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Journal articles on the topic 'Leaf area'

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Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Payne, W. A., C. W. Wendt, L. R. Hossner, and C. E. Gates. "Estimating Pearl Millet Leaf Area and Specific Leaf Area." Agronomy Journal 83, no. 6 (November 1991): 937–41. http://dx.doi.org/10.2134/agronj1991.00021962008300060004x.

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2

Chinnamuthu, C. R., C. Kailasam, and Dr S. Sankaran. "Sorghum Leaf Area as a Function of Sixth Leaf Area." Journal of Agronomy and Crop Science 162, no. 5 (May 1989): 300–304. http://dx.doi.org/10.1111/j.1439-037x.1989.tb00720.x.

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3

Awal, M. A., Wan Ishak ., J. Endan ., and M. Haniff . "Determination of Specific Leaf Area and Leaf Area-leaf Mass Relationship in Oil Palm Plantation." Asian Journal of Plant Sciences 3, no. 3 (April 15, 2004): 264–68. http://dx.doi.org/10.3923/ajps.2004.264.268.

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4

Pierce, Lars L., Steven W. Running, and Joe Walker. "Regional-Scale Relationships of Leaf Area Index to Specific Leaf Area and Leaf Nitrogen Content." Ecological Applications 4, no. 2 (May 1994): 313–21. http://dx.doi.org/10.2307/1941936.

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5

Smith, Duncan D., John S. Sperry, and Frederick R. Adler. "Convergence in leaf size versus twig leaf area scaling: do plants optimize leaf area partitioning?" Annals of Botany 119, no. 3 (December 27, 2016): 447–56. http://dx.doi.org/10.1093/aob/mcw231.

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Background and Aims Corner’s rule states that thicker twigs bear larger leaves. The exact nature of this relationship and why it should occur has been the subject of numerous studies. It is obvious that thicker twigs should support greater total leaf area (Atwig) for hydraulical and mechanical reasons. But it is not obvious why mean leaf size (A-) should scale positively with Atwig. We asked what this scaling relationship is within species and how variable it is across species. We then developed a model to explain why these relationships exist. Methods To minimize potential sources of variability, we compared twig properties from six co-occurring and functionally similar species: Acer grandidentatum, Amelanchier alnifolia, Betula occidentalis, Cornus sericea, Populus fremontii and Symphoricarpos oreophilus. We modelled the economics of leaf display, weighing the benefit from light absorption against the cost of leaf tissue, to predict the optimal A- :Atwig combinations under different canopy openings. Key Results We observed a common A- by Atwig exponent of 0.6, meaning that A -and leaf number on twigs increased in a specific coordination. Common scaling exponents were not supported for relationships between any other measured twig properties. The model consistently predicted positive A- by Atwig scaling when twigs optimally filled canopy openings. The observed 0·6 exponent was predicted when self-shading decreased with larger canopy opening. Conclusions Our results suggest Corner’s rule may be better understood when recast as positive A- by Atwig scaling. Our model provides a tentative explanation of observed A- by Atwig scaling and suggests different scaling may exist in different environments.

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6

CURRAN, P. J., and N. W. WARDLEY. "Radiometric leaf area index." International Journal of Remote Sensing 9, no. 2 (February 1988): 259–74. http://dx.doi.org/10.1080/01431168808954850.

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7

Toebe, M., P. J. Melo, R. R. Souza, A. C. Mello, and F. L. Tartaglia. "Leaf area estimation in triticale by leaf dimensions." Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences 14, no. 2 (June 30, 2019): 1–9. http://dx.doi.org/10.5039/agraria.v14i2a5656.

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8

Cargnelutti Filho, Alberto, Rafael Vieira Pezzini, Ismael Mario Márcio Neu, and Gabriel Elias Dumke. "Estimation of buckwheat leaf area by leaf dimensions." Semina: Ciências Agrárias 42, no. 3Supl1 (April 22, 2021): 1529–48. http://dx.doi.org/10.5433/1679-0359.2021v42n3supl1p1529.

