Leaf area estimation by simple measurements and evaluation of leaf area prediction models in Cabernet-Sauvignon grapevine leaves

For two growing seasons (2005 and 2006), leaves of grapevine cv. Cabernet-Sauvignon were collected at three growth stages (bunch closure, veraison, and ripeness) from 10-year-old vines grafted on 1103 Paulsen and SO4 rootstocks and subjected to three watering regimes in a commercial vineyard in central Greece. Leaf shape parameters (leaf area-LA, perimeter-Per, maximum midvein length-L, maximum width-W, and average radial-AR) were determined using an image analysis system. Leaf morphology was affected by sampling time but not by year, rootstock, or irrigation treatment. The rootstock×irrigation×sampling time interaction was significant for all the leaf shape parameters (LA, Per, L, W, and AR) and the means of the interaction were used to establish relationships between them. A highly significant linear function between L and LA could be used as a non-destructive LA prediction model for Cabernet-Sauvignon. Eleven models proposed for the non-destructive LA estimation in various grapevine cultivars were evaluated for their accuracy in predicting LA in this cultivar. For all the models, highly significant linear functions were found between calculated and measured LA. Based on r ² and the mean square deviation (MSD), the model proposed for LA estimation in cv. Cencibel [LA = 0.587(L×W)] was the most appropriate.     

Leaf area estimation in a sugar beet cultivar by linear models

An indirect method of leaf area measurement for Rizor sugar beet cultivar was tested. Leaves were sampled during two growing seasons in a Randomised Complete Block Design experiment. For 2002 samplings, leaf area [cm²] was linearly correlated with maximum leaf width [cm] using all leaf samples (r ² = 0.83, p < 0.001) or using the means of the 8 sampling occasions (r ² = 0.97, p < 0.001). Correlations between leaf area and leaf mid vein length [cm] were weaker (r ² = 0.75, p < 0.001 and r ² = 0.93, p < 0. 001, respectively). For 2003 samplings, the area estimated by the equations was highly correlated to the measured leaf area.     

Leaf area estimation in muskmelon by allometry

This study developed a method for estimating the leaf area (LA) of muskmelon by using allometry. The best linear measure was evaluated first, testing both a leaf length and width (W). Leaf samples were collected from plants grown in containers of different sizes, leaves of four cultivars, at different develpoment stages, and of different leaf sizes. Two constants of a power equation were determined for relating allometrically a linear leaf measure and LA, in a greenhouse crop. W proved to be a better fit than the leaf length. The maximum attainable W and LA were estimated at W_x = 15.4 cm and LA_x = 174.1 cm². The indicators of fit quality showed that the function was properly related to LA and W as: LA/LA_x = A_o × (W/W_Lx)^b; the allometric exponent was b = 1.89, where R ² = 0.9809 (n = 484), the absolute sum of squares, 0.4584, and the standard deviation of residues, 0.03084, based on relative values calculations (LA/LA _x and W/W_Lx). The relationship was not affected by the cultivar, crop age, leaf size or stress treatment in the seedling stage. The empirical value of allometric constant (A₀) was estimated as 0.963.     

Allometric models for leaf area estimation across different leaf-age groups of evergreen broadleaved trees in a subtropical forest

The accurate and nondestructive determination of individual leaf area (LA) of plants, by using leaf length (L) and width (W) measurement or combinations of them, is important for many experimental comparisons. Here, we propose reliable and simple regressions for estimating LA across different leaf-age groups of eight common evergreen broadleaved trees in a subtropical forest in Gutianshan Natural Reserve, eastern China. During July 2007, the L, W, and LA of 2,923 leaves (202 to 476 leaves for each species) were measured for model construction and the respective measurements on 1,299 leaves were used for model validation. Mean L, W, LA and leaf shape (L:W ratio) differed significantly between current and older leaves in four out of the eight species. The coefficients of one-dimension LA models were affected by leaf age for most species while those incorporating both leaf dimensions (L and W) were independent of leaf age for all the species. Therefore, the regressions encompassing both L and W (LA = a L W + b), which were independent of leaf age and also allowed reliable LA estimations, were developed. Comparison between observed and predicted LA using these equations in another dataset, conducted for model validation, exhibited a high degree of correlation (R ² = 0.96−0.99). Accordingly, these models can accurately estimate the LA of different age groups for the eight evergreen tree species without using instruments.     

