Study of parental models: building an instrument for their exploration

Objective: This research presents the construction of an attributional questionnaire concerning the different parental models and factors that are involved in family interactions. Method: A mixed methodology was used as a foundation to develop items and respective pilots that allowed checking the validity and internal consistency of the instrument using expert judgment. Results: An instrument of 36 statements was organized into 12 categories to explore the parental models according to the following factors: parental models, breeding patterns, attachment bonds and guidelines for success, and promoted inside family contexts. Analyzing these factors contributes to the children’s development within the familiar frown, and the opportunity for socio-educational intervention. Conclusion: It is assumed that the family context is as decisive as the school context; therefore, exploring the nature of parental models is required to understand the features and influences that contribute to the development of young people in any social context.





Technologies of birth and models of midwifery care

This article is based on a study of a reform in the organisation of maternity services in the United Kingdom, which aimed towards developing a more woman-centred model of care. After decades of fragmentation and depersonalisation of care, associated with the shift of birth to a hospital setting, pressure by midwives and mothers prompted government review and a relatively radical turnaround in policy. However, the emergent model of care has been profoundly influenced by concepts and technologies of monitoring. The use of such technologies as ultrasound scans, electronic foetal monitoring and oxytocic augmentation of labour, generally supported by epidural anaesthesia for pain relief, have accompanied the development of a particular ecological model of birth – often called active management –, which is oriented towards the idea of an obstetric norm. Drawing on analysis of women’s narrative accounts of labour and birth, this article discusses the impact on women’s embodiment in birth, and the sources of information they use about the status of their own bodies, their labour and that of the child. It also illustrates how the impact on women’s experiences of birth may be mediated by a relational model of support, through the provision of caseload midwifery care.

Children's health care assistance according to their families: a comparison between models of Primary Care

OBJECTIVE To compare the health assistance models of Basic Traditional Units (UBS) with the Family Health Strategy (ESF) units for presence and extent of attributes of Primary Health Care (APS), specifically in the care of children. METHOD A cross-sectional study of a quantitative approach with families of children attended by the Public Health Service of Colombo, Paraná. The Primary Care Assessment Tool (PCA-Tool) was applied to parents of 482 children, 235 ESF units and 247 UBS units covering all primary care units of the municipality, between June and July 2012. The results were analyzed according to the PCA-Tool manual. RESULTS ESF units reached a borderline overall score for primary health care standards. However, they fared better in their attributes of Affiliation, Integration of care coordination, Comprehensiveness, Family Centeredness and Accessibility of use, while the attributes of Community Guidance/Orientation, Coordination of Information Systems, Longitudinality and Access attributes were rated as insufficient for APS. UBS units had low scores on all attributes. CONCLUSION The ESF units are closer to the principles of APS (Primary Health Care), but there is need to review actions of child care aimed at the attributes of APS in both care models, corroborating similar studies from other regions of Brazil.

Ecological niche models and patterns of richness and endemism of the southern Andean genus Eurymetopum (Coleoptera, Cleridae)

Eurymetopum is an Andean clerid genus with 22 species. We modeled the ecological niches of 19 species with Maxent and used them as potential distributional maps to identify patterns of richness and endemicity. All modeled species maps were overlapped in a single map in order to determine richness. We performed an optimality analysis with NDM/VNDM in a grid of 1º latitude-longitude in order to identify endemism. We found a highly rich area, located between 32º and 41º south latitude, where the richest pixels have 16 species. One area of endemism was identified, located in the Maule and Valdivian Forest biogeographic provinces, which extends also to the Santiago province of the Central Chilean subregion, and contains four endemic species (E. parallelum, E. prasinum, E. proteus, and E. viride), as well as 16 non-endemic species. The sympatry of these phylogenetically unrelated species might indicate ancient vicariance processes, followed by episodes of dispersal. Based on our results, we suggest a close relationship between these provinces, with the Maule representing a complex area.

