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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
06/06/2019 |
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Actualizado : |
06/06/2019 |
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Autor : |
PÉREZ GOMAR, E.; REICHERT, M.M.; REINERT, D.J.; GARCÍA, F. |
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Afiliación : |
ENRIQUE PEREZ GOMAR CAPURRO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Universidade Federal de Santa Maria, Brasil.; Universidade Federal de Santa Maria, Brasil.; Universidad de la República (UdelaR)/ Facultad de Agronomía. |
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Título : |
Atributos do solo e biomassa radicular após quatro anos de semeadura direta de forrageiras de estação fria em campo natural dessecado com herbicidas ? [Soil properties and root system after four years of direct drilling of winter growing forage on a native grassfield dissected with herbicides]. |
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Fecha de publicación : |
2002 |
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Fuente / Imprenta : |
Revista Brasileira de Ciência do Solo, 2002;26(1):211-223 |
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ISSN : |
1806-9657 |
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DOI : |
10.1590/S0100-06832002000100022 |
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Idioma : |
Portugués |
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Notas : |
Parte da Tese de Mestrado do primeiro autor, apresentada ao Programa de Pós-Graduação em Agronomia, UFSM. Parcialmente financiada pelo Pronex-CNPq/FINEP. Article history: Recebido para publicação em agosto de 2000; aprovado em setembro de 2001. |
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Contenido : |
RESUMO: Os campos naturais, desenvolvidos sobre solos arenosos da região norte do Uruguai, são compostos por espécies forrageiras, sobretudo de gramíneas de produção estacional, com baixa produtividade no inverno. O objetivo deste estudo foi avaliar o efeito da dessecação do campo natural no estabelecimento de espécies de estação fria em atributos do solo e biomassa radicular. O estudo, iniciado em 1994, utilizou delineamento experimental de blocos ao acaso com parcelas subsubdivididas, com três repetições. Nas parcelas principais, em 1994, foram aplicados os tratamentos com herbicidas (paraquat 0,60 g ha-1 i.a., glifosate 0,36 g ha-1 i.a. e glifosate 1,44 g ha-1 i.a.) e testemunha sem herbicida em campo natural para a semeadura de pastagens de inverno. Nessas parcelas, a pastagem de inverno foi aveia preta (Avena strigosa L.), triticale (X Triticosecale Wittmack) e azevém (Lolium multiflorum L.). As subparcelas foram formadas pela reaplicação ou não dos herbicidas em 1995 e as subsubparcelas foram formadas pela reaplicação ou não dos herbicidas em 1996. As amostras de solo para determinar a biomassa radicular, a densidade do solo, o carbono (C) orgânico do solo, bases trocáveis, Al trocável e o pH do solo foram extraídas separadamente, em três subamostras, usando cilindro metálico de 7,65 cm de diâmetro e 40 cm
de comprimento. Os monolitos extraídos foram estratificados até 30 cm de profundidade nas camadas de 0-5, 5-10, 10-15, 15-20 e 20-30 cm. A biomassa
radicular foi maior na testemunha do que a média dos tratamentos com herbicidas somente na camada de 0-5 cm, e, entre os tratamentos com herbicidas, a biomassa radicular foi maior com paraquat do que com o glifosate. A reaplicação de herbicidas, em 1995 e 1996, também ocasionou redução da biomassa radicular. Houve alta correlação positiva de C orgânico com a biomassa radicular. A redução de C orgânico para o tratamento mais agressivo de controle químico (glifosate 1,44 g i.a. ha-1) foi de 13%. Não houve efeito dos tratamentos sobre as bases trocáveis, porém houve aumento no teor de Al trocável e na densidade do solo e redução na estabilidade de agregados com a redução do teor de matéria orgânica. O sistema com maior aplicação de herbicidas, desenvolvido para maior produção de forragem invernal, e com maior agressividade no controle do campo natural provocou maior transformação na comunidade vegetal, resultando em menor biomassa radicular e C orgânico, com conseqüências negativas quanto à acidez do solo e estrutura do solo. SUMMARY: Natural grassfields developed on sandy soils from Northern