ISSN e: 2007-4018 / ISSN print: 2007-4018

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Vol. XXII, issue 3 September - December 2016

ISSN: ppub: 2007-3828 epub: 2007-4018

Review article

Forest biometric models in Hidalgo, Mexico: state of the art

http://dx.doi.org/10.5154/r.rchscfa.2015.09.043

Vásquez-Bautista, Nehemías 1 ; Zamudio-Sánchez, Francisco J. 1 * ; Alvarado-Segura, Arturo A. 1 2 ; Romo-Lozano, José L. 1

  • 1Universidad Autónoma Chapingo, División de Ciencias Forestales. Carretera México-Texcoco km 38.5. C. P. 56230. Chapingo, Texcoco, Estado de México.
  • 2Instituto Tecnológico Superior del Sur del Estado de Yucatán. C. P. 97880. Oxkutzcab, Yucatán, México.

Corresponding author. fzamudios@taurus.chapingo.mx

Received: September 30, 2015; Accepted: June 26, 2016

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

Abstract

Historically, logging has been the main reason for encouraging forest research. Since 2000, searching information about carbon capture and content has increased through the use of biometric models and remote sensing technology. The aim of this paper was to compile, systematize, and analyze scientific and technological reports related to biometric models that have been used in forest management in a region of central Mexico (Hidalgo). A total of 32 research studies were published from 1976 to 2015 reporting 289 models. These researches emphasize the use of growth, volume, biomass, carbon, site index, density, and mortality models. The growth models have been the most studied models while biomass and carbon models have consistently increased since 2007. Pinus has been the most studied genus, but research on Quercus was practically not found. Five species do not have fitted models, despite their economic importance: Pinus leiophylla, P. michoacana, P. oocarpa, Cupressus lindleyi, and Arbutus xalapensis. The reliability of all published models is based in statistical criteria, but it has not been reported if they have satisfied final user’s demand.

Keywords:Volume; logging; biomass; carbon.

Introduction

Forest Biometrics refers to the use of statistical and mathematical modeling in the evaluation and analysis of forest resources (Gregoire & Köhl, 2001; Salas & Real 2013). Growth and site index models and those used for estimation of volume, biomass and carbon content are part of forest biometrics. The information generated from biometric models is of great importance in forest management; however, its application is based on quantitative and qualitative verifications and validations of the model behavior, which characterizes its complexity (Salas & Real, 2013).

The first biometric model was proposed by Cotta in 1804 (Spurr, 1952). Since then, models have emerged for the various existing weather conditions, slope, exposure or soil types. These models have been adapted with the addition of new parameters to describe and explain the factors influencing the biological behavior of trees, which has allowed us to develop and validate models per species, for regional and local uses (Corral, Barrio, Aguirre, & Diéguez, 2007; Shao & Reynolds, 2006).

The state of the art in biometric models can measure the impact they have had and the distribution of its use; also describes how the issue has been addressed, the degree of advancement of knowledge and their tendencies (Londoño, Maldonado, & Calderón, 2014). On forest biometric models, several authors agree on the widespread use of growth models, the tendency to the integration of simulators from already created models and the growing interest in models of biomass and carbon content by fitting allometric equations (Cheng, Gamarra, & Birigazzi, 2014; Fernández, 2005; Hong-gang, Jian-guo, Ai-oguo, & Cai-yun, 2007; Porté & Bartelink, 2002; Vacchiano, Magnani & Collati, 2012). Others authors such as Landsberg (2003), Mäkelä et al. (2000) and Peng (2000) have presented the state of the art of forest modeling to a wider scale. These authors note that process-based models should be combined with static (volume, height-diameter) and dynamic (growth) models; identify the needs of users; and continue research on the behavior of processes of carbon, nutrients and its consumption.

