Evaluation of biomass, carbon storage capability, agroforestry interest of <i>Pinus pinea</i> L. and management practices to increase carbon stocks: a review

Mechergui, Kaouther; Naghmouchi, Souheila; Altamimi, Amal Saleh; Jaouadi, Wahbi

doi:10.1590/01047760202127012938

ABSTRACT

Background:

Stone pine (Pinus pinea L.) is one of the characteristic species of the Mediterranean flora. This species has been used since ancient times because of its economic importance. This study was carried out because stone pine forests in the Mediterranean area, as carbon sinks, are one of the mitigation strategies considered to face climate change. Also, stone pine forests are of great socio-economic importance on an international scale due to the high demand for pine nuts and the important role of this emblematic species in agroforestry. The objectives of this study are, firstly, the evaluation of biomass and carbon storage capability and, secondly, the study of agroforestry interest of Pinus pinea L. and management practices to increase carbon stocks.

Results:

A review of research and knowledge was carried out on the subject based on a selection of publications that have been made in the Mediterranean area. The main conclusions are that stone pine forests have a very high biomass stock and a high storage potential in the future. However, these forests could be a potential carbon reservoir in the coming years and thus on climate change mitigation. In addition, the practice of agroforestry in the stone pine ecosystem can create jobs and many sources of income for the local population and improve their living standards.

Conclusion:

Finally, this review of research results can serve as an initial basis for refining management practices to improve the establishment of pine trees and tools to help forest managers in quantifying biomass, thereby contributing to the accurate estimation of carbon sequestration and stocks in stone pine stands and agroforestry practices..

Keywords:
Stone pine; biomass; carbon stocks; agroforestry interest; management practices

HIGHLIGHTS

Forest management and quantification of stone pine biomass and carbon stocks.

Biomass accumulation under the stone pine and agroforestry interest of species.

Ground biomass estimation with very high spatial resolution satellite images.

Management practices used to increase biomass and carbon stocks.

[1] ✤ jaouadiwahbi@gmail.com

Biomass (kg.ha-1.yr-1)	Structure	Aboveground	Belowground	cones	Total
	Even-aged	1721.1	525.3	76.1	2322.5
	Uneven-aged	2034.4	520.9	108.2	2663.5
C fixation (Mg.C.ha-1.yr-1)	Even-aged	0.873	0.267	0.055	1.195
C fixation (Mg.C.ha-1.yr-1)	Uneven-aged	1.031	0.265	0.038	1.334

General Model	Country	Aboveground biomass model	References
	Spain	0.11694 d2.424	Montero et al. (2005MONTERO, G. ; RUIZ-PEINADO, R. ; MUÑOZ, M. Producción de biomasa y fijación de CO2 por los bosques españoles. Monografías INIA, Serie Forestal no. 13, Madrid, Spain, pp. 270. 2005.)
	Italy	0.054 d2.594	Cutini et al. 2013
	Portugal	280.1 C2.22h0.19	Correira et al. 2010

Allometric function	R²	R_2aj
Wps = 12.4076 x CHPps	0.893	0.890
Wpscs = 14.1471 x CHPps	0.995	0.995
Wps : above ground biomass per plot ; CHPps : Tree crown horizontal projection ; W psc : cumulative above ground biomass per plot

Allometric function	Biomass component	Model	Published statistics
Italy D range : Cutini et al. (2013)	Stem	0.091 d2.346	RMSE = 70.85
	Needles	0.009 d2.537	RMSE by local 3.91;16.39 ; 2.74; 4.93
	Woody	0.045 d2.602	RMSE = 85.14
	Aboveground	∑ components
Portugal D range : Correira et al. (2010)	Wood	18.85 c1.68 h0.95	R² _aj = 0.93
	Bark	8.08 c1.55 h0.47	R² _aj = 0.82
	Needles	22.27 c1.76 (h/d)(-0.5)	R² _aj = 0.63
	Branches	184.94 c3.03	R² _aj = 0.74
	Aboveground	∑ components
Spain D range : Ruiz-Peinado et al. (2011)	Stem	0.0224 d1.923h1.0193	R² _aj = 0.99 RMSE = 36.76
	Needles with thin branches (< 2 cm)	21.927-2.827h+ 0.0707 d2	R² _aj = 0.90 RMSE = 19.65
	Medium branches (2-7 cm)	0.0525 d²	R² _aj = 0.80 RMSE = 29.46
	Thick branches	Z If d ≤ 22.5 cm then Z = 0 If d > 22.5 cm then Z = 1	R² _aj = 0.86 RMSE = 46.17
	Aboveground	∑ components
	Roots	0.117 d²	R² _aj = 0.98 RMSE = 14.86

Allometric function	Biomass component	Model	k	α	β	R² _aj	RMSE
MT-d	Stem	kd^α	0.002423 (<0.0001)	3.246929 (<0.0001)		0.931	98.2
	Branches	kd^α	0.618358 (<0.0001)	1.678759 (<0.0001)		0.797	76.7
	Needles	k (d/100)^α	2061.774 (<0.0001)	4.248054 (<0.0001)		0.776	33.0
	Aboveground	∑ components				0.961	115.7
MT-dh	Stem	K d^α (h/d)^β	0.008797 (<0.0001)	2.871281 (<0.0001)	-0.19326 (<0.0001)	0.930	98.9
	Branches	K d^α h^β	0.060502 (<0.0001)	1.897782 (<0.0001)	0.527179 (<0.0001)	0.852	65.5
	Needles	K (d/100)^α (h/d)^β	49.37346 (<0.0001)	2.528243 (<0.0001)	-2.01953 (<0.0001)	0.888	23.4
	Aboveground	∑ components				0.960	116.9

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