Growth and adaptability of provenances and progenies of Pinus maximinoi h. E. Moore in northern Mozambique

Growth and adaptability of provenances and progenies of Pinus maximinoi h. E. Moore in northern Mozambique | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Growth and adaptability of provenances and progenies of Pinus maximinoi h. E. Moore in northern Mozambique Cremildo Riba Gouveia Dias, Laurina Adriano Guacha, Aires Afonso Mbanze This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2456600/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study was to evaluate the growth and adaptability of Pinus maximinoi provenances and progenies and to estimate the genetic variation that could be exploited in future breeding work. The company Floresta de Niassa Lda established a trial with four provenances and nineteen progenies in the Chimbonila district of northern Mozambique. The field trial was set up in a randomized block design with four repetitions and six-plant linear plots. At two, four and eight years of age, performance in total height, diameter at breast height (DBH) and survival were analysed. The results at eight years of age showed no significant variation between provenances for all variables analysed, and any of these provenances can be used for reforestation in northern Mozambique. The first group was composed of 9 (45%) of the best performing progenies, mostly from Yuscaran and San Jeronimo, and the second (last) group was composed of 11 (55%) of the worst performing progenies, mostly from Tatumbla and Coban. Most of the nine best classified progenies based on the predicted additive genetic effect and genetic gain belonged to Yuscaran and Tatumbla. On the other hand, most of the progenies at the level of the seventeen best classified individuals belonged to San Jeronimo and can be used for future breeding projects in northern Mozambique. Adaptability Growth Precedencies and progenies trial Pinus maximinoi and Phenotypic and Genotypic Selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Information available indicates that the first plantations in Mozambique date from the 19th century with the planting predominantly of Eucalyptus in the then Loureço Marques now Maputo, with the aim of drying the marshes in the lower part of the city (MINAG - Ministério da Agricultura 2009 ). The post-national independence period was marked by the development of plantations with fast-growing forest species to supply firewood and charcoal to the populations of the three largest urban centres, Maputo, Beira and Nampula and their surroundings, with the aim of reducing the pressure that was already beginning to be felt on the native forest around the large urban centres (MINAG 2009). In this framework, the FO1 projects were created in Manica and FO2 in Marracuene, most of them with the species Eucalyptus saligna , Eucalyptus grandis , Pinus patula and Casuarina equisetifolia , which were abandoned at the height of the civil war of the 16 years, between 1977 and 1992 (MINAG 2006). Around 1980, the first forestry research was started, where the introduction and selection tests of species and provenances were carried out, seedling production tests in nurseries and tests of forestry techniques in the establishment of plantations. However, the expected success was not achieved, but they did contribute to doubling the forest area the country had at the time of the proclamation of independence, from 20,000 ha in 1975 to approximately 42,000 ha in 1992 (MINAG 2006). After the end of the civil war, there was a need to attract new investments for plantations with fast-growing exotic species, with the aim of i) recovering unproductive land abandoned due to itinerant agriculture (INDE - Instituto Nacional do Desenvolvimento da Educação 2009 ); ii) creating jobs; and iii) reducing the pressure on native forests due to population growth (Landry and Chirwa 2011 ; Nube et al. 2016 ; Zanella et al. 2018). Niassa province attracted most of the investments due to the availability of land due to low population density. Mozambique is well placed to expand the afforestation of multipurpose plantations, including an increasing need for forest products and the availability of land. Increasing the country's forest plantation area from the current 60,000 ha to over one million by 2030 would have the potential to create 250,000 jobs and produce $ 1.5 billion in manufactured goods (MINAG 2009). According to the (Governo da Província do Niassa 2007 ) of the estimated potential for the province, the establishment of forest plantations of fast-growing species is based on selection criteria for areas suitable for commercial plantations. The Lichinga Plateau is the region with the greatest potential for the development of commercial plantations, and the most productive areas are located in the districts of Ngaúma, Sanga, Muembe and Lichinga. The main species used to establish these plantations were Eucalyptus grandis , Pinus elliottii , P. patula and P. taeda , which already existed in the settlements dating from colonial times. Later, they were also introduced, P. maximinoi , P. tecunumanii and P. oocarpa , with the recommendation of the Central America and Mexico Coniferous Resources Cooperative (CAMCORE). The introduction of Pinus maximinoi species for the first time in Niassa, particularly in the district of Chimbonila, was established at the same time as the experiments, since there was no information on adaptability, growth, and production of exotic species in the Niassa environment. However, the literature shows that this species is compatible with Niassa's soil and climate conditions. For its success and profitability, it is important to identify species that best adapt and grow rapidly, and this is only possible through species and provenance testing. In this context, Empresa Florestas de Niassa Limitada (FdN), with the help of CAMCORE, established seven (7) experiments of tropical pine (López [s.n.]). One of these experiments is the object of the present study, which aims to evaluate the growth and adaptability of provenances and progenies of P. maximinoi H. E. Moore in the environment of Chimbonila, northern Mozambique. The research questions raised by Dias and Mbanze ( 2020 ) in the study on the growth and adaptability of provenances and family-within-provenance of Pinus tecunumanii in northern Mozambique are part of this study. These questions were answered from the evaluation of results at 2, 4 and 8 years of age of a P. maximinoi trial established in the test fields of the company Niassa Forests. 2. Materials And Methods 2.1. The sites The experimental site was at Chimbonila, Niassa Province in Mozambique’s North Region, belongs to Company Floresta de Niassa limited. The trial was situated at latitude 13 o 14'14,8''S and longitude 35 o 30'46,5''E at an altitude of 1180 meters above sea level (Fig. 1 ). The climate of the site is humid temperate (Cwb), with two well-defined seasons: Summers temperate and rainy and cold winters and dry. The area receives a mean annual rainfall over 1200 mm and may exceed this value and reach 1400 mm of rainfall and experience mean annual temperatures ranging from 18 to 24°C, but generally less than 22°C (Ministério da Administração Estatal 2014 ). The soils at the site are mostly clay, deep reds and have low susceptibility to erosion (Shimanikire 2011 ). ------------------------------------- Insert Fig. 1 -------------------------------------- 2.2. Seed Sources The seeds used in this experiment are only part of the existing variation for the species, including Nicarágua, Guatemala and Honduras, which were obtained from the CAMCORE. This comprised 19 half-sib families and four (4) provenances of P. maximinoi and was used as a seed control from the Guatemala seed production area. The relationship provenances evaluated with their respective geographical locations are presented in Table 1 . ------------------------------------- Insert Table 1 -------------------------------------- 2.3. Experimental design and establishment The seedlings were planted on site in January 2011 using a randomized complete block design at the provenance level, followed by randomisation of families within provenances. There were four (4) replications and six (6) trees per family planted in line plots. The spacing adopted was 3.0 m x 3.0 m between plants with a double border around the experiment (Fig. 6 of appendix ). There was no soil preparation, only manual pits with a depth of 35 cm were used for seedling planting. At the time of planting, a hydrogel system was applied to radical the seedlings by immersion in a solution at a concentration of 10 g/litre. The cover fertilization was performed thirty days following planting, with NPK 100 g (17.38% − 28.51% − 4%) and adding 5% sulphur, 0.8% zinc and 0.5% boron per hole. These dosages were determined by analysis of soil samples made earlier. Formation pruning was effectuated in 2013 to 2,8 years of age. Production pruning was effectuated in August 2015 and September 2017. Additionally, total cleaning was performed (weeds) once a year in the first 4 years after planting. Figure 2 shows two pictures of the test. ------------------------------------- Insert Fig. 2 -------------------------------------- 2.4. Data collection and analysis 2.4.1. Data collection In this trial, data from three (3) measurements over time, from 2013 to 2018, i.e., from 2 to 8 years old, were considered. The parameters measured in the trial were height in meters (2, 4 and 8 years) and diameter at breast height (DBH) only at 4 and 8 years old. The height measurement was made with a "Vertex IV" hypsometer and a graduated stick with 10 cm accuracy when needed. The DBH of the trees was measured with a calliper. Survival was also evaluated from the living tree count in the second, fourth and eighth years. Volume per tree was calculated using the following formula (Ladrach 1986) cited in Hodge and Dvorak ( 1999 ); Gapare et al. ( 2001 ); Hodge and Dvorak (2015): \(\text{V}\text{o}\text{l}=\text{0,0003}\times {\text{D}\text{B}\text{H}}^{2}\times \text{H}\text{e}\text{i}\text{g}\text{h}\text{t}\) (1). where the volume is in cubic meters (m 3 ) and DBH is the over bark diameter at breast height (cm) and height in meters (m). Survival was obtained from the count number of living trees and was expressed as a percentage relative to the number of unique experimental trees. According to the formula used by Cornacchia et al. ( 1998 ): $$Surv\%=\frac{NIV}{NTI}\times 100$$ 2 where NIV is the number of living individuals by provenance, and NTI is the total number of individuals per provenance. A comparison of the relative position of each provenance in height growth was made on performance over the known methods discussed by Burdon (1998) and used by (Mora ( 2002 ). Thus, various ratings range from 0 to 100, and the formula is: $$DR=\frac{X-{X}_{minor}}{{X}_{major}-{X}_{minor}}\times 100$$ 3 where DR is the relative performance (%); X is the average of the evaluated origin; X minor is the provenance of the worst performance; and X major is the origin of better performance. For comparison, the average annual volume increments of provenances were calculated by the formula used by Shimizu and Higa ( 1981 ); Kietzka ( 1988 ): $${m}^{3}/ha year=\frac{\stackrel{-}{V}\times n\times \% Surv}{1\times 100}$$ 4 where \(\stackrel{-}{V}\) is the arithmetic average volume per tree; n is the number of trees planted per hectare; % Surv is the percentage of survival; and 1 is the age in years. 2.4.2. Data analysis All analyses and results in this present work were based on eight years of data. Data were analysed using Selegen Reml/Blup (Federal University of Viçosa, Minas Gerais, Brazil) and the SPSS computer programme. Analysis of variance (ANOVA) was performed on DBH, height and survival at both sites for both provenances and families. If the ANOVA was significant, Tukey’s test was subsequently performed to identify different groups of families in the provenances. Clustering dendrogram analysis and representation were performed to illustrate the above results of the ANOVA between different sources and progeny and thus identify similar groups regarding the growth characteristics (height, DBH and volume). The method used was the agglomeration Ward's and the Minkowski distance since all variables were continuous. The value of Eta 2 (variation percentage between the clusters) and the mean test between groups were used to determine the optimal number of clusters of provenances and progeny by retaining and thus obtaining a good standard view of joint variation among the provenances and progeny. After identification of groups, the 9 best progenies were selected based on the predicted additive genetic effect and genetic gain (selection 45% of progeny). Based on the genetic improvement of the general average of the variable volume, they were also ranked in the top 17 subjects (4% will check the level of individuals of the progenies within provenances). This classification aims to select the best progenies and individuals with higher volumetric production for subsequent generations of breeding and commercial plantations. To select the variable with the highest influence on the productivity of the progeny, the Pearson correlation coefficient (PCC) between the volume and the height or DBH was used, depending on the variable with the highest PCC. The estimates of the variance components and genetic parameters were based on the restricted maximum likelihood method (REML) of the individual progenies from statistical model 5 (Test half-sib progeny, with a randomized block design. This was done with the aid of the computer program SELEGEN-REM / BLUP (Resende 2014 ). The calculation of variance components was obtained by the formulas proposed for (Resende 2007 ). $${h}_{a}^{2}=\frac{{\sigma }_{a}^{2}}{{\sigma }_{f}^{2}}$$ 5 where \({h}_{a}^{2}\) is the individual heritability in the narrow sense; \({\sigma }_{a}^{2}\) is the additive variance; and \({\sigma }_{f}^{2}\) is the individual phenotypic variance. $${CV}_{gi}\left(\%\right)=\frac{\sqrt{{\sigma }_{a}^{2}}}{\stackrel{-}{X}}\times 100$$ 6 where \({CV}_{gi}\) (%) is the individual additive genetic coefficient of variation and \(\stackrel{-}{X}\) is the overall mean of the original population. $${CV}_{d}\left(\%\right)=\frac{\sqrt{{\sigma }_{d}^{2}}}{\stackrel{-}{X}}\times 100$$ 7 where \({CV}_{d}\) (%) is the residual coefficient of variation of the experiment and \({\sigma }_{d}^{2}\) is the residual variance of the experiment. Estimates of the gains with the selection of progeny were obtained according to the proposal by (Cruz 2005 ). \(GS \left(\%\right)=\frac{GS}{\stackrel{-}{{X}_{0}}}\times 100\) , being \(GS={h}_{a}^{2}\times DS\) (8) where GS is the genetic gain by selection; DS is the selection differential; and \({\stackrel{-}{X}}_{0}\) is the overall mean of the original population. $$DS={\stackrel{-}{X}}_{s}-{\stackrel{-}{X}}_{0}$$ 9 where \({\stackrel{-}{X}}_{s}\) is the average of the selected progenies. 