Leaf mass per area, not total leaf area, drives differences in above-ground biomass distribution among woody plant functional types

Fungal decomposition of lignin leads to the whitening, or bleaching, of leaf litter, especially in temperate and tropical forests, but less is known about such bleaching in forests of cooler regions, such as boreal and subalpine forests. The purposes of the present study were to examine the extent of bleached area on the surface of leaf litter and its variation with environmental conditions in subboreal and subalpine forests in Japan and to examine the microfungi associated with the bleaching of leaf litter by isolating fungi from the bleached portions of the litter. Bleached area accounted for 21.7%–32.7% and 2.0%–10.0% of total leaf area of Quercus crispula and Betula ermanii, respectively, in subboreal forests, and for 6.3% and 18.6% of total leaf area of B. ermanii and Picea jezoensis var. hondoensis, respectively, in a subalpine forest. In subboreal forests, elevation, C/N ratio and pH of the FH layer, and slope aspect were selected as predictor variables for the bleached leaf area. Leaf mass per area and lignin content were consistently lower in the bleached area than in the nonbleached area of the same leaves, indicating that the selective decomposition of acid unhydrolyzable residue (recalcitrant compounds such as lignin, tannins, and cutins) enhanced the mass loss of leaf tissues in the bleached portions. Isolates of a total of 11 fungal species (6 species of Ascomycota and 5 of Basidiomycota) exhibited leaf-litter-bleaching activity under pure culture conditions. Two fungal species (Coccomyces sp. and Mycena sp.) occurred in both subboreal and subalpine forests, which were separated from each other by approximately 1100 km.

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Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d221042 ◽

2021 ◽

Vol 22 (10) ◽

Author(s):

Benyamin Lakitan ◽

Kartika Kartika ◽

Laily Ilman Widuri ◽

Erna Siaga ◽

Lya Nailatul Fadilah

Keyword(s):

Regression Model ◽

Leaf Area ◽

Morphological Traits ◽

Leafy Vegetables ◽

Stem Cuttings ◽

Total Leaf Area ◽

Tropical Ecosystem ◽

Total Leaf ◽

Tropical Conditions ◽

Non Destructive

Abstract. Lakitan B, Kartika K, Widuri LI, Siaga E, Fadilah LN. 2021. Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch. Biodiversitas 22: 4487-4495. Talinum paniculatum known as Java ginseng is an ethnic vegetable in Indonesia that has also been utilized as a medical plant. Young leaves are the primary economic part of T. paniculatum, which can be eaten fresh or cooked. This study was focused on characterizing morphological traits of T. panicultaum and developing a non-destructive yet accurate and reliable model for predicting total area per leaf cluster on each elongated branch per flush growth cycle. The non-destructive approach allows frequent and timely measurements. In addition, the developed model can be used as guidance for deciding the time to harvest for optimum yield. Results indicated that T. paniculatum flourished rapidly under wet tropical conditions, especially if they were propagated using stem cuttings. The plants produced more than 50 branches and more than 800 leaves, or on average produced more than 15 leaves per branch at the age of nine weeks after planting (WAP). The zero-intercept linear model using a combination of two traits of length x width (LW) as a predictor was accurate and reliable for predicting a single leaf area (R2 = 0.997). Meanwhile, the estimation of total area per leaf cluster was more accurate if three traits, i.e., number of leaves, the longest leaf, and the widest leaf in each cluster were used as predictors with the zero-intercept linear regression model (R2 = 0.984). However, the use of a single trait of length (L) and width (W) of the largest leaf within each cluster as a predictor in the power regression model exhibited moderately accurate prediction at the R2 = 0.883 and 0.724, respectively.

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Genetic variance of sunflower yield components - Heliantus annuus L.

