draft · 2018. 10. 2. · draft 100 usually, the term canopy coverage refers to the proportion of...

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The canopy coverage is correlated with the number of shoots produced by Eucalyptus clones in a clonal

minigarden

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2018-0197.R1

Manuscript Type: Note

Date Submitted by the Author: 31-Jul-2018

Complete List of Authors: Saudade de Aguiar, Natália; Universidade do Estado de Santa CatarinaNavroski, Marcio ; Universidade do Estado de Santa CatarinaMiranda, Letícia ; Klabin FlorestalSehnen Mota, Clenilso; Instituto Federal CatarinenseEstopa, Regiane; Klabin FlorestalNicoletti, Marcos; Universidade do Estado de Santa CatarinaKonzen, Enéas; Universidade do Estado de Santa Catarina,

Keyword: Eucalyptus, mini-cuttings, shoots, percentage of leaf coverage, clonal forestry

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/cjfr-pubs

Canadian Journal of Forest Research

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1 The canopy coverage is correlated with the number of shoots produced by Eucalyptus

2 clones in a clonal mini-garden

3 Natália Saudade de Aguiar1, Marcio Carlos Navroski1, Letícia Miranda2, Clenilso Sehnen Mota3,

4 Regiane Abjaud Estopa2, Marcos Felipe Nicoletti1, Enéas Ricardo Konzen1*

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6 1 Centro de Ciências Agroveterinárias, Universidade do Estado de Santa Catarina, Luís de

7 Camões Av. 2090, Lages, SC, Brazil. E-mails: [email protected],

8 [email protected], [email protected]

9 2 Klabin Florestal, Av. Brasil, 26 - Harmonia, 84275-00, Telêmaco Borba, PR, Brasil. E-mails:

10 [email protected], [email protected]

11 3 Instituto Federal Catarinense, Estrada do Redentor, 5665, Rio do Sul, SC, Brazil. E-mail:

12 [email protected]

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14 *Corresponding author:

15 Enéas Ricardo Konzen

16 Telephone: +55 49 3289-9248

17 E-mail: [email protected]

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31 Abstract

32 In this work, we analyzed the correlation between the canopy coverage of two commercial clones

33 of Eucalyptus benthamii and one of E. dunnii and their shoot yields in a clonal mini-garden

34 system. By canopy coverage, we referred to the area of a picture occupied by leaves (green area)

35 when analyzed using computational resources. The mini-garden was set up to yield shoots on a

36 regular time schedule (between 20 and 30 days) to obtain mini-cuttings for clonal propagation.

37 Pictures were taken at approximately 30 cm above the upper leaves from the plots containing

38 mini-stumps of each clone on the day before the collection of mini-cuttings for six consecutive

39 harvests (approximately six months). The leaf coverage was obtained using the computational

40 package Easy Leaf Area. Our results indicated a significantly high Pearson correlation coefficient

41 (r = 0,744, P < 0.001) between the canopy coverage and the number of shoots produced by each

42 clone. A logistic regression model was adjusted to this dataset, enabling a prediction of the

43 number of shoots based on the canopy coverage. This approach has the potential for assisting

44 forest nurseries in predicting the yield of mini-cuttings while conducting clonal propagation of

45 their genetic materials.

46 Keywords: Eucalyptus; mini-cuttings; shoots; percentage of leaf coverage.

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56 1. Introduction

57 Eucalypts are hardwood species that produce wood of commercial interest in Brazil and

58 other countries. Currently, Eucalyptus plantations occupy 5.7 million hectares in Brazil (IBA

59 2017). In southern Brazil, two economically important species are Eucalyptus dunnii Maiden and

60 Eucalyptus benthamii Maiden & Cambage. These two species are recommended for these areas

61 as they tolerate temperatures as low as -5 and -10ºC for E. dunnii and E. benthamii, respectively,

62 and the occurrence of regular frosts (Paludzyszyn-Filho et al. 2006). However, limited breeding

63 has been conducted with such species, as their cultivation is relatively recent in southern Brazil.