In Mexico, forest growth modeling has been done since the 1970s (Garzón & Flores, 1977; Ramírez & Musalem, 1977). However, it is necessary to update, validate and calibrate existing biometric systems, otherwise considerable volumes of wood could be underestimated or overestimated and to schedule cutting intensities outside the range of forestry potential of a site (Comisión Nacional Forestal [CONAFOR], 2014). In the state of Hidalgo, the forest area (temperate forests, rainforest, arid areas and disturbed vegetation) covers approximately 51 % of the state territory (20,813 km2); the wooded area covers 403,685 ha, of which 57 % are temperate forests and the remaining percentage corresponds to rainforests (Instituto Nacional de Estadística y Geografía [INEGI], 2013, 2015). From this wooded area, on average, 123,592 m3 of roundwood is extracted (Secretaría de Medio Ambiente y Recursos Naturales [SEMARNAT], 2010, 2011, 2012, 2013, 2014), which represents approximately 2 % national. The harvesting method used in Hidalgo has been the Silvicultural Development Method (Castelán-Lorenzo & Arteaga-Martínez, 2009), Pinus and Quercus are the most harvested in order of importance (SEMARNAT, 2014). The state has 36 protected natural areas covering 139,357 ha and account for nearly 7 % of the state territory, (Consejo Nacional de Ciencia y Tecnología [CONACYT], 2015).

The aim of this study was to collect, organize, analyze and synthesize research papers, dissemination documents and publications related to biometric models used for forest management in Hidalgo, Mexico. With the above, it is intended to present the current state of forestry research and show the tendencies in the study area.

Materials and methods

The analysis focuses on biometric models developed in the state of Hidalgo, located between 21° 24’ - 19° 36’ N and 97° 58’ - 99° 53’ W. The state of Hidalgo borders the states of Mexico, Puebla, Querétaro, San Luis Potosí, Tlaxcala and Veracruz (INEGI, 2013). The state of Hidalgo is listed as a state with low-timber production (SEMARNAT, 2013); Pinus and Quercus provide greater volume to the state timber production with 70 and 23 %, respectively. The logging percentage of the state for Pinus coincides with the national percentage (70 %), but in the case of Quercus is two times higher than the 10 % national (SEMARNAT, 2010, 2011, 2012, 2013). Figure 1 shows the main types of vegetation in the state of Hidalgo.

Figure 1. Type of vegetation in the state of Hidalgo, Mexico. Most models (92 %) were developed in uneven-aged forests of Pinus, Quercus, Pinus-Quercus and Abies, which together represent 10 % of the state territory. The remaining models (8 %) were developed in rainforest, scrubland and mountain cloud forest occupying 9 % of the territory. Agricultural land and secondary vegetation cover 43 % and 23 %, respectively (INEGI, 2015).

The state of the art was constructed by a review in thesis, journals, brochures and technical reports on aspects related to biometric systems for economically important forest species in Hidalgo. The search for information was made in libraries of academic and research institutions related to forestry, through site visits (Table 1). Also, digital libraries (Table 1) and scientific journals (Table 2) were consulted online.

Table 1. Institutional libraries consulted as a source of information for analyzing the development of forest biometric models in Hidalgo, Mexico.

Institution State Type of query
Universidad Nacional Autónoma de México (UNAM) Ciudad de México Site visit Presencial
Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP) Ciudad de México Site visit
Universidad Autónoma Metropolitana (UAM) Estado de México Site visit Presencial
Universidad Autónoma Chapingo (UACh) Estado de México Site visit Presencial
Colegio de Postgraduados (ColPos) Estado de México Site visit Presencial
Tecnológico de Estudios Superiores de Valle de Bravo Estado de México Site visit

Table 2. Scientific journals consulted as a source of information for analyzing the development of forest biometric models in Hidalgo, Mexico.

Journal Institution concerned Type of query
Revista Bosque Universidad Austral de Chile Online
Interciencia Asociación Interciencia, Venezuela Online
UNASYLVA FAO Online
Revista Mexicana de Ciencias Forestales Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias Site visit Presencial
Revista Chapingo Serie Ciencias Forestales y del Ambiente Universidad Autónoma Chapingo Site visit Presencial
Botanical Sciences Sociedad Botánica de México Online
Madera y Bosques Instituto de Ecología Online
Revista Fitotecnia Mexicana Sociedad Mexicana de Fitogenética Online
Terra Latinoamericana Sociedad Mexicana de la Ciencia del Suelo Online
Agrociencia Colegio de Postgraduados Site visit Presencial

The information was collected using the technique of “snowball”, where documents through their literature suggested other documents with the same topic that have been developed in the study area. The information was classified into seven groups of models according to their use: volume and taper equations (static models); site index (productivity indicator); biomass, carbon and growth estimation (dynamic models); and density and mortality (description of the stand). The literature cited in each of the collected documents was also collected to know the sources of information that support them. References were grouped according to the type of source (articles, reports, books and theses) and language of publication.

