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 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.     

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。     

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 .     

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.     

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.     

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.     

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.     

泉州湾河口湿地主要红树植物叶面积测算

植物叶面积的测算对于评价生态系统初级生产力具有重要意义.本研究分别选用“最大叶长”、“最大叶宽”以及“最大叶长×最大叶宽”等指标,利用不同类型的线性或非线性回归方程,对泉州湾河口湿地主要红树植物秋茄、桐花树和白骨壤的叶面积进行测算,从而确定各自最佳拟合回归方程.结果表明:二元非线性回归方程Y=0.7297X10.8698 X2.11600、幂指数方程Y=0.9740X0.9634和Y=0.7773X 0.9954分别为秋茄、桐花树和白骨壤叶面积的最佳拟合回归方程.进一步的0-1回归检验和相对误差值分析显示,以上回归方程均能精确地估算各自的叶面积,其中,白骨壤叶面积测算更为精确.     

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.     

胡杨叶功能性状特征及其对地下水埋深的响应

叶片性状反映了植物对环境的适应能力及其自我调控能力。以塔里木干旱荒漠区建群种胡杨（Populus euphratica）为研究对象,通过分析自然生长状况下胡杨叶功能性状对地下水埋深（GWD）的响应及功能性状间的权衡关系,揭示胡杨对极端干旱荒漠环境的生态适应策略。结果表明：胡杨7个叶功能性状种内变异程度不同（9.20%-40.02%）,叶面积（LA）变异程度最大,叶干物质含量（LDMC）与叶片含水量（LWC）变异程度较低,GWD梯度上表现出较大的分化变异特征。叶性状在不同GWD之间差异显著（P<0.05）,与GWD呈极显著相关（P<0.01）。比叶面积（SLA）、LA、LWC与叶干重（LDM）呈极显著正相关（P<0.01）,与叶厚度（LT）、叶组织密度（LTD）、LDMC呈极显著的负相关（P<0.01）;LDMC与LT、LTD,LWC与LA、SLA呈极显著正相关（P<0.01）,反映胡杨通过叶性状间的相互调节与权衡来适应干旱荒漠环境。逐步回归分析表明LA、LT对GWD变化最敏感,可间接借助这2个性状来预测干旱荒漠区地下水埋深变化。随GWD降低,胡杨SLA、LA、LDM、LWC减小,而LT、LTD、LDMC增大,其由高生长速率、资源利用能力的开拓型策略转变为以增强自身养分储存、防御能力的保守型策略,拓宽了生态幅和增强其在干旱逆境的适合度。可见,极端干旱荒漠区胡杨形成了小的LA、SLA、LDM,大的LT、LDMC、LTD等一系列有利于减少水分散失、储存养分和增强耐旱能力的干旱性状组合,这可能是其适应干旱贫瘠环境的生态策略。     

Effects of leaf position and nitrogen supply on the expansion of leaves of field grown sunflower (Helianthus annuus L.)

Leaf growth responses to N supply and leaf position were studied using widely-spaced sunflower plants growing under field conditions. Both N supply (range 0.25 to 11.25 g added N per plant) and leaf position significantly (p=0.001) affected maximum leaf area (LA_max) of target leaves through variations in leaf expansion rate (LER); effects on duration of expansion were small. Specific leaf nitrogen (SLN, g N m^-2) fell quite rapidly during the initial leaf expansion phase (LA < 35% LA_max) but leveled off during the final 65% increase of leaf area. This pattern held across leaf positions and N supply levels. Leaf nitrogen accumulation after 35% LA_max continued up to achievement of LA_max; reductions in the higher SLN characteristic of the initial phase were insufficient to cover the nitrogen requirements for expansion during the final phase. LER in the quasi-linear expansion phase (35 to 100% of LA_max) was strongly associated with SLN above a threshold that varied with leaf position (mean 1.79±0.225 g N m^-2). This contrasts with the response of photosynthesis at high irradiance to SLN, which has previously been shown to have a threshold of 0.3 g N m^-2; in the present work saturation of photosynthetic rate was evident when SLN reached 1.97 g N m^-2. Thus, once the area of a leaf exceeds 35% of LA_max, expansion proceeds provided SLN values are close to the levels required for maximum photosynthesis. However, growth of leaves during the initial expansion phase ensures a minimum production of leaf area even at low N supply levels.     