64 Large-scale plantations of eucalypts rely on the selection of desirable genotypes with superior

65 qualities for industrial demands (Konzen et al., 2017). To achieve industrial demand levels,

66 efficient methods for propagating superior genetic materials are also necessary (Brondani et al.

67 2012a, b).

68 For various species, including the eucalypts, vegetative propagation is the most suitable

69 alternative for the production of clonal plants in large-scale field plantations (Nakhooda and Jain

70 2016). Most of the currently cultivated areas of eucalypts have been developed using clonal

71 propagation strategies. One of the most prominent techniques involves the use of mini-cuttings,

72 which has been widely employed due to its simplicity and the low costs involved, compared to

73 micropropagation (Brondani et al. 2012a, b). Mini-cuttings are shoots detached from cuttings or

74 from plants grown from seeds that have meristematic regions that are able to develop roots and

75 new shoots, thus generating whole new plants, genetically identical to the original genotype

76 (Wendling et al. 2010).

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77 The ability to produce shoots is essential, and the mini-cutting technique is a viable option

78 for the production of clonal plants (Brondani et al. 2012b). The differential ability for developing

79 new shoots is associated with genotypes, irrigation and the nutrients available during conduction

80 of the mini-stumps, and the status of nutrition is one of the key aspects for growing vigorous

81 shoots. Temperature variation, the juvenility of the mini-stumps, seasonality and the time

82 between harvests of mini-cuttings are other factors that significantly contribute to the production

83 of shoots (Hartmann et al. 2011). Alfenas et al. (2009) include other contributing factors, such as

84 the temperature, light incidence and intensity and photoperiod throughout the year. Additionally,

85 the harvest of mini-cuttings must be selective and continuous. Selection is based on desirable

86 characteristics, such as the presence of young and turgid leaves with no sanitary issues, such as

87 the presence of fungi and bacteria that may cause contamination and disease. The harvest process

88 is repeated every 15-30 days, stimulating the production of new shoots and enabling the growth

89 of new saplings in the nurseries.

90 Considering that the aforementioned aspects must be as highly controlled as possible,

91 forest nurseries might be able to predict the number of shoots that will be produced by each clone

92 of interest. Moreover, they might also analyze whether the production of mini-cuttings was

93 regular and steady throughout the year, which has an economic impact and may interfere with the

94 strategies adopted for delivering clonal plants in proper condition to field plantations. However,

95 this raises another question: is there a consistent variable that predicts the number of shoots that

96 will be produced by each clone? It is standard to predict the shoots yield based on the average

97 yield of the clone from previous collections or from productivity data already available from

98 clonal mini-gardens that are being harvested. Alternatively, we propose a novel variable that has

99 the potential for such predictions: canopy coverage.

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100 Usually, the term canopy coverage refers to the proportion of the forest floor that is

101 occupied by the projection of the tree crowns (Jennings et al. 1999). In agriculture, however, it

102 has been associated with imaging of crop areas and the determination of the area of the picture

103 that is occupied by leaves (green area). The latter definition is used in this work. Previous

104 analyses have shown a high and significant correlation between leaf area indexes and the canopy

105 coverage for wheat, triticale and corn (Nielsen et al. 2012). A number of computational resources

106 have been developed for measuring the leaf area from digital photographs for such studies.

107 Among them, Easy Leaf Area was developed for batch-processing of hundreds of pictures at once

108 (Easlon and Bloom, 2014). Easy Leaf Area distinguishes leaves from the background in digital

109 images, enabling the user to obtain the leaf area or the canopy coverage for each of the images.

