Pedotransfer functions related to spatial variability of water retention attributes for lowland soils

The estimation of non available soil variables through the knowledge of other related measured variables can be achieved through pedotransfer functions (PTF) mainly saving time and reducing cost. Great differences among soils, however, can yield non desirable results when applying this method. This study discusses the application of developed PTFs by several authors using a variety of soils of different characteristics, to evaluate soil water contents of two Brazilian lowland soils. Comparisons are made between PTF evaluated data and field measured data, using statistical and geostatistical tools, like mean error, root mean square error, semivariogram, cross-validation, and regression coefficient. The eight tested PTFs to evaluate gravimetric soil water contents (Ug) at the tensions of 33 kPa and 1,500 kPa presented a tendency to overestimate Ug 33 kPa and underestimate Ug1,500 kPa. The PTFs were ranked according to their performance and also with respect to their potential in describing the structure of the spatial variability of the set of measured values. Although none of the PTFs have changed the distribution pattern of the data, all resulted in mean and variance statistically different from those observed for all measured values. The PTFs that presented the best predictive values of Ug33 kPa and Ug1,500 kPa were not the same that had the best performance to reproduce the structure of spatial variability of these variables.

Impacts of land leveling on lowland soil physical properties

The practice of land leveling alters the soil surface to create a uniform slope to improve land conditions for the application of all agricultural practices. The aims of this study were to evaluate the impacts of land leveling through the magnitudes, variances and spatial distributions of selected soil physical properties of a lowland area in the State of Rio Grande do Sul, Brazil; the relationships between the magnitude of cuts and/or fills and soil physical properties after the leveling process; and evaluation of the effect of leveling on the spatial distribution of the top of the B horizon in relation to the soil surface. In the 0-0.20 m layer, a 100-point geo-referenced grid covering two taxonomic soil classes was used in assessment of the following soil properties: soil particle density (Pd) and bulk density (Bd); total porosity (Tp), macroporosity (Macro) and microporosity (Micro); available water capacity (AWC); sand, silt, clay, and dispersed clay in water (Disp clay) contents; electrical conductivity (EC); and weighted average diameter of aggregates (WAD). Soil depth to the top of the B horizon was also measured before leveling. The overall effect of leveling on selected soil physical properties was evaluated by paired "t" tests. The effect on the variability of each property was evaluated through the homogeneity of variance test. The thematic maps constructed by kriging or by the inverse of the square of the distances were visually analyzed to evaluate the effect of leveling on the spatial distribution of the properties and of the top of the B horizon in relation to the soil surface. Linear regression models were fitted with the aim of evaluating the relationship between soil properties and the magnitude of cuts and fills. Leveling altered the mean value of several soil properties and the agronomic effect was negative. The mean values of Bd and Disp clay increased and Tp, Macro and Micro, WAD, AWC and EC decreased. Spatial distributions of all soil physical properties changed as a result of leveling and its effect on all soil physical properties occurred in the whole area and not specifically in the cutting or filling areas. In future designs of leveling, we recommend overlaying a cut/fill map on the map of soil depth to the top of the B horizon in order to minimize areas with shallow surface soil after leveling.

Compared performance of penetrometers and effect of soil water content on penetration resistance measurements

Modern agriculture techniques have a great impact on crops and soil quality, especially by the increased machinery traffic and weight. Several devices have been developed for determining soil properties in the field, aimed at managing compacted areas. Penetrometry is a widely used technique; however, there are several types of penetrometers, which have different action modes that can affect the soil resistance measurement. The objective of this study was to compare the functionality of two penetrometry methods (manual and automated mode) in the field identification of compacted, highly mechanized sugarcane areas, considering the influence of soil water volumetric content (θ) on soil penetration resistance (PR). Three sugarcane fields on a Rhodic Eutrudrox were chosen, under a sequence of harvest systems: one manual harvest (1ManH), one mechanized harvest (1MH) and three mechanized harvests (3MH). The different degrees of mechanization were associated to cumulative compaction processes. An electronic penetrometer was used on PR measurements, so that the rod was introduced into the soil by hand (Manual) and by an electromechanical motor (Auto). The θ was measured in the field with a soil moisture sensor. Results showed an effect of θ on PR measurements and that regression models must be used to correct data before comparing harvesting systems. The rod introduction modes resulted in different mean PR values, where the "Manual" overestimated PR compared to the "Auto" mode at low θ.