Uruguay are formed by communities of forage species, composed mainly of seasonal growing grasses, with low biomass production during the fall/winter period. The objective of this study was to evaluate the effect of herbicides, applied on native grassfield to established winter forage species, on soil properties and root biomass. This experiment began in 1994 and was established as a complete block design, with three replications. In the main plots, to establish winter-growing the forage-species black oat (Avena strigosa L.), triticale (X Triticosecale Wittmack), and ryegrass (Lolium multiflorum L.) on native grassfield, herbicides were applied (paraquat 0,60 g ha-1 a.i., glyphosate 0,36 g ha-1 a.i., glyphosate 1,44 g ha-1 a.i.) and a test without herbicide was used for comparison. The main plots were divided in 1995 forming a splitplot
design, where each plot received the same treatments on a half plot. The split-plots were divided in split-split-plot design in 1996, where each split-plot received the same treatments on a half split-plot. Soil samples to measure root biomass, bulk density, soil organic carbon, exchangeable bases and aluminum, and soil pH were taken in three separate samplings using a 7.65 cm diameter by 40 cm long metallic cylinder. The extracted soil
monoliths were stratified down to 30 cm to the following depths: 0-5, 5-10, 10-15, 15-20 and 20-30 cm. Root biomass was higher where no herbicide was applied, as compared to herbicide treatments only at 0-5 cm depth as well as for paraquat application, as compared to glyphosate. The continuous herbicide application in 1995 and 1996 produced progressive root biomass reduction. There was a high positive correlation between root biomass and
soil organic carbon (SOC) and the latter reduced 13% due to the higher rate of chemical control (1.44 g ha-1 a.i. of glyphosate applied in 1994, 95 and 96). There was no effect of tested treatments on exchangeable bases, but Al3+ was affected. SOC changes were closely related to changes in soil structure, increasing bulk density and decreasing aggregate stability. The system with high rate of herbicide use, developed to obtain high winter forage biomass production and suppression of native grasses, induced great changes on plant community presenting, as a consequence, less root biomass and SOC with negative effect on soil acidity and aggregation. MenosRESUMO: Os campos naturais, desenvolvidos sobre solos arenosos da região norte do Uruguai, são compostos por espécies forrageiras, sobretudo de gramíneas de produção estacional, com baixa produtividade no inverno. O objetivo deste estudo foi avaliar o efeito da dessecação do campo natural no estabelecimento de espécies de estação fria em atributos do solo e biomassa radicular. O estudo, iniciado em 1994, utilizou delineamento experimental de blocos ao acaso com parcelas subsubdivididas, com três repetições. Nas parcelas principais, em 1994, foram aplicados os tratamentos com herbicidas (paraquat 0,60 g ha-1 i.a., glifosate 0,36 g ha-1 i.a. e glifosate 1,44 g ha-1 i.a.) e testemunha sem herbicida em campo natural para a semeadura de pastagens de inverno. Nessas parcelas, a pastagem de inverno foi aveia preta (Avena strigosa L.), triticale (X Triticosecale Wittmack) e azevém (Lolium multiflorum L.). As subparcelas foram formadas pela reaplicação ou não dos herbicidas em 1995 e as subsubparcelas foram formadas pela reaplicação ou não dos herbicidas em 1996. As amostras de solo para determinar a biomassa radicular, a densidade do solo, o carbono (C) orgânico do solo, bases trocáveis, Al trocável e o pH do solo foram extraídas separadamente, em três subamostras, usando cilindro metálico de 7,65 cm de diâmetro e 40 cm
de comprimento. Os monolitos extraídos foram estratificados até 30 cm de profundidade nas camadas de 0-5, 5-10, 10-15, 15-20 e 20-30 cm. A biomassa
radicular foi mai... Presentar Todo |
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Palabras claves : |
CAMPO NATURAL; EXCHANGEABLE ALUMINIUM; PASTURAS; PASTURE MANAGEMENT; ROOTS; SOIL ORGANIC CARBON; SOIL STRUCTURE; URUGUAY. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12784/1/Perez-Gomar-2002.pdf
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Marc : |
LEADER 06321naa a2200289 a 4500
001 1059822
005 2019-06-06
008 2002 bl uuuu u00u1 u #d
022 $a1806-9657
024 7 $a10.1590/S0100-06832002000100022$2DOI
100 1 $aPÉREZ GOMAR, E.
