Relevant bibliographies by topics / Leaf area estimation

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Academic literature on the topic 'Leaf area estimation'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Leaf area estimation"

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Fanourakis, Dimitrios, Filippos Kazakos, and Panayiotis A. Nektarios. "Allometric Individual Leaf Area Estimation in Chrysanthemum." Agronomy 11, no. 4 (April 18, 2021): 795. http://dx.doi.org/10.3390/agronomy11040795.

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A model for estimating the area of individual leaves (LA) by employing their dimensions was developed for chrysanthemum. Further hypotheses were tested: (a) LA estimation is improved by considering blade length (Lb) rather than leaf length (L), and (b) a reasonable LA estimation can be attainable by considering L in conjunction to a shape trait, which is cultivar dependent. For the model development, six cultivars were employed (1500 leaves in total), while for model validation, an independent set of nine cultivars was utilized (1125 leaves in total). Several characteristics were digitally assessed in fully expanded leaves which included petiole length, leaf L, width (W), perimeter, shape traits (aspect ratio, circularity, roundness, solidity), together with LA. LA estimation was more accurate by considering both L and W, as compared to a single dimension. A linear model, employing the product of L by W as independent variable, provided the most accurate LA estimation (R2 = 0.84). The model validation indicated a highly significant correlation between computed and measured LA (R2 = 0.88). Replacing L by Lb reasonably predicted LA (R2 = 0.832) but at some expense of accuracy. Contrary to expectation, considering L (or W) and a cultivar-specific shape trait generally led to poor LA estimations.
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S, THIMMEGOWDA. "ESTIMATION OF LEAF AREA IN WHEAT GENOTYPES." Madras Agricultural Journal 73, May (1986): 278–80. http://dx.doi.org/10.29321/maj.10.a02268.

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Leaf product cons ants for night wheat genotypes, ware investigated. The test product constants varied significantly 10.729 to 0.709 for flag-leat, 0.737 10 0.864. for other than flag leaf and 0 740 to 0.831 for all leaves) among the genotypes. This indicated that a singla constant for whost cras as such cannot be accepted." However, a lea! product constant of flag leaf only could be used for estimating leaf area in wheat genotypes as the high releationship existed between the actual leal area and the estimated leaf area in all the genotypes as compared to other than flag leaf and all leaves
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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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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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Deng, Yangbo, Kunyong Yu, Xiong Yao, Qiaoya Xie, Yita Hsieh, and Jian Liu. "Estimation of Pinus massoniana Leaf Area USING Terrestrial Laser Scanning." Forests 10, no. 8 (August 6, 2019): 660. http://dx.doi.org/10.3390/f10080660.

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The accurate estimation of leaf area is of great importance for the acquisition of information on the forest canopy structure. Currently, direct harvesting is used to obtain leaf area; however, it is difficult to quickly and effectively extract the leaf area of a forest. Although remote sensing technology can obtain leaf area by using a wide range of leaf area estimates, such technology cannot accurately estimate leaf area at small spatial scales. The purpose of this study is to examine the use of terrestrial laser scanning data to achieve a fast, accurate, and non-destructive estimation of individual tree leaf area. We use terrestrial laser scanning data to obtain 3D point cloud data for individual tree canopies of Pinus massoniana. Using voxel conversion, we develop a model for the number of voxels and canopy leaf area and then apply it to the 3D data. The results show significant positive correlations between reference leaf area and mass (R2 = 0.8603; p < 0.01). Our findings demonstrate that using terrestrial laser point cloud data with a layer thickness of 0.1 m and voxel size of 0.05 m can effectively improve leaf area estimations. We verify the suitability of the voxel-based method for estimating the leaf area of P. massoniana and confirmed the effectiveness of this non-destructive method.
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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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K, BALAKRISHNAN, NATARAJARATNAM N, and SUNDARUM K.M. "A RAPID METHOD FOR THE ESTIMATION OF LEAF AREA IN FIELD BEAN." Madras Agricultural Journal 72, November (1985): 633–35. http://dx.doi.org/10.29321/maj.10.a02415.

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The present investigation aimed to establish a relationship between leaf length x leal breadth and leat area in field bean CV. Co. 1. The regression equation fitted against leaf area and the product between terminal leaf length and breadth was Y = 3 09+1.63 (X) (r = 0.9647**), where Y = leaf area (trifoliate leaf) per leaf. X = Length X Breadth of the terminal leaf let of the trifoliate leaf (L x B). The leaf area was also predicted by using the formula A = 1,685 (L x B). A significant correlation (r=0.9630) was also obtained with actual and predicted leaf area by using the above constants. It was found that the predicted leaf area by regression equation was more accurate than by using A= 1685 (L x B) method. This study will be helpful to estimate the leat area in situ without destroying canopy.
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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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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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Jiang, Ni, Wanneng Yang, Lingfeng Duan, Guoxing Chen, Wei Fang, Lizhong Xiong, and Qian Liu. "A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images." Journal of Innovative Optical Health Sciences 08, no. 02 (March 2015): 1550002. http://dx.doi.org/10.1142/s1793545815500029.