Macronutrient uptake, accumulation and export by the irrigated 'vitória' pineapple plant

The nutritional state of the pineapple plant has a large effect on plant growth, on fruit production, and fruit quality. The aim of this study was to assess the uptake, accumulation, and export of nutrients by the irrigated 'Vitória' pineapple plant during and at the end of its development. A randomized block statistical design with four replications was used. The treatments were defined by different times of plant collection: at 270, 330, 390, 450, 510, 570, 690, 750, and 810 days after planting (DAP). The collected plants were separated into the following components: leaves, stem, roots, fruit, and slips for determination of fresh and dry matter weight at 65 ºC. After drying, the plant components were ground for characterization of the composition and content of nutrients taken up and exported by the pineapple plant. The results were subjected to analysis of variance, and non-linear regression models were fitted for the significant differences identified by the F test (p<0.01). The leaves and the stem were the plant components that showed the greatest accumulation of nutrients. For production of 72 t ha-1 of fruit, the macronutrient accumulation in the 'Vitória' pineapple exhibited the following decreasing order: K > N > S > Ca > Mg > P, which corresponded to 898, 452, 134, 129, 126, and 107 kg ha-1, respectively, of total accumulation. The export of macronutrients by the pineapple fruit was in the following decreasing order: K > N > S > Ca > P > Mg, which was equivalent to 18, 17, 11, 8, 8, and 5 %, respectively, of the total accumulated by the pineapple. The 'Vitória' pineapple plant exported 78 kg ha-1 of N, 8 kg ha-1 of P, 164 kg ha-1 of K, 14 kg ha-1 of S, 10 kg ha-1 of Ca, and 6 kg ha-1 of Mg by the fruit. The nutrient content exported by the fruits represent important components of nutrient extraction from the soil, which need to be restored, while the nutrients contained in the leaves, stems and roots can be incorporated in the soil within a program of recycling of crop residues.

PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

Dry matter production of Tanzania grass as a function of agrometeorological variables

The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry‑field conditions in São Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree‑days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in São Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree‑days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.

Determinação de açúcar total em café cru por espectroscopia no infravermelho próximo e regressão por mínimos quadrados parciais

In this work a fast method for the determination of the total sugar levels in samples of raw coffee was developed using the near infrared spectroscopy technique and multivariate regression. The sugar levels were initially obtained using gravimety as the reference method. Later on, the regression models were built from the near infrared spectra of the coffee samples. The original spectra were pre-treated according to the Kubelka-Munk transformation and multiplicative signal correction. The proposed analytical method made possible the direct determination of the total sugar levels in the samples with an error lower by 8% with respect to the conventional methodology.

Air pollution and emergency admissions for cardiorespiratory diseases in Lisbon (Portugal)

Daily records of hospital admissions due to cardiorespiratory diseases and levels of PM10, SO2, CO, NO, NO2, and O3 were collected from 1999-2004 to evaluate the relationship between air pollution and morbidity in Lisbon. Generalised additive Poisson regression models were adopted, controlling for temperature, humidity, and both short and long-term seasonality. Significant positive associations, lagged by 1 or 2 days, were found between markers of traffic-related pollution (CO and NO2) and cardiocirculatory diseases in all age groups. Increased childhood emergency admissions for respiratory illness were significantly correlated with the 1-day lagged SO2 levels coming from industrial activities.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

VIS-NIR SPECTROMETRY, SOIL PHOSPHATE EXTRACTION METHODS AND INTERACTIONS OF SOIL ATTRIBUTES

The objective of this study was to evaluate the relationships between the spectra in the Vis-NIR range and the soil P concentrations obtained from the PM and Prem extraction methods as well as the effects of these relationships on the construction of models predicting P concentration in Oxisols. Soil samples' spectra and their PM and Prem extraction solutions were determined for the Vis-NIR region between 400 and 2500 nm. Mineralogy and/or organic matter content act as primary attributes allowing correlation of these soil phosphorus fractions with the spectra, mainly at wavelengths between 450-550, 900-1100 nm, near 1400 nm and between 2200-2300 nm. However, the regression models generated were not suitable for quantitative phosphate analysis. Solubilization of organic matter and reactions during the PM extraction process hindered correlations between the spectra and these P soil fractions. For Prem,, the presence of Ca in the extractant and preferential adsorption by gibbsite and iron oxides, particularly goethite, obscured correlations with the spectra.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Thermal comfort temperature update for broiler chickens up to 21 days of age

Due to changes in genetics and nutrition, as well as in acclimatization of broiler chickens to the Brazilian climate, temperature values currently accepted as optimal may be outdated. The objective of this research was to update the environment temperatures that characterize the thermal comfort for broilers chickens from one to 21 days of age, under Brazilian production conditions. This research was conducted with 600 COBB birds, which were distributed in five growth chambers maintained at different temperatures during the first three weeks of age. During the experimental period, temperature values were progressively reduced, consisting in five treatments: T2724/21, T30/27/24, T33/30/27, T36/33/30 and T39/36/33. It was observed that the birds maintained in the T30(27-24) treatment presented better performance compared to other environment conditions. Based on the obtained regression models, the environment temperature values that provide greater weighing gain for the broiler chicken growth in the initial period were 31.3, 25.5 and 21.8 ºC, respectively for the first, second and third week of age.

