88 resultados para Linear growth


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O conhecimento da área foliar de plantas daninhas pode auxiliar o estudo das relações de interferência entre elas e as culturas agrícolas. O objetivo desta pesquisa foi determinar uma equação matemática que estime a área foliar de Merremia cissoides, a partir da relação entre as dimensões lineares dos limbos foliares. Folhas da espécie foram coletadas de diferentes locais na Universidade Estadual Paulista, Jaboticabal, Estado de São Paulo, Brasil, medindo-se o comprimento (C), a largura máxima (L) e a área foliar de três tipos de folíolos. Foram estimadas equações lineares Y = a x (X) para cada tipo de folíolo. Houve sobreposição dos intervalos de confiança das equações dos folíolos primário e secundário, por isso considerou-se uma única equação da média desses folíolos, além da equação do folíolo principal, para caracterização da área foliar de M. cissoides. Assim, a área foliar dessa espécie pode ser estimada pelo somatório das áreas dos limbos foliares dos folíolos principal e primário + secundário, por meio da equação AFnest = 0,501 x (X) + 2,181 x (Z), em que X indica C x L do folíolo principal e Z indica C x L médios dos folíolos primário + secundário, respectivamente.

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Growth curves models provide a visual assessment of growth as a function of time, and prediction body weight at a specific age. This study aimed at estimating tinamous growth curve using different models, and at verifying their goodness of fit. A total number 11,639 weight records from 411 birds, being 6,671 from females and 3,095 from males, was analyzed. The highest estimates of a parameter were obtained using Brody (BD), von Bertalanffy (VB), Gompertz (GP,) and Logistic function (LG). Adult females were 5.7% heavier than males. The highest estimates of b parameter were obtained in the LG, GP, BID, and VB models. The estimated k parameter values in decreasing order were obtained in LG, GP, VB, and BID models. The correlation between the parameters a and k showed heavier birds are less precocious than the lighter. The estimates of intercept, linear regression coefficient, quadratic regression coefficient, and differences between quadratic coefficient of functions and estimated ties of quadratic-quadratic-quadratic segmented polynomials (QQQSP) were: 31.1732 +/- 2.41339; 3.07898 +/- 0.13287; 0.02689 +/- 0.00152; -0.05566 +/- 0.00193; 0.02349 +/- 0.00107, and 57 and 145 days, respectively. The estimated predicted mean error values (PME) of VB, GP, BID, LG, and QQQSP models were, respectively, 0.8353; 0.01715; -0.6939; -2.2453; and -0.7544%. The coefficient of determination (RI) and least square error values (MS) showed similar results. In conclusion, the VB and the QQQSP models adequately described tinamous growth. The best model to describe tinamous growth was the Gompertz model, because it presented the highest R-2 values, easiness of convergence, lower PME, and the easiness of parameter biological interpretation.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Soil porosity influences plant development since root growth and crop yield are determined by the root depth. The objective of this study was to investigate the linear and spatial variability and correlations between common bean yield and soil porosity. The bean grain yield of the irrigated cultivar Carioca IAC was analyzed in the growing season 2004/2005, in Selviria-MS, as well as macroporosity (MA), microporosity (MI) and total porosity (TP), in a Dystroferric Red Latosol, at four depths: 1 (0.0-0.10 m), 2 (0.10-0.20 M), 3 (0.20-0.30 m) and 4 (0.30-0.40 m). Soil and plant data were collected in a geostatistical grid with 135 points spaced 10 m apart, covering an area of 50 x 150 m. The data of the studied attributes did not vary randomly and the values were intermediate to low. They followed well-defined spatial standards, reaching between 11.70-104.40 m. on the other hand, the linear correlation between the plant and soil attributes was low, due to the high number of observations. Grain yield had the best linear correlations with MA1b, MI1 and TP3. From the spatial point of view, the inverse correlation between PG and #TP2 was outstanding. At the sites where #TP2 diminished (0.030-0.045 m(3) m(-3)) the yield varied from 2,173 to 3,529 kg ha(-1) and where it increased (0.045-0.076 m(3) m(-3)), the yield was between 1,630 and 2,173 kg ha(-1). Therefore, the total soil porosity, evaluated in the 0.10-0.20 m layer (#TP2), indicated the importance of the contact root/soil and was in turn a satisfactory indicator of soil physical quality, with a view to the grain yield of irrigated common bean.

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The growth hormone receptor (GHR) is the cell surface receptor for growth hormone (GH) and is required for GH to carry out its effects on target tissues. The objectives of the present study were to estimate the allele and genotype frequencies of the GHR/Alu I gene polymorphism located in the regulatory region in beef cattle belonging to different genetic groups and to determine associations between this polymorphism and growth and carcass traits. Genotyping was performed on 384 animals, including 79 Nellore (Zebu), 30 Canchim (5/8 Charolais+3/8 Zebu), 30 Simmental X Nellore crossbred and 245 Angus x Nellore crossbred cattle. Alleles Alu I(+), Alu I(-) and Alu I(N)-null allele-were evidenced for the GHR/Alu I polymorphism and the frequency of the Alu I(N) allele was significantly higher than the frequency of the Alu I(+) and Alu I(-) alleles in all genetic groups. Genotype Alu I(N/N) of the GHRIAlu I predominated in Nellore animals, while the Alu I(N/+) and Alu I(N/-) predominated in the other genetic groups. In the association studies, traits of interest were analyzed using the General Linear Model (GLM) procedure of the SAS program and least squares means of the genotypes were compared by the Tukey test. Significant associations (P < 0.05) were observed between the Alu I(N/N) genotype of the GHRIAlu I polymorphism and lower weight gain and body weight at slaughter, although a confounding between genotypes and genetic groups may have occurred. (c) 2005 Elsevier B.V. All rights reserved.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The buffalo is a domestic animal species of growing world-wide importance. Research to improve genetic improvement programs is important to maintain the productivity of buffalo. The objective this research was to evaluate the growth of Brazilian buffalo to two years of age with different growth curves. Growth curves consolidate the information contained in the weight-age data into three or four biologically meaningful parameters. The data included 31,452 weights at birth and 120, 205, 365, 550 and 730 days of buffalo (n = 5,178) raised on pasture without supplementation. Logistic, Gompertz, quadratic logarithmic, and linear hyperbolic curves (designated L, G, QL, and LH, respectively) were fitted to the data by using proc NUN of SAS (SAS Institute, Inc., Cary, NC, USA). The parameters estimates for L [WT= A * (((1 + exp (-k * AGE)))**-m)] were A = 865.1 +/- 5.42; k= 0.0028 +/- 0.00002; M= 3.808 +/- 0.007; R(2) = 0.95. For G [WT= A * exp (-b * exp (-k * age)] the parameters estimates were A= 967.6 +/- 7.23; k = 0.00217 +/- 0.000015; b = -2.8152 +/- 0.00532. For QL [WT= A + b*age + k*(age*age) + m*log (age)] parameters estimates were A= 37.41 +/- 0.48; k= 0.00019 +/- 6.4E(-6); b= 0.539 +/- 0.006; m= 2.32 +/- 0.23; R(2)=0.96. For LH [WT= A + b*AGE + k*(1/AGE)] the parameters estimates were A= 23.15 +/- 0.44; k=15.16 +/- 0.66; b= 0.707 +/- 0.001; R(2)= 0.96. Each of these curves fit these data equally well and could be used for characterizing growth to two years in beef buffalo.