Cjfr-2016-0392.Pdf

Canadian Journal of Forest Research

Minimum age for clear -cutting native species with energetic potential in the Brazilian semi-arid

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2016-0392.R3

Manuscript Type: Note

Date Submitted by the Author: 02-Dec-2016

Complete List of Authors: Santana, Otacilio; Universidade Federal de Pernambuco, Biophysics and Radiobiology

Keyword: growth rings,Draft firewood, Semi-Arid, rotation age, bioenergy

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1 Minimum age for clear-cutting native species with energetic potential in the Brazilian

2 semi-arid region

4 Otacilio Antunes Santana 1

5 Av. Prof. Moraes Rego, n° 1235, Departamento de Biofísica e Radiobiologia (DBR),

6 Mestrado Profissional em Rede Nacional para o Ensino das Ciências Ambientais

7 (PROFCIAMB), Centro de Biociências (CB), Universidade Federal de Pernambuco (UFPE),

8 Cidade Universitaria, Recife, Pernambuco, Brazil, 50670901

9 [email protected]

10 [email protected]

11 +55 81 997371266

12 1Author for correspondence Draft

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26 Abstract

27 What is the minimum age for clearcutting native species with energetic potential in the

28 Brazilian semiarid region? This was the central question of this study, which aimed at

29 estimating the minimum cutting cycle for native woody species from an area covered by a

30 sustainable forest management plan. Individuals (n = 240) of twelve species native to the

31 semiarid region were measured and wood discs were taken from them to collect data relative

32 to: i) volume, by an industrial 3D scanner; ii) density, using a highfrequency densitometer;

33 iii) age, by a growth ring measuring device; and iv) the lower heating value, by an oxygen

34 bomb calorimeter and an elemental analyzer. Data were fitted to mathematical models by

35 regression analysis to determine the relationship and significance between the variables

36 analyzed. The estimated minimum age for the harvesting of native woody species was 47

37 years, determined by the age at whichDraft there was stabilization of volume growth curves over

38 time, and an increase in density and heating power (wood retraction).

40 Keywords: growth rings; firewood; rotation age; bioenergy.

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51 1. Introduction

52 The mean annual increment (MAI) and the current annual increment (CAI) are parameters

53 that allow estimation of the optimal age of timber harvest under sustainable forest

54 management, but their calculation is laborious (costly, expensive and requires a large data set)

55 and time consuming in woody species of the Brazilian semiarid region. Although sustainable

56 forest management plans are in place for this region, such difficulty results in ages for clear

57 cut cycles without a scientific basis to justify these possible cycles. Unlike rapidly growing

58 species of Eucalyptus and Pinus that invest their biomass mainly in shoots, native woody

59 species of the Brazilian semiarid region have slow initial growth, investing most of their

60 biomass initially in roots (high root to shoot ratio); this as a support structure, to reduce water

61 loss and against herbivorous attack (from which organs find refuge in the soil) (Lima and

62 Rodal 2010, Costa et al. 2014, SantanaDraft and Encinas 2016). In addition, the individual

63 architecture of woody plants in the Brazilian semiarid region is based on broad ramifications

64 of the trunk and branches, which makes it difficult to estimate the independent variables,

65 namely height and diameter, used in volumetric calculations (Barros et al. 2010).

66 In addition to shoot volume, other variables are essential for estimating the energy

67 potential of wood. The bulk density, chemical composition (e.g. cellulose and lignin),

68 percentage of vessels and parenchyma, moisture content and size of fibers are some

69 parameters that influence the heating value of wood. However, these parameters are

70 correlated and represented by the density of the wood, which has a significant direct and

71 proportional relationship with the heating value of wood (Gebreegziabher et al. 2013). The

72 age of the individual plant is another important factor, because, with advancing age and the

73 stabilization of growth in height, woody individuals increase the carbon accumulation on their

74 trunks and branches, thus increasing the value of the mass/volume ratio of the timber. Adult

75 wood is characterized by high density, long tracheids, thick cell walls, high percentage of

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76 latewood, low percentage of spiral grain, low percentage of nodes, high percentage of

77 cellulose, low percentage of compression wood, high transverse shrinkage, smaller microfibril

78 angle and greater mechanical strength (Rowell 2012).