Results and discussion

Institutions, information sources and species studied

Institutions and information sources. A total of 32 research documents (Appendix 1) were found in two of the five Forest Management Units (UMAFOR) in the state of Hidalgo: 52 % at the UMAFOR 1302 Zacualtipán-Molango (Table 3) and 43 % at the UMAFOR 1303 Pachuca-Tulancingo (Table 4). The forest inventory (Secretaría de Agricultura y Ganadería [SAG], 1976) is the only study carried out at state level (Table 5). Most research papers, 27 in total, were published in the last eight years (2007-2015). In 2013, the year with the highest number of publications, seven researches were found.

Table 3. Research documents in the area of forest management generated in the UMAFOR 1302 Zacualtipán-Molango, Hidalgo, Mexico.

Authors* Area of influence Equations Type of model Number of samples Species studied Type of pulbication
Brosovich (1998) Zacualtipán de Ángeles 10 Density, site index and volume 52 (D) Pinus patula Thesis
Tenorio (2003) Estatal 2 Volume 101 (D) Pinus patula Thesis
Carrillo, Acosta, y Tenorio (2004) Estatal 1 Volume 101 (D) Pinus patula Brochure
Cruz (2007) Zacualtipán de Ángeles 13 Biomass, volume 62 (D) Pinus patula, Pinus teocote y latifoliadas Thesis
Aguirre et al. (2008) Zacualtipán de Ángeles 1 Cabon 75 (ND)** Pinus patula Article
Santiago (2009) Zacualtipán de Ángeles 23 Growth, density, site index, mortality and volume 84 (ND) Pinus patula Thesis
Cruz, Valdez, Ángeles, y De los Santos (2010) Zacualtipán de Ángeles 4 Volume 114 (ND)** Pinus patula and Pinus teocote Article
Figueroa (2010) Zacualtipán de Ángeles 9 Biomass 18 (D) Alnus spp., Clethra sp., Pinus patula and Quercus spp. Thesis
Olvera (2010) Barranca de Metztitlán 4 Volume 87 (D) Pinus greggii Thesis
Acosta, Carrillo, y Gómez (2011) Zacualtipán de Ángeles 4 Biomasa y carbono 40 (D) Alnus acuminata and Clethra mexicana Article
Vásquez (2011) Zacualtipán de Ángeles 5 Carbon 18 (D) Pinus patula Thesis
Hernández (2012) Zacualtipán de Ángeles 12 Volume 78 (D) Pinus patula Thesis
Muñoz et al. (2012) Barranca de Metztitlán 4 Volume 87 (D) Pinus greggii Article
Santiago (2013) Zacualtipán de Ángeles 1 Volume 42 (ND)** Pinus patula Thesis
Soriano, Ángeles, Martínez, Plascencia, y Razo (2013) Zacualtipán de Ángeles 3 Biomass 25 (D) Latifoliadas and Pinus patula Chapter
González (2014) UMAFOR 1302 Zacualtipán - Molango 16 Site index and Volume 159 (D) Pinus patula and Pinus teocote Report
Soriano (2014) Zacualtipán de Ángeles 12 Biomass and volume 71 (D) Pinus patula, Liquidambar macrophylla, Quercus spp., Alnus jorullensis, Cletra mexicana, Prunus serotina, Carpinus caroliniana and Virburum ciliatum Thesis
D: Destructive; ND: Non destructive. *Full references in Appendix 1. **Sampling site.

Table 4. Research documents in the area of forest management generated in the UMAFOR 1303 Pachuca-Tulancingo, Hidalgo, Mexico.

