Inflorescence characteristics, seed composition, and allometric relationships predicting seed yields in the biomass crop <i>Cynara cardunculus</i>

Archontoulis, S.V.; Struik, P.C.; Yin, Xinyou; Bastiaans, L.; Vos, J.; Danalatos, N.G.

doi:10.1111/j.1757-1707.2010.01045.x

Cited by 23 publications

(13 citation statements)

References 22 publications

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“…Archontoulis et al. (2010) also found that the contents of oleic acid and linoleic acid accounted for 81.91% of the total fatty acids contents in the cardoon ( Cynara cardunculus L.).…”

Section: Discussionmentioning

confidence: 96%

“…The results of Zhang et al (2016) on Korean pine (Pinus koreansis) showed that oleic acid (C18:1) and linoleic acid (C18:2) were the most abundant fatty acids. Archontoulis et al (2010) also found that the contents of oleic acid and linoleic acid accounted for 81.91% of the total fatty acids contents in the cardoon (Cynara cardunculus L.).…”

Section: Oil Content and Fatty Acid Analysismentioning

confidence: 89%

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Oil content, fatty acid composition and biodiesel properties among natural provenances of Siberian apricot (Prunus sibirica L.) from China

Wang

Liu

et al. 2020

GCB Bioenergy

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“…Archontoulis et al. (2010) also found that the contents of oleic acid and linoleic acid accounted for 81.91% of the total fatty acids contents in the cardoon ( Cynara cardunculus L.).…”

Section: Discussionmentioning

confidence: 96%

Section: Oil Content and Fatty Acid Analysismentioning

confidence: 89%

Oil content, fatty acid composition and biodiesel properties among natural provenances of Siberian apricot (Prunus sibirica L.) from China

Wang

Liu

et al. 2020

GCB Bioenergy

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“…However, as previously mentioned, the three oleaginous crops compared here show different seed yield and straw production. Although an overall range of 80-250 g· m −2 potential of seed yield is reported for cardoon [64], the average grain yield of 0.75 t· ha −1 and 10 t· ha −1 for straw considered in this paper is in accordance with the recent findings of [65]. In fact, those authors reported a mean seed yield of 603 kg· ha −1 in a large scale cultivation (77 ha) of the cardoon in southern Portugal and confirmed that this species is suitable for biomass production in Mediterranean regions.…”

Section: Oleaginous Crop Cultivationmentioning

confidence: 99%

LCA Study of Oleaginous Bioenergy Chains in a Mediterranean Environment

et al. 2014

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This paper reports outcomes of life cycle assessments (LCAs) of three different oleaginous bioenergy chains (oilseed rape, Ethiopian mustard and cardoon) under Southern Europe conditions. Accurate data on field practices previously collected during a three-year study at two sites were used. The vegetable oil produced by oleaginous seeds was used for power generation in medium-speed diesel engines while the crop residues were used in steam power plants. For each bioenergy chain, the environmental impact related to cultivation, transportation of agricultural products and industrial conversion for power generation was evaluated by calculating cumulative energy demand, acidification potential and global warming potential. For all three bioenergy chains, the results of the LCA study show a considerable saving of primary energy (from 70 to 86 GJ· ha −1 ) and greenhouse gas emissions (from 4.1 to 5.2 t CO2· ha −1 ) in comparison to power generation from fossil fuels, although the acidification potential of these bioenergy chains may be twice that of OPEN ACCESSEnergies 2014, 7 6259 conventional power generation. In addition, the study highlights that land use changes due to the cultivation of the abovementioned crops reduce soil organic content and therefore worsen and increase greenhouse gas emissions for all three bioenergy chains. The study also demonstrates that the exploitation of crop residues for energy production greatly contributes to managing environmental impact of the three bioenergy chains.

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“…[3.1]). For application examples, see Vega et al (2000), Vega and Sadras (2003), and Archontoulis et al (2010). The Michaelis-Menten equation (Eq.…”

Section: Group Vi-othersmentioning

confidence: 99%

Nonlinear Regression Models and Applications in Agricultural Research

2015

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In data analysis, we often ask the following questions:Which is the best model to describe our data? Which is the best statistical index to judge the goodness of fit? How do we choose among competing models? There are no simple answers to these questions. Here we attempt to provide agronomists with a general framework on how to approach these questions appropriately. Our specific objectives are: (i) to provide a succinct overview of nonlinear models and to develop a guideline to understand the family of functions used in agricultural applications; (ii) to indicate techniques to modify nonlinear models and how to cope with multiple nonlinear models; (iii) to discuss key methodological issues on parameter estimation, model performance, and comparison; and (iv) to demonstrate step-by-step analysis of experimental data using a nonlinear regression model. The structure follows the flow diagram in Fig. 1. We start with the definition of nonlinear regression models and discuss their main advantages and disadvantages. Then we present 77 nonlinear functions (including those in supplemental tables) with references to applications in agriculture. We offer an updated overview of methodologies to fit models, choose starting values, assess goodness of fit, select the best models, and evaluate residuals. Finally, we reanalyze experimental data on biomass growth with time (Danalatos et al., 2009). NONLINEAR REGRESSION MODELS DefinitionIn general, statistical models used in agricultural applications can be described with the following notation:where y is the response variable, f is the function or model, x are the inputs, q denotes the parameters to be estimated, and e is the error. Each parameter can be evaluated for whether it is linear or not: if the second derivative of the function with respect to a parameter is not equal to zero, then the parameter is nonlinear. Thus a given function ( f ) can have a mix of linear and nonlinear parameters. Why Should We Use Nonlinear Models?The main advantages of nonlinear models are parsimony, interpretability, and prediction (Bates and Watts, 2007). In general, nonlinear models are capable of accommodating a vast variety of mean functions, although each individual nonlinear model can be less flexible than linear models (i.e., polynomials) in terms of the variety of data they can describe; however, nonlinear models appropriate for a given application can be more parsimonious (i.e., there will be fewer parameters involved) and more easily interpretable. Interpretability comes from the fact that the parameters can be associated with a biologically meaningful process. For example, one of the most widely used nonlinear models is the logistic equation (Eq. [2.1] in Table 1). This model describes the pervasive S-shaped growth curve. The ABSTRACT Nonlinear regression models are important tools because many crop and soil processes are better represented by nonlinear than linear models. Fitting nonlinear models is not a single-step procedure but an involved process that requires careful examinati...

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Inflorescence characteristics, seed composition, and allometric relationships predicting seed yields in the biomass crop Cynara cardunculus

Cited by 23 publications

References 22 publications

Oil content, fatty acid composition and biodiesel properties among natural provenances of Siberian apricot (Prunus sibirica L.) from China

Oil content, fatty acid composition and biodiesel properties among natural provenances of Siberian apricot (Prunus sibirica L.) from China

LCA Study of Oleaginous Bioenergy Chains in a Mediterranean Environment

Nonlinear Regression Models and Applications in Agricultural Research

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