Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

Spatial variability of leaf nutrient contents in a drip irrigated citrus orchard

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.

Spatial variability of air dry bulb temperature and black globe humidity index in a broiler house during the heating phase

The air dry-bulb temperature (t db),as well as the black globe humidity index (BGHI), exert great influence on the development of broiler chickens during their heating phase. Therefore, the aim of this study was to analyze the structure and the magnitude of the t db and BGHI spatial variability, using geostatistics tools such as semivariogram analysis and also producing kriging maps. The experiment was conducted in the west mesoregion of the states of Minas Gerais in 2010, in a commercial broiler house with heating system consisting of two furnaces that heat the air indirectly, in the firsts 14 days of the birds' life. The data were registered at intervals of five minutes in the period from 8 a.m. to 10 a.m. The variables were evaluated by variograms fitted by residual maximum likelihood (REML) testing the Spherical and Exponential models. Kriging maps were generated based on the best model used to fit the variogram. It was possible to characterize the variability of the t db and BGHI, which allowed observing the spatial dependence by using geostatistics techniques. In addition, the use of geostatistics and distribution maps made possible to identify problems in the heating system in regions inside the broiler house that may harm the development of chicks.

Spatial variability of mechanical resistance to penetration evaluated with mobile unit on haplic vertisol cultivated with mango

Since the advent of mechanized farming and intensive use of agricultural machinery and implements on the properties, the soil began to receive greater load of machinery traffic, which can cause increased soil compaction. The aim of this study was to evaluate the spatial variability of soil mechanical resistance to penetration (RP) in the layers of 0.00-0.10, 0.10-0.20, 0.20-0.30 and 0.30-0.40m, using geostatistics in an area cultivated with mango in Haplic Vertisol of the northeastern semi-arid, with mobile unit equipped with electronic penetrometer. The RP data was collected in 56 points from an area of 3 ha, and random soil samples were collected to determine the soil moisture and texture. For RP data analysis we used descriptive statistics and geostatistics. The soil mechanical resistance to penetration presented increased variability, with adjustment of the spherical and exponential semivariograms in the layers. We found that 42% of the area in the layer of 0.10-0.20m showed RP values above 2.70 MPa. Maximum values of RP were found in the layer of 0.19-0.27m, predominantly in 56% of the area.

Outlining precision boundaries among areas with different variability standards using magnetic susceptibility and geomorphic surfaces

There is an increasing demand for detailed maps that represent in a simplified way the knowledge of the variability of a particular area or region maps. The objective was to outline precision boundaries among areas with different accuracy variability standards using magnetic susceptibility and geomorphic surfaces. The study was conducted in an area of 110 ha, which identified three compartment landscapes based on the geomorphic surfaces model. To determinate pH, organic matter, phosphorus, potassium and magnesium, the total sand and clay, 514 soil samples were collected at depths of 0-0.20 m and 0.60-0.80 m. The sum of base, cationic exchange capacity and base saturation were calculated and the magnetic susceptibility was evaluated in the laboratory using a system based on a balance of analytical precision method. Geomorphic surfaces identification allowed setting specific management areas (locations with maximum homogeneity of soil attributes). The map of spatial variability of magnetic susceptibility can be used to validate the precise boundaries among geomorphic surfaces identified in the field and infer the variability of clay content and soil base saturation.

Spatial variability of apparent electrical conductivity and soil properties in a coffee production field

Precision agriculture based on the physical and chemical properties of soil requires dense sampling to determine the spatial variability of these properties. This dense sampling is often expensive and time-consuming. One technique used to reduce sample numbers involves defining management zones based on information collected in the field. Some researchers have demonstrated the importance of soil electrical variables in defining management zones. The objective of this study was to evaluate the relationship between the spatial variability of the apparent electrical conductivity and the soil properties in the coffee production of mountain regions. Spatial variability maps were generated using a geostatistical method. Based on the spatial variability results, a correlation analysis, using bivariate Moran's index, was done to evaluate the relationship between the apparent electrical conductivity and soil properties. The maps of potassium (K) and remaining phosphorus (P-rem) were the closest to the spatial variability pattern of the apparent electrical conductivity.

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Intrapartum hypoxia and power spectral analysis of fetal heart rate variability

Reliable detection of intrapartum fetal acidosis is crucial for preventing morbidity. Hypoxia-related changes of fetal heart rate variability (FHRV) are controlled by the autonomic nervous system. Subtle changes in FHRV that cannot be identified by inspection can be detected and quantified by power spectral analysis. Sympathetic activity relates to low-frequency FHRV and parasympathetic activity to both low- and high-frequency FHRV. The aim was to study whether intra partum fetal acidosis can be detected by analyzing spectral powers of FHRV, and whether spectral powers associate with hypoxia-induced changes in the fetal electrocardiogram and with the pH of fetal blood samples taken intrapartum. The FHRV of 817 R-R interval recordings, collected as a part of European multicenter studies, were analyzed. Acidosis was defined as cord pH ≤ 7.05 or scalp pH ≤ 7.20, and metabolic acidosis as cord pH ≤ 7.05 and base deficit ≥ 12 mmol/l. Intrapartum hypoxia increased the spectral powers of FHRV. As fetal acidosis deepened, FHRV decreased: fetuses with significant birth acidosis had, after an initial increase, a drop in spectral powers near delivery, suggesting a breakdown of fetal compensation. Furthermore, a change in excess of 30% of the low-to-high frequency ratio of FHRV was associated with fetal metabolic acidosis. The results suggest that a decrease in the spectral powers of FHRV signals concern for fetal wellbeing. A single measure alone cannot be used to reveal fetal hypoxia since the spectral powers vary widely intra-individually. With technical developments, continuous assessment of intra-individual changes in spectral powers of FHRV might aid in the detection of fetal compromise due to hypoxia.

