Allometric Equations to Predict <i>Pinus palustris</i> Biomass in the Southeastern United States

Farias, Aline Araújo; Gezan, Salvador A.; de Carvalho, Melissa Pisaroglo; Ferraz Filho, Antonio Carlos; Soares, Carlos Pedro Boechat

doi:10.1590/2179-8087.040318

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Original Article, Forest Management • Floresta Ambient. 26 (spe 1) • 2019 • https://doi.org/10.1590/2179-8087.040318 copy

Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palustris stump-biomass. Biomass data were collected at different locations in the southeastern United States. A total of 119 allometric equations were fitted from the combination of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G), number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE) was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass prediction for trees from different sites.

Keywords:
forest management; modeling; regression

Attribute	Units	Mean	Standard deviation	Minimum	Maximum
I	years	28.4	24.2	5	87
DBH	cm	16.0	13.7	0.81	54.3
H	m	11.5	7.7	0.55	30.4
N	n.ha^-1	924	589.6	50	2150
BA	m² ha^-1	14.2	8.6	0.21	30.2
Dq	cm	16.9	10.7	1.80	51.1
W	kg	262.3	492.5	0.20	2149.9
SI	m	21.27	4.08	13.74	33.86

Model	S_y.x	R² Adjust.
1	0.7206	0.9155
2	0.6908	0.9223
3	0.5883	0.9437
4	0.3834	0.9761
5	0.3783	0.9767
6	0.546	0.515
7	0.4624	0.9652
8	0.456	0.9661
9	0.3641	0.9784
10	0.6999	0.9203
11	0.5266	0.9548
12	0.4417	0.9682

Model	S_y.x	R² Adjust.
13	0.3058	0.9848
14	0.3067	0.9847
15	0.2898	0.9863
16	0.2948	0.9859
17	0.3201	0.9833
18	0.3502	0.98
19	0.3264	0.9827
20	0.3363	0.9816
21	0.3529	0.9797
22	0.3084	0.9845
23	0.3191	0.9834
24	0.348	0.9803
25	0.4009	0.9738
26	0.4859	0.9616

Models	RMSE
5^th: $l n (W) = - 1.8702 + 0.1114. (H) + 0.5818. l n (H . D B H)$	0.2838
9^th: $l n (W) = - 1.7709 + 0.0544. (D B H) + 0.6273. l n (H . D B H)$	0.2688
15^th: $l n (W) = - 0.7785 + 0.7124. (H) + 0.0979. l n (H . D B H) - 0.6353. (G) - 0.0004. l n (N) + 0.0122. l n (D q)$	0.2156
16^th: $l n (W) = - 0.9978 + 0.7082. (H) + 0.1009. l n (H . D B H) - 0.531. (N) - 0.0003. l n (D q)$	0.2090

Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro Rodovia BR 465 Km 7, CEP 23897-000, Tel.: (21) 2682 0558 | (21) 3787-4033 - Seropédica - RJ - Brazil
E-mail: floram@ufrrj.br

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[1] Aline Araújo FariasDepartamento de Engenharia Florestal, Universidade Federal de Viçosa – UFV, Avenida Purdue, s/n, Edif. Reinaldo de Jesus Araújo, CEP 36570-900, Viçosa, MG, Brasil e-mail: araujo.91@hotmail.com