110 Therefore, in this work, we evaluated the possibility of using estimates of canopy

111 coverage to estimate the number of shoots prior to their collection for two clones of E. benthamii

112 and one clone of E. dunnii in a clonal mini-garden. This is the first report that uses the percentage

113 of canopy coverage, hereafter also referred to as leaf coverage percentage, as an indirect

114 measurement of shoots yield. For this purpose, we used the computation package Easy Leaf Area

115 (Easlon and Bloom 2014) to estimate the leaf area, the projected leaf area and the percentage of

116 canopy coverage in a precise, fast and nondestructive fashion.

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118 2. Materials and Methods

119

120 2.1. Location of the experiment and genetic materials

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122 The study was conducted from May to November 2017 in a clonal mini-garden at Klabin

123 S.A., located at Otacílio Costa, Santa Catarina State, Brazil. This area has a Cfb (temperate)

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124 climate according to a recent update to Koppen’s categorizations (Alvares et al. 2013). In this

125 area, cloudy days prevail throughout the year, especially in fall and winter, reducing the

126 availability of light for the mini-stumps, which means that a highly controlled environment is

127 required for optimizing the performance of mini-stumps and the production of shoots for mini-

128 cuttings. Therefore, in our study, the mini-stumps were located in plastic incubators in a

129 greenhouse with controlled irrigation and fertigation, schematically represented in Figure 1,

130 which shows a partial view of the experimental system.

131 We used one plastic incubator placed within a greenhouse to perform the experiment, in

132 which two clones of E. benthamii (clones A and B) and one of E. dunnii were evaluated. The

133 incubator was constructed with a length of 16.6 m, a width of 0.8 m and a height of 0.8 m

134 (partially represented in Figure 1). A layer of crushed stone covered with a layer of sand with a

135 cumulative depth of 30 cm was used as the substrate for growing the mini-stumps of the three

136 clones.

137 Mini-stumps originated from cuttings of each of the clones were planted in consecutive

138 lines spaced 10 x 10 cm apart. Eight lines per clone, each with eight mini-stumps, were planted in

139 the incubator with sand as the substrate. The eight lines designated a plot for each clone. In total,

140 four plots were planted per clone in the incubator. The mini-stumps were progressively adapted to

141 the incubator and received daily irrigation supplemented with a balanced nutrient solution for

142 their growth. The nutrient solution contained the following concentrations of each nutrients: 165

143 mg.L-1 N, 32.7 mg.L-1 P, 255 mg.L-1 K, 200 mg.L-1 Ca, 40 mg.L-1 Mg, 52 mg.L-1 S, 0.4 mg.L-1 B,

144 0.05 mg.L-1 Zn, 0.06 mg.L-1 Cu, 1.6 mg.L-1 Fe, 1.04 mg.L-1 Mn, and 0.02 mg.L-1 Mo. During

145 winter, fertigation was performed one or twice per day. In summer, the frequency was increased

146 to two to four times per day. In both seasons, the first fertigation was performed for 4 min. in the

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147 morning. In winter, the second fertigation lasted 2 min. In summer, the duration of the remaining

148 fertigation events was 3 min each.

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150 2.2. Collection of shoots and evaluation of canopy coverage

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152 Following the adaptation period, we started collecting the shoots and measuring the

153 percentage of leaf cover. Immediately preceding each collection of shoots, we took a panoramic

154 picture encompassing a partial area (more than 50%) of each of the four plots of the three clones.

155 The picture was taken 30 cm above the canopy of each plot using a Samsung tablet model Tab3.

156 After taking the pictures, all the shoots in adequate condition for use as mini-cuttings were

157 collected from the incubator and counted.

158 From the harvests of shoots, we counted the total number of shoots per plot. In total, we

159 performed six evaluations over the course of the experimental period (each of the four plots for

160 each clone was evaluated six times). For the measurement of the percentage of leaf cover in the

161 pictures, we used the computational package Easy Leaf Area (http://www.plant-image-

162 analysis.org/software/easy-leaf-area) (Easlon and Bloom 2014). Using the leaf canopy

163 application, we determined the percentage of the photographs occupied by leaves (green area) by

164 adjusting the green ratio and other settings provided by the software, as shown in Figure 2.

