Article
Using Acoustic Tomography to Model Wood Deterioration in
Cedrelinga cateniformis Ducke in the Peruvian Amazon
Gloria P. Cardenas-Rengifo 1,* , Juan Rodrigo Baselly-Villanueva 2 , Sheyla Y. Chumbimune-Vivanco 3,4 ,
Arturo T. Macedo-Ramírez 5 , Evelin Salazar 4 , Benjamín Minaya 4 , Saron Quintana 5 , Abrahan Cabudivo 5 ,
Stella S. A. Palma 6 , Pedro Álvarez-Álvarez 7,* and Jimmy A. Ocaña-Reyes 1
1 Estación Experimental Agraria Pucallpa, Instituto Nacional de Innovación Agraria (INIA), Carretera Federico
Basadre Km 4200, Pucallpa 25004, Peru; jimmyunheval@gmail.com
2 Estación Experimental Agraria San Roque, Instituto Nacional de Innovación Agraria (INIA), Calle San Roque
209, Loreto 16430, Peru; jrbasellyv@gmail.com
3 Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural (ETSIAMN), Universidad
Politécnica de Valencia (UPV), Camino de Vera, s/n, E-46022 Valencia, Spain; ychv8290@gmail.com
4 Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina
1981, Lima 15024, Peru; esalazar@inia.gob.pe (E.S.); b.minaya@outlook.com (B.M.)
5 Facultad de Ciencias Forestales, Universidad Nacional de la Amazonía Peruana (UNAP), Calle Pevas N◦ 548,
Iquitos 16002, Peru; amacedoramirez@gmail.com (A.T.M.-R.); saron.quintana@unapiquitos.edu.pe (S.Q.);
abraham.cabudivo@unapiquitos.edu.pe (A.C.)
6 FEAGRI, Unicamp—School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil;
ssapalma@gmail.com
7 Department of Organisms and Systems Biology, Polytechnic School of Mieres, University of Oviedo,
E-33600 Mieres, Asturias, Spain
* Correspondence: agpres_pucallpa@inia.gob.pe (G.P.C.-R.); alvarezpedro@uniovi.es (P.Á.-Á.)
Abstract: Forest plantations can be established in order to restore degraded areas. Acoustic tomog-
Citation: Cardenas-Rengifo, G.P.; raphy, which is of increasing importance in forest management, was used in the present study to
Baselly-Villanueva, J.R.; obtain information for managing plantations of Cedrelinga cateniformis Ducke in the Peruvian Amazon.
Chumbimune-Vivanco, S.Y.; The species is valuable in the timber sector of Peru, but the core wood tends to deteriorate and
Macedo-Ramírez, A.T.; Salazar, E.; develop cavities. The main objective of the study was to model wood deterioration in Cedrelinga
Minaya, B.; Quintana, S.; cateniformis Ducke using the data obtained through acoustic tomography. Eight plantations of varying
Cabudivo, A.; Palma, S.S.A.; ages were analyzed using acoustic tomography in order to obtain indicators of wood deterioration.
Álvarez-Álvarez, P.; et al. Using Biometric, climatic, and edaphic data (explanatory variables) were also measured in each plantation.
Acoustic Tomography to Model Wood The indicator variables and explanatory variables were compared and evaluated using correlation
Deterioration in Cedrelinga cateniformis
and principal component analysis. Wood deterioration was modelled using stepwise regression. The
Ducke in the Peruvian Amazon.
indicator variables differed significantly between plantations and were mainly correlated with the
Forests 2024, 15, 778. https://doi.org/
10.3390/f15050778 biometric variables (age and diameter at breast height). The models explained 81% of the variability
of pith rot. The percentage rotten area was minimal in young plantations (1%), and the opposite was
Academic Editor: Miha Humar observed in mature trees (21.5 to 25.6%). The study findings provide valuable information, enabling
Received: 22 March 2024 foresters to determine the optimal age and diameter for felling Cedrelinga cateniformis in plantations
Revised: 24 April 2024 in the Peruvian Amazon.
Accepted: 25 April 2024
Published: 29 April 2024 Keywords: non-destructive evaluation; acoustic waves; wood quality; internal defects; regression
Copyright: © 2024 by the authors. 1. Introduction
Licensee MDPI, Basel, Switzerland.
Forests and forest plantations are invaluable natural resources for humans [1]. How-
This article is an open access article
distributed under the terms and ever, in the forestry industry in Peru, wood is selected and extracted from natural forests
conditions of the Creative Commons without considering the need to restore these plant formations or to maintain a permanent
Attribution (CC BY) license (https:// balance between growth and productivity [2]. Wood is a valuable material with a vital role,
creativecommons.org/licenses/by/ and its properties determine the applications and economic value of the final products [3].
4.0/). Likewise, the loss of the structural functionality of trees due to the deterioration of core
Forests 2024, 15, 778. https://doi.org/10.3390/f15050778 https://www.mdpi.com/journal/forests
Forests 2024, 15, 778 2 of 16
wood can negatively impact the value [4]. In the context of the high demand and restricted
availability of resources from natural forests [5], wood extraction from forest plantations
must be regulated in order to ensure that high-quality products are obtained [6]. Addition-
ally, the use of innovative technologies is essential to optimize the use of forest resources,
ensuring sustainability and the supply of good quality raw material [7].
Wood evaluation methods can be categorized according to the level of destruction
of the material being evaluated; they are classified as destructive, semi-destructive, and
non-destructive [8], the latter of which is the most commonly used [9]. Non-destructive
evaluation (NDE) methods that do not alter the structure of the wood can be used to deter-
mine the physical and mechanical properties, thus enabling the use of the tested wood [10].
Different tests with different principles are used in this method of evaluating wood quality,
including mechanical tests, ultrasound, resonance, and acoustic tomography [11]. NDE
methods provide valuable information for different applications, such as performing clonal
selection, tree classification, and tree risk management in urban areas [12].
For decades, researchers have used invasive (destructive) methods to detect internal
defects in wood [13]. However, since the 1960s, NDE methods have been used to determine
the growth characteristics and physical–mechanical properties of standing wood and to
detect internal defects in the stem [14]. At present, forest managers and arborists use non-
destructive techniques to locate and quantify defects and deterioration in wood at different
stands and forest scales [5,15,16]. Maximizing the use of forest resources by considering
variations in wood properties at multiple scales can increase the final yields [17]. Acoustic
technology has become an essential tool for evaluating material via the use of NDE methods.
The technology is also used for other applications in the forestry industry, including quality
control and product classification [18].
Sound is produced by wave motion in an elastic medium (solid, liquid, or gas) and
requires a source of mechanical vibration [19]. In wood, wave propagation is a dynamic
process that is internally related to physical and mechanical properties, particularly the
moduli of elasticity, density, and humidity [20,21]. Acoustic tomography relies on measur-
ing sound waves that travel through a material (in this case, tree stems) from one sensor to
another [16]. This method can detect the presence of anomalies or deterioration in trees
via analyzing the propagation of sound waves generated when sensors are tapped with an
electronic impact hammer. The result of this measurement process is a tomogram, which
represents the speed of the sound waves in the cross-section [22]. Tomographic images are
essential in internal tree inspection [23]. The velocity of sound propagation is generally
faster in healthy (more solid) wood than it is in degraded wood [24], although the acoustic
properties of wood can also be affected by various factors such as age and phytosanitary
status, as well as natural defects such as grain deviation, knots, and resin pockets.
Acoustic tomography is used by professionals in the forestry sector and by arborists [25].
In a study conducted in 2005, researchers concluded that the resolution of the images
obtained through stress waves can be improved via increasing the frequency applied
and the number of sensors used [26]. In 2014, a demonstration in the Czech Republic
showed non-invasive methods to be a promising tool for managing and protecting forest
ecosystems [27]. In 2022, the use of the dynamic modulus of elasticity was found to improve
the acoustic tomographic evaluation of standing trees, demonstrating that sound velocity
is related to the mechanical parameters of wood [24]. The inspection of standing trees in
China and Panama showed that acoustic tomography is an effective, non-invasive method
for assessing internal decay, cavities, and structural integrity, even in irregularly shaped
trees [4,28,29]. Other studies have used tomography for the numerical simulation of wave
propagation to determine the size of the cavity and for developing a new approach to the
quantitative analysis of acoustic tomography images, demonstrating the effectiveness of
the method relative to others [30]. Acoustic tomography studies generally involve the use
of the technique in urban trees or the development of new methodologies [15,23]; however,
modelling studies of internal tree health are scarce [16]. At present, acoustic tomography is
not widely used in Peru. The techniques have been used to evaluate the health of forest
Forests 2024, 15, 778 3 of 16
plantations in the Amazon [5,31,32] and to determine the risk of falling urban trees in
Lima [33]. The number of trunks affected was determined in all of these studies.
Cedrelinga cateniformis Ducke is a monotypic species, with a restricted distribution in
the Neotropical region, with the Amazon as the natural center of distribution [34]. It is a
low-demanding species, which leads to the rapid early growth of the trees. Annual growth
can reach up to 2.56 m in the first 6 years, and tends to continue increasing [35]. In Peru,
the species is distributed in the Amazonas, Madre de Dios, Huánuco, Junín, Loreto, Pasco,
San Martín, Ucayali, and Cuzco territories [36]. The wood is classified as being of medium
density, easy to work with, and with a good surface finish [37]. It is used to produce wood
panels, cellulose, and paper, and in civil and marine construction [38]. C. cateniformis is
important in the Peruvian timber market [39], representing 9.2% of the national market,
90% of which is obtained from tropical forests, mainly in areas such as Loreto, Madre de
Dios, and Ucayali [37]. The demand for the wood has increased constantly due to its good
physical and mechanical properties [2]. However, the presence of medullary rotting in the
stem [39] discourages producers in plantations using it [40].
The primary aim of this research was to model wood deterioration in Cedrelinga
cateniformis Ducke with data acquired from acoustic tomography. We hypothesized that
wood decay in this species is influenced by biometric, climatic, and/or edaphic factors. The
research findings will help to establish a more efficient forest management system for the
species and generate more income for forest producers [41].
2. Materials and Methods
2.1. Study Area
For the study, a total of eight C. cateniformis plantations, distributed in two properties,
located in Loreto Department, Maynas province, San Juan Bautista district (Peru), were
evaluated. The “Puerto Almendra” property (geographical coordinates 3◦50.046′ S and
73◦22.670′ W), which belongs to the State University of the Peruvian Amazon (UNAP),
is located at an elevation of 95 m.a.s.l. The second property, the “El Dorado” Experimen-
tal Annex (geographical coordinates 3◦56.279′ S and 73◦25.342′ W), belongs to the San
Forests 2024, 15, x FORRo qPEuEeR AREgVrIiE cWul tural Experimental Station (INIA), and is located at an elevation of 120 m 4 of 18 
m.a.s.l. (Figure 1).
 