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Total green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented. Using projected areas of the plant in images, linear, quadratic, exponential and power regression models for estimating total GLA were evaluated. Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area. And power models fit better than other models. In addition, the use of multiple side-view images was an efficient method for reducing the estimation error. The inclusion of the top-view projected area as a second predictor provided only a slight improvement of the total leaf area estimation. When the projected areas from multi-angle images were used, the estimated leaf area (ELA) using the power model and the actual leaf area had a high correlation coefficient (R2 > 0.98), and the mean absolute percentage error (MAPE) was about 6%. The method was capable of estimating the total leaf area in a nondestructive, accurate and efficient manner, and it may be used for monitoring rice plant growth.
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Dissertations / Theses on the topic "Leaf area estimation"

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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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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
N/A
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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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Fang, Hongliang. "Improving the estimation of leaf area index from multispectral remotely sensed data." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/304.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2003.
Thesis research directed by: 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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Pacheco, Anna. "Contribution of hyperspectral remote sensing to the estimation of leaf area index in the context of precision agriculture." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26734.

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The estimation of Leaf Area Index (LAI) is a key parameter controlling biophysical processes of the vegetation canopy, and ultimately yield. Defined as one half the total green leaf area per unit ground surface area, LAI is an essential component of precision crop management. Direct field techniques are tedious, time-consuming and labour-intensive. Indirect techniques, such as determining gap fraction with optical instruments have proven to be a good alternative, but their use is limited to rigid field sampling techniques. Vegetation indices have been useful to estimate LAI but are limited mostly due to its background reflectance noise. LAI can be estimated using different types of data, but only hyperspectral remote sensing has the potential to distinguish effectively the crop from other field components using spectral mixture analysis. Once the crop fraction has been derived, LAI is estimated using a crop fraction inversion technique. The application of this technique under agricultural field conditions has been very limited and not rigorously validated. The main objective of this study is to validate the crop fraction inversion technique for LAI estimation, and to examine the potential for LAI estimation using hyperspectral remote sensing data in the context of precision agriculture. This research will provide a unique scientific contribution to the field of hyperspectral remote sensing and greatly contribute to the advancement of remote sensing agriculture applications in Canada. (Abstract shortened by UMI.)
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Banskota, Asim. "The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39188.

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The need for an efficient dimensionality reduction technique has remained a critical challenge for effective analysis of hyperspectral data for vegetation applications. Discrete wavelet transform (DWT), through multiresolution analysis, offers oppurtunities both to reduce dimension and convey information at multiple spectral scales. In this study, we investigated the utility of the Haar DWT for AVIRIS hyperspectral data analysis in three different applications (1) classification of three pine species (Pinus spp.), (2) estimation of leaf area index (LAI) using an empirically-based model, and (3) estimation of LAI using a physically-based model. For pine species classification, different sets of Haar wavelet features were compared to each other and to calibrated radiance. The Haar coefficients selected by stepwise discriminant analysis provided better classification accuracy (74.2%) than the original radiance (66.7%). For empirically-based LAI estimation, the models using the Haar coefficients explained the most variance in observed LAI for both deciduous plots (cross validation R2 (CV-R2) = 0.79 for wavelet features vs. CV-R2 = 0.69 for spectral bands) and all plots combined (CV R2 = 0.71 for wavelet features vs. CV-R2 = 0.50 for spectral bands). For physically-based LAI estimation, a look-up-table (LUT) was constructed by a radiative transfer model, DART, using a three-stage approach developed in this study. The approach involved comparison between preliminary LUT reflectances and image spectra to find the optimal set of parameter combinations and input increments. The LUT-based inversion was performed with three different datasets, the original reflectance bands, the full set of the wavelet extracted features, and the two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R2 = 0.77) than the original spectral bands (RMSE = 0.69, R2 = 0.42). This study has demonstrated that the application of the discrete wavelet transform can provide more accurate species discrimination within the same genus than the original hyperspectral bands and can improve the accuracy of LAI estimates from both empirically- and physically-based models.
Ph. D.
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Kandasamy, Sivasathivel. "Leaf Area Index (LAI) monitoring at global scale : improved definition, continuity and consistency of LAI estimates from kilometric satellite observations." Phd thesis, Université d'Avignon, 2013. http://tel.archives-ouvertes.fr/tel-00967319.

