Incorporating rainfall data to better plan eucalyptus clones deployment in eastern Brazil

Scolforo, Henrique Ferraço; Scolforo, José Roberto Soares; Stape, José Luiz; McTague, J. P.; Burkhart, Harold E.; McCarter, James B.; Neto, Fernando de Castro; Loos, Rodolfo Araujo; Sartório, Robert Cardoso

doi:10.1016/j.foreco.2017.02.025

Cited by 31 publications

(15 citation statements)

References 19 publications

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“…The use of clonal material as a categorical variable proved significant when included in the model, which was expected since different clonal materials are expected to have different height growth behaviour (Calegario et al, 2005;Scolforo et al, 2017). In this case, however, the variable contributed to a far lesser extent than age or dominant height, and it was justified to present a model version with a common intercept, since it can expand the applicability of the model in those cases with limited information concerning the clonal material.…”

Section: Figurementioning

confidence: 99%

HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL

Filho

Mola‐Yudego

Ribeiro

et al. 2018

CERNE

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HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZILFERRAZ FILHO, A. C.; MOLA-YUDEGO, B.; RIBEIRO, A.; SCOLFORO, J. R. S.; LOOS, R. A.; SCOLFORO, H. F. Height-diameter models for Eucalyptus sp. plantations in Brazil. CERNE, v. 24, n. 1, p. 9-17, 2018. HIGHLIGHTSHeight-diameter models are provided for eucalyptus plantations in Brazil.A wide variety of stand conditions (age and tree densities) were covered in the database.Mixed modeling was used due to the hierarchical structure of the data (trees in plots).The models use diameter, age, and dominant height as predictor variables. ABSTRACTThe use of height-diameter models have important applications in the management of eucalyptus plantations. The aim of this paper is to provide adequate height models based on the diameter and stand variables that could be easily used and applied to plantation's management. The data was based on tests located in Espírito Santo and Bahia. A total of 312 plots, including 35 different treatments (different initial planting densities, number and severity of thinning operations, fertilization regimes, among others), and four different clones. This resulted in 2,407 conditions for 85,608 tree measurements. The constructed equations were based on mixed-effect models, in order to accommodate possible autocorrelations, to address variations between sites and to obtain locally calibrated estimates. Among the different model structures and combination of variables tested and presented, the best model showed a coefficient of determination of 0.953 and 0.988, for the fixed and fixed+random parts, respectively. The model included a random factor for measurements-within-tree and tree-within-plot. The bias of the fixed part was 0.208 cm (0.0659%). We believe the models in this study can have broad applications in management and inventory methods applied to plantations. HEIGHT-DIAMETER MODELS FOR EUCALYPTUS SP. PLANTATIONS IN BRAZIL 10 CERNEFERRAZ FILHO et al.

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Section: Figurementioning

confidence: 99%

HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL

Filho

Mola‐Yudego

Ribeiro

et al. 2018

CERNE

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“…Annual variations in precipitation can alter gross primary production and wood production by one-third to one-half (Stape et al, 2008), and any regional changes in climate would likely result in regional changes in production. Statistical and ecophysiological models that incorporate rainfall are commonly used to predict growth (Almeida et al, 2010;Scolforo et al, 2017). Short rotations (commonly about 6-8 years) allow land owners to change land use in responses to changing markets.…”

Section: Introductionmentioning

confidence: 99%

“…Deployment of Eucalyptus clones requires many years of silvicultural evaluation (Resende et al, 2012). A long-standing goal of tree breeders and ecophysiologists is understanding the survival and growth of a large and diverse group of clones (derived from hybrids of various species) across large areas, and how this knowledge can be used to develop manageable ideotypes for tactical and operational decisions (Marcatti et al, 2017;Scolforo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The interactions of climate, spacing and genetics on clonal Eucalyptus plantations across Brazil and Uruguay

Binkley

Campoe

Álvares

et al. 2017

Forest Ecology and Management

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Intensively managed plantations account for 1.5% of the world's forests, but they meet one-third of the demand for wood products. Eucalyptus plantations are among the most productive, with rates of growth depending heavily on genetics, silviculture, and climate. The TECHS Project examines productivity at 36 locations across a 3500 km gradient from Brazil to Uruguay, testing the interacting influences of genetics, temperature and precipitation on stemwood production. Across all sites and genotypes, stemwood production in the middle of the 6year rotation (the peak period of growth) averaged 22 Mg ha −1 yr −1. Production varied by fivefold across sites, and by about 2-fold among genotypes within each site. The best clones at each location grew 1.5-4 Mg ha −1 yr −1 more than the average for all clones, underscoring the importance of matching genotypes to local site conditions. Contrary to patterns for natural forests across geographic gradients, Eucalyptus production declined with increasing temperature, dropping by 2.5 Mg ha −1 yr −1 for a 1°C temperature increase. The temperature effect was likely driven in part by the geographic covariance of temperature and rainfall, as rainfall tended to decline by 78 mm yr −1 for each 1°C increase in temperature. Stemwood production increased an average of 1.5 Mg ha −1 yr −1 for each 100 mm yr −1 increase in precipitation, but when the covariation of temperature and precipitation were included the apparent influence of precipitation declined to 0.4 Mg ha −1 yr −1 for each 100 mm yr −1 increase in precipitation. Future results will determine if within-site reductions in ambient rainfall have the same apparent influences as the rainfall pattern across the geographic gradient, as well as quantifying the importance of insects and pests in affecting growth. The supply of wood from intensively managed plantations will be strongly influenced by both temperature and precipitation at plantation locations, and with changing climates.

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“…FERRAZ FILHO et al, 2011;PAULO et al, 2014;BONTEMPS;BOURIAUD, 2014;ADAMEC;DRÁPELA, 2016;MARCATTI et al 2017;SCOLFORO et al, 2017).…”

Section: Introductionunclassified

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

CERNE

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Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history.

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Incorporating rainfall data to better plan eucalyptus clones deployment in eastern Brazil

Cited by 31 publications

References 19 publications

HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL

HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL

The interactions of climate, spacing and genetics on clonal Eucalyptus plantations across Brazil and Uruguay

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

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