A simple model for nondestructive leaf area estimation in bedding plants

Measurement of leaf area is commonly used in many horticultural research experiments, but it is generally destructive, requiring leaves to be removed for measurement. Determining the individual leaf area (LA) of bedding plants like pot marigold (Calendula officinalis L.), dahlia (Dahlia pinnata), sweet William (Dianthus barbatus L.), geranium (Pelargonium × hortorum), petunia (Petunia × hybrida), and pansy (Viola wittrockiana) involves measurements of leaf parameters such as length (L) and width (W) or some combinations of these parameters. Two experiments were carried out during spring 2010 (on two pot marigold, four dahlia, three sweet William, four geranium, three petunia, and three pansy cultivars) and summer 2010 (on one cultivar per species) under greenhouse conditions to test whether a model could be developed to estimate LA of bedding plants across cultivars. Regression analysis of LA versus L and W revealed several models that could be used for estimating the area of individual bedding plants leaves. A linear model having LW as the independent variable provided the most accurate estimate (highest R ², smallest mean square error, and the smallest predicted residual error sum of squares) of LA in all bedding plants. Validation of the model having LW of leaves measured in the summer 2010 experiment coming from other cultivars of bedding plants showed that the correlation between calculated and measured bedding plants leaf areas was very high. Therefore, these allometric models could be considered simple and useful tools in many experimental comparisons without the use of any expensive instruments.     

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

In a two-year experiment (2002–2003), five N application rates [0, 60, 120, 180, and 240 kg(N) ha⁻¹, marked N₀, N₆₀, N₁₂₀, N₁₈₀, and N₂₄₀, 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 (N₂₄₀) 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.     

Needle area measurement by the cut method and estimation of specific leaf area inCryptomeria japonica

Needle surface area inCryptomeria japonica was measured using a newly proposed cut method. Sample needles of various lengths were taken from foliage shoots belonging to various height layers of nine trees growing at three sites. Needles were cut into small pieces with a hand-made cutter made of razor blades and washers. By measuring the circumference and thickness of each piece, its lateral area was calculated and summed to give the total surface area of the needle. For estimating the surface area of a needle (s), two linear parameters of needle size termedy _n* and /were proposed:y _n* was the distance between the needle tip and the uppermost point of attachment of the needle to the shoot, whilel was the distance between the needle tip and the lowermost point of attachment. The power-form relationship betweens andl was superior to thes-y _n* relationship, since the former did not differ significantly among shoots of different diameter. Based on thes-l relationship, the total surface area of a shoot was estimated from thel-census of the shoot. Specific leaf area of a shoot (SLA), defined as half of the shoot surface area divided by the dry weight of the shoot, decreased from 90 to 3 [cm²g(dry wt)⁻¹] with the diameter of the woody tissue of the foliage shoot.     

Leaf area estimation of sunflower leaves from simple linear measurements

Simple, accurate, and non-destructive methods for determining leaf area (LA) of plants are important for many experimental comparisons. Determining the individual LA of sunflower (Helianthus annuus L.) involves measurements of leaf parameters such as length (L) and width (W), or some combinations of these parameters. Two field experiments were carried out during 2003 and 2004 to compare predictive equations of sunflower LAs using simple linear measurements. Regression analyses of LA vs. L and W revealed several equations that could be used for estimating the area of individual sunflower leaves. A linear equation having W² as the independent variable provided the most accurate estimate (r ² = 0.98, MSE = 985) of sunflower LA. Validation of the equation having W² of leaves measured in the 2004 experiment showed that the correlation between calculated and measured areas was very high.     