Figure 1. Location of Cedrelinga cateniformis plantations, Peru. (a) study area, (b) distribution of plots.
Figure 1. Location of Cedrelinga cateniformis plantations, Peru. (a) study area, (b) distribution of 
plots. 
2.2. Methodology 
In October 2022, all plantations were inventoried using the Field-Map (FM) software 
and hardware (version X16). The trees were georeferenced at a precision of 0.03 m. The 
diameter at breast height (DBH) (1.3 m) was subsequently measured with a diametric 
tape, and the commercial height (CH) and total height (TH) were determined with a hyp-
someter. In total, 8 plantations of ages ranging from 15 to 53 years were evaluated as fol-
lows: 4 belonging to the UNAP (P1, P2, P3, and P4), and the other 4 to the “El Dorado” 
site (P5, P6, P7, and P8). The seeds used to establish the plantations were obtained from 
seed trees of the same population located in the surrounding natural forests [37]. Enrich-
ment planting (EP), forest massifs (FMs), and agroforestry systems (AFSs) were estab-
lished at different spacings (Table 1). EP aims to add valuable forest species to degraded 
forest; FMs correspond to plantations in open areas where the plants are uniformly dis-
tributed; and AFSs are production systems where crops and trees are planted sequentially. 
The DBH of the trees evaluated ranged from 11.4 to 152.6 cm, the CH ranged from 1.8 to 
19.7 m, and the TH ranged from 9.7 to 45.7 m (Table 1). 
Table 1. Structural characteristics of Cedrelinga cateniformis plantations, Peru. 
Property UNAP A.E. El Dorado 
Plot P1 P2 P3 P4 P5 P6 P7 P8 
Age (years) 15 43 53 24 24 18 33 26 
System of plantation  EP EP FM AFS AFS AFS FM AFS 
Area (ha) 4.46 0.60 0.41 0.98 0.54 1.30 0.27 0.40 
Spacing (m) 7 × 7 5 × 5 10 × 10 7 × 7 8 × 6 35 × 5 2.7 × 2.7 5 × 5 
N 43 72 17 28 40 51 91 66 
Minimum 13.3 22.9 57.0 24.2 21.9 16.2 11.4 7.3 
DBH 
Mean 34.6 46.5 73.3 54.9 49.6 43.5 40.3 31.0 
(cm) 
Maximum 56.5 84.9 120.1 152.6 85.6 70.0 96.0 62.0 
 
Forests 2024, 15, 778 4 of 16
The Department of Loreto occupies an area of 369,852 km2, representing 28.7% of the
surface area of Peru. It is located in the extreme northeast of Peru, and is divided into
7 provinces and 51 districts. The Department borders with Ecuador, Colombia, and Brazil,
and it belongs to the so-called “Amazonian Plain”, whose elevational gradient ranges from
61 to 220 m.a.s.l. [42].
San Juan Bautista has a typical jungle environment below 350 m.a.s.l., with mean and
minimum temperatures of, respectively, 25.0 and 23.0 ◦C; the precipitation ranges from
2000 to 3000 mm annually [43]. The minimum, mean, and maximum temperatures in
the study area are 21.17, 26.18, and 31.25 ◦C, respectively; and the mean precipitation is
2680.88 mm year−1. The soils are mainly clay loam and loamy sand, which are strongly
acidic, with low to medium levels of organic matter.
2.2. Methodology
In October 2022, all plantations were inventoried using the Field-Map (FM) software
and hardware (version X16). The trees were georeferenced at a precision of 0.03 m. The
diameter at breast height (DBH) (1.3 m) was subsequently measured with a diametric tape,
and the commercial height (CH) and total height (TH) were determined with a hypsometer.
In total, 8 plantations of ages ranging from 15 to 53 years were evaluated as follows:
4 belonging to the UNAP (P1, P2, P3, and P4), and the other 4 to the “El Dorado” site
(P5, P6, P7, and P8). The seeds used to establish the plantations were obtained from seed
trees of the same population located in the surrounding natural forests [37]. Enrichment
planting (EP), forest massifs (FMs), and agroforestry systems (AFSs) were established at
different spacings (Table 1). EP aims to add valuable forest species to degraded forest; FMs
correspond to plantations in open areas where the plants are uniformly distributed; and
AFSs are production systems where crops and trees are planted sequentially. The DBH of
the trees evaluated ranged from 11.4 to 152.6 cm, the CH ranged from 1.8 to 19.7 m, and the
TH ranged from 9.7 to 45.7 m (Table 1).
Table 1. Structural characteristics of Cedrelinga cateniformis plantations, Peru.
Property UNAP A.E. El Dorado
Plot P1 P2 P3 P4 P5 P6 P7 P8
Age (years) 15 43 53 24 24 18 33 26
System of plantation EP EP FM AFS AFS AFS FM AFS
Area (ha) 4.46 0.60 0.41 0.98 0.54 1.30 0.27 0.40
Spacing (m) 7 × 7 5 × 5 10 × 10 7 × 7 8 × 6 35 × 5 2.7 × 2.7 5 × 5
N 43 72 17 28 40 51 91 66
Minimum 13.3 22.9 57.0 24.2 21.9 16.2 11.4 7.3
DBH Mean 34.6 46.5 73.3 54.9 49.6 43.5 40.3 31.0
(cm) Maximum 56.5 84.9 120.1 152.6 85.6 70.0 96.0 62.0
Deviation 10.6 16.5 17.0 24.1 12.8 11.8 16.4 10.2
Minimum 1.8 3.4 2.3 3.1 8.0 4.2 4.3 3.5
CH Mean 5.1 8.6 8.1 9.2 13.9 9.0 12.7 8.4
(m) Maximum 11.3 15.8 14.1 17.4 19.7 17.5 19.7 16.2
Deviation 2.0 3.2 3.2 3.3 2.8 3.0 4.0 3.1
Minimum 12.2 16.4 23.4 17.2 19.2 12.5 11.7 9.7
TH Mean 18.3 26.6 31.0 25.7 28.3 22.4 33.2 21.5
(m) Maximum 24.7 38.8 45.7 35.2 37.0 28.2 48.3 31.3
Deviation 3.3 5.2 6.1 4.8 3.7 4.0 7.7 4.8
Minimum 0.0272 0.0822 0.5665 0.1076 0.3046 0.0852 0.0216 0.0087
CV Mean 0.2722 0.8822 1.6308 1.3234 1.4798 0.7616 1.0016 0.3607
(m3) Maximum 1.0820 3.0943 3.4895 8.6903 5.5719 2.6911 5.9236 1.0969
Deviation 0.2147 0.7241 0.7725 1.5871 0.9721 0.5736 0.9376 0.2672
N: Number of individuals, DBH: diameter at breast height (1.3 m), CH: commercial height, TH: total height,
CV: commercial volume, EP: enrichment planting, FM: forest massif, AFS: agroforestry system.
Forests 2024, 15, x FOR PEER REVIEW 5 of 18 
 