79 The optimal age for harvesting a planted forest stand for wood production is when the

80 current annual increment curve intersects the mean annual increment curve. At that point, the

81 rate of growth in wood volume begins to reduce over time, and the economic profitability in

82 relation to forest investment also begins to fall (Prodan, 1968). In semiarid regions, in

83 addition to a reduced rate of wood volume growth as trees age, wood density often increases

84 over time as an ecophysiological strategy of native tree species to cope with moisture

85 limitations (Liu et al. 2013, Klein et al. 2014, Santana and Encinas 2016). This wood

86 retraction is evident in the aerial part of the plant and can enhance the root:shoot ratio even if

87 the leaf area index continues to developDraft over time (Santana and Encinas 2016). Before of the

88 wood retraction occurs, a reduction of the rate of growth in wood volume and an increasing of

89 the rate of growth in wood density are observed (Paula 1993, Santana and Encinas 2016).

90 Thus, in semiarid regions, the time at which wood volume growth levels off and when the

91 density and LHV curves change their inclination angle in relation to age could represent a

92 parameter defines the best time for harvesting wood destined for bioenergy uses (Liu et al.

93 2013, Klein et al. 2014). There is not an analytical or exact method for calculating this point

94 of levelling off or for detecting a critical change in the inclination angle of wood density and

95 LHV curves, but these trends are supported by observations in the literature (Althoff et al.

96 2016, Santana and Encinas 2016).

97 In 2013, 9.1% of the Brazilian energy matrix was based on wood used as firewood and

98 charcoal, which has increased the demand for this resource by approximately 5% per year

99 (Brasil 2013). In the semiarid region of the state of Pernambuco, the extracted wood is used

100 as an energy source of the Polo Gesseiro do Araripe (Araripina Microregion), which has 39

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101 gypsum mines and 869 industries to cement manufacture, which fill 73% of their energy

102 requirements with wood, but the small manufacturers use 100% wood to meet their energy

103 needs. The production of a 10,000 kg of plaster requires 0.5 st native wood, which has an

104 annual demand of 5.5·10 4 million kg/year (Silva 2008/2009). The sustainable production of

105 wood fuel would require a total area of 155,000 hectares specifically for the plaster industry,

106 with a cutting cycle ranging from 10 to 15 years. But the Northeastern Plants Association

107 (APNE 2014) has the registration of 94 sustainable forest management plans in the state of

108 Pernambuco, covering an area of 39,748 hectares, insufficient to meet the energy demands of

109 the Polo, which grows 10% (Sindugesso 2011) to 25% per year, in addition to a growing

110 ceramics industry (CPRH 2014).

111 The goal of this study was to estimate the minimum age for a clear cutting cycle of

112 native woody species from an area governedDraft by a sustainable forest management plan in the

113 semiarid region, according to requirements for implementation and maintenance of Annual

114 Production Unit, described by the Normative Instruction of the Brazil Ministry of the

115 Environment (number 1 of 06/25/2009 – MMA 2009).

116

117 2. Materials and Methods

118 The wood analyzed came from a forest management farm (Figure 1A) in the state of

119 Pernambuco (8°35’S and 37°59’W), having a semiarid climate with annual rainfall below

120 1,000 mm (550 mm in 2015, APAC, 2016) and mean annual temperature greater than 27°C,

121 or a BSh climate according to the Köppen classification (Peel et al. 2007). This farm is

122 located on the ‘Sertaneja Meridional e Raso da Catarina’ geomorphological depression. The

123 soils have been classified into four classes: Haplic Cambisol, Salic Gleysol, Yellow Oxisol,

124 Haplic Planosol, and the vegetation has been classified into Closed Arboreal, Open Arboreal

125 and Shrub structural classes (Aguiar et al. 2013).

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126 The vegetation analyzed came from the clearcutting of experimental plots in a

127 sustainably managed forest of the SemiArid Forest Management Network (MMA 2009). The

128 twelve predominant tree species have a cumulative importance value index of 96%, measured

129 in 20 plots of 50 x 100 m (10 ha), with a 1,271 stems/ha density (diameter at breast height ≥ 1

130 cm) (Aguiar et al. 2013), evaluated according to the Permanent Plot Measurements Protocol

131 of the SemiArid Forest Management Network, and Rodal et al. (1992).

