SciELO - Scientific Electronic Library Online

 
vol.28 número1Estequiometría de la caída de acículas de Pinus hartwegii Lindl. en dos bosques alpinos del centro de MéxicoSuministro y calidad del agua de escurrimientos en la selva mediana subcaducifolia de la Costa de Oaxaca, México índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • No hay artículos similaresSimilares en SciELO

Compartir


Revista Chapingo serie ciencias forestales y del ambiente

versión On-line ISSN 2007-4018versión impresa ISSN 2007-3828

Rev. Chapingo ser. cienc. for. ambient vol.28 no.1 Chapingo ene./abr. 2022  Epub 02-Feb-2024

https://doi.org/10.5154/r.rchscfa.2020.10.067 

Scientific articles

Genetic parameters of a progeny trial of Pinus greggii Engelmann ex Parlatore var. australis Donahue & López in the Mixteca Alta of Oaxaca, Mexico

Gina I. Reyes-Esteves1 

Javier López-Upton1  * 

Mario V. Velasco-García2 

Marcos Jiménez-Casas1 

1 Colegio de Postgraduados, Campus Montecillo. km 36.5 carretera México-Texcoco. C. P. 56230. Montecillo, Texcoco, Estado de México, México.

2 Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP), Centro Nacional de Investigación Disciplinaria en Conservación y Mejoramiento de Ecosistemas Forestales (CENID-COMEF). Av. Progreso 5, Santa Catarina. C. P. 04010. Coyoacán, Ciudad de México, México.


Abstract

Introduction:

The Mixteca Alta of Oaxaca has high environmental degradation. Several species have been planted to recover vegetation cover; Pinus greggii Engelmann ex Parlatore var. australis Donahue & López has grown successfully even without selected material.

Objective:

To determine growth performance and genetic parameters of 90 families at early ages, for use in the selection of superior trees.

Materials and methods:

In San Miguel Achiutla, Oaxaca, genetic variation in growth, conformation and straightness of 90 selected open-pollinated families of P. greggii var. australis was evaluated in a progeny trial five years after planting in two different quality sites. Additive genetic variation, heritability (h2) and genetic and phenotypic correlations were calculated.

Results and discussion:

h2 were superior in the more fertile site. Height, whorls, straightness and volume had greater genetic control (0.09 < h2 i < 0.18). h2 i of stem straightness was higher when evaluated as a whole than when divided into three sections. Genetic correlations were high and positive among growth variables (rg > 0.81; diameter vs. volume = 0.99) and low to moderate for the rest. Volume had a higher genetic correlation with other traits and higher genetic variation and heritability, so it could be used as a selection criterion for breeding cycle. Some genetic correlations differed contrastingly between sites in equal pairs of variables.

Conclusion:

Differences between families and heritability will allow the identification of superior individuals for germplasm sources for regional use.

Keywords: genetic control; heritability; stem straightness; genetic correlation; superior trees.

Resumen

Introducción:

La Mixteca Alta Oaxaqueña posee alta degradación ambiental. Para recuperar la cobertura vegetal se han plantado varias especies; Pinus greggii Engelmann ex Parlatore var. australis Donahue & López ha crecido con éxito aun sin material seleccionado.

Objetivo:

Determinar el desempeño del crecimiento y los parámetros genéticos de 90 familias a edades tempranas, para su uso en la selección de árboles superiores.

Materiales y métodos:

En San Miguel Achiutla, Oaxaca, se evaluó la variación genética del crecimiento, conformación y rectitud de 90 familias seleccionadas de polinización libre de P. greggii var. australis, en un ensayo de progenies a los cinco años de plantación en dos sitios de calidad diferentes. La variación genética aditiva, la heredabilidad (h2) y las correlaciones genética y fenotípica se calcularon.

Resultados y discusión:

En general, las h2 fueron superiores en el sitio más fértil. La altura, verticilos, rectitud y volumen presentaron mayor control genético (0.09 < h2 i < 0.18). La h2 i de la rectitud del fuste fue mayor cuando se evaluó completo que dividido en tres secciones. Las correlaciones genéticas fueron altas y positivas entre las variables de crecimiento (rg > 0.81; diámetro vs. volumen = 0.99) y bajas a moderadas en el resto. El volumen tuvo mayor correlación genética con otras características y mayor variación genética y heredabilidad, por lo que pudiera usarse como criterio de selección en el ciclo de mejora. Algunas correlaciones genéticas difirieron contrastantemente entre sitios en iguales pares de variables.

Conclusión:

Las diferencias entre familias y la heredabilidad permitirán la identificación de individuos superiores para fuentes de germoplasma de uso regional.