3. Results 3.1. Relative performance of the provenances Figure 3 presents the relative performance of 4 origins of P. maximinoi in 2, 4 and 8 years of evaluation, showing that the provenance of the San Jeronimo (SJ) maintained the same position qualifying of the relative performance in 2 and 4 years equal to 100% and having lowered its performance relative to 8 years for 93.79%. Another type of situation happens with the control (Common lot) that his relative performance is always increasing over the years (10.72%, 69.18% and 100%), having if detached like the second-best relative performance. Yuscaran (Yus) showed, on average, the third best relative performance, especially at 8 years. ------------------------------------- Insert Fig. 3 -------------------------------------- The provenance of the Tatumbla (Tla) not only presented a performance very much down to 2 and 4 years but also tended to increase his performance with growth. For the original Coban (Cob), he presented the worst performance every year, except for 2 years, when it was 19.43%, which surpassed the control by nearly 9% to more. 3.2. Growth and adaptability of the provenances The results of the ANOVA and mean test (Tukey) of different origins of P. maximinoi tested in Chimbonila are shown in Table 2 . The degree of survival was significant at the 5% level at 2 years, and no differences were found in the last two years studied and ranged from 83.33 to 97.92% in the period. There were no marked differences from the heights in year 4, although the origins San Jeronimo (SJ) were higher than in the rest. At both two and eight years, there were no significant differences between the provenances. The DBH and volume parameters at 4 years or 8 years did not reveal significant differences between sources. The experimental coefficients of variation (CV%) in provenances were low for all parameters in the period studied, except for the volume to 4 years age. ------------------------------------- Insert Table 2 -------------------------------------- The annual average increase (m 3 * ha − 1 yr − 1 ) from all sources was higher than that of the control except for the Yuscaran origin (Yus) at 4 years, and there were no differences after 8 years (Fig. 4 ). The Mean Annual Increments of Coban origins (Cob), Tatumbla (Tla) and Yuscaran (Yus) produced the equivalents of 113.65% and 114.34%, 102.8% and 112.06% and 100.46% and 106.2% of the Annual Average Increase control at 4 and 8 years old, respectively. San Jeronimo (SJ) had a higher mean annual increment than the other origins and the control (150.81% and 127.58%) for years 4 and 8, respectively, although there were no significant differences for 8 years. ------------------------------------- Insert Fig. 4 -------------------------------------- 3.3. Variation in growth among the progenies The results of Tukey's test for averaging pairs among the progeny from all sources are shown in Table 3 . The Tukey test was performed after the ANOVA assumed that the progeny showed significant values of the DBH and volume to 8 years old, as shown in the accompanying Table 8 in the appendix . For the DBH parameter, the best progeny were Coban (15–884), San Jeronimo (15–868 and 15–875) and Tatumbla (15–962), whereas no statistically significant differences between them were observed, and they had a relative superiority over the other, contrary to Tatumbla (15–950), which had the worst performance of all progenies. Regarding the volume parameter, the average progeny 15–962 is the best of all, and the worst progeny is 15–950 for both Tatumbla provenances. An important observation is in relation to the control, which showed a growth above the average of progeny. ------------------------------------- Insert Table 3 -------------------------------------- Person's correlation coefficients (CCP) showed a strong positive (> 0.9) and significant (P < 0.01) correlation between the DBH and volume parameters for all progenies (see the last column of Table 3 ). To better visualize the average of the test results of the origins of Table 2 and Table 3 , the progeny of a cluster analysis were made, and subsequently, an ANOVA and mean test were performed on their respective clusters. The results are shown in Table 4 and Fig. 5 and it can be easily observed that the two-cluster solution was the best choice. ------------------------------------- Insert Table 4 -------------------------------------- The first cluster comprised the 9 (45%) best progenies, mainly of origins Yuscaran (Yus) and San Jeronimo (SJ), and showed superiority in all parameters (height, DBH and volume). The last cluster contained 11 (55%) progenies with performance below the overall average and was mainly composed of the family of the Tatumbla (Tla) provenance. The control also belongs to the last cluster. ------------------------------------- Insert Fig. 5 -------------------------------------- 3.4. Selection of progenies based on phenotypic characters Table 5 presents estimates of variance components and genetic parameters of the progenies of P. maximinoi at 8 years old. It is noted that by breaking the individual variance components, most of the total phenotypic variance of all the studied parameters was residual variance due to ( \({\sigma }_{d}^{2}\) ) and individual phenotypic variance ( \({\sigma }_{f}^{2}\) ). The individual heritability in the narrow sense ( \({h}_{a}^{2}\) ) allows us to predict moderately favourable conditions for the selection of families. However, the coefficient of determination of plot effects ( \({\complement }_{e}^{2}\) ) and the effect of provenances ( \({\complement }_{p}^{2}\) ) showed low magnitude values. The individual additive genetic variation coefficient ( \({CV}_{gi}\) %) and genotypic variation coefficients among progenies ( \({CV}_{gp}\) %) were high for all characters, and residual variation ( \({CV}_{d}\) %) was of low magnitude for height and DBH and medium magnitude for the variable volume. Finally, the relationship between the genetic coefficient of variation and the residual coefficient of variation showed values close to and above one (1). \({\sigma }_{a}^{2}\): additive genetic variance; \({\sigma }_{e}^{2}\): environmental variance between plots; \({\sigma }_{p}^{2}\): genetic variance among provenances; \({\sigma }_{d}^{2}\): residual variance; \({\sigma }_{f}^{2}\): phenotypic variance at the individual level in the experiment; \({h}_{a}^{2}\): individual heritability in the narrow sense; \({\complement }_{e}^{2}\): coefficient of determination of plot effects; \({\complement }_{p}^{2}\): coefficient of determination of the effects of origins; \({CV}_{gi}\)%: individual additive genetic coefficient of variation; \({CV}_{gp}\)%: coefficient of genotypic variation among progenies; \({CV}_{d}\)%: coefficient of residual variation. ------------------------------------- Insert Table 5 -------------------------------------- Based on the predicted additive genetic effect (a) and genetic gain, nine (9) improved progenies were then classified, corresponding to 45% of the parent selection by volume, as shown in Table 6 . It is noted that provenances Yuscaran (Yus) and Tatumbla (Tla) had a higher number of selected progenies, with three each. This was followed by the provenance San Jeronimo, with two selected progenies, and the Coban origin contributed only progeny. ------------------------------------- Insert Table 6 -------------------------------------- Table 7 shows the desirable rating of the 17 (4%) best individuals selected from 20 progeny Pinus maximinoi continued for breeding. The classification criterion was based on the genetic improvement of the general average volume parameter. The San Jeronimo (SJ) origin contributed to the greater number of individuals selected, totalling 34% (6), followed by the Coban origin (Cob) to 29.4% (5) of selected individuals, all individuals of the progeny 15–884. The Tatumbla origin (Tla) contributed 23.5% (4) of individuals, and finally, the Yuscaran (Yus) contributed 11.8% (2) of individuals. ------------------------------------ Insert Table 7 -------------------------------------- 4. Discussion 4.1. Relative performance, growth, and adaptability of the provenances The control (common lot) presented superior relative performance in relation to the other provenances tested in the Chimbonila environment (Fig. 2 ), demonstrating a greater potential for growth of seeds collected in the production area of seeds of P. maximinoi originating from Guatemala, as has been verified in other studies (Fier 2001 ). According to the general averages of height, DBH and volume in Table 2 , they are similar to the values found by Gapare et al. ( 2001 ) for Pinus maximinoi in experiments that were evaluated in South Africa (12.6 m, 17.8 cm and 0.12 m 3 /tree). When analysing the survival rates, there were no statistically significant differences, and all the varieties, including the control (common lot), showed average values above 90% with a coefficient of variation of high precision. Similar trends can be observed in the studies of Dias and Mbanze ( 2020 ) in Mozambique, leading to the assumption that for this species, the varieties show good potential for adaptability in the Chimbonila environment. The survival values of the present research are above those found (see, for example, (Kietzka 1988 ; Wright 1992 ; Gapare et al. 2001 ; Ettori et al. 2004 ; Lopez-Upton et al. 2005 ; Santos et al. 2018 ). According to the results provided in Table 2 , the growth and adaptability of P. maximinoi provenances of the trial established in the Chimbonila environment indicated an acceptable level of experimental accuracy by presenting values of low magnitude in all parameters evaluated and showing good statistical efficiency for the analysis. Gapare et al. ( 2001 ) and Santos et al. ( 2018 ) found coefficients of variation higher than those in the present research. However, there are also tests with coefficient values close to the present study, as is the case of Fier ( 2001 ). The low values of the experimental coefficient of variation suggest a small phenotypic variation within plots, which explains the lack of genetic differences between provenances. However, in general, the experimental design was very reliable in capturing the growth and adaptability of progenies of P. maximinoi in the Chimbonila environment, since the coefficient of variation values of the measured variables were below 20%, as recommended by Pimental Gomes ( 1990 ); Pimentel-Gomes and Garcia ( 2002 ). The mean annual volumetric increment revealed no significant differences between the provenances at 8 years. Therefore, the volumetric increment is high, presenting an average of 21.22 m 3 /ha per year; however, it should be considered that this variable is directly influenced by the number of trees in the forest stand. The mean annual increment of the same provenances was well above the control but also above the results of other studies conducted in Africa (see, for example, (Hodge and Dvorak, 2012 ; Dias and Mbanze, 2020 )). 4.2. Variation in growth among the progenies The author did not detect significant differences at the 5% level between the means of the P. maximinoi varieties tested in Chimbonila at eight years of age for the characteristic height, DBH and volume. These results corroborate those of Nyoka ( 1994 ) and Fier ( 2001 ) that were observed in the regions of Zimbabwe and Brazil, respectively. Atie et al. ( 2018 ); Santos et al. ( 2018 ) reported significant variations among provenances of P. maximinoi in Brazil for height, DBH and volume, indicating that they can be explored in subsequent breeding cycles. However, the percentage variation between the best and worst mean DAP and volume of the progenies were 26.78% and 43.34%, respectively, which are characterized as significant differences, as presented in Table 3 , suggesting that the population has moderate genetic variation and therefore a weighted possibility of genetic improvement by selection among progenies. According to the analysis unfolding at the progeny level, it is possible to see two distinct groups of good and bad performance, indicating that not all progenies contribute in the same way to the overall performance of the progeny. The group with higher participation in the performance of the progenies contributed 45% of the progenies, as it was possible to identify with the help of cluster analysis. These progenies are predominantly from Yuscaran (Yus) and San Jeronimo (SJ). High correlation coefficient values (CC ≥ 90%) were found between DBH and volume in tests of provenances and progenies of P. maximinoi , demonstrating that the selection can be based on DBH, without prejudice to the volume character, agreeing with what occurred with studies carried out by other authors for forest species such as (Munthali and Stewar 1998 ; Sampaio et al. 2000 ; Fier 2001 ; Sampaio et al. 2002 ; Nascimento 2010 ; Moreira et al. 2014 ; Souza et al. 2016 ; Biernaski 2018 ; Dias and Mbanze, 2020 ), among others. 4.3. Selection of progenies based on phenotypic characters The estimates of heritability in the restricted sense (h 2 a ) showed values of medium magnitude for all growth characters ranging from 0.18 to 0.32, indicating the existence of moderate genetic control for all characteristics. Heritability values from 0.01 to 0.15 are considered low; from 0.15 to 0.50 medium; and above 0.50 high (Resende 1995 , 2002 ). The heritability found is similar to those obtained by Sampaio et al. ( 2000 ); Gapare et al. ( 2001 ); Biernaski ( 2018 ) and lower than those reported by (Wright et al. 1993 ). Resende ( 2002 ) noted that a heritability of 0.20 is a reasonable value for growth characteristics in forest species. The coefficients of determination of plot effects ( \({\complement }_{e}^{2}\) ) were less than 5% for the DBH and volume characters and greater than 10% for the height character. According to Resende ( 2002 ), the recommended values for estimates of the coefficients of determination of plot effects ( \({\complement }_{e}^{2}\) ) are values equal to or lower than 10%. According to Farias Neto et al. ( 2008 ), these values demonstrate that there is greater genetic control than environmental variation between the plots and within blocks. Judging by the values of coefficient of individual additive genetic variation ( \({CV}_{gi}\) %) and coefficient of genotypic variation among progenies ( \({CV}_{gp}\) %) obtained in all characters were high, indicating the existence of heritable genetic variation in the population, therefore, selection can be done at both individual and progeny levels. Coefficients of genetic variation above 7% are considered high by (Sebbenn et al. 1998 ). Regarding the relationship between individual additive genetic variation coefficient ( \({CV}_{gi}\) %) and coefficient of residual variation ( \({CV}_{d}\) %), values were found to be close (height) and greater than one unit for DBH and volume characters. These results show a favourable situation for early selection at the DBH character level. This relationship, according to Vencovsky and Barriga ( 1992 ), when close to or greater than one unit indicates a very favourable situation for obtaining genetic gains with progeny selection. Through the individual BLUP (best linear prediction without bias) procedure, the 9 best P. maximinoi progenies were selected with potential for a second-generation breeding programme, highlighting the Yuscaran (Yus) and Tatumbla (Tla) provenances with the contribution of 3 progenies within each provenance. It is important to report that the individual BLUP procedure is equivalent to the multi-effect index selection method (Resende et al. 1994 ; Resende 2002 ). The genetic values of the 9 progenies provided a genetic gain of 5.19%, increasing the average volume character from 0.1552 m 3 /tree to 0.1803 m 3 /tree. The same method of progeny selection was applied to individual trees, where the 17 best individuals were selected for an effective population size (Ne) equal to 10.8 based on the volume character. It is noteworthy that the progenies 884 (Cob), 868 and 875, both from San Jeronimo, showed the best results, especially progeny 884, which was repeated 5 times. This selection increased the average of the character from 0.1552 m 3 /tree to 0.2089 m3/tree, presenting a genetic gain of at least 11.11%. The gains estimated with the selection can be considered promising due to the early age of the evaluation. Therefore, the effective population size is small (10.8), which limits the possibility of new selections. Resende ( 1995 ); Vieira and Shimizu ( 1998 ) emphasise that an effective population size of 60 is necessary to maintain genetic variability in recurrent selection in short- and long-term improvement. In the present study, to meet (effective population size of 60) the recommendations of Resende ( 1995 ); Vieira and Shimizu ( 1998 ), it would be necessary to select 352 individuals for the volume character to maintain the effective size at an acceptable level. With this number of selected individuals, it would provide a genetic gain of 1.7%, increasing the average volume from 0.1552 m 3 /tree to 0.1634 m 3 /tree. With this new average, it would still be acceptable to form an improved population for future generations since it is higher than the original average of the population. 