132 The species and number of evaluated individuals of each species were: Acacia

133 kallunkiae J.W.Grimes & Barneby, Fabaceae (n = 13); Acacia piauhiensis Benth., Fabaceae

134 (n = 15); Anadenanthera colubrina (Vell.) Brenan. Var. cebil (Griseb.) Reis, Fabaceae (n =

135 27); Aspidosperma pyrifolium Mart., Apocynaceae (n = 35); Poincianella pyramidalis (Tul.)

136 L. P. Queiroz, Fabaceae (n = 12); Croton blanchetianus Baill., Euphorbiaceae (n = 9);

137 Erythrina velutina Willd., PapilionoideaeDraft (n = 17); Jatropha elliptica (Pohl.) Mull.Arg.,

138 Euphorbiaceae (n = 33); Maytenus rigida (Mart.) Benth., Celastraceae (n = 16), Mimosa

139 caesalpiniifolia Benth, Fabaceae (n = 8); Myracrodruon urundeuva All., Anacardiaceae (n =

140 29); and Peltophorum dubium (Spreng.) Taub., Fabaceae (n = 26).

141 Harvested trees were analyzed for volumetry (Figure 1B), densitometry (Figure 1C),

142 dating (Figure 1C) and calorimetry (Figure 1D). The volume of the wood (m 3) was measured

143 by an industrial 3D scanner (3D scanner SKDKFX four Lens, Foshan Shangke

144 Machinery Co., Ltd., Guangdong Province, China), in which all parts (trunk + branches) were

145 reduced to 1 m length (Figure 1B) in a mobile studio in locus. The density of the wood and

146 the age (growth rings) were measured from sectioned discs, 2 cm thick, by means of high

147 frequency densitometry (Schinker et al. 2003), in LignoStation TM using LignoScop (Rinntech,

148 Heidelberg, Germany) (Figure 1C) to scan surfaces using cameras attached to the microscope,

149 and LignoScan to scan the wood surface using highfrequency (1600 dpi) with 0.001 mm

150 precision (Shchupakivskyy et al. 2014). Standard procedures (Fritts 1976, Lisi et al. 2008)

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151 and COFECHA software (The University of Tennessee, Knoxville, GrissinoMayer 2001)

152 were used for crossdating and standardization. The irregularity of interannual rain, or of

153 another climate event, could be revised by the crossdating analysis. This analysis could

154 identify growth zone and false growth rings in an interannual period (Pagotto et al. 2015).

155 The equipment performed 1,200 factorial (1,200!) crossdating analyzes (240 studied

156 individuals with five replicates each).

157 The Higher Heating Value (HHV) of the wood was calculated by collecting data

158 measured with a calorimeter (C6000 global standards 2/10 IKA ®, Staufen, Germany)

159 (Günther et al. 2012). Samples were ground to a maximum size of 25 µm. Lower Heating

160 Value (LHV) was determined by subtracting the heat of vaporization of the water vapor from

161 the higher heating value. The water vapor was determined by variance of the hydrogen

162 content of the sample measured throughDraft the ElementAnalyzer (Vario EL, Elementar

163 Analysesystem GmbH, Langenselbold, Germany) (Figure 1D). These procedures followed the

164 standards DIN EN ISO 17162009, DIN 519001 2000, DIN 519003 2005 (Günther et al.

165 2012) and ABNT/NBR 8633/84.

166 The relationships between variables (Y = volume and X = density; Y= volume and X

167 = age; Y = lower heating value and X = density; Y = density and X = age; Y = lower heating

168 value and X = volume; and Y = lower heating value and X = age) were performed by fitting

169 the data to a wide array of mathematical growth models (Table 1) using regression analysis to

170 calculate a coefficient of determination (R2), rootmeansquare error (RMSE), significance

171 level (p) and the fit curve; and the selection of the best fit model (based on maximizing R2,

172 minimizing RMSE, and minimizing p) for each case (Zar 1999). The regression analysis was

173 preceded by the D’Agostino Normality test (D'Agostino et al. 1990) for each variable to

174 validate statistical premises. The models were chosen as indicated by Kleinbaum et al. (2013).

