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.     

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.     

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。     

Nondestructive,simple, and accurate model for estimation of the individual leaf area of som (<Emphasis Type="Italic">Persea bombycina</Emphasis>)

Nondestructive approach of modeling leaf area could be useful for plant growth estimation especially when number of available plants is limited and/or experiment demands repeated estimation of leaf area over a time scale. A total of 1,280 leaves were selected randomly from eight different morphotypes of som (Persea bombycina) established at randomized complete block design under recommended cultural regimes in field. Maximum leaf laminar width (B), length (L) and their squares B², L²; leaf area (LA), and lamina length × width (L×B) were determined over two successive seasons. Leaf parameters were significantly affected by morphotypes; but seasons had nonsignificant impacts on tested features. Therefore, pooled seasonal morphotype means of each parameter were used to establish relationship with LA. L and its square L² did not provide accurate models for LA predictions. Considerably better models were obtained by using B (y = 2.984 + 7.9664 x, R ² = 0.615, P≥0.001, n = 119) and B² (y = 12.784+ 0.9604 x, R ² = 0.605, P≥0.001, n = 119) as independent variables. However, maximum accuracy of prediction of LA could be achieved through a simple linear relationship of L×B (y = 8.2203 + 0.4224 x, R ² = 0.843, P≥0.0001, n = 119). The model (LA:L×B) was validated with randomly selected leaf samples (n = 360) of som morphotypes and highly significant (P≤0.001) linear function was found between actual and predicted LAs. Therefore, the last model may consider adequate to predict leaf area of all cultivars of som with sufficient fidelity.     

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 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.     

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.     

Assessment of a non-destructive method to estimate the leaf area of <Emphasis Type="Italic">Armoracia rusticana</Emphasis>

The aim of the study was to analyze horseradish growth for developing a mathematical model to estimate the leaf area based on linear measurements of the leaf surface. Leaf area (LA), number, and morphometric characteristics of the leaves including lamina length (L) and width (W) were evaluated on two horseradish accessions (Cor and Mon) throughout a 2 year growing cycle. In both accessions, increased values of LA and leaf number were found by comparing the second with the first-growing season. Leaf development occurs along with variations in size and not in shape during the plant growth. The leaves are elliptical in shape but tend to be wider and bigger in Cor accession and tapered and similar to narrow ellipses in Mon showing different length/width relationship. Consequently, several regression models relating to the LA and L, W, L², and W² individually or in combination were fitted for each accession based on a set of 1000 leaves. The horseradish LA can be predicted based on either length or width alone. However, the regression linear model LA?=?aLW?+?b (LA?=?0.71LW ??0.27 and LA?=?0.76LW ??3.22 for Cor and Mon, respectively) provided the best LA estimation (R²?>?0.95). The validation of this latter model showed high correlation between LA measured and LA predicted in both accessions (R²?=?0.98). Considering the type of foliage of horseradish, the proposed model can be used to estimate the leaf area throughout the entire crop cycle.     

Leaf shape and its relationship with Leaf Area Index in a sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.) cultivar

Heteroblasty of sugar beet cultivar Rizor was studied under field conditions for three growing seasons (2003, 2005, 2006) in a Randomized Complete Block (RCB) design experiment. Eleven leaf samplings, from early June till the end of October, were conducted each year and leaf shape parameters [leaf area (LA), centroid X or Y (CX or CY), length (L), width (W), average radial (AR), elongation (EL), shape factor (SF)] were determined by an image analysis system. During samplings, Leaf Area Index (LAI) was measured non-destructively. Significant year and sampling effects were found for all traits determined. With the progress of the growing season, leaves became smaller (LA, L, W, and AR were decreased) and rounded. The largest leaves were sampled in 2006 when LAI was highest. LA was strongly correlated with L and W with simple functions (y = 0.1933 x^2.2238, r ² = 0.96, p<0.001, and y = 28.693 x − 192.33, r ² = 0.97, p< 0.001, respectively), which could be used for non-destructive LA determination. Also, LAI was significantly related with LA and leaf dimensions (L, W) suggesting that an easy, non-destructive determination of LAI under field conditions is feasible for sugar beet cv. Rizor. An erratum to this article is available at .     

