| 1. | Title | ##plugins.generic.mcfnsReadingTools.titleLabel## | Quantification and Incorporation of Uncertainty in Forest Growth and Yield Projections Using A Bayesian Probabilistic Framework: A Demonstration for Plantation Coastal Douglas-fir in the Pacific Northwest, USA |
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| 2. | Creator | ##plugins.generic.mcfnsReadingTools.creators## | Duncan Willson; Oklahoma State University, Department of Natural Resource Ecology & Management, Stillwater, OK, USA, 74078; Estados Unidos |
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| 2. | Creator | ##plugins.generic.mcfnsReadingTools.creators## | Vicente Monleon; USDA Forest Service, PNRS, FIA Program, Corvallis, OR, USA, 97331; Estados Unidos |
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| 2. | Creator | ##plugins.generic.mcfnsReadingTools.creators## | Aaron Weiskittel; University of Maine, Center for Research on Sustainable Forests, Orono, ME, USA, 04469 |
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| 3. | Subject | ##plugins.generic.mcfnsReadingTools.keywords## | forest growth and yield; error propagation; model uncertainty; error budgets; individual tree growth models |
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| 3. | Subject | ##plugins.generic.mcfnsReadingTools.disciplines## | Forest biometrics and growth and yield modeling |
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| 4. | Description | ##plugins.generic.mcfnsReadingTools.abstract## | A Bayesian probabilistic modeling platform was used and evaluated for application in a relatively complex individual-tree growth and yield model for coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco), which was expressed as a mixed discrete and continuous Bayesian Network for annual projections. The modeling platform used a common and open-source Bayesian analysis program (JAGS v3.3.0), and was sufficiently flexible to handle a relatively complex model structure; namely, a differential form, highly dynamic, recursive, hierarchical, non-linear system of equations with a rather complex error structure. This novel probabilistic modeling platform met certain desirable criteria, including (1) accurate and tractable projections that included full error propagation; (2) flexible and comprehensive analytic capabilities; (3) full consideration of hierarchical and multi-level model structures; (4) capacity for random effects calibration; (5) allowance of hypothesis testing and updating knowledge across different system components, simultaneously with different sources of information (i.e., new data); (6) computational efficiency; and (7) relatively simple implementation as demonstrated in a complied scripting language. Probabilistic projections of forest growth and yield included all sources of errors and uncertainty (e.g., estimated parameters, ... |
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| 5. | Publisher | ##plugins.generic.mcfnsReadingTools.publisher## | Contemporary Journal Concept Press |
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| 6. | Contributor | ##plugins.generic.mcfnsReadingTools.contributors## | |
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| 7. | Date | ##plugins.generic.mcfnsReadingTools.date## | 2019-10-30 |
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| 8. | Type | ##plugins.generic.mcfnsReadingTools.type## | Text.Serial.Journal |
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| 9. | Format | ##plugins.generic.mcfnsReadingTools.format## | PDF (inglés); application/pdf; SUP (inglés); SUP2 (inglés); SUP3 (inglés) |
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| 10. | Identifier | ##plugins.generic.mcfnsReadingTools.identifier## | https://mcfns.com/index.php/Journal/es/article/view/11.3 |
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| 11. | Source | ##plugins.generic.mcfnsReadingTools.source## | Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol. 11 Núm. 2: MCFNS October 30, 2019; 264-285 (22) |
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| 11. | Source | ##plugins.generic.mcfnsReadingTools.onlineIssn## | 1946-7664 |
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| 12. | Language | ##plugins.generic.mcfnsReadingTools.language## | en |
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| 13. | Relation | ##plugins.generic.mcfnsReadingTools.suppFiles## | SUP (inglés); SUP2 (inglés); SUP3 (inglés) |
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| 14. | Coverage | ##plugins.generic.mcfnsReadingTools.coverageGeo## | |
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| 15. | Rights | ##plugins.generic.mcfnsReadingTools.rights## | Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) 2019 |
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