Diagnostic techniques of local influence in spatial analysis of soybean yield

A modelagem da estrutura de dependência espacial pela abordagem da geoestatística é fundamental para a definição de parâmetros que definem esta estrutura, e que são utilizados na interpolação de valores em locais não amostrados pela técnica de krigagem. Entretanto, a estimação de parâmetros pode ser muito afetada pela presença de observações atípicas nos dados amostrados. O desenvolvimento deste trabalho teve por objetivo utilizar técnicas de diagnóstico de influência local em modelos espaciais lineares gaussianos, utilizados em geoestatística, para avaliar a sensibilidade dos estimadores de máxima verossimilhança e máxima verossimilhança restrita na presença de dados discrepantes. Estudos com dados experimentais mostraram que tanto a presença de valores atípicos como de valores considerados influentes, pela análise de diagnóstico, pode exercer forte influência nos mapas temáticos, alterando, assim, a estrutura de dependência espacial. As aplicações de técnicas de diagnóstico de influência local devem fazer parte de toda análise geoestatística a fim de garantir que as informações contidas nos mapas temáticos tenham maior qualidade e possam ser utilizadas com maior segurança pelo agricultor.Modeling of spatial dependence structure, concerning geoestatistics approach, is an indispensable tool for fixing parameters that define this structure, applied on interpolation of values in places that are not sampled, by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations on sampled data. Thus, this trial aimed at using diagnostics techniques of local influence in spatial linear Gaussians models, applied at geoestatistics in order to evaluate sensitivity of maximum likelihood estimators and restrict maximum likelihood to small perturbations in these data. So, studies with simulated and experimental data were performed. Those results, obtained from the study of real data, allowed us to conclude that the presence of atypical values among the sampled data can have a strong influence on thematic maps, changing, therefore, the spatial dependence. The application of diagnostics techniques of local influence should be part of any geoestatistic analysis, ensuring that the information from thematic maps has better quality and can be used with greater security by farmers.CNPq, CONAB e Fundação Araucária, pelo apoio financeiro, e à UNIOESTE - Universidade Estadual do Oeste do Paran

Soybean yield modeling using bootstrap methods for small samples

One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters

Statistical methods for identifying anisotropy in the Spodoptera frugiperda spatial distribution

Corn is a very important agricultural product, however, some pests may cause damage to the corn productivity such as Spodoptera frugiperda, which prevents the plant from growing in a regular manner. Since the indiscriminate use of the pesticide may cause an increasing resistance of the insect besides an environmental damage, it is important to estimate the areas and the dominant directions where the insect may propagate. The main aim of this work was to study the spreading of the fall armyworm in a commercial agricultural area in the South of Brazil. For this, we considered a set including the location of each corn plant attacked by the insect. In particular, we assumed that the spatial locations given by the geographic coordinates constitute a spatial point pattern following a stationary Poisson point process. In order to detect the presence of possible dominant directions in the distribution of the fall armyworm infestation we studied the anisotropic features of the data by using some second-order spatial point-pattern analysis techniques such as the K directional test, the wavelet-based test, and the quadrat counting test. All the results showed that spatial distribution of fall armyworm may follow a clustered Poisson point process with the presence of an evident anisotropy mainly due to the shape and the distance between corn plants of the experimental area. These preliminary results could be used for reducing and optimizing the use of pesticides with a consequent decrease of the environmental impact

Autocorrelação espacial dos índices ndvi e gvi derivados de imagens landsat/tm para cultura da soja no oeste paranaense e ano agrícola de 2004/2005