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The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the plants, totaling 1,815 leaves. In these 1,815 leaves, L and W were measured and the LW of the leaf blade was calculated, which were used as independent variables in the model. The leaf area of each leaf was determined using the digital photo method (Y), which was used as a dependent variable of the model. For each sowing date, cultivar and thirds of the plant, 80% of the leaves (1,452 leaves) were randomly separated for the generation of the models and 20% of the leaves (363 leaves) for the validation of the models of leaf area estimation as a function of linear dimensions. For buckwheat, IPR91-Baili and IPR92-Altar cultivars, the quadratic model (Ŷ = 0.5217 + 0.6581LW + 0.0004LW2, R2 = 0.9590), power model (Ŷ = 0.6809LW1.0037, R2 = 0.9587), linear model (Ŷ = 0.0653 + 0.6892LW, R2 = 0.9587) and linear model without intercept (Ŷ = 0.6907LW, R2 = 0.9587) are indicated for the estimation of leaf area determined by digital photos (Y) based on the LW of the leaf blade (x), and, preferably, the linear model without intercept can be used, due to its greater simplicity.

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9

Simón, M. R. "Inheritance of flag-leaf angle, flag-leaf area and flag-leaf area duration in four wheat crosses." Theoretical and Applied Genetics 98, no. 2 (February 1999): 310–14. http://dx.doi.org/10.1007/s001220051074.

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10

Antognozzi, E., A. Tombesi, and A. Palliotti. "RELATIONSHIP BETWEEN LEAF AREA, LEAF AREA INDEX AND FRUITING IN KIWIFRUIT (ACTINDIA DELICIOSA)." Acta Horticulturae, no. 297 (April 1992): 435–42. http://dx.doi.org/10.17660/actahortic.1992.297.57.

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11

Stancioiu, Petru Tudor, and Kevin L. O'Hara. "Sapwood area leaf area relationships for coast redwood." Canadian Journal of Forest Research 35, no. 5 (May 1, 2005): 1250–55. http://dx.doi.org/10.1139/x05-039.

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Coast redwood (Sequoia sempervirens (D. Don) Endl.) trees in different canopy strata and crown positions were sampled to develop relationships between sapwood cross-sectional area and projected leaf area. Sampling occurred during the summers of 2000 and 2001 and covered tree heights ranging from 7.7 to 45.2 m and diameters at breast height ranging from 9.4 to 92.7 cm. Foliage morphology varied greatly and was stratified into five types based on needle type (sun or shade) and twig color. A strong linear relationship existed between projected leaf area and sapwood area at breast height or sapwood at the base of the live crown despite the variability in foliage morphology. Ratios of leaf area to sapwood were 0.40 m2/cm2 at breast height and 0.57 m2/cm2 at crown base. Measurements of sapwood at the base of the live crown improved leaf area predictions because of sapwood taper below the crown base. A sapwood taper model was also developed.

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12

Silva, Jocélia Rosa da, Arno Bernardo Heldwein, Andressa Janaína Puhl, Adriana Almeida do Amarante, Daniella Moreira Salvadé, Cadmo João Onofre Gregory dos Santos, and Mateus Leonardi. "Leaf Area Estimation in Chamomile." Journal of Agricultural Science 11, no. 2 (January 15, 2019): 429. http://dx.doi.org/10.5539/jas.v11n2p429.

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The knowledge of the variables specific leaf area and leaf area index is important for direct or indirect quantification of plant growth, development and yield. However, there is a lack of these information due to the difficulty in measuring the leaf area of chamomile. Measuring leaf area by direct methods, such as the use of leaf area integrator is a very laborious and time consuming activity because the plant has many leaves and with small size. The use of leaf dry matter is a promising variable for the leaf area estimation. As an important measure to evaluate plant growth, the present study aimed to obtain a model for chamomile leaf area estimation through leaf dry matter. The experiment was conducted in two sowing dates (March 18 and June 30, 2017) at different plant densities (66, 33, 22, 16, 13, 11 and 8 plants m-2). The leaves of chamomile plants were collected in the plant vegetative and reproductive phases. The leaf area determination was performed using the electronic integration method of leaf area. The specific leaf area was 133 cm2 g-1, with no differences between sowing dates, plant densities and phenological phases of plant collection. The leaf area measured with the electronic leaf area integrator exhibited high correlation with chamomile leaf dry matter and the resulting model of leaf area data by the integrator presented optimum performance. This model is indicated for leaf area determination of chamomile when there is availability of leaf dry matter data.

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13

Lafarge, T. A., and G. L. Hammer. "Predicting plant leaf area production:." Field Crops Research 77, no. 2-3 (September 2002): 137–51. http://dx.doi.org/10.1016/s0378-4290(02)00085-0.