Palabras clave: control genético; heredabilidad; rectitud del fuste; correlación genética; árboles superiores

Highlights:

  1. Additive genetic variation, heritability, and genetic and phenotypic correlation were calculated.

  2. The heritability of stem straightness was higher when evaluated as a whole than in three sections.

  3. Genetic parameters indicate that genotypes should be selected based on volume.

  4. Some genetic correlations differed contrastingly between sites for equal pairs of variables.

Introduction

The Mixteca Alta region in Oaxaca is one of the most drastic examples of degradation of forest areas with water scarcity problems (Martínez, 2006). Several species have been planted to recover vegetation cover, including Pinus greggii Engelmann ex Parlatore var. australis Donahue & López because of its adaptability and tolerance to limiting conditions of degraded areas in Oaxaca (Vásquez-García, Cetina-Alcalá, Campos-Bolaños & Casal-Ángeles, 2016; Velasco-Velasco, Enríquez-del Valle, Rodríguez-Ortiz, Campos-Ángeles, Gómez-Cárdenas, & García-García, 2012). Although some provenances of var. australis have shown excellent results in the Mixteca Alta of Oaxaca (Valencia-Manzo, Velasco-García, Gómez, Ruiz-Muñoz, & Capó-Arteaga, 2006; Velasco-Velasco et al., 2012), no genetic material development has been appreciated through progeny trial evaluations nor has genetic control via heritability estimation been determined. Heritability estimates in several growth and tree form traits are necessary for selection of superior trees, to obtain germplasm that will generate more productive reforestations (White, Adams, & Neale, 2007). The present study analyzed the performance of 90 half-sib families five years after planting in a progeny trial at two sites in San Miguel Achiutla, Oaxaca. The objective was to evaluate growth differences, tree conformation, branching quality and stem straightness in the progeny of Pinus greggii, in two limiting sites, and to estimate genetic parameters for use in the selection of superior trees.

Materials and Methods

Trial establishment and biological material

In the fall of 2014, a progeny trial with 90 open-pollinated families of P. greggii var. australis was established on land in the community of San Miguel Achiutla, Oaxaca. The site has a semi-warm temperate sub-humid (A)C(w1) climate, precipitation and mean annual temperature of 700 mm and 18.1 °C, without frost (Instituto Nacional de Estadística y Geografía [INEGI], 2005). The trial consisted of two sites 1 km apart (site 1: 17.29° N and 97.47° W, 1 924 m elevation, western exposure; site 2: 17.29° N and 97.48° W, 1 915 m elevation, eastern exposure), both with slight erosion and shallow soil with pH of 8.4. Site 2 soil had higher levels of nitrogen (0.6 vs. 0.3 mg∙kg-1), iron (2.7 vs. 2.2 mg∙kg-1), copper (0.5 vs. 0.2 mg∙kg-1), zinc (0.3 vs. 0.2 mg∙kg-1), less sodium (15.8 vs. 20.7 mg∙kg-1), equal amount of phosphorus (1.1 vs. 1.1 mg∙kg-1) and higher percentage of clay than site 1. N concentrations were determined by the micro-Kjeldahl method and those of P, K, Fe, Cu and Zn by inductively coupled plasma optical emission spectrometry (ICP-OES model 725, Agilent; Mulgrave, Australia) (Alcántar & Sandoval, 1999). A randomized complete block experimental design was used at each site, 20 replicates for each family, in one-tree plots at 3 x 3 m spacing (1 800 trees per site). A row of trees of the same species was established around the perimeter of both plantations to ensure complete competition between plants.

Variables evaluated

Survival, total height with a graduated level staff, diameter at 1 m above the ground using a digital vernier, number of whorls with two or more branches, and tree conformation on a scale of 1 to 5 (1 = greater straightness and branches with flat angles., 5 = sinuous trunk and branches with acute angles) were evaluated after five years (winter 2019). Tree conformation was determined by two independent observers to avoid bias in appreciation (Reyes de la Barra, Ponce-Donoso, Vallejo-Barra, Daniluk-Mosquera, & Coelho-Duarte, 2018) and average was determined. In the whorl closest to the height of 1 m, the angle was obtained with a graduated protractor, regarding the average of the thickest branch and its opposite (Bustillos-Aguirre, Vargas-Hernández, López-Upton, & Ramírez-Valverde, 2018; Escobar-Sandoval, Vargas-Hernández, López-Upton, Espinosa-Zaragoza, & Borja de la Rosa, 2018). Diameter was measured in the same branches where the angle was evaluated, obtaining an average. The stem straightness was evaluated with two criteria: straightness 1, that of the entire stem with a scale from 1 (straight stem) to 5 (with pronounced twists) (Gutiérrez-Vázquez, Cornejo-Oviedo, Zermeño-González, Valencia-Manzo, & Mendoza-Villarreal, 2010); and straightness 2, the average value of straightness of three equal sections of the stem, each graded on a scale of 1 = straight, 2 = with a single curve and 3 = with two curvatures (Sierra de Grado, Diez-Barra, & Alia-Miranda, 1999). The volume of the trunk (dm3) was calculated as V = 0.0003 * diameter2 * height (Dvorak, Kietzka, Donahue, Hodge, & Stanger, 2000).