5. Conclusions Pinus maximinoi showed suitable growth and performance in the 4 provenances, including the control (common lot) in the Chimbonila region, and can be recommended for commercial plantations in the region. There were no significant differences between the provenances studied in all growth characteristics at 8 years of age, and any of the provenances can be used for reforestation. The phenotypic association between the growth characteristics DBH and volume is strong for all progenies, suggesting that selection can be made based on DBH, which is the variable of easy access. The genetic control of the growth characters measured by the coefficient of determination of plot effects and coefficient of additive genetic variation demonstrated a high potential for genetic improvement of the population. The selection of the 17 individuals with higher genetic values within the provenances evaluated allowed the elevation of the average volume from 0.1552 m 3 /tree to 0.2089 m 3 /tree, with genetic gains of 11.11%. Declarations Acknowledgments The authors are grateful to the company Florestas de Niassa, Lda; the engineer José Bernardo Manteiga, engineer Custódio da Conceição and Misters John Mkumbira, Hélio Raul, Abudo Issufo, Candawele Abudo and Agostinho Adriano for their assistance and collaboration during data collection. Authors’ contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cremildo Riba Gouveia Dias, Laurina Adriano Guacha and Aires Afonso Mbanze. The first draft of the manuscript was written by Cremildo Riba Gouveia Dias, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received. Declaration of Competing Interest : The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. References Atie W, Mbinga J, Cherono F, Omondi S, Bala P, Muchiri MN, Chagala-Odera E (2018) Growth Performance of Second-Generation Pinus maximinoi and P. tecunumanii Progeny Trials at Turbo, Kenya. In E. Chagala–Odera, D. Ochieng, J. Wanjiku, M. Muchiri, M. Gichora, P. Tuwei, B. Kamondo, E. Mengich, D. Langat, P. Oballa, G. Muthike, R. Chiteva, J. Kagombe, L. Cherotich, M. Gathogo, A. Muthama, & N. Oduor (Eds.), Contribution of Forestry Research to Sustainable Development Biernaski FA (2018) Pinus maximinoi H. E. 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For Ecol Manag 62(1–4):313–322 Tables Table 1 Provenance, progenies and soil and climate characterization of the place where the seeds were collected Provenance Country State or Department Number of Progenies Latitude Longitude Elevation Range (m) Rainfall (mm/year) Temperature ( o C) Coban (Cob) Guatemala Alta Verapaz 4 15 o 28’ 90 o 24’ 1420-1440 2109 13.7 - 23.7 San Jeronimo (SJ) Guatemala Baja Verapaz 4 15 o 04’ 90 o 14’ 1280-1590 970 16.3 - 27.6 Tatumbla (Tla) Honduras Fco. Morazan 6 14 o 01’ 87 o 07’ 1400-1600 908 15.0 - 24.9 Yuscaran (Yus) Honduras El Paraiso 4 13 o 50’ 86 o 55’ 1500-1700 1300 16.9 - 27.7 Common lot Guatemala n.a n.a n.a n.a n.a Source : Dovorak et al. (2000) Table 2 Growth means (height, DBH and volume) and survival of the provenances of P. maximinoi (15-64-36C1) Provenances Height (m) DBH (Cm) Volume (m 3 /tree) Survival (%) 2 years 4 years 8 years 4 years 8 years 4 years 8 years 2 years 4 years 8 years Cob 3.17 6.51 ab 15.04 8.20 17.67 0.0149 0.1623 96.88 ab 96.88 95.83 SJ 3.53 7,23 a 15.87 8.95 18.10 0.0196 0.1778 100 a 98.96 97.92 Tla 3.08 6.36 b 15.52 7.93 17.38 0.0137 0.1639 93.75 ab 93.75 93.75 Yus 3.20 6.50 ab 15.65 7.78 16.93 0.0135 0.1554 95 ab 94.17 93.33 Common lot 3.13 6.84 ab 15.93 8.15 17.02 0.0157 0.1667 83.33 b 83.33 83.33 Mean 1 3.22 6.69 15.60 8.20 17.42 0,0154 0.1652 93.79 93.42 92.83 F ratio 2.23 ns 3.42 * 1.38 ns 1.87 ns 1.57 ns 2.29 ns 0.66 ns 3.47 * 2.93 ns 2.20 ns CV exp (%) 7.43 5.69 3.90 8.05 4.40 20.19 12.22 7.21 7.53 8.15 Means followed by the same letter in the column were not significantly different by Tukey’s test. ns = not significant * = significant at 5%; ** = significant at 1%; 1 = overall average of sources, including the control; CV Exp = experimental coefficient of variation Table 3 Means of comparison between progenies and correlation between height and DAP with volume for P. maximinoi (15-64-36C1) at 8 years Country Provenances Progenies Means (m) Means (Cm) Means (m 3 /tree) PCC Guatemala Cob 15-884 15.76 18.69 a 0.1892 ab 0,964 ** Guatemala Cob 15-886 14.90 17.75 ab 0.1602 ab 0,957 ** Guatemala Cob 15-887 14.98 17.07 ab 0.1543 ab 0,961 ** Guatemala Cob 15-888 14.41 17.07 ab 0.1424 ab 0,968 ** Guatemala SJ 15-868 16.16 18.60 a 0.1915 ab 0,950 ** Guatemala SJ 15-870 16.03 17.09 ab 0.1604 ab 0,934 ** Guatemala SJ 15-872 14.84 17.87 ab 0.1624 ab 0,933 ** Guatemala SJ 15-875 16.49 18.73 a 0.1956 ab 0,966 ** Honduras Tla 15-949 14.46 18.22 ab 0.1629 ab 0,961 ** Honduras Tla 15-954 15.27 16.82 ab 0.1466 ab 0,974 ** Honduras Tla 15-957 16.63 17.79 ab 0.1822 ab 0,949 ** Honduras Tla 15-962 16.13 18.86 a 0.1998 a 0,965 ** Honduras Tla 15-950 12.97 13.81 b 0.1132 b 0,984 ** Honduras Tla 15-956 15.63 16.15 ab 0.1408 ab 0,951 ** Honduras Yus 15-899 15.64 17.82 ab 0.1753 ab 0,946 ** Honduras Yus 15-900 15.43 14.92 ab 0.1177 ab 0,950 ** Honduras Yus 15-902 15.61 17.26 ab 0.1584 ab 0,917 ** Honduras Yus 15-903 15.79 17.27 ab 0.1603 ab 0,960 ** Honduras Yus 15-893 15.56 16.90 ab 0.1541 ab 0.945 ** Guatemala Common lot 15.93 17.02 ab 0.1667 ab 0,969 ** DMS 4.02 4.44 0,08 Means followed by the same letter in the same column are not significantly different by Tukey test for (p ≤5%); DMS - for the least significant difference test. PCC = Pearson correlation coefficient, ** significant at (p-value = 0.1). All values are correlated with the DBH. Table 4 Results from ANOVA, Eta 2 and Tukey test of means for clusters of the different progenies and precedencies Cluster 1 Cluster 2 Total 9 (45%) 11 (55%) 20 (100%) F Eta 2 Height (m) 15.85 a 14.53 b 15.12 41.27*** 0.696 DBH (cm) 17.73 a 16.01 b 16.79 16.51*** 0. 478 Volume (m 3 ) 0.175 a 0.137 b 0.154 32.32*** 0.642 Values with the same letter in the row do not differ statistically by the Tukey test. **** = Significant at p≤0.01 Table 6 Selection of the 9 (45%) best progenies of P. maximinoi (15-64-36C1) at 8 years Sequence Provenances Progenies a Gain New Average 1 SJ 15-875 0.0518 0.0518 0.2070 2 Tla 15-962 0.0470 0.0494 0.2046 3 Cob 15-884 0.0443 0.0477 0.2029 4 SJ 15-868 0.0364 0.0449 0.2001 5 Tla 15-957 0.0180 0.0395 0.1947 6 Yus 15-899 0.0156 0.0355 0.1907 7 Yus 15-903 0.0078 0.0316 0.1868 8 Yus 15-902 0.0041 0.0281 0.1833 9 Tla 15-949 0.0009 0.0251 0.1803 Original population average 0.1552 a: predicted additive genetic effect. Table 7 Classification of the 17 (4%) best individuals of P. maximinoi (15-64-36C1), selected within progenies at 8 years Sequence Block Prog. Proc. Tree f a U + a Gain New Average Ne 1 3 962 Tat 6 0.4989 0.1082 0.2634 0.1082 0.2634 1.0000 2 1 868 SJ 1 0.3687 0.0692 0.2245 0.0887 0.2439 2.0000 3 4 875 SJ 3 0.3247 0.0643 0.2195 0.0806 0.2358 3.0000 4 4 899 Yus 5 0.3398 0.0548 0.2101 0.0741 0.2294 4.0000 5 1 957 Tat 3 0.3412 0.0542 0.2095 0.0702 0.2254 5.0000 6 1 884 Cob 1 0.3103 0.0534 0.2086 0.0674 0.2226 6.0000 7 2 957 Tat 4 0.3215 0.0511 0.2063 0.0650 0.2203 6.4972 8 1 884 Cob 4 0.2984 0.0502 0.2054 0.0632 0.2184 7.0588 9 3 884 Cob 2 0.2776 0.0497 0.2050 0.0617 0.2169 7.2483 10 1 875 SJ 6 0.2763 0.0485 0.2038 0.0604 0.2156 7.8947 11 1 884 Cob 6 0.2861 0.0468 0.2021 0.0591 0.2144 7.8870 12 1 899 Yus 4 0.3232 0.0468 0.2020 0.0581 0.2133 8.5714 13 4 884 Cob 6 0.2622 0.0461 0.2013 0.0572 0.2124 8.4324 14 2 868 SJ 3 0.2629 0.0423 0.1975 0.0561 0.2113 9.1304 15 1 875 SJ 3 0.2528 0.0422 0.1974 0.0552 0.2104 9.5847 16 4 868 SJ 6 0.2586 0.0421 0.1974 0.0544 0.2096 10.0524 17 2 962 Tat 2 0.2462 0.0421 0.1973 0.0537 0.2089 10.7668 Original population average 0.1552 Proc: Provenances; Prog: Progenies f: phenotypic value individual or field measurement; a: predicted additive genetic effect; u + a: genetic value predicted additive; Ne: effective population size. Table 8 Analysis of variance between progenies of P. maximinoi (15-64-36C1) at 8 years ( Appendix ) Mean Squares Source DF Height (m) DBH (Cm) Volume (m 3 /tree) Survival (%) Block 3 0.2528 ns 1.0887 ns 0.00121 ns 114.583 ns Progenies 19 2.8310 ns 6.2688 * 0.00218 * 221.308 ns Residue 57 2.3198 2.9767 0.00103 153.570 CV exp 9.87 10.13 19.83 13.13 Mean 1 15.43 8,16 0.1617 94.38 where * = significant at 5% probability; ** = significant at 1% probability; ns = not significant; 1 = overall mean of provenances, including the control; DF = degrees of freedom; CV Exp = experimental coefficient of variation. Table 9 Analysis of variance for the progeny level within provenances of P. maximinoi (15-64-36C1) at 8 years ( Appendix ) Provenances DF Height (m) DBH (Cm) Volume (m 3 /tree) MSq Z Cv exp (%) MSq Z Cv exp (%) MSq Z Cv exp (%) Cob 3 1.2584 ns 5.81 2.3717 ns 6.29 0.0016 ns 17.25 SJ 3 2.0611 ns 6.88 2.2852 ns 5.03 0.0014 ns 11.95 Tla 5 6.8787 ns 15.30 13.1658 ns 17.22 0.0038 ns 29.33 Yus 4 0.0711 ns 5.64 5.0349 ** 3.88 0.0018** 10.80 Overall mean 1 15.42 17.37 0.1625 where * = significant at 5% probability; ** = significant at 1% probability; ns = not significant; 1 = overall mean of provenance, not including control; DF = degrees of freedom; Z = F test for progenies within provenances; CV Exp = experimental coefficient of variation. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2456600","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":166505569,"identity":"c6759120-46ad-46b0-b776-7ad6ad8ecb6a","order_by":0,"name":"Cremildo Riba Gouveia Dias","email":"data:image/png;base64,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","orcid":"","institution":"Agricultural Research Institute of Mozambique - Northwest Zonal Centre - Agricultural Station of Lichinga","correspondingAuthor":true,"prefix":"","firstName":"Cremildo","middleName":"Riba Gouveia","lastName":"Dias","suffix":""},{"id":166505571,"identity":"0c89d920-6391-40f6-bfeb-2156d22af84d","order_by":1,"name":"Laurina Adriano Guacha","email":"","orcid":"","institution":"Green Resources Niassa, SA","correspondingAuthor":false,"prefix":"","firstName":"Laurina","middleName":"Adriano","lastName":"Guacha","suffix":""},{"id":166505573,"identity":"b8bea7e6-4c77-48b1-af7b-953049e4d27f","order_by":2,"name":"Aires Afonso Mbanze","email":"","orcid":"","institution":"Universidade Lúrio","correspondingAuthor":false,"prefix":"","firstName":"Aires","middleName":"Afonso","lastName":"Mbanze","suffix":""}],"badges":[],"createdAt":"2023-01-08 19:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2456600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2456600/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31484518,"identity":"9ae204c9-bb3b-451a-84aa-9671a5621e06","added_by":"auto","created_at":"2023-01-12 14:35:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202653,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the study area\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFonte:\u003c/strong\u003e O Autor\u003csup\u003e1\u003c/sup\u003e (2022)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/aa4fa84187f88842c3c2f0ef.jpeg"},{"id":31483818,"identity":"f876bfff-3e83-453d-b207-d08c164a6b4a","added_by":"auto","created_at":"2023-01-12 14:27:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":411276,"visible":true,"origin":"","legend":"\u003cp\u003eField trial of \u003cem\u003eP. maximinoi \u003c/em\u003eestablished in the company Florestas do Niassa, Mussa locality. Photo taken at the age of 4, on the left, is the Camcore plaque of three experiments described, including the one of \u003cem\u003eP. maximinoi\u003c/em\u003e evaluated in this study.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/4f8fd8a23115bb327589161c.png"},{"id":31484519,"identity":"b841b5d0-998e-476d-a41b-bb7c2e5108f8","added_by":"auto","created_at":"2023-01-12 14:35:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52561,"visible":true,"origin":"","legend":"\u003cp\u003eRelative performance of \u003cem\u003eP. maximinoi\u003c/em\u003e(15-64-36C1)\u003cem\u003e \u003c/em\u003eprovenances in Chimbonila district\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/e740feed2adecdb54ebc7a29.jpg"},{"id":31483812,"identity":"9e7eb066-2cab-4b3b-af9a-7caef65a06f6","added_by":"auto","created_at":"2023-01-12 14:27:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68871,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual increment (in volume) of the provenances of\u003cem\u003e P. maximinoi\u003c/em\u003e(15-64-36C1)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/5b04129cd72f128d1fda9c67.jpg"},{"id":31483816,"identity":"877b8167-39c8-4341-a88b-e4a46b0b4f06","added_by":"auto","created_at":"2023-01-12 14:27:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59009,"visible":true,"origin":"","legend":"\u003cp\u003eCluster dendrogram of the different progenies and procedures of\u003cem\u003e P. maximinoi\u003c/em\u003e (15-64-36C1) at 8 years\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/0bae2abacd7f4e1742a51916.jpg"},{"id":34319123,"identity":"62b7ca9c-8523-4db0-b39b-912de4a39be8","added_by":"auto","created_at":"2023-03-15 21:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1431983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/4ce0f764-6ecb-4187-afba-97622a20a082.pdf"},{"id":31483814,"identity":"bf7e6b3e-2431-4b84-9116-58e7106dacf8","added_by":"auto","created_at":"2023-01-12 14:27:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28667,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-2456600/v1/17dc895b3e726a0daa64f20f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Growth and adaptability of provenances and progenies of Pinus maximinoi h. E. Moore in northern Mozambique","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInformation available indicates that the first plantations in Mozambique date from the 19th century with the planting predominantly of Eucalyptus in the then Loure\u0026ccedil;o Marques now Maputo, with the aim of drying the marshes in the lower part of the city (MINAG - Minist\u0026eacute;rio da Agricultura \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The post-national independence period was marked by the development of plantations with fast-growing forest species to supply firewood and charcoal to the populations of the three largest urban centres, Maputo, Beira and Nampula and their surroundings, with the aim of reducing the pressure that was already beginning to be felt on the native forest around the large urban centres (MINAG 2009).\u003c/p\u003e \u003cp\u003eIn this framework, the FO1 projects were created in Manica and FO2 in Marracuene, most of them with the species \u003cem\u003eEucalyptus saligna\u003c/em\u003e, \u003cem\u003eEucalyptus grandis\u003c/em\u003e, \u003cem\u003ePinus patula\u003c/em\u003e and \u003cem\u003eCasuarina equisetifolia\u003c/em\u003e, which were abandoned at the height of the civil war of the 16 years, between 1977 and 1992 (MINAG 2006). Around 1980, the first forestry research was started, where the introduction and selection tests of species and provenances were carried out, seedling production tests in nurseries and tests of forestry techniques in the establishment of plantations. However, the expected success was not achieved, but they did contribute to doubling the forest area the country had at the time of the proclamation of independence, from 20,000 ha in 1975 to approximately 42,000 ha in 1992 (MINAG 2006).\u003c/p\u003e \u003cp\u003eAfter the end of the civil war, there was a need to attract new investments for plantations with fast-growing exotic species, with the aim of i) recovering unproductive land abandoned due to itinerant agriculture (INDE - Instituto Nacional do Desenvolvimento da Educa\u0026ccedil;\u0026atilde;o \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); ii) creating jobs; and iii) reducing the pressure on native forests due to population growth (Landry and Chirwa \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nube et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zanella et al. 2018). Niassa province attracted most of the investments due to the availability of land due to low population density.\u003c/p\u003e \u003cp\u003eMozambique is well placed to expand the afforestation of multipurpose plantations, including an increasing need for forest products and the availability of land. Increasing the country's forest plantation area from the current 60,000 ha to over one million by 2030 would have the potential to create 250,000 jobs and produce \u003cspan\u003e$\u003c/span\u003e1.5\u0026nbsp;billion in manufactured goods (MINAG 2009). According to the (Governo da Prov\u0026iacute;ncia do Niassa \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) of the estimated potential for the province, the establishment of forest plantations of fast-growing species is based on selection criteria for areas suitable for commercial plantations. The Lichinga Plateau is the region with the greatest potential for the development of commercial plantations, and the most productive areas are located in the districts of Nga\u0026uacute;ma, Sanga, Muembe and Lichinga.