Bibliografie tematiche / Leaf area

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Letteratura scientifica selezionata sul tema "Leaf area"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 12 febbraio 2022

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Articoli di riviste sul tema "Leaf area"

1

Payne, W. A., C. W. Wendt, L. R. Hossner e C. E. Gates. "Estimating Pearl Millet Leaf Area and Specific Leaf Area". Agronomy Journal 83, n. 6 (novembre 1991): 937–41. http://dx.doi.org/10.2134/agronj1991.00021962008300060004x.

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Chinnamuthu, C. R., C. Kailasam e Dr S. Sankaran. "Sorghum Leaf Area as a Function of Sixth Leaf Area". Journal of Agronomy and Crop Science 162, n. 5 (maggio 1989): 300–304. http://dx.doi.org/10.1111/j.1439-037x.1989.tb00720.x.

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Awal, M. A., Wan Ishak ., J. Endan . e M. Haniff . "Determination of Specific Leaf Area and Leaf Area-leaf Mass Relationship in Oil Palm Plantation". Asian Journal of Plant Sciences 3, n. 3 (15 aprile 2004): 264–68. http://dx.doi.org/10.3923/ajps.2004.264.268.

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4

Pierce, Lars L., Steven W. Running e Joe Walker. "Regional-Scale Relationships of Leaf Area Index to Specific Leaf Area and Leaf Nitrogen Content". Ecological Applications 4, n. 2 (maggio 1994): 313–21. http://dx.doi.org/10.2307/1941936.

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Smith, Duncan D., John S. Sperry e Frederick R. Adler. "Convergence in leaf size versus twig leaf area scaling: do plants optimize leaf area partitioning?" Annals of Botany 119, n. 3 (27 dicembre 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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CURRAN, P. J., e N. W. WARDLEY. "Radiometric leaf area index". International Journal of Remote Sensing 9, n. 2 (febbraio 1988): 259–74. http://dx.doi.org/10.1080/01431168808954850.

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Toebe, M., P. J. Melo, R. R. Souza, A. C. Mello e F. L. Tartaglia. "Leaf area estimation in triticale by leaf dimensions". Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences 14, n. 2 (30 giugno 2019): 1–9. http://dx.doi.org/10.5039/agraria.v14i2a5656.

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Cargnelutti Filho, Alberto, Rafael Vieira Pezzini, Ismael Mario Márcio Neu e Gabriel Elias Dumke. "Estimation of buckwheat leaf area by leaf dimensions". Semina: Ciências Agrárias 42, n. 3Supl1 (22 aprile 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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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, n. 2 (febbraio 1999): 310–14. http://dx.doi.org/10.1007/s001220051074.

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Antognozzi, E., A. Tombesi e A. Palliotti. "RELATIONSHIP BETWEEN LEAF AREA, LEAF AREA INDEX AND FRUITING IN KIWIFRUIT (ACTINDIA DELICIOSA)". Acta Horticulturae, n. 297 (aprile 1992): 435–42. http://dx.doi.org/10.17660/actahortic.1992.297.57.

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Tesi sul tema "Leaf area"

1

Back, Merri, e A. K. Dobrenz. "Increasing the Leaf Area of Alfalfa". College of Agriculture, University of Arizona (Tucson, AZ), 1985. http://hdl.handle.net/10150/200495.

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Malone, Sean M. "Assessment of Soybean Leaf Area for Redefining Management Strategies for Leaf-Feeding Insects". Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/29252.