Deviation 10.6 16.5 17.0 24.1 12.8 11.8 16.4 10.2 
Minimum 1.8 3.4 2.3 3.1 8.0 4.2 4.3 3.5 
CH Mean 5.1 8.6 8.1 9.2 13.9 9.0 12.7 8.4 
(m) Maximum 11.3 15.8 14.1 17.4 19.7 17.5 19.7 16.2 
Deviation 2.0 3.2 3.2 3.3 2.8 3.0 4.0 3.1 
Minimum 12.2 16.4 23.4 17.2 19.2 12.5 11.7 9.7 
TH Mean 18.3 26.6 31.0 25.7 28.3 22.4 33.2 21.5 
(m) Maximum 24.7 38.8 45.7 35.2 37.0 28.2 48.3 31.3 
Deviation 3.3 5.2 6.1 4.8 3.7 4.0 7.7 4.8 
Minimum 0.0272 0.0822 0.5665 0.1076 0.3046 0.0852 0.0216 0.0087 
CV Mean 0.2722 0.8822 1.6308 1.3234 1.4798 0.7616 1.0016 0.3607 
(m3) Maximum 1.0820 3.0943 3.4895 8.6903 5.5719 2.6911 5.9236 1.0969 
Deviation 0.2147 0.7241 0.7725 1.5871 0.9721 0.5736 0.9376 0.2672 
N: Number of individuals, DBH: diameter at breast height (1.3 m), CH: commercial height, TH: total 
height, CV: commercial volume, EP: enrichment planting, FM: forest massif, AFS: agroforestry sys-
tem. 
The wood deterioration was evaluated using acoustic tomography (ArborSonic 3D, 
Forests 2024, 15, 778 Fakopp Enterprise Ltd., Sopron, Hungary). Trees were classified into 10 cm 5doifa1m6 eter clas-
ses in all plantations. Between 3 and 13 individuals were evaluated, depending on the total 
number oTfh tereweoso idndcelutedrieodra itnio neawcahs celvaaslus a[t4e4d]u; s3i0n,g 4a0c,o 1u7st,i c24to, m40o,g r4a0p, h5y1(,A 4r0b,o arSnodn i4c23 Din,dividuals 
were FeavkaoplupaEtnetder pinri spelLottds., PSo1p, rPon2,,H Pu3n,g Par4y,) .PT5re, ePs 6w,e Pre7c,l aasnsidfie dP8in, tore1s0pcemctdiivameleyt.e rTchlaes seesvaluations 
were incaarlrl ipeldan otautito nbse.tBwetewenee n403 andd1 360in dcmivi daubaolsvwe etrheee vgarlouuatned, dbepceanudsien,g aosn pthreetvoitoalus reports 
have ninumdibceartoefdt,r eiens tinhcilsu dspedecinieesa,c rhoctl amssa[i4n4]l;y3 0o,c4c0u, 1rs7, i2n4 ,t4h0e,  4b0a, s5a1,l 4s0e,catnidon42 oinf dthiveid sutaelms s [45,46]. 
were evaluated in plots P1, P2, P3, P4, P5, P6, P7, and P8, respectively. The evaluations
Between 8 and 10 sensors were used, depending on the diameter of the shaft, i.e., more 
were carried out between 40 and 60 cm above the ground because, as previous reports
than hthavee 6i nrdeiccoatmedm, iennthdiesdsp becyi e[s4, 7ro].t mThaien lsyeoncscourrss iwn ethree bianssaelrsteecdtio anroofutnheds ttehme st[r4u5n,4k6 ].on a hori-
zontaBle ptwlaenene.8 Tanhde 1t0resen csoirrcsuwmerfeeurseendc, edse paendi ndgisotnatnhceedsi awmerter mofethaesushraefdt,  ip.er.,emciosreltyh avnia the sen-
sors athned6 wreecroem rmecenodrdededby w[4i7th]. TAhrebsoern-sSoorsnwice3rDe i nvs5e.r3te.1d2a5r osuonftdwthaeretr.u Onkncoen tahheo rsieznonstoarls were in-
stallepdl,a nthe.eT threatnresemciirttcuemr bfeorexnecse swanerded pisltaancceeds wine rseitmue.a Asucroedusptriecc isseoluynvdia twheavseenss owrsearned generated 
were recorded with Arbor-Sonic3D v5.3.125 software. Once the sensors were installed, the
by retpraenastmeditlteyr tbaopxepsinwger eeapclahc esdeninssoitru w. Aitchou as tisctesoeul nhdawmamveesrw aetr ethgeen searamteed binytreenpesaittyed. lTyhe sensor 
transmtapittpiendg etahceh sseonusnordw withavaesste aelnhda mfomremr aetdt htehsea mdaetian tmenasittryi.xT.h Te hseen ssorutrnadns vmeitltoecdittyhe was auto-
maticsaolulnyd cwalacvuelsaatned f oinrm tehdet shoefdtawtaamrea,t raixn.dT hae tsoomunodgvrealmoci twy awsa sgaeunteormaatteicda l[ly29ca,4lc6u,l4a8te]d. 
Tinhteh evsaorfitawbalrees,  aenvdaalutoamteodg raams iwndasicgaentoerrast eodf [w29o,4o6d,4 8d].eterioration were the wave velocity 
The variables evaluated as indicators of wood deterioration were the wave velocity in
in m/ms /(Ws (WV)V, )i,nincciiddeenncceep perecrecnetnagteag(Ie), (aIn)d, athnedp ethrcee nptaegreceonf tthaegreo totef ntharee aro(RttAe)n.  Tahreeara (dRiaAl ). The ra-
dial waavveessw wereerem emaseuarseud,reasdt,h aesse tphaessset hproausgs hththreocuegnhte rtohfet hceenpittehra ondf tehneab pleitthhe abnetdte renable the 
bettera naanlyasliyssoifst ohef itnhter innatlehrenaaltlh hoef ahletahlt hoyf ahnedalatfhfeyct aedndtr eaeffs (eFcitgeudre t2r)e[e4s9 ](.Figure 2) [49]. 
 
Figure 2. Radial velocities considered for evaluating wood decay in C. cateniformis plantations (dotted
black lines). (a) healthy tree, (b) affected tree.
The I was determined using Equation (1), which related the number of deteriorated
wood trees to the total number of trees in the plantation [50] as follows:
 
Total tress a f f ected
I = ·100 (1)
Total trees evaluated
Biometric, climatic, and edaphic variables were considered as predictors for wood
deterioration modelling (Tables 1 and 2). The commercial volume (CV) was calculated
using Equation (2), where π = 3.1416 and “f” is the species form factor, with a value of
0.496 [51].
π.DBH2
CV = · CH· f (2)
4
Forests 2024, 15, 778 6 of 16
Table 2. Climatic and edaphic characteristics of Cedrelinga cateniformis plantations, Peru.
Variable Minimum Mean Maximum Deviation
T◦min (◦C) 21.17 21.21 21.26 0.04
T◦mean (◦C) 26.12 26.18 26.24 0.06
Climatic T◦max (◦C) 31.08 31.17 31.25 0.09
pp
(mm·year−1) 2663.00 2680.88 2700.00 17.46
S (%) 27.33 62.54 81.33 18.19
Si (%) 5.33 21.25 41.67 11.97
Edaphic C (%) 9.00 16.71 32.00 7.68
pH 4.50 4.64 4.77 0.11
EC (dS/m) 0.01 0.03 0.03 0.01
T◦min: minimum annual temperature, T◦mean: mean annual temperature, T◦max: maximum annual temperature,
pp: annual precipitation, S: sand, Si: silt, C: clay, EC: electrical conductivity.
Climate variables were acquired from NASA’s Global Energy Resources Prediction [52],
accessed 7 September 2023. The center point of each plantation was used as the reference
point for downloading the data, and the period considered for the analysis was 2001 to 2021.
Soil sampling was carried out in October 2022. In each plantation, a composite sample was
extracted through systematic collection, according to the methodology established by [53],
and the subsamples were extracted with a sampler tube at 30 cm depth. The samples were
sent to the Soil, Water and Foliar Laboratory of the E.E.A. Canaán–INIA for analysis.
The statistical analysis was performed with Rstudio 4.3.3 statistical software (Boston,
MA, USA). The existence of any significant differences in the wood deterioration indicator
variables (WV, I, and RA) between plantations was evaluated, and the mean values for
the individuals in the same diameter classes (10 cm) in each plantation were considered
as replicates [54]. The analysis of variance (ANOVA) was conducted and the means were
compared by applying the Tukey’s HSD test (p < 0.05) in the “agricolae” package [55]. The
assumptions of normality and variance homogeneity were evaluated using Shapiro–Wilk
and Bartlett tests (p < 0.05), respectively; when the WV did not meet the required assump-
tions, the Kruskal–Wallis nonparametric test was applied. In addition, the association
between the variables through correlation tests and, subsequently, the predictor variables
(biometric, climatic, and edaphic) for the wood deterioration models were identified via
correlation. The Pearson’s correlation coefficient was determined (p < 0.05) using the cor
function in Rstudio [56]. Furthermore, in order to explain all of the existing variability of
the variables evaluated, principal component analysis (PCA) was carried out using the R
studio packages FactoMineR and factoextra [57,58].
Modelling was conducted using stepwise regression, which has been used to estimate
various forest variables in different studies [59–61]. The response variable was specified,
and a list of possible explanatory variables was provided (Equation (3)). The explanatory
variable most closely correlated with the response variable was chosen, and additional
explanatory variables were included iteratively or eliminated until no further significant
correlations between predictive variables were found [62].
Y = β0 + β1·X1 + . . . + βn·Xn + ϵ (3)
where Y is the variable indicating wood deterioration (WS, I, and RA), X1, . . ., Xn are
the explanatory variables, β1, . . ., βn are the model parameters, and ϵ is the error term.
The regression was carried out using the lm and step functions in Rstudio [63,64], and
some of the variables modelled were transformed by Ln. The statistical significance of the
regression models generated, and their parameters was verified (F and t at p < 0.05).
Forests 2024, 15, 778 7 of 16
3. Results
3.1. Wood Deterioration
The wood deterioration indicator variables differed significantly (p < 0.05) between
plantations (Table 3). Significant differences in the WV between the youngest plantation (P1)
and the oldest plantation (P3 and P7) were observed, with values of 1650.75 and 1293.21 and
1365.68 m/s, respectively. The highest value of I was recorded in plantation P3 (95.24%),
and was statistically significantly different from the value in plot P6 (51.66%). The RA was
highest in plantation P3, with 22.52% of the stem cross section, differing significantly from
those in plantations P1, P5, P6, and P8, in which low percentages of 1.13, 2.88, 1.16, and
2.16%, respectively, were observed.
Table 3. Comparison of the means of indicator variables of wood deterioration.
Plot Age WV I RA(m/s) (%) (%)
P1 15 1650.75 ± 136.00 c 69.17 ± 21.67 ab 1.13 ± 0.25 c
P2 43 1537.40 ± 201.08 b 73.58 ± 20.93 ab 7.26 ± 6.10 ab
P3 53 1293.21 ± 311.74 a 95.24 ± 8.25 a 22.52 ± 2.69 a
P4 24 1585.19 ± 156.59 bc 91.67 ± 13.94 a 7.78 ± 4.45 ab
P5 24 1769.71 ± 208.54 d 88.33 ± 12.91 a 2.88 ± 0.88 bc
P6 18 1783.74 ± 118.38 d 51.66 ± 29.27 b 1.16 ± 0.79 c
P7 33 1365.68 ± 295.92 a 83.73 ± 21.10 a 13.45 ± 3.9 a
P8 26 1589.28 ± 202.72 bc 58.08 ± 33.99 ab 2.16 ± 1.36 bc
Sig. Kruskal wallis (0.00) ANOVA (0.04) ANOVA (0.00)
Different letters indicate significant differences, according to Tukey or Kruskal–Wallis tests (p < 0.05), WV: wave
velocity, I: incidence percentage, RA: percentage of the rotten area.
For the young plantations, the tomograms showed regions of high WVs in the cross
section, except for small regions in the peripheral section, in which low velocities were
Forests 2024, 15, x FOR PEER REVIEW 
 attributed to an edge effect (Figure 3). As the plantations aged, the WV decreased, mainly 8 of 18 
in the stem center, indicating deterioration in the stem medulla (P7, P2, and P3).
 