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14

Solov’ev, A. V., and M. K. Kayumov. "Determining optimal leaf surface area." Russian Agricultural Sciences 33, no. 1 (February 2007): 16–18. http://dx.doi.org/10.3103/s1068367407010065.

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15

Çırak, C., M. S Odabaş, B. Sağlam, and A. K Ayan. "Relation between leaf area and dimensions of selected medicinal plants." Research in Agricultural Engineering 51, No. 1 (February 7, 2012): 13–19. http://dx.doi.org/10.17221/4896-rae.

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In this research, leaf area prediction models were developed for some leaf-used medicinal plants namely Calamintha nepeta, Datura stromonium, Melissa officinalis, Mentha piperita, Nerium oleander, Origanum onites and Urtica dioica growing wild in Black Sea region of Turkey. Lamina width, length and leaf area were measured non-destructively to develop the models. The actual leaf areas of the plants were measured by PLACOM Digital Planimeter, and multiple regression analysis with Excel 7.0 computer package program was performed for the plants separately. The produced leaf area prediction models in the present study were formulized as LA = (a) + (b<sub>1</sub> × L) + [(b<sub>2</sub> × (L × W)]  + (b<sub>3</sub> × L<sup>2</sup>) + (b<sub>4</sub> × W<sup>2</sup>) + [b<sub>5</sub> × (L × W<sup>2</sup>)] + [b<sub>6</sub> × (L<sup>2</sup> × W)] + [b<sub>7</sub> × (L<sup>2</sup> × W<sup>2</sup>)] where LA is leaf area, W is leaf width, L is leaf length and a, b<sub>1</sub>, b<sub>2</sub>, b<sub>3</sub>, b<sub>4</sub>, b<sub>5</sub>, b<sub>6</sub>, and b<sub>7</sub> are coefficients. R<sup>2</sup> values for medicinal plants tested varied with species from 0.82 in Origanum onites to 0.98 in Urtica dioica. All R² values and standard errors were found to be significant at the P < 0.001 level.

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16

Bhagsari, A. S., and R. H. Brown. "Leaf Photosynthesis and its Correlation with Leaf Area 1." Crop Science 26, no. 1 (January 1986): 127–32. http://dx.doi.org/10.2135/cropsci1986.0011183x002600010030x.

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17

Thanisawanyangkura, Sornprach, Herve Sinoquet, Pierre Rivet, Michel Cretenet, and Eric Jallas. "Leaf orientation and sunlit leaf area distribution in cotton." Agricultural and Forest Meteorology 86, no. 1-2 (August 1997): 1–15. http://dx.doi.org/10.1016/s0168-1923(96)02417-3.

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18

Firman, D. M., and E. J. Allen. "Estimating individual leaf area of potato from leaf length." Journal of Agricultural Science 112, no. 3 (June 1989): 425–26. http://dx.doi.org/10.1017/s0021859600085889.

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Measurements of the area of individual leaves in crops are useful in the analysis of canopy architecture as they allow determination of the structure of leaf area index in a vertical profile. This information may be of use in modelling leaf growth and the assessment of photosynthetic potential of different strata of the canopy with ontogeny (cf. Firman & Allen, 1988).

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19

Schrader, Julian, Giso Pillar, and Holger Kreft. "Leaf-IT: An Android application for measuring leaf area." Ecology and Evolution 7, no. 22 (October 18, 2017): 9731–38. http://dx.doi.org/10.1002/ece3.3485.

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20

Liu, Guofan, and Kent D. Kobayashi. "250 Using Leaf Area Devices (LADS) to Estimate Total Leaf Area of Coffee Plants." HortScience 34, no. 3 (June 1999): 485C—485. http://dx.doi.org/10.21273/hortsci.34.3.485c.

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It is difficult to estimate the total leaf area of coffee plants with accuracy due to the large number of leaves and the high leaf density of the plant canopy. In 1996, on Maui, Hawaii, 98 leaves of various sizes were randomly collected for each of five cultivars of Coffea arabica L. The cultivars used were `Guadalupe', `Guatemalan', `Mokka', `Red Catuai', and `Yellow Caturra'. Leaf length, width, and area were measured. Seventy-five leaves were used to develop leaf area models, and the remaining leaves were used to test the accuracy of the models using a 1:1 line. We then developed leaf area devices (LADs), which were made of sheet plastic and shaped to resemble coffee leaves. There were three groups of areas in the leaf area devices, based on leaf sizes. Total leaf area (TLA) contained three components. Each component related to the mean leaf area (k) and the number of leaves (n) in that group. The model for the total leaf area was: TLA = k1n1 + k2n2 + k3n3, where k is a constant in each group. The estimation errors for the different cultivars ranged from 5.6% to 12.3% for 1-year-old plants (four cultivars) and from 1.9% to 7.8% for mature plants (five cultivars). By using the LADs and counting the number of leaves, we can obtain the total leaf area for coffee plants in the field.

