Leaf allometry and prediction of specific leaf area (SLA) in a sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.) cultivar

Sugar beet cv. Rizor was grown for five growing seasons (2002–2006) in field conditions in Thessaly, central Greece. A total of 55 samplings took place during the growing seasons and allometric growth of the leaves was monitored. Highly significant (p<0.001) quadratic relationships were found between individual leaf mass (LM), individual leaf area (LA), aboveground dry biomass (ADB), and leaf area index (LAI). Only the LM-LA relationship (LA = 43.444 LM² − 10.693 LM + 118.34) showed a relatively high r ² (0.63) and thus could be used for prediction of LA. Specific leaf area (SLA) was significantly related with leaf water content (LWC) (SLA = 26 279 LWC² − 44 498 LWC + 18 951, r ² = 0.91, p<0.001) and thus LWC could be a good indirect predictor of SLA in this cultivar.     

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.     

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 prediction model for sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.) cultivars

In two successive years (2003 and 2004), a set of 16 commercial sugar beet cultivars was established in Randomized Complete Block experiments at two sites in central Greece. Cultivar combination was different between years, but not between sites. Leaf sampling took place once during the growing season and leaf area, LA [cm²], leaf midvein length, L [cm] and maximum leaf width, W [cm] were determined using an image analysis system. Leaf parameters were mainly affected by cultivars. Leaf dimensions and their squares (L², W²) did not provide an accurate model for LA predictions. Using L×W as an independent variable, a quadratic model (y = 0.003 x² − 1.3027 x + 296.84, r ² = 0.970, p<0.001, n = 32) provided the most accurate estimation of LA. With compromises in accuracy, the linear relationship between L×W and LA (y = 0.5083 x + 31.928, r ² = 0.948, p<0.001, n = 32) could be used as a prediction model thanks to its simplicity.     

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.     

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.     

Evaluation of a leaf area prediction model proposed for sunflower

Six leaf samplings were conducted in two sunflower (Helianthus annuus L.) hybrids during the 2006 growing season in order to evaluate a simple model proposed for leaf area (LA) estimation. A total of 144 leaves were processed using an image analysis system and LA, maximum leaf width (W) [cm], and midvein length (L) [cm] were measured. Also, LA was estimated using the model proposed by Rouphael et al. (2007). Measured LA was exponentially related with L and W, and the W-LA relationships showed higher r ². Estimated LA was strongly and exponentially related with L. Strong, linear relationships with high r ² between estimated and measured LA confirmed the high predictability of the proposed model.     

五种火棘属植物的叶面积回归分析

以采集于贵州、云南、广西、湖南等地的火棘、密花火棘、全缘火棘、细圆齿火棘和窄叶火棘共5种火棘属植物26 401个成熟叶样为材料,利用WinFOLIA软件测量叶的多项形态指标并与叶面积进行11种模拟方程回归分析。结果表明:五种火棘属植物的叶面积(LA)与叶长×叶宽(LW)相关性最高,幂函数方程、三次方程、二次方程和线性方程能较好拟合其关系,且均以幂函数方程的解释程度最高(R²均大于0.970),5个物种的幂函数方程分别为LA=0.743(LW)0.936、LA=0.748(LW)0.936、LA=0.742(LW)0.955、LA=0.732(LW)0.952、LA=0.766(LW)0.954。这说明基于叶长×叶宽的叶面积幂函数方程能很好地来模拟五种火棘属植物的叶面积。     

Modeling individual leaf area of rose (<Emphasis Type="Italic">Rosa hybrida</Emphasis> L.) based on leaf length and width measurement

Accurate and nondestructive methods to determine individual leaf areas of plants are a useful tool in physiological and agronomic research. Determining the individual leaf area (LA) of rose (Rosa hybrida L.) involves measurements of leaf parameters such as length (L) and width (W), or some combinations of these parameters. Two-year investigation was carried out during 2007 (on thirteen cultivars) and 2008 (on one cultivar) under greenhouse conditions, respectively, to test whether a model could be developed to estimate LA of rose across cultivars. Regression analysis of LA vs. L and W revealed several models that could be used for estimating the area of individual rose leaves. A linear model having L×W as the independent variable provided the most accurate estimate (highest r ² , smallest MSE, and the smallest PRESS) of LA in rose. Validation of the model having L×W of leaves measured in the 2008 experiment coming from other cultivars of rose showed that the correlation between calculated and measured rose LA was very high. Therefore, this model can estimate accurately and in large quantities the LA of rose plants in many experimental comparisons without the use of any expensive instruments.     

