Spatial variability of soil CO2 emission in a sugarcane area characterized by secondary information

Soil CO2 emission (FCO2) is governed by the inherent properties of the soil, such as bulk density (BD). Mapping of FCO2 allows the evaluation and identification of areas with different accumulation potential of carbon. However, FCO2 mapping over larger areas is not feasible due to the period required for evaluation. This study aimed to assess the quality of FCO2 spatial estimates using values of BD as secondary information. FCO2 and BD were evaluated on a regular sampling grid of 60 m × 60 m comprising 141 points, which was established on a sugarcane area. Four scenarios were defined according to the proportion of the number of sampling points of FCO2 to those of BD. For these scenarios, 67 (F67), 87 (F87), 107 (F107) and 127 (F127) FCO2 sampling points were used in addition to 127 BD sampling points used as supplementary information. The use of additional information from the BD provided an increase in the accuracy of the estimates only in the F107, F67 and F87 scenarios, respectively. The F87 scenario, with the approximate ratio between the FCO2 and BD of 1.00:1.50, presented the best relative improvement in the quality of estimates, thereby indicating that the BD should be sampled at a density 1.5 time greater than that applied for the FCO2. This procedure avoided problems related to the high temporal variability associated with FCO2, which enabled the mapping of this variable to be elaborated in large areas.

Geochemical characterization of heavy metal contaminated area using multivariate factorial kriging

This paper describes a geostatistical method, known as factorial kriging analysis, which is well suited for analyzing multivariate spatial information. The method involves multivariate variogram modeling, principal component analysis, and cokriging. It uses several separate correlation structures, each corresponding to a specific spatial scale, and yields a set of regionalized factors summarizing the main features of the data for each spatial scale. This method is applied to an area of high manganese-ore mining activity in Amapa State, North Brazil. Two scales of spatial variation (0.33 and 2.0 km) are identified and interpreted. The results indicate that, for the short-range structure, manganese, arsenic, iron, and cadmium are associated with human activities due to the mining work, while for the long-range structure, the high aluminum, selenium, copper, and lead concentrations, seem to be related to the natural environment. At each scale, the correlation structure is analyzed, and regionalized factors are estimated by cokriging and then mapped.

Variabilidade espacial do fósforo e das frações granulométricas de um Latossolo Vermelho Amarelo

This study was conducted to study the spatial variability of phosphorus, estimating it through cokriging taking as covariables the size fractions of soil. The study was conducted at the experimental farm INCAPER-ES. The soil was sampled in the canopy projection of culture and depth of 0-0.20 meters in an irregular mesh with 109 points. The data were initially submitted to a descriptive analysis and correlation. Through geostatistics was made the adjustment of the variograms. The P showed significant correlation with the sand and clay fractions indicating that areas with higher concentrations of clay have lower availability of this nutrient. Both fractions have equal performance as co-variable in the estimate of the levels of P in the soil.

Correlação espacial entre a altitude e as precipitaç ões pluviométricas no estado de São Paulo

The rain occurrence is one of the most important phenomena to determinate the climate and, as most of the other climatic phenomena, shows a continuous spatial variability that can be detected through special geostatistical methods. Besides the great influence of the topographic relief on the specific climate of each region, it is normal to expect spatial correlations of this variable with the precipitations; the determinism of this correlation may help to elaborate more precise conclusions involving these phenomena. In this paper, the spatial variability of altitude and of the pluviometric precipitations was rigorously analyzed, besides the existing correlation between these variables. It was concluded that these variables show strong spatial dependence and they are directly correlated. The mapping of the occurrence of both the phenomena was done by geostatistical methods pased on the infomiation concerning the spatial variability of each one.

Estudo comparativo de métodos geoestatísticos aplicados em agricultura de precisão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desempenho das redes neurais artificiais na estimativa das variáveis físicas e químicas do solo

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Correlações de Pearson e geoestatísticas entre a produtividade de cana-de-açúcar, estabelecida em dois métodos de colheita, e atributos físico-químicos do solo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Correlação da produtividade vegetal em função de atributos físico-químicos do solo na integração lavoura-pecuária

Pós-graduação em Agronomia - FEIS

Geoestatística multivariada na delimitação de zonas de manejo na cultura de cana-de-açúcar

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Eficiência da cokrigagem na estimativa da produtividade do café conilon

The objective of this research was to estimate the productivity (PRODUT) in sc/ha of coffee conilon through the technique of cokriging, using as covariate the production of humid coffee (PROD) in kg and compare the results with estimates obtained by kriging ordinary. The study was conducted in a commercial area of conilon coffee, Coffea canephora Pierre var. conilon, located in São Mateus Municipality, Espirito Santo State. For the field work was sampled the humid coffee production in a sampling grid irregular of 18.5 ha, 87 sampling points in the total. We also determined the production of dry coffee beans and coffee benefited 12% moisture, to obtain the PRODUT variable. After exploratory data analysis, which showed the correlation between variables in the order of 0.899, was performed variogram analysis. Were adjusted theoretical variograms to PROD and PRODUT and cross variogram between two variables. Finally we estimated the value of productivity, both by ordinary kriging as per cokriging. The validation of the estimation by cokriging not shows, however, significant gains in relation to validation by ordinary kriging.