Cover plants with potential use for crop-livestock integrated systems in the Cerrado region

The objective of this work was to evaluate the effects of lignin, hemicellulose, and cellulose concentrations in the decomposition process of cover plant residues with potential use in no-tillage with corn, for crop-livestock integrated system, in the Cerrado region. The experiment was carried out at Embrapa Cerrados, in Planaltina, DF, Brazil in a split plot experimental design. The plots were represented by the plant species and the subplots by harvesting times, with three replicates. The cover plants Urochloa ruziziensis, Canavalia brasiliensis, Cajanus cajan, Pennisetum glaucum, Mucuna aterrima, Raphanus sativus, Sorghum bicolor were evaluated together with spontaneous plants in the fallow. Cover plants with lower lignin concentrations and, consequently, higher residue decomposition such as C. brasiliensis and U. ruziziensis promoted higher corn yield. High concentrations of lignin inhibit plant residue decomposition and this is favorable for the soil cover. Lower concentrations of lignin result in accelerated plant decomposition, more efficient nutrient cycling, and higher corn yield.

Soil biological attributes in pastures of different ages in a crop-livestock integrated system

The objective of this work was to evaluate the effect of the pasture (Urochloa brizantha) component age on soil biological properties, in a crop-livestock integrated system. The experiment was carried out in a Brazilian savannah (Cerrado) area with 92 ha, divided into six pens of approximately 15 ha. Each pen represented a different stage of the pasture component: formation, P0; one year, P1; two years, P2; three years, P3; and final with 3.5 years, Pf. Samples were taken in the 0-10 cm soil depth. The soil biological parameters - microbial biomass carbon (MBC), microbial biomass respiration (C-CO2), metabolic quotient (qCO2), microbial quotient (q mic), and total organic carbon (TOC) - were evaluated and compared among different stages of the pasture, and between an adjacent area under native Cerrado and another area under degraded pasture (PCD). The MBC, q mic and TOC increased and qCO2 reduced under the different pasture stages. Compared to PCD, the pasture stages had higher MBC, q mic and TOC, and lower qCO2. The crop-livestock integrated system improved soil microbiological parameters and immobilized carbon in the soil in comparison to the degraded pasture.

Smooth height/age curves from stem analysis with linear programming

[Abstract]

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Abstract

Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images

In this paper, an advanced technique for the generation of deformation maps using synthetic aperture radar (SAR) data is presented. The algorithm estimates the linear and nonlinear components of the displacement, the error of the digital elevation model (DEM) used to cancel the topographic terms, and the atmospheric artifacts from a reduced set of low spatial resolution interferograms. The pixel candidates are selected from those presenting a good coherence level in the whole set of interferograms and the resulting nonuniform mesh tessellated with the Delauney triangulation to establish connections among them. The linear component of movement and DEM error are estimated adjusting a linear model to the data only on the connections. Later on, this information, once unwrapped to retrieve the absolute values, is used to calculate the nonlinear component of movement and atmospheric artifacts with alternate filtering techniques in both the temporal and spatial domains. The method presents high flexibility with respect to the required number of images and the baselines length. However, better results are obtained with large datasets of short baseline interferograms. The technique has been tested with European Remote Sensing SAR data from an area of Catalonia (Spain) and validated with on-field precise leveling measurements.

Impulse response recovery of linear systems through weighted cumulant slice

Identifiability of the so-called ω-slice algorithm is proven for ARMA linear systems. Although proofs were developed in the past for the simpler cases of MA and AR models, they were not extendible to general exponential linear systems. The results presented in this paper demonstrate a unique feature of the ω-slice method, which is unbiasedness and consistency when order is overdetermined, regardless of the IIR or FIR nature of the underlying system, and numerical robustness.