is necessary to fit a certain mathematical model to the variogram, represented by the highest possible coefficient of determination between the model and the variogram [58]. Each mathematical model is uniquely determined by nugget, sill, and range parameters.…”

Section: Geostatistical Spatial Interpolation Methodsmentioning

confidence: 99%

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

2022

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The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.

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“…A large number of applications of geostatistics on groundwater geophysics include the application by Ahmed et al (2007);Yeh et al (2002); Liu (2000, 2004), and Arora, 2013. Recently Pardo-Iguzquiza andDowd (2013) have shown the importance of variogram estimation and modeling for the case of sparse datasets. These workers have adopted the integrative approach for monitoring water movement in the vadose.…”

Section: Variographic Analysismentioning

confidence: 99%

Non-intrusive hydro-geophysical characterization of the unsaturated zone of South India-A case study

Arora¹,

Boisson²,

Ahmed³

2016

Journal of African Earth Sciences

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Comparison of inference methods for estimating semivariogram model parameters and their uncertainty: The case of small data sets

Cited by 17 publications

References 37 publications

Variabilidade Espacial Da Precipitação No Cultivo De Milho Segunda Safra No Paraná Utilizando O Modelo Wave

Variabilidade Espacial Da Precipitação No Cultivo De Milho Segunda Safra No Paraná Utilizando O Modelo Wave

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

Non-intrusive hydro-geophysical characterization of the unsaturated zone of South India-A case study

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