\u003c/p\u003e \u003cp\u003eThe main species used to establish these plantations were \u003cem\u003eEucalyptus grandis\u003c/em\u003e, \u003cem\u003ePinus elliottii\u003c/em\u003e, \u003cem\u003eP. patula\u003c/em\u003e and \u003cem\u003eP. taeda\u003c/em\u003e, which already existed in the settlements dating from colonial times. Later, they were also introduced, \u003cem\u003eP. maximinoi\u003c/em\u003e, \u003cem\u003eP. tecunumanii\u003c/em\u003e and \u003cem\u003eP. oocarpa\u003c/em\u003e, with the recommendation of the Central America and Mexico Coniferous Resources Cooperative (CAMCORE). The introduction of \u003cem\u003ePinus maximinoi\u003c/em\u003e species for the first time in Niassa, particularly in the district of Chimbonila, was established at the same time as the experiments, since there was no information on adaptability, growth, and production of exotic species in the Niassa environment. However, the literature shows that this species is compatible with Niassa's soil and climate conditions. For its success and profitability, it is important to identify species that best adapt and grow rapidly, and this is only possible through species and provenance testing.\u003c/p\u003e \u003cp\u003eIn this context, Empresa Florestas de Niassa Limitada (FdN), with the help of CAMCORE, established seven (7) experiments of tropical pine (L\u0026oacute;pez [s.n.]). One of these experiments is the object of the present study, which aims to evaluate the growth and adaptability of provenances and progenies of \u003cem\u003eP. maximinoi\u003c/em\u003e H. E. Moore in the environment of Chimbonila, northern Mozambique. The research questions raised by Dias and Mbanze (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in the study on the growth and adaptability of provenances and family-within-provenance of \u003cem\u003ePinus tecunumanii\u003c/em\u003e in northern Mozambique are part of this study. These questions were answered from the evaluation of results at 2, 4 and 8 years of age of a \u003cem\u003eP. maximinoi\u003c/em\u003e trial established in the test fields of the company Niassa Forests.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. The sites\u003c/h2\u003e \u003cp\u003eThe experimental site was at Chimbonila, Niassa Province in Mozambique\u0026rsquo;s North Region, belongs to Company Floresta de Niassa limited. The trial was situated at latitude 13\u003csup\u003eo\u003c/sup\u003e14'14,8''S and longitude 35\u003csup\u003eo\u003c/sup\u003e30'46,5''E at an altitude of 1180 meters above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The climate of the site is humid temperate (Cwb), with two well-defined seasons: Summers temperate and rainy and cold winters and dry. The area receives a mean annual rainfall over 1200 mm and may exceed this value and reach 1400 mm of rainfall and experience mean annual temperatures ranging from 18 to 24\u0026deg;C, but generally less than 22\u0026deg;C (Minist\u0026eacute;rio da Administra\u0026ccedil;\u0026atilde;o Estatal \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The soils at the site are mostly clay, deep reds and have low susceptibility to erosion (Shimanikire \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e-------------------------------------\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e--------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Seed Sources\u003c/h2\u003e \u003cp\u003eThe seeds used in this experiment are only part of the existing variation for the species, including Nicar\u0026aacute;gua, Guatemala and Honduras, which were obtained from the CAMCORE. This comprised 19 half-sib families and four (4) provenances of \u003cem\u003eP. maximinoi\u003c/em\u003e and was used as a seed control from the Guatemala seed production area. The relationship provenances evaluated with their respective geographical locations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e-------------------------------------\u003c/p\u003e \u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e--------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Experimental design and establishment\u003c/h2\u003e \u003cp\u003eThe seedlings were planted on site in January 2011 using a randomized complete block design at the provenance level, followed by randomisation of families within provenances. There were four (4) replications and six (6) trees per family planted in line plots. The spacing adopted was 3.0 m x 3.0 m between plants with a double border around the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e of \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e). There was no soil preparation, only manual pits with a depth of 35 cm were used for seedling planting.\u003c/p\u003e \u003cp\u003eAt the time of planting, a hydrogel system was applied to radical the seedlings by immersion in a solution at a concentration of 10 g/litre. The cover fertilization was performed thirty days following planting, with NPK 100 g (17.38% \u0026minus;\u0026thinsp;28.51% \u0026minus;\u0026thinsp;4%) and adding 5% sulphur, 0.8% zinc and 0.5% boron per hole. These dosages were determined by analysis of soil samples made earlier. Formation pruning was effectuated in 2013 to 2,8 years of age. Production pruning was effectuated in August 2015 and September 2017. Additionally, total cleaning was performed (weeds) once a year in the first 4 years after planting. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows two pictures of the test.\u003c/p\u003e \u003cp\u003e-------------------------------------\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e--------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data collection and analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Data collection\u003c/h2\u003e \u003cp\u003eIn this trial, data from three (3) measurements over time, from 2013 to 2018, i.e., from 2 to 8 years old, were considered. The parameters measured in the trial were height in meters (2, 4 and 8 years) and diameter at breast height (DBH) only at 4 and 8 years old. The height measurement was made with a \"Vertex IV\" hypsometer and a graduated stick with 10 cm accuracy when needed. The DBH of the trees was measured with a calliper. Survival was also evaluated from the living tree count in the second, fourth and eighth years.\u003c/p\u003e \u003cp\u003eVolume per tree was calculated using the following formula (Ladrach 1986) cited in Hodge and Dvorak (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e); Gapare et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); Hodge and Dvorak (2015): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{V}\\text{o}\\text{l}=\\text{0,0003}\\times {\\text{D}\\text{B}\\text{H}}^{2}\\times \\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\)\u003c/span\u003e\u003c/span\u003e (1).\u003c/p\u003e \u003cp\u003ewhere the volume is in cubic meters (m\u003csup\u003e3\u003c/sup\u003e) and DBH is the over bark diameter at breast height (cm) and height in meters (m).\u003c/p\u003e \u003cp\u003eSurvival was obtained from the count number of living trees and was expressed as a percentage relative to the number of unique experimental trees. According to the formula used by Cornacchia et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Surv\\%=\\frac{NIV}{NTI}\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere NIV is the number of living individuals by provenance, and NTI is the total number of individuals per provenance.\u003c/p\u003e \u003cp\u003eA comparison of the relative position of each provenance in height growth was made on performance over the known methods discussed by Burdon (1998) and used by (Mora (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Thus, various ratings range from 0 to 100, and the formula is:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$DR=\\frac{X-{X}_{minor}}{{X}_{major}-{X}_{minor}}\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere DR is the relative performance (%); X is the average of the evaluated origin; X\u003csub\u003eminor\u003c/sub\u003e is the provenance of the worst performance; and X\u003csub\u003emajor\u003c/sub\u003e is the origin of better performance.\u003c/p\u003e \u003cp\u003eFor comparison, the average annual volume increments of provenances were calculated by the formula used by Shimizu and Higa (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1981\u003c/span\u003e); Kietzka (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1988\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${m}^{3}/ha year=\\frac{\\stackrel{-}{V}\\times n\\times \\% Surv}{1\\times 100}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{V}\\)\u003c/span\u003e\u003c/span\u003e is the arithmetic average volume per tree; n is the number of trees planted per hectare; % Surv is the percentage of survival; and 1 is the age in years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Data analysis\u003c/h2\u003e \u003cp\u003eAll analyses and results in this present work were based on eight years of data. Data were analysed using Selegen Reml/Blup (Federal University of Vi\u0026ccedil;osa, Minas Gerais, Brazil) and the SPSS computer programme.\u003c/p\u003e \n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003e\u003cspan style=\"text-align: inherit;\"\u003eAnalysis of variance (ANOVA) was performed on DBH, height and survival at both sites for both provenances and families. If the ANOVA was significant, Tukey\u0026rsquo;s test was subsequently performed to identify different groups of families in the provenances. Clustering dendrogram analysis and representation were performed to illustrate the above results of the ANOVA between different sources and progeny and thus identify similar groups regarding the growth characteristics (height, DBH and volume). The method used was the agglomeration Ward\u0026apos;s and the Minkowski distance since all variables were continuous. The value of Eta\u003c/span\u003e\u003csup style=\"text-align: inherit;\"\u003e2\u003c/sup\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u0026nbsp;(variation percentage between the clusters) and the mean test between groups were used to determine the optimal number of clusters of provenances and progeny by retaining and thus obtaining a good standard view of joint variation among the provenances and progeny.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eAfter identification of groups, the 9 best progenies were selected based on the predicted additive genetic effect and genetic gain (selection 45% of progeny). Based on the genetic improvement of the general average of the variable volume, they were also ranked in the top 17 subjects (4% will check the level of individuals of the progenies within provenances). This classification aims to select the best progenies and individuals with higher volumetric production for subsequent generations of breeding and commercial plantations. To select the variable with the highest influence on the productivity of the progeny, the Pearson correlation coefficient (PCC) between the volume and the height or DBH was used, depending on the variable with the highest PCC.\u003c/li\u003e\n \u003cli\u003eThe estimates of the variance components and genetic parameters were based on the restricted maximum likelihood method (REML) of the individual progenies from statistical model 5 (Test half-sib progeny, with a randomized block design. This was done with the aid of the computer program SELEGEN-REM / BLUP (Resende \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe calculation of variance components was obtained by the formulas proposed for (Resende \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${h}_{a}^{2}=\\frac{{\\sigma }_{a}^{2}}{{\\sigma }_{f}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the individual heritability in the narrow sense; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the additive variance; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{f}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the individual phenotypic variance.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${CV}_{gi}\\left(\\%\\right)=\\frac{\\sqrt{{\\sigma }_{a}^{2}}}{\\stackrel{-}{X}}\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{gi}\\)\u003c/span\u003e\u003c/span\u003e (%) is the individual additive genetic coefficient of variation and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e is the overall mean of the original population.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${CV}_{d}\\left(\\%\\right)=\\frac{\\sqrt{{\\sigma }_{d}^{2}}}{\\stackrel{-}{X}}\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{d}\\)\u003c/span\u003e\u003c/span\u003e (%) is the residual coefficient of variation of the experiment and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{d}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the residual variance of the experiment.\u003c/p\u003e \u003cp\u003eEstimates of the gains with the selection of progeny were obtained according to the proposal by (Cruz \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(GS \\left(\\%\\right)=\\frac{GS}{\\stackrel{-}{{X}_{0}}}\\times 100\\)\u003c/span\u003e\u003c/span\u003e, being \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(GS={h}_{a}^{2}\\times DS\\)\u003c/span\u003e\u003c/span\u003e (8)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere GS is the genetic gain by selection; DS is the selection differential; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\stackrel{-}{X}}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the overall mean of the original population.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$DS={\\stackrel{-}{X}}_{s}-{\\stackrel{-}{X}}_{0}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\stackrel{-}{X}}_{s}\\)\u003c/span\u003e\u003c/span\u003eis the average of the selected progenies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.1. Relative performance of the provenances\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the relative performance of 4 origins of \u003cem\u003eP. maximinoi\u003c/em\u003e in 2, 4 and 8 years of evaluation, showing that the provenance of the San Jeronimo (SJ) maintained the same position qualifying of the relative performance in 2 and 4 years equal to 100% and having lowered its performance relative to 8 years for 93.79%. Another type of situation happens with the control (Common lot) that his relative performance is always increasing over the years (10.72%, 69.18% and 100%), having if detached like the second-best relative performance. Yuscaran (Yus) showed, on average, the third best relative performance, especially at 8 years.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n \u003cp\u003eThe provenance of the Tatumbla (Tla) not only presented a performance very much down to 2 and 4 years but also tended to increase his performance with growth. For the original Coban (Cob), he presented the worst performance every year, except for 2 years, when it was 19.43%, which surpassed the control by nearly 9% to more.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e3.2. Growth and adaptability of the provenances\u003c/h2\u003e\n \u003cp\u003eThe results of the ANOVA and mean test (Tukey) of different origins of \u003cem\u003eP. maximinoi\u003c/em\u003e tested in Chimbonila are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The degree of survival was significant at the 5% level at 2 years, and no differences were found in the last two years studied and ranged from 83.33 to 97.92% in the period. There were no marked differences from the heights in year 4, although the origins San Jeronimo (SJ) were higher than in the rest. At both two and eight years, there were no significant differences between the provenances. The DBH and volume parameters at 4 years or 8 years did not reveal significant differences between sources. The experimental coefficients of variation (CV%) in provenances were low for all parameters in the period studied, except for the volume to 4 years age.