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| Registros recuperados : 6 | |
| 6. |  | NOVAES, E.; DROST, D. R.; FARMERIE, W. G.; PAPPAS JUNIOR, G. J.; GRATTAPAGLIA, D.; SEDEROFF, R. R.; KIRST, M. High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomic, v.9, p. 312, 2008.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 6 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
28/02/2024 |
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Data da última atualização: |
28/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
TESHOME, M.; BRAZ, E. M.; TORRES, C. M. M. E.; RAPTIS, D. I.; MATTOS, P. P. de; TEMESGEN, H.; RUBIO-CAMACHO, E. A.; SILESHI, G. W. |
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Afiliação: |
MINDAYE TESHOME, UNIVERSIDADE FEDERAL DE VIÇOSA; EVALDO MUNOZ BRAZ, CNPF; CARLOS MOREIRA MIQUELINO ELETO TORRES, UNIVERSIDADE FEDERAL DE VIÇOSA; DIMITRIOS IOANNIS RAPTIS, INTERNATIONAL HELLENIC UNIVERSITY; PATRICIA POVOA DE MATTOS, CNPF; HAILEMARIAM TEMESGEN, OREGON STATE UNIVERSITY; ERNESTO ALONSO RUBIO-CAMACHO, INSTITUTO NACIONAL DE INVESTIGACIONES FORESTALES, AGRÍCOLAS Y PECUARIAS; GUDETA WOLDESEMAYAT SILESHI, ADDIS ABABA UNIVERSITY. |
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Título: |
Mixed-effects height prediction model for Juniperus procera trees from a Dry Afromontane Forest in Ethiopia. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Forests, v. 15, n. 3, 443, p. 1-19, 2024. |
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DOI: |
https://doi.org/10.3390/f15030443 |
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Idioma: |
Inglês |
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Conteúdo: |
Tree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands. |
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Palavras-Chave: |
Altura das árvores; Calibração; Modelo de predição; Native tree; Prediction model; Stand volume; Tree height. |
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Thesagro: |
Inventário Florestal. |
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Thesaurus NAL: |
Allometry; Calibration; Forest inventory; forestry; Juniperus procera. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1162360/1/Forest-2024-Braz.pdf
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Marc: |
LEADER 02445naa a2200373 a 4500
001 2162360
005 2024-02-28
008 2024 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/f15030443$2DOI
100 1 $aTESHOME, M.
245 $aMixed-effects height prediction model for Juniperus procera trees from a Dry Afromontane Forest in Ethiopia.$h[electronic resource]
260 $c2024
520 $aTree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands.
650 $aAllometry
650 $aCalibration
650 $aForest inventory
650 $aforestry
650 $aJuniperus procera
650 $aInventário Florestal
653 $aAltura das árvores
653 $aCalibração
653 $aModelo de predição
653 $aNative tree
653 $aPrediction model
653 $aStand volume
653 $aTree height
700 1 $aBRAZ, E. M.
700 1 $aTORRES, C. M. M. E.
700 1 $aRAPTIS, D. I.
700 1 $aMATTOS, P. P. de
700 1 $aTEMESGEN, H.
700 1 $aRUBIO-CAMACHO, E. A.
700 1 $aSILESHI, G. W.
773 $tForests$gv. 15, n. 3, 443, p. 1-19, 2024.
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Embrapa Florestas (CNPF) |
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