Quantile regression for modelling the impact of climate in cork growth quantiles in Portugal

被引:6
|
作者
Paulo, Joana Amaral [1 ]
Firmino, Paulo Neves [1 ]
Faias, Sonia Pacheco [1 ]
Tome, Margarida [1 ]
机构
[1] Univ Lisbon, Inst Super Agron, Ctr Estudos Florestais, P-1349017 Lisbon, Portugal
关键词
Quercus suber L; Climate change; Cork stopper; Cork growth index; Cork ring; Cork thickness; OAK QUERCUS-SUBER; L; STANDS; WATER; TREE; AGROFORESTRY; VARIABILITY; DEBARKING; VARIABLES; SCHEDULE; FORESTS;
D O I
10.1007/s10342-021-01379-8
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
The annual growth and the thickness of cork are known to be highly variable between trees located in the same geographical location. Researching how climate variables affect different trees within the same site is a step forward for the management of cork production since current knowledge focusses only on the average tree response. Quantile regression methodology was applied for the first time to a large data set containing measurements of cork growth, sampled in 35 stands across the cork oak distribution area in Portugal. This methodology proved to be useful for testing the hypothesis raised: does climate affect differently the annual cork growth, and ultimately cork thickness of individual trees located in the same stand? Estimating the amount of cork produced by one stand that has the required thickness for the production of natural cork stoppers is essential to support cork oak management. However, no model, before this work, had been developed to provide managers with this information. A downward parabolic relationship between annual cork growth and annual precipitation was determined for all quantiles, with optimum annual average precipitation value ranging from 1103 to 1007 mm. April to August monthly temperatures, spring average temperature or summer average temperature, showed a negative relationship with annual cork growth, in particular for lower quantiles. Maximum annual temperature was shown to negatively affect annual cork thickness, in particular for the trees under the 6th quantile. The ratio between annual precipitation and average temperature, that define the Lang index (LI), showed a downward parabolic relationship with annual cork growth. Best cork growth conditions are found for Lang index values around 60, corresponding for the transition between semi-arid climate and humid climate. The application of the final model developed for estimating cork thickness of an eight years' cork growth period allowed the prediction and mapping of the percentage of cork suitable for natural cork stopper production. It showed that higher values are expected in the Southern and Central coastal regions and along the Tagus River basin. The Northern coastal and mountain regions, characterised by Lang index values higher to 60 (humid climates), present lower estimated values for the percentage of cork suitable for natural cork stopper production. The estimated values are expected to be reduced under climate change scenarios in the Southern and Central coastal regions.
引用
收藏
页码:991 / 1004
页数:14
相关论文
共 50 条
  • [1] Quantile regression for modelling the impact of climate in cork growth quantiles in Portugal
    Joana Amaral Paulo
    Paulo Neves Firmino
    Sónia Pacheco Faias
    Margarida Tomé
    [J]. European Journal of Forest Research, 2021, 140 : 991 - 1004
  • [2] On elliptical quantiles in the quantile regression setup
    Hlubinka, Daniel
    Siman, Miroslav
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2013, 116 : 163 - 171
  • [3] On generalized elliptical quantiles in the nonlinear quantile regression setup
    Hlubinka, Daniel
    Siman, Miroslav
    [J]. TEST, 2015, 24 (02) : 249 - 264
  • [4] ESTIMATION FOR EXTREME CONDITIONAL QUANTILES OF FUNCTIONAL QUANTILE REGRESSION
    Zhu, Hanbing
    Zhang, Riquan
    Li, Yehua
    Yao, Weixin
    [J]. STATISTICA SINICA, 2022, 32 : 1767 - 1787
  • [5] On generalized elliptical quantiles in the nonlinear quantile regression setup
    Daniel Hlubinka
    Miroslav Šiman
    [J]. TEST, 2015, 24 : 249 - 264
  • [6] Multiscale Exploratory Analysis of Regression Quantiles Using Quantile SiZer
    Park, Cheolwoo
    Lee, Thomas C. M.
    Hannig, Jan
    [J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2010, 19 (03) : 497 - 513
  • [7] Nonparametric estimation of conditional quantiles using quantile regression trees
    Chaudhuri, P
    Loh, WY
    [J]. BERNOULLI, 2002, 8 (05) : 561 - 576
  • [8] ESTIMATION OF QUANTILE DENSITY-FUNCTION BASED ON REGRESSION QUANTILES
    DODGE, Y
    JURECKOVA, J
    [J]. STATISTICS & PROBABILITY LETTERS, 1995, 23 (01) : 73 - 78
  • [9] THE DETERMINATION OF A LEAST QUANTILE OF SQUARES REGRESSION LINE FOR ALL QUANTILES
    CARRIZOSA, E
    PLASTRIA, F
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 1995, 20 (05) : 467 - 479
  • [10] Modelling the impact of uncertainty on sectoral GHG emissions in Saudi Arabia using the causality-in-quantiles and quantile-on-quantile approaches
    Raggad, Bechir
    Ben-Salha, Ousama
    Zrelly, Houyem
    Jbir, Rafik
    [J]. ENERGY STRATEGY REVIEWS, 2024, 51