Maternal age and adverse perinatal outcomes in a birth cohort (BRISA) from a Northeastern Brazilian city

PURPOSE:To verify the existence of associations between different maternal ages and the perinatal outcomes of preterm birth and intrauterine growth restriction in the city of São Luís, Maranhão, Northeastern Brazil.METHODS:A cross-sectional study using a sample of 5,063 hospital births was conducted in São Luís, from January to December 2010. The participants comprise the birth cohort for the study "Etiological factors of preterm birth and consequences of perinatal factors for infant health: birth cohorts from two Brazilian cities" (BRISA). Frequencies and 95% confidence intervals were used to describe the results. Multiple logistic regression models were applied to assess the adjusted odds ratio (OR) of maternal age associated with the following outcomes: preterm birth and intrauterine growth restriction.RESULTS:The percentage of early teenage pregnancy (12–15 years old) was 2.2%, and of late (16–19 years old) was 16.4%, while pregnancy at an advanced maternal age (>35 years) was 5.9%. Multivariate analyses showed a statistically significant increase in preterm births among females aged 12–15 years old (OR=1.6; p=0.04) compared with those aged 20–35 years. There was also a higher rate in preterm births among females aged 16–19 years old (OR=1.3; p=0.01). Among those with advanced maternal age (>35 years old), the increase in the prevalence of preterm birth had only borderline statistical significance (OR=1.4; p=0.05). There was no statistically significant association between maternal age and increased prevalence of intrauterine growth restriction.

Predictors of fluid intravasation during operative hysteroscopy: a preplanned prospective observational study with 200 cases

PURPOSE: To verify the predictors of intravasation rate during hysteroscopy.METHODS: Prospective observational study (Canadian Task Force classification II-1). All cases (n=200 women; 22 to 86 years old) were treated in an operating room setting. Considering respective bag overfill to calculate water balance, we tested two multiple linear regression models: one for total intravasation (mL) and the other for absorption rate (mL.min-1). The predictors tested (independent variables) were energy (mono/bipolar), tube patency (with/without tubal ligation), hysterometry (cm), age≤50 years, body surface area (m2), surgical complexity (with/without myomectomy) and duration (min).RESULTS: Mean intravasation was significantly higher when myomectomy was performed (442±616 versus 223±332 mL; p<0.01). In the proposed multiple linear regression models for total intravasation (adjusted R2=0.44; p<0.01), the only significant predictors were myomectomy and duration (p<0.01).In the proposed model for intravasation rate (R2=0.39; p<0.01), only myomectomy and hysterometry were significant predictors (p=0.02 and p<0.01, respectively).CONCLUSIONS: Not only myomectomy but also hysterometry were significant predictors of intravasation rate during operative hysteroscopy.

Babesia canis vogeli infection in dogs and ticks in the semiarid region of Pernambuco, Brazil

Abstract:This study aimed to report the prevalence of Babesia canis vogeli in dogs and ticks in the urban and rural areas of Petrolina, Pernambuco. Serum and peripheral blood samples of 404 dogs were tested by indirect immunofluorescence assay (IFA) and by blood smears, respectively. The presence of tick infestation was evaluated, and some specimens were submitted to DNA amplification by polymerase chain reaction (PCR). The presence of antibodies anti-B. canis vogeli was determinate in 57.9% (234/404) of dogs. The direct detection of Babesia spp was obtained in 0.5% (2/404) dogs by visualization of intraerythrocytic forms. Infestation by Rhipicephalus sanguineus sensu lato was observed in 54.5% (220/404) of dogs in both urban and rural areas. DNA of Babesia canis vogeli were obtained by PCR in 6% individual (3/50) and 8.7% of pool of ticks (7/80). The risk factors for the presence of anti-B. canis vogeli antibodies, as determined through the application of logistic regression models (P<0.05), were the following: medium breed size variables (P<0.001); contact with areas of forest (P=0.021); and access on the street (P=0.046). This study describes, for the first time, the confirmation of infection of B. canis vogeli in dogs and ticks in the semiarid region of Pernambuco, Brazil.