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n \u003cp\u003eThe annual average increase (m\u003csup\u003e3\u003c/sup\u003e * ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) from all sources was higher than that of the control except for the Yuscaran origin (Yus) at 4 years, and there were no differences after 8 years (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The Mean Annual Increments of Coban origins (Cob), Tatumbla (Tla) and Yuscaran (Yus) produced the equivalents of 113.65% and 114.34%, 102.8% and 112.06% and 100.46% and 106.2% of the Annual Average Increase control at 4 and 8 years old, respectively. San Jeronimo (SJ) had a higher mean annual increment than the other origins and the control (150.81% and 127.58%) for years 4 and 8, respectively, although there were no significant differences for 8 years.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.3. Variation in growth among the progenies\u003c/h2\u003e\n \u003cp\u003eThe results of Tukey\u0026apos;s test for averaging pairs among the progeny from all sources are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The Tukey test was performed after the ANOVA assumed that the progeny showed significant values of the DBH and volume to 8 years old, as shown in the accompanying Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e in the \u003cspan class=\"InternalRef\"\u003eappendix\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFor the DBH parameter, the best progeny were Coban (15\u0026ndash;884), San Jeronimo (15\u0026ndash;868 and 15\u0026ndash;875) and Tatumbla (15\u0026ndash;962), whereas no statistically significant differences between them were observed, and they had a relative superiority over the other, contrary to Tatumbla (15\u0026ndash;950), which had the worst performance of all progenies.\u003c/p\u003e\n \u003cp\u003eRegarding the volume parameter, the average progeny 15\u0026ndash;962 is the best of all, and the worst progeny is 15\u0026ndash;950 for both Tatumbla provenances. An important observation is in relation to the control, which showed a growth above the average of progeny.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n \u003cp\u003ePerson\u0026apos;s correlation coefficients (CCP) showed a strong positive (\u0026gt;\u0026thinsp;0.9) and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) correlation between the DBH and volume parameters for all progenies (see the last column of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo better visualize the average of the test results of the origins of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the progeny of a cluster analysis were made, and subsequently, an ANOVA and mean test were performed on their respective clusters. The results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and it can be easily observed that the two-cluster solution was the best choice.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n \u003cp\u003eThe first cluster comprised the 9 (45%) best progenies, mainly of origins Yuscaran (Yus) and San Jeronimo (SJ), and showed superiority in all parameters (height, DBH and volume). The last cluster contained 11 (55%) progenies with performance below the overall average and was mainly composed of the family of the Tatumbla (Tla) provenance. The control also belongs to the last cluster.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.4. Selection of progenies based on phenotypic characters\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents estimates of variance components and genetic parameters of the progenies of \u003cem\u003eP. maximinoi\u003c/em\u003e at 8 years old. It is noted that by breaking the individual variance components, most of the total phenotypic variance of all the studied parameters was residual variance due to (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{d}^{2}\\)\u003c/span\u003e\u003c/span\u003e) and individual phenotypic variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{f}^{2}\\)\u003c/span\u003e\u003c/span\u003e). The individual heritability in the narrow sense (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e) allows us to predict moderately favourable conditions for the selection of families. However, the coefficient of determination of plot effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\complement }_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e) and the effect of provenances (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\complement }_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e) showed low magnitude values. The individual additive genetic variation coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{gi}\\)\u003c/span\u003e\u003c/span\u003e%) and genotypic variation coefficients among progenies (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{gp}\\)\u003c/span\u003e\u003c/span\u003e%) were high for all characters, and residual variation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{d}\\)\u003c/span\u003e\u003c/span\u003e%) was of low magnitude for height and DBH and medium magnitude for the variable volume. Finally, the relationship between the genetic coefficient of variation and the residual coefficient of variation showed values close to and above one (1).\u003c/p\u003e\n \u003cdiv\u003e\\({\\sigma }_{a}^{2}\\): additive genetic variance; \\({\\sigma }_{e}^{2}\\): environmental variance between plots; \\({\\sigma }_{p}^{2}\\): genetic variance among provenances; \\({\\sigma }_{d}^{2}\\): residual variance; \\({\\sigma }_{f}^{2}\\): phenotypic variance at the individual level in the experiment; \\({h}_{a}^{2}\\): individual heritability in the narrow sense; \\({\\complement }_{e}^{2}\\): coefficient of determination of plot effects; \\({\\complement }_{p}^{2}\\): coefficient of determination of the effects of origins; \\({CV}_{gi}\\)%: individual additive genetic coefficient of variation; \\({CV}_{gp}\\)%: coefficient of genotypic variation among progenies; \\({CV}_{d}\\)%: coefficient of residual variation.\u003c/div\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n \u003cp\u003eBased on the predicted additive genetic effect (a) and genetic gain, nine (9) improved progenies were then classified, corresponding to 45% of the parent selection by volume, as shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. It is noted that provenances Yuscaran (Yus) and Tatumbla (Tla) had a higher number of selected progenies, with three each. This was followed by the provenance San Jeronimo, with two selected progenies, and the Coban origin contributed only progeny.\u003c/p\u003e\n \u003cp\u003e-------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the desirable rating of the 17 (4%) best individuals selected from 20 progeny \u003cem\u003ePinus maximinoi\u003c/em\u003e continued for breeding. The classification criterion was based on the genetic improvement of the general average volume parameter. The San Jeronimo (SJ) origin contributed to the greater number of individuals selected, totalling 34% (6), followed by the Coban origin (Cob) to 29.4% (5) of selected individuals, all individuals of the progeny 15\u0026ndash;884. The Tatumbla origin (Tla) contributed 23.5% (4) of individuals, and finally, the Yuscaran (Yus) contributed 11.8% (2) of individuals.\u003c/p\u003e\n \u003cp\u003e------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e--------------------------------------\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Relative performance, growth, and adaptability of the provenances\u003c/h2\u003e \u003cp\u003eThe control (common lot) presented superior relative performance in relation to the other provenances tested in the Chimbonila environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), demonstrating a greater potential for growth of seeds collected in the production area of seeds of \u003cem\u003eP. maximinoi\u003c/em\u003e originating from Guatemala, as has been verified in other studies (Fier \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). According to the general averages of height, DBH and volume in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, they are similar to the values found by Gapare et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) for \u003cem\u003ePinus maximinoi\u003c/em\u003e in experiments that were evaluated in South Africa (12.6 m, 17.8 cm and 0.12 m\u003csup\u003e3\u003c/sup\u003e/tree).\u003c/p\u003e \u003cp\u003eWhen analysing the survival rates, there were no statistically significant differences, and all the varieties, including the control (common lot), showed average values above 90% with a coefficient of variation of high precision. Similar trends can be observed in the studies of Dias and Mbanze (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Mozambique, leading to the assumption that for this species, the varieties show good potential for adaptability in the Chimbonila environment. The survival values of the present research are above those found (see, for example, (Kietzka \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Wright \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Gapare et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ettori et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lopez-Upton et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the results provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the growth and adaptability of \u003cem\u003eP. maximinoi\u003c/em\u003e provenances of the trial established in the Chimbonila environment indicated an acceptable level of experimental accuracy by presenting values of low magnitude in all parameters evaluated and showing good statistical efficiency for the analysis. Gapare et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Santos et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found coefficients of variation higher than those in the present research. However, there are also tests with coefficient values close to the present study, as is the case of Fier (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe low values of the experimental coefficient of variation suggest a small phenotypic variation within plots, which explains the lack of genetic differences between provenances. However, in general, the experimental design was very reliable in capturing the growth and adaptability of progenies of \u003cem\u003eP. maximinoi\u003c/em\u003e in the Chimbonila environment, since the coefficient of variation values of the measured variables were below 20%, as recommended by Pimental Gomes (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1990\u003c/span\u003e); Pimentel-Gomes and Garcia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean annual volumetric increment revealed no significant differences between the provenances at 8 years. Therefore, the volumetric increment is high, presenting an average of 21.22 m\u003csup\u003e3\u003c/sup\u003e/ha per year; however, it should be considered that this variable is directly influenced by the number of trees in the forest stand. The mean annual increment of the same provenances was well above the control but also above the results of other studies conducted in Africa (see, for example, (Hodge and Dvorak, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dias and Mbanze, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Variation in growth among the progenies\u003c/h2\u003e \u003cp\u003eThe author did not detect significant differences at the 5% level between the means of the \u003cem\u003eP. maximinoi\u003c/em\u003e varieties tested in Chimbonila at eight years of age for the characteristic height, DBH and volume. These results corroborate those of Nyoka (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Fier (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) that were observed in the regions of Zimbabwe and Brazil, respectively. Atie et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Santos et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported significant variations among provenances of \u003cem\u003eP. maximinoi\u003c/em\u003e in Brazil for height, DBH and volume, indicating that they can be explored in subsequent breeding cycles. However, the percentage variation between the best and worst mean DAP and volume of the progenies were 26.78% and 43.34%, respectively, which are characterized as significant differences, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, suggesting that the population has moderate genetic variation and therefore a weighted possibility of genetic improvement by selection among progenies.\u003c/p\u003e \u003cp\u003eAccording to the analysis unfolding at the progeny level, it is possible to see two distinct groups of good and bad performance, indicating that not all progenies contribute in the same way to the overall performance of the progeny. The group with higher participation in the performance of the progenies contributed 45% of the progenies, as it was possible to identify with the help of cluster analysis. These progenies are predominantly from Yuscaran (Yus) and San Jeronimo (SJ).\u003c/p\u003e \u003cp\u003eHigh correlation coefficient values (CC\u0026thinsp;\u0026ge;\u0026thinsp;90%) were found between DBH and volume in tests of provenances and progenies of \u003cem\u003eP. maximinoi\u003c/em\u003e, demonstrating that the selection can be based on DBH, without prejudice to the volume character, agreeing with what occurred with studies carried out by other authors for forest species such as (Munthali and Stewar \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sampaio et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fier \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sampaio et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Nascimento \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Moreira et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Souza et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Biernaski \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dias and Mbanze, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), among others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Selection of progenies based on phenotypic characters\u003c/h2\u003e \u003cp\u003eThe estimates of heritability in the restricted sense (h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e) showed values of medium magnitude for all growth characters ranging from 0.18 to 0.32, indicating the existence of moderate genetic control for all characteristics. Heritability values from 0.01 to 0.15 are considered low; from 0.15 to 0.50 medium; and above 0.50 high (Resende \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The heritability found is similar to those obtained by Sampaio et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); Gapare et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); Biernaski (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and lower than those reported by (Wright et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Resende (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) noted that a heritability of 0.20 is a reasonable value for growth characteristics in forest species.\u003c/p\u003e \u003cp\u003eThe coefficients of determination of plot effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\complement }_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e) were less than 5% for the DBH and volume characters and greater than 10% for the height character. According to Resende (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the recommended values for estimates of the coefficients of determination of plot effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\complement }_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e) are values equal to or lower than 10%. According to Farias Neto et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), these values demonstrate that there is greater genetic control than environmental variation between the plots and within blocks.\u003c/p\u003e \u003cp\u003eJudging by the values of coefficient of individual additive genetic variation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{gi}\\)\u003c/span\u003e\u003c/span\u003e%) and coefficient of genotypic variation among progenies (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{gp}\\)\u003c/span\u003e\u003c/span\u003e%) obtained in all characters were high, indicating the existence of heritable genetic variation in the population, therefore, selection can be done at both individual and progeny levels. Coefficients of genetic variation above 7% are considered high by (Sebbenn et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the relationship between individual additive genetic variation coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{gi}\\)\u003c/span\u003e\u003c/span\u003e%) and coefficient of residual variation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CV}_{d}\\)\u003c/span\u003e\u003c/span\u003e%), values were found to be close (height) and greater than one unit for DBH and volume characters. These results show a favourable situation for early selection at the DBH character level. This relationship, according to Vencovsky and Barriga (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), when close to or greater than one unit indicates a very favourable situation for obtaining genetic gains with progeny selection.