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Monitoring biophysical variables at a global scale over long time periods is vital to address the climatechange and food security challenges. Leaf Area Index (LAI) is a structure variable giving a measure of the canopysurface for radiation interception and canopy-atmosphere interactions. LAI is an important variable in manyecosystem models and it has been recognized as an Essential Climate Variable. This thesis aims to provide globaland continuous estimates of LAI from satellite observations in near-real time according to user requirements to beused for diagnostic and prognostic evaluations of vegetation state and functioning. There are already someavailable LAI products which show however some important discrepancies in terms of magnitude and somelimitations in terms of continuity and consistency. This thesis addresses these important issues. First, the nature ofthe LAI estimated from these satellite observations was investigated to address the existing differences in thedefinition of products. Then, different temporal smoothing and gap filling methods were analyzed to reduce noiseand discontinuities in the time series mainly due to cloud cover. Finally, different methods for near real timeestimation of LAI were evaluated. Such comparison assessment as a function of the level of noise and gaps werelacking for LAI.Results achieved within the first part of the thesis show that the effective LAI is more accurately retrievedfrom satellite data than the actual LAI due to leaf clumping in the canopies. Further, the study has demonstratedthat multi-view observations provide only marginal improvements on LAI retrieval. The study also found that foroptimal retrievals the size of the uncertainty envelope over a set of possible solutions to be approximately equal tothat in the reflectance measurements. The results achieved in the second part of the thesis found the method withlocally adaptive temporal window, depending on amount of available observations and Climatology as backgroundestimation to be more robust to noise and missing data for smoothing, gap-filling and near real time estimationswith satellite time series.
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Mazumdar, Deepayan Dutta. "Multiangular crop differentiation and LAI estimation using PROSAIL model inversion." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2011, 2011. http://hdl.handle.net/10133/3103.

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Understanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring.
xiii, 161 leaves : ill., map ; 29 cm
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Soma, Maxime. "Estimation de la distribution spatiale de surface et de biomasse foliaires de couverts forestiers méditerranéens à partir de nuages de points acquis par un LIDAR terrestre." Thesis, Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0111.

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Pour mieux comprendre le fonctionnement des écosystèmes forestiers à échelle fine, les modèles écophysiologiques cherchent à intégrer les flux d’énergie et de matière. Ces échanges dépendent de la distribution de la végétation. Leur modélisation nécessite donc une description de la structure de la végétation en trois dimensions (3D) à un niveau de détail que seule la télédétection peut produire à haut débit. Les LiDAR terrestre (Light Detection And Ranging) possèdent un fort potentiel pour caractériser en 3D la végétation au sein des canopées. De précédents travaux qui relient la densité de points à la quantité de végétation ont montré des résultats prometteurs. Cette thèse développe ces approches en explorant les diverses sources d’erreurs. Les biais systématiques sont corrigés à l’échelle de la branche, de l’arbre et de la placette. Ce travail s’appuie à la fois sur des travaux théoriques et expérimentaux. Nous avons d’abord évalué des estimateurs théoriques sur des branches. Sur cette végétation réelle, les estimateurs se sont révélés sensibles à la taille de voxel utilisée et à la distance de mesure. Les corrections apportées sont demeurées robustes sur des arbres entiers. Cependant, l’échantillonnage au LiDAR terrestre est limité par l’occlusion végétale. Un travail spécifique a été conduit pour optimiser les estimations en tirant avantage des corrélations spatiales présentes dans la végétation. Cette approche permet de limiter les sous-estimations systématiques liées à l’occlusion. L’ensemble des outils présentés offrent la possibilité de dresser des cartes de végétation à l’échelle de la placette en fournissant des estimateurs non biaisés de la surface foliaire
To better understand functioning of forest ecosystems at fine scale, ecophysiological model attempt to include energy and material fluxes. Such exchanges depend on the distribution of vegetation. Hence, these models require a tridimensional (3D) description of vegetation structure, at a level of detail which can only be retrieve with remote sensing at large scale. Terrestrial LiDAR (Light Detection And Ranging) have a great potential to provide 3D description of vegetation elements in canopy. Previous studies established promising relations between the point density and quantity of vegetation. This work develop these statistical methods, focusing on source of errors. Systematic biases are corrected at branch, tree and plot scales. This study relies on both numerical simulations and field experiments. First, we test estimators on branches in laboratory conditions. On this natural vegetation, estimators are sensitive to voxel size and distance from instrument with phase-shift LiDAR. Developed corrections from this branch experiment are valid at tree scale. However, difficulties arising from sampling limitations due to occlusion and instrument sampling pattern cause negative biases in dense areas. Specific investigations are conducted to identify source of errors and to optimize multiscan estimations. A statistical method called LAD-kriging, based on spatial correlation within vegetation, improves local accuracy of estimations and limits underestimations due to occlusion. The tools produced in this work allow to map vegetation at plot scale by providing unbiased estimator of leaf area. Some of these tools are currently implemented within open access Computree software
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Pinjuv, Guy L. "Hybrid forest modelling of Pinus Radiata D. Don in Canterbury, New Zealand." Thesis, University of Canterbury. New Zealand School of Forestry, 2006. http://hdl.handle.net/10092/1102.