The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera, Arctiidae)

Species' geographic ranges are usually considered as basic units in macroecology and biogeography, yet it is still difficult to measure them accurately for many reasons. About 20 years ago, researchers started using local data on species' occurrences to estimate broad scale ranges, thereby establishing the niche modeling approach. However, there are still many problems in model evaluation and application, and one of the solutions is to find a consensus solution among models derived from different mathematical and statistical models for niche modeling, climatic projections and variable combination, all of which are sources of uncertainty during niche modeling. In this paper, we discuss this approach of ensemble forecasting and propose that it can be divided into three phases with increasing levels of complexity. Phase I is the simple combination of maps to achieve a consensual and hopefully conservative solution. In Phase II, differences among the maps used are described by multivariate analyses, and Phase III consists of the quantitative evaluation of the relative magnitude of uncertainties from different sources and their mapping. To illustrate these developments, we analyzed the occurrence data of the tiger moth, Utetheisa ornatrix (Lepidoptera, Arctiidae), a Neotropical moth species, and modeled its geographic range in current and future climates.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Calibration of the century, apsim and ndicea models of decomposition and n mineralization of plant residues in the humid tropics

The aim of this study was to calibrate the CENTURY, APSIM and NDICEA simulation models for estimating decomposition and N mineralization rates of plant organic materials (Arachis pintoi, Calopogonium mucunoides, Stizolobium aterrimum, Stylosanthes guyanensis) for 360 days in the Atlantic rainforest bioma of Brazil. The models´ default settings overestimated the decomposition and N-mineralization of plant residues, underlining the fact that the models must be calibrated for use under tropical conditions. For example, the APSIM model simulated the decomposition of the Stizolobium aterrimum and Calopogonium mucunoides residues with an error rate of 37.62 and 48.23 %, respectively, by comparison with the observed data, and was the least accurate model in the absence of calibration. At the default settings, the NDICEA model produced an error rate of 10.46 and 14.46 % and the CENTURY model, 21.42 and 31.84 %, respectively, for Stizolobium aterrimum and Calopogonium mucunoides residue decomposition. After calibration, the models showed a high level of accuracy in estimating decomposition and N- mineralization, with an error rate of less than 20 %. The calibrated NDICEA model showed the highest level of accuracy, followed by the APSIM and CENTURY. All models performed poorly in the first few months of decomposition and N-mineralization, indicating the need of an additional parameter for initial microorganism growth on the residues that would take the effect of leaching due to rainfall into account.

Phosphorus accumulation and pollution potential in a hapludult fertilized with pig manure

Successive applications of pig litter to the soil surface can increase the phosphorus (P) content and alter its adsorption, promoting P transfer to surface or subsurface waters. The purpose of this study was to evaluate P accumulation and the pollution potential of a soil after application of pig litter. In March 2010, eight years after the installation of an experiment in Braço do Norte, Santa Catarina, SC, Brazil, on a Typic Hapludult, soil was sampled (layers 0-2.5, 2.5-5, 5-10, 10-15, 15-20 and 20-30 cm) after the following fertilization treatments: no pig litter fertilization, pig slurry application and pig manure application. In this period, 694 and 1,890 kg P2O5 ha-1 were applied in the treatments with pig slurry and pig manure, respectively. The P content was determined, based on Mehlich-1, anion exchange resin (AER), 0.01 mol L-1 CaCl2 and total P in the samples. The adsorption isotherm parameters were also determined by the Langmuir and Koski-Vähälä & Hartikainem models in the layers 0-2.5 and 20-30 cm. The application of 1,890 kg P2O5 ha-1 in the form of pig manure led to P accumulation, as evidenced by Mehlich-1, down to a depth of 15 cm, by AER and 0.01 mol L-1 CaCl2 down to 20 cm and by total P to 30 cm. After application of 1,890 kg P2O5 ha-1 in the form of pig manure, the values of maximum P adsorption capacity were lowest in the deepest layer (20-30 cm), indicating the occupation of part of the adsorption sites of the particles. The application of swine manure to the soil over eight years increased the P quantity in the soil solution of the surface layer, indicating environmental contamination risk for surface and subsurface waters.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Organic acid adsorption and mineralization in oxisols with different textures

