Determination of physical, chemical and biological attributes with individual analyses is inadequate for improving the understanding of soil conditions as a function of land-use change (LUC) in comparison to the natural state of soil. For a more accurate soil condition diagnostic, it is necessary to consider various indicators related to these characteristics, which requires the use of multivariate statistical analysis. The aim of this work was to characterize, through multivariate analysis, different types of LUCs in an Oxisol as a function of the physical, chemical and biological attributes and to clarify the relationship of these attributes with the quality of the soil in comparison to these attributes in natural soil conditions, in the southern Amazon in Brazil. The land uses evaluated in the municipality of Alta Floresta, state of Mato Grosso (MT), Brazil, were native amazon forest (ma), degraded pasture (pd), managed renewed pasture (pn), permanent preservation area in recovery (app), crop area (rice), forage sugarcane (ca) and reforested area with eucalyptus (eu). To characterize the physical and chemical soil attributes, samples were collected in each land-use area, at depths of 0-0.10 and 0.10-0.20 m, and the determination of soil microbial activity (biological attributes) was evaluated at a depth of 0-0.10 m. The interrelationship between the analyzed attributes was described by multivariate techniques, which included hierarchical and non-hierarchical cluster analyses, principal component analysis, canonical correlation, and structural equation modeling. The multivariate approach for the analysis of soil attribute data was efficient in the identification of anthropogenic actions on areas in comparison to natural conditions. Together, the cluster analysis and principal components analysis identified two groups that differed mainly in terms of anthropic operations of soil tillage and liming. The land use that was most similar to the natural condition was degraded pasture, which was mainly due to K and H + Al contents, soil microporosity and soil basal respiration. Structural equation modeling indicated that the latent factor soil chemical attributes had three times greater interference (-0.5828) than the latent factor soil physical attributes (0.1735) on the latent factor soil biological attributes. Therefore, anthropic actions, especially the liming, modified soil acidity conditions, affecting the microorganisms of its flora and changing the native fungal community of the soil that was evaluated. large areas, mainly through the inadequate conversion of natural environments into agricultural areas (Fonseca et al., 2007; Rojas et al., 2016). The main impact of LUC is on in the soils, which are directly responsible for the sustainability and productivity of natural and agricultural ecosystems (Castilho et al., 2016; Novak et al., 2017; Sanabria et al., 2016). Studying the physical, chemical and biological attributes of soil in different applications and comparing these attributes to those in areas

Este trabalho foi realizado com o objetivo de estimar a divergência genética entre progênies de Pinus caribaea var. hondurensis, por meio de caracteres quantitativos. O experimento foi instalado em delineamento látice 10 x 10, triplo, com 100 tratamentos (96 progênies oriundas de polinização aberta de um pomar clonal da espécie e quatro testemunhas). Foram avaliados os caracteres: diâmetro a 1,30 m do solo, altura total de planta, volume cilíndrico, produção de resina total e resina por área de painel. Utilizou-se a distância generalizada de Mahalanobis (D2) e o método de otimização de Tocher. A maior distância genética observada entre as progênies foi de 100% (D2 = 65,51) e a menor foi de 0,09% (D2 = 0,15). O caractere volume foi o que mais contribuiu para a divergência genética entre os grupos avaliados. O agrupamento a partir do método de otimização de Tocher possibilitou a separação das progênies em quatro grupos, com concentração de 96,9% das progênies em um único grupo. Para que estas progênies possam ser incluídas em programas de melhoramento genético para produção de resina e madeira, cruzamentos controlados deverão ser priorizados entre indivíduos mais produtivos, que apresentaram maior divergência genética.Genetic divergence genetic between Pinus caribaea var. hondurensis progenies based on quantitative traitsThe proposal of this study was to estimate the genetic divergence among Pinus caribaea var. hondurensis progenies through quantitative traits. Trail was established in lattice design 10 x 10, triple, with 100 treatments (96 progenies from clonal seed orchard of P. caribaea var. hondurensis and four controls). The genetic divergence was estimated using the generalized Mahalanobis distance (D2) and Torcher’s optimization method. Diameter at 1.30 above the ground, total plant height, cylindrical volume, total resin production and resin per panel area were evaluated. The largest genetic distance observed between the progenies was 100% (D2 = 65.51) and the lowest was 0,09% (D2 = 0.15). Clustering by Torcher’s optimization method separated the progeny in four groups, with a concentration of 96.9% of the progenies in a single group. Volume was the largest contributor to the genetic divergence among groups. To include these progeny in breeding programs for resin and wood production controlled crossings should be prioritized among the most productive individuals that presented greater genetic divergence.Index terms: Genetic breeding; Generalized Mahalanobis distance; Tocher optimization

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We define a new four-parameter model called the odd log-logistic generalized inverse Gaussian distribution which extends the generalized inverse Gaussian and inverse Gaussian distributions. We obtain some structural properties of the new distribution. We construct an extended regression model based on this distribution with two systematic structures, which can provide more realistic fits to real data than other special regression models. We adopt the method of maximum likelihood to estimate the model parameters. In addition, various simulations are performed for different parameter settings and sample sizes to check the accuracy of the maximum likelihood estimators. We provide a diagnostics analysis based on case-deletion and quantile residuals. Finally, the potentiality of the new regression model to predict price of urban property is illustrated by means of real data.

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