Weighting coefficients for the estimation of sensory threshold

This paper discusses the determ-nation of difference thresholds(ED50) by least squares procedures. A table of weights (W) tocorrect ED50 for chance probability was constructed for pairedand triangle designs. The probability (P) of a correct judgmentabove chance for the paired design is P = 2p^*–1, wherep^* is the actual proportion of correct responses. For the triangledesign, P = (3p^*–l)/2. In both designs, P was weightedby W = y²/p^*q^*, where y² is the squared ordinate of the normalcurve based on P, and q^* = 1–p^*. An example of the useof the table of weights is provided.     

Leaf habit of tree species does not strongly predict leaf litter decomposition but alters climate-decomposition relationships

Plant and Soil - Leaf habit of tree species (evergreen versus deciduous) is proposed to be an important determinant of leaf litter decomposition, but it remains largely understudied as to how...     

A structured approach for selection among candidate metabolic network models and estimation of unknown stoichiometric coefficients

A metabolic network model is one of the cornerstones of the emerging Metabolic Engineering methodology. In this article, special attention is therefore, given to the phase of model building. A five-stage structured approach to metabolic network modeling is introduced. The basic steps are: (1) to collect a priori knowledge on the reaction network and to build candidate network models, (2) to perform an a priori check of the model, (3) to estimate the unknown parameters in the model, (4) to check the identified model for acceptability from a biological and thermodynamic point of view, and (5) to validate the model with new data. The approach is illustrated with a growth system involving baker's yeast growing on mixtures of substrates. Special attention is given to the central uncertainties in metabolic network modeling, i.e., estimation of energetic parameters in the network and the choice of the source of anabolic reducing equivalents NADPH.     

Leaf age and season influence the relationships between leaf nitrogen, leaf mass per area and photosynthesis in maple and oak trees

Abstract. Seasonal changes in photosynthesis, leaf nitrogen (N) contents and leaf mass per area (LMA) were observed over three growing seasons in open-grown sun-lit leaves of red maple ( Acer rubrum ), sugar maple ( A. sacchamm ) and northern pin oak ( Quereus ellipsoidalis ) trees in southern Wisconsin. Net photosynthesis and leaf N were highly linearly correlated on both mass and area bases within all species from late spring until leaf senescence in fall. Very early in the growing season leaves had high N concentrations, but low photosynthetic rates per unit leaf N, suggesting that leaves were not fully functionally developed at that time. Leaf N per unit area and LMA had nonparallel seasonal patterns, resulting in differing relationships between leaf N/area and LMA in the "early versus late growing season. As a result of differences in seasonal patterns between leaf N/area and LMA, net photosynthesis/area was higher for a given LMA in the spring than fall, and the overall relationships between these two parameters were poor.     

Black Spot: a platform for automated and rapid estimation of leaf area from scanned images

Leaf area and its derivatives (e.g. specific leaf area) are widely used in ecological assessments, especially in the fields of plant–animal interactions, plant community assembly, ecosystem functioning and global change. Estimating leaf area is highly time-consuming, even when using specialized software to process scanned leaf images, because manual inputs are invariably required for scale detection and leaf surface digitisation. We introduce Black Spot Leaf Area Calculator (hereafter, Black Spot), a technique and stand-alone software package for rapid and automated leaf area assessment from images of leaves taken with standard flatbed scanners. Black Spot operates on comprehensive rule-sets for colour band ratios to carry out pixel-based classification which isolates leaf surfaces from the image background. Importantly, the software extracts information from associated image meta-data to detect image scale, thereby eliminating the need for time-consuming manual scale calibration. Black Spot’s output provides the user with estimates of leaf area as well as classified images for error checking. We tested this method and software combination on a set of 100 leaves of 51 different plant species collected from the field. Leaf area estimates generated using Black Spot and by manual processing of the images using an image editing software generated statistically identical results. Mean error rate in leaf area estimates from Black Spot relative to manual processing was ?0.4 % (SD = 0.76). The key advantage of Black Spot is the ability to rapidly batch process multi-species datasets with minimal user effort and at low cost, thus making it a valuable tool for field ecologists.