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During this study two models were developed to predict growth of Pinus radiata D.Don plantations in Canterbury, New Zealand. The first, CanSPBL(1.2), is a model for whole rotations of stands owned by Selwyn Plantation Limited in Canterbury. The second model, CanSPBL(water) is a hybrid growth model for the Selwyn estate in Canterbury that incorporates an index of root zone water balance over the simulation period. An existing stand growth and yield model CanSPBL was examined using a validation dataset of PSP measurements that were not used in model fitting. Projection bias was shown for mean top height, basal area per hectare, and residual stand stocking particularly for stands at elevations exceeding 450 metres. The new model, CanSPBL(1.2) showed an increase in precision of 4 - 46% over CanSPBL(1.0) at a stand level. The components of the stand model include mean top height, basal area per hectare, stems per hectare, and diameter distribution. The mortality model was made in conjunction with managers at CanSPBL to exclude catastrophic mortality events from model projections. Data used for model fitting was filtered using a mortality index based on the -3/2 power law. An examination of this model with an independent dataset showed little apparent bias. The new model, CanSPBL(water) was developed to include an index of water balance over the simulation period. Water balance estimates were made using a sub model for root zone water balance included in the hybrid physiological model 3-PG (Landsberg and Waring, 1997). The new model showed an increase in precision of 1 - 4% over CanSPBL(1.2) at a stand level (with the exception of the model for maximum diameter which showed a decrease in precision of 0.78%) using climatic inputs that included yearly variation. However the model showed increases of precision from 0.5 to 8% (with the exception of maximum diameter again, showing a decrease in precision of 0.13%) using long term monthly average climatic inputs. The components of the stand model also include mean top height, basal area per hectare, stems per hectare, and diameter distribution. The mortality model was also fitted with a data set filtered using a mortality severity index based on the -3/2 power law to exclude catastrophic mortality events. An examination of this model with an independent dataset showed little apparent bias. Two models to predict a one sided canopy leaf area index (LAI) of radiata pine stands in the Canterbury Plains of New Zealand were also developed. The models were fitted using non-linear least squares regression of LAI estimates against stem measurements and stand characteristics. LAI estimates were derived from digital analysis of fisheye lens photography. The models were kept simple to avoid computational circularity for physiological modelling applications. This study included an objective comparison and validation of a range of model types. The models CANTY (Goulding, 1995), CanSPBL(1.2) (Pinjuv, 2005), CanSPBL-water (Pinjuv, 2005), and 3-PG (Landsberg and Waring, 1997) were compared and validated with the main criteria for comparison being each model s ability to match actual historical measurements of forest growth in an independent data set. Overall, the models CanSPBL(water), and CanSPBL(1.2) performed the best in terms of basal area and mean top height prediction. Both models CanSPBL(water), and CanSPBL(1.2) showed a slightly worse fit in predictions of stocking than did the model CANTY. The hybrid model 3PG showed a better fit for the prediction of basal area than the statistically based model CANTY, but showed a worse fit for the prediction of final stocking than all other models. In terms of distribution of residuals, CanSPBL(1.2) had overall the lowest skewness, kurtosis, and all model parameters tested significant for normality. 3PG performed the worst on average, in terms of the distribution of residuals, and all models tested positively for the normality of residual distribution.
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Books on the topic "Leaf area estimation"

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Frazer, G. W. A method for estimating canopy openness, effective leaf area index, and photosynthetically active photon flux density using hemispherical photography and computerized image analysis techniques. Victoria, B.C: Pacific Forestry Centre, 1997.

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Stolker, Robert Jan, and Felix van Lier. Choice and interpretation of preoperative investigations. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0041.

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Preoperative risk assessment is one of the most important steps in perioperative management. In the last decades, considerable progress has been achieved. However, as more high-risk procedures are performed in more aged patients, suffering more morbidity, this may lead to an increased risk of adverse outcomes. The goal of preoperative assessment is to identify patients at extreme risk and discuss whether they should be operated on, or undergo an alternative procedure with a lower risk profile, or if conservative treatment should be continued. Furthermore, it gives the opportunity to optimize patients prior to surgery, adapt intraoperative anaesthetic management and monitoring, and select patients for postoperative treatment at an intensive care unit or post-anaesthesia care unit. The cornerstone of preoperative assessment is the estimation of functional capacity. Accurate anamnesis and physical examination are crucial. Several procedures have been used to optimize the preoperative risk stratification. In this chapter, the value of these additional preoperative investigations is reviewed. These investigations are to be performed only in patients with considerable co-morbidity undergoing high-risk surgery. As cardiovascular adverse events are a major determinant of postoperative outcome, the chapter focuses on the management of the two most important cardiac risk factors, that is, myocardial ischaemia and impaired left ventricular function.
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Addison, Tony, and Atanu Ghoshray. Pandemics and their impact on oil and metal prices. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/914-3.

