scholarly journals Inoculation of containerized Pinus halepensis (Miller) seedlings with basidiospores of Pisolithus arhizus (Pers) Rauschert, Rhizopogon roseolus (Corda) Th M Fr and Suillus collinitus (Fr) O Kuntze

1994 ◽  
Vol 51 (5) ◽  
pp. 521-528 ◽  
Author(s):  
P Torres ◽  
M Honrubia
Keyword(s):  
Pinus Halepensis ◽  
Rhizopogon Roseolus ◽  
Pisolithus Arhizus

Growth response of Pinus halepensis to inoculation with Pisolithus arhizus in a terraced rangeland amended with urban refuse

1996 ◽  
Vol 179 (1) ◽  
pp. 35-43 ◽  
Author(s):  
A. Roldan ◽  
I. Querejeta ◽  
J. Albaladejo ◽  
V. Castillo
Keyword(s):  
Growth Response ◽  
Pinus Halepensis ◽  
Pisolithus Arhizus

scholarly journals Short Communication. Physiological effects of Rhizopogon Roseolus on Pinus halepensis seedlings

2013 ◽  
Vol 22 (3) ◽  
pp. 568
Author(s):  
J.A. Alfonso Domínguez Núñez ◽  
M. Saiz ◽  
C. Calderon ◽  
J.A. Saiz de Omeñaca
Keyword(s):  
Short Communication ◽  
Pinus Halepensis ◽  
Physiological Effects ◽  
Rhizopogon Roseolus

Modélisation de la croissance en hauteur du pin d’Alep (Pinus halepensis Mill.) en région méditerranéenne française

2004 ◽  
Vol 30 (2) ◽  
pp. 205-218 ◽  
Author(s):  
Cyrille Rathgeber ◽  
Laurence Blanc ◽  
Christian Ripert ◽  
Michel Vennetier
Keyword(s):  
Pinus Halepensis ◽  
Région Méditerranéenne

Croissance et production du pin d’Alep (Pinus halepensis Mill.) en Tunisie

2012 ◽  
Vol 38 (1) ◽  
pp. 39-57 ◽  
Author(s):  
Tahar Sghaier ◽  
Youssef Ammari
Keyword(s):  
Pinus Halepensis

Tree ring to climate relationships of Aleppo pine (Pinus halepensis Mill.) in Greece

2001 ◽  
Vol 27 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Andreas Papadopoulos ◽  
Françoise Serre-Bachet ◽  
Lucien Tessier
Keyword(s):  
Tree Ring ◽  
Pinus Halepensis ◽  
Aleppo Pine

Biomass of understory species of Pinus halepensis Mill. forests in the Kassandra Peninsula - Chalkidiki

1998 ◽  
Vol 63 ◽  
Author(s):  
P. Smiris ◽  
F. Maris ◽  
K. Vitoris ◽  
N. Stamou ◽  
P. Ganatsas
Keyword(s):  
Moisture Content ◽  
Aboveground Biomass ◽  
Quercus Ilex ◽  
Pinus Halepensis ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Pistacia Lentiscus ◽  
Quercus Coccifera ◽  
Understory Species ◽  
Cistus Incanus

This  study deals with the biomass estimation of the understory species of Pinus halepensis    forests in the Kassandra peninsula, Chalkidiki (North Greece). These  species are: Quercus    coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus  unedo, Erica arborea, Erica    manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis,  Fraxinus ornus. A sample of    30 shrubs per species was taken and the dry and fresh weights and the  moisture content of    every component of each species were measured, all of which were processed  for aboveground    biomass data. Then several regression equations were examined to determine  the key words.

scholarly journals Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

2021 ◽  
Vol 13 (12) ◽  
pp. 2307
Author(s):  
J. Javier Gorgoso-Varela ◽  
Rafael Alonso Ponce ◽  
Francisco Rodríguez-Puerta
Keyword(s):  
Probability Density ◽  
Linear Models ◽  
Pinus Halepensis ◽  
Beta Function ◽  
Probability Density Functions ◽  
Lidar Data ◽  
Density Functions ◽  
Minimum Diameter ◽  
Location Parameters ◽  
Diameter Distributions

The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.

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