Figure 3. 2D tomograms of Cedrelinga cateniformis at different ages, Peru. 
Figure 3. 2D tomograms of Cedrelinga cateniformis at different ages, Peru.
 
 
 
FFoorreessttss 22002244,, 1155,, x7 7F8OR PEER REVIEW 9 of 18  8 of 16
TThhee WV waass ssiiggnniifificcaannttllyy aannd  nneeggaattiivveellyy ccoorrrreelalatteeddw witihtht htheeR RAA( F(iFgiugruere4 )4, )s,o soth taht,aitn, 
iann aanv aevraegraegtere ter,eleo, wloewr espr esepdesedinsd iincdaticeadteadh aig hiegrhpeerr pceenrctaegnetaogfer otft irnogtt.inAglt. hAoluthgohutghhe Ithwea Is 
wpaoss iptiovseiltyivaenlyd annedg antievgealtyivceolryr ecloartreedlawteidth woitthhe rotihnedri ciantdoircvataorria vbalerisa,bthlees,c othrere claotriroenlastwioenrse 
wneortes tnaotits stticaatilslyticsaigllnyi fisicgannitfi.cant. 
 
FFiigguurree 44.. CCoorrrreellooggrraamm ooff tthheei ninddiciacatotorrv avrairaibalbelseos fowf owoododde dteertieorriaotrioatni.oWn. VW: wVa: vweavveel ovceiltoy,cIi:tyin, cIi:d inencic-e
dpeenrccee npteargcee,nRtaAg:ep, eRrAce:n pteargceenoftatghee orof tttheen raortteaen. * a*r=eap. <** 0=. 0p1 <.  0.01. 
3.2. Relationship between Wood Deterioration and Predictive Variables
3.2. Relationship between Wood Deterioration and Predictive Variables 
All biometric variables, except for the CH, were significantly correlated with the wood
All biometric variables, except for the CH, were significantly correlated with the 
decay indicator variables (p < 0.05) (Table 4). Age was the biometric variable most closely
wcoororde ldateecdayw iinthditchaetoirn dvaicraiatobrlevs a(rpi a<b 0le.0s5()t h(TeaWbleV 4a)n. AdgReA w).aIsn ththee beiodmapehtriicc vvaarriiaabbllees ,moonslty 
ctlhoeseplyH cowrareslastigedn iwficitahn ttlhye cinordrieclaattoerd vwariitahblaesw (othoed WdVet earniodr RatAio).n Inin tdhiec aetdoarpvhaicr ivaabrlieab(RleAs,) .
oNnolyn ethoef pthHe cwliams astiigcnviafirciaanbtlleys cwoerrreelsaitgendi fiwciatnht lay wcoororedl adteedterwioitrhattiohne iinnddiiccaattoorr vvaarriiaabbllees 
((RTAab).l eN4o)n. eT ohfe thWe Vcliwmaastisci gvnarifiiacbalnestl ywnereeg saitgivneifilycacnotrlrye lcaotrerdelwatietdh wagiteh( trh=e i−n0d.i8c7a3to).r Tvahrei-I
awblaess s(iTganbilfiec 4a)n. tTlyhea nWdVp wosaisti svieglnyificocarrnetllayt endegwatiitvheltyh ecoDrrBeHlataendd wCitVh a(rge= (0r .=7 2−10.a8n7d3).0 T.8h2e2 ,
Ir ewsapse cstigivneilfiyc)a. nTthlye aRnAd wpaossistiigvneliyfi ccaonrtrleylaatnedd pwoitshit itvheel yDcBoHrr ealnadte dCVw i(trh =a g0.e7,2t1h eanDdB 0H.8, 2th2,e 
rTeHsp,eacntidveplHy).( rT=he0 .R8A58 ;w0a.7s6 s7i;g0n.i7fi7c5a;natnlyd a0n.7d5 8p,orseistpiveecltyiv ceolyrr. elated with age, the DBH, the 
TH, aInndt hpeHP (Cr A= ,07.825.885; %0.7o6f7t;h 0e.7v7a5r;i aabnidlit 0y.7in58th, reedspateactwivaeslye.x p lained by the first (46.2%) and
second (26.6%) components (Figure 5). The oldest P3 plot (53 years) yielded the highest
Tvaablluee 4s. CofortrheelatIioannsd beRtwAeeann dwothode dloewcaeys itndviaclautoers voafritahbelesW anVd.  PplroedtsicPto1rsa. nd P6, which were
15 anTdyp1e8 oyfe aPrrseodficatgivee,  rVesapreiacbtilvee ly, yieldedWthVe  lowest values oI f the I and RA.RAeg arding
the association between variaAblgees , the I a−n0d.08R7A3 *w* ere posit0iv.5e0l9y nas ssociated w0.8it5h8 m**o st of
the biometric variables, except for the CH. The Wns V was negatively associated with the IDBH −0.413  0.721 * 0.767 * 
and the RA. The climatic and edaphic variables were not associated with any of the wood
Biometric CH 0.080 ns 0.363 ns 0.102 ns 
deterioration indicators, except for pH.
TH −0.605 ns 0.706 ns 0.775 * 
CV −0.268 ns 0.822 * 0.649 
T°mean −0.318 ns 0.343 ns 0.299 ns 
Climate T°min −0.347 ns 0.465 ns 0.440 ns 
T°max −0.369 ns 0.363 ns 0.303 ns 
 
Forests 2024, 15, x FOR PEER REVIEW 10 of 18 
 Forests 2024, 15, 778 9 of 16
Table 4. Correlationpspb etween w−0o.o3d71d encs ay indicato0r.3v5a3ri ansb les and pre0d.i2c4to5r sn.s 
S −0.282 ns 0.178 ns 0.382 ns 
Type of Predictive Variable WV I RA
Si 0.298 ns −0.213 ns −0.360 ns 
ns
Edaphic C Age0.232 ns −0.08−703.*1*17 ns 0.50−90.367 ns 0.858 **
DBH −0.413 ns 0.721 * 0.767 *
ns ns
Biometric pH CH−0.635  0.0800n.s456  0.3630n.7s 58 * 0.102 ns
EC TH0.219 ns −0.6005.n0s87 ns 0.70−60n.s235 ns 0.775 *
WV: wave velocity, I: incidence percentageC, VRA: percentage− o0f. 2t6h8e nrsotten area, D0B.8H22: d* iameter at 0.649
breast height at 1.3 m, CH: commercial hTe◦igmheta, nTH: total he−ig0h.t3,1 C8Vns: commercia0l. 3v4o3lunms e, T°min: 0.299 ns
minimum temperature, T°mean: mean temTp◦emrainture, T°max: −m0a.x3i4m7 unsm temperatu0r.4e6, 5ppns: precipita- 0.440 ns
tion, S: Sand, Si: silt, CC: clilmaya, tEeC: electrical cTo◦nmdauxctivity, * = p <− 00.0.356, 9**n =s  p < 0.01, ns 0=. 3n6o3nn-s ignificant. 0.303 ns
pp −0.371 ns 0.353 ns 0.245 ns
In the PCA, 72.85% of the variabilitSy in the data −w0a.s2 8e2xnpslained by t0h.1e7 8finrsst (46.2%) 0.382 ns
and second (26.6%) components (Figure S5i). The oldest P03.2 p9l8onts (53 years) y−ie0.l2d1e3dn sthe high- −0.360 ns
est values of the I aEnda RphAic and the lowesCt values of the 0W.2V32. nPslots P1 and− P0.61,1 w7 nhsich were −0.367 ns
15 and 18 years of age, respectively, yielpdHed the lowest− v0a.l6u3e5sn sof the I and0 .R45A6. nRs egarding 0.758 *
ns ns ns
the association between variables, the IE aCnd RA were p0o.2s1it9ively associate0d.0 8w7 ith most of −0.235
the biometric vWaVr:iawbalveesv, eelxoccietyp, tI :fioncri dthenec eCpHer.c Tenhtaeg We, RVA w: paersc ennetgagaetiovfethlye raoststeoncaiaretae,dD BwHit:hd ia◦ th
mee tIe r at breast height
at 1.3 m, CH: commercial height, TH: total height, CV: commercial volume, T min: minimum temperature,
and the RA. ThTe◦m celiamn:amtieca nantedm epderaaptuhriec,  Tv◦amriaaxb: lmesa xwimeurem ntoemt apsesraotcuirae,tepdp :wpirtehci paintayti oonf, tSh:eS awndo,oSdi: silt, C: clay, EC:
deterioration ienledcitcriactaol crosn, deuxcteivpity ,f*o=r p <H0.. 05, ** = p < 0.01, ns = non-significant.
 