Authors* Area of influence Equations Type of model Number of samples Species studied Type of pulbication
Rodríguez (2000) Acaxochitlán 8 Growth 12 (D) Pinus patula Thesis
Pacheco et al. (2007) Cuaunepantla y Acaxochitlán 2 Biomass and Carbon 20 (D) Pinus greggii Article
Acosta and Carrillo (2008) UMAFOR 1303, Pachuca-Tulancingo 2 Volume 43 (D) Pinus montezumae Brochure
Rodríguez (2009) Singuilucan, Zempoala, Tepeapulco y Cuautepec de Hinojosa 2 Density 122 (ND) Pinus montezumae Brochure
Hernández (2012) Sureste de Hidalgo, Singuilucan 1 Growth 36 (D) Pinus montezumae Thesis
Velarde (2012) UMAFOR 1303 Pachuca-Tulancingo 106 Growth and Volume 185 (D) Pinus montezumae y Pinus patula Report Informe
González (2013) Mineral del Monte 2 Biomass and Volume 4 (D) Pinus patula Thesis
Hernández et al. (2013) UMAFOR 1303, Pachuca-Tulancingo 2 Density 131 (ND) Pinus teocote Article
Razo, Gordillo, Rodríguez, Maycotte, y Acevedo (2013) Parque Nacional El Chico 2 Biomass and Carbon 5 (ND) Abies religiosa Article
Rodríguez and Calva (2013) Parque Nacional El Chico 2 Biomass and Carbon 250 (ND) Abies religiosa Chapter
Rodríguez (2013) Sierra de Pachuca 12 Biomass Carbon and Growth 250 (ND) Abies religiosa Thesis
Hernández et al. (2014) Metztitlán 3 Site index 25 (D) Pinus greggii Article
Velarde (2014) UMAFOR 1303 Pachuca-Tulancingo 8 Site index and Volume 120 (D) Pinus rudis y P. teocote Report
Hernández et al. (2015) Acaxochitlán, Cuautepec de Hinojosa, Singuilucan y Tulancingo de Bravo 1 Site index 345 (ND) Pinus teocote Article
D: Destructive; ND: Non destructive. *Full references in Appendix 1.

Author Area of influence Equations Type of Model Number of samples Species studied Type of publication
Secretaría de agricultura y Ganadería (SAG, 1976) State level 12 Volume 899 (D) Alnus sp., Quercus sp., Cedrela odorata, Inga spuria, Cupania dentata, Bursera simaruba, Juniperus flaccida, Pinus cembroides, Pinus patula, Pinus ayacahuite, Pinus teocote, Pinus greggii, Pinus pseudostrobus, Platanus sp., Liquidambar styraciflua, Psidum guajava and Dendropanax arborea Brochure
D: Destructive; ND: Non destructive. *Full references in Appendix 1.

The institutions that have generated the greatest number of theses (bachelor, master and PhD) are the Colegio de Postgraduados (ColPos) and the Universidad Autónoma Chapingo (UACh) with six and four theses, respectively. The Universidad Nacional Autónoma de México (UNAM) contributed with two theses. The Universidad Michoacana de San Nicolás de Hidalgo (UMSNH) and the Universidad Agraria Autónoma Antonio Narro (UAAAN) had. The fact that the ColPos and the UACh have generated greater quantity of theses, it is due to the age of their academic programs, because the UACh started the bachelor’s programs in 1933 and the master’s program in 1986, while the ColPos created the forestry postgraduate program in 1976 (Caballero, 2004).

All articles analyzed were published in Mexican journals. The brochures have been created by government institutions (Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias [INIFAP] and State of Hidalgo Government), aimed at forest service providers. Technical reports, in turn, have been created by firms backed by a renowned researcher and under CONAFOR funding. This scenario shows that the information is only generated and disseminated locally, in Spanish language and to a reduced scientific community sector. Thus, it is important to promote institutional strategies so that future documents will be published in journals, because journals have greater spread spectrum. The results of the research should be aimed at finding general principles that rule and describe the processes occurring in forest areas.

Species studied. The most studied species belong to the Pinus genus, whose importance based on the number of studies that used it as an object of study are: P. patula Schltdl. & Cham. (17), P. teocote Schltdl. & Cham. (7), P. greggii Engelm. ex Parl. (5), P. montezumae Lamb. (5), P. cembroides Gordon (1), P. ayacahuite C. Ehrenb. ex Schltdl. (1), P. pseudostrobus Lindl. (1) and P. rudis Endl. (1). Other species such as Abies religiosa (Kunth) Schltdl. & Cham. (3), Alnus sp. (4), Clethra sp. (3), Quercus sp. (3), Cedrela odorata L. (1), Inga spuria Humb.

