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

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

Medidas de comparação de mapas gerados por métodos geoestatísticos

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps

COMPARISON MEASURES OF MAPS GENERATED BY GEOSTATISTICAL METHODS

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.CNPqCNPqCAPESCAPESFundacao AraucariaFundacao Araucari

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

RELATIONSHIP BETWEEN SAMPLE DESIGN AND GEOMETRIC ANISOTROPY IN THE PREPARATION OF THEMATIC MAPS OF CHEMICAL SOIL ATTRIBUTES

<div><p>ABSTRACT Spatial variability depends on the sampling configuration and characteristics associated with the georeferenced phenomenon, such as geometric anisotropy. This study aimed to determine the influence of the sampling design on parameter estimation in an anisotropic geostatistical model and the spatial estimation of a georeferenced variable at unsampled locations. Datasets were simulated with geometric anisotropy, considering five values for the anisotropic ratio (1, 2, 3, 4, 5), and three sampling designs: lattice, random and lattice plus close pairs. The simulation results were used as a reference to select anisotropic models to describe the spatial dependence structure in chemical soil properties. For each dataset (with either simulated or chemical soil properties), the values of the georeferenced variables at unsampled locations were estimated by kriging, considering estimated isotropic and anisotropic geostatistical models. The choice of the sampling design influenced the spatial estimation of the georeferenced variable and the quality of the estimation of the geostatistical anisotropic model. The incorporation of geometric anisotropy in the spatial estimation of simulated data sets and soil chemical properties produced differences in the spatial estimation and improved the level of detail of subregions in thematic maps.</p></div