Genetika ◽

10.2298/gensr0301001h ◽

2003 ◽

Vol 35 (1) ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Nada Hladni ◽

Dragan Skoric ◽

Marija Kraljevic-Balalic

Keyword(s):

Leaf Area ◽

Plant Height ◽

Leaf Number ◽

Total Leaf Area ◽

Gene Effects ◽

Partial Dominance ◽

Mean Values ◽

Total Leaf ◽

Additive Gene ◽

Mode Of Inheritance

The main goals of sunflower breeding in Yugoslavia and abroad are increased seed yield and oil content per unit area and increased resistance to diseases, insects and stress conditions via an optimization of plant architecture. In order to determine the mode of inheritance, gene effects and correlations of total leaf number per plant, total leaf area and plant height, six genetically divergent inbred lines of sunflower were subjected to half diallel crosses. Significant differences in mean values of all the traits were found in the F1 and F2 generations. Additive gene effects were more important in the inheritance of total leaf number per plant and plant height, while in the case of total leaf area per plant the nonadditive ones were more important looking at all the combinations in the F1 and F2 generations. The average degree of dominance (Hi/D)1/2 was lower than one for total leaf number per plant and plant height, so the mode of inheritance was partial dominance, while with total leaf area the value was higher than one, indicating super dominance as the mode of inheritance. Significant positive correlation was found: between total leaf area per plant and total leaf number per plant (0.285*) and plant height (0.278*). The results of the study are of importance for further sunflower breeding work.

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Concentration and periods of application of prohexadione calcium in the growth of strawberry seedlings

Semina Ciências Agrárias ◽

10.5433/1679-0359.2018v39n5p1937 ◽

2018 ◽

Vol 39 (5) ◽

pp. 1937 ◽

Cited By ~ 1

Author(s):

Caroline Farias Barreto ◽

Leticia Vanni Ferreira ◽

Savana Irribarem Costa ◽

Andressa Vighi Schiavon ◽

Tais Barbosa Becker ◽

...

Keyword(s):

Leaf Area ◽

Chlorophyll Concentration ◽

Growth Control ◽

Dry Mass ◽

Energy Costs ◽

Petiole Length ◽

Total Leaf Area ◽

Total Leaf ◽

Control Growth ◽

Prohexadione Calcium

For strawberry cultivation in Brazil, producers are dependent on imported seedlings. An alternative strategy to reduce this dependence is the use of seedlings obtained from nursery plants grown in a protected environment. However, as these seedlings are produced in the summer and planted at the end of this season or the spring of the following year, it is necessary to control growth to reduce the energy costs of the plants. The objective of this study was to evaluate the effect of different concentrations and periods of application of proexadione calcium (ProCa) on growth control of strawberry seedlings. The experiment was carried out in a greenhouse with seedlings of the cultivars ‘Aromas’ and ‘Camarosa’, produced by rooting stolons and kept in polystyrene trays of 72 cells in a substrate of carbonized rice husk. The experimental design was completely randomized, with a 4 × 2 factorial scheme (4 concentrations of ProCa: 0, 100, 200, and 400 mg L-1 × 2 periods of application: at 20 and 30 days after the planting period of rooting stolon). Plant survival, crown diameter, petiole length, total leaf area, specific leaf area, chlorophyll concentration, and dry mass of the crown and shoot were evaluated. The application of ProCa at 20 days after the planting period of the rooting stolon at the concentrations of 200 and 400 mg L-1 favored the reduction of petiole length in plants of ‘Aromas’ strawberry and total leaf aerial in ‘Camarosa’ strawberry. The application of ProCa from the concentration of 100 mg L-1 reduced the vegetative growth of ‘Aromas’ and ‘Camarosa’ strawberry seedlings cultivated in substrate.

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A Comparison of Methods for Estimating Total Leaf Area of Barley Plants 1

Crop Science ◽

10.2135/cropsci1965.0011183x000500060041x ◽

1965 ◽

Vol 5 (6) ◽

pp. 602-603 ◽

Cited By ~ 14

Author(s):

Albert E. Carleton ◽

Wilson H. Foote

Keyword(s):

Leaf Area ◽

Comparison Of Methods ◽

Total Leaf Area ◽

Total Leaf

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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Remote Sensing ◽

10.3390/rs12020269 ◽

2020 ◽

Vol 12 (2) ◽

pp. 269 ◽

Cited By ~ 2

Author(s):

Shunfu Xiao ◽

Honghong Chai ◽

Ke Shao ◽

Mengyuan Shen ◽

Qing Wang ◽

...