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Paraná. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.Este trabalho apresenta um estudo de estatística espacial de áreas baseado no NDVI (índice de vegetação por diferença normalizada) e no GVI (índice de vegetação verde) da cultura da soja, obtidos de imagens de sensoriamento remoto da região oeste do Paraná. As imagens foram coletadas pelo sensor TM (Thematic Mapper) do satélite Landsat-5, durante a safra de 2004/2005. Os dados foram agrupados em cinco classes de igual amplitude, o que permitiu criar um mapa da evolução temporal da cultura da soja. Foi utilizado o índice I de Moran para estudar a autocorrelação espacial em um nível global e o índice LISA (Local Indicators of Spatial Association) para estudar a autocorrelação espacial em um nível local. Por meio destes índices, foi possível conhecer o perfil da cultura de soja nos municípios da região oeste do Paraná, permitindo identificar épocas diferentes do plantio desta cultura e subsidiar os membros da cadeia produtiva da soja que utilizam dados de produtividade em seus planejamentos.525537Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

A genetic algorithm for resizing and sampling reduction of non-stationary soil chemical attributes optimizing spatial prediction

Aim of study: To evaluate the influence of the parameters of the geostatistical model and the initial sample configuration used in the optimization process; and to propose and evaluate the resizing of a sample configuration, reducing its sample size, for simulated data and for the study of the spatial variability of soil chemical attributes under a non-stationary with drift process from a commercial soybean cultivation area.Area of study: Cascavel, BrazilMaterial and methods: For both, the simulated data and the soil chemical attributes, the Genetic Algorithm was used for sample resizing, maximizing the overall accuracy measure.Main results: The results obtained from the simulated data showed that the practical range did not influence in a relevant way the optimization process. Moreover, the local variations, such as variance or sampling errors (nugget effect), had a direct relationship with the reduction of the sample size, mainly for the smaller nugget effect. For the soil chemical attributes, the Genetic Algorithm was efficient in resizing the sampling configuration, since it generated sampling configurations with 30 to 35 points, corresponding to 29.41% to 34.31% of the initial configuration, respectively. In addition, comparing the optimized and initial configurations, similarities were obtained regarding spatial dependence structure and characterization of spatial variability of soil chemical attributes in the study area.Research highlights: The optimization process showed that it is possible to reduce the sample size, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in future experiments

Gaussian Markov random field spatial models in GAMLSS

This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized Additive-Model for Location, Scale and Shape (GAMLSS) model. This allows modelling of any or all the parameters of the distribution for the response variable using explanatory variables and spatial effects. The response variable distribution is allowed to be a non-exponential family distribution. A new package developed in R to achieve this is presented. We use Gaussian Markov random fields to model the spatial effect in Munich rent data and explore some features and characteristics of the data. The potential of using spatial analysis within GAMLSS is discussed. We argue that the flexibility of parametric distributions, ability to model all the parameters of the distribution and diagnostic tools of GAMLSS provide an ideal environment for modelling spatial features of data

Spectral characteristics of soybean during the vegetative cycle with landsat 5/TM images in the western Paraná, Brazil

The objective of this study was to analyze changes in the spectral behavior of the soybean crop through spectral profiles of the vegetation indexes NDVI and GVI, expressed by different physical values such as apparent bi-directional reflectance factor (BRF), surface BRF, and normalized BRF derived from images of the Landsat 5/TM. A soybean area located in Cascavel, Paraná, was monitored by using five images of Landsat 5/TM during the 2004/2005 harvesting season. The images were submitted to radiometric transformation, atmospheric correction and normalization, determining physical values of apparent BRF, surface BRF and normalized BRF. NDVI and GVI images were generated in order to distinguish the soybean biomass spectral response. The treatments showed different results for apparent, surface and normalized BRF. Through the profiles of average NDVI and GVI, it was possible to monitor the entire soybean cycle, characterizing its development. It was also observed that the data from normalized BRF negatively affected the spectral curve of soybean crop, mainly, during the phase of vegetative growth, in the 12-9-2004 image.O objetivo deste trabalho foi estudar as mudanças no comportamento espectral da cultura da soja, por meio dos perfis espectrais temporais dos índices de vegetação NDVI e GVI, expressos em diferentes valores físicos: fator de reflectância bidirecional (FRB) aparente, de superfície e normalizado derivados de imagens Landsat 5/TM. Foi monitorada área de cultura de soja localizada próxima ao município de Cascavel - PR, utilizando cinco imagens da safra de 2004/2005, sendo realizados nessas imagens os procedimentos de transformação radiométrica, correção atmosférica e normalização, determinando valores físicos dos fatores de reflectância bidirecional aparente, de superfície e normalizado, respectivamente. Com o intuito de caracterizar a resposta espectral da biomassa da soja, geraram-se imagens referentes aos índices de vegetação NDVI e GVI. Como resultado, a cultura mostrou-se diferente para os tratamentos dos fatores de reflectância bidirecional aparente, de superfície e de normalização. Por meio dos perfis médios espectrais do NDVI e GVI, foi possível acompanhar todo o ciclo da cultura da soja, caracterizando o seu desenvolvimento. Observou-se, ainda, que os dados provenientes do fator de reflectância bidirecional normalizado descaracterizaram a curva espectral da cultura da soja, principalmente em meio à fase de crescimento vegetativo, na data de 9-12-2004.32833

