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A whole-stand model for estimating the productivity of uneven-aged temperate pine-oak forests in Mexico

Abstract:
This study presents a model for estimating forest productivity based on a sample of 2048 permanent field plots covering a wide range of growing sites in Mexico. Our state-space approach assumes that the growth behavior of any stand over time can be estimated on the basis of its current state, defined by the dominant height (H), number of trees per hectare (N), and stand basal area (BA). We used transition functions to estimate the change in states as a function of the current state. We also present transition functions for the change in stand volume (V) and total above-ground biomass (AGB). The first transition function relates dominant height to dominant diameter by using the guide-curve method to estimate site form. The transition function for N consists of two models, one for estimating natural mortality and the other for estimating recruitment. These models were developed in two steps: in the first step, the logistic regression and maximum likelihood approach were used to estimate the probability of the occurrence of mortality or recruitment, and in the second step, the rate of change associated with each event was modeled when mortality or recruitment was assumed to have occurred as a result of the first step. The remaining three transition functions (BA, V, and AGB) were fitted simultaneously to account for possible correlations between errors. The model estimating total above-ground biomass (AGB), which can be considered a state variable that summarizes the performance of the whole model, explained more than 97% of the observed variability, with a root mean square error value of 10.57 Mg/ha.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/su17083393

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Role:
Author
ORCID:
0000-0002-5206-9128
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Role:
Author
ORCID:
0000-0002-2851-7517
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Role:
Author
ORCID:
0000-0002-1463-6523
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Author
ORCID:
0000-0003-0732-4908


Publisher:
MDPI
Journal:
Sustainability More from this journal
Volume:
17
Issue:
8
Article number:
3393
Publication date:
2025-04-10
Acceptance date:
2025-04-09
DOI:
EISSN:
2071-1050


Language:
English
Keywords:
Pubs id:
2118291
Local pid:
pubs:2118291
Deposit date:
2025-05-19
ARK identifier:

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