Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co)variances with age adequately and larger breeding value accuracies can be expected using this model. © South African Society for Animal Science.

Conservation and use of palm trees in Brazil's landscaping

Palm trees belong to the Arecaceae family and are widely utilized in landscaping of tropical countries. Although there are many species, just a few are used in Brazil's landscaping, the majority coming from other countries. So, this study had the objective to characterize the plants in the collection of palm trees at UNESP/FCAV, Campus of Jaboticabal, Sao Paulo State, Brazil. The germination of seeds was also studied to increase the production of seedlings for use in landscaping, as this is the biggest difficulty for spreading new species. The introduction of new species in the collection was accomplished. It was also made a survey of major pests and diseases that occur in the collection. The most common pests found were: Brassolis sophorae, Parisoschoenus obesulus, Rhynchophorus palmarum and Coraliomela sp., and the most common disease causing fungi: Colletrotrichum sp., Curvularia sp., Bipolaris sp., Helminthosporium sp., Alternaria sp., Phyllosticta sp., Pestalotia sp., Exosporium palmivorum and Ceratocystis pardoxa.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Wavelets and decision trees for target detection over sea surface using cosmo-skymed SAR data

We are investigating the combination of wavelets and decision trees to detect ships and other maritime surveillance targets from medium resolution SAR images. Wavelets have inherent advantages to extract image descriptors while decision trees are able to handle different data sources. In addition, our work aims to consider oceanic features such as ship wakes and ocean spills. In this incipient work, Haar and Cohen-Daubechies-Feauveau 9/7 wavelets obtain detailed descriptors from targets and ocean features and are inserted with other statistical parameters and wavelets into an oblique decision tree. © 2011 Springer-Verlag.

Relation between green spaces and bird community structure in an urban area in Southeast Brazil

Increased urbanization typically leads to an increase in abundance of a few species and a reduction in bird species richness. Understanding the structure of biotic communities in urban areas will allow us to propose management techniques and to decrease conflicts between wild species and human beings. The objective of this study was to describe the structure of the bird community in an urban ecosystem. The study was carried out in the city of Taubaté in southeastern Brazil. Point-counts were established in areas with different levels of tree density ranging from urban green spaces to predominantly built-up areas. We looked for a correlation between the richness/abundance of birds and the size of the area surveyed, the number of houses, the number of tree species and the number of individual trees. The results of multiple regression showed that bird richness had a direct relationship with vegetation complexity. The abundance and diversity of tree species were better predictors of bird species than the number of houses and size of the area surveyed. We discuss implications of this study for conservation and management of bird diversity in urban areas, such as the need to increase green areas containing a large diversity of native plant species. © 2011 Springer Science+Business Media, LLC.

Delimitação entre os lenhos juvenil e adulto de pinus elliottii engelm

Trees produce different types of woody tissue during growth. The xylem produced in the first years until a certain cambial age is called juvenile wood and has different physical, mechanical, chemical and anatomical characteristics from that produced later, when the cambial cells have matured. Thus, prior knowledge of the quality of the wood is of fundamental importance for choosing the best use of the material. This work aimed at using data from length of the tracheid and wood density to define the age transition from juvenile to mature wood. For this, three Pinus elliottii var. elliottii trees, aged 35 years, were collected at the Itapetininga Experimental Station, located in the state of São Paulo, Brazil. From each tree, disks of about 5 cm thick were obtained 0.05 m above the ground and used to determine the radial density by the gamma ray attenuation method and to measure the length of the tracheids. The results obtained by linear regression analysis showed that juvenile wood is limited to the first seven years of tree growth and the formation of mature wood occurs after twenty years. For the mature wood, there are significant differences among the averages obtained from the tracheid length and wood density.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and Bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. © 2013 American Dairy Science Association.

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data. © FUNPEC-RP.

Water availability determines the richness and density of fig trees within Brazilian semideciduous forest landscapes

The success of fig trees in tropical ecosystems is evidenced by the great diversity (+750 species) and wide geographic distribution of the genus. We assessed the contribution of environmental variables on the species richness and density of fig trees in fragments of seasonal semideciduous forest (SSF) in Brazil. We assessed 20 forest fragments in three regions in Sao Paulo State, Brazil. Fig tree richness and density was estimated in rectangular plots, comprising 31.4 ha sampled. Both richness and fig tree density were linearly modeled as function of variables representing (1) fragment metrics, (2) forest structure, and (3) landscape metrics expressing water drainage in the fragments. Model selection was performed by comparing the AIC values (Akaike Information Criterion) and the relative weight of each model (wAIC). Both species richness and fig tree density were better explained by the water availability in the fragment (meter of streams/ha): wAICrichness = 0.45, wAICdensity = 0.96. The remaining variables related to anthropic perturbation and forest structure were of little weight in the models. The rainfall seasonality in SSF seems to select for both establishment strategies and morphological adaptations in the hemiepiphytic fig tree species. In the studied SSF, hemiepiphytes established at lower heights in their host trees than reported for fig trees in evergreen rainforests. Some hemiepiphytic fig species evolved superficial roots extending up to 100 m from their trunks, resulting in hectare-scale root zones that allow them to efficiently forage water and soil nutrients. The community of fig trees was robust to variation in forest structure and conservation level of SSF fragments, making this group of plants an important element for the functioning of seasonal tropical forests. © 2013 Elsevier Masson SAS. All rights reserved.