& Bonpl. ex Willd. (1), Cupania dentata Moc. et Sessé ex D.C. (1), Bursera simaruba (L.) Sarg. (1), Juniperus flaccida Schltdl. (1), Platanus sp. (1), Liquidambar styraciflua L.

(1), Psidium guajava L. (1) and Dendropanax arboreus (L.) Decne. & Planch. (1) are less frequent (Tables 3, 4 and 5). The species P. patula, besides being the most studied, it is reported since 1976.

Current state of biometric models in the study area

Distribution of models per species. Figure 2 outlines the importance of the species studied and types of biometric models developed in the state of Hidalgo. A total of 289 models were found, which are distributed among the genera Pinus, Abies, Quercus and other broadleaf trees. Pinus concentrated 86 % of the fitted equations (249) distributed in the following species: 148 in P. patula, 58 in P. montezumae, 23 in P. teocote, 13 in P. greggii, four in P. rudis and one in P. cembroides. Meanwhile, A. religiosa concentrated 5 % (16) and Quercus only 1 % (3); the remaining 8 % of equations (22) distributed in 18 species.

Figure 2. Importance of biometric models and forest species studied in the state of Hidalgo. The most important genera by the number of studies carried out are Pinus, Abies and Quercus. The rest of the studies (8 %) is distributed in 18 species. Most models are focused on logging (growth: 116, volume: 82, site index: 23, density: 7, mortality: 1) and a few others on models of biomass (44) and estimation of carbon content (16). The number of models reported in the research work is shown in the middle of the figure, volume and growth models are most relevant.

The economic importance of some species from the genera Pinus and Abies in the study area coincides with the number of studies carried out. On the other hand, the genus Quercus has been little studied despite the exploited wood volume, perhaps because of the difficulty of their taxonomic identification, high morphological variability (Bárcenas, 2011) and the ability to form hybrids (Zúñiga, Sánchez-González, & Granados, 2009). Moreover, there are other species of Pinus, conifers and broadleaf trees that despite of being exploited, are not reported in research studies (Pinus leiophylla Schiede ex Schltdl. & Cham., P. michoacana Martínez, P. oocarpa Schiede ex Schltdl., Cupressus lindleyi Klotzsch ex Endl. and Arbutus xalapensis Kunth), so it is suggested to extend the base of models for these forest species.

No models developed for the mixed pine-oak or oak-pine forests were reported, which together occupy 17 % of the state wooded area (INEGI, 2015; Figure 1). However, the forest inventory of the state of Hidalgo (SAG, 1976) contains volume equations for two pine species groups: for the group of P. montezumae, P. patula and P. ayacahuite, and for the group P. teocote, P. greggii and P. pseudostrobus. Since models were fitted for mixed forests, it is necessary to validate if they make good estimates for monospecific masses of the corresponding species. Models for pine-oak forest start to develop for other Mexican forests, using the diameter growth dynamics (Návar, 2014).

With regard to increment and yield models, it is recommended to develop at individual tree level, diameter classes, groups of species or stand-level to meet different purposes as obtaining roundwood or logs used for cellulose or poles; individual tree models are important especially in processes of validation of models used in an area (Návar-Chaidez & Domínguez-Calleros, 2013).

Classification of models. Of all models, forest management studies are those that have received greater attention: 116 growth, 82 of volume, 23 of site index, seven of density and one of mortality models. Volume models were the most reported in research studies. The studies’ approach shows two major groups, on the one hand, the logging and on the other hand, the environmental services (estimation of biomass and carbon) (Figure 2). From 2007, models of biomass (44) and carbon estimation (16) have become more frequent; also from the same year, the genus Pinus was incorporated to the studies on environmental services in the study area. Under this approach, A. religiosa. and broadleaved trees are the most frequent species.

Most models use mainly diameter at breast height (d) and total height (h) as input variables. The models have been fitted with data from established silvicultural sites, so the use of national forest inventory sites as permanent sample plots is proposed to understand the behavior of forests.

Most used models. Table 6 shows the forest biometric models used in the state of Hidalgo. The most commonly used model is the Schumacher’s model for growth curves and site index (Schumacher, 1939) and that of Schumacher and Hall to estimate volume (Schumacher & Hall, 1933). Given the large number of models fitted so far, it is suggested to create growth simulators (Santiago-García, de los Santos-Posadas, Ángeles-Pérez, Valdéz-Lazalde, & Ramírez-Valverde, 2013), that bring together mathematical models in a program to predict and calculate different growth scenarios (Salas & Real, 2013; Santiago-García et al., 2013). It is appropriate to verify, validate and update existing models to assess whether they are valid and can spread to other areas with similar conditions.