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We examine the effect of pandemics on selected commodity prices—in particular, those of zinc, copper, lead, and oil. We set up a vector autoregressive model and analyse data since the mid-nineteenth century to determine how prices reacted to pandemics such as the 1918 Spanish Flu, 1957 Asian Flu, and 1968 Hong Kong Flu. We control for demand and supply fundamentals to generate forecasts from the point of outbreak, and we consider whether any pattern can be deduced in reactions to adverse global shocks. Results are varied, depending on choice of commodity and magnitude and type of response. No clear conclusions are possible from past pandemics, and we conclude that at the time of writing, forecasts are difficult to make in the ongoing current pandemic too. We conclude by estimating impulse response functions to assess likely impact and the subsequent response of commodity prices to the shock.
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Book chapters on the topic "Leaf area estimation"

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Raj, Rahul, Saurabh Suradhaniwar, Rohit Nandan, Adinarayana Jagarlapudi, and Jeffrey Walker. "Drone-Based Sensing for Leaf Area Index Estimation of Citrus Canopy." In Lecture Notes in Civil Engineering, 79–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37393-1_9.

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Korhonen, Lauri, and Felix Morsdorf. "Estimation of Canopy Cover, Gap Fraction and Leaf Area Index with Airborne Laser Scanning." In Forestry Applications of Airborne Laser Scanning, 397–417. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-017-8663-8_20.

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Ying-Ying, Dong, Wang Ji-Hua, Li Cun-Jun, Wang Qian, and Huang Wen-Jiang. "Integration of Ground Observations and Crop Simulation Model for Crop Leaf Area Index Estimation." In Advances in Intelligent and Soft Computing, 831–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29637-6_112.

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Feng, Haikuan, Fuqin Yang, Guijun Yang, and Haojie Pei. "Hyperspectral Estimation of Leaf Area Index of Winter Wheat Based on Akaike’s Information Criterion." In Computer and Computing Technologies in Agriculture X, 528–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06155-5_54.

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Brown, Daniel G. "A Spectral Unmixing Approach to Leaf Area Index (LAI) Estimation at the Alpine Treeline Ecotone." In GIS and Remote Sensing Applications in Biogeography and Ecology, 7–21. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1523-4_2.

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Vichev, B. I., and K. G. Kostov. "Estimation of Leaf and Branch Area Indexes of Deciduous Trees Using Dual-Frequency Microwave Radiometric Data." In Microwave Physics and Techniques, 407–12. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5540-3_41.

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Korzukhin, Michael, and Vasily Grabovsky. "Estimation of Leaf Area Index (LAI) of Russian Forests Using a Mechanical Model and Forest Inventory Data." In Innovations in Landscape Research, 341–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37421-1_18.

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Li, Dan, Hao Jiang, Shuisen Chen, Chongyang Wang, Siyu Huang, and Wei Liu. "Leaf Area Index Estimation of Winter Pepper Based on Canopy Spectral Data and Simulated Bands of Satellite." In Communications in Computer and Information Science, 515–26. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3966-9_57.

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Udayakumar, M., and T. Sekar. "Estimation of Leaf Area–Wood Density Traits Relationship in Tropical Dry Evergreen Forests of Southern Coromandel Coast, Peninsular India." In Wood is Good, 169–87. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3115-1_17.

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Leal-Ramirez, Cecilia, Héctor Echavarría-Heras, and Oscar Castillo. "Exploring the Suitability of a Genetic Algorithm as Tool for Boosting Efficiency in Monte Carlo Estimation of Leaf Area of Eelgrass." In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, 291–303. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17747-2_23.

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Conference papers on the topic "Leaf area estimation"

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Lee, Sang-Ho, Myung-Min Oh, and Jong-Ok Kim. "Plant Leaf Area Estimation via Image Segmentation." In 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2022. http://dx.doi.org/10.1109/itc-cscc55581.2022.9894907.

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Ghazal, Mohammed, and 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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BURG, Patrik, Jana BURGOVÁ, Vladimír MAŠÁN, and 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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Hajjdiab, Hassan, and Abdellatif Obaid. "A vision-based approach for nondestructive leaf area estimation." In 2010 2nd Conference on Environmental Science and Information Application Technology (ESIAT). IEEE, 2010. http://dx.doi.org/10.1109/esiat.2010.5568973.

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Soni, Amar Prasad, Amar Kumar Dey, and Manisha Sharma. "An image processing technique for estimation of betel leaf area." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253691.

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Wang, Peicheng, Ling Tong, Xing Zhou, Xun Gang, Bo Gao, Yuxia Li, and Yuan Sun. "Estimation of Leaf Area Index Based on Hemispherical Canopy Photography." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554699.