175 The possible multicollinearity among variables was calculated by the FarrarGlauber test

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176 (Farrar & Glauber 1976). Adjustments, graphics, statistical deviation and coefficient of

177 variation were calculated using Statistica 12 (Statsoft, Dell, Tulsa, USA).

178

179 3. Results

180 The mean age of the study population was 46 years (CV% = 15.2%; Figure 2A), ranging from

181 98 years ( Peltophorum dubium ) to 5 years ( Croton blanchetianus ). The mean volume was

182 0.12 m 3 (CV% = 11.9%; Figure 2B) per individual tree, with individuals ranging in size from

183 a minimum volume of 0.003 m 3 (Croton blanchetianus ) to a maximum volume of 0.40 m 3

184 (Acacia kallunkiae ). The mean wood density was 0.8 g · cm 3 (CV% = 7.1%; Figure 2C),

185 varying between 0.4 g · cm 3 (Jatropha elliptica ) and 1.05 g · cm 3 (Acacia kallunkiae ). The

186 mean lower heating value was 4,863 kcal · kg 1 (CV% = 6.2%; Figure 2D), ranging from

187 3,783 kcal · kg 1 (Acacia piauhiensis )Draft to 5,697 kcal · kg 1 (Erythrina velutina ).

188 Data reflecting the relationship between volume and density, and between volume and

189 age, were well fit to sigmoidal models (5 parameters) significantly (R2 > 0.86; RMSE < 0.05;

190 p < 0.001; Figure 3A and 3B, Table 2). In both relationships, volume began to rise sharply

191 (curve growth > 45° slope relative to the dependent variable) to the point where it became

192 constant (curve growth < 5° slope in relation to the dependent variable) and then slowed

193 relative to growth of the independent variable (density or age). The relationship between the

194 lower heating value (LHV) and density was the most significant (R2 > 0.97; RMSE < 0.03; p

195 < 0.001), directly and significantly increasing with increasing density (Figure 3C, Table 2).

196 Multicollinearity was not found in the models tested (p > 0.800). Considering the relationship

197 between density and LHV with the age of individuals, data fitted to the exponential growth

198 model (double and with five parameters), in which, from a given year, 47 years for density

199 and 49.5 for LHV, there is a levelling off of the curve (Figure 3D and 3F; Table 2), with a

200 more marked increase in the values of these variables (curve growth> 45 °slope relative to the

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201 dependent variable) than previously (growth curve ≈ 30 ° slope relative to the dependent

202 variable). As for the relationship between LHV and volume, data fitted to the Chapman model

203 (four parameters) (Figure 3E; Table 2), indicating that in the higher volume growth phase, the

204 increase in LHV value is less pronounced (curve growth < 15° slope in relation to the

205 dependent variable) than when the volume growth begins to stabilize (growth curve > 45°

206 slope in relation to the dependent variable).

207 The minimum age for clear cutting cycle in an area of Sustainable Forest Management

208 Plan in the semiarid region, with the destination of wood with energy purposes, suggested by

209 the variables analyzed, was from 46.8 (47) years (Figure 3B). This is the average tree age at

210 which there is a stabilization of the volume growth (curve growth < 5° slope in relation to the

211 dependent variable), and pronounced growth of the LHV (from 49.5 years; Figure 3F) and

212 density (from 47 years; Figure 3D). Draft

213

214 4. Discussion

215 One limitation of this study lies in the impracticality of working with a larger sample size,

216 given the time required for cutting and loading for transportation (about 24 hours), making it

217 difficult to scan other woody individuals in the semiarid region. However, 240 individuals

218 were measured with five replicates of each measurement or analysis, improving the reliability

219 and accuracy of data collected.