Table 6. Most used forest biometric models for estimating variables in the state of Hidalgo, Mexico.

B: biomass, C: carbon, d: diameter at breast height, Dc: crown diameter, A: age, h: height, SI: site index, N: number of trees, V: volume; β0, β1, β2, β3: regression parameters.

On the other hand, it is highly recommended the use of models for sustainable management of forest communities of Hidalgo, especially those focused on forest protection. Some studies with this approach have been developed in Durango, the state with greater timber production in Mexico (SEMARNAT, 2013), where fire behavior and magnitude have been studied regarding the anthropogenic factor, the ecological role of forest fires, climatic and soil variables, socioeconomic conditions of the area, population density and access roads (Návar-Chaidez, 2011; Pérez-Verdín, Márquez-Linares, Cortés-Ortiz, & Salmerón-Macías, 2013; Rodríguez-Trejo & Fulé, 2003).

Validation and model selection. Different authors used criteria such as the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation, number of parameters of the equation and number of variables to validate and select a model. The parsimony criterion has been included in recent studies (Akaike Information Criterion [AIC], Bayesian information criterion [BIC] and Schwarz selection criteria). Graphical adjustment and of the model were used as secondary selection criterion. No values ​of R2, RMSE, range in diameter and height range were reported in many of the models fitted in the study area, information that would facilitate the subsequent verification and validation of the models.

Sample size. Sample sizes used in fitting models vary according to the purpose of the study. Logging modeling was carried out with larger destructive samples compared to the modeling of biomass and carbon content (Tables 3, 4 and 5).

Analysis of the documentary references

In the 32 studies reviewed (Appendix 1), a total of 1,547 cited references were found, which corresponded to 1,022 documents. This means that about 34 % of the references were cited in two or more articles.

Regarding the origin of documentary references, 19.2 % comes from the main forestry journals with an impact factor the JCR (Journal Citation Report): Forest Ecology and Management (7.4 %), Forest Science (3.8 %), Agrociencia (2.7 %), Canadian Journal of Forest Research (2.7 %) and Madera y Bosques (2.6 %). About 10.2 % of the references comes from UACh and ColPos (6.8 % and 3.4 %, respectively). Approximately 7.2 % of the sources comes from sourcebooks. Another 3.4 % came from conference proceedings or union meetings. The remaining information (60 %) derived from articles published in journals of lower impact, brochures, technical reports, theses in other institutions and unpublished documents. According to the language of publication, 56.7 % of the documents are in Spanish (580), 42.4 % in English (434 documents) and less than 1 % in German (5) and Portuguese (3).

The 10 most frequent citations reported within the 32 studies reviewed are: Clutter, Fortson, Pienaar, Brister, and Bailey (1983) in 13 articles; Romahn de la Vega, Ramírez, and Treviño (1994) in nine; Spurr (1952) and Figueroa (2010) in eight; Acosta Vargas, Velázquez, and Etchevers (2002), Aguirre et al. (2008), Caballero (1972), Díaz et al. (2007), Perry (1991) and Torres and Magaña (2001), in seven each. References come from a small number of institutions and authors that cited each other, so the link with academic groups at national and international level as well as networks of scientists related to the forestry area should be searched. The interaction between different disciplines related to the forestry sector favor the increase of information with inter and multidisciplinary approach, which is of vital importance in modern science (Borut, Levnajic, Povh, & Perc, 2014).

Conclusions

Biometric models in Hidalgo, Mexico, have been fitted mainly for the genus Pinus. It is proposed to expand the base of models for other economically important genera such as Abies, Quercus, Arbutus and Cupressus, and verify, validate and update existing models. Reported models based their reliability by the coefficient of determination (R2) but more studies using selection criteria with biological, economic and management significance are needed. In the studies reviewed, it is not mentioned if the fitted models are valid or have managed to meet user demand. Studies are restricted to local use and have been carried out by a small group of authors. It is recommended that forestry research will focus on identifying general principles that describe the factors underlying processes inherent to the forest, and have importance for forest management. In practical terms, the focus must be that volume models reduce economic losses as a result of underestimation or overestimation. Finally, it is suggested to integrate a state or regional forestry information system.