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Commercially available leaf area index (LAI) meters are tools that can be used in making insect management decisions. However, proper technique must be determined for LAI estimation, and accuracy must be validated for the meters. Full-season soybean require LAI values of at least 3.5 to 4.0 by early to mid-reproductive developmental stages to achieve maximum yield potential, but the relationship between double-crop soybean LAI and yield is unknown. This research (1) evaluated minimum plot size requirements for mechanically defoliated soybean experiments using the LAI-2000 Plant Canopy Analyzer, (2) compared LAI estimates among LAI-2000 detector types which respond to different wavelengths of light, (3) compared LAI-2000 estimates with directly determined LAI values for 0, 33, 66, and 100% mechanical defoliation levels, (4) used linear and non-linear models to describe the response of full-season and double-crop soybean yields to reductions in LAI through mechanical defoliation, and (5) evaluated the response of double-crop soybean yields to reductions in LAI through insect defoliation. The minimum plot size for obtaining accurate LAI estimates of defoliated canopies in soybean with 91 cm row centers is four rows by 2 m, with an additional 1 m at the ends of the two middle rows also defoliated. The wide-blue detector, which is found in newer LAI-2000 units and responds to wavelengths of light from 360 to 460 nm, gave higher LAI estimates than the narrow-blue detector, which responds to light from 400 to 490 nm. The unit with the narrow-blue detector gave estimates equal to directly determined LAI in two of three years for 0, 33, and 66% defoliation levels, while the units with the wide-blue detectors gave estimates higher than directly determined LAI in the two years that they were studied, except for a few accurate 33% defoliation estimates. Therefore, the LAI-2000 usually provides reasonable estimates of LAI. Yield decreased linearly with LAI when LAI values were below 3.5 to 4.0 by developmental stages R4 to R5 in both full-season and double-crop soybean. Usually, there was no relationship between yield and LAI at LAI values greater than 4.0. There was an average yield reduction of 820 ± 262 kg ha-1 for each unit decrease in LAI below the critical 3.5 to 4.0 level; maximum yields ranged from 1909 to 3797 kg ha-1. Insect defoliators did not defoliate double-crop soybean plots to LAI levels less than 4.0, and there was no yield difference between insect-defoliated and control plots. Therefore, double-crop soybean that maintains LAI values above the 3.5 to 4.0 critical level during mid-reproductive developmental stages is capable of tolerating defoliating pest
Ph. D.
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Duffy, Natasha Michelle. "Design limitations to potential leaf area in urban forests". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ46004.pdf.

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Ebodaghe, Denis Abumere. "Estimating daily green leaf area index for corn in Virginia". Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/74731.

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A model to predict the daily green leaf area index (GLAI) for corn has been developed for Indiana conditions. Using daily maximum and minimum temperatures the GLAI was predicted for the vegetative stage, reproductive and grain filling stage, and the leaf senescing stage of corn. Predictions of GLAI for corn can be made on a daily basis from the day corn is planted until it is harvested for grain. The GLAI model was tested under Virginia conditions using green leaf area measurements collected from corn plants grown on Davidson silty clay loam, Davidson silty clay, and Mayodan sandy loam soils in the Piedmont region of the State. Maximum and minimum temperature data were also collected at the three sites. Measurements were made for two growing seasons using corn hybrid Pioneer 3369A, three plant population densities and two irrigation schedules. Short duration temperature data were also collected to compare with the daily maximum and minimum temperature data for the Mayodan site. Also a combination of soil temperature at 10 cm depth and air temperatures were used for the temperature functions accumulated from date of planting at the Mayodan site. Results of this study show that the predicted and measured GLAI values compare favorably under irrigated conditions on the Davidson soil. The results were not as favorable on the irrigated corn on the Mayodan soil. When the corn is subjected to severe moisture stress on either soil, GLAI cannot be predicted with this model. Short duration temperature data resulted in a better prediction of GLAI on the Mayodan soil. When applying nitrogen fertilizer to the corn through the irrigation system through the grain filling stage, the measured GLAI values compared favorably with the predicted GLAI values. However, the application of nitrogen and sulfur fertilizer together resulted in GLAI being maintained above that predicted for a longer period of time during the grain filling stage before its decline.
Ph. D.
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Coker, Graham William Russell. "Leaf Area Index in Closed Canopies: An indicator of site quality". Thesis, University of Canterbury. School of Forestry, 2006. http://hdl.handle.net/10092/1128.

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This study examined leaf area index (LAI) and relationships with corresponding tree growth, climate and soil characteristics across New Zealand forest plantations. The aim of this study was to determine if quick measures of projected leaf area across environmental gradients of New Zealand were an accurate indicator of site quality. Projected leaf areas of Pinus radiata D Don and Cupressus lusitanica Mills seedlings were measured using a Li-Cor LAI-2000 plant canopy analyser at 22 locations representing the soil and climatic diversity across New Zealand plantation forests. Seedlings planted at 40 000 stems per hectare were used to test treatment effects of fertiliser, site disturbance and species over a 4 year period. It was hypothesised that collected climate and soil information would explain differences in LAI development patterns across sites as the canopies approached site and seasonal maxima. Averaged across sites Cupressus lusitanica 7.28 (± 2.59 Std.) m2 m-2 had significantly (p = 0.0094) greater projected LAIs than Pinus radiata 6.47 (± 2.29) m2m-2. Maximum site LAI (LAImax) varied from 2.9 to 11.8 m2 m-2 for Pinus radiata and from 3.1 to 12.6 m2 m-2 for Cupressus lusitanica. LAImax of both species was significantly and positively correlated with vapour pressure deficit, soil carbon, nitrogen, phosphorous and CEC, but negatively with solar radiation, temperature and soil bulk density. A seasonal model of LAI across sites illustrated an 8.5% fluctuation in LAI of established canopies over the course of a year. Despite considerable variation in climate and soil characteristics across sites the combined effects of LAI at harvest and temperature were significantly correlated with site productivity (r2 = 0.84 and 0.76 for Pinus radiata and Cupressus lusitanica respectively). A national model of LAImax (r2 = 0.96) was proposed for Pinus radiata across climate and soil environments and the significance of LAImax as a component of site quality monitoring tools is discussed.
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Johnson, Ryan L., e University of Lethbridge Faculty of Arts and Science. "Airborne remote sensing of forest leaf area index in mountainous terrain". Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2000, 2000. http://hdl.handle.net/10133/90.