Statistical analysis and estimation of genetic parameters

The pooled analysis of the two evaluation sites showed significant differences between all variables; therefore, analysis by each evaluation trial was selected. The analysis of variance for each site was performed with the MIXED procedure of SAS (Littell, Milliken, Stroup, Wolfinger, & Schabenberger, 2006) and variance components were obtained by the restricted maximum likelihood method. The model of the completely randomized blocks design was:

Yjk = u + Bj + Fk + ejk

where,

Yjk = measured value of the individual of the k-th family, within the j-th block (repetition)

µ = population mean

Bj = block fixed effect

Fk = random effect of the k-th family ∼ NID (0, σ2 f)

ejk = error associated with these effects ∼ NID (0, σ 2 e )

j = 1, 2..., 20 blocks

k = 1, 2..., 90 families.

The additive genetic variation coefficient was estimated with the relation: CVGA = (σ2 A)1/2/ *100.

Strict sense heritability at the individual level (h2 i) and of family means (h2 f), for all variables per site, was calculated with variance components using the following equations (Falconer, 2017):

hi2=3σf2/σf2+σe2

hf2=(34σf2 )/(σf2+σe2b)

where,

σ2 f = variance of families

σ2 e = error variance

b = harmonic mean of the number of plants per family for each site.

A coefficient of genetic determination of 3 was used to avoid overestimating the additive variance (σ2 A = 3σ2 f) and heritability.

The standard error of individual heritability [EE(h2i)] was estimated with the Falconer (2017) equation:

EEh2=2(1+nf-1hi2)2(1-hi2)2)/(na(na-1)(nf-1)0.5

where,

nf = number of families

na = number of trees per family.

Phenotypic correlations between pairs of variables were evaluated based on Pearson's correlation coefficient, while genetic correlations (rg (XY)) per site were obtained with the equation described by Falconer (2017):

rg(XY)=COVf(X,Y)/(σf(X)2*σf(Y)2)0.5

where,

COVf(X,Y) = covariance of families between X and Y

σ2 f(X) and σ2 f(Y) = variances of families for the same traits.

COVf (X,Y) was estimated from the sum of X and Y (Rice, 1988): COVf(X,Y)= &#091;σf(X + Y)2-σfX2* σfY2&#093; / 2, where σ2 f (X+Y) is the variance of families of variable X + Y.

The standard error of genetic correlations [EE(rg)] was calculated according to the procedure described by Falconer (2017):

EErg=1-r  g2EE h x2  EE h y22 h x2 h y20.5.

Results and Discussion

Survival of the trial at five years was 89 % at site 1 and 84 % at site 2. According to Table 1, significant differences (P < 0.05) between families were obtained for all variables. Averages were higher at site 2 for the variables of growth, branch quality, conformation and straightness. This may be due to higher content of N, Cu, Fe and Zn in soil of site 2. Velasco-Velasco et al. (2012) reported superior growth in sites with higher fertility in a trial in the Mixteca Alta of Oaxaca.

Height, stem diameter and number of whorls were similar to those obtained in P. greggii in the Mixteca Alta of Oaxaca, in sites with pH of 7.6 and 8.1 (Valencia-Manzo et al., 2006; Velasco-Velasco et al., 2012). Lower average values at similar ages and in poor environmental conditions have been reported for this species (Domínguez-Calleros, Rodríguez-Laguna, Capulín-Grande, Razo-Zárate, & Díaz-Vásquez, 2017). This could be because the families evaluated in this research correspond to provenances reported as outstanding in studies conducted in the Mixteca Alta of Oaxaca (Valencia-Manzo et al., 2006; Vásquez-García et al., 2016; Velasco-Velasco et al., 2012). Growth values should be related to the strategy of adaptation to water deficit, seeking the selection of larger trees with tolerance to this deficit (Martínez-Trinidad, Vargas-Hernández, Muñoz, & López-Upton, 2002).

Table 1 Average, minimum and maximum values of characters evaluated in families of Pinus greggii var. australis five years after planting in two sites in the Mixteca Alta of Oaxaca, Mexico. 

Variable / site Site 1 Site 2
Probability Mean Average per family Probability Mean Average per family
Minimum Maximum Minimum Maximum
Total height (cm) 0.0001 218 159 297 0.0009 281 229 328
Diameter (cm) 0.0034 2.47 1.65 3.83 0.0096 3.4 2.48 4.52
Volume (dm3) 0.0013 0.8 0.27 2.32 0.0017 1.46 0.75 2.82
Branch diameter (mm) 0.0119 10.8 7.6 14.2 0.0017 12.5 9.9 16.1
Branch angle (°) 0.0001 57.9 49.5 65.3 0.0095 58.1 51 65
Whorls 0.0001 7.4 5.6 10.3 0.0001 11.5 8 15.6
Conformation 0.001 2.28 2.88 1.81 0.0001 1.77 2.34 1.39
Straightness 1 0.0001 2.54 2.94 2.16 0.0001 1.57 2 1.14
Straightness 2 0.0236 1.51 1.73 1.31 0.0158 1.51 1.76 1.38

Diameter = stem diameter at 1 m; straightness 1 = straightness of the entire stem; straightness 2 = average of three stem sections.