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21

Darshan, Muchadia, and Vrinda S. Thaker. "Leaf Area Meter (LAM): Software for the Measurement of Leaf Area and Related Analysis." Vegetos- An International Journal of Plant Research 30, special (2017): 64. http://dx.doi.org/10.5958/2229-4473.2017.00035.0.

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22

Borghetti, M., G. G. Vendramin, and R. Giannini. "Specific leaf area and leaf area index distribution in a young Douglas-fir plantation." Canadian Journal of Forest Research 16, no. 6 (December 1, 1986): 1283–88. http://dx.doi.org/10.1139/x86-227.

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The spatial distribution of specific leaf area and leaf area index of needles in different age classes has been investigated in a young and unthinned Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco) plantation in Central Italy through the destructive analysis of 12 trees sampled in four diameter size classes. Specific leaf area decreased with leaf age and from crown base to apex. A clear interaction between the effects of age and position on specific leaf area was demonstrated. For the whole canopy the vertical distribution of leaf area was well fitted by a normal curve equation, which explained 97% of the variation. The midpoint of the leaf area distribution, estimated as a parameter of the normal curve, was found to be 1.2 m below the mean canopy depth. The standard deviation of leaf area with respect to height was 16.4%. The midpoint of leaf area distribution decreased as leaf age increased and increased as diameter size class increased. Strong and significant linear relationships were found between leaf biomass, leaf area, sapwood area, and diameter at breast height.

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23

Wu, Dan, Stuart Phinn, Kasper Johansen, Andrew Robson, Jasmine Muir, and Christopher Searle. "Estimating Changes in Leaf Area, Leaf Area Density, and Vertical Leaf Area Profile for Mango, Avocado, and Macadamia Tree Crowns Using Terrestrial Laser Scanning." Remote Sensing 10, no. 11 (November 6, 2018): 1750. http://dx.doi.org/10.3390/rs10111750.

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Vegetation metrics, such as leaf area (LA), leaf area density (LAD), and vertical leaf area profile, are essential measures of tree-scale biophysical processes associated with photosynthetic capacity, and canopy geometry. However, there are limited published investigations of their use for horticultural tree crops. This study evaluated the ability of light detection and ranging (LiDAR) for measuring LA, LAD, and vertical leaf area profile across two mango, macadamia and avocado trees using discrete return data from a RIEGL VZ-400 Terrestrial Laser Scanning (TLS) system. These data were collected multiple times for individual trees to align with key growth stages, essential management practices, and following a severe storm. The first return of each laser pulse was extracted for each individual tree and classified as foliage or wood based on TLS point cloud geometry. LAD at a side length of 25 cm voxels, LA at the canopy level and vertical leaf area profile were calculated to analyse tree crown changes. These changes included: (1) pre-pruning vs. post-pruning for mango trees; (2) pre-pruning vs. post-pruning for macadamia trees; (3) pre-storm vs. post-storm for macadamia trees; and (4) tree leaf growth over a year for two young avocado trees. Decreases of 34.13 m2 and 8.34 m2 in LA of mango tree crowns occurred due to pruning. Pruning for the high vigour mango tree was mostly identified between 1.25 m and 3 m. Decreases of 38.03 m2 and 16.91 m2 in LA of a healthy and unhealthy macadamia tree occurred due to pruning. After flowering and spring flush of the same macadamia trees, storm effects caused a 9.65 m2 decrease in LA for the unhealthy tree, while an increase of 34.19 m2 occurred for the healthy tree. The tree height increased from 11.13 m to 11.66 m, and leaf loss was mainly observed between 1.5 m and 4.5 m for the unhealthy macadamia tree. Annual increases in LA of 82.59 m2 and 59.97 m2 were observed for two three-year-old avocado trees. Our results show that TLS is a useful tool to quantify changes in the LA, LAD, and vertical leaf area profiles of horticultural trees over time, which can be used as a general indicator of tree health, as well as assist growers with improved pruning, irrigation, and fertilisation application decisions.