Physiological changes and essential oil composition of clary sage (<Emphasis Type="Italic">Salvia sclarea</Emphasis> L.) rosette leaves as affected by salinity

Since soil salinity is a widespread problem, we proposed to focus on its effect on seedling growth, mineral composition and particularly on essential oil composition known to be reliable to abiotic conditions. Clary sage seedlings were hydroponically cultivated under different salt concentrations (0, 25, 50, and 75 mM NaCl). The dry biomass and the mineral element contents were determined. The essential oils were extracted and analyzed by GC and GC–MS. Results showed that growth was reduced by 42% at 75 mM. This growth decrease was accompanied by a decrease in tissue hydration and a slight restriction in K⁺ uptake, as well as an increase in Na⁺ levels. Concerning essential oil yields, the application of 25 mM NaCl increased significantly the oil yield which decreased with increasing salt concentration. Besides, the chemical composition of clary sage was found to be also strongly affected by salt treatment since each salt concentration appeared to induce a different new chemotype in clary sage essential oil.     

按单叶叶面积估测法评估蒿柳（Salix viminalis L．）第一生长季整株叶面积

本文在蒿柳（ＳａｌｉｘｖｉｍｉｎａｌｉｓＬ．）单叶叶面积（ＡＬ）估测的基础上预测枝条水平上的叶面积．ＡＬ与叶特征度量叶长（Ｌ）、宽（Ｗ）、Ｌ２、Ｗ２、乘积（ＬＷ）,叶干重（ＷＬ）之间相关性分析表明,尽管大多数相关关系本质上为非线性,但线性（Ｙ＝ｂ×Ｘ）和非线性指数方程（Ｙ＝ｂ×Ｘｃ）均有较高的复相关系数ｒ２和较好的预测能力,且以ＬＷ最好。ＡＬ估测方程的建立必须考虑植物生长阶段、枝类型及叶片着生的相对高度的影响．椭圆和抛物面的组合能成功地拟合叶片形状,反映叶形变化和较准确的计算单叶面积．以主技基径Ｄ,枝长Ｈ,Ｄ２Ｈ以及主枝上的叶片数与基径的乘积（ＮＬ·Ｄ）为独立交量来估测主枝叶面积（Ａｐ）的非线性方程好于线性方程,但方程的估计精度因腋生枝的萌生而受影响．腋生枝数与主枝基径的乘积组合（ＮＳＳ·Ｄ）、腋生枝干重（ＷＳ）的非线性方程可用于估测胶生枝叶面积（Ａｓ）,枝水平上叶面积的估测方程都因植物生长阶段的不同而有差异．     

EFFECTS OF ELEVATED CARBON DIOXIDE ON ESTIMATION OF LEAF AREA AND LEAF DRY WEIGHT OF SOYBEAN

The objectives of this study were to determine the effects of elevated CO₂ on relationships between leaf area (A) and linear leaf dimensions (length [L] and width [W]) and leaf dry weight (M) in soybeans (Glycine max (L.) Merr. cv. Bragg). Based on dimensional measurements made on trifoliolates 1–6 for plants grown under three CO₂ levels (348, 502 and 645 μl l^−-1), the best predictor for both trifoliolate leaf area and for fully expanded central leaflets of the trifoliolates was an equation of the form A = b_o + b₁L·W; these relationships were unaffected by CO₂, although there was a small effect of leaf position. For expanding central leaflets of the fifth trifoliolate, no CO₂, leaf size (age) or CO₂ × leaf size effect was found. Specific leaf weight (i.e., M/A) was significantly affected by CO₂, increasing with increasing CO₂. Hence, trifoliolate dry weight can be nondestructively estimated from trifoliolate area using the equation M = 0.097 + (6.71 × 10^−-3 + 1.04 × 10^−-6[CO₂])A, where [CO₂] is mean daytime CO₂ concentration of the growth environment.     

Relationship between specific leaf area,leaf thickness,leaf water content and <Emphasis Type="Italic">SPAD-502</Emphasis> readings in six Amazonian tree species

The aim of this work was to assess the effect of leaf thickness, leaf succulence (L_S), specific leaf area (SLA), specific leaf mass (W_s) and leaf water content (LWC) on chlorophyll (Chl) meter values in six Amazonian tree species (Carapa guianensis, Ceiba pentandra, Cynometra spruceana, Pithecolobium inaequale, Scleronema micranthum and Swietenia macrophylla). We also tested the accuracy of a general calibration equation to convert Minolta Chl meter (SPAD-502) readings into absolute Chl content. On average, SPAD values (x) increased with fresh leaf thickness (FLT [μm] = 153.9 + 0.98 x, r ² = 0.06**), dry leaf thickness (DLT [μm] = 49.50 + 1.28 x, r ² = 0.16**), specific leaf mass (W_s [g (DM) m⁻²] = 6.73 + 1.31 x, r ² = 0.43**), and leaf succulence (L_S [g(FM)] m⁻² = 94.2 + 1.58 x, r ² = 0.19**). However, a negative relationship was found between SPAD values and either specific leaf area [SLA (m² kg⁻¹) = 35.1 − 0.37 x, r ² = 0.38**] or the leaf water content (LWC [%]= 80.0 − 0.42 x, r ² = 0.58**). Leaf Chl contents predicted by the general calibration equation significantly differed (p<0.01) from those estimated by species-specific calibration equations. We conclude that to improve the accuracy of the SPAD-502 leaf thickness and LWC should be taken into account when calibration equations are to be obtained to convert SPAD values into absolute Chl content.     