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.     

Leaf and fine root carbon stocks and turnover are coupled across Arctic ecosystems

Estimates of vegetation carbon pools and their turnover rates are central to understanding and modelling ecosystem responses to climate change and their feedbacks to climate. In the Arctic, a region containing globally important stores of soil carbon, and where the most rapid climate change is expected over the coming century, plant communities have on average sixfold more biomass below ground than above ground, but knowledge of the root carbon pool sizes and turnover rates is limited. Here, we show that across eight plant communities, there is a significant positive relationship between leaf and fine root turnover rates (r² = 0.68, P < 0.05), and that the turnover rates of both leaf (r² = 0.63, P < 0.05) and fine root (r² = 0.55, P < 0.05) pools are strongly correlated with leaf area index (LAI, leaf area per unit ground area). This coupling of root and leaf dynamics supports the theory of a whole‐plant economics spectrum. We also show that the size of the fine root carbon pool initially increases linearly with increasing LAI, and then levels off at LAI = 1 m² m^?2, suggesting a functional balance between investment in leaves and fine roots at the whole community scale. These ecological relationships not only demonstrate close links between above and below‐ground plant carbon dynamics but also allow plant carbon pool sizes and their turnover rates to be predicted from the single readily quantifiable (and remotely sensed) parameter of LAI, including the possibility of estimating root data from satellites.     

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.     

Application of linear models for estimation of leaf area in soybean [<Emphasis Type="Italic">Glycine max</Emphasis> (L.) Merr]

Leaf area estimation is an important measurement for comparing plant growth in field and pot experiments. In this study, determination of the leaf area (LA, cm²) in soybean [Glycine max (L.) Merr] involves measurements of leaf parameters such as maximum terminal leaflet length (L, cm), width (W, cm), product of length and width (LW), green leaf dry matter (GLDM) and the total number of green leaflets per plant (TNLP) as independent variables. A two-year study was carried out during 2009 (three cultivars) and 2010 (four cultivars) under field conditions to build a model for estimation of LA across soybean cultivars. Regression analysis of LA vs. L and W revealed several functions that could be used to estimate the area of individual leaflet (LE), trifoliate (T) and total leaf area (TLA). Results showed that the LW-based models were better (highest R ² and smallest RMSE) than models based on L or W and models that used GLDM and TNLP as independent variables. The proposed linear models are: LE = 0.754 + 0.655 LW, (R ² = 0.98), T = −4.869 + 1.923 LW, (R ² = 0.97), and TLA = 6.876 + 1.813 ΣLW (summed product of L and W terminal leaflets per plant), (R ² = 0.99). The validation of the models based on LW and developed on cv. DPX showed that the correlation between calculated and measured LA was strong. Therefore, the proposed models can estimate accurately and massively the LA in soybeans without the use of expensive instrumentation.     

Leaf area prediction for corn (<Emphasis Type="Italic">Zea mays</Emphasis> L.) cultivars with multiregression analysis

Leaf area is one of the most important parameter for plant growth. Reliable equations were offered to predict leaf area for Zea mays L. cultivars. All equations produced for leaf area were derived as affected by leaf length and leaf width. As a result of ANOVA and multiregression analysis, it was found that there was a close relationship between actual and predicted growth parameters. The produced leaf-area prediction model in the present study is LA = a + b L + c W + d LZ where LA is leaf area, L is leaf length, W is maximum leaf width, LZ is leaf zone and a, b, c, d are coefficients. R ² values were between 0.88–0.97 and standard errors were found to be significant at the p<0.001 significance level.     