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24

Easlon, Hsien Ming, and Arnold J. Bloom. "Easy Leaf Area: Automated Digital Image Analysis for Rapid and Accurate Measurement of Leaf Area." Applications in Plant Sciences 2, no. 7 (July 2014): 1400033. http://dx.doi.org/10.3732/apps.1400033.

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25

Barclay, H. J. "Conversion of total leaf area to projected leaf area in lodgepole pine and Douglas-fir." Tree Physiology 18, no. 3 (March 1, 1998): 185–93. http://dx.doi.org/10.1093/treephys/18.3.185.

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26

Xiao, Chun-Wang, I. A. Janssens, J. Curiel Yuste, and R. Ceulemans. "Variation of specific leaf area and upscaling to leaf area index in mature Scots pine." Trees 20, no. 3 (March 3, 2006): 304–10. http://dx.doi.org/10.1007/s00468-005-0039-x.

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27

Sellin, Arne, and Priit Kupper. "Spatial variation in sapwood area to leaf area ratio and specific leaf area within a crown of silver birch." Trees 20, no. 3 (January 12, 2006): 311–19. http://dx.doi.org/10.1007/s00468-005-0042-2.

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28

Singh, A. "APPROXIMATION OF LEAF AREA BY USING LEAF DIMENSIONS IN GUAVA." Acta Horticulturae, no. 735 (March 2007): 321–24. http://dx.doi.org/10.17660/actahortic.2007.735.44.

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29

Cristian, Manuel Agudelo Restrepo, Eduardo Roa-Guerrero Edgar, and Numpaque López Humberto. "Leaf detector box: Artificial vision system for leaf area identification." African Journal of Agricultural Research 12, no. 20 (May 18, 2017): 1702–12. http://dx.doi.org/10.5897/ajar2016.11698.

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30

Koubouris, Georgios, Dimitris Bouranis, Efraim Vogiatzis, Abdolhossein Rezaei Nejad, Habtamu Giday, Georgios Tsaniklidis, Eleftherios K. Ligoxigakis, Konstantinos Blazakis, Panagiotis Kalaitzis, and Dimitrios Fanourakis. "Leaf area estimation by considering leaf dimensions in olive tree." Scientia Horticulturae 240 (October 2018): 440–45. http://dx.doi.org/10.1016/j.scienta.2018.06.034.

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31

FARGO, W. S., E. L. BONJOUR, and T. L. WAGNER. "AN ESTIMATION EQUATION FOR SQUASH LEAF AREA USING LEAF MEASUREMENTS." Canadian Journal of Plant Science 66, no. 3 (July 1, 1986): 677–82. http://dx.doi.org/10.4141/cjps86-089.

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An equation was developed which may be used to estimate the area of all sizes of developing squash (Cucurbita pepo L.) leaves. The equation uses two leaf measurements (midrib length (ML) and the distance between tertiary lobes (TD)) which may be taken quickly in the laboratory or field without disturbing the host plant. The equation is:[Formula: see text]The equation is applicable in monitoring individual leaf expansion as well as total plant leaf area increase and in examining the dynamics of the plant under various environmental conditions.Key words: Cucurbita pepo L., leaf area, growth, development, leaf expansion

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32

Mencuccini, Maurizio, Teresa Rosas, Lucy Rowland, Brendan Choat, Hans Cornelissen, Steven Jansen, Koen Kramer, et al. "Leaf economics and plant hydraulics drive leaf : wood area ratios." New Phytologist 224, no. 4 (July 15, 2019): 1544–56. http://dx.doi.org/10.1111/nph.15998.

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33

Reddy, V. R., B. Acock, D. N. Baker, and M. Acock. "Seasonal Leaf Area‐Leaf Weight Relationships in the Cotton Canopy." Agronomy Journal 81, no. 1 (January 1989): 1–4. http://dx.doi.org/10.2134/agronj1989.00021962008100010001x.

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34

Abani, M. S. C. "Leaf Area Formulae for Estimating Leaf Areas of "Okazi" (Gnetum afrianum) and "Nchuanwu" (Ocimum viridis)." Journal of Agronomy and Crop Science 160, no. 3 (March 1988): 180–82. http://dx.doi.org/10.1111/j.1439-037x.1988.tb00315.x.

