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。     

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).     

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.     

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.     

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.     

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.     

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.     

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 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 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 .     

Models for leaf area estimation of three forest species in a short coppice rotation

It was obtained statistical models to estimate the leaf area (LA) based in the length (L) and the width (W) of Bambusa vulgaris, two different eucalypt clones, AEC-144 (spontaneous hybrid of Eucalyptus urophylla) and LW07 (Eucalyptus urophylla x Eucalyptus grandis), and Salix nigra leaves. The trees or clumps were provbrided from a short rotation coppice (SRC) for bioenergy, mainly characterized by the high tree density, in Botucatu, Sao Paulo, Brazil. It was collected, by chance, more than 4000 leaves that represented a quarter of the coppices. The bamboo and AEC-144 clone were, at the time, 22 months old, while the willow and LW07 clone were 18 months old. Young, intermediate and old leaves were mixed and measured. The measured leaves were correlated to obtain the simple linear eqs. (LA in function of L and W) and multiple linear regression (LA in function of L × W), to each species. All the species shown a positive correlation coefficient (r) to L (r = 0.75 to 0.95), W (r = 0.70 to 0.82) e L × W (r = 0.87 to 0.95), significative to p ≤ .05. The multiple linear models, that used L × W, are the most appropriated once it had better adjustments with the determination coefficients (R²) between 0.76 and 0.91 with exception in the case of S. nigra (willow), the R² of the simple linear regression using L was similar to the multiple linear regression, 0.90 and 0.91 respectively, showing that it is possible to estimate the LA in willow just using length.     

Screening of wild accessions resistant to gray mold (Botrytis cinerea Pers.) in Lycopersicon

Tomato gray mold (Botrytis cinerea Pers.) is a common disease worldwide, and often causes serious production loss by infecting leaves, stems, flowers and fruits. Presently, no resistant cultivars are available. To find new breeding materials for gray mold resistance, assessment for resistance of the leaflet and stem in six tomato cultivars, 44 wild tomato accessions and a Solanum lycopersicoides accession was performed. Although no correlation was observed (r=−0.127^ns) between resistance of the leaflet and the stem, L. peruvianum LA2745, L. hirsutum LA2314 and L. pimpinellifolium LA1246 showed high resistance both in the leaflet and in the stem. Particularly, in the leaves of LA2745, no lesions were observed even more than two weeks after the inoculation with conidia, and F1s between a cultivated tomato and LA2745 also showed high resistance as observed in LA2745. From these results, LA2745 is thought to be a promising material for breeding gray-mold resistant cultivars.     

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.     

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.     

龙船花两变种叶面积回归方程的建立

以龙船花两变种橙红龙船花Ixora coccinea var. coccinea、邦德胡卡红仙丹草I. coccinea var. bandhuca的成熟叶为材料,利用WinFolia软件测定多项叶形态指标,并对叶面积进行回归分析,分别建立其8种回归方程以及总的适用回归方程。结果表明,龙船花两变种的叶片长×叶水平宽、叶周长、叶垂直长、叶片长、叶水平宽、叶片长×叶片长以及叶水平宽×叶水平宽与叶面积之间的相关系数及复相关系数均呈极显著水平(P＜0.01),可分别用来建立龙船花的叶面积回归方程;叶面积与叶片长×叶水平宽的相关系数及复相关系数最高,基于叶片长×叶水平宽的8种叶面积回归方程更好地估测两种龙船花的叶面积。经检验发现,二次函数、复合函数、幂函数能更准确地估测叶面积;由两种龙船花共同建立的3种总的回归方程中,复合函数与幂函数能更好地模拟估测叶面积。     

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.     

EFFECT OF CARBON DIOXIDE ENRICHMENT ON DEVELOPMENT OF THE FIRST SIX MAINSTEM LEAVES IN SOYBEAN

Many conclusions concerning plant responses to CO₂ enrichment have been based on assumptions of increased leaf size derived from observations of average leaf area measured at some time well into the growth period. The objectives of this study were to study the effect of elevated CO₂ on 1) the timing of mainstem leaflet appearance, 2) the rate and duration of leaflet expansion, and 3) the final area of individual leaflets of soybeans (Glycine max [L.] Merr. cv. Bragg) grown from seed at 348, 502, and 645 μl 1^–1 CO₂ concentrations. Central leaflet areas from mainstem trifoliolates 1–6 were measured every two days from time of appearance to full expansion. Leaflets tended to appear earlier in elevated CO₂ treatments; leaflets 2 through 6 appeared an average of 0.4 days earlier in the 502 μl 1^–1 treatment and 1.2 days earlier in the 645 μl 1^–1 treatment than in the 349 μl 1^–1 treatment. Relative rates of expansion were different among leaflets in their response to elevated CO₂; expansion rates of leaflets 1 and 4 were significantly higher at the highest CO₂ concentration. However, final area of leaflets was not affected by CO₂, or (in leaflet 5 only) was slightly smaller at the highest CO₂ treatment. Apparently, higher expansion rates of leaflets 1 and 4 at high CO₂ were offset by a tendency for decreased duration of expansion. It appears that there are morphological constraints on final leaflet area in soybean seedlings which limit the effects of elevated CO₂ on the early development of mainstem 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 growth relationships of East Africa highland bananas (Musa AAA-EAHB) cv. Kisansa and Mbwazirume

Highland bananas are an important staple food in East Africa, but there is little information on their physiology and growth patterns. This makes it difficult to identify opportunities for yield improvement. We studied allometric relationships by evaluating different phenological stages of highland banana growth for use in growth assessment, understanding banana crop physiology and yield prediction. Pared corms of uniform size (cv. Kisansa) were planted in a pest‐free field in Kawanda (central Uganda), supplied with fertilizers and irrigated during dry periods. In addition, tissue‐cultured plants (cv. Kisansa) were planted in an adjacent field and in Ntungamo (southwest Uganda), with various nutrient addition treatments (of N, P, K, Mg, S, Zn, B and Mo). Plant height, girth at base, number of functional leaves and phenological stages were monitored monthly. Destructive sampling allowed derivation of allometric relationships to describe leaf area and biomass distribution in plants throughout the growth cycle. Individual leaf area was estimated as LA (m²) = length (m) × maximum lamina width (m) × 0.68. Total plant leaf area (TLA) was estimated as the product of the measured middle leaf area (MLA) and the number of functional leaves. MLA was estimated as MLA (m²) = ?0.404 + 0.381 height (m) + 0.411 girth (m). A light extinction coefficient (k = 0.7) was estimated from photosynthetically active radiation measurements in a 1.0 m grid over the entire day. The dominant dry matter (DM) sinks changed from leaves at 1118 °C days (47% of DM) and 1518 °C days (46% of DM), to the stem at 2125 °C days (43% of DM) and 3383 °C days (58% of DM), and finally to the bunch at harvest (4326 °C days) with 53% of DM. The allometric relationship between above‐ground biomass (AGB in kg DM) and girth (cm) during the vegetative phase followed a power function, AGB = 0.0001 (girth)^2.35 (R² = 0.99), but followed exponential functions at flowering, AGB = 0.325 e^0.036(girth) (R² = 0.79) and at harvest, AGB = 0.069 e^0.068(girth) (R² = 0.96). Girth at flowering was a good parameter for predicting yields with R² = 0.7 (cv. Mbwazirume) and R² = 0.57 (cv. Kisansa) obtained between actual and predicted bunch weights. This article shows that allometric relationship can be derived and used to assess biomass production and for developing banana growth models, which can help breeders and agronomists to further exploit the crop's potential.