Keyword(s):

Sugar Beet ◽

Convex Hull ◽

Leaf Area ◽

Plant Phenotyping ◽

Phenotypic Traits ◽

Individual Plant ◽

Total Leaf Area ◽

Automatic Data ◽

Total Leaf ◽

Canopy Area

Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

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Evaluation of vegetative growth traits and their relation to time to first flowering of olive seedlings

Australian Journal of Agricultural Research ◽

10.1071/ar02131 ◽

2003 ◽

Vol 54 (4) ◽

pp. 371 ◽

Cited By ~ 20

Author(s):

T. S. Pritsa ◽

D. G. Voyiatzis ◽

C. J. Voyiatzi ◽

M. S. Sotiriou

Keyword(s):

Leaf Area ◽

Specific Leaf Area ◽

Developmental Stages ◽

Growth Traits ◽

Leaf Shape ◽

Total Leaf Area ◽

Total Leaf ◽

Vegetative Traits ◽

Number Of Leaves ◽

Shape Characteristics

The aim of this work was to assess growth traits during the initial developmental stages of olive seedlings, which could be correlated to time to first flowering, facilitating fast selection in olive breeding programs. The experimental material consisted of 232 olive seedlings derived from controlled crosses of 'Kalamon' with self (KA × KA), with 'Amphissis' (KA × AM), and with 'Koroneiki' (KA × KO) and from open pollination of 'Kalamon', 'Amphissis', 'Koroneiki', 'Chalkidikis', and 'Manzanillo'. Vegetative traits of the seedlings, including canopy height and diameter, length of lateral vegetation, number of leaves, mean and total leaf area per plant, leaf shape characteristics, and specific leaf area (SLA), were recorded until 15 months after sowing. The first seedlings to initiate flowers, 4 years after sowing, were also recorded. The existence of correlations between the above growth traits and time to first flowering was investigated. In single-branched seedlings 6 months after sowing, height measured at this stage was significantly correlated with the mean and total leaf area per plant, specific leaf area, and other vegetative traits measured 15 months after sowing. Seedlings with high values of these parameters were the first to initiate flowers 33 months later. Our results indicated that pre-selection of olive seedlings for earliness of first flowering is possible, based on vegetative characteristics assessed very early in their development.

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Estimation of total leaf area and D leaf area of pineapple from biometric characteristics

Revista Brasileira de Fruticultura ◽

10.1590/0100-29452018556 ◽

2018 ◽

Vol 40 (6) ◽

Cited By ~ 1

Author(s):

Marlúcia Pereira dos Santos ◽

Victor Martins Maia ◽

Fernanda Soares Oliveira ◽

Rodinei Facco Pegoraro ◽

Silvânio Rodrigues dos Santos ◽

...

Keyword(s):

Leaf Area ◽

Regression Models ◽

Carbon Fixation ◽

Pearson Correlation ◽

Leaf Length ◽

Total Leaf Area ◽

Total Leaf ◽

Pearson Correlation Analysis ◽

Biometric Measurements ◽

Non Destructive

Abstract The estimation of pineapple total leaf area by simple, fast and non-destructive methods allow inferences related to carbon fixation estimative, biotic and abiotic damages and correlating positively with yield. The objective was to estimate D leaf area and total leaf area and of ‘Pérola’ pineapple plants from biometric measurements. For this purpose, 125 slips were selected and standardized by weight for planting in pots. Nine months after planting in a greenhouse, the plants were harvested to evaluate the total leaf area of the plant, D leaf area and D leaf length and width using a portable leaf area meter. Pearson correlation analysis was made and it was observed significative positive and strong correlation among the studied variables. Then, regression models were adjusted. It was observed that the D leaf area of ‘Pérola’ pineapple can be estimated from the length and width of this same leaf and the total leaf area can be estimated from the D leaf area.

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Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

Remote Sensing ◽

10.3390/rs12193121 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3121

Author(s):

Roya Mourad ◽

Hadi Jaafar ◽

Martha Anderson ◽

Feng Gao

Keyword(s):

Linear Regression ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Surface Reflectance ◽

Above Ground Biomass ◽

Area Index ◽

Ground Biomass ◽

Semi Arid ◽

Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

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