165

166 2.3. Statistical analysis

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168 The number of shoots per mini-stump and the canopy coverage percentage were first

169 analyzed using descriptive statistics in R (R Developmental Core Team 2015) and graphically

170 diagramed with boxplot graphics. We followed the analysis with an ANOVA (P < 0.05) after

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http://www.plant-image-analysis.org/software/easy-leaf-area

http://www.plant-image-analysis.org/software/easy-leaf-area

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171 initially checking the data for their fit to normal distribution and their variance homogeneities

172 (P<0.05). The laercio package in R (Silva 2015) was used to perform a Tukey test (P<0.05) for

173 mean comparisons. The Pearson correlation coefficient between the two variables analyzed

174 (number of shoots and canopy coverage percentage) and its significance (P < 0.05) were

175 calculated using Past (Hammer et al. 2001).

176 The data distributions of the two variables were then adjusted using an appropriate

177 regression model for estimating the production of shoots according to the canopy coverage. After

178 testing for linear, exponential, polynomial and logistic models, the logistic model showed the

179 best-fitting results. For the logistic regression, three models were tested: with four parameters,

180 three parameters and an asymptotic. Their quality was further evaluated to select the best model,

181 using the Acaike information criterium (AIC) (Acaike, 1973), the Bayesian criterium (BIC) and

182 the standard error of the estimate (Syx). The logistic regression model of this work was then

183 adjusted according to the function with three parameters:

184 𝐶𝐶𝑖𝑗 = 𝜑1

1 + exp (𝜑2 ‒ 𝑠𝑖𝑗

𝜑3 )+ 𝑒𝑖𝑗

185 where CCij is the canopy coverage of the ith clone of the jth replicate, sij is the number of shoots of

186 the ith clone of the jth replicate and φi are the coefficients of the regression with fixed and/or

187 random effects, and eij is the residual effect. The validation of the model was conducted with a

188 chi-square test using a sample of 20% of the data, to check whether the two variables still fitted to

189 the same model. The analyses were performed with R.

190

191 3. Results

192

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193 We evaluated the clones six times (shoots harvests), from the end of fall (first collection

194 after 30 days) to mid-spring (180 days later) in the Southern Hemisphere. Considering that the

195 first collections were performed in winter, a lower production of shoots would be expected, as

196 shown in Figure 3a. After that, the production of shoots increased as soon as the temperature and

197 light availability increased in the spring. The mean number of shoots per square meter varied

198 from approximately 115 in the second harvest to 422 in the sixth and last harvest (Figure 3a). The

199 first three collections (winter) differed from the last three (spring) based on Tukey’s comparison

200 (P < 0.05).

201 Significant differences (P < 0.05) were also detected among the three clones. The E.

202 dunnii clone yielded a mean of 375 shoots.m-2, while the E. benthamii clone A yielded 193

203 shoots.m-2, and clone B yielded 190 shoots.m-2. The variability of the data obtained for the

204 variable number of shoots is represented separately for each clone in Figure 3b.

205 The percentage of leaf cover, in general, also exhibits an increasing trend across the

206 collections. However, it was slightly lower in the last collection (180 days) in comparison to the

207 previous analysis (150 days) (Figure 3c). Similarly, the percentage of leaf coverage was higher

208 for E. dunnii (mean of 69%) than for the E. benthamii clone A (31%) and clone B (30%). The

209 box plot in Figure 3d shows the variability of the leaf canopy coverage for each clone.

210 With analysis of the correlation between canopy coverage and the number of shoots

211 produced, we detected an association between the two variables. The Pearson correlation

212 coefficient obtained was r = 0,744, significant at P < 0.001.

213 Furthermore, by performing a regression analysis associating the percentage of canopy

214 coverage with the number of shoots, we obtained an improved adjustment using a logistic model

215 (Figure 4) with three parameters. The model with three parameters showed the lowest Acaike

216 (AIC = 181.9), Bayesian (BIC = 186.6) and standard errors of the estimates (Syx = 9.63), fitting

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217 as the best choice to adjust the canopy coverage with the number of shoots. The clones of E.