245 $aAtributos do solo e biomassa radicular após quatro anos de semeadura direta de forrageiras de estação fria em campo natural dessecado com herbicidas ? [Soil properties and root system after four years of direct drilling of winter growing forage on a native grassfield dissected with herbicides].$h[electronic resource]
260 $c2002
500 $aParte da Tese de Mestrado do primeiro autor, apresentada ao Programa de Pós-Graduação em Agronomia, UFSM. Parcialmente financiada pelo Pronex-CNPq/FINEP. Article history: Recebido para publicação em agosto de 2000; aprovado em setembro de 2001.
520 $aRESUMO: Os campos naturais, desenvolvidos sobre solos arenosos da região norte do Uruguai, são compostos por espécies forrageiras, sobretudo de gramíneas de produção estacional, com baixa produtividade no inverno. O objetivo deste estudo foi avaliar o efeito da dessecação do campo natural no estabelecimento de espécies de estação fria em atributos do solo e biomassa radicular. O estudo, iniciado em 1994, utilizou delineamento experimental de blocos ao acaso com parcelas subsubdivididas, com três repetições. Nas parcelas principais, em 1994, foram aplicados os tratamentos com herbicidas (paraquat 0,60 g ha-1 i.a., glifosate 0,36 g ha-1 i.a. e glifosate 1,44 g ha-1 i.a.) e testemunha sem herbicida em campo natural para a semeadura de pastagens de inverno. Nessas parcelas, a pastagem de inverno foi aveia preta (Avena strigosa L.), triticale (X Triticosecale Wittmack) e azevém (Lolium multiflorum L.). As subparcelas foram formadas pela reaplicação ou não dos herbicidas em 1995 e as subsubparcelas foram formadas pela reaplicação ou não dos herbicidas em 1996. As amostras de solo para determinar a biomassa radicular, a densidade do solo, o carbono (C) orgânico do solo, bases trocáveis, Al trocável e o pH do solo foram extraídas separadamente, em três subamostras, usando cilindro metálico de 7,65 cm de diâmetro e 40 cm de comprimento. Os monolitos extraídos foram estratificados até 30 cm de profundidade nas camadas de 0-5, 5-10, 10-15, 15-20 e 20-30 cm. A biomassa radicular foi maior na testemunha do que a média dos tratamentos com herbicidas somente na camada de 0-5 cm, e, entre os tratamentos com herbicidas, a biomassa radicular foi maior com paraquat do que com o glifosate. A reaplicação de herbicidas, em 1995 e 1996, também ocasionou redução da biomassa radicular. Houve alta correlação positiva de C orgânico com a biomassa radicular. A redução de C orgânico para o tratamento mais agressivo de controle químico (glifosate 1,44 g i.a. ha-1) foi de 13%. Não houve efeito dos tratamentos sobre as bases trocáveis, porém houve aumento no teor de Al trocável e na densidade do solo e redução na estabilidade de agregados com a redução do teor de matéria orgânica. O sistema com maior aplicação de herbicidas, desenvolvido para maior produção de forragem invernal, e com maior agressividade no controle do campo natural provocou maior transformação na comunidade vegetal, resultando em menor biomassa radicular e C orgânico, com conseqüências negativas quanto à acidez do solo e estrutura do solo. SUMMARY: Natural grassfields developed on sandy soils from Northern Uruguay are formed by communities of forage species, composed mainly of seasonal growing grasses, with low biomass production during the fall/winter period. The objective of this study was to evaluate the effect of herbicides, applied on native grassfield to established winter forage species, on soil properties and root biomass. This experiment began in 1994 and was established as a complete block design, with three replications. In the main plots, to establish winter-growing the forage-species black oat (Avena strigosa L.), triticale (X Triticosecale Wittmack), and ryegrass (Lolium multiflorum L.) on native grassfield, herbicides were applied (paraquat 0,60 g ha-1 a.i., glyphosate 0,36 g ha-1 a.i., glyphosate 1,44 g ha-1 a.i.) and a test without herbicide was used for comparison. The main plots were divided in 1995 forming a splitplot design, where each plot received the same treatments on a half plot. The split-plots were divided in split-split-plot design in 1996, where each split-plot received the same treatments on a half split-plot. Soil