Figure 5. PCA pFliogtu orfe w5.ooPdC dAetpelroiot roaftiwono oindddiceatteorrio arnatdi opnreinddicitcoart ovrarainabdlepsr.e WdiVct:o wr vavaeri avbelleosc.ityW, VI: : wave velocity,
incidence percenI:tiangcei,d RenAc:e ppeerrcceenntataggee o, Rf tAh:ep reorttcent agreao, fDtBhHe r: odttiaenmaertera ,aDt BbrHea: sdti ahmeiegthetr at b1.r3e amst, height at 1.3 m,
CH: commerciaCl hHe:igchomt, TmHer: chiaelighheti gtohtta, lT, HCV: h: eciogmhtmtoertacila, lC vVo:lcuommem, Te°rmciainl :v moliunmimeu, Tm◦ mteminp: meriantuimreu, m temperature,
T°mean: mean tTe◦mmpeearna:tumreea, nT°temmapxe: rmatauxriem, Tu◦mm atexm: mpearxaimtuurem, ptepm: pperreactiupritea,tpiopn: ,p Sre: csiapnitda,t iSoin: ,sSil:ts, aCn:d , Si: silt, C: clay,
clay, EC: electricEaCl :coelnedcturcictiavlictyo.n ductivity.
3.3. Relationshi3p. 3b.etRweeleanti oWnsohoidp DbettwereieonraWtioono danDde tPereiodricattoiorn VanridabPlreesd ictor Variables
The predictiveT mheodperlesd oicft wivoeomd oddeeclasyo, fwwitoho adgdee accatyin, wg iatsh aa pgereadcitcitnogr avsaraiapbrleed fiocrto trhve ariable for the
WV, explaininWg V6,4e%x polfa itnhien gva6r4i%aboilfitthye inv atrhiaeb liilniteyairn mthoedleinl,e aarrem sohdoewl,na riens hTaobwlen i5n. TIna btlhee5 . In the young
young trees, thtree Wes,Vt hbeetWwVeebne tthwee reanditahle sreandsioarlss wenasso 1r6s5w0.a7s51 m65/s0;. 7in5 mth/e sm; iantuthree tmreaetsu, rtehter ees, the value
value decreasedde csrteeaadseildy sttoe a1d29il3y.2t1o m12/9s3 (.F2i1gumr/e s6(aF).i gFuorre t6hae) I. , Ftohre tohnelyI, pthreedoinctloyrp vraerdiaicbtloer variable was
 
Forests 2024, 15, 778 10 of 16
the CV, which explained 68% of the existing variability; both variables increased linearly;
for each unit of volume, the I increased by 26.43% (Figure 6b). A high proportion (81%)
of the variability in the RA was explained by age and the DBH; in young plantations, the
percentage was minimal, with an average of 1.1%, and there were no differences between
the diameter classes (e.g., in P1, 1% in trees of DBHs of 25 and 55 cm). In older plantations,
the highest percentage of damage, 22.5% on average, occurred in larger diameter trees
(e.g., in P3, 21.5 and 25.6% in trees of DBHs of 55 and 75 cm, respectively) (Figure 6c).
Table 5. Predictive models for indicators of wood deterioration.
Parameter
Variable Model 2β R0 β1 β2
WV WV = β0 + (β1·Age) + ϵ 1890.1780 *** −10.7901 * - 0.64 **
I I = β0 + (b1·CV) + ϵ 50.9461 *** 26.4361 ** - 0.68 *
RA Ln(RA) = β0 + β1·Ln(Age) + β2·Ln(DBH) + ϵ −8.8233 * 1.9469 * 1.0151 * 0.81 *
WV: wave velocity, I: incidence percentage, RA: percentage of rotten area, Age (years), DBH: diameter at breast
3
Forests 2024, 15, x FOR PEER REVIEWh eight at 1.3 m (cm), CV: commercial volume (m ), Ln: natural logarithm, β0, β1, β12: opf a1r8a meters of the models,
 
ϵ: error term, * = p < 0.05, ** = p < 0.01, *** = p < 0.001, R2: coefficient of determination.
 
FFiigguurree 6.6 M. Modoeld inedl iicnadtoircsa otfo Crs. coatfenCif.ocrmaties nwiofoordm diestewriooroadtiodne itne prilaonrtaattiioonns.i n(a)p WlaVn: twataivoen vse.lo(ca-) WV: wave velocity,
ity, (b) I: incidence percentage and (c) RA: percentage of the rotten area. 
(b) I: incidence percentage and (c) RA: percentage of the rotten area.
4. Discussion 
4.1. Wood Deterioration 
The wood quality indicators varied significantly among plantations. This can be at-
tributed to the influence of environmental conditions and the tree age on the wood char-
acteristics and properties [65]. 
The velocity at which sound waves are propagated in wood depends on the tree spe-
cies, moisture content, measurement direction, and density [66,67]. In general, the wave 
velocity is correlated with the rigidity of the wood and the lignin content. Rigid wood will 
propagate the sound faster, and wood with a high moisture content will propagate the 
sound waves more slowly because the capillaries contain water rather than air [68]. One 
 