Genetic control of traits evaluated

Table 2 shows that individual heritabilities (h2 i) at both sites were classified as low (Cornelius, 1994); family heritabilities (h2 f) at site 2 were slightly higher. Height, volume, number of whorls and straightness 1 had the highest individual and family mean heritabilities in both evaluation sites; site 1 also had it for branch angle and site 2 for stem conformation, perhaps due to environmental differences in soil fertility and texture. In trials carried out on Pinus patula Schiede ex Schltd. et Cham. heritability differences between sites have been determined with values that double the numbers from one trial to another (Salaya-Domínguez, López-Upton, & Vargas-Hernández, 2012; Morales González, López-Upton, Vargas-Hernández, Ramírez-Herrera, & Gil-Muñoz, 2013).

Heritabilities of traits of interest such as stem straightness, height, diameter at breast height and volume per tree are in the range of 0.1 to 0.3 for forest species (Bustillos-Aguirre et al., 2018; Escobar-Sandoval et al., 2018; Mora & Zamudio, 2006; Morales-González et al., 2013; Sánchez-Vargas, Cambrón-Sandoval, Sáenz-Romero, & Vargas-Hernández, 2014). Therefore, the values found in this trial are also within the range of realistic values and are conservatives for these variables.

Table 2 Individual heritability (h2 i) and family means (h2 f), and additive genetic variation (CVGA) coefficient for growth and stem quality traits evaluated in a progeny trial of Pinus greggii var. australis after five years of planting at two sites in the Mixteca Alta of Oaxaca, Mexico. 

Traits Site 1 Site 2
CVGA (%) h2 i h2 f CVGA (%) h2 i h2 f
Height 11.18 0.11 (0.021) 0.3 7.76 0.10 (0.020) 0.27
Diameter 16.11 0.08 (0.018) 0.25 10.11 0.07 (0.017) 0.21
Volume 46.01 0.09 (0.019) 0.26 27.61 0.09 (0.019) 0.25
Branch diameter 11.69 0.06 (0.016) 0.21 10.76 0.09 (0.019) 0.26
Branch angle 6.69 0.14 (0.024) 0.35 3.86 0.07 (0.017) 0.21
Whorls 13.59 0.15 (0.026) 0.37 15.43 0.18 (0.028) 0.39
Conformation 9.91 0.09 (0.019) 0.26 13.45 0.17 (0.027) 0.37
Straightness 1 7.79 0.11 (0.022) 0.31 13.45 0.14 (0.025) 0.34
Straightness 2 4.94 0.05 (0.015) 0.19 4.18 0.06 (0.016) 0.19

Standard error of h2 i in parentheses. Straightness 1 = straightness of the entire stem; Straightness 2 = average of three stem sections.

h2 i and h2 f values in diameter are lower than that in height and volume; this trend has been observed in other studies with values in diameter of 0.10 < h2 i < 0.30 compared to height and volume (0.10 < h2 i < 0.35) in P. pinaster Ait. (Zas-Arregui, Merlo, & Fernández-López, 2004) and in P. patula (Bustillos-Aguirre et al., 2018; Escobar-Sandoval et al., 2018; Salaya-Domínguez et al., 2012). Values of h2 i = 0.28 for diameter and h2 i = 0.25 for height have been reported for P. greggii, but lower than volume (h2 i = 0.32) (Azamar-Oviedo, López-Upton, Vargas-Hernández, & Plancarte-Barrera, 2000). Height and diameter are sensitive to microenvironmental effects and competition between trees (Vargas-Hernández, Adams, & Joyce, 2003).

Conformation and straightness 1 had slightly higher heritability than the more precisely measured traits (diameter and angle of branches and straightness 2) as reported by Haapanen, Velling, and Annala (1997), except for branch angle at site 1. Reports on heritabilities for stem straightness show great variability and differences in both ages and material used, environments and evaluation methods may be influencing such discrepancies (Sierra de Grado et al., 1999; Zas-Arregui et al., 2004).

Bustillos-Aguirre et al. (2018) reported low to moderate genetic control values of 0.00 < h2 i < 0.23 and 0.00 < h2 f < 0.42 for branching traits of P. patula. In the present study, branch angle and diameter showed less genetic control than number of whorls. Low heritabilities in branch diameter agree with the results obtained by Bustillos-Aguirre et al. (2018) for P. patula, and Jansons, Baumanis, and Haapanen (2009) for P. sylvestris L., who indicate that branch size is weakly heritable (reduced additive variance) and is influenced by environmental factors. Branch traits have an important effect on wood quality (Lowell et al., 2014) and evaluation should be incorporated as a long-term selection criterion. For practicality, two branches were evaluated in the whorl closest to the height of the point where stem diameter was measured; however, under open field conditions, high variability was observed within and between whorls, so obtaining data in a single whorl may not be the best indicator of branch quality. In this regard, for the selection it was better to consider the number of whorls, because it has a higher heritability and it is more practical to measure it in the open-field, as recommended by Zas-Arregui et al. (2004).

The number of whorls, which is related to the annual growth pattern of the terminal bud and height growth rate, had a moderate to high genetic control. Jansons et al. (2009) indicate that longer shoot length result in more knot-free wood.