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Figures:

Figure 1. Type of vegetation in the state of Hidalgo, Mexico. Most models (92 %) were developed in uneven-aged forests of Pinus, Quercus, Pinus-Quercus and Abies, which together represent 10 % of the state territory. The remaining models (8 %) were developed in rainforest, scrubland and mountain cloud forest occupying 9 % of the territory. Agricultural land and secondary vegetation cover 43 % and 23 %, respectively (INEGI, 2015).
Figure 2. Importance of biometric models and forest species studied in the state of Hidalgo. The most important genera by the number of studies carried out are Pinus, Abies and Quercus. The rest of the studies (8 %) is distributed in 18 species. Most models are focused on logging (growth: 116, volume: 82, site index: 23, density: 7, mortality: 1) and a few others on models of biomass (44) and estimation of carbon content (16). The number of models reported in the research work is shown in the middle of the figure, volume and growth models are most relevant.

Tables:

Table 1. Institutional libraries consulted as a source of information for analyzing the development of forest biometric models in Hidalgo, Mexico.
Institution State Type of query
Universidad Nacional Autónoma de México (UNAM) Ciudad de México Site visit Presencial
Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP) Ciudad de México Site visit
Universidad Autónoma Metropolitana (UAM) Estado de México Site visit Presencial
Universidad Autónoma Chapingo (UACh) Estado de México Site visit Presencial
Colegio de Postgraduados (ColPos) Estado de México Site visit Presencial
Tecnológico de Estudios Superiores de Valle de Bravo Estado de México Site visit
Table 2. Scientific journals consulted as a source of information for analyzing the development of forest biometric models in Hidalgo, Mexico.
Journal Institution concerned Type of query
Revista Bosque Universidad Austral de Chile Online
Interciencia Asociación Interciencia, Venezuela Online
UNASYLVA FAO Online
Revista Mexicana de Ciencias Forestales Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias Site visit Presencial
Revista Chapingo Serie Ciencias Forestales y del Ambiente Universidad Autónoma Chapingo Site visit Presencial
Botanical Sciences Sociedad Botánica de México Online
Madera y Bosques Instituto de Ecología Online
Revista Fitotecnia Mexicana Sociedad Mexicana de Fitogenética Online
Terra Latinoamericana Sociedad Mexicana de la Ciencia del Suelo Online
Agrociencia Colegio de Postgraduados Site visit Presencial
Table 3. Research documents in the area of forest management generated in the UMAFOR 1302 Zacualtipán-Molango, Hidalgo, Mexico.
Authors* Area of influence Equations Type of model Number of samples Species studied Type of pulbication
Brosovich (1998) Zacualtipán de Ángeles 10 Density, site index and volume 52 (D) Pinus patula Thesis
Tenorio (2003) Estatal 2 Volume 101 (D) Pinus patula Thesis
Carrillo, Acosta, y Tenorio (2004) Estatal 1 Volume 101 (D) Pinus patula Brochure
Cruz (2007) Zacualtipán de Ángeles 13 Biomass, volume 62 (D) Pinus patula, Pinus teocote y latifoliadas Thesis
Aguirre et al. (2008) Zacualtipán de Ángeles 1 Cabon 75 (ND)** Pinus patula Article
Santiago (2009) Zacualtipán de Ángeles 23 Growth, density, site index, mortality and volume 84 (ND) Pinus patula Thesis
Cruz, Valdez, Ángeles, y De los Santos (2010) Zacualtipán de Ángeles 4 Volume 114 (ND)** Pinus patula and Pinus teocote Article
Figueroa (2010) Zacualtipán de Ángeles 9 Biomass 18 (D) Alnus spp., Clethra sp., Pinus patula and Quercus spp. Thesis