samples to measure root biomass, bulk density, soil organic carbon, exchangeable bases and aluminum, and soil pH were taken in three separate samplings using a 7.65 cm diameter by 40 cm long metallic cylinder. The extracted soil monoliths were stratified down to 30 cm to the following depths: 0-5, 5-10, 10-15, 15-20 and 20-30 cm. Root biomass was higher where no herbicide was applied, as compared to herbicide treatments only at 0-5 cm depth as well as for paraquat application, as compared to glyphosate. The continuous herbicide application in 1995 and 1996 produced progressive root biomass reduction. There was a high positive correlation between root biomass and soil organic carbon (SOC) and the latter reduced 13% due to the higher rate of chemical control (1.44 g ha-1 a.i. of glyphosate applied in 1994, 95 and 96). There was no effect of tested treatments on exchangeable bases, but Al3+ was affected. SOC changes were closely related to changes in soil structure, increasing bulk density and decreasing aggregate stability. The system with high rate of herbicide use, developed to obtain high winter forage biomass production and suppression of native grasses, induced great changes on plant community presenting, as a consequence, less root biomass and SOC with negative effect on soil acidity and aggregation.
653 $aCAMPO NATURAL
653 $aEXCHANGEABLE ALUMINIUM
653 $aPASTURAS
653 $aPASTURE MANAGEMENT
653 $aROOTS
653 $aSOIL ORGANIC CARBON
653 $aSOIL STRUCTURE
653 $aURUGUAY
700 1 $aREICHERT, M.M.
700 1 $aREINERT, D.J.
700 1 $aGARCÍA, F.
773 $tRevista Brasileira de Ciência do Solo, 2002;26(1):211-223
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INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
16/03/2022 |
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Actualizado : |
16/03/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
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Afiliación : |
ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CRISTINA ACOSTA-MUÑOZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANTONIO JESÚS ARIZA SALAMANCA, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; MARIA ÁNGELES VARO-MARTINEZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANA CECILIA RACHID CASNATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE FRANCO, Faculty of Agronomy, University of the Republic, Paysandú, Uruguay; RAFAEL NAVARRO-CERRILLO, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain. |
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Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Annals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073 |
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ISSN : |
1844-8135 |
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DOI : |
10.15287/afr.2021.2073 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021.
Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes. |
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Contenido : |
ABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved. MenosABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied throu... Presentar Todo |
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Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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URL : |
https://www.afrjournal.org/index.php/afr/article/viewFile/2073/1177
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Marc : |
LEADER 03380naa a2200265 a 4500
001 1062842
005 2022-03-16
008 2021 bl uuuu u00u1 u #d
022 $a1844-8135
024 7 $a10.15287/afr.2021.2073$2DOI
100 1 $aHIRIGOYEN, A.
245 $aA machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data.$h[electronic resource]
260 $c2021
500 $aArticle history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021. Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes.
520 $aABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved.
653 $aIntensive silviculture
653 $aLAI canopy
653 $aMachine learning algorithms
700 1 $aACOSTA-MUÑOZ, C.
700 1 $aSALAMANCA, A.J.A.
700 1 $aVARO-MARTINEZ, M.Á.
700 1 $aRACHID, C.
700 1 $aFRANCO, J.
700 1 $aNAVARRO-CERRILLO, R.
773 $tAnnals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073
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