Mixing Eucalyptus and Acacia trees leads to fine root over-yielding and vertical segregation between species

The consequences of diversity on belowground processes are still poorly known in tropical forests. The distributions of very fine roots (diameter <1 mm) and fine roots (diameter <3 mm) were studied in a randomized block design close to the harvest age of fast-growing plantations. A replacement series was set up in Brazil with mono-specific Eucalyptus grandis (100E) and Acacia mangium (100A) stands and a mixture with the same stocking density and 50 % of each species (50A:50E). The total fine root (FR) biomass down to a depth of 2 m was about 27 % higher in 50A:50E than in 100A and 100E. Fine root over-yielding in 50A:50E resulted from a 72 % rise in E. grandis fine root biomass per tree relative to 100E, whereas A. mangium FR biomass per tree was 17 % lower than in 100A. Mixing A. mangium with E. grandis trees led to a drop in A. mangium FR biomass in the upper 50 cm of soil relative to 100A, partially balanced by a rise in deep soil layers. Our results highlight similarities in the effects of directional resources on leaf and FR distributions in the mixture, with A. mangium leaves below the E. grandis canopy and a low density of A. mangium fine roots in the resource-rich soil layers relative to monospecific stands. The vertical segregation of resource-absorbing organs did not lead to niche complementarity expected to increase the total biomass production. © 2012 Springer-Verlag Berlin Heidelberg.

Genetic evaluation using multi-trait and random regression models in Simmental beef cattle

The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Seasonal variation in leaf traits between congeneric savanna and forest trees in Central Brazil: Implications for forest expansion into savanna

The ecology of forest and savanna trees species will largely determine the structure and dynamics of the forest-savanna boundaries, but little is known about the constraints to leaf trait variation imposed by selective forces and evolutionary history during the process of savanna invasion by forest species. We compared seasonal patterns in leaf traits related to leaf structure, carbon assimilation, water, and nutrient relations in 10 congeneric species pairs, each containing one savanna species and one forest species. All individuals were growing in dystrophic oxisols in a fire-protected savanna of Central Brazil. We tested the hypothesis that forest species would be more constrained by seasonal drought and nutrient-poor soils than their savanna congeners. We also hypothesized that habitat, rather than phylogeny, would explain more of the interspecific variance in leaf traits of the studied species. We found that throughout the year forest trees had higher specific leaf area (SLA) but lower integrated water use efficiency than savanna trees. Forest and savanna species maintained similar values of predawn and midday leaf water potential along the year. Lower values were measured in the dry season. However, this was achieved by a stronger regulation of stomatal conductance and of CO2 assimilation on an area basis (A area) in forest trees, particularly toward the end of the dry season. Relative to savanna trees, forest trees maintained similar (P, K, Ca, and Mg) or slightly higher (N) leaf nutrient concentrations. For the majority of traits, more variance was explained by phylogeny, than by habitat of origin, with the exception of SLA, leaf N concentration, and A area, which were apparently subjected to different selective pressures in the savanna and forest environments. In conclusion, water shortage during extended droughts would be more limiting for forest trees than nutrient-poor soils. © 2013 Springer-Verlag Berlin Heidelberg.

Growth and carbon isotopes of Mediterranean trees reveal contrasting responses to increased carbon dioxide and drought

Forest dynamics will depend upon the physiological performance of individual tree species under more stressful conditions caused by climate change. In order to compare the idiosyncratic responses of Mediterranean tree species (Quercus faginea, Pinus nigra, Juniperus thurifera) coexisting in forests of central Spain, we evaluated the temporal changes in secondary growth (basal area increment; BAI) and intrinsic water-use efficiency (iWUE) during the last four decades, determined how coexisting species are responding to increases in atmospheric CO2 concentrations (Ca) and drought stress, and assessed the relationship among iWUE and growth during climatically contrasting years. All species increased their iWUE (ca. +15 to +21 %) between the 1970s and the 2000s. This increase was positively related to Ca for J. thurifera and to higher Ca and drought for Q. faginea and P. nigra. During climatically favourable years the study species either increased or maintained their growth at rising iWUE, suggesting a higher CO2 uptake. However, during unfavourable climatic years Q. faginea and especially P. nigra showed sharp declines in growth at enhanced iWUE, likely caused by a reduced stomatal conductance to save water under stressful dry conditions. In contrast, J. thurifera showed enhanced growth also during unfavourable years at increased iWUE, denoting a beneficial effect of Ca even under climatically harsh conditions. Our results reveal significant inter-specific differences in growth driven by alternative physiological responses to increasing drought stress. Thus, forest composition in the Mediterranean region might be altered due to contrasting capacities of coexisting tree species to withstand increasingly stressful conditions. © 2013 Springer-Verlag Berlin Heidelberg.