Variability in the fecal egg count and the parasitic burden of hair sheep after grazing in nematode infected paddocks

This study aimed to evaluate the variability in the fecal egg count (FEC) and the parasitic burden of naive hair sheep after grazing in nematode infected paddocks. The research was carried out in Tabasco, Mexico, during two periods (August and December). In each period 32 lambs were grazed for one month on African star grass (Cynodon plectostachyus) contaminated with gastrointestinal parasitic nematodes. FEC, packed cell volume (PCV) and body weight (BW) were recorded. Gastrointestinal worms were recovered at necropsy. Data were analyzed with the MIXED procedure of SAS using a model of repeated measurements over time. A higher number of Haemonchus contortus worms was found in December (2814±838) than in August (1166±305). The opposite occurred with Cooperia curticei (2167±393 and 3638±441, respectively). The FEC and correlation coefficient in respect to the worm burden were higher in December (6516 ± 1599, r=0.83, respectively) than in August (4364±771, r=0.44, respectively). A high variability in resistance-susceptibility to gastrointestinal nematodes (GIN) occurred in Katahdin × Pelibuey lambs after grazing.

Estimating Attractor Dimension on the Nonlinear Pendulum Time Series

Chaotic dynamical systems exhibit trajectories in their phase space that converges to a strange attractor. The strangeness of the chaotic attractor is associated with its dimension in which instance it is described by a noninteger dimension. This contribution presents an overview of the main definitions of dimension discussing their evaluation from time series employing the correlation and the generalized dimension. The investigation is applied to the nonlinear pendulum where signals are generated by numerical integration of the mathematical model, selecting a single variable of the system as a time series. In order to simulate experimental data sets, a random noise is introduced in the time series. State space reconstruction and the determination of attractor dimensions are carried out regarding periodic and chaotic signals. Results obtained from time series analyses are compared with a reference value obtained from the analysis of mathematical model, estimating noise sensitivity. This procedure allows one to identify the best techniques to be applied in the analysis of experimental data.

Genetic variability among Commelina weed species from the states of Paraná and São Paulo, Brazil

This work aims to carry out a comparative analysis using RAPD molecular markers in four Commelina weed species from the state of Paraná and C. benghalensis populations from the states of Paraná and São Paulo, Brazil. The genomic plant DNA sample was extracted from the leaves, separated, randomly fragmented and amplified by PCR. Random amplified polymorphic DNA fragments (RAPD markers) were analyzed by using POPGENE statistical program. Eighty-five primer sequences were tested but only three were suitable as molecular markers producing 37 DNA polymorphic fragments for comparisons among four Commelina species and 22 polymorphic fragments for comparisons among C. benghalensis populations. The results showed that there were inter-specific and intra-specific genetic variabilities among Commelina plant genera. Genetic diversity analysis between species indicated four mono-specific clusters and it was suggested to keep C. villosa as one species. Regarding the intra-specific genetic variability of C. benghalensis alone, three groups were verified, although there were 13 populations from two geographical areas. However, these clusters do not correspond to the distinct characteristics verified.

Phytosociological variability of weeds in soybean field

Understanding spatial distribution of weeds in the crop enables to perform localized herbicide applications, increasing the technical and economic efficiency of operations and reducing environmental impacts. This work aimed to characterize the spatial and phytosociological variability of weeds occurring in soybean commercial field. It was conducted in an agricultural area located at the municipality of Boa Vista das Missões - RS, during the 2010/2011 harvest season. The area, that had been managed under no-tillage with soybean monoculture (summer) for five years, was divided in regular squares of 50 x 50 m (0.25 ha), totalizing 356 points. For species identification, 0.5 x 0.5 m sample squares were used. During the survey, 1,739 individuals were identified, distributed in 19 species of 13 families. The weed species Cardiospermum halicacabum, Digitaria horizontalis, Urochloa plantaginea and Raphanus raphanistrum showed the highest population variation in the area; however, only C. halicacabum, U. plantaginea and R. raphanistrum stood out based on the Importance Index Value (IVI). Localized management strategies considering the spatial variability of weed species placed in the Magnoliopsidas and Liliopsidas group show a high potential for use in soybean crop. The results show that the sampling method through regular grid was capable of characterizing the occurrence, population density and spatial variability of weed species in soybean crop.