218 dunnii and E. benthamii A showed asymptotic values higher than 80%, both with similar

219 adjustment curves. The parameters and statistical significance of the model are shown in Figure

220 4. Further validation of the model with a qui-square test showed a X² value of 58.7, non-

221 significant in comparison to a tabled value of 77.9, confirming the validity of the model for the

222 data.

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224

225

226

227 4. Discussion

228

229 From the data, we have demonstrated the application of correlating the leaf canopy

230 coverage percentage with the number of shoots produced by eucalypts mini-stumps in a clonal

231 mini-garden.

232 The mean number of shoots showed a gradual increase over the period of evaluations

233 (Figure 3a). Similarly, Cunha (2009) demonstrated that an increase in temperature and natural

234 light enhanced the productivity of Eucalyptus mini-stumps.

235 The progressive increase in the number of shoots produced, in addition to being

236 associated with seasonality, can also be due to the progressive adaptation of the mini-stumps to

237 the incubators. Another study conducted by Souza-Júnior and Wendling (2003) suggested that the

238 increase in the number of shoots in later stages of collection is associated with the continuous

239 breakage of the apical dominance of the mini-stumps in the course of their experiments.

240 Moreover, this trend indicates a proper physiological status and a more developed adventitious

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241 root system after a few months. The change in hormonal balance due to pruning is also partly

242 responsible for the increase in the production of shoots (Wendling et al., 2010).

243 In general, the variation in the canopy coverage over six months followed a similar trend

244 as the number of shoots. When the number of shoots increased, the canopy coverage was also

245 higher. Only the last harvest did not follow this trend, likely the result of the leaf area of the

246 shoots being lower in that period, although a high number of shoots were produced. The Pearson

247 correlation coefficient between the number of shoots and the canopy coverage was almost 75%,

248 which can be considered a strong correlation, according to the categorization of Cohen (1988).

249 The regression analysis also suggested good precision in the estimates, as shown by the logistic

250 model (Figure 4). Analysis of the models, however, suggests that the clones of E. benthamii

251 should follow a similar trend (curve), which was not the result that we obtained. Fine adjustments

252 of the three coefficients ( ) account for the minor differences between the values of both 𝜑1

253 variables (canopy coverage and number of shoots), therefore estimating the best fit between them.

254 Even small differences between the values of each clone can result in distinct adjustment curves.

255 An implication of these results is that each clone will have a specific model (adjustment) that

256 needs to be established. Moreover, other regression methods should be tested to check the best-fit

257 model for each case. Our study, after prior testing, indicated that the logistic model was the best

258 fit for our data. Other regression models were not described here since they are outside the focus

259 of this article. The most important implication from the data is that both correlation analysis and

260 regression modeling suggest a high goodness-of-fit between the canopy coverage and the number

261 of shoots.

262 Our results present a novel perspective for evaluating the performance of mini-gardens

263 with respect to the number of shoots produced by each species, clone, mini-stump or other

264 established unit. Companies and research institutions usually perform manual counting of the

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265 number of shoots produced by their genetic materials and use that data to estimate their future

266 production. The approach presented here offers an opportunity for indirect and accurate

267 predictions of the number of shoots produced, based solely on the percentage of canopy coverage

268 as analyzed from simple photographs using computational resources such as Easy Leaf Area,

269 developed and released by Easlon and Bloom (2014).

270

271 5. Conclusion

272

273 Our data demonstrate a significant correlation and reliable adjustment of the data to a

274 logistic model for the estimation of the number of shoots with considerable precision based on the

275 leaf canopy coverage. This result might represent an important contribution for nurseries that

276 routinely collect mini-cuttings for massive propagation of desirable genotypes. We highlight that

277 this is the first report concerned with an association between the leaf canopy coverage and the

278 number of shoots produced by mini-stumps. Software, such as the Easy Leaf Area package, as

279 well as other computational resources that enable the measurement of canopy coverage, are

280 useful in establishing this association.