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Fu, Wenxue, Huadong Guo, and Xinwu Li. "Estimation of leaf area index (LAI) using POLInSAR: Preliminary research." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6048982.

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Ge, Yunjian, Zhenbo Liu, Jian Chen, and Tao Sun. "Estimation of paddy rice leaf area index using digital photography." In 2014 7th International Congress on Image and Signal Processing (CISP). IEEE, 2014. http://dx.doi.org/10.1109/cisp.2014.7003865.

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Cheng, Yuanlei, Yunping Chen, Shuaifeng Jiao, Haichang Wei, Wangyao Shen, Yan Chen, Shilong Li, and Hua Zhan. "Leaf Area Index Estimation from Hemisphere Image Based on GhostNet." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884777.

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Yang, Zhiliang, Jiapei Tong, Mingchen Feng, Guoliang Hu, Jinqiao Wu, and Yingchun Fan. "Soybean Leaf Segmentation and Area Estimation Based on Extreme Points." In ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3587716.3587782.

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Reports on the topic "Leaf area estimation"

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Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, Theodore D. Leininger, Paul B. Hamel, Kristina F. Connor, Nathan M. Schiff, A. Dan Wilson, and 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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Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, Theodore D. Leininger, Paul B. Hamel, Kristina F. Connor, Nathan M. Schiff, A. Dan Wilson, and 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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Duncan, A. Estimation of Leak Rate from the Emergency Pump Well in L-Area Complex Basin. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/890207.

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Azzi, Elias S., Cecilia Sundberg, Helena Söderqvist, Tom Källgren, Harald Cederlund, and Haichao Li. Guidelines for estimation of biochar durability : Background report. Department of Energy and Technology, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.lkbuavb9qc.

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Biochar is produced by heating biomass in the total or partial absence of oxygen. This report addresses the long-term persistence of biochar in soil and how this can be managed in climate calculations and reporting. The report consists of this summary and four chapters, which can be read independently. Different terms have been used to describe the durability of biochar carbon storage, but also the physical presence of biochar in soils, e.g. persistence, permanence, recalcitrance, residence times, stability. Today, the term “durability of carbon storage” is preferred in policy contexts, but various academic disciplines such as soil science have other established terms like “persistence”. Here, both durability and persistence are used, rather interchangeably. It is important to be aware of differences in meaning that exist between disciplines. The purpose of this report is to present the state of knowledge regarding the proportion of carbon in biochar that remains in the soil over time and provide recommendations for calculating this. There is a need to calculate the persistence of biochar in soil for national climate reporting, corporate climate reporting, carbon credit trading, and life cycle assessments for various purposes. On the persistence of biochar The amount of biochar remaining after a certain time depends on the properties of the biochar and the environment in which it is located. Nearly all research on biochar persistence has focused on its application in agricultural soils. The main reason for the high durability of biochar carbon storage is the formation of fused aromatic stable structures during biomass pyrolysis. A high degree of fused aromatic structures makes biochar much less prone to microbial decomposition than fresh biomass. Different biochars have different properties, and this influences how long they persist in the soil. To achieve biochar with properties that provide higher persistence, it should be produced at higher temperatures for a sufficient duration. Measuring and calculating biochar persistence Established quantification methods of 100-year biochar persistence (e.g. referenced in IPCC inventory guidelines and used in voluntary carbon markets, to date) extrapolate short-term soil decomposition processes, and do not fully consider the processes that may explain millennial persistence. Calculations regarding biochar persistence have traditionally used a time span of 100 years to describe the amount of remaining carbon after a certain time. The use of specifically 100 years lacks a well founded scientific reason, but has been regarded as “far enough” into the future from a climate perspective and close enough for modelling to be meaningful. An active area of research relevant for the understanding biochar carbon storage durability is the development of advanced analytical characterisation methods of biochar that will enable measurement of the physicochemical heterogeneity in carbon structures present in biochar. Another area of continued research is biochar incubation, with a focus on field conditions, to elucidate both differences from laboratory conditions, and how transport processes affect biochar in the field. Recommendation and conclusion In the project, available research data has been aggregated into a functional model that calculates how much of the carbon in biochar remains after a given number of years. The model is based on the H/C ratio of the biochar placed in the soil and the annual average temperature at the location. The model is made freely accessible to provide biochar market actors with the best available knowledge for estimating the durability of biochar carbon. Existing research results provide a sufficient foundation for estimation of the amount of biochar expected to remain over time. Future research results are expected to lead to increased knowledge regarding the decomposition properties of biochar, in particular biochars with a very low H/C ratio. Therefore, this recommendation will be revised by the end of the project in 2025.
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Estache, Antonio, Ronaldo Seroa da Motta, and Grégoire Garsous. Shared Mandates, Moral Hazard, and Political (Mis)alignment in a Decentralized Economy. Inter-American Development Bank, March 2015. http://dx.doi.org/10.18235/0011691.