220 Our findings corroborate what has already been reported for the area (Aguiar et al.

221 2013), which before the Sustainable Forest Management Plan, had reduced logging (> 20

222 individuals / ha / year). The values of volume, density, and LHV varied within the 95%

223 confidence intervals reported in the literature for the mean values of the species analyzed:

224 volume (from biomass) from 0.001 m 3 (Santana and Souto 2006) to 0.50 m 3 (Silva and

225 Sampaio 2008); density from 0.3 g · cm 3 (Lima and Rodal 2010) to 1.2 g · cm 3 (Paula 1993);

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226 and LHV from 3,800 kcal · kg 1 (Medeiros Neto et al. 2012) to 5,800 kcal · kg 1 (Quirino et al.

227 2004).

228 The rapid growth in the volume of tree species, over the years, and a subsequent

229 stabilization of this growth is commonly observed in the literature, not only for fast growing

230 plantation species (Encinas et al. 2011), but also for native species (Felker 1986, Lima 1986,

231 Vico et al. 2015, Santana and Encinas 2016). This stabilization is considered as the saturation

232 period of the plant individual, both by limited environmental resources, by competition with

233 others in their environment, but also by individual senescence marked by the genetics of the

234 species (Hunt 1982, Felker 1986, Soares et al. 2011). The onset of the saturation, senescence

235 or slow growth process, as shown in Figure 3B from 47 years, is marked in forestry science

236 by the beginning of the cutting cycle for vegetation intended for production of firewood and

237 charcoal (Rode et al. 2014, Althoff Draftet al. 2016). When the volume tends to stabilize, other

238 variables, such as density (Figure 3 D) and heating value (Figure 3F), which have a high

239 correlation (R2 > 0.97), continue to grow in value. While a saturation of those values was not

240 observed in this work, it is, however, emphasized in the literature (Hunt 1982, Felker 1986).

241 This reduction of the rate of growth of the wood volume and an increasing of the rate of

242 growth of the wood density and lower heating value (LHV) infer in the begin of wood

243 retraction. Thus, from the age of 47, the harvesting is strongly indicated.

244 In other dry forests on Earth, the cutting cycle of wood is ranges from 63 to 97 years,

245 much longer than the 47 years derived here. The causes for these longer rotation periods are:

246 i) in India, to avoid urban occupation (Agarwal et al., 2016); ii) in Australia and Africa, to

247 conserve the habitat for large mammals (Bhadouria et al. 2016); iii) in West Africa, to reduce

248 wildfire susceptibility and microclimate changes (Scheitera and Savadogo, 2016); iv) in

249 Cameroon and Panama, to preserve forest stands that are used in popular religious and sacred

250 rituals (Kemeuze et al. 2016, Seijo et al. 2016); v) in Costa Rica, India, Papua New Guinea,

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251 and Southern Africa, to produce medicinal bioproducts (Schmiedel et al. 2016); vi) in

252 Morocco and Argentina, to perpetuate ethnobotanical and other cultural uses of natural

253 resources (Martínez 2015, Blanco and Carrière 2016); and vii) in China, to maintain the

254 groundwater flow near to surface by hydraulic lift from trees (Xiao and Huang 2016). In all

255 cases, like in this work, biodiversity preservation and firewood sustainability also were

256 objectives of forest management (Althoff et al. 2016, Santana 2016).

257

258 5. Conclusion

259 The estimate of the minimum age for cutting cycle of native woody species in the evaluated

260 area in Brazil’s semiarid region was 47 years, determined by the age at which there was

261 stabilization of volume growth over time, and an increase in density and heating power (wood

262 retraction). Draft

263

264 Acknowledgements

265 The author is grateful to ProReitoria de Pesquisa e PosGraduacao of the Universidade

266 Federal de Pernambuco (PROPESQ/UFPE) for the financial and logistical support, and to

267 Research Group ‘Educometria’ (UFPE/CNPq) by discussion and survey support. The author

268 is very grateful to the reviewers for their careful and meticulous reading of the paper.

269

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Draft

Figure 1. Map showing the origin of the tree individuals studied (A); and methodological steps: volumetry (B); densitometry and dating (C) and calorimetry (D).

714x851mm (72 x 72 DPI)

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Draft

Figure 2. Age (A), volume (B), density (C) and lower heating value (LHV) (D) of twelve native species analyzed in the semi-arid region.