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Leaf area index (LAI) provides forestry information that is important for regional scale ecological models and in studies of global change. This research examines the effects of mountainous terrain on the radiometric properties of multispectral CASI imagery in estimating ground-based optical measurements of LAI, obtained using the TRAC and LAI- 2000 systems. Field and image data were acquired summer 1998 in Kananaskis, Alberta, Canada. To account for the influence of terrain a new modified approach using the Li and Strahler Geometric Optical Mutual Shadowing (GOMS) model in 'multiple forward mode' (MFM) was developed. This new methodology was evaluated against four traditional radiometric corrections used in comination with spectral mixture analysis (SMA) and NDVI. The MFM approach provided the best overall predictions of LAI measured with ground-based optical instruments, followed by terrain normalized SMA, SMA without terrain normalization and NDVI.
xiv, 151 leaves : ill. (some col.), map ; 29 cm.
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Wang, Dongdong. "Improving satellite Leaf Area Index estimation based on various integration methods". College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9872.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Dept. of Geography. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Johnson, Ryan L. "Airborne remote sensing of forest leaf area index in mountainous terrain". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ49131.pdf.

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Winkler, Tobias. "Empirical models for grape vine leaf area estimation on cv. Trincadeira". Master's thesis, ISA-UL, 2016. http://hdl.handle.net/10400.5/13008.

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Mestrado Vinifera Euromaster - Viticulture and Enology - Instituto Superior de Agronomia - UL / Institut National D'Etudes Superieures Agronomiques de Montpellier
Estimating a Vineyard’s leaf area is of great importance when evaluating the productive and quality potential of a vineyard and for characterizing the light and thermal microenvironments of grapevine plants. The aim of the present work was to validate the Lopes and Pinto method for determining vineyard leaf area in the vineyards of Lisbon’s wine growing region in Portugal, with the typical local red grape cultivar Trincadeira, and to improve prediction quality by providing cultivar specific models. The presented models are based on independent datasets of two consecutive years 2015 and 2016. Fruiting shoots were collected and analyzed during all phenological stages. Primary leaf area of shoots is estimated by models using a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves, as presented by Lopes and Pinto (2005). Lateral Leaf area additionally uses the area of the biggest lateral leaf as predictor. Models based on Shoot length and shoot diameter and number of lateral leaves were tested as less laborious alternatives. Although very fast and easy to assess, models based on shoot length and diameter were not able to predict variability of lateral leaf area sufficiently and were susceptible to canopy management. The Lopes and Pinto method is able to explain a very high proportion of variability, both in primary and lateral leaf area, independently of the phenological stage, as well as before and after trimming. They are inexpensive, universal, practical, non-destructive methods which do not require specialized staff or expensive equipment
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Phinopoulos, Victoras Georgios. "Estimation of leaf area in grapevine cv. Syrah using empirical models". Master's thesis, ISA/UL, 2014. http://hdl.handle.net/10400.5/8631.

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Mestrado Vinifera EuroMaster - Instituto Superior de Agronomia
Empirical models for the estimation of the Area of single Primary and Lateral leaves, and total Primary and Lateral Leaf Area of a shoot, are presented for the grapevine cv. Syrah (Vitis vinifera L.). The Area of single Leaves is estimated with models using the sum of the lengths of the two lateral veins of each leaf, with logarithmic transformation of both variables. Separate models are proposed for Primary and Lateral Leaves. Models based on the Lopes and Pinto (2005) method, using Mean Leaf Area multiplied by the number of Leaves as predictors, are proposed for the estimation for Total Primary and Lateral Leaf Area. It is suggested, that failure to locate the Largest Leaf of a Primary or Lateral shoot, would not significantly impair the accuracy of the models. All models explain a very high proportion of variability in Leaf Area and they can by applied in research and viticulture for the frequent estimation of Leaf Area in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialised staff or expensive equipment
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Libri sul tema "Leaf area"

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Warchałowski, Andrzej. Chrysomelidae: The leaf-beetles of Europe and the Mediterranean area. Warszawa: Natura Optima dux Foundation, 2003.