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.     

The relationship between cytokinins and the amount of nitrogen in the wintering organs of herbaceous plants

The dynamics of cytokinin content and the total protein and nonprotein forms of nitrogen in tissues of wintering organs of clary sage Salvia sclarea L. and cinquefoil Potentilla alba L. in abnormally cold (2005–2006 years) and abnormally warm (2006–2007 years) winters in Moscow have been studied. A direct correlation between the content of total cytokinins and the total and protein nitrogen forms in tissues of wintering leaves and buds has been determined. A correlation link between the level of single cytokinins and the protein nitrogen forms has been found.     

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.     

Estimation of leaf number and leaf area of hydroponic pak-choi plants (<Emphasis Type="Italic">Brassica campestns</Emphasis> ssp,<Emphasis Type="Italic">chinensis</Emphasis>) using growing degree-days

Temperature is a principal environmental factor that directly affects the growth and timing of appearance for crop leaves. To estimate the leaf number and leaf area of ‘Seoul’ pak-choi plants (Brassica campestns ssp.chinensis), we applied the concept of growing degree-days GDD=(T_avg-T_base) × days, where T_avg, T_base and days were the daily average air temperature, base temperature, and days after transplanting, respectively. Leaves that were beginning to unfold with a leaf area ≥1 cm₂ were counted every 2 to 3 d. Linear relationships were found between leaf number and days after transplanting as well as between leaf number and GDD. The rate of appearance and the number of leaves per GDD were 0.542 leaves d^-1 and 0.051 leaves oC^-1 d^-1, respectively. In contrast, the relationship was non-linear between leaf number and leaf area, with the latter being calculated as [(128.9+11.6×GDD-0.03×GDD²)/1+(0.051×GDD+3.5) /13.7)^-3.9] (cm₂oC¹ d^-1). Using model validation, we found that the estimated leaf number and leaf area showed strong agreement with measured values. our results demonstrate the usefulness of modeling to estimate total leaf area and growth from hydroponically grown pak-choi plants.     

Soybean leaf nitrogen,chlorophyll content,and chlorophyll <Emphasis Type="Italic">a/b</Emphasis> ratio

The objective of this study was to assess genotypic variation in soybean chlorophyll (Chl) content and composition, and to test if these data could be used as a rapid screening method to predict genotypic variation in leaf tissue N content. Chl contents and composition were examined among 833 soybean (Glycine max L. Merr.) accessions and related to SPAD meter readings and leaf N content. In the initial year of the study (2002), the relationship between leaf Chl and leaf N contents (r ² = 0.043) was not sufficiently close for Chl to be useful as a predictive tool for leaf N content. Therefore, leaf N content was not determined in 2004 but samples were again collected for determination of Chl content and composition. In 2002, the soybean accessions separated into two distinct groups according to leaf Chl a/b ratios, with the majority of a mean ratio of 3.79. However, approximately 7 % (60) of the genotypes could be readily assigned to a group with a mean Chl a/b ratio of 2.67. Chl a/b analyses in 2004 confirmed the results obtained in 2002 and of 202 genotypes, all but 6 fell into the same group as in 2002.     

Simulation of PSII-operating efficiency from chlorophyll fluorescence in response to light and temperature in chrysanthemum (<Emphasis Type="Italic">Dendranthema grandiflora</Emphasis>) using a multilayer leaf model

Chlorophyll fluorescence serves as a proxy photosynthesis measure under different climatic conditions. The objective of the study was to predict PSII quantum yield using greenhouse microclimate data to monitor plant conditions under various climates. Multilayer leaf model was applied to model fluorescence emission from actinic light-adapted (F') leaves, maximum fluorescence from light-adapted (Fm') leaves, PSII-operating efficiency (F_q'/F_m'), and electron transport rate (ETR). A linear function was used to approximate F' from several measurements under constant and variable light conditions. Model performance was evaluated by comparing the differences between the root mean square error (RMSE) and mean square error (MSE) of observed and predicted values. The model exhibited predictive success for Fq'/Fm' and ETR under different temperature and light conditions with lower RMSE and MSE. However, prediction of F' and Fm' was poor due to a weak relationship under constant (R² = 0.48) and variable (R² = 0.35) light.