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

Abstract

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.

Leaflet shape analysis separates rose cultivars and estimates leaf area

“…Blanco and Folegatti (2005) predicted leaf area by a linear model LA=-4.27 + 0.88 L×W with high accuracy and only little differences observed between the cultivars and environment. Tsialtas and Maslaris (2008) documented that in sugar beet a model developed based on leaf dimensions [LA= 31.928 + 0.5083 L×W] satisfactorily predicted LA better than when the leaf width or length was solely used. Similarly, Lu et al (2004) proposed that the simple and linear relationships between leaf area and leaf dimensions (length and width) might be useful for nondestructive estimation of plants leaf area.…”

Prediction of Leaf Area, Fresh and Dry Weight in Stinging Nettle (Urtica dioica) by Linear Regression Models

“…The most important part of stinging nettle is leaf and leaf area is an important variable in physiological and agronomical researches. As well as plant yield and quality are influenced by photosynthesis and transpiration rate that are intimately linked with leaf area [23]. Therefore recording of leaf area during growth and development can be valuable.…”

“…They predicted leaf area by model LA=-4.27 + 0.88 L×W with high accuracy and only little differences between the cultivars, location and growing systems. For leaf area prediction models in sugar beet, the results of study of Tsialtas and Maslaris [23] showed model based on both leaf dimensions [LA= 31.928 + 0.5083 L×W] predicted LA better than that using only leaf width or length. Similar reports from Kumar and Sharma [24], Silva et al [25] and Rouphael et al [26] in leaf area estimation of clary sage (Salvia sclarea L.), potato (Solanum tubersum L.) and rose (Rosa hybrid L.) respectively.…”

“…Similary, Mokhtarpour et al [17] showed to predict of leaf fresh and dry weigh in maize, power function gives a better estimation than linear models. But the main advantage of the linear relationships relative to power function is its simplicity of use [23].…”