Olvera (2010) Barranca de Metztitlán 4 Volume 87 (D) Pinus greggii Thesis
Acosta, Carrillo, y Gómez (2011) Zacualtipán de Ángeles 4 Biomasa y carbono 40 (D) Alnus acuminata and Clethra mexicana Article
Vásquez (2011) Zacualtipán de Ángeles 5 Carbon 18 (D) Pinus patula Thesis
Hernández (2012) Zacualtipán de Ángeles 12 Volume 78 (D) Pinus patula Thesis
Muñoz et al. (2012) Barranca de Metztitlán 4 Volume 87 (D) Pinus greggii Article
Santiago (2013) Zacualtipán de Ángeles 1 Volume 42 (ND)** Pinus patula Thesis
Soriano, Ángeles, Martínez, Plascencia, y Razo (2013) Zacualtipán de Ángeles 3 Biomass 25 (D) Latifoliadas and Pinus patula Chapter
González (2014) UMAFOR 1302 Zacualtipán - Molango 16 Site index and Volume 159 (D) Pinus patula and Pinus teocote Report
Soriano (2014) Zacualtipán de Ángeles 12 Biomass and volume 71 (D) Pinus patula, Liquidambar macrophylla, Quercus spp., Alnus jorullensis, Cletra mexicana, Prunus serotina, Carpinus caroliniana and Virburum ciliatum Thesis
D: Destructive; ND: Non destructive. *Full references in Appendix 1. **Sampling site.
Table 4. Research documents in the area of forest management generated in the UMAFOR 1303 Pachuca-Tulancingo, Hidalgo, Mexico.
Authors* Area of influence Equations Type of model Number of samples Species studied Type of pulbication
Rodríguez (2000) Acaxochitlán 8 Growth 12 (D) Pinus patula Thesis
Pacheco et al. (2007) Cuaunepantla y Acaxochitlán 2 Biomass and Carbon 20 (D) Pinus greggii Article
Acosta and Carrillo (2008) UMAFOR 1303, Pachuca-Tulancingo 2 Volume 43 (D) Pinus montezumae Brochure
Rodríguez (2009) Singuilucan, Zempoala, Tepeapulco y Cuautepec de Hinojosa 2 Density 122 (ND) Pinus montezumae Brochure
Hernández (2012) Sureste de Hidalgo, Singuilucan 1 Growth 36 (D) Pinus montezumae Thesis
Velarde (2012) UMAFOR 1303 Pachuca-Tulancingo 106 Growth and Volume 185 (D) Pinus montezumae y Pinus patula Report Informe
González (2013) Mineral del Monte 2 Biomass and Volume 4 (D) Pinus patula Thesis
Hernández et al. (2013) UMAFOR 1303, Pachuca-Tulancingo 2 Density 131 (ND) Pinus teocote Article
Razo, Gordillo, Rodríguez, Maycotte, y Acevedo (2013) Parque Nacional El Chico 2 Biomass and Carbon 5 (ND) Abies religiosa Article
Rodríguez and Calva (2013) Parque Nacional El Chico 2 Biomass and Carbon 250 (ND) Abies religiosa Chapter
Rodríguez (2013) Sierra de Pachuca 12 Biomass Carbon and Growth 250 (ND) Abies religiosa Thesis
Hernández et al. (2014) Metztitlán 3 Site index 25 (D) Pinus greggii Article
Velarde (2014) UMAFOR 1303 Pachuca-Tulancingo 8 Site index and Volume 120 (D) Pinus rudis y P. teocote Report
Hernández et al. (2015) Acaxochitlán, Cuautepec de Hinojosa, Singuilucan y Tulancingo de Bravo 1 Site index 345 (ND) Pinus teocote Article
D: Destructive; ND: Non destructive. *Full references in Appendix 1.
Author Area of influence Equations Type of Model Number of samples Species studied Type of publication
Secretaría de agricultura y Ganadería (SAG, 1976) State level 12 Volume 899 (D) Alnus sp., Quercus sp., Cedrela odorata, Inga spuria, Cupania dentata, Bursera simaruba, Juniperus flaccida, Pinus cembroides, Pinus patula, Pinus ayacahuite, Pinus teocote, Pinus greggii, Pinus pseudostrobus, Platanus sp., Liquidambar styraciflua, Psidum guajava and Dendropanax arborea Brochure
D: Destructive; ND: Non destructive. *Full references in Appendix 1.
Table 6. Most used forest biometric models for estimating variables in the state of Hidalgo, Mexico.
B: biomass, C: carbon, d: diameter at breast height, Dc: crown diameter, A: age, h: height, SI: site index, N: number of trees, V: volume; β0, β1, β2, β3: regression parameters.