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This paper investigates the effects of political (mis)alignment on public service delivery when mandates are shared between state and local governments. We analyze sewage treatment policies in the state of São Paulo, Brazil. Relying on difference-in-differences estimations, we establish a causal relationship between political alignment and higher sewage treatment provision. Conceptually, we find that, with uncertain local commitment and weakly enforceable local obligations, shared mandates lead to a moral hazard issue implying service under-provision. Our results show that political alignment attenuates such moral hazard effects.
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Hertel, Thomas, David Hummels, Maros Ivanic, and Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, June 2003. http://dx.doi.org/10.21642/gtap.wp26.

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With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gains, whereas large trade elasticities lead to the opposite result. Critics are understandably wary of results being determined largely by the authors’ choice of trade elasticities. Where do these trade elasticities come from? CGE modelers typically draw these elasticities from econometric work that uses time series price variation to identify an elasticity of substitution between domestic goods and composite imports (Alaouze, 1977; Alaouze, et al., 1977; Stern et al., 1976; Gallaway, McDaniel and Rivera, 2003). This approach has three problems: the use of point estimates as “truth”, the magnitude of the point estimates, and estimating the relevant elasticity. First, modelers take point estimates drawn from the econometric literature, while ignoring the precision of these estimates. As we will make clear below, the confidence one has in various CGE conclusions depends critically on the size of the confidence interval around parameter estimates. Standard “robustness checks” such as systematically raising or lowering the substitution parameters does not properly address this problem because it ignores information about which parameters we know with some precision and which we do not. A second problem with most existing studies derives from the use of import price series to identify home vs. foreign substitution, for example, tends to systematically understate the true elasticity. This is because these estimates take price variation as exogenous when estimating the import demand functions, and ignore quality variation. When quality is high, import demand and prices will be jointly high. This biases estimated elasticities toward zero. A related point is that the fixed-weight import price series used by most authors are theoretically inappropriate for estimating the elasticities of interest. CGE modelers generally examine a nested utility structure, with domestic production substitution for a CES composite import bundle. The appropriate price series is then the corresponding CES price index among foreign varieties. Constructing such an index requires knowledge of the elasticity of substitution among foreign varieties (see below). By using a fixed-weight import price series, previous estimates place too much weight on high foreign prices, and too small a weight on low foreign prices. In other words, they overstate the degree of price variation that exists, relative to a CES price index. Reconciling small trade volume movements with large import price series movements requires a small elasticity of substitution. This problem, and that of unmeasured quality variation, helps explain why typical estimated elasticities are very small. The third problem with the existing literature is that estimates taken from other researchers’ studies typically employ different levels of aggregation, and exploit different sources of price variation, from what policy modelers have in mind. Employment of elasticities in experiments ill-matched to their original estimation can be problematic. For example, estimates may be calculated at a higher or lower level of aggregation than the level of analysis than the modeler wants to examine. Estimating substitutability across sources for paddy rice gives one a quite different answer than estimates that look at agriculture as a whole. When analyzing Free Trade Agreements, the principle policy experiment is a change in relative prices among foreign suppliers caused by lowering tariffs within the FTA. Understanding the substitution this will induce across those suppliers is critical to gauging the FTA’s real effects. Using home v. foreign elasticities rather than elasticities of substitution among imports supplied from different countries may be quite misleading. Moreover, these “sourcing” elasticities are critical for constructing composite import price series to appropriate estimate home v. foreign substitutability. In summary, the history of estimating the substitution elasticities governing trade flows in CGE models has been checkered at best. Clearly there is a need for improved econometric estimation of these trade elasticities that is well-integrated into the CGE modeling framework. This paper provides such estimation and integration, and has several significant merits. First, we choose our experiment carefully. Our CGE analysis focuses on the prospective Free Trade Agreement of the Americas (FTAA) currently under negotiation. This is one of the most important FTAs currently “in play” in international negotiations. It also fits nicely with the source data used to estimate the trade elasticities, which is largely based on imports into North and South America. Our assessment is done in a perfectly competitive, comparative static setting in order to emphasize the role of the trade elasticities in determining the conventional gains/losses from such an FTA. This type of model is still widely used by government agencies for the evaluation of such agreements. Extensions to incorporate imperfect competition are straightforward, but involve the introduction of additional parameters (markups, extent of unexploited scale economies) as well as structural assumptions (entry/no-entry, nature of inter-firm rivalry) that introduce further uncertainty. Since our focus is on the effects of a PTA we estimate elasticities of substitution across multiple foreign supply sources. We do not use cross-exporter variation in prices or tariffs alone. Exporter price series exhibit a high degree of multicolinearity, and in any case, would be subject to unmeasured quality variation as described previously. Similarly, tariff variation by itself is typically unhelpful because by their very nature, Most Favored Nation (MFN) tariffs are non-discriminatory in nature, affecting all suppliers in the same way. Tariff preferences, where they exist, are often difficult to measure – sometimes being confounded by quantitative barriers, restrictive rules of origin, and other restrictions. Instead we employ a unique methodology and data set drawing on not only tariffs, but also bilateral transportation costs for goods traded internationally (Hummels, 1999). Transportation costs vary much more widely than do tariffs, allowing much more precise estimation of the trade elasticities that are central to CGE analysis of FTAs. We have highly disaggregated commodity trade flow data, and are therefore able to provide estimates that precisely match the commodity aggregation scheme employed in the subsequent CGE model. We follow the GTAP Version 5.0 aggregation scheme which includes 42 merchandise trade commodities covering food products, natural resources and manufactured goods. With the exception of two primary commodities that are not traded, we are able to estimate trade elasticities for all merchandise commodities that are significantly different form zero at the 95% confidence level. Rather than producing point estimates of the resulting welfare, export and employment effects, we report confidence intervals instead. These are based on repeated solution of the model, drawing from a distribution of trade elasticity estimates constructed based on the econometrically estimated standard errors. There is now a long history of CGE studies based on SSA: Systematic Sensitivity Analysis (Harrison and Vinod, 1992; Wigle, 1991; Pagon and Shannon, 1987) Ho
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Gelain, Paolo, Marco Lorusso, and Saeed Zaman. Oil Price Fluctuations and US Banks. Federal Reserve Bank of Cleveland, May 2024. http://dx.doi.org/10.26509/frbc-wp-202411.