Forests 2024, 15, 778 11 of 16
4. Discussion
4.1. Wood Deterioration
The wood quality indicators varied significantly among plantations. This can be
attributed to the influence of environmental conditions and the tree age on the wood
characteristics and properties [65].
The velocity at which sound waves are propagated in wood depends on the tree
species, moisture content, measurement direction, and density [66,67]. In general, the wave
velocity is correlated with the rigidity of the wood and the lignin content. Rigid wood will
propagate the sound faster, and wood with a high moisture content will propagate the
sound waves more slowly because the capillaries contain water rather than air [68]. One
of the basic principles of tomography states that the first wave to reach a sensor will have
travelled the fastest path. However, in the case of decomposed wood, the wave travels more
slowly [69], increasing the transmission time from the sender to the receiver [70]. Relative
to the WV, which was higher in young plantations (e.g., P1 and P6, with 1650.75 and
1783.74 m/s), these values reflect healthy wood and transmission times that are consistent
with those reported in previous studies [67,71–73]. The slowest WVs were recorded in the
oldest plots (e.g., 1293.21 and 1365.68 m/s in P3 and P7). Acoustic tomography analysis
demonstrated that C. cateniformis was susceptible to the deterioration of the medullary area,
as observed in plots P7, P2, and P3 (Figure 3). This condition was reported for species
in plantations in the Amazon region [40]. The WV was significantly correlated with the
RA (r = −0.906) (Figure 4). The correlation depended on the time and speed of the wave
propagation and the ability of acoustic tomography to identify internal defects in tree
trunks [67,71–74].
4.2. Relationship between Wood Deterioration and Predictor Variables
Wood deterioration was closely related to the biometric variables (Table 4 and Figure 5),
and the positive association showed that older and larger trees were more susceptible to
stem rot. In previous studies with other tree species, wood decomposition was found to
increase proportionally with age [2,75–77]. The incidence in C. cateniformis was directly
related to age [2,45,75], and over-mature trees were found to have a high percentage of
medullary rot [2,75]. The susceptibility of the medullary region to deterioration may be
due to the small amount of lignin and the subsequent lack of rigidity. The absence of lignin
generates a suitable environment for the proliferation of fungi [69,78], and the species is
therefore predisposed to attack by pathogenic fungi that can cause wood decay [2,45,75].
The pathogens that cause rotting in the stem center enter via the root system and colonize
the tree upwards through the heartwood xylem [4]. The humid conditions at the base of
the tree in contact with the soil favor the development of xylophages [5,32], and it has been
reported that the humidity influences the proliferation and decomposition of wood [79].
Minimum and maximum temperatures and precipitation affect wood deterioration,
and higher or lower levels (extreme rain, extreme heat, extreme cold, strong winds) can
cause stress in tree species [80]. Changes in climatic variables may be beneficial for decom-
poser organisms [80,81]. In the case of C. cateniformis, climatic conditions do not directly
affect wood deterioration (Table 4), but extreme events could create conditions that promote
the development of pathogens, considering the low tolerance of the species to flooding [82].
The low (or no) correlation between the deterioration indicators and edaphic variables
may be associated with the plasticity of C. cateniformis, although it requires clayey, acidic
soils with low (or zero) levels of stoniness [83,84]. The RA was the only variable that was
positively and significantly correlated with pH (r = 0.758) (Table 4), probably because C.
cateniformis growing in alkaline soil is more susceptible to wood deterioration due to the
acidophilic nature of the species [85,86].
4.3. Wood Deterioration Modelling
The models yielded robust coefficients of determination, with values ranging from
0.68 to 0.93 (Table 5). Values close to 1 indicate the strongest relationship between the
Forests 2024, 15, 778 12 of 16
measured values and the predicted values [80,87]. The main model predictor variables
were age and the DBH (the WV and RA), which were strongly correlated with the wood
deterioration indicators (Table 4). The model estimating the WV yielded a negative param-
eter β1, indicating that the propagation of sound waves between sensors was slower in
older trees (Figure 6a). The CV acted as a predictor variable for the I, and the parameter β1
demonstrated a positive value, indicating that trees with a larger CV are more susceptible
to wood deterioration, as reported by in other studies [2,88].
The percentage of the rotten area was influenced by the tree age and diameter. Young
plantations had a minimum percentage of the rotten area, while older plantations (53 years)
had a higher percentage, ranging from 21.5% to 25.6%. The silvicultural rotation of C.
cateniformis occurs at a young age, but limited diameter growth and immature wood both
restrict the use of timber [36,89]. The plantations should therefore be maintained until an
optimal age. According to the study findings, in trees of 25–30 years old, with a DBH from
45.0 to 48.5 cm and a CV from 0.9069 to 1.0276 m3, the WV ranges between 1620.43 and
1566.47 m/s, the I between 74.9 and 78.1%, and the RA between 3.9 and 5.9% (Figure 6b,c).
The loss of relative resistance was twice as high in trees with a high percentage of cavities
than in trees with a low percentage of cavities (Figure 6a–c) [90]. Thus, plantations should
not be maintained for more than 30 years, as this could lead to economic losses. The
condition of the species beyond 30 years will directly affect the harvestable volume at the
time of harvesting [2,88].
Acoustic tomography is a useful tool for identifying damaged areas of tree stems [91].
The proposed inspection protocols can assist in detecting such damage and obtaining more
detailed data for managing forest resources [71]. This study proposes the use of this tool
to enable the better control of the quality of C. cateniformis wood in forest plantations,
generating essential information for timber quality control in plantations, and promoting
the planting of the species in the Peruvian Amazon [36]. This is essential in the forestry
sector in Peru, which is currently undervalued and faces various challenges that must be ad-
dressed [83,84,92]. The results of this study are consistent with those of the National Forest
Strategy, which promotes adaptive research for efficient and diversified production [80,93].
5. Conclusions
The study examined tree decay in eight C. cateniformis Ducke plantations of ages
between 15 and 53 years in the Peruvian Amazon by using acoustic tomography data. The
species began to show variations in trunk pith at 15 years, with a minimal incidence of
rot, and a higher incidence of damage due to rot and cavities at 30 years. Based on the
information obtained, we recommend harvesting the species at a maximum age of 30 years
in order to ensure the presence of a higher percentage of usable volumes of wood for
producers. New, less labor-intensive methods of evaluating wood degradation in forestry
plantations should be developed using acoustic tomography to obtain accurate data.
Author Contributions: Conceptualization, G.P.C.-R., J.R.B.-V., P.Á.-Á. and S.Y.C.-V.; methodology,
G.P.C.-R., J.R.B.-V., S.Y.C.-V., A.T.M.-R. and B.M.; software, G.P.C.-R., J.R.B.-V. and S.Y.C.-V.; validation,
G.P.C.-R., J.R.B.-V., S.Y.C.-V., B.M., E.S. and J.A.O.-R.; formal analysis, G.P.C.-R., J.R.B.-V., S.Y.C.-V.,
P.Á.-Á. and S.S.A.P.; investigation, E.S., S.Q., A.T.M.-R. and A.C.; resources, G.P.C.-R., J.R.B.-V., S.Y.C.-
V. and J.A.O.-R.; data curation, G.P.C.-R., J.R.B.-V., S.Y.C.-V., B.M., A.T.M.-R., P.Á.-Á. and S.S.A.P.;
writing—original draft preparation, G.P.C.-R., J.R.B.-V., S.Y.C.-V. and. J.A.O.-R.; writing—review and
editing, G.P.C.-R., J.R.B.-V., S.Y.C.-V., P.Á.-Á. and S.S.A.P.; visualization, E.S., S.Q., P.Á.-Á. and J.R.B.-V.;
supervision, E.S. and J.A.O.-R.; project administration, E.S. and J.A.O.-R.; funding acquisition, E.S.
and J.A.O.-R. All authors have read and agreed to the published version of the manuscript.
Funding: This research was financed by the National Program of Scientific Research and Advanced
Studies CONCYTEC, project PE501078898-2022-PROCIENCIA: “Sanidad interna de plantaciones de
Cedrelinga cateniformis (Ducke) Ducke de diferentes edades mediante tomografía acústica en Amazonía
baja, Loreto-Perú, 2022” and executed by the National Institute of Agricultural In-novation-INIA
of Peru”.
Forests 2024, 15, 778 13 of 16
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: We thank Wilfredo Eric Rodriguez Felipa, Roiser Lobato Galvez, and Angela
Pillpe Huaman for supporting the logistic activities.
Conflicts of Interest: The authors declare that they have no conflicts of interest.
References
1. Li, G.; Wang, X.; Feng, H.; Wiedenbeck, J.; Ross, R.J. Analysis of Wave Velocity Patterns in Black Cherry Trees and Its Effect on
Internal Decay Detection. Comput. Elecftron. Agric. 2014, 104, 32–39. [CrossRef]
2. López, R. Estudio Silvicultural Del Tornillo (Cedrelinga Catenaeformis DUCKE). Rev. For. del Perú v 2005, 10, 1–7.
3. Živanović, I.; Šurjanac, N.; Jovanović, F.; Ðord̄ević, I.; Češljar, G. Transversal Stress Wave Velocity in the Tree of Serbian Spruce
(Picea Omorika (Pančić) Purkyně). Sustain. For. Collect. 2022, 85–86, 87–96. [CrossRef]
4. Gilbert, G.; Ballesteros, J.O.; Barrios-Rodriguez, C.A.; Bonadies, E.F.; Cedeño-Sánchez, M.L.; Fossatti-Caballero, N.J.; Trejos-
Rodríguez, M.M.; Pérez-Suñiga, J.M.; Holub-Young, K.S.; Henn, L.A.; et al. Use of Sonic Tomography to Detect and Quantify
Wood Decay in Living Trees. Appl. Plant Sci. 2016, 4, 1600060. [CrossRef]
5. Angulo-Ruiz, W.E.; Fasabi-Pashanasi, H.; Rengifo-Pérez, C.P.; Valdivia-Marquez, L.N. Non-Destructive Technique Based on
