Spatial variability of forage yield and soil physical attributes of a Brachiaria decumbens pasture in the Brazilian Cerrado

The objective of this study was to analyze variability, linear and spatial correlations of forage dry mass yield (FDM) and dry matter percentage (DM%) of Brachiaria decumbens with the bulk density (BD), gravimetric (GM) and volumetric (VM) moisture, mechanical resistance to penetration (RP) and organic matter content (OM), at depths 1 (0-0.10 m) and 2 (0.10-0.20 m), in a Red Latosol (Oxisol), in order to select an indicator of soil physical quality and identify possible causes of pasture degradation. The geostatistical grid was installed to collect soil and plant data, with 121 sampling points, over an area of 2.56 ha. The linear correlation between FDM × DM% and FDM × BD2 was low, but highly significant. Spatial correlations varied inversely and positively, respectively. Except for DM% and BD, at both depths, the other attributes showed average to high variability, indicating a heterogeneous environment. Thus, geostatistics emerges as an important tool in understanding the interactions in pasture ecosystems, in order to minimize possible causes of degradation and indicate better alternatives for soil-plant-animal management. The decrease in FDM and increased BD1 are indicators of physical degradation (compaction) of Red Latosol (Oxisol), particularly in the places with the highest concentration of animals and excessive trampling, in Cerrado conditions, in the municipality of Selvíria, Mato Grosso do Sul State, Brazil.

Spatial and linear correlations between soil and corn

O conjunto de tecnologias aplicadas ao sistema de produção agrícola tem como características principais a verticalização da produtividade, diminuição de custos, melhoria nas características físicas, químicas e biológicas do solo para proporcionarem o crescimento sustentável do meio de produção. Desta forma, o trabalho teve como objetivo determinar a variabilidade e as correlações lineares e especiais entre os atributos da planta e do solo, visando selecionar um indicador da qualidade física do solo de boa representatividade para produção de forragem. No ano agrícola de 2006, na Fazenda Bonança, município de Pereira Barreto (SP), foram analisados a produtividade de forragem do milho outonal (FDM) em sistema plantio direto irrigado e os atributos físicos do solo, num Latossolo Vermelho distrófico. O objetivo foi estudar a variabilidade e as correlações lineares e espaciais entre os atributos da planta e os do solo, visando selecionar um indicador da qualidade física do solo de boa representatividade para a produtividade da forragem. Foi instalada a malha geoestatística, para coleta de dados do solo e planta, contendo 125 pontos amostrais, numa área de 2.500 m². Os atributos estudados, além de não terem variado aleatoriamente, apresentaram variabilidade dos dados entre baixa e muito alta e seguiram padrões espaciais bem definidos, com alcances entre 7,8 e 38,0 m. Por outro lado, a correlação linear entre os atributos da planta com o do solo foi baixa e extremamente significativa. Os pares Massa Seca de forragem versus Microporosidade e Diâmetro do colmo versus Densidade do Solo foram melhor correlacionados na camada de 0-0.10m, enquanto os outros pares - Massa Seca de Forragem versus Macroporosidade - e Porosidade Total - apresentaram correlação inversa para a mesma camada. Entretanto, do ponto de vista espacial, houve uma alta correlação inversa entre Massa Seca de Forragem com Microporosidade, de modo que a microporosidade na camada de 0-0.10m pode ser considerada um bom indicador de qualidade física do solo, tendo em vista a produção de forragem de milho.

Adaptive control charts: A Markovian approach for processes subject to independent disturbances

Recent studies have shown that adaptive X control charts are quicker than traditional X charts in detecting small to moderate shifts in a process. In this article, we propose a joint statistical design of adaptive X and R charts having all design parameters varying adaptively. The process is subjected to two independent assignable causes. One cause changes the process mean and the other changes the process variance. However, the occurrence of one kind of assignable cause does not preclude the occurrence of the other. It is assumed that the quality characteristic is normally distributed and the time that the process remains in control has exponential distribution. Performance measures of these adaptive control charts are obtained through a Markov chain approach. (c) 2005 Elsevier B.V. All rights reserved.

Carboxylic acids of marine evaporitic oils from Sergipe-Alagoas Basin, Brazil

A. suite of 10 different marine evaporitic oil samples from Sergipe-Alagoas Basin, Brazil was studied for its biomarker content, in particular its acidic constituents. The oils showed different molecular distributions and relative abundances of n-alkanoic, isoprenoid and hopanoic acids. The observed differences were assigned to the incorporation of immature organic matter in the oils and fractionation along the migration pathway. The diagenetic precursor functionality (alcohol/ether or acid) was proposed based on the comparison of the relative abundances of the neutral and acidic biomarkers (hopanoids, isoprenoids, alkyl-steranes, monoaromatic alkyl-steroids). In the acidic fraction, 3 series of steroid-alkanoic acids and monoaromatic steroid-alkanoic acids (steroid-methanoic, ethanoic and propanoic acids and monoaromatic steroid-methanoic, ethanoic and propanoic acids) were detected, while in the neutral fraction only 2 series of each corresponding class could be observed (methyl and ethyl-steranes and monoaromatic methyl and ethyl-steroids). These carbon shifts suggest that decarboxylation is an important process in the formation of the alkyrsteranes and monoaromatic alkyl-steroids, and we infer that carboxylic acids are the diagenetic precursors of these classes of compounds. When alcohol or ether are the diagenetic precursors (isoprenoids and hopanoids), no significant differences in the molecular distributions between neutral and acidic fractions were observed. (C) 2000 Elsevier B.V. Ltd. All rights reserved.

Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.