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 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.     

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.     

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。     

Replication of tomato yellow leaf curl virus (TYLCV) DNA in agroinoculated leaf discs from selected tomato genotypes

The leaf disc agroinoculation system was applied to study tomato yellow leaf curl virus (TYLCV) replication in explants from susceptible and resistant tomato genotypes. This system was also evaluated as a potential selection tool in breeding programmes for TYLCV resistance. Leaf discs were incubated with a head-to-tail dimer of the TYLCV genome cloned into the Ti plasmid ofAgrobacterium tumefaciens. In leaf discs from susceptible cultivars (Lycopersicon esculentum) TYLCV single-stranded genomic DNA and its double-stranded DNA forms appeared within 2–5 days after inoculation. Whiteflies (Bemisia tabaci) efficiently transmitted the TYLCV disease to tomato test plants following acquisition feeding on agroinoculated tomato leaf discs. This indicates that infective viral particles have been produced and have reached the phloem cells of the explant where they can be acquired by the insects. Plants regenerated from agroinfected leaf discs of sensitive tomato cultivars exhibited disease symptoms and contained TYLCV DNA concentrations similar to those present in field-infected tomato plants, indicating that TYLCV can move out from the leaf disc into the regenerating plant. Leaf discs from accessions of the wild tomato species immune to whitefly-mediated inoculation,L. chilense LA1969 andL. hirsutum LA1777, did not support TYLCV DNA replication. Leaf discs from plants tolerant to TYLCV issued from breeding programmes behaved like leaf discs from susceptible cultivars.The Hebrew University of Jerusalem, Faculty of Agriculture, Department of Field and Vegetable Crops     

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.     

The effect of NaCl on water relations,chlorophyll, and protein and proline contents of two cultivars of blackgram (Vigna mungo L.)

The physiological basis of salt tolerance of two cultivars of blackgram, cv Candhari Mash (relatively salt tolerant) and cv Mash 654 (salt sensitive), was assessed in salinized sand culture at the flowering stage. Increasing NaCl concentration in the rooting medium significantly reduced the chlorophyll a, chlorophyll b, and total chlorophyll, leaf water potential (Ψ_w), leaf solute potential (Ψ_s), and leaf turgor potential (Ψ_p) in both the cultivars. Leaf protein and proline content was increased as a result of increasing salt concentration in both cultivars. High salt concentrations had no significant effect on the seed protein content of both cultivars. At high salinities, cv Candhari Mash had significantly greater chlorophyll a, chlorophyll b and total chlorophyll, leaf water potential, solute potential, and turgor potential than cv Mash 654, but the latter had greater leaf proline content than cv Candhari Mash. Cultivars did not differ significantly for both leaf and seed protein contents. The relatively salt tolerant cv Candhari Mash maintained high leaf water potential and turgor potential to resist salt injury. Leaf proline content had negative correlation with salt tolerance in blackgram.     

Artificial neural networking to estimate the leaf area for invasive plant Wedelia trilobata

Leaf area are very important parameter for the understanding of growth and physiological responses of invasive plant species under different environmental factors. This study was conducted to build non-destructive leaf area model of Wedelia trilobata that were grown in greenhouse. Regression analysis and artificial neural network (ANN) approaches were used for the development of leaf area model with the help of leaf length and width of 262 plants samples. In selection of best method under both techniques, the lower value of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and higher value of R² were considered. According to the results it was found that ANN have higher value of (R² = 0.96) and lower value of error (MAE = 0.023, RMSE = 0.379, MAPE = 0.001) than regression analysis (R² = 0.94, MAE = 0.111, RMSE = 1.798, MAPE = 0.0005). It was concluded that error between predicted and actual value was less under ANN. Therefore, ANN model approach can be used as an alternating method for the estimation of leaf area. Through estimation of leaf area, invasive plant growth can predict under different environment conditions.