Additive genetic variability (CVGA) was considerable in most variables. In the case of Pinus, CVGA values close to 10 % indicate substantial gains per selection, with values between 5 and 15 % being common for growth variables (Cornelius, 1994). Volume had the highest CVGA values (46 and 27.6 % for sites 1 and 2, respectively). These results were lower than those reported in P. patula by Salaya-Domínguez et al. (2012), where diameter, height and volume reached ranges of 24 to 39 %, 19 to 30 % and 55 to 80 %, respectively.

Phenotypic and genetic correlations between variables

Genetic correlations were high and positive among growth traits ((rg ≥ 0.776, Tables 3 and 4). Based on correlations, it will be appropriate to use diameter as a classification criterion, due to simplicity and precision of its measurement, contrary to what occurs with the height of trees greater than 7 m if branches make it difficult to observe the crown of the tree. High rg values are attributed to the presence of common genes that influence traits and to the effect of linkage between close genes (Falconer, 2017).

Table 3 Genetic (right diagonal, standard error in parentheses) and phenotypic (left diagonal) correlations between growth and stem quality traits at site 1, five years after planting in a progeny trial of Pinus greggii var. australis in the Mixteca Alta of Oaxaca, Mexico. 

Variable Height Diameter Volume BD BA Whorls Conformation Straightness 1 Straightness 2
Height 0.93 (0.02) 0.81 (0.05) 0.85 (0.04) 0.28 (0.12) 0.12 (0.13) 0.06 (0.15) -0.02 (0.14) -0.18 (0.16)
Diameter 0.94 0.99 (0.00) 0.97 (0.01) 0.40 (0.12) 0.12 (0.14) 0.22 (0.15) 0.35 (0.13) -0.17 (0.17)
Volume 0.86 0.89 0.85 (0.05) 0.44 (0.11) 0.43 (0.11) -0.07 (0.15) 0.25 (0.13) -0.33 (0.15)
BD 0.77 0.8 0.65 0.29 (0.14) -0.33 (0.13) 0.61 (0.11) 0.69 (0.08) 0.50 (0.14)
BA 0.24 0.28 0.2 0.19 0.26 (0.11) -0.37 (0.12) 0.45 (0.10) 0.14 (0.14)
Whorls 0.4 0.42 0.35 0.65 0.2 -0.60 (0.09) -0.03 (0.13) -0.55 (0.10)
Conformation -0.37 -0.4 -0.29 -0.25 -0.23 -0.56 0.39 (0.12) 0.97 (0.01)
Straightness 1 -0.12 -0.1 -0.11 -0.02 0.07 -0.19 0.22 0.99 (0.00)
Straightness 2 -0.1 -0.08 -0.09 -0.01 0.04 -0.19 0.29 0.36

BD = branch diameter; BA = branch angle; Straightness 1 = straightness of the entire stem; Straightness 2 = average of three stem sections.

Table 4 Genetic (right of diagonal, standard error in parentheses) and phenotypic (left of diagonal) correlations between growth and quality of stems traits of site 2, after five years of planting in a progeny trial of Pinus greggii var. australis in the Mixteca Alta of Oaxaca, Mexico. 

Variable Height Diameter Volume BD BA Whorls Conformation Straightness _1 Straightness _2
Height 0.77 (0.06) 0.84 (0.04) 0.09 (0.15) 0.03 (0.16) 0.02 (0.13) -0.42 (0.11) -0.34 (0.12) -0.52 (0.12)
Diameter 0.94 0.96 (0.01) 0.37 (0.14) 0.03 (0.17) 0.03 (0.13) -0.30 (0.13) -0.18 (0.14) -0.29 (0.16)
Volume 0.87 0.91 0.29 (0.14) 0.12 (0.15) 0.11 (0.12) -0.36 (0.11) -0.14 (0.13) -0.30 (0.15)
BD 0.69 0.72 0.62 -0.33 (0.14) -0.67 (0.07) 0.50 (0.10) 0.39 (0.11) 0.14 (0.16)
BA 0.08 0.13 0.1 0.004 -0.05 (0.13) 0.32 (0.12) 0.21 (0.13) 0.38 (0.15)
Whorls 0.45 0.43 0.34 0.11 0.05 -0.66 (0.06) -0.45 (0.09) -0.54 (0.10)
Conformation -0.41 -0.4 -0.29 -0.14 -0.08 -0.5 0.62 (0.07) 0.98 (0.01)
Straightness _1 -0.17 -0.15 -0.1 -0.01 0.03 -0.22 0.34 0.71 (0.07)
Straightness _2 -0.15 -0.12 -0.09 0.0002 0.06 -0.23 0.29 0.27

BD = branch diameter; BA = branch angle; Straightness _1 = straightness of the entire stem; Straightness _2 = average of three stem sections.