1103x1446mm (72 x 72 DPI)

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Figure 3. Relationship between the analyzed variables: volume, lower heating value (LHV), density and age of the 240 woody individuals measured.

1431x1577mm (72 x 72 DPI)

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Table 1. Mathematical models used for data fit.

Model Equation*

Polynomial (linear) Yi=β 0 + β 1i ⋅ X + ε i

2 Polynomial (Quadratic) Yi=ββ 01i +⋅+⋅ X β 2i X + ε i

2 3 Polynomial (Cubic) Yi=+⋅+⋅ββ 01i2i XX β +⋅ β 3i X + ε i

β1 Yi= + ε i Sigmoidal (3 parameters) X1−β 2  −  1+ e β3 

β1 Yi=β 0 + + ε i Sigmoidal (4 parameters) Xi−β 2  −  1+ e β3 

β1 Yi=β 0 +β + ε i X −β  4 Sigmoidal (5 parameters) −i 2   1+ e β3    

β1 Yi=β + ε i Logistic (3 parameters) X  3 Draft1+ i  β2 

β1 Yi=β 0 +β + ε i Logistic (4 parameters) X  3 1+ i  β2 

β 1  4  X−β + β ln2 β4  −i 2 3   β  5     Weibull (4 parameters) Yi=β 1  1 − e  + ε i      

β 1  4  X−β + β ln2 β4  −i 2 3   β  5     Weibull (5 parameters) Yi=+−β 0 β 1  1 e  + ε i      

Xi−β 2    Gompertz (3 parameters) −eβ3  Yi=β 1 e + ε i

Xi−β 2    Gompertz (4 parameters) −eβ3  Yi01=β + β e + ε i

β X β2 Hill (3 parameters) Y =1 i + ε iβ2 β 2 i β3+ X i

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β X β2 Hill (4 parameters) Y =β +1 i + ε i 0β2 β 2 i β3+ X i

β3 Chapman (3 parameters) −β2X i Yi=β 1( 1 − e ) + ε i

β3 Chapman (4 parameters) −β2X i Yi01=+β β( 1 − e ) + ε i

β1⋅X i Exponential Growth (Simple 1 parameter) Yi= e + ε i

β2⋅X i Exponential Growth (Simple 2 parameters) Yi=β 1 ⋅ e + ε i

β2⋅X i Exponential Growth (Simple 3 parameters) Yi01=β +⋅ β e + ε i

β2i⋅X β 4i ⋅ X Exponential Growth (Double 1 parameter) Yi1=⋅β e +⋅ β 3 e + ε i

β2i⋅X β 4i ⋅ X Exponential Growth (Double 2 parameter) Yi01=+⋅ββ e +⋅ β 3 e + ε i

*Y i = observed value of the dependent variable; X i = observed value of the independent variable; βi = regression equation

parameters; εi = fit error. Draft

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Table 2. Significant fit of data to models for six analyzed relationships: volume vs. density (A); volume vs. age (B); lower heating value vs. density (C); density vs. age (D); lower heating value vs. volume (E); and lower heating value vs. age (F).

Model Function R2 RMSE p

0.509 Sigmoidal (5 Yi = − 0.12 + 0.005 Xi − 0.423  A −   0.86 0.044 < 0.001 0.001  parameters) 1+ e   

0.031 Sigmoidal (5 Yi = 0.007 + 3.799 Xi − 23.861  B −   0.89 0.037 < 0.001 4.287  parameters) 1+ e   

Polynomial

C Yi= 3.736 + 1.738 ⋅ X i 0.97 0.026 < 0.001 (linear)

Exponential

0.001⋅ Xi 0.019 ⋅ X i D Growth (Double 5 Yi =− 0.193 + 0.248 ⋅ e + 0.210 ⋅ e 0.91 0.034 < 0.001

parameters)

Chapman (4 6.438 E Y= 0.004 + 0.001Draft 1 − e −6.297 ⋅ X i 0.90 0.048 < 0.001 i ( ) parameters)

Exponential

0.001⋅ Xi 0.044 ⋅ X i F Growth (Double 5 Yi =− 1415.94 + 5142.98 ⋅ e + 76.118 ⋅ e 0.94 0.050 < 0.012

parameters)

Source: Author.

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