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We document a sizable effect of oil price fluctuations on US banking variables by estimating an SVAR with sign restrictions as in Baumeister and Hamilton (2019). We find that oil market shocks that lead to a contraction in world economic activity unambiguously lower the amount of bank credit to the US economy, tend to decrease US banks' net worth, and tend to increase the US credit spread. The effects can be strong and long-lasting, or more modest and short-lived, depending on the source of the oil price fluctuations. The effects are stronger for smaller and lower leveraged banks.
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Granado, Camilo, and Daniel Parra-Amado. Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations. Banco de la República, September 2023. http://dx.doi.org/10.32468/be.1249.

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This study examines whether and how important it is to adjust output gap frameworks during the COVID-19 pandemic and similar unprecedentedly large-scale episodes. Our proposed modelling framework comprises a Bayesian Structural Vector Autoregressions with an identification setup based on a permanent-transitory decomposition that exploits the long-run relationship of consumption with output and whose residuals are scaled up around the COVID-19 period. Our results indicate that (i) a single structural error is usually sufficient to explain the permanent component of the gross domestic product (GDP); (ii) the adjusted method allows for the incorporation of the COVID-19 period without assuming sudden changes in the modelling setup after the pandemic; and (iii) the proposed adjustment generates approximation improvements relative to standard filters or similar models with no adjustments or alternative ones, but where the specific rare observations are not known. Importantly, abstracting from any adjustment may lead to over or underestimating the gap, to too-quick gap recoveries after downturns, or too-large volatility around the median potential output estimations.
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Douglas, Thomas A., Christopher A. Hiemstra, Stephanie P. Saari, Kevin L. Bjella, Seth W. Campbell, M. Torre Jorgenson, Dana R. N. Brown, and Anna K. Liljedahl. Degrading Permafrost Mapped with Electrical Resistivity Tomography, Airborne Imagery and LiDAR, and Seasonal Thaw Measurements. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41185.

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Accurate identification of the relationships between permafrost extent and landscape patterns helps develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools are particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT was a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. The transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies, suggesting attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. The results from our study indicated limitations in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.
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Rodríguez, Francisco. Cleaning Up the Kitchen Sink: On the Consequences of the Linearity Assumption for Cross-Country Growth Empirics. Inter-American Development Bank, January 2006. http://dx.doi.org/10.18235/0011322.

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Existing work in growth empirics either assumes linearity of the growth function or attempts to capture non-linearities by the addition of a small number of quadratic or multiplicative interaction terms. Under a more generalized failure of linearity or if the functional form taken by the non-linearity is not known ex ante, such an approach is inadequate and will lead to biased and inconsistent OLS and instrumental variables estimators. This paper uses non-parametric and semiparametric methods of estimation to evaluate the relevance of strong non-linearities in commonly used growth data sets. Our tests decisively reject the linearity hypothesis. A preponderance of our tests also rejects the hypothesis that growth is a separable function of its regressors. Absent separability, the approximation error of estimators of the growth function grows in proportion to the number of relevant dimensions, substantially increasing the data requirements necessary to make inferences about the growth effects of regressors. We show that appropriate non-parametric tests are commonly inconclusive as to the effects of policies, institutions and economic structure on growth.
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