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Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Feiveson, A. H. Error analysis of leaf area estimates made from allometric regression models. [Washington, DC: National Aeronautics and Space Administration, 1987.

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Sarabandi, J. Effect of curvature on the backscattering from leaves. [Washington, DC: National Aeronautics and Space Administration, 1988.

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Plummer, Stephen E. BOREAS RSS-4 1994 jack pine leaf biochemistry and modeled spectra in the SSA. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Chen, Jing. BOREAS RSS-7 Landsat TM LAI images of the SSA and NSA. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Chen, Jing. BOREAS RSS-7 Landsat TM LAI images of the SSA and NSA. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Capitoli di libri sul tema "Leaf area"

1

Liang, Shunlin, Xiaotong Zhang, Zhiqiang Xiao, Jie Cheng, Qiang Liu e Xiang Zhao. "Leaf Area Index". In Global LAnd Surface Satellite (GLASS) Products, 3–31. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02588-9_2.

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Keane, Robert E. "LAI: Leaf Area Index". In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_237-1.

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Keane, Robert E. "LAI: Leaf Area Index". In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 730–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_237.

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Wirth, Rainer, Hubert Herz, Ronald J. Ryel, Wolfram Beyschlag e Bert Hölldobler. "The Study Area — Barro Colorado Island". In Herbivory of Leaf-Cutting Ants, 49–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05259-4_3.

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Sánchez-de-Miguel, Patricia, Pilar Baeza, Pedro Junquera e José Ramón Lissarrague. "Vegetative Development: Total Leaf Area and Surface Area Indexes". In Methodologies and Results in Grapevine Research, 31–44. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9283-0_3.

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Qu, Yonghua. "Leaf Area Index: Advances in Ground-Based Measurement". In Observation and Measurement of Ecohydrological Processes, 359–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-48297-1_11.

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Chen, Baisong, Zhuo Fu, Yuchun Pan, Jihua Wang e Zhixuan Zeng. "Single Leaf Area Measurement Using Digital Camera Image". In Computer and Computing Technologies in Agriculture IV, 525–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18336-2_64.

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Han, Dianyuan, e Fengqing Zhang. "Leaf Area Measurement Embeded in Smart Mobile Phone". In Advances in Intelligent Systems, 207–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27869-3_27.

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Strachan, I. B., D. W. Stewart e E. Pattey. "Determination of Leaf Area Index in Agricultural Systems". In Agronomy Monographs, 179–98. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/agronmonogr47.c9.

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Fujita, K., T. Oka, H. Sato, N. Sakurai, S. Sendo, H. Saneoka e H. Nobuyasu. "Factors controlling leaf area development in husk leaf of flint corn (Zea mays L.)". In Plant Nutrition for Sustainable Food Production and Environment, 907–8. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0047-9_293.

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Atti di convegni sul tema "Leaf area"

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BURG, Patrik, Jana BURGOVÁ, Vladimír MAŠÁN e Miroslav VACHŮN. "LEAF SURFACE AREA ESTIMATION IN DIFFERENT GRAPES VARIETIES USING A AM 300 LEAF AREA METER". In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.037.

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Experimental measurements focused on evaluation of grapevine leaf surface area development in nine varieties, in the viticultural conditions of South Moravia. The dynamics of leaf surface area development was measured by using a device called leaf area meter AM 300. The device operates on the principle of a scanner and the resulting values are expressed through the leaf area index - LAI. The measurements were carried out in five dates during phenophases of growth, flowering, initial development of fruits, and ripening of berries. The results show a significant differences in increase in leaf area between the evaluated varieties, especially during flowering. The size of the leaf area, depending on the year, corresponds to values between 7.615 and 13.483 square metres per hectare. The largest leaf area was reached in growth stage 8, which is ripening of fruit. The leaf area reached the largest size in the varieties Grüner Veltliner, Zweigelt, and Sauvignon, with values ranging from 20.560 to 26.481 square metres per hectare. The results suggest that a significant proportion of leaf area is also represented by lateral shoots whose size in the ripening phase, depending on variety, ranges from 33.7 to 52.9 per cent of the total leaf area.
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Ghazal, Mohammed, e Hassan Hajjdiab. "Leaf spot area index: A nondestructive mangrove leaf spot estimation technique". In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091414.