Genetic correlations between branch diameter and growth traits were high (rg ≥ 0.85) for site 1. When selecting tall trees with larger stem diameter and volume, their progeny will have branches with larger diameters, but only at this site, because at site 2 there is almost no genetic correlation between these variables. For both sites, genetic correlations were positive between conformation and straightness 2 (average of three stem sections); trees with optimal conformation values will also have the best straightness values. Correlations of branch diameter with conformation and straightness of the total stem were positive at a medium level (0.38 < rg < 0.70), so selecting trees with smaller diameters would moderately improve conformation and straightness of the stem (given the scale of measurement).

For site 1, branch angle showed positive, but moderate correlation (0.27 < rg < 0.46), with total stem growth and straightness traits. This means that genotypes with more horizontal branches will be those with higher productivity (Escobar-Sandoval et al., 2018), but will be prone to generate sinuous stems (higher values in straightness scale). On the other hand, the number of whorls was negatively correlated with conformation and straightness of the three stem sections (-0.67 < rg < -0.54), indicating there will be better conformation and straight stems when the number of whorls is higher. Site 2 shows negative genetic correlations among conformation, growth variables and number of whorls (-0.67< rg < -0.29), so that trees with better conformation will generate progenies with higher growth and a greater number of whorls (Bustillos-Aguirre et al., 2018).

Some genetic correlations differed contrastingly between sites 1 and 2; for example, the pairs of traits where rg were positive (high and moderate) for site 1, but negative (moderate) for site 2, were diameter vs. conformation, and angle vs. branch diameter. Correlations between branch diameter vs. stem diameter and height showed positive values for both sites, but with different magnitudes, being high for site 1. On the contrary, branch angle vs. conformation had negative genetic correlation (high) for site 1, while site 2 had positive correlation (moderate or null). Differences between sites may be because the environment influences correlations through different physiological mechanisms (Falconer, 2017), in this case, differences in fertility and soil texture. Genetic correlations depend on gene frequencies, so they also vary by different half-siblings of the same family among sites (Valencia-Manzo & Vargas-Hernández, 2001).

In terms of phenotypic correlation coefficients, high and positive values were found for both sites between growth traits (rp > 0.85), as well as between branch diameter and growth traits (rp > 0.61). In this regard, Vargas-Hernández et al. (2003) indicate that trees with higher productivity are those that regularly have fewer branches with reduced size and long internodes, and higher quality wood.

When selecting genotypes, genetic correlations should be considered, otherwise severe deterioration of growth and stem quality may result as well as subsequent loss of economic gain (Haapanen et al., 1997). Reports on genetic correlations between growth and stem traits show great variability (Zas-Arregui et al., 2004). The results of the present study suggest that, while improving growth will not be detrimental to stem quality, neither will it result in a simultaneous improvement of both.

The families with larger stem volume, which represent 20 %, have an overall average of 1.55 dm3; 1.14 dm3 for site 1 and 1.95 dm3 for site 2. The overall trial average was 1.13 dm3 with an average of 0.80 dm3 for site 1 and 1.46 dm3 for site 2. This is a five-year selection differential in volume of 0.42 dm3 in a general average and 0.34 dm3 and 0.49 dm3 for site 1 and 2, respectively. Although the selection of the best individuals within families has yet to be considered, it is possible to understand the progress that can be achieved in a first breeding cycle, and based on genetic correlations observed, to make gains for most of the variables evaluated. Usually, selection in fast-growing pines has been carried out up to the age of eight years (Dvorak et al., 2000). At this age it is possible to find the best parents by selecting the best families and the best individuals of these, and to take advantage of the precocity of the species for seed collection to have more productive trees for the area.

Conclusions

Open-pollinated families of Pinus greggii var. australis evaluated in San Miguel Achiutla, Oaxaca, were different in growth, branch quality, conformation and straightness after five years of planting. The highest values of averages and heritabilities were recorded in the site with higher fertility and more clay in the soil. Between sites, differences in genetic correlations between variables reflected the possible interaction of genotypes with soil traits. Volume stood out for obtaining greater genetic correlation with other traits, greater genetic variation and heritability, which could be used as a selection criterion in the breeding cycle. Heterogeneous site conditions and early evaluation reduce the estimated values of heritabilities, so evaluations should continue to optimize genetic gains.

Acknowledgments

The authors thank CONACyT for the funding support granted to the first author. To CONAFOR for supporting the project "Establishment of two progeny trials of Pinus greggii Engelm. var. australis Donahue & López". The authors also thank the community of San Miguel Achiutla, Oaxaca and Ing. Aarón Ruiz Pérez for the technical support provided.

