Abstract The habitat loss is the main threat for many parrot species worldwide. However, the disturbed environments can influence the occurrence of the species positively or negatively, depending on its structure and potential use as an alternative environment. Therefore, this work aims to determine the relationship between land use types and the occurrence of the threatened Red-browed Amazon, identifying the land use types related to species’ occurrence and the direction of these relationships. Seven land use types were significantly associated with the species’ occurrence: Primary/Secondary Advanced/Medium Natural Forest, Secondary Natural Vegetation, Pasture, Outcrop/Bare Soil, Mangroves, Rivers and Urban Areas. We found that some land use types that are structurally different from the original environments (Pasture, Outcrop/Bare Soil and Urban Areas) positively affected the occurrence of the species. The relationship between intensity of occurrence and highly anthropized land use types, suggests a plasticity of the species in habitat use that may be contributing to the maintenance of Red-browed Amazon populations in highly disturbed habitats. Therefore, we proposed that the management of disturbed areas, along with usual conservation strategies (e.g., conservation of forest remnants, restoration of degraded areas), could contribute to Red-browed Amazon conservation.

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.

RESUMO Neste trabalho, teve-se o objetivo de estudar e comparar mapas temáticos da produtividade da soja para diferentes grades amostrais, utilizando métodos geoestatísticos (função semivariância e krigagem). A análise foi realizada com dados da produtividade de soja em t ha-1 numa área comercial com grades regulares e distâncias entre pontos de 25x25 m, 50x50 m, 75x75 m e 100x100 m, com 549; 188; 66 e 44 pontos amostrais, respectivamente, e dados obtidos pelo monitor de colheita. Também foram gerados esquemas amostrais otimizados, com o algoritmo chamado Têmpera Simulada, usando a maximização da medida de acurácia exatidão global, como critério de otimização. Foi verificado que fatores como o tamanho da amostra e a densidade amostral influenciaram na descrição da distribuição espacial da produtividade da soja feita por meio dos mapas temáticos. Quando se aumentou o tamanho amostral, houve aumento da eficiência dos mapas temáticos quanto à descrição da variabilidade espacial da produtividade da soja (maiores valores dos índices de acurácia e os menores valores para a soma quadrada do erro de estimação). Além disso, os mapas mais acurados foram obtidos considerando as configurações amostrais otimizadas com 188 e 549 pontos amostrais.

ABSTRACT This study aimed to compare thematic maps of soybean yield for different sampling grids, using geostatistical methods (semivariance function and kriging). The analysis was performed with soybean yield data in t ha-1 in a commercial area with regular grids with distances between points of 25x25 m, 50x50 m, 75x75 m, 100x100 m, with 549, 188, 66 and 44 sampling points respectively; and data obtained by yield monitors. Optimized sampling schemes were also generated with the algorithm called Simulated Annealing, using maximization of the overall accuracy measure as a criterion for optimization. The results showed that sample size and sample density influenced the description of the spatial distribution of soybean yield. When the sample size was increased, there was an increased efficiency of thematic maps used to describe the spatial variability of soybean yield (higher values of accuracy indices and lower values for the sum of squared estimation error). In addition, more accurate maps were obtained, especially considering the optimized sample configurations with 188 and 549 sample points.

The spatial variability of structures in regionalized variables are defined with the aid of geostatistical techniques, which facilitate the estimation of values for these variables in unsampled localizations and generate thematic maps to be used in decision making for localized treatments in the area under study. The quality of these maps depends on the trustworthiness of these estimates that can be modified with the choice for the sample design. The objective of this work was to establish an optimal size and shape of the sample designs in order to enhance the efficiency of sampling plans for the prediction of space dependent variables. These designs were obtained with the use of a stochastic search method called Simulated Annealing. This method is based on a sampling grid with a large number of points. Here, it is initially used to consider simulated data sets with distinct spatial dependence structures and is then used to consider real data on soy productivity. The simulated results are used as reference for the achievement of the best sample design with the lowest number of sample points that can efficiently represent the spatial dependence structure of soy productivity in a commercial area harvested by the harvester monitor. The results reported for the simulations and soy productivity data show that the optimization process was efficient in determining sample designs with reduced size, especially when using the Global Accuracy as the measurement to be maximized.

La definición de la estructura de la variabilidad espacial de las variables regionalizadas por medio de técnicas geoestadísticas, permite estimar los valores de estas variables en lugares no incluidos en el muestreo, generando mapas temáticos que serán utilizados en la construcción de sectores agrícolas para aplicación diferenciada de tratamientos del suelo. La calidad de estos mapas depende de las estimativas confiables, que pueden cambiar por la selección la configuración del muestreo. El objetivo de este estudio fue determinar el tamaño de la muestra y la configuración de muestreo óptima, con el fin de maximizar la eficiencia del plano de muestreo, en la predicción de las variables con dependencia espacial. Las configuraciones muestrales optimizadas fueron obtenidas utilizando un método de búsqueda estocástica llamado Recocido simulado, a partir de una cuadrícula de muestreo con un gran número de puntos, inicialmente teniendo en cuenta un conjunto de datos simulados, con diferentes estructuras de dependencias espaciales y posteriormente un conjunto de datos reales de la productividad de la soya. Los resultados obtenidos de la simulación y del conjunto de datos reales de la productividad de la soya mostraron que el proceso de optimización fue eficiente en la determinación de configuración de muestreo óptima con tamaño de muestrea reducido, principalmente usando el índice de Exactitud Global como medida para maximizarla.

O objetivo do presente trabalho foi estudar a variabilidade espacial de macro e micronutrientes foliares. O pomar com plantas de 5 anos de idade, plantado com espaçamento regular de 8 x 7 m, foi manejado sob irrigação localizada. Foram coletadas amostras foliares de cada planta do pomar para serem analisadas em laboratório. Os dados foram analisados por meio do programa computacional R, versão 2.5.1 Copyright (C) 2007, juntamente com o pacote GeoR. Todos os teores de macro e micronutrientes estudados ajustaram-se à distribuição normal e apresentaram dependência espacial. Os modelos de melhor ajuste, com base na verosimilhança, para os macro e micronutrientes, foram esférico e matern. Para os macronutrientes nitrogênio, fósforo, potássio, cálcio, magnésio e enxofre, sugerem-se distâncias mínimas entre amostras de 37; 58; 29; 63; 46 e 15 m, respectivamente, enquanto para os micronutrientes boro, cobre, ferro, manganês e zinco, essas distâncias são de 29; 9; 113; 35 e 14 m, respectivamente.

This study aimed to evaluate the spatial variability of leaf content of macro and micronutrients. The citrus plants orchard with 5 years of age, planted at regular intervals of 8 x 7 m, was managed under drip irrigation. Leaf samples were collected from each plant to be analyzed in the laboratory. Data were analyzed using the software R, version 2.5.1 Copyright (C) 2007, along with geostatistics package GeoR. All contents of macro and micronutrients studied were adjusted to normal distribution and showed spatial dependence.The best-fit models, based on the likelihood, for the macro and micronutrients were the spherical and matern. It is suggest for the macronutrients nitrogen, phosphorus, potassium, calcium, magnesium and sulfur the minimum distances between samples of 37; 58; 29; 63; 46 and 15 m respectively, while for the micronutrients boron, copper, iron, manganese and zinc, the distances suggests are 29; 9; 113; 35 and 14 m, respectively.