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Lü, Chaohui, Hui Ren, Yibin Zhang e Yinhua Shen. "Leaf Area Measurement Based on Image Processing". In 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmtma.2010.141.

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4

Zhao, Chunjiang, Jihua Wang, Wenjiang Huang e Liangyun Liu. "Influence of leaf water status on leaf area index and leaf nitrogen concentration inversion of wheat canopy". In Multispectral and Hyperspectral Remote Sensing Instruments and Applications II. SPIE, 2005. http://dx.doi.org/10.1117/12.578698.

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5

Han, Dianyuan. "Leaf area measurement based on Markov random field". In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6526258.

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6

Monje, O., G. W. Stutte, H. T. Wang e C. J. Kelly. "NDS Water Pressures Affect Growth Rate By Changing Leaf Area, Not Single Leaf Photosynthesis". In 31st International Conference On Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2001. http://dx.doi.org/10.4271/2001-01-2277.

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7

Zhen LI, Tian-sheng HONG, Wei-bin WU e Min-juan LIU. "A Novel Method of Object Identification and Leaf Area Calculation in Multi-leaf Image". In 2007 Minneapolis, Minnesota, June 17-20, 2007. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2007. http://dx.doi.org/10.13031/2013.23173.

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8

Junlei Tan, Mingguo Ma, Yi Song, Guanghui Huang e Yi Song. "Retrieval of leaf area index, leaf chlorophyll content based on SLC model and CHRIS data". In 2010 Second IITA International Conference on Geoscience and Remote Sensing (IITA-GRS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iita-grs.2010.5602344.

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9

Kaiyan, Lin, Wu Junhui, Chen Jie e Si Huiping. "Measurement of Plant Leaf Area Based on Computer Vision". In 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2014. http://dx.doi.org/10.1109/icmtma.2014.99.

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10

Yang, Qingfeng, Weiguo Fu, Bing Pan, Liang Zhang, Baolin Feng e Lu Li. "Design of Intelligent Acquisition System for Tomato Leaf Area". In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE, 2019. http://dx.doi.org/10.1109/siprocess.2019.8868325.

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Rapporti di organizzazioni sul tema "Leaf area"

1

Wang, S. Leaf area index. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/298882.

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2

Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, Theodore D. Leininger, Paul B. Hamel, Kristina F. Connor, Nathan M. Schiff, A. Dan Wilson e Margaret S. Devall. Nondestructive estimation of leaf area for pondberry. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-rn-14.

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3

Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, Theodore D. Leininger, Paul B. Hamel, Kristina F. Connor, Nathan M. Schiff, A. Dan Wilson e Margaret S. Devall. Nondestructive estimation of leaf area for pondberry. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-rn-14.

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4

Dogan, Adnan, Cuneyt Uyak, Nurhan Keskin, Anil Akcay, Ruhan Ilknur, Gazioglu Sensoy e Sezai Ercisli. Grapevine Leaf Area Measurements by Using Pixel Values. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, giugno 2018. http://dx.doi.org/10.7546/crabs.2018.06.07.

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5

Scurlock, JMO. Worldwide Historical Estimates of Leaf Area Index, 1932-2000. Office of Scientific and Technical Information (OSTI), febbraio 2002. http://dx.doi.org/10.2172/814100.

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6

Maloley, MJ, R. Fernandes, F. Canisius e C. Butson. Peak season leaf area index for the Nanaimo Aquifer - 2011. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2013. http://dx.doi.org/10.4095/293342.

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7

Kaszycki, C. A., L. A. Dredge e H. Groom. Surficial geology and glacial history, Lynn Lake - Leaf Rapids area, Manitoba. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/225935.

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8

Chen, J. M., e J. Cihlar. Retrieving Leaf Area Index of boreal conifer forests using Landsat TM images. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/218507.

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9

Percival, J. A., K. D. Card, R. A. Stern e N. J. Begin. A Geological Transect of the Leaf River area, northeastern Superior Province, Ungava Peninsula, Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/132560.

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10

L.T. Rader. Biomass, Leaf Area, and Resource Availability of Kudzu Dominated Plant Communities Following Herbicide Treatment. Office of Scientific and Technical Information (OSTI), ottobre 2001. http://dx.doi.org/10.2172/807682.

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