Acoustic Tomography for the Identification of Internal Defects in Trees. Sci. Agropecu. 2021, 12, 65–71. [CrossRef]
6. Panebra Salirrosas, P.I. Evaluacion No Destructiva de La Densidad En Madera de Plantaciones de Dos Especies Forestales. 2019,
pp. 1–70. Available online: https://repositorio.lamolina.edu.pe/handle/20.500.12996/3877 (accessed on 6 December 2023).
7. Vega Cueto, A.; Canga Libano, E.; Sánchez García, S.; Hevia Canal, A.; Feito Díaz, I.; Menéndez-Miguélez, M.; González-García,
M.; Martínez Alonso, C.; Majada Guijo, J.P. Innovación de Procesos y Productos En El Sector de La Madera. Cuad. la Soc. Española
Ciencias For. 2016, 336, 323–336. [CrossRef]
8. Jaskowska-Lemańska, J.; Przesmycka, E. Semi-Destructive and Non-Destructive Tests of Timber Structure of Various Moisture
Contents. Materials 2021, 14, 96. [CrossRef]
9. Lopez, A.J.; Yolanda, P.; Adolfo, J. Evaluacion No-Destructiva de la Densidad de la Madera de Arboles Vivos en pie de Eucalyptus
Grandis Utilizando Resistógrafo. Forestry 2010, 1–9. Available online: https://www.jornadasforestales.com.ar/jornadas/2010
/trab_res_pos/445.15.T.LOPEZ.pdf (accessed on 6 December 2023).
10. Ondrejka, V.; Gergel’, T.; Bucha, T.; Pástor, M. Innovative Methods of Non-Destructive Evaluation of Log Quality. Cent. Eur. For. J.
2021, 67, 3–13. [CrossRef]
11. Acuña, L.; Spavento, E.M.; Casado, M.M.; Basterra, L.A.; Keil, G.D. Metodología De Ensayo No Destructivo Aplicada a Madera
Estructural De Populus X Euramericana I-214.Populus X Euramericana. 2014. Available online: https://www.researchgate.net/
profile/Luis-Acuna-5/publication/329990800_Metodologia_de_ensayo_no_destructivo_y_destructivo_aplicada_a_madera_
de_Populus_x_canadensis_%E2%80%99I-214%E2%80%99_de_procedencia_Argentina/links/5c27cb7892851c22a34e7b05/
Metodologia-de-ensayo-no-destructivo-y-destructivo-aplicada-a-madera-de-Populus-x-canadensis-I-214-de-procedencia-
Argentina.pdf (accessed on 6 December 2023).
12. Arciniegas, A.; Brancheriau, L.; Lasaygues, P. Tomography in Standing Trees: Revisiting the Determination of Acoustic Wave
Velocity. Ann. For. Sci. 2014, 72, 685–691. [CrossRef]
13. Liang, S.; Fu, F. Effect of Sensor Number and Distribution on Accuracy Rate of Wood Defect Detection with Stress Wave
Tomography. Wood Res. 2014, 59, 521–532.
14. Wei, X.; Du, C.; Xu, S.; Tian, C.; Yang, X.; Hu, L.; Pang, P. Research on Stress Wave Wood Nondestructive Testing Technology.
J. Phys. Conf. Ser. 2022, 2366, 012035. [CrossRef]
15. Palma, S.; dos Reis, M.N.; Gonçalves, R. Tomographic Images Generated from Measurements in Standing Trees Using Ultrasound
and Postprocessed Images: Methodological Proposals for Cutting Velocity, Interpolation Algorithm and Confusion Matrix Metrics
Focusing on Image Quality. Forests 2022, 13, 1935. [CrossRef]
16. Viera, G.; Dos santos, D.; Cerqueira, B.; Monteiro, A.; de-Figueiredo-Latorraca, J.V. Historic Urban Trees: Assessing the Trunks
Internal Integrity. Bosque 2023, 44, 481–491.
17. Schimleck, L.; Dahlen, J.; Apiolaza, L.A.; Downes, G.; Emms, G.; Evans, R.; Moore, J.; Pâques, L.; Van den Bulcke, J.; Wang, X.
Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation. Forests 2019, 10, 728. [CrossRef]
18. Wang, X. Acoustic Measurements on Trees and Logs: A Review and Analysis. Wood Sci. Technol. 2013, 47, 965–975. [CrossRef]
19. Sonorización, S. Conceptos Fundamentales Del Sonido. Sintec 2012, 1–13. Available online: https://www.acdacustics.com/files/
conceptos.pdf (accessed on 6 December 2023).
20. Pereira-Rollo, L.C. Tomografia de Impulso Para Estimativa Da Densidade Da Madeira. Master’s Dissertation, Universidade sao
Paulo, Piracicaba, Brazil, 2009.
21. Omonte, M.; Valenzuela-Hurtado, L. Relationship between Acoustic Wave Velocity and Different Characteristics of Wood in
Eucalyptus Nitens Trees with Sawing Dimensions. Maderas Cienc. Tecnol. 2020, 22, 559–568. [CrossRef]
22. Hanum, S.F.; Iryadi, R.; Rahayu, A.; Bangun, T.M.; Darma, I.D.P. Sutomo Wood Decay Diagnostic of Joannesia Princeps Vellozo at
Bali Botanical Garden Using Arborsonic Acoustic 3D Tomograph. IOP Conf. Ser. Mater. Sci. Eng. 2020, 935, 012069. [CrossRef]
Forests 2024, 15, 778 14 of 16
23. Liu, L.; Li, G. Acoustic Tomography Based on Hybrid Wave Propagation Model for Tree Decay Detection. Comput. Electron. Agric.
2018, 151, 276–285. [CrossRef]
24. Cristini, V.; Tippner, J.; Tomšovský, M.; Zlámal, J.; Mařík, R. Acoustic Tomography Outputs in Comparison to the Properties of
Degraded Wood in Beech Trees. Eur. J. Wood Wood Prod. 2022, 80, 1377–1387. [CrossRef]
25. Du, X.; Li, J.; Feng, H.; Chen, S. Image Reconstruction of Internal Defects in Wood Based on Segmented Propagation Rays of
Stress Waves. Appl. Sci. 2018, 8, 1778. [CrossRef]
26. Fathi, S.; Divos, F.; Bejo, L. Comparison between Two Methods of Non-Destructive Evaluation of Standing Trees. Nondestruct.
Test. 2019, 2, 8.
27. Simon, J.; Machar, I.; Brus, J.; Pechanec, V. Combining a Growth-Simulation Model with Acoustic-Wood Tomography as a
Decision-Support Tool for Adaptive Management and Conservation of Forest Ecosystems. Ecol. Inform. 2015, 30, 309–312.
[CrossRef]
28. Wu, X.; Li, G.; Jiao, Z.; Wang, X. Reliability of Acoustic Tomography and Ground-Penetrating Radar for Tree Decay Detection.
Appl. Plant Sci. 2018, 6, e01187. [CrossRef]
29. Ostrovský, R.; Kobza, M.; Gažo, J. Extensively Damaged Trees Tested with Acoustic Tomography Considering Tree Stability in
Urban Greenery. Trees—Struct. Funct. 2017, 31, 1015–1023. [CrossRef]
30. Schubert, S.; Gsell, D.; Dual, J.; Motavalli, M.; Niemz, P. Acoustic Wood Tomography on Trees and the Challenge of Wood
Heterogeneity. Holzforschung 2009, 63, 107–112. [CrossRef]
31. Angulo, W.E. Estudio de Sanidad Forestal Mediante Tecnicas Acusticas No Destructivas de Una Plantacion Forestal “Tornillo” Proveniente
de La Region Loreto; INIA—Instituto Nacional de Innovación Agraria: Pucallpa, Peru, 2018; Available online: http://repositorio.
inia.gob.pe/handle/20.500.12955/732 (accessed on 6 December 2023).
32. Cuellar-Bautista, J.; Miriam, R.; Chumbimune, S.Y.; Lara, V.A. Caracterización Radial de La Densidad de La Madera de Cedrelinga
Cateniformis y Calycophyllum Spruceanum Mediante El Diseño de Una Red Neuronal Artificial Utilizando Imágenes Tomográficas.
2019. Available online: https://www.researchgate.net/publication/338115628_CARACTERIZACION_RADIAL_DE_LA_
DENSIDAD_DE_LA_MADERA_DE_Cedrelinga_cateniformis_y_Calycophyllum_spruceanum_MEDIANTE_EL_DISENO_
DE_UNA_RED_NEURONAL_ARTIFICIAL_UTILIZANDO_IMAGENES_TOMOGRAFICAS (accessed on 6 December 2023).
33. Mendosa Saucedo, G. Uso de Metodos No Destructivos Para Determinar El Riesgo de Caida de Arboles Urbanos, En El Parque de
Las Leyendas. Tesis Pregr. 2018, 100. Available online: https://repositorio.lamolina.edu.pe/handle/20.500.12996/3755 (accessed
on 6 December 2023).
34. Valderrama, H. Anatomia Comparativa del Xilema del Tronco y de la Rama de Cedrelinga Catenaeformis Ducke (FABACEAE).
Folia Amaz. 1998, 9, 5–28. [CrossRef]
35. Pashanasi-Amasifuen, B.; Aponte-Jaramillo, A.N.; Mathios-Flores, M.A. Crecimiento de Tornillo (Cedrelinga Catenaeformis) y
Marupa (Simarouba Amara) Dentro de Un Sistema Agroforestal En Multiestratos. Rev. Peru. Investig. Agropecu. 2022, 1, e10.
[CrossRef]
36. Baluarte, J. El Control de Calidad de La Madera de Plantaciones, Una Alternativa Para Alebtar El Cultivo de Arboles de Especies
Forestales Maderables, Estudio de Caso de Cedrelinga Catenaeformis “Tornillo”. Folia Amaz. 2019, 28, 43–51. [CrossRef]
37. Cruz, W.; Saldaña, C.; Ramos, H.; Baselly, R.; Loli, J.C.; Cuellar, E. Genetic Structure of Natural Populations of Cedrelinga
Cateniformis “tornillo” from the Oriental Region of Peru. Sci. Agropecu. 2020, 11, 521–528. [CrossRef]
38. Melo, M.d.F.F.; Varela, V.P. Aspectos Morfológicos de Frutos, Sementes, Germinação e Plântulas de Duas Espécies Florestais
Da Amazônia: I. Dinizia Excelsa Ducke (Angelim-Pedra). II Cedrelinga Catenaeformis Ducke (Cedrorana)—Leguminosae:
Mimosoideae. Rev. Bras. Sementes 2006, 28, 54–62. [CrossRef]
39. Vallejos-Torres, G.; Gonzales-Polar, L.E.T.; Arévalo-López, L.A. Enraizamiento de Brotes de Tornillo (Cedrelinga Catenaeformis
Ducke), En La Amazonía Peruana. Rev. For. Mesoam. Kurú 2014, 11, 60. [CrossRef]
40. Yepes-Alza, F.; Linares-Bensimón, C. Rendimiento de Trozas Aserradas de Cedrelinga Cateniformis Ducke Obtenidas Del Raleo
Silvicultural de Plantaciones En Jenaro Herra, Loreto-Perú. Folia Amaz. 2007, 16, 115. [CrossRef]
41. Álvarez Gómez, L.; Ríos Torres, S. Evaluación Económica de Plantaciones de Tornillo, Cedrilenga Catenaeformis, En El Departamento de
Loreto; Instituto de Investigaciones de la Amazonia Peruana—IIAP: Iquitos, Peru, 2009; ISBN 9789972667695.
42. Instituto Nacional de Estadistica e Informatica-INEI. Carpeta Georeferencial Región Loreto Perú. Of. Gestión la Inf. y Estadística
Dir. 2019, 1–20.
43. Distrito de San Juan Bautista de La Provincia de Maynas, Región Loreto. Available online: https://www.iperu.org/distrito-de-
san-juan-bautista-provincia-de-maynas (accessed on 31 August 2023).
44. Loto, D.E.; Gasparri, I.; Azcona, M.; García, S.; Spagarino, C. Estructura y Dinámica de Bosques de Palo Santo En El Chaco Seco.
Ecol. Austral 2018, 28, 064–073. [CrossRef]
45. Díaz, R.; Jeilit, N. Identificación de Patógenos Fúngicos Causantes de La “Pudrición Medular” En Cedreliga Cateniformis,
(Ducke) Ducke “Tornillo” En Plantaciones Forestales de Jenaro Herrera-Loreto, Perú. 2009. Available online: https://repositorio.
unapiquitos.edu.pe/handle/20.500.12737/3109 (accessed on 6 December 2023).
46. Muñoz, O. Uso Del Tomografo Para Determinar Sanidad En Plantaciones Forestales de Tornillo En El Dorado-INIA, Distrito de
San Juan Bautiusta, Loreto. Tesis Pregr. 2023, 2003–2005. Available online: https://repositorio.unapiquitos.edu.pe/handle/20.500
.12737/9485 (accessed on 6 December 2023).
Forests 2024, 15, 778 15 of 16
47. Palma, S.S.A.; Gonçalves, R. Tomographic Images of Tree Trunks Generated Using Ultrasound and Post-Processed Images:
Influence of the Number of Measurement Points. BioResources 2022, 17, 6638–6655. [CrossRef]