Leaf shape alters the coefficients of leaf area estimation models for Saussurea stoliczkai in central Tibet

Nondestructive methods to estimate individual leaf area (LA) accurately, by leaf length (L) and/or width (W), is helpful for the in situ and successive LA measurements. However, leaf shape and size may covary with environment and thus alter the coefficients of LA estimation models. To test such hypothesis, we carried out an experiment by measuring Saussurea stoliczkai C. B. Clarke leaves along an altitudinal transect in Damxung county, central Tibet. In July 2011, we selected seven sites at about every 150 m in altitude from 4,350 m to 5,250 m a.s.l. A total of 1,389 leaves (182 to 203 leaves for each site) were measured. For each site, models developed by two leaf dimensions [LA = a (L×W) + b] could estimate LA more accurately than those by single dimension. L, W, LA and leaf shape index (L:W ratio) all decreased with increasing altitude, leading to significant differences in coefficients of two-dimension model between almost every two sites. Accordingly, a common two-dimension model is unlikely to occur for S. stoliczkai across the whole altitudinal transect, indicating that the varying leaf shape may alter the coefficient of LA estimation models.     

Leaf area index (LAI) map of a protected area within the caldera of Vico Lake (Italy)

ABSTRACT

Structural traits of the vegetation types and plantations occurring in a protected area within the caldera of Vico Lake (Italy) were analysed. There were significant correlations among structural traits, at leaf and stand level. Leaf area index (LAI) and specific leaf area (SLA) were the most significantly correlated traits. LAI rose according to stand plant density, tree size and SLA; the highest LAI value monitored in the Fagus sylvatica L. forest was justified by the largest tree size (28.9±2.8 m height and 53±15 cm diameter) and the highest SLA (212±23 cm² g^-1). The main traits determining the variations in leaf structure among species were analysed by Principal Component Analysis (PCA). The LAI values were used to realise a map allowing us to delimit high LAI values (4.1–5.0), corresponding to the F. sylvatica forest and to the F. sylvatica forest with the sporadic presence of Quercus cerris L. and Castanea sativa Miller, mean LAI values (classes 3.1–4.0) corresponding to Corylus avellana L. plantations and to the Phragmites australis (Cav.) Trin. vegetation type, low LAI values (classes 2.6–3.0) corresponding to Q. cerris forests and C. sativa plantations.     

Leaf Construction Cost of the Most Abundant Species in an Upland Grassland Area of Northern Greece

The leaf construction cost, i.e., the energy expenditure required for the production of plant biomass (CC, g glucose/g dry biomass), is considered to be a major determinant of species success in various habitats. Nitrogen, carbon, and mineral contents in leaves were used to measure leaf CC. The aboveground biomass was sampled from the most abundant plant species (Poa pratensis L., Lolium perenne L., Festuca valida (Uechtr.) Penzes, Trifolium repens L., Taraxacum officinale Weber ex Wigg, Plantago lanceolata L., and Achillea millefolium L.) during the 1997 growing season in an upland grassland dominated by C₃ species. Soil samplings were performed in parallel with leaf samplings in order to determine soil inorganic nitrogen. T. repens leaves had the highest nitrogen concentration; grasses had the highest carbon content, while the highest mineral content was observed in the leaves of the forb species. The highest leaf CC was calculated for the legume T. repens followed by the grass F. valida. The grass L. perenne had the cheapest leaves, since it had the lowest CC. A positive correlation between leaf CC and soil inorganic nitrogen was evident for grasses (P. pratensis, L. perenne, F. valida) and P. lanceolata.     