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35

Keane, M. G., and G. F. Weetman. "Leaf area – sapwood cross-sectional area relationships in repressed stands of lodgepole pine." Canadian Journal of Forest Research 17, no. 3 (March 1, 1987): 205–9. http://dx.doi.org/10.1139/x87-036.

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To better understand the phenomenon of growth "stagnation" in high-density lodgepole pine (Pinuscontorta Dougl. ex Loud.), leaf area and its relationship with sapwood cross-sectional area were examined on both an individual tree and stand basis. Leaf areas of individual trees in a 22-year-old stand varied from 30.8 m2 (dominants in stands of low stocking) to 0.05 m2 (suppressed trees in stands of high stocking). Leaf area indices ranged from 13.4 to 2.3 m2 m−2 between low and high stocking levels, respectively. Over the same stocking range, the ratio of leaf area to sapwood cross-sectional area was reduced from 0.3 to 0.15 m2 cm−2. Intraring wood density profiles showed that ovendry density increased from 0.52 to 0.7 g cm−3 and the proportion of early wood decreased over a stocking level range of 6500–109 000 trees/ha. A reduction in hydraulic conductivity in the stems of stagnant trees, suggested by the greater proportion of narrow-diameter tracheids present, may lead to a greater resistance to water transport within the boles of trees from stagnant stands, leading to low leaf areas.

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36

Westoby, Warton, and Reich. "The Time Value of Leaf Area." American Naturalist 155, no. 5 (2000): 649. http://dx.doi.org/10.2307/3078987.

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37

Çi̇rak, C., M. Odabaş, A. Ayan, B. Sağlam, and K. Kevseroğlu. "Estimation of leaf area in selectedHypericumspecies." Acta Botanica Hungarica 50, no. 1-2 (March 2008): 81–91. http://dx.doi.org/10.1556/abot.50.2008.1-2.5.

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38

NeSmith, D. S. "Estimating Summer Squash Leaf Area Nondestructively." HortScience 27, no. 1 (January 1992): 77. http://dx.doi.org/10.21273/hortsci.27.1.77.

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39

Kanuma, Takahiro, K. Ganno, S. Hayashi, and O. Sakaue. "Leaf Area Measurement Using Stereo Vision." IFAC Proceedings Volumes 31, no. 5 (April 1998): 157–62. http://dx.doi.org/10.1016/s1474-6670(17)42115-x.

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40

Erlacher, Wellington A., Fábio L. Oliveira, Gustavo S. Fialho, Diego MN Silva, and Arnaldo HO Carvalho. "Models for estimating yacon leaf area." Horticultura Brasileira 34, no. 3 (September 2016): 422–27. http://dx.doi.org/10.1590/s0102-05362016003019.

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ABSTRACT The recent exploration of yacon demands scientific information for improving the crop production technology. This study aimed to set a leaf area estimate model for yacon plants, using non-destructive measurements of leaf length (L) and/or width (W). Sixty-four representative yacon plants were randomly selected in an experimental field during the full vegetative growth. One thousand leaves of various sizes were taken from those plants for setting and validating a model. The logarithmic model best fitted this purpose, the result of multiplying length by width being used as independent variable. Yacon leaf area can be determined with high precision and accuracy by LALW = (-27.7418 + (3.9812LW / ln LW ) , disregarding the leaf size.

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41

Mack, Laura, Filippo Capezzone, Sebastian Munz, Hans-Peter Piepho, Wilhelm Claupein, Tim Phillips, and Simone Graeff-Hönninger. "Nondestructive Leaf Area Estimation for Chia." Agronomy Journal 109, no. 5 (September 2017): 1960–69. http://dx.doi.org/10.2134/agronj2017.03.0149.

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42

Westoby, Mark, David Warton, and Peter B. Reich. "The Time Value of Leaf Area." American Naturalist 155, no. 5 (May 2000): 649–56. http://dx.doi.org/10.1086/303346.

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43

Balakrishnan, K., N. Natarajaratnam, and C. Rajendran. "Critical Leaf Area Index in Pigeonpea." Journal of Agronomy and Crop Science 159, no. 3 (September 1987): 164–66. http://dx.doi.org/10.1111/j.1439-037x.1987.tb00081.x.

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MANGKOEDIHARDJO, SARWOKO. "LEAF AREA FOR PHYTOPUMPING OF WASTEWATER." Applied Ecology and Environmental Research 5, no. 1 (July 1, 2007): 37–42. http://dx.doi.org/10.15666/aeer/0501_037042.