Linear regression models to soybean yield estimate in the west region of the state of paraná, brazil, using spectral data

The main objective of this work was to evaluate the linear regression between spectral response and soybean yield in regional scale. In this study were monitored 36 municipalities from the west region of the states of Parana using five images of Landsat 5/TM during 2004/05 season. The spectral response was converted in physical values, apparent and surface reflectances, by radiometric transformation and atmospheric corrections and both used to calculate NDVI and GVI vegetation indices. Those ones were compared by multiple and simple regression with government official yield values (IBGE). Diagnostic processing method to identify influents values or collinearity was applied to the data too. The results showed that the mean surface reflectance value from all images was more correlated with yield than individual dates. Further, the multiple regressions using all dates and both vegetation indices gave better results than simple regression.O trabalho teve o objetivo de avaliar modelos lineares de regressão entre resposta espectral e produtividade em soja, na escala regional. Para isso, foram monitorados 36 municípios do oeste do Paraná, utilizando cinco imagens do satélite Landsat 5/TM da safra de 2004/2005. Foram realizados os procedimentos de transformação radiométrica e correção atmosférica nas imagens, determinando valores físicos das refletâncias aparente e de superfície. Posteriormente, foram calculados os índices de vegetação NDVI e GVI, os quais, por meio de regressões lineares simples e múltiplas, compararam-se com as produtividades oficiais dos municípios, obtidas das estatísticas IBGE. Aplicou-se também uma análise de diagnóstico, para detectar pontos influentes e de colinearidade. Os resultados mostraram que a média dos valores de NDVI e GVI de todas as imagens foi mais bem relacionada com a produtividade do que para cada data separadamente. O uso de regressões múltiplas com os dois índices, em todas as datas, propiciou melhores resultados de relação com a produtividade.50451

Sampling redesign of soil penetration resistance in spatial t-Student models

 Aim of study: To reduce the sample size in an agricultural area of 167.35 hectares, cultivated with soybean, to analyze the spatial dependence of soil penetration resistance (SPR) with outliers.Area of study: Cascavel, BrazilMaterial and methods: The reduction of sample size was made by the univariate effective sample size ( ) methodology, assuming that the t-Student model represents the probability distribution of SPR.Main results: The radius and the intensity of spatial dependence have an inverse relationship with the estimated value of the . For the depths of SPR with spatial dependence, the highest estimated value of the  reduced the sample size by 40%. From the new sample size, the sampling redesign was performed. The accuracy indexes showed differences between the thematic maps with the original and reduced sampling designs. However, the lowest values of the standard error in the parameters of the spatial dependence structure evidenced that the new sampling design was appropriate. Besides, models of semivariance function were efficiently estimated, which allowed identifying the existence of spatial dependence in all depth of SPR.Research highlights: The sample size was reduced by 40%, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in the next mappings in the agricultural area. The spatial t-Student model was able to reduce the influence of outliers in the spatial dependence structure