Optimum Mean Temperature for a Plant Growth Calculated by a New Method of Summation

Abrami, Giovanni

doi:10.2307/1934305

Cited by 24 publications

(15 citation statements)

References 8 publications

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“…We chose the logistic equation for three reasons: (1) In some of our higher densities, we noticed a slowed rate of growth of individual plants, suggesting that they had or were nearing an asymptote, (2) for plants that had not yet leveled off in growth, the logistic equation has the advantage of showing exponential growth in its first phase (i.e., before height reaches half of its eventual asymptote), and (3) the parameters are biologically interpretable— K indicates the asymptotic height of a plant, whereas r describes the intrinsic rate of increase of plants. The logistic equation has been widely used in models of plant growth (e.g., Abrami 1972; Weiner and Thomas 1986; Tsoularis 2001). Using the logistic equation thus allows us to fit a single growth model to all of our data and account for the fact that growth for some plants had started to level off, whereas others had not.…”

Section: Methodsmentioning

confidence: 99%

Across-Environment Genetic Correlations and the Frequency of Selective Environments Shape the Evolutionary Dynamics of Growth Rate in Impatiens Capensis

et al. 2010

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Trade-offs can exist within and across environments, and constrain evolutionary trajectories. To examine the effects of competition and resource availability on trade-offs, we grew individuals of recombinant inbred lines of Impatiens capensis in a factorial combination of five densities with two light environments (full light and neutral shade) and used a Bayesian logistic growth analysis to estimate intrinsic growth rates. To estimate across-environment constraints, we developed a variance decomposition approach to principal components analysis, which accounted for sample size, model-fitting, and within-RIL variation prior to eigenanalysis. We detected negative across-environment genetic covariances in intrinsic growth rates, although only under fulllight. To evaluate the potential importance of these covariances, we surveyed natural populations of I. capensis to measure the frequency of different density environments across space and time. We combined our empirical estimates of across-environment genetic variance-covariance matrices and frequency of selective environments with hypothetical (yet realistic) selection gradients to project evolutionary responses in multiple density environments. Selection in common environments can lead to correlated responses to selection in rare environments that oppose and counteract direct selection in those rare environments. Our results highlight the importance of considering both the frequency of selective environments and the across-environment genetic covariances in traits simultaneously. 4 Authors contributed equally.

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Section: Methodsmentioning

confidence: 99%

Across-Environment Genetic Correlations and the Frequency of Selective Environments Shape the Evolutionary Dynamics of Growth Rate in Impatiens Capensis

et al. 2010

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“…15 is moved downwards by the amount (P -+-Q) -1 (cf. Abrami 1972). Thus the equation for the logistic curve passing through the origin becomes The upper limit of the dry-matter yield is now Q• P I (P+ Q) L Equation 4.13 can be rewritten in more general form (cf.…”

Section: Growth Curvesmentioning

confidence: 99%

A dynamic model for determining the optimum cutting schedule of Italian ryegrass

Pohjonen

1975

AFSci

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A quantitative approach to the determination of the optimum cutting schedule of Italian ryegrass (Lolium multiflorum Lam.) was presented. Optimization was based on a dynamic growth model which included the concepts crop growth rate, development rate and proper growth rate. The proper growth rate measured the growth potential associated with the stages of development in the sward. The crop growth rate of Italian ryegrass was studied at the Arctic Circle Experiment Station during 1973 and 1974. The proper growth rate was determined from primary observations as the derivative of an ordinary logistic curve which passes through origin. The maximum theoretical daily growth of Italian ryegrass was calculated as approx. 300 kg • ha-1• day-1. The optimum cutting schedules using gradient method were sought for Italian ryegrass sward. First, the maximum total dry-matter yield was looked for. Then the maximization was extended to the case, when the yield was weighted with the digestibility of the dry-matter. The maximum yield was obtained when the sward was cut three times, and when the cuttings were concentrated into the latter half of the growing season. The yield of the optimum cutting schedule was not sensitive to small changes in the cutting dates. In the conditions of Finnish Lapland the optimum cutting schedule of Italian ryegrass was: first cut at the end of July, second cut at the end of August and the third cut at the end of September.

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“…The method was later tested against phenology data of Japanese Cherry (Prunus serrulata; Lindsey 1963). The concept was refined in many subsequent works (Abrami 1972;Allen 1976;Arnold 1960;Baskerville and Emin 1969) and transferred to other biological systems (e.g. Eckenrode and Chapman 1972;Gilbert and Gutierrez 1973;Sevacherian et al 1977).…”

Section: Introductionmentioning

confidence: 99%

Grapevine bud break prediction for cool winter climates

Nendel

2009

Int J Biometeorol

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Statistical analysis of bud break data for grapevine (Vitis vinifera L. cvs. Riesling and Müller-Thurgau) at 13 sites along the northern boundary of commercial grapevine production in Europe revealed that, for all investigated sites, the heat summation method for bud break prediction can be improved if the starting date for the accumulation of heat units is specifically determined. Using the coefficient of variance as a criterion, a global minimum for each site can be identified, marking the optimum starting date. Furthermore, it was shown that the application of a threshold temperature for the heat summation method does not lead to an improved prediction of bud break. Using site-specific parameters, bud break of grapevine can be predicted with an accuracy of +/- 2.5 days. Using average parameters, the prediction accuracy is reduced to +/- 4.5 days, highlighting the sensitivity of the heat summation method to the quality and the representativeness of the driving temperature data.

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Optimum Mean Temperature for a Plant Growth Calculated by a New Method of Summation

Cited by 24 publications

References 8 publications

Across-Environment Genetic Correlations and the Frequency of Selective Environments Shape the Evolutionary Dynamics of Growth Rate in Impatiens Capensis

Across-Environment Genetic Correlations and the Frequency of Selective Environments Shape the Evolutionary Dynamics of Growth Rate in Impatiens Capensis

A dynamic model for determining the optimum cutting schedule of Italian ryegrass

Grapevine bud break prediction for cool winter climates

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