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Chapman, S. C., G. L. Hammer, and J. A. Palta. "Predicting leaf area development of sunflower." Field Crops Research 34, no. 1 (July 1993): 101–12. http://dx.doi.org/10.1016/0378-4290(93)90114-3.

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46

Righetti, Timothy L., Carmo Vasconcelos, David R. Sandrock, Samuel Ortega-Farias, Yerko Moreno, and Francisco J. Meza. "Assessments of CO2 Assimilation on a Per-leaf-area Basis are Related to Total Leaf Area." Journal of the American Society for Horticultural Science 132, no. 2 (March 2007): 230–38. http://dx.doi.org/10.21273/jashs.132.2.230.

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Net photosynthetic rates often are dependent on leaf size when expressed on a leaf-area basis (CO2 assimilation as μmol·m−2·s−1). Therefore, distinguishing between leaf-size-related and other causes of differences in net photosynthetic rate cannot be determined when data are presented on a leaf-area basis. From a theoretical perspective, CO2 assimilation expressed on a leaf-area basis (μmol·m−2·s−1) will be independent of leaf area only when total net CO2 assimilation (leaf CO2 assimilation as μmol·s−1) is linearly related to leaf area and the function describing this relationship has a nonzero y intercept. This situation was not encountered in the data sets we evaluated; therefore, ratio-based estimates of CO2 assimilation were often misleading. When CO2 assimilation data are expressed on a per-leaf-area basis (the standard procedure in the photosynthesis literature), it is difficult to determine how photosynthetic efficiency changes as leaves or plants mature and difficult to compare the efficiency of treatments or cultivars when leaf size or total plant leaf area varies.

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Xuan-Ran, LI, LIU Qi-Jing, CAI Zhe, and MA Ze-Qing. "SPECIFIC LEAF AREA AND LEAF AREA INDEX OF CONIFER PLANTATIONS IN QIANYANZHOU STATION OF SUBTROPICAL CHINA." Chinese Journal of Plant Ecology 31, no. 1 (2007): 93–101. http://dx.doi.org/10.17521/cjpe.2007.0012.

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ZHONG, X., S. PENG, J. E. SHEEHY, R. M. VISPERAS, and H. LIU. "Relationship between tillering and leaf area index: quantifying critical leaf area index for tillering in rice." Journal of Agricultural Science 138, no. 3 (May 2002): 269–79. http://dx.doi.org/10.1017/s0021859601001903.

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A field study was conducted at the International Rice Research Institute (IRRI), Philippines during the dry seasons of 1997 and 1998 under irrigated conditions. The objectives of this study were to quantify the critical leaf area index (LAIc) at which tillering stops based on the relationship between tillering rate and LAI, and to determine the effect of nitrogen (N) on LAIc in irrigated rice (Oryza sativa L.) crop. Results showed that the relative tillering rate (RTR) decreased exponentially as LAI increased at a given N input level. The coefficient of determination for the equation quantifying the RTR-LAI relationship ranged from 0·87 to 0·99. The relationship between RTR and LAI was affected by N input level, but not by planting density. The N input level had a significant effect on LAIc with a high N input level causing an increase in LAIc. Tillering stopped at LAI of 3·36 to 4·11 when N was not limiting. Under N limited conditions LAIc reduced to as low as 0·98. Transplanting spacing and number of seedlings per hill had little effect on LAIc. Results from this study suggest that LAI and plant N status are two major factors that influence tiller production in rice crops. The possibility that LAI influences tillering by changing light intensity and/or light quality at the base of the canopy where tiller buds and young tillers are located is discussed.

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Ghadami Firouzabadi, Ali, Mahmoud Raeini-Sarjaz, Ali Shahnazari, and Hamid Zareabyaneh. "Non-destructive estimation of sunflower leaf area and leaf area index under different water regime managements." Archives of Agronomy and Soil Science 61, no. 10 (February 23, 2015): 1357–67. http://dx.doi.org/10.1080/03650340.2014.1002776.

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Bouriaud, O., K. Soudani, and N. Bréda. "Leaf area index from litter collection: impact of specific leaf area variability within a beech stand." Canadian Journal of Remote Sensing 29, no. 3 (January 2003): 371–80. http://dx.doi.org/10.5589/m03-010.

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