References

Alcántar, G., & Sandoval, M. (1999). Manual de análisis químico de tejido vegetal. Chapingo, México: Sociedad Mexicana de las Ciencias del Suelo. [ Links ]

Azamar-Oviedo, M., López-Upton, J., Vargas-Hernández, J. J., & Plancarte-Barrera, A. (2000). Evaluación de un ensayo de procedencias-progenie de Pinus greggii y su conversión a huerto semillero. Retrieved from https://www.researchgate.net/profile/Javier_Upton/publication/288834300_Evaluacion_de_un_ensayo_de_procedenciasprogenies_de_Pinus_greggii_y_su_conversion_a_huerto_semillero/links/5685ccc308ae1e63f1f36f41/Evaluacion-de-un-ensayo-de-procedencias-progenies-de-Pinus-greggii-y-su-conversion-a-huerto-semillero.pdfLinks ]

Bustillos-Aguirre, C. V., Vargas-Hernández, J. J., López-Upton, J., & Ramírez-Valverde, G. (2018). Repetibilidad de parámetros genéticos de las características de ramificación en progenies de Pinus patula. Madera y Bosques, 24(1), e2411131. doi: 10.21829/myb.2018.2411131 [ Links ]

Cornelius, J. P. (1994). Heritabilities and additive genetic coefficients of variation in forest trees. Canadian Journal of Forest Research, 24(2), 372‒379. doi: 10.1139/x94-050 [ Links ]

Domínguez-Calleros, P. A., Rodríguez-Laguna, R., Capulín-Grande, J., Razo-Zárate, R., & Díaz-Vásquez, M. A. (2017). Influencia de factores edáficos en el crecimiento de una plantación de Pinus greggii Engelm. en Santiago de Anaya, Hidalgo, México. Madera y Bosques, 23(2), 145‒154. doi: 10.21829/myb.2017.2321522 [ Links ]

Dvorak, W. S., Kietzka, J. E., Donahue, J. K., Hodge, G. R., & Stanger, T. K. (2000). Pinus greggii. In CAMCORE Cooperative (Ed.), Conservation and testing of tropical and subtropical forest tree species (pp. 52‒73). Raleigh, NC, USA: College of Natural Resources, North Caroline State University. [ Links ]

Escobar-Sandoval, M. C., Vargas-Hernández, J. J., López-Upton, J., Espinosa-Zaragoza, S., & Borja de la Rosa, A. (2018). Parámetros genéticos de calidad de madera, crecimiento y ramificación en Pinus patula. Madera y Bosques, 24(2), e2421595. doi: 10.21829/myb.2018.2421595 [ Links ]

Falconer, D. S. (2017). Introduction to quantitative genetics. San Francisco, CA, USA: Forgotten Books. [ Links ]

Gutiérrez-Vázquez, B. N., Cornejo-Oviedo, E. H., Zermeño-González, A., Valencia-Manzo, S., & Mendoza-Villarreal, R. (2010). Conversión de un ensayo de progenies de Pinus greggii var. greggii a huerto semillero mediante eigen-análisis. Bosque, 31(1), 45‒52. doi: 10.4067/S0717-92002010000100006 [ Links ]

Haapanen, M., Veiling, P., & Annala, M. L. (1997). Progeny trial estimates of genetic parameters for growth and quality traits in Scots pine. Silva Fennica, 31(1), 3‒12. Retrieved from http://hdl.handle.net/1975/8506Links ]

Instituto Nacional de Estadística y Geografía (INEGI). (2005). Prontuario de información geográfica municipal de los Estados Unidos Mexicanos, San Miguel Achiutla, Oaxaca. Clave geoestadística 20258. México: Author. [ Links ]

Jansons, A., Baumanis, I., & Haapanen, M. (2009). Branch traits as selection criteria in Scots pine breeding in Latvia. Latvijas Lauksaimniecības Universitāte Rakski, 23(318), 45‒56. Retrieved from https://llufb.llu.lv/proceedings/n23/5/LLU-raksti-nr23-45-56.pdfLinks ]

Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., & Schabenberger, O. (2006). SAS ® for mixed models (2nd. ed.). Cary, NC, USA: Author. doi: 10.1111/j.1541-0420.2006.00596_6.x [ Links ]

Lowell, E. C., Maguire, D. A., Briggs, D. G., Turnblom, E. C., Jayawickrama, K. J. S., & Bryce, J. (2014). Effects of silviculture and genetics on branch/knot attributes of Coastal Pacific Northwest Douglas-fir and implications for wood quality-A synthesis. Forest, 5(7), 1717‒1736. doi: 10.3390/f5071717 [ Links ]

Martínez, J. (2006). Manejo del agua y restauración productiva en la región indígena mixteca de Puebla y Oaxaca. Resultados de los estudios y recomendaciones para los tomadores de decisiones de las comunidades y Organizaciones de la Sociedad Civil. Retrieved from http://centro.paot.org.mx/documentos/bm/man_agua_res_pro.pdfLinks ]

Martínez-Trinidad, T., Vargas-Hernández, J. J., Muñoz-Orozco, A., & López-Upton, J. (2002). Respuesta al déficit hídrico en Pinus leiophylla: consumo de agua y crecimiento en plántulas de diferentes poblaciones. Agrociencia, 36(3), 365‒376. Retrieved from https://www.colpos.mx/agrocien/Bimestral/2002/may-jun/art-10.pdfLinks ]

Mora, F., & Zamudio, F. (2006). Variabilidad genética del crecimiento en progenies selectas de Pinus radiata. Ciência Florestal, 16(4), 399‒405. doi: 10.5902/198050981921 [ Links ]