48. Chumbimune, S.Y.; Cardenas, G.P.; Saravia, D.; Valqui, L.; Salazar, W.; Arbizu, C.I. Methodology for Avocado (Persea Americana
Mill.) Orchard Evaluation Using Different Measurement Technologies. Chil. J. Agric. Anim. Sci. 2022, 38, 259–273. [CrossRef]
49. Palma, S.S.A.; Gonçalves, R.; Trinca, A.J.; Costa, C.P.; Reis, M.N.; Martins, G.A. Interference from Knots, Wave Propagation
Direction, and Effect of Juvenile and Reaction Wood on Velocities in Ultrasound Tomography. BioResources 2018, 13, 2834–2845.
[CrossRef]
50. Armengot, L.; Ferrari, L.; Milz, J.; Velásquez, F.; Hohmann, P.; Schneider, M. Cacao Agroforestry Systems Do Not Increase Pest
and Disease Incidence Compared with Monocultures under Good Cultural Management Practices. Crop Prot. 2020, 130, 105047.
[CrossRef]
51. Otarola-Acevedo, E.; Linares-Bensimón, C. Tablas de Volumen Total y Comercial de Cedrelinga Catenaeformis Ducke “Tornillo”
Para Las Plantaciones En Loreto, Peru. Folia Amaz. 2006, 13, 151. [CrossRef]
52. POWER|Data Access Viewer. Available online: https://power.larc.nasa.gov/data-access-viewer/ (accessed on 6 December 2023).
53. Osorio, W. Toma De Muestras De Suelos Para Evaluar La Fertiidad Del Suelo. Suelos Ecuat. 2010, 41, 23–28.
54. Sotomayor Garretón, A.; Moya Navarro, I.A.; Acuña Aroca, B. Comportamiento de Las Variables Dasométricas En Plantaciones
de Pinus Contorta Doug. Ex Loud., Bajo Manejo Silvopastoral y Forestal En La Región de Aysén, Chile. Cienc. Investig. For. 2010,
16, 265–290. [CrossRef]
55. Mendiburu, F. De Manual Práctico Para El Uso de Agricolae. Univ. Nac. Agrar. La Molina 2010, 58. Available online:
https://www.google.com/search?q=Manual+Pr%C3%A1tico+para+el+uso+de+agricolae&oq=manual&aqs=chrome.0.69i59l3
j69i57j0l4.4175j0j8&sourceid=chrome&ie=UTF-8 (accessed on 6 December 2023).
56. Lalinde, H.; Diego, J.; Castro, E.; Johel, E.; Rangel, C.; Sierra, T.; Andrés, C.; Torrado, A.; Karina, M.; Sierra, C.; et al. Sobre El Uso
Adecuado Del Coeficiente de Correlación de Pearson: Definición, Propiedades y Suposiciones. AVFT Arch. Venez. Farmacol. y Ter.
2018, 18. Available online: https://www.redalyc.org/journal/559/55963207025/55963207025.pdf (accessed on 6 December 2023).
57. Lê, S.; Josse, J.; Husson, F. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 2008, 25, 1–18. [CrossRef]
58. Kassambara, A.; Mundt, F. _factoextra: Extract and Visualize the Results of Multivariate Data Analyses_.R Package Version 1.0.7.
2022. Available online: https://CRAN.R-project.org/package=factoextra (accessed on 6 December 2023).
59. Menéndez-Miguélez, M.; Álvarez-Álvarez, P.; Majada, J.; Canga, E. Effects of Soil Nutrients and Environmental Factors on Site
Productivity in Castanea Sativa Mill. Coppice Stands in NW Spain. New For. 2015, 46, 217–233. [CrossRef]
60. Farrelly, N.; Dhubháin, Á.N.; Nieuwenhuis, M. Sitka Spruce Site Index in Response to Varying Soil Moisture and Nutrients in
Three Different Climate Regions in Ireland. For. Ecol. Manag. 2011, 262, 2199–2206. [CrossRef]
61. Villabrille, J.D. Modelización Del Crecimiento y La Producción de Plantaciones de Eucalyptus Globulus Labill. En El Noroeste de
España. Doctoral Thesis, Universidad Santiago de Compostela España, Lugo, Spain, 2015.
62. Vanclay, J.K. Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests. CAB Int. 1994. Available online:
https://espace.library.uq.edu.au/view/UQ:8211/ModellingForestG.pdf (accessed on 6 December 2023).
63. Venables, W.N.; Ripley, B.D. Modern Applied Statistics with S, 4th ed.; 2002; Volume 53, ISBN 0387954570. Available online:
https://www.stats.ox.ac.uk/pub/MASS4/VR4stat.pdf (accessed on 6 December 2023).
64. John, M.; Chambers, T.J.H. Statistical Models in S; Routledge: Washington, DC, USA, 1991; ISBN 9780412830402.
65. Arciniegas, A.; Prieto, F.; Brancheriau, L.; Lasaygues, P. Literature Review of Acoustic and Ultrasonic Tomography in Standing
Trees. Trees 2014, 28, 1559–1567. [CrossRef]
66. Kloiber, M.; Reinprecht, L.; Hrivnák, J.; Tippner, J. Comparative Evaluation of Acoustic Techniques for Detection of Damages in
Historical Wood. J. Cult. Herit. 2016, 20, 622–631. [CrossRef]
67. Proto, A.R.; Cataldo, M.F.; Costa, C.; Papandrea, S.F.; Zimbalatti, G. A Tomographic Approach to Assessing the Possibility of Ring
Shake Presence in Standing Chestnut Trees. Eur. J. Wood Wood Prod. 2020, 78, 1137–1148. [CrossRef]
68. Makýš, O.; Krušinský, P.; Korenková, R.; Šrobárová, D. Diagnostics of Wooden Poles Situated in the Open—Air Museum Using
Sonic Tomography. Civ. Environ. Eng. 2018, 14, 54–60. [CrossRef]
69. Perlin, L.P.; Pinto, R.C.d.A.; Valle, Â. do Ultrasonic Tomography in Wood with Anisotropy Consideration. Constr. Build. Mater.
2019, 229, 116958. [CrossRef]
70. Espinosa, L.; Prieto, F.; Brancheriau, L.; Lasaygues, P. Effect of Wood Anisotropy in Ultrasonic Wave Propagation: A Ray-Tracing
Approach. Ultrasonics 2019, 91, 242–251. [CrossRef]
71. Papandrea, S.F.; Cataldo, M.F.; Zimbalatti, G.; Proto, A.R. Comparative Evaluation of Inspection Techniques for Decay Detection
in Urban Trees. Sens. Actuators A Phys. 2022, 340, 113544. [CrossRef]
72. Wang, X.; Bruce, R. Detección de Decaimiento En Robles Rojos Mediante Una Combinación de Inspección Visual, Pruebas
Acústicas y Microperforación de Resistencia. Int. Soc. Arbiculture 2008, 34, 1–4. [CrossRef]
73. Soge, A.O.; Popoola, O.I.; Adetoyinbo, A.A. Detection of Wood Decay and Cavities in Living Trees: A Review. Can. J. For. Res.
2021, 51, 937–947. [CrossRef]
74. Basterchea, M. Comparación de Las Técnicas No Destructivas de Tomografía Ultrasónica y Resistencia a La Perforación En La
Evaluación de Discos de Madera. J. Penelit. Pendidik. Guru Sekol. Dasar 2016, 6, 128.
Forests 2024, 15, 778 16 of 16
75. Franco, W.; Radwan, A. Cedrelinga Cateniformis (Ducke) Ducke (Chuncho o Tornillo): Una Oportunidad de Desarrollo Sostenible Para La
Amazonía Ecuatoriana y La Industria Forestal Mediante Plantaciones y Sistemas Agroforestales; Tena, Ecuador, 2023; Available online:
https://www.researchgate.net/publication/373976460_Cedrelinga_cateniformis_Ducke_Ducke_Chuncho_o_Tornillo_una_
oportunidad_de_desarrollo_sostenible_para_la_Amazonia_ecuatoriana_y_la_Industria_Forestal_mediante_plantaciones_y_
sistemas_Agroforestales (accessed on 6 December 2023).
76. Niemtur, S.; Chomicz, E.; Kapsa, M. Computer Tomography in Wood-Decay Assessment of Silver Fir (Abies Alba Mill.) Stands in
the Polish Part of the Carpathians. Environ. Sci. Eng. 2013, 629–637. [CrossRef]
77. Bouslimi, B.; Koubaa, A.; Bergeron, Y. Variation of Brown Rot Decay in Eastern White Cedar (Thuja occidentalis L.). BioResources
2013, 8, 4735–4755. [CrossRef]
78. González, F.V.R.; Ames de Icochea, T.I. Pudricion de La Madera de Diez Especies Forestales Por Acción de Cinco Hongos
Xilofagos. Rev. For. del Perú 1981, 10, 1–38.
79. Kamperidou, V. The Biological Durability of Thermally-and Chemically-Modified Black Pine and Poplarwood against Basid-
iomycetes and Mold Action. Forests 2019, 10, 1111. [CrossRef]
80. Falvai, D.; Saláta, D.; Baltazár, T.; Czóbel, S. Instrumental Study of the Health Status of Picea Abies [l.] Karst and Pinus Mugo
(Turra) and Their Relation to Environmental Parameters in the Eastern Alps. Forests 2021, 12, 716. [CrossRef]
81. Austin, A.T.; Classen, A.T.; Eggleton, P.; Okada, K.; Parr, C.L.; Adair, E.C.; Adu-bredu, S.; Alam, A.; Alvarez-garzón, C.; Barber,
N.A.; et al. Termite Sensitivity to Temperature Affects Global Wood Decay Rates. Science 2022, 377, 1440–1444.
82. Flores Bendezú, Y. Fichas Técnicas Para Plantaciones Con Especies Nativas En Zona De Selva Baja; Instituto Nacional de Innovación
Agraria-INIA: Pucallpa, Peru, 2019.
83. Aldana, R.; García, C.R.; Hidalgo, C.G.; Flores, G.; Del Castillo, D.; Reynel, C.; Pariente, E.; Honorio, E. Morphometric Analysis of
the Species of Dipteryx in the Peruvian Amazon Folia. Inst. Investig. la Amaz. Peru. Folia Amaz. 2016, 25, 101–118.
84. Monteverde-Calderón, E.G. Evaluación Rápida de La Regeneración Natural de Cedrelinga Cateniformis En Un Bosque Premon-
tano de Satipo, Perú. Xilema 2021, 31, 75–83. [CrossRef]
85. Angulo, W.E. Crecimiento y Productividad de La Plantación de Cedrelinga Catenaeformis Ducke, Establecida En Diferentes
Condiciones de Sitio, En Suelo Inceptisol En El Bosque Alexander von Humboldt. Inst. Nac. Innov. Agrar.-INIA 2014, 31.
Available online: http://repositorio.inia.gob.pe/bitstream/inia/578/1/Angulo-crecimiento_productividad.pdf (accessed on
6 December 2023).
86. Soto Sabino, G. Efecto de Indicadores Fisicoquimos Del Suelos En El Crecimiento de Cedrelinga Cateniformis (Tornillo) En Supte
San Jorge, Leoncio Prado. 2023. Available online: https://repositorio.unas.edu.pe/handle/20.500.14292/2357 (accessed on 6
December 2023).
87. Nakagawa, S.; Schielzeth, H. A General and Simple Method for Obtaining R2 from Generalized Linear Mixed-Effects Models.
Methods Ecol. Evol. 2013, 4, 133–142. [CrossRef]
88. Hazir, E.; Koc, K.H. A Modeling Study to Evaluate the Quality of Wood Surface. Maderas Cienc. y Tecnol. 2018, 20, 691–702.
[CrossRef]
89. Baluarte, J.; Alvarez, J. Modelamiento Del Crecimiento de Tornillo Cedrelinga Catenaeformis Ducke En Plantaciones En Jenaro
Herrera, Departamento de Loreto, Perú. Folia Amaz. 2015, 24, 21–32. [CrossRef]
90. Rinn, B.F. Basic Aspects of Mechanical Stablility of Tree Cross-Sections. Arborist News 2011, 20, 52–54.
91. Szabó, K.; Sütöri-Diószegi, M.; Szabo, V.; Bagdi-Fekete, O.; Doma-Tarcsányi, J. Examination of Tthe Condition of the Oldest Trees
in the Buda Arboretum. Proc. Fábos Conf. Landsc. Greenw. Plan. 2022, 7, 45.
92. Pari, G.; Santana, L.; Villanueva, E.; Zarate, D. Planeamiento Estrategico Del Sector Forestal En El Perú. Tesis pregrado, Pontificia
Universidad católica del Peru, Lima, Peru, 2017. Available online: https://tesis.pucp.edu.pe/repositorio/handle/20.500.12404/8
020 (accessed on 6 December 2023).
93. MINAGRI, Ministerio de Agricultura y riego. Estrategia Nacional Forestal Versión Concertada Con Instituciones Y Actores
Forestales 2002. pp. 1–120. Available online: https://faolex.fao.org/docs/pdf/per136311anx.pdf (accessed on 6 December 2023).
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