137 resultados para Gompertz


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Digital Image

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In recent work (Kroll, 1990) on dairy cattle growth using its monthly weights, a Gompertz function, first considering the autocorrelated error structure, and second, the independent error structure, was fitted. The last explained 25% more of the total variance than the former. In this work, a Gompertz function was fitted with autocorrelated error structure adding seasonality to the model. This was suggested taking into account the time series autocorrelation function. In order to estimate the six parameters, an iterative procedure was used, employing a matrix X = [G\S], where G is the usual matrix for fitting the Gompertz function, and S is a 0s, 1s, -1s matrix necessary to take into account seasonality. With this model, the adjustment was better in terms of the coefficient determination.

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Incluye Bibliografía

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El documento presenta la aplicacion del modelo de Gompertz para: a).ajustes de las estructuras de fecundidad por edades para diferentes periodos aplicado a Chile, Costa Rica, Guatemala, Panama y Peru; y b).investigacion de las tendencias de los parametros alfa y beta, tratando de establecer la asociacion entre aquellos y los cambios en los niveles de fecundidad. La informacion basica usada consiste en un conjunto de tasas de fecundidad por edades para cada pais y periodos las cuales se promediaron para obtener la estructura standard. Al graficar los valores de alfa y beta, se concluye que existe una clara tendencia en los casos de Chile, Costa Rica y Peru. La tendencia de los valores de alfa, en estos casos, esta directamente asociada con la tendencia del nivel de fecundidad y con beta ocurre lo contrario. Para estos tres paises, se realizo una proyeccion grafica de los dos parametros

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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Documento de trabajo para el Panel de América Latina, Santiago, 16-20 julio 1979

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Pós-graduação em Biometria - IBB

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This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone (Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected

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Records of shrimp growth and water quality made during 12 crops from each of 48 ponds, over a period of 6.5 years, were provided by a Queensland, Australia, commercial shrimp farm, These data were analysed with a new growth model derived from the Gompertz model. The results indicate that water temperature, mortality and pond age significantly affect growth rates. After 180 days, shrimp reach 34 g at constant 30 degrees C, but only 15 g after the same amount of time at 20 degrees C. Mortality, through thinning the density of shrimp in the ponds, increased the growth rate, but the effect is small. With continual production, growth rates at first remained steady, then appeared to decrease for the sixth and seventh crop, after which they have increased steadily with each crop. It appears that conservative pond management, together with a gradual improvement in husbandry techniques, particularly feed management, brought about this change. This has encouraging implications for the long-term sustainability of the farming methods used. The growth model can be used to predict productivity, and hence, profitability, of new aquaculture locations or new production strategies.

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This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone (Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected. (C) 2013 Elsevier B.V. All rights reserved.

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The critical crop-weed competition period in a dry-seeded rice system is an important consideration in formulating weed management strategies. Field experiments were conducted in the summer seasons of 2012 and 2013 at the Punjab Agricultural University, Ludhiana, India, to determine the extent of yield loss in two different rice cultivars (PR 114 and PR 115) with different periods of weed interference. Twelve weed control timings were used to identify critical periods of weed competition in dry-seeded rice. PR 114, a long-duration rice cultivar (145 d) having slower initial growth than PR 115 (125 d), was more prone to yield losses. In both years, 100% yield loss was observed where weeds were not controlled throughout the season. In weed-free plots, the grain yield of PR 114 was 6.39-6.80 t ha-1, for PR 115, it was 6.49-6.87 t ha-1. Gompertz and logistic equations fitted to yield data in response to increasing periods of weed control and weed interference showed that, PR 114 had longer critical periods than PR 115. Critical weed-free periods to achieve 95% of weed-free yield for PR 114 was longer than for PR 115 by 31 days in 2012 and 26 days in 2013. Weed infestation also influenced the duration of critical periods. Higher weed pressure in 2012 than in 2013 increased the duration of the critical period of crop-weed competition in that year. The identification of critical crop-weed competition periods for different cultivars will facilitate improved decision-making regarding the timing of weed control and the adoption of cultivars having high weed-suppressing abilities. This will also contribute to the development of integrated weed management in dry-seeded rice systems.