Leaf litterfall,leaf area index,and radiation transmittance in cypress wetlands and slash pine plantations in north-central Florida

The seasonal dynamics of leaf litterfall and leaf area index (LAI, all-sided basis), light penetration and the vertical distribution of surface area index, and the feasibility of estimating LAI from radiation transmittance were studied from April 1993 to March 1994 in the canopies of three cypress (Taxodium ascendens) wetlands and their surrounding slash pine (Pinus elliottii) uplands in Florida flatwoods. Annual leaf litterfall ranged from 324 to 359 g m^–2 in the wetlands, which was very close to the average for 11 sites throughout Florida of 340±26 g m^–2. The seasonal pattern of the normalized LAI obtained for the dominant tree species in the ecosystems could be used to construct the seasonal dynamics of LAI at the ecosystem scale. The vertical distribution of surface area index in the wetlands was significantly different from that in the surrounding pine uplands. The maximum LAI of cypress wetlands in this area was about 8 m² m^–2, which was higher than the maximum of slash pine plantations of 6 m² m^–2. Cypress leaves were strongly erectophile in space. Results showed that the LAI-2000 canopy analyzer could generally be used to estimate forest LAI, whether the forest canopy was closed or not, if an overall clumping index of 2.00 was applied. However, as LAI decreased, the relative error contained in the radiation-based LAI estimates increased. This indicated that foliage clumping at the stand scale was more important than that at the tree or branch scale.     

Allometric model for nondestructive leaf area estimation in clary sage (<Emphasis Type="Italic">Salvia sclarea</Emphasis> L.)

Leaf area estimation is an important biometrical observation recorded for evaluating plant growth in field and pot experiments. In this study, conducted in 2009, a leaf area estimation model was developed for aromatic crop clary sage (Salvia sclarea L.), using linear measurements of leaf length (L) and maximum width (W). Leaves from four genotypes of clary sage, collected at different stages, were used to develop the model. The actual leaf area (LA) and leaf dimensions were measured with a Laser Area meter. Different combinations of prediction equations were obtained from L, W, product of LW and dry mass of leaves (DM) to create linear (y = a + bx), quadratic (y = a + bx + cx²), exponential (y = ae^bx), logarithmic (y = a + bLnx), and power models (y = ax^b) for each genotype. Data for all four genotypes were pooled and compared with earlier models by graphical procedures and statistical measures viz. Mean Square Error (MSE) and Prediction Sum of Squares (PRESS). A linear model having LW as the independent variables (y = −3.4444 + 0.729 LW) provided the most accurate estimate (R ² = 0.99, MSE = 50.05, PRESS = 12.51) of clary sage leaf area. Validation of the regression model using the data from another experiment showed that the correlation between measured and predicted values was very high (R ² = 0.98) with low MSE (107.74) and PRESS (26.96).     

Leaf area establishment of a maize (Zea Mays L.) field crop under potassium deficiency

The effect of K deficiency on leaf area index (LAI) establishment of a maize field crop (Zea Mays L.) was studied. The experimental work was carried out in 2000 and 2001 on a long-term K fertilization trial. Three K fertilization regimes (K0, K1 and K4) have been applied since 1995, thus leading to contrasted levels of available K in soils (14, 23 and 44 µg exchangeable K per g of dry soil for the three fertilization regimes, respectively). The rate of leaf appearance, the leaf elongation rate (LER), the leaf elongation duration (LED), their final length and width and the number of senescent leaves were investigated. K concentrations in shoot tissue water were lower in K0 plants, whereas concentrations of Ca and Mg were higher. The LAI was reduced in the K0 treatment, mainly because of a slower rate of leaf appearance and a reduced final size of individual leaves. The reduced final length of individual leaves was almost entirely accounted for by a reduced LER during the quasi linear elongation phase. The LED was only slightly affected. A rough parallelism was observed between the relative reduction of leaf length and the relative reduction of plant water content during leaf elongation. Conversely, there was no evidence that leaf elongation was limited by carbohydrate availability in leaf growing zones. This suggests that K deficiency reduced LER probably because of altered plant-water relationships. On the whole, these results strengthen the idea that leaf growth is a key variable for analyzing, and later on modeling, crop growth under K deficiency.