5 resultados para Yield Response

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)


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Carbon emissions related to human activities have been significantly contributing to the elevation of atmospheric [CO(2)] and temperature. More recently, carbon emissions have greatly accelerated, thus much stronger effects on crops are expected. Here, we revise literature data concerning the physiological effects of CO(2) enrichment and temperature rise on crop species. We discuss the main advantages and limitations of the most used CO(2)-enrichment technologies, the Open-Top Chambers (OTCs) and the Free-Air Carbon Enrichment (FACE). Within the conditions expected for the next few years, the physiological responses of crops suggest that they will grow faster, with slight changes in development, such as flowering and fruiting, depending on the species. There is growing evidence suggesting that C(3) crops are likely to produce more harvestable products and that both C(3) and C(4) crops are likely to use less water with rising atmospheric [CO(2)] in the absence of stressful conditions. However, the beneficial direct impact of elevated [CO(2)] on crop yield can be offset by other effects of climate change, such as elevated temperatures and altered patterns of precipitation. Changes in food quality in a warmer, high-CO(2) world are to be expected, e.g., decreased protein and mineral nutrient concentrations, as well as altered lipid composition. We point out that studies related to changes in crop yield and food quality as a consequence of global climatic changes should be priority areas for further studies, particularly because they will be increasingly associated with food security. (c) 2009 Elsevier Ltd. All rights reserved.

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The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model, Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the rth moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin er al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214-223]. The usefulness of these models is illustrated in a Simulation study and in applications to three real data sets. (C) 2009 Elsevier B.V. All rights reserved.

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This work evaluated the effect of pressure and temperature on yield and characteristic flavour intensity of Brazilian cherry (Eugenia uniflora L) extracts obtained by supercritical CO(2) using response surface analysis, which is a simple and efficient method for first inquiries. A complete central composite 2(2) factorial experimental design was applied using temperature (ranging from 40 to 60 degrees C) and pressure (from 150 to 250 bar) as independent variables. A second order model proved to be predictive (p <= 0.05) for the extract yield as affected by pressure and temperature, with better results being achieved at the central point (200 bar and 50 degrees C). For the flavour intensity, a first order model proved to be predictive (p <= 0.05) showing the influence of temperature. Greater characteristic flavour intensity in extracts was obtained for relatively high temperature (> 50 degrees C), Therefore, as far as Brazilian cherry is concerned, optimum conditions for achieving higher extract yield do not necessarily coincide to those for obtaining richer flavour intensity. Industrial relevance: Supercritical fluid extraction (SFE) is an emerging clean technology through which one may obtain extracts free from organic solvents. Extract yields from natural products for applications in food, pharmaceutical and cosmetic industries have been widely disseminated in the literature. Accordingly, two lines of research have industrial relevance, namely, (i) operational optimization studies for high SFE yields and (ii) investigation on important properties extracts are expected to present (so as to define their prospective industrial application). Specifically, this work studied the optimization of SFE process to obtain extracts from a tropical fruit showing high intensity of its characteristic flavour, aiming at promoting its application in natural aroma enrichment of processed foods. (C) 2008 Elsevier Ltd. All rights reserved.

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The objective of the study was assessing the effect of the nitrogen and the aspersion irrigation on the growth and dry matter yield of black oats (Avena strigosa Schreb). The experiment was conducted in the Campus of USP in Pirassununga, Sao Paulo State. In the study were evaluated four nitrogen levels (0, 50, 100 and 150 kg of N ha(-1)) and also the presence or absence of the irrigation. The plant variables evaluated were: mean height, dry matter percentage, yield and growth rate. The results had disclosed to greater height of plant in the irrigated condition, reflecting in the higher production of dry matter. In dry land area, percentage of DM was 24.7% and in irrigated area 18.7%. The nitrogen was significantly only for plant mean height that showed linear fit when carried through the irrigation. The effect of the irrigation was better for the production of black oats than nitrogen.

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Allyl 1-naphthyl ethers are useful compounds for different purposes, but reported methods to synthesize them require long reaction times. In this work, we have obtained allyl 1-naphthyl ether in good yield using ultrasonic-assisted methodology in a 1-h reaction. A central composite design was used to obtain a statistical model and a response surface (p < 0.05; R(2) = 0.970; R(adj)(2) = 0.949; R(pred)(2) = 0.818) that can predict the optimal conditions to maximize the yield, validated experimentally. (C) 2010 Elsevier B.V. All rights reserved.