Updated generalized biomass equations for North American tree species

Chojnacky, David C.; Heath, Linda S.; Jenkins, Jennifer C.

doi:10.1093/forestry/cpt053

Cited by 256 publications

(323 citation statements)

References 97 publications

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“…These biomass estimates were consistent with predictions from models #13, #14 and Jenkins et al (Table 7). However, when compared to the adjusted Jenkins' et l. models proposed by Chojnacky et al (2014) the differences were substantial, especially for trees with specific gravity greater than 0.40. There were notable differences when comparing these predictions between states and ecoregions, this may be attributed to larger diameter P. ponderosa trees in Nebraska than in other states.…”

Section: Suitability Of Allometric Models For Estimating Biomassmentioning

confidence: 79%

“…However, the coefficients of Jenkins et al (2003) as modified by Chojnacky et al (2014) did not necessarily produce values consistent (2003) model overestimated biomass for the most abundant tree species in the FIA inventory. The same trend was reported by Zhou and Hemstrom (2009) when estimating aboveground tree biomass on forest land in the Pacific Northwest.…”

Section: Discussionmentioning

confidence: 90%

“…In the validation process (Table 4), Jenkins' et al generalized model (Model #15) was included to test the relationship between the overestimation reported for forest stands (Zhou and Hemstrom 2009;Domke et al 2012;Chojnacky et al 2014) and the biomass estimates for open-grown trees.…”

Section: Suitability Of Allometric Models For Estimating Biomassmentioning

confidence: 99%

“…The models #13, #14 and Jenkins et al (#15) were evaluated again, including the adjustment made by Chojnacky et al (2014) to Jenkins et al, using data from FIA and destructively sampled P. ponderosa, in NE, MT, ecoregions 331, and 332, projected to 50 years. These biomass estimates were consistent with predictions from models #13, #14 and Jenkins et al (Table 7).…”

Section: Suitability Of Allometric Models For Estimating Biomassmentioning

confidence: 99%

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Estimating carbon storage in windbreak trees on U.S. agricultural lands

et al. 2016

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Assessing carbon (C) capture and storage potential by the agroforestry practice of windbreaks has been limited. This is due, in part, to a lack of suitable data and associated models for estimating tree biomass and C for species growing under more opengrown conditions such as windbreaks in the Central Plains region of the United States (U.S.). We evaluated 15 allometric models using destructively sampled Pinus ponderosa (Lawson & C. Lawson) data from field windbreaks in Nebraska and Montana. Several goodness-of-fit metrics were used to select the optimal model. The Jenkins' et al. model was then used to estimate biomass for 16 tree species in windbreaks projected over a 50 year time horizon in nine continental U.S. regions. Carbon storage potential in the windbreak scenarios ranged from 1.07 ± 0.21 to 3.84 ± 0.04 Mg C ha -1 year -1 for conifer species and from 0.99 ± 0.16 to 13.6 ± 7.72 Mg C ha -1 year -1 for broadleaved deciduous species during the 50 year period. Estimated mean C storage potentials across species and regions were 2.45 ± 0.42 and 4.39 ± 1.74 Mg C ha -1 year -1 for conifer and broadleaved deciduous species, respectively. Such information enhances our capacity to better predict the C sequestration potential of windbreaks associated with whole farm/ranch operations in the U.S.

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Section: Suitability Of Allometric Models For Estimating Biomassmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 90%

Section: Suitability Of Allometric Models For Estimating Biomassmentioning

confidence: 99%

Section: Suitability Of Allometric Models For Estimating Biomassmentioning

confidence: 99%

See 2 more Smart Citations

Estimating carbon storage in windbreak trees on U.S. agricultural lands

et al. 2016

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show abstract

“…Tiers 2 and 3) are available for estimating tree biomass. The most widely used approach is the use of generic equations based on trees harvested in several sites (Brown 1997;Jenkins et al 2003;Chave et al 2005;Chave et al 2014;Chojnacky et al 2014). However, such generic equations may lead to systematic errors of up to 400 % at the site level (Alvarez et al 2012;Ngomanda et al 2014;Chave et al 2014).…”

Section: Introductionmentioning

confidence: 99%