Morales-González, E., López-Upton, J., Vargas-Hernández, J. J., Ramírez-Herrera, C., & Gil-Muñoz, A. (2013). Parámetros genéticos de Pinus patula en un ensayo de progenies establecido en dos altitudes. Revista Fitotecnia Mexicana, 36(2), 155‒162. doi: 10.35196/rfm.2013.2.155 [ Links ]

Reyes de la Barra, J., Ponce-Donoso, M., Vallejo-Barra, O., Daniluk-Mosquera, G., & Coelho-Duarte, A. P. (2018). Comparación de cuatro métodos de evaluación visual del riesgo de árboles urbanos. Colombia Forestal, 21(2), 161‒173. doi: 10.14483/2256201X.12604 [ Links ]

Rice, J. A. (1988). Mathematical statistics and data analysis. Pennsylvania, USA: Wadsworth and Brooks/Cole Statistics/Probability Series. [ Links ]

Salaya-Domínguez, J. M., López-Upton, J., & Vargas-Hernández, J. J. (2012). Variación genética y ambiental en dos ensayos de progenies de Pinus patula. Agrociencia, 46(5), 519‒534. Retrieved from https://agrociencia-colpos.mx/index.php/agrociencia/article/view/973Links ]

Sánchez-Vargas, N. M., Cambrón-Sandoval, V. H., Sáenz-Romero, C., & Vargas-Hernández, J. J. (2014). Parámetros genéticos del crecimiento temprano de familias de medios hermanos de Pinus pseudostrobus Lindl. en Michoacán, México. Revista Forestal Venezolana, 58, 65‒76. Retrieved from https://www.researchgate.net/publication/323432143_PARAMETROS_GENETICOS_DEL_CRECIMIENTO_TEMPRANO_DE_FAMILIAS_DE_MEDIOS_HERMANOS_DE_PINUS_PSEUDOSTROBUS_LINDL_EN_MICHOACAN_MEXICOLinks ]

Sierra de Grado, R., Diez-Barra, R., & Alia-Miranda, R. (1999). Evaluación de la rectitud del fuste en seis procedencias de Pinus pinaster Ait. Investigación Agraria. Sistemas y Recursos Forestales, 8(2), 264‒278. Retrieved from https://www.researchgate.net/publication/28052601_Evaluacion_de_la_rectitud_del_fuste_en_seis_procedencias_de_Pinus_pinaster_AitLinks ]

Valencia-Manzo, S., & Vargas-Hernández, J. J. (2001). Correlaciones genéticas y selección simultánea del crecimiento y densidad de la madera en Pinus patula. Agrociencia, 35(1), 109‒120. Retrieved from https://agrociencia-colpos.mx/index.php/agrociencia/article/view/95Links ]

Valencia-Manzo, S., Velasco-García, M. V., Gómez-Cárdenas, M., Ruiz-Muñoz, M., & Capó-Arteaga, M. A. (2006). Ensayo de procedencias de Pinus greggii Engelm. en dos localidades de la Mixteca Alta de Oaxaca, México. Revista Fitotecnia Mexicana 29(1), 27‒32. Retrieved from http://revistafitotecniamexicana.org/documentos/29-1/4a.pdfLinks ]

Vargas-Hernández, J. J., Adams, W. T., & Joyce, D. G. (2003). Quantitative genetic structure of stem form and branching traits in Douglas-fir seedlings and implications for early selection. Silvae Genetica, 52(1), 36‒44. Retrieved from https://www.thuenen.de/media/institute/fg/PDF/Silvae_Genetica/2003/Vol._52_Heft_1/52_1_36.pdfLinks ]

Vásquez-García, I., Cetina-Alcalá, V. M., Campos-Bolaños, R., & Casal-Ángeles, L. F. (2016). Evaluación de plantaciones forestales en tres comunidades de la Mixteca Alta Oaxaqueña. Agroproductividad, 9(2), 12‒19. Retrieved from http://revista-agroproductividad.org/index.php/agroproductividad/article/view/716Links ]

Velasco-Velasco, V. A., Enríquez del Valle, J. R., Rodríguez-Ortiz, G., Campos-Ángeles, G. V., Gómez-Cárdenas, M., & García-García, M. L. (2012). Evaluación de procedencias de Pinus greggii Engelm. en dos plantaciones en la Mixteca Oaxaqueña. Revista Mexicana de Ciencias Forestales, 3(9), 41‒50. Retrieved from http://cienciasforestales.inifap.gob.mx/index.php/forestales/article/view/534Links ]

White, T. L., Adams, W. T., & Neale, D. B. (2007). Forest genetics. Cambridge, USA: CABI Publishing. [ Links ]

Zas-Arregui, R., Merlo, E., & Fernández-López, J. (2004). Genetic parameter estimates for maritime pine in the Atlantic coast of north-west Spain. Forest Genetics, 11(1), 45‒53. Retrieved from http://hdl.handle.net/10261/101373Links ]

Received: October 30, 2020; Accepted: October 22, 2021

*Corresponding author: jlopezupton@gmail.com; tel.: +52 595 952 0200 ext. 1463.

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License