\u003c/p\u003e \u003cp\u003eThrough the individual BLUP (best linear prediction without bias) procedure, the 9 best \u003cem\u003eP. maximinoi\u003c/em\u003e progenies were selected with potential for a second-generation breeding programme, highlighting the Yuscaran (Yus) and Tatumbla (Tla) provenances with the contribution of 3 progenies within each provenance. It is important to report that the individual BLUP procedure is equivalent to the multi-effect index selection method (Resende et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Resende \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The genetic values of the 9 progenies provided a genetic gain of 5.19%, increasing the average volume character from 0.1552 m\u003csup\u003e3\u003c/sup\u003e/tree to 0.1803 m\u003csup\u003e3\u003c/sup\u003e/tree.\u003c/p\u003e \u003cp\u003eThe same method of progeny selection was applied to individual trees, where the 17 best individuals were selected for an effective population size (Ne) equal to 10.8 based on the volume character. It is noteworthy that the progenies 884 (Cob), 868 and 875, both from San Jeronimo, showed the best results, especially progeny 884, which was repeated 5 times. This selection increased the average of the character from 0.1552 m\u003csup\u003e3\u003c/sup\u003e/tree to 0.2089 m3/tree, presenting a genetic gain of at least 11.11%. The gains estimated with the selection can be considered promising due to the early age of the evaluation. Therefore, the effective population size is small (10.8), which limits the possibility of new selections. Resende (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); Vieira and Shimizu (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) emphasise that an effective population size of 60 is necessary to maintain genetic variability in recurrent selection in short- and long-term improvement. In the present study, to meet (effective population size of 60) the recommendations of Resende (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); Vieira and Shimizu (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), it would be necessary to select 352 individuals for the volume character to maintain the effective size at an acceptable level. With this number of selected individuals, it would provide a genetic gain of 1.7%, increasing the average volume from 0.1552 m\u003csup\u003e3\u003c/sup\u003e/tree to 0.1634 m\u003csup\u003e3\u003c/sup\u003e/tree. With this new average, it would still be acceptable to form an improved population for future generations since it is higher than the original average of the population.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cem\u003ePinus maximinoi\u003c/em\u003e showed suitable growth and performance in the 4 provenances, including the control (common lot) in the Chimbonila region, and can be recommended for commercial plantations in the region.\u003c/p\u003e \u003cp\u003eThere were no significant differences between the provenances studied in all growth characteristics at 8 years of age, and any of the provenances can be used for reforestation.\u003c/p\u003e \u003cp\u003eThe phenotypic association between the growth characteristics DBH and volume is strong for all progenies, suggesting that selection can be made based on DBH, which is the variable of easy access.\u003c/p\u003e \u003cp\u003eThe genetic control of the growth characters measured by the coefficient of determination of plot effects and coefficient of additive genetic variation demonstrated a high potential for genetic improvement of the population.\u003c/p\u003e \u003cp\u003eThe selection of the 17 individuals with higher genetic values within the provenances evaluated allowed the elevation of the average volume from 0.1552 m\u003csup\u003e3\u003c/sup\u003e/tree to 0.2089 m\u003csup\u003e3\u003c/sup\u003e/tree, with genetic gains of 11.11%.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the company Florestas de Niassa, Lda; the engineer Jos\u0026eacute; Bernardo Manteiga, engineer Cust\u0026oacute;dio da Concei\u0026ccedil;\u0026atilde;o and Misters John Mkumbira, H\u0026eacute;lio Raul, Abudo Issufo, Candawele Abudo and Agostinho Adriano for their assistance and collaboration during data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cremildo Riba Gouveia Dias, Laurina Adriano Guacha and Aires Afonso Mbanze. The first draft of the manuscript was written by Cremildo Riba Gouveia Dias,\u0026nbsp;and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAtie W, Mbinga J, Cherono F, Omondi S, Bala P, Muchiri MN, Chagala-Odera E (2018) Growth Performance of Second-Generation \u003cem\u003ePinus maximinoi\u003c/em\u003e and \u003cem\u003eP. tecunumanii\u003c/em\u003e Progeny Trials at Turbo, Kenya. 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Universidade de S\u0026atilde;o Paulo\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNube TG, Santos A, Junior RT, Silva IC (2016) \u003cem\u003eImpactos Socioecon\u0026ocirc;micos das Planta\u0026ccedil;\u0026otilde;es Florestais no Socioeconomic Impacts of Forest Plantations in Niassa, Mozambique\u003c/em\u003e. \u003cem\u003e23\u003c/em\u003e(1), 52\u0026ndash;60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyoka BI (1994) Provenance variation in \u003cem\u003ePinus maximinoi\u003c/em\u003e: a promising species for commercial afforestation in Zimbabwe. Commonw Forestry Association 73(1):47\u0026ndash;53\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePimental Gomes F (1990) \u003cem\u003eCurso de Estatistica Experimental\u003c/em\u003e (L. Nobel (ed.); 13th ed.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePimentel-Gomes F, Garcia CH (2002) Estat\u0026iacute;stica aplicada a experimentos agron\u0026ocirc;micos e florestais: exposi\u0026ccedil;\u0026atilde;o com exemplos e orienta\u0026ccedil;\u0026otilde;es para uso de aplicativos. FEALQ, p 309\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResende MDV (2002) \u003cem\u003eGen\u0026eacute;tica biom\u0026eacute;trica e estat\u0026iacute;stica no melhoramento de plantas perenes\u003c/em\u003e (Embrapa (ed.)). Embrapa Informa\u0026ccedil;\u0026atilde;o Tecnol\u0026oacute;gica, Colombo: Embrapa Florestas\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResende MDV (1995) Delineamento de experimentos de sele\u0026ccedil;\u0026atilde;o para maximiza\u0026ccedil;\u0026atilde;o da acur\u0026aacute;cia seletiva e do progresso gen\u0026eacute;tico. Revista \u0026Aacute;rvore 19(4):479\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResende MDV (2014) Selegen Reml/Blup \u0026ndash; Sistema Estat\u0026iacute;stico e Sele\u0026ccedil;\u0026atilde;o Gen\u0026eacute;tica Computadorizada - Manual Complementar do Selegen-Reml/Blup 2014, vol 2014. Universidade Federal de Vi\u0026ccedil;osa\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResende MDV (2007) \u003cem\u003eMatem\u0026aacute;tica e estat\u0026iacute;stica na an\u0026aacute;lise de experimento e no melhoramento gen\u0026eacute;tico\u003c/em\u003e (Embrapa Florestas (ed.); 1\u003csup\u003ea\u003c/sup\u003e Edi\u0026ccedil;\u0026atilde;o). Colombo: Embrapa Florestas\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResende M, Oliveira E, Goulart Junior F, Oaida G (1994) \u003cem\u003eSele\u0026ccedil;\u0026atilde;o Gen\u0026eacute;tica Computadorizada - Selegen - M\u0026oacute;dulo 1 - Best Prediction - Manual do Usu\u0026aacute;rio\u003c/em\u003e (EMBRAPA (ed.))\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampaio PDTB, Resende MDV, Ara\u0026uacute;jo AJ (2000) Estimativas de par\u0026acirc;metros gen\u0026eacute;ticos e m\u0026eacute;todos de sele\u0026ccedil;\u0026atilde;o para o melhoramento gen\u0026eacute;tico de \u003cem\u003ePinus caribaea\u003c/em\u003e var. hondurensis. Pesquisa Agropecu\u0026aacute;ria Brasileira 35(11):2243\u0026ndash;2253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampaio PTB, Resende MDV, Ara\u0026uacute;jo AJ (2002) Estimativas de par\u0026acirc;metros gen\u0026eacute;ticos e m\u0026eacute;todos de sele\u0026ccedil;\u0026atilde;o para o melhoramento gen\u0026eacute;tico de \u003cem\u003ePinus oocarpa\u003c/em\u003e Schiede. Pesquisa Agropecu\u0026aacute;ria Brasileira 37(5):625\u0026ndash;636\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos W, Silva MSC, Deniz LD, Kieras WS, Shimizu JY, Sousa VA, Aguiar AV (2018) Identifica\u0026ccedil;\u0026atilde;o de Proced\u0026ecirc;ncias e Prog\u0026ecirc;nies de \u003cem\u003ePinus maximinoi\u003c/em\u003e com potencial produtivo para madeira. Scientia Forestalis 46(117):127\u0026ndash;136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSebbenn AM, Siqueira ACMDF, Kageyama PY, Machado JAR (1998) Par\u0026acirc;metros gen\u0026eacute;ticos na conserva\u0026ccedil;\u0026atilde;o da cabre\u0026uacute;va - \u003cem\u003eMyroxylon peruiferum\u003c/em\u003e L.F. Alem\u0026atilde;o. Scientia Forestalis 53:31\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimanikire T (2011) \u003cem\u003eSoil Assessment Report for areas Earmarked for Eucalyptus Production (2011/12) Lichinga \u0026ndash; 1201 (Blocks A, B, C, E, F, G and O) and Massangulo \u0026ndash; 2101 (Blocks J and L)\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimizu JY, Higa AR (1981) Varia\u0026ccedil;\u0026atilde;o Racial do \u003cem\u003ePinus taeda\u003c/em\u003e L. no sul do Brasil at\u0026eacute; o Sexto ano de Idade. Bol de Pesquisa Florestal 2:1\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouza FB, Freitas MLM, Moraes MLT, Vilas Boas O, Sebbenn AM (2016) Selection of \u003cem\u003ePinus\u003c/em\u003e species and provenances for Assis region, State of S\u0026atilde;o Paulo. Scientia Forestalis/Forest Sciences 44(111):675\u0026ndash;682\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVencovsky R, Barriga P (1992) Gen\u0026eacute;tica biom\u0026eacute;trica no fitomelhoramento. Sociedade Brasileira de Gen\u0026eacute;tica (ed.)). Sbg\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVieira AH, Shimizu JY (1998) November) Avalia\u0026ccedil;\u0026atilde;o do potencial de produtividade de madeira de \u003cem\u003ePinus tecunumanii\u003c/em\u003e no Sul de Rond\u0026ocirc;nia.Boletim de Pesquisa, \u003cem\u003e24\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JA (1992) Eight Year Results from Provenance trials of \u003cem\u003ePinus caribaea\u003c/em\u003e var. hondurensis, \u003cem\u003eP. oocarpa\u003c/em\u003e and \u003cem\u003eP. tecunumanii\u003c/em\u003e in the Valle del Cauca, Colombia. Manager Forestal Research Smurfit Carton de Colombia 42(3):401\u0026ndash;407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JA, Osorio LF, Lambeth CC (1993) Development of a tree improvement program with \u003cem\u003ePinus maximinoi\u003c/em\u003e in Colombia. For Ecol Manag 62(1\u0026ndash;4):313\u0026ndash;322\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Provenance, progenies and soil and climate characterization of the place where the seeds were collected\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"902\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.742793791574279%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvenance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.312638580931264%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.747228381374724%\"\u003e\n \u003cp\u003e\u003cstrong\u003eState or Department\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Progenies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.42350332594235%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.977827050997783%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.645232815964523%\"\u003e\n \u003cp\u003e\u003cstrong\u003eElevation Range (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.534368070953438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall (mm/year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.64079822616408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.742793791574279%\"\u003e\n \u003cp\u003eCoban (Cob)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.312638580931264%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.747228381374724%\"\u003e\n \u003cp\u003eAlta Verapaz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.975609756097562%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.42350332594235%\"\u003e\n \u003cp\u003e15\u003csup\u003eo\u003c/sup\u003e 28\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.977827050997783%\"\u003e\n \u003cp\u003e90\u003csup\u003eo\u003c/sup\u003e 24\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.645232815964523%\"\u003e\n \u003cp\u003e1420-1440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.534368070953438%\"\u003e\n \u003cp\u003e2109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.64079822616408%\"\u003e\n \u003cp\u003e13.7 - 23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.742793791574279%\"\u003e\n \u003cp\u003eSan Jeronimo (SJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.312638580931264%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.747228381374724%\"\u003e\n \u003cp\u003eBaja Verapaz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.975609756097562%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.42350332594235%\"\u003e\n \u003cp\u003e15\u003csup\u003eo\u003c/sup\u003e 04\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.977827050997783%\"\u003e\n \u003cp\u003e90\u003csup\u003eo\u003c/sup\u003e 14\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.645232815964523%\"\u003e\n \u003cp\u003e1280-1590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.534368070953438%\"\u003e\n \u003cp\u003e970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.64079822616408%\"\u003e\n \u003cp\u003e16.3 - 27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.742793791574279%\"\u003e\n \u003cp\u003eTatumbla (Tla)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.312638580931264%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.747228381374724%\"\u003e\n \u003cp\u003eFco. Morazan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.975609756097562%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.42350332594235%\"\u003e\n \u003cp\u003e14\u003csup\u003eo\u003c/sup\u003e 01\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.977827050997783%\"\u003e\n \u003cp\u003e87\u003csup\u003eo\u003c/sup\u003e 07\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.645232815964523%\"\u003e\n \u003cp\u003e1400-1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.534368070953438%\"\u003e\n \u003cp\u003e908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.64079822616408%\"\u003e\n \u003cp\u003e15.0 - 24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.742793791574279%\"\u003e\n \u003cp\u003eYuscaran (Yus)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.312638580931264%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.747228381374724%\"\u003e\n \u003cp\u003eEl Paraiso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.975609756097562%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.42350332594235%\"\u003e\n \u003cp\u003e13\u003csup\u003eo\u003c/sup\u003e 50\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.977827050997783%\"\u003e\n \u003cp\u003e86\u003csup\u003eo\u003c/sup\u003e 55\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.645232815964523%\"\u003e\n \u003cp\u003e1500-1700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.534368070953438%\"\u003e\n \u003cp\u003e1300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.64079822616408%\"\u003e\n \u003cp\u003e16.9 - 27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.742793791574279%\"\u003e\n \u003cp\u003eCommon lot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.312638580931264%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.747228381374724%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.42350332594235%\"\u003e\n \u003cp\u003en.a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.977827050997783%\"\u003e\n \u003cp\u003en.a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.645232815964523%\"\u003e\n \u003cp\u003en.a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.534368070953438%\"\u003e\n \u003cp\u003en.a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.64079822616408%\"\u003e\n \u003cp\u003en.a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Dovorak et al. (2000)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Growth means (height, DBH and volume) and survival of the provenances of\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eP. maximinoi\u003c/em\u003e (15-64-36C1)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"948\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12.239%;\" width=\"12.526315789473685%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvenances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 21.9524%;\" valign=\"top\" width=\"21.473684210526315%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16.8043%;\" valign=\"top\" width=\"16.210526315789473%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBH (Cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17.0957%;\" valign=\"top\" width=\"18.210526315789473%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVolume (m\u003csup\u003e3\u003c/sup\u003e/tree)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 18.9412%;\" valign=\"top\" width=\"24%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"8.13810110974106%\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"9.001233045622689%\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"8.014796547472256%\"\u003e\n \u003cp\u003e8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"9.741060419235511%\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"9.3711467324291%\"\u003e\n \u003cp\u003e8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"9.98766954377312%\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"11.344019728729963%\"\u003e\n \u003cp\u003e8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"10.480887792848335%\"\u003e\n \u003cp\u003e2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"8.508014796547473%\"\u003e\n \u003cp\u003e4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"9.124537607891492%\"\u003e\n \u003cp\u003e8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"13.53811149032992%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"7.508532423208191%\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"8.304891922639364%\"\u003e\n \u003cp\u003e6.51 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"7.39476678043231%\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.987485779294653%\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"8.646188850967008%\"\u003e\n \u003cp\u003e17.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"9.215017064846416%\"\u003e\n \u003cp\u003e0.0149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"10.466439135381115%\"\u003e\n \u003cp\u003e0.1623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"9.670079635949943%\"\u003e\n \u003cp\u003e96.88 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.849829351535837%\"\u003e\n \u003cp\u003e96.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"8.418657565415245%\"\u003e\n \u003cp\u003e95.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e7,23 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e15.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e18.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e0.0196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e0.1778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e100 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e98.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e97.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e6.36 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e15.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e0.1639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e93.75 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e93.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e93.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e6.50 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e15.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e16.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e0.0135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e0.1554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e95 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e94.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eCommon lot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e6.84 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e15.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e17.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e0.0157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e0.1667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e83.33 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eMean\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e15.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e17.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e0,0154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e0.1652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e93.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e93.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e92.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eF ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e2.23\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e3.42\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e1.38\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e1.87\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e1.57\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e2.29\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e0.66\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e3.47\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e2.93\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e2.20\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.239%;\" valign=\"top\" width=\"12.513144058885384%\"\u003e\n \u003cp\u003eCV\u003csub\u003eexp\u0026nbsp;\u003c/sub\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0908%;\" valign=\"top\" width=\"6.94006309148265%\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7708%;\" valign=\"top\" width=\"7.676130389064143%\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.9937%;\" valign=\"top\" width=\"6.8349106203995795%\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.5478%;\" valign=\"top\" width=\"8.307045215562566%\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.1593%;\" valign=\"top\" width=\"7.991587802313354%\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8392%;\" valign=\"top\" width=\"8.517350157728707%\"\u003e\n \u003cp\u003e20.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0049%;\" valign=\"top\" width=\"9.674027339642482%\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0335%;\" valign=\"top\" width=\"8.937960042060988%\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.255520504731861%\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.9539%;\" valign=\"top\" width=\"7.781282860147213%\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeans followed by the same letter in the column\u0026nbsp;were\u0026nbsp;not significantly different by\u0026nbsp;Tukey\u0026rsquo;s\u0026nbsp;test. ns = not significant * = significant at 5%; ** = significant at 1%; 1 = overall average of sources, including the control; CV\u003csub\u003eExp\u003c/sub\u003e = experimental coefficient of variation\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Means of comparison between progenies and correlation between height and DAP with volume for\u003cem\u003e\u0026nbsp;P. maximinoi\u003c/em\u003e (15-64-36C1) at 8 years\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"674\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvenances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgenies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeans (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeans (Cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeans (m\u003csup\u003e3\u003c/sup\u003e/tree)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e18.69 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1892 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,964\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.75 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1602 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,957\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.07 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1543 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,961\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.07 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1424 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,968\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e16.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e18.60 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1915 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,950\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e16.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.09 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1604 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,934\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e14.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.87 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1624 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,933\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e18.73 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1956 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,966\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e14.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e18.22 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1629 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,961\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e16.82 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1466 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,974\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.79 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1822 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,949\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e16.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e18.86 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1998 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,965\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e12.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e13.81 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1132 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,984\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e16.15 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1408 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,951\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.82 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1753 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,946\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e14.92 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1177 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,950\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.26 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1584 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,917\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.27 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1603 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,960\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003e15-893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e16.90 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1541 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0.945\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003eCommon lot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e15.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e17.02 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0.1667 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e0,969\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.759643916913946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.985163204747774%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.908011869436201%\"\u003e\n \u003cp\u003eDMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.094955489614243%\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.765578635014837%\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.655786350148368%\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.830860534124628%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeans followed by the same letter in the same column are not significantly different by Tukey test for (p \u0026le;5%); DMS - for the least significant difference test. PCC = Pearson correlation coefficient, ** significant at (p-value = 0.1). All values are correlated with the DBH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Results from ANOVA, Eta\u003csup\u003e2\u003c/sup\u003e and Tukey test of means for clusters of the different progenies and precedencies\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"495\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.004048582995953%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.574898785425102%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.59919028340081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e9 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.004048582995953%\"\u003e\n \u003cp\u003e11 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.574898785425102%\"\u003e\n \u003cp\u003e20 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.59919028340081%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003eEta\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003eHeight\u0026nbsp;(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e15.85 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.004048582995953%\"\u003e\n \u003cp\u003e14.53 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.574898785425102%\"\u003e\n \u003cp\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.59919028340081%\"\u003e\n \u003cp\u003e41.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003eDBH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e17.73 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.004048582995953%\"\u003e\n \u003cp\u003e16.01 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.574898785425102%\"\u003e\n \u003cp\u003e16.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.59919028340081%\"\u003e\n \u003cp\u003e16.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0. 478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003eVolume (m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.979757085020243%\"\u003e\n \u003cp\u003e0.175 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.004048582995953%\"\u003e\n \u003cp\u003e0.137 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.574898785425102%\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.59919028340081%\"\u003e\n \u003cp\u003e32.32***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues with the same letter in the row do not differ statistically by the Tukey test.\u003c/p\u003e\n\u003cp\u003e**** = Significant at p\u0026le;0.01\u003c/p\u003e\n\u003cp\u003e\u003cimg 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width=\"712\" height=\"573\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Selection of the 9 (45%) best progenies of\u003cem\u003e\u0026nbsp;P. maximinoi\u003c/em\u003e (15-64-36C1) at 8 years\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvenances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgenies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew Average\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.2070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.2046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.2029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.2001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.1947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.1907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.1868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.1833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.728476821192054%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.39072847682119%\"\u003e\n \u003cp\u003eTla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15-949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e0.0251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.1803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" width=\"49.33774834437086%\"\u003e\n \u003cp\u003eOriginal population average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.913907284768213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.066225165562914%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68211920529801%\"\u003e\n \u003cp\u003e0.1552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ea: predicted additive genetic effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Classification of the 17 (4%) best individuals of\u003cem\u003e\u0026nbsp;P. maximinoi\u003c/em\u003e (15-64-36C1), selected within progenies at 8 years\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"686\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProg.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProc.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e\u003cstrong\u003eU + a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew Average\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eTat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.4989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.1082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.1082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e2.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e3.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e4.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eTat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e5.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e6.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eTat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e6.4972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e7.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e7.2483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e7.8947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e7.8870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eYus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.3232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e8.5714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eCob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e8.4324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e9.1304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.1974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e9.5847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eSJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.1974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e10.0524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.06259097525473%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.714701601164483%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.132459970887918%\"\u003e\n \u003cp\u003e962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.986899563318778%\"\u003e\n \u003cp\u003eTat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.404657933042213%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.8981077147016%\"\u003e\n \u003cp\u003e0.2462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.442503639010189%\"\u003e\n \u003cp\u003e0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.879184861717613%\"\u003e\n \u003cp\u003e0.1973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"8.58806404657933%\"\u003e\n \u003cp\u003e0.0537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.283842794759826%\"\u003e\n \u003cp\u003e0.2089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.606986899563319%\"\u003e\n \u003cp\u003e10.7668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"57.5801749271137%\"\u003e\n \u003cp\u003eOriginal population average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.1552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.7667638483965%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eProc: Provenances; Prog: Progenies f: phenotypic value individual or field measurement; a: predicted additive genetic effect; u + a: genetic value predicted additive; Ne: effective population size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e Analysis of variance between progenies of\u003cem\u003e\u0026nbsp;P. maximinoi\u003c/em\u003e (15-64-36C1) at 8 years (\u003cstrong\u003eAppendix\u003c/strong\u003e)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"100%\"\u003e\n \u003ch5\u003eMean Squares\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.639209225700164%\"\u003e\n \u003ch5\u003eSource\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.589785831960461%\"\u003e\n \u003ch5\u003eDF\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.627677100494235%\"\u003e\n \u003ch5\u003eHeight (m)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.42833607907743%\"\u003e\n \u003ch5\u003eDBH (Cm)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.746293245469523%\"\u003e\n \u003ch5\u003eVolume (m\u003csup\u003e3\u003c/sup\u003e/tree)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.968698517298186%\"\u003e\n \u003ch5\u003eSurvival (%)\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.639209225700164%\"\u003e\n \u003ch5\u003eBlock\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.589785831960461%\"\u003e\n \u003ch5\u003e3\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.627677100494235%\"\u003e\n \u003ch5\u003e0.2528\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.42833607907743%\"\u003e\n \u003ch5\u003e1.0887\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.746293245469523%\"\u003e\n \u003ch5\u003e0.00121\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.968698517298186%\"\u003e\n \u003ch5\u003e114.583\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.639209225700164%\"\u003e\n \u003ch5\u003eProgenies\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.589785831960461%\"\u003e\n \u003ch5\u003e19\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.627677100494235%\"\u003e\n \u003ch5\u003e2.8310\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.42833607907743%\"\u003e\n \u003ch5\u003e6.2688\u003csup\u003e*\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.746293245469523%\"\u003e\n \u003ch5\u003e0.00218\u003csup\u003e*\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.968698517298186%\"\u003e\n \u003ch5\u003e221.308\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.639209225700164%\"\u003e\n \u003ch5\u003eResidue\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.589785831960461%\"\u003e\n \u003ch5\u003e57\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.627677100494235%\"\u003e\n \u003ch5\u003e2.3198\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.42833607907743%\"\u003e\n \u003ch5\u003e2.9767\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.746293245469523%\"\u003e\n \u003ch5\u003e0.00103\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.968698517298186%\"\u003e\n \u003ch5\u003e153.570\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.639209225700164%\"\u003e\n \u003ch5\u003eCV\u003csub\u003eexp\u003c/sub\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.589785831960461%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.627677100494235%\"\u003e\n \u003ch5\u003e9.87\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.42833607907743%\"\u003e\n \u003ch5\u003e10.13\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.746293245469523%\"\u003e\n \u003ch5\u003e19.83\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.968698517298186%\"\u003e\n \u003ch5\u003e13.13\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.639209225700164%\"\u003e\n \u003ch5\u003eMean\u003csup\u003e1\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.589785831960461%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.627677100494235%\"\u003e\n \u003ch5\u003e15.43\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.42833607907743%\"\u003e\n \u003ch5\u003e8,16\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.746293245469523%\"\u003e\n \u003ch5\u003e0.1617\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.968698517298186%\"\u003e\n \u003ch5\u003e94.38\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere\u0026nbsp;* = significant at 5% probability; ** = significant at 1% probability; ns = not significant; 1 = overall mean of provenances, including the control; DF = degrees of freedom; CV\u003csub\u003eExp\u003c/sub\u003e = experimental coefficient of variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9\u003c/strong\u003e Analysis of variance for the progeny level within provenances of\u003cem\u003e\u0026nbsp;P. maximinoi\u003c/em\u003e (15-64-36C1)\u003cem\u003e\u0026nbsp;\u003c/em\u003eat 8 years (\u003cstrong\u003eAppendix\u003c/strong\u003e)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"668\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.282511210762333%\"\u003e\n \u003ch5\u003eProvenances\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"5.381165919282512%\"\u003e\n \u003ch5\u003eDF\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"23.766816143497756%\"\u003e\n \u003ch5\u003eHeight (m)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"26.905829596412556%\"\u003e\n \u003ch5\u003eDBH (Cm)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"24.663677130044842%\"\u003e\n \u003ch5\u003eVolume (m\u003csup\u003e3\u003c/sup\u003e/tree)\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.50099403578529%\"\u003e\n \u003ch5\u003eMSq\u003csup\u003eZ\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.109343936381709%\"\u003e\n \u003ch5\u003eCv\u003csub\u003eexp\u003c/sub\u003e (%)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.687872763419485%\"\u003e\n \u003ch5\u003eMSq\u003csup\u003eZ\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.898608349900595%\"\u003e\n \u003ch5\u003eCv\u003csub\u003eexp\u003c/sub\u003e (%)\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.898608349900595%\"\u003e\n \u003ch5\u003eMSq\u003csup\u003eZ\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.904572564612327%\"\u003e\n \u003ch5\u003eCv\u003csub\u003eexp\u003c/sub\u003e (%)\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.311377245508982%\"\u003e\n \u003ch5\u003eCob\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.389221556886228%\"\u003e\n \u003ch5\u003e3\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.425149700598803%\"\u003e\n \u003ch5\u003e1.2584\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.377245508982035%\"\u003e\n \u003ch5\u003e5.81\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.071856287425149%\"\u003e\n \u003ch5\u003e2.3717\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e6.29\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e0.0016\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.976047904191617%\"\u003e\n \u003ch5\u003e17.25\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.311377245508982%\"\u003e\n \u003ch5\u003eSJ\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.389221556886228%\"\u003e\n \u003ch5\u003e3\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.425149700598803%\"\u003e\n \u003ch5\u003e2.0611\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.377245508982035%\"\u003e\n \u003ch5\u003e6.88\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.071856287425149%\"\u003e\n \u003ch5\u003e2.2852\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e5.03\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e0.0014\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.976047904191617%\"\u003e\n \u003ch5\u003e11.95\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.311377245508982%\"\u003e\n \u003ch5\u003eTla\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.389221556886228%\"\u003e\n \u003ch5\u003e5\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.425149700598803%\"\u003e\n \u003ch5\u003e6.8787\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.377245508982035%\"\u003e\n \u003ch5\u003e15.30\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.071856287425149%\"\u003e\n \u003ch5\u003e13.1658\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e17.22\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e0.0038\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.976047904191617%\"\u003e\n \u003ch5\u003e29.33\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.311377245508982%\"\u003e\n \u003ch5\u003eYus\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.389221556886228%\"\u003e\n \u003ch5\u003e4\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.425149700598803%\"\u003e\n \u003ch5\u003e0.0711\u003csup\u003ens\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.377245508982035%\"\u003e\n \u003ch5\u003e5.64\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.071856287425149%\"\u003e\n \u003ch5\u003e5.0349\u003csup\u003e**\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e3.88\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e0.0018**\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.976047904191617%\"\u003e\n \u003ch5\u003e10.80\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.311377245508982%\"\u003e\n \u003ch5\u003eOverall mean\u003csup\u003e1\u003c/sup\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.389221556886228%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.425149700598803%\"\u003e\n \u003ch5\u003e15.42\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.377245508982035%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.071856287425149%\"\u003e\n \u003ch5\u003e17.37\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.724550898203592%\"\u003e\n \u003ch5\u003e0.1625\u003c/h5\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.976047904191617%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere\u0026nbsp;* = significant at 5% probability; ** = significant at 1% probability; ns = not significant; 1 = overall mean of provenance, not including control; DF = degrees of freedom; Z = F test for progenies within provenances; CV\u003csub\u003eExp\u003c/sub\u003e = experimental coefficient of variation.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adaptability, Growth, Precedencies and progenies trial, Pinus maximinoi and Phenotypic and Genotypic Selection","lastPublishedDoi":"10.21203/rs.3.rs-2456600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2456600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study was to evaluate the growth and adaptability of Pinus maximinoi provenances and progenies and to estimate the genetic variation that could be exploited in future breeding work. The company Floresta de Niassa Lda established a trial with four provenances and nineteen progenies in the Chimbonila district of northern Mozambique. The field trial was set up in a randomized block design with four repetitions and six-plant linear plots. At two, four and eight years of age, performance in total height, diameter at breast height (DBH) and survival were analysed. The results at eight years of age showed no significant variation between provenances for all variables analysed, and any of these provenances can be used for reforestation in northern Mozambique. The first group was composed of 9 (45%) of the best performing progenies, mostly from Yuscaran and San Jeronimo, and the second (last) group was composed of 11 (55%) of the worst performing progenies, mostly from Tatumbla and Coban. Most of the nine best classified progenies based on the predicted additive genetic effect and genetic gain belonged to Yuscaran and Tatumbla. On the other hand, most of the progenies at the level of the seventeen best classified individuals belonged to San Jeronimo and can be used for future breeding projects in northern Mozambique.\u003c/p\u003e","manuscriptTitle":"Growth and adaptability of provenances and progenies of Pinus maximinoi h. E. Moore in northern Mozambique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-12 14:27:20","doi":"10.21203/rs.3.rs-2456600/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e25142a0-1a79-4f9d-97ef-7edba7e62864","owner":[],"postedDate":"January 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-15T21:29:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-12 14:27:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2456600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2456600","identity":"rs-2456600","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}