Leaf shape and its relationship with Leaf Area Index in a sugar beet (Beta vulgaris L.) cultivar

Tsialtas, J. T.; Maslaris, N.

doi:10.1007/s11099-008-0009-9

Cited by 9 publications

(17 citation statements)

References 25 publications

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“…For large quantities of measurements, laboratory instruments, such as the scanning planimeter LiCor Model: Li3100 (Li-Cor Corp. Lincol, Nebrasca, USA) or PC-connected systems, like the WinDIAS2.0 colour conveyor image analyser (Delta-T Devices Ltd., Cambridge, UK), which can record images to a computer, can be used. These techniques have proven themselves to be useful in grapevine research (Smith & Kliewer, 1984;Intrigliolo et al, 2009) and in other fields of horticulture (Tsialtas & Maslaris, 2007;Klamkowski & Treder, 2008). Caldas et al (1992) used a hand-scanner and pixel based software for the estimation of the area of grapevine leaves.…”

Section: Introductionmentioning

confidence: 99%

GRA.LE.D. (GRApevine LEaf Digitalization) Software for the Detection and Graphic Reconstruction of Ampelometric Differences Between Vitis Leaves

Bodor

Baranyai

Bálo

et al. 2016

SAJEV

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Section: Introductionmentioning

confidence: 99%

GRA.LE.D. (GRApevine LEaf Digitalization) Software for the Detection and Graphic Reconstruction of Ampelometric Differences Between Vitis Leaves

Bodor

Baranyai

Bálo

et al. 2016

SAJEV

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“…Leaf area (LA) is one of the most useful parameters for understanding many aspects of functional plant performance, such as light and water use (Bunce 1989, Lott et al 2000, Merilo et al 2006, Tsialtas and Maslaris 2007. Leaf size is often linked to plant adaptations that determine the success or failure of a plant to settle in a given habitat (Meier and Leuschner 2008).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Nondestructive Methods of Leaf Area Estimation

우효진¹,

박용목²

2009

J. Ecol. Environ.

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ABSTRACT:We compared two nondestructive methods for leaf area estimation using leaves of 16 common plant species classified into six types depending on leaf shape. Relatively good linear relationships between actual leaf area (LA) and leaf length (L), width (W), or the product of length and width (LW) were found for ordinary leaves with lanceolate, oblanceolate, linear and sagitttate shapes with entire margins, serrate margins, mixed margins with a entire form and shallow lobes, and ordinary incised margins. LA was better correlated with LW than L or W, with R 2 > 0.91. However, for deeply incised lobes, LA estimation using LW showed low correlation coefficient values, indicating low accuracy. On the other hand, a method using photographic paper showed a good correlation between estimates of area based on the mass of a cut-out leaf image on a photographic sheet (PW) and actual leaf area for all types of leaf shape. Thus, the PW method for LA estimation can be applied to all shapes of leaf with high accuracy. The PW method takes a little more time and has a higher cost than leaf estimation methods using LW based on leaf dimensions. These results indicate that researchers should choose their nondestructive LA estimation method according to their research goals.

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“…Sugar beet shows different leaf morphology depending on cultivar ploidy (Bosemark 1993) and environmental effects (Tsialtas and Maslaris 2007). Leaf shape determination can be useful for both taxonomic (Iwata et al 2002, Camargo Neto et al 2006) and physiological (Goudriaan andvan Laar 1994, Bhatt andChanda 2003) studies.…”

Section: Introductionmentioning

confidence: 99%

“…However, its use in field physiology studies is restricted due to reasons analogous to those referred to LA determination in the field. Thus, non-destructive models for LAI estimation based on LA measurements have already been developed for beans (de Jesus et al 2001), tomato (Blanco and Folegatti 2003), and sugar beet (Tsialtas and Maslaris 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Evaluation of models proposed for LA and LAI estimation under different environmental or growing conditions is necessary since leaf morphology is affected by growth, nutrition, and temperature (Njoku 1957, Bhatt and Chanda 2003, Tsialtas and Maslaris 2007. Till now, models based on W and LA were proposed for LA and LAI estimation, respectively, for sugar beet cv.…”

Section: Introductionmentioning

confidence: 99%

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Nitrogen fertilization effects on leaf morphology and evaluation of leaf area and leaf area index prediction models in sugar beet

Tsialtas

Maslaris

2008

Photosynt.

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In a two-year experiment (2002)(2003), five N application rates [0, 60, 120, 180, and 240 kg(N) ha -1 , marked N 0 , N 60 , N 120 , N 180 , and N 240 , respectively] were applied to sugar beet cv. Rizor arranged in a Randomized Complete Block design with six replications. Leaf shape parameters [leaf area (LA), maximum length (L), maximum width (W), average radial (AR), elongation (EL), and shape factor (SF)] were determined using an image analysis system, and leaf area index (LAI) was non-destructively measured every two weeks, from early August till mid-September (four times). Years, samplings, and their interaction had significant effects on the determined parameters. Fertilization at the highest dose (N 240 ) increased L and sampling×fertilization interaction had significant effects on LA, L, W, and SF. For this interaction, W was the best-correlated parameter with LA and LAI meaning that W is a good predictor of these parameters. Two proposed models for LA estimation were tested. The model based on both leaf dimensions [LA = 0.5083 (L×W) + 31.928] predicted LA better than that using only W (LA = 21.686 W -112.88). Instrumentally measured LAI was highly correlated with predicted LAI values derived from a quadratic function [LAI = -0.00001 (LA) 2 + 0.0327 LA -2.0413]. Thus, both LA and LAI can be reliably predicted non-destructively by using easily applied functions based on leaf dimensions (L, W) and LA estimations, respectively.Additional key words: Beta vulgaris; leaf area; leaf length; leaf width; non-destructive methods.

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Leaf shape and its relationship with Leaf Area Index in a sugar beet (Beta vulgaris L.) cultivar

Cited by 9 publications

References 25 publications

GRA.LE.D. (GRApevine LEaf Digitalization) Software for the Detection and Graphic Reconstruction of Ampelometric Differences Between Vitis Leaves

GRA.LE.D. (GRApevine LEaf Digitalization) Software for the Detection and Graphic Reconstruction of Ampelometric Differences Between Vitis Leaves

Comparison of Two Nondestructive Methods of Leaf Area Estimation

Nitrogen fertilization effects on leaf morphology and evaluation of leaf area and leaf area index prediction models in sugar beet

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