281

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dunnii. Acta Scientiarum. Agronomy 34(2), 169-178. DOI: 10.4025/actasciagron.v34i2.13059

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Eucalyptus benthamii mini-cuttings. Journal of Forestry Research, 23(4), 583-592. DOI:

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Cohen J., 1988. Statistical power analysis for the behavioral sciences. Erlbaum: Hillsdale, NJ. 567

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Cunha, A.C.M.C., Nogueira de Paiva, H., Garcia Leite, H., Barros, N.F.D., Palha Leite, F. 2009.

Relações entre variáveis climáticas com produção e enraizamento de miniestacas de eucalipto.

Revista Árvore 33(2):195-203. DOI: 10.1590/S0100-67622009000200001

Easlon, H.M.; Bloom, A.J 2014. Easy Leaf Area: automated digital image analysis for rapid and

accurate measurement of leaf area. Applications in Plant Sciences 2(7).

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Hammer Ø., Harper D.A.T., Ryan P.D. 2001. PAST-Palaeontological statistics. Paleontologica

Eletronica 4(1):9pp.

Hartmann, H.T., Kester, D. E., Davies Junior, F.T, Geneve, R.L. 2011. Plant propagation:

principles and practices. 8th ed. São Paulo, SP, Brazil: Prentice-Hall, 915 p.

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Jennings, S.B., Brown, N.D., Sheil, D. 1999. Assessing forest canopies and understorey

illumination: canopy closure, canopy cover and other measures. Forestry 72(1), 59-74.

Konzen, E.R., Navroski, M.C., Pereira, M.D.O., Nascimento, B., Meneguzzi, A., Souza, P. F. D.

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2017. Genetic variation for growth variables of Eucalyptus benthamii Maiden & Cambage and E.

smithii RT Baker provenances in southern Brazil. Cerne, 23(3), 359-366.

Nakhooda, M., Jain, S.M. 2016. A review of eucalyptus propagation and conservation. Propagation

of Ornamental Plants 16(4):101-119.

Nielsen, D.C., Miceli-Garcia, J.J., Lyon D.J. 2012. Canopy cover and leaf area index relationships

for wheat, triticale, and corn. Agronomy Journal 104(6), 1569-1573.

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Paludzyszyn Filho, E., dos Santos, P.E.T., Ferreira, C.A. 2006. Eucaliptos indicados para plantios

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Souza-Junior, L., Wendling, I. 2003. Propagação vegetativa de Eucalyptus dunnii via miniestaquia

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009-9175-2

282

283 Figures captions

Figure 1. Overview of the experimental system adopted in this work. Dripping pipes are represented

in gray. Four pipes were used for fertigation.

Figure 2. Images of the canopy of two clones of Eucalyptus used for determining the percentage of

canopy coverage (percentage of leaf coverage) in a clonal mini-garden. Partial images of the

canopy of E. dunnii (a) with its analysis (b) and E. benthamii clone B (c) followed by its analysis

(d) are shown, alongside the images processed with Easy Leaf Area – Leaf Canopy, showing the

green area as representative of the leaves.

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Figure 3. The number of shoots and the percentage of leaf coverage for six collections of shoots of

clones of E. benthamii and E. dunnii in a clonal mini-garden. a – Total number of shoots per clone

throughout the collection points. b – Box plot showing the variability in the number of shoots across

six replicates (collections of shoots in intervals of approximately 30 days) for each clone. c –

Percentage of leaf coverage for all the collections. d – Box plot showing the variability of the

percentage of leaf coverage for each clone.

Figure 4. Logistic regression model representing the association between the canopy coverage (%)

and the number of shoots of three Eucalyptus clones. a – General model for the whole set of data.

b – Model for each clone.

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