Organic acids play an important role in the nutritional conditions of plants. Their relevance is related to their formation dynamics, mineralization rate and adsorption by soil colloids. This study was carried out to evaluate the dynamics of mineralization and adsorption of organic acid (acetic acid - AA, citric acid - CA and humic acid - HA) applied to the soil. Samples of two Oxisols were used: Rhodic Haplustox (LV) and Typic Haplustox (LVA). The mineralization experiment was arranged in a 2 x 3 x 5 factorial design, based on the factors: two soils (LV and LVA) x three organic acid (OA) types (AA, CA and HA) x five OA rates (0, 1, 2, 4, and 8 mmol dm-3). Organic carbon mineralization in samples was measured by the C-CO2 efflux, produced by the microbial activity, in a 30-day (measurements after 4, 8, 12, 21, and 30 days) and in a 4-day experiment (measured after 24, 48, 72 and 96 h). Organic acid adsorption was tested in a 2 x 2 x 5 x 4 factorial design, with the factors and levels: two Oxisols; two organic acids (AA and CA); five OA rates (0, 1, 2, 4, and 8 mmol dm-3) and four adsorption periods (6, 24, 48, and 72 h). The C-CO2 production of soil treated with CA was highest. In the adsorption experiment, the affinity of CA to soil adsorption sites was greatest. The adsorption of organic acids to soils may be an important mechanism by which bioavailability and thus mineralization capacity by microbial activity are reduced.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Surface Complexation Modeling in Variable Charge Soils: Charge Characterization by Potentiometric Titration

ABSTRACT Intrinsic equilibrium constants of 17 representative Brazilian Oxisols were estimated from potentiometric titration measuring the adsorption of H+ and OH− on amphoteric surfaces in suspensions of varying ionic strength. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. The former was fitted by calculating total site concentration from curve fitting estimates and pH-extrapolation of the intrinsic equilibrium constants to the PZNPC (hand calculation), considering one and two reactive sites, and by the FITEQL software. The latter was fitted only by FITEQL, with one reactive site. Soil chemical and physical properties were correlated to the intrinsic equilibrium constants. Both surface complexation models satisfactorily fit our experimental data, but for results at low ionic strength, optimization did not converge in FITEQL. Data were incorporated in Visual MINTEQ and they provide a modeling system that can predict protonation-dissociation reactions in the soil surface under changing environmental conditions.

Liming in Agricultural Production Models with and Without the Adoption of Crop-Livestock Integration

ABSTRACT Perennial forage crops used in crop-livestock integration (CLI) are able to accumulate large amounts of straw on the soil surface in no-tillage system (NTS). In addition, they can potentially produce large amounts of soluble organic compounds that help improving the efficiency of liming in the subsurface, which favors root growth, thus reducing the risks of loss in yield during dry spells and the harmful effects of “overliming”. The aim of this study was to test the effects of liming on two models of agricultural production, with and without crop-livestock integration, for 2 years. Thus, an experiment was conducted in a Latossolo Vermelho (Oxisol) with a very clayey texture located in an agricultural area under the NTS in Bandeirantes, PR, Brazil. Liming was performed to increase base saturation (V) to 65, 75, and 90 % while one plot per block was maintained without the application of lime (control). A randomized block experimental design was adopted arranged in split-plots and four plots/block, with four replications. The soil properties evaluated were: pH in CaCl2, soil organic matter (SOM), Ca, Mg, K, Al, and P. The effects of liming were observed to a greater depth and for a long period through mobilization of ions in the soil, leading to a reduction in SOM and Al concentration and an increase in pH and the levels of Ca and Mg. In the first crop year, adoption of CLI led to an increase in the levels of K and Mg and a reduction in the levels of SOM; however, in the second crop year, the rate of decline of SOM decreased compared to the decline observed in the first crop year, and the level of K increased, whereas that of P decreased. The extent of the effects of liming in terms of depth and improvement in the root environment from the treatments were observed only partially from the changes observed in the chemical properties studied.

Boron adsorption in soils from the State of São Paulo, Brazil

Boron adsorption was studied in five representative soils (Rhodic Hapludox, Arenic Paleudalf and three Typic Hapludox) from the State of São Paulo, Brazil. Adsorption was higher in the clayey Oxisols, followed by the Alfisol and the coarser Oxisols. Calcium carbonate promoted an increase in the amount of adsorbed boron in all soils, with the most pronounced effect in the coarser-textured Oxisols. High correlation coefficients were found between adsorbed boron and clay and amorphous aluminum oxide contents and specific surface area (r = 0.79, 0.76 and 0.73, respectively, p < 0.01). Clay content, free aluminum oxide, and hot CaCl2 (0.01 mol L-1)-extracted boron explained 93% of the variation of adsorbed boron. Langmuir and Freundlich isotherms fitted well to the adsorbed data, and highest values for maximum boron adsorption were found in clayey soils, which were significantly correlated with contents of total, free and amorphous iron and aluminum oxides, as well with the physical attributes. Ninety four percent of the variation in the maximum adsorption could be related to the free iron content.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.