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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
21/02/2014 |
Actualizado : |
22/02/2014 |
Autor : |
Rabbinge, R. ; Goudriaan, J. ; Keulen, H. ; Penning de Vries, F.W.T. ; Laar, H.H. (ed) |
Título : |
Theoretical production ecology : reflections and prospects |
Fecha de publicación : |
1990 |
Fuente / Imprenta : |
Wageningen (Holanda): Pudoc, 1990. |
Páginas : |
301p |
Serie : |
Simulation monographs |
ISBN : |
90-220-1004-x (bound) |
Idioma : |
Español |
Thesagro : |
AGRICULTURA; ANALISIS ECONOMICO; COMPETICION VEGETAL; CRECIMIENTO; CULTIVOS DE INVERNADERO; ENFERMEDADES DE LAS PLANTAS; EXPLOTACION AGRICOLA INTENSIVA; FACTORES CLIMATICOS; FISIOLOGIA VEGETAL; INVESTIGACION; MALEZAS; MODELOS DE SIMULACION; NITROGENO; PAISES EN DESARROLLO; RESPUESTA DE LA PLANTA; TRANSPIRACION. |
Asunto categoría : |
-- |
Marc : |
LEADER 01012nam a2200361 a 4500 001 1037362 005 2014-02-22 008 1990 bl uuuu u00u1 u #d 100 1 $aRABBINGE, R. 245 $aTheoretical production ecology$breflections and prospects 260 $aWageningen (Holanda): Pudoc$c1990 300 $a301p 490 $aSimulation monographs 650 $aAGRICULTURA 650 $aANALISIS ECONOMICO 650 $aCOMPETICION VEGETAL 650 $aCRECIMIENTO 650 $aCULTIVOS DE INVERNADERO 650 $aENFERMEDADES DE LAS PLANTAS 650 $aEXPLOTACION AGRICOLA INTENSIVA 650 $aFACTORES CLIMATICOS 650 $aFISIOLOGIA VEGETAL 650 $aINVESTIGACION 650 $aMALEZAS 650 $aMODELOS DE SIMULACION 650 $aNITROGENO 650 $aPAISES EN DESARROLLO 650 $aRESPUESTA DE LA PLANTA 650 $aTRANSPIRACION 700 1 $aGOUDRIAAN, J. 700 1 $aKEULEN, H. 700 1 $aPENNING DE VRIES, F.W.T. 700 1 $aLAAR, H.H.
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INIA La Estanzuela (LE) |
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha actual : |
02/12/2019 |
Actualizado : |
02/12/2019 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
MEDEIROS, W.; PUPIN, S.; TORRES, D.; PAVAN, B.E.; FERRAUDO, A.S.; DE MORAES, M.L.T.; DE PAULA, R.C. |
Afiliación : |
WILLIAM MEDEIROS; SILVELISE PUPIN; DIEGO GABRIEL TORRES DINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BRUNO ETTORE PAVAN; ANTONIO SÉRGIO FERRAUDO; MARIO LUIZ TEIXEIRA DE MORAES; RINALDO CESAR DE PAULA. |
Título : |
Artificial neural networks for predicting the genetic value of Eucalyptus progenies. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
In: Pesquisa florestal brasileira = Brazilian journal of forestry research., v. 39, e201902043, Special issue, 2019. Colombo : Embrapa Florestas, 2019. Congreso IUFRO, 25., Curitiba, Brasil, 29 setiembre-05 octubre, 2019. Abstracts. |
Páginas : |
p. 187-188 |
Idioma : |
Inglés |
Contenido : |
The main goal of researchers in genetic breeding programs is to select superior genotypes and recommend varieties through effective selection methods. Thus, the objective of this study was to evaluate the performance of Artificial Neural Networks (ANN) in predicting genetic values for progeny selection of Eucalyptus sp. For the training of ANN, 64 experiments were simulated that varied among means (5, 10, 15 and 20), heritability (10, 20, 30 and 40%) and coefficient of variation (10, 20, 30 and 40%). For validation of ANN, data from a progeny test of Eucalyptus camaldulensis was used. The genetic values of both the simulated and progeny data were obtained by the REML / BLUP procedure. The ANN used was a multiple layer type with three inputs (phenotype value, block means, and progeny mean), a hidden layer containing four neurons and one exit layer. The algorithm used was backpropagation. The correlation between genetic values predicted by the BLUP methodology and those obtained by ANN was 99% in the training phase and 91% in the validation stage. The good performance of ANN in the validation stage reflected in the correlation of the ordering of individuals (92%) and families (99%) of E. camaldulensis by the two methods. Thus, multiple layer ANN showed good performance in predicting genetic values in progeny tests of Eucalyptus sp. for DBH which are promising tools for selection of progenies in forest breeding programs. |
Palabras claves : |
EUCALYPTS. |
Asunto categoría : |
K10 Producción forestal |
Marc : |
LEADER 02218nam a2200205 a 4500 001 1060487 005 2019-12-02 008 2019 bl uuuu u01u1 u #d 100 1 $aMEDEIROS, W. 245 $aArtificial neural networks for predicting the genetic value of Eucalyptus progenies.$h[electronic resource] 260 $aIn: Pesquisa florestal brasileira = Brazilian journal of forestry research., v. 39, e201902043, Special issue, 2019. Colombo : Embrapa Florestas, 2019. Congreso IUFRO, 25., Curitiba, Brasil, 29 setiembre-05 octubre, 2019. Abstracts.$c2019 300 $ap. 187-188 520 $aThe main goal of researchers in genetic breeding programs is to select superior genotypes and recommend varieties through effective selection methods. Thus, the objective of this study was to evaluate the performance of Artificial Neural Networks (ANN) in predicting genetic values for progeny selection of Eucalyptus sp. For the training of ANN, 64 experiments were simulated that varied among means (5, 10, 15 and 20), heritability (10, 20, 30 and 40%) and coefficient of variation (10, 20, 30 and 40%). For validation of ANN, data from a progeny test of Eucalyptus camaldulensis was used. The genetic values of both the simulated and progeny data were obtained by the REML / BLUP procedure. The ANN used was a multiple layer type with three inputs (phenotype value, block means, and progeny mean), a hidden layer containing four neurons and one exit layer. The algorithm used was backpropagation. The correlation between genetic values predicted by the BLUP methodology and those obtained by ANN was 99% in the training phase and 91% in the validation stage. The good performance of ANN in the validation stage reflected in the correlation of the ordering of individuals (92%) and families (99%) of E. camaldulensis by the two methods. Thus, multiple layer ANN showed good performance in predicting genetic values in progeny tests of Eucalyptus sp. for DBH which are promising tools for selection of progenies in forest breeding programs. 653 $aEUCALYPTS 700 1 $aPUPIN, S. 700 1 $aTORRES, D. 700 1 $aPAVAN, B.E. 700 1 $aFERRAUDO, A.S. 700 1 $aDE MORAES, M.L.T. 700 1 $aDE PAULA, R.C.
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