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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
16/03/2022 |
Actualizado : |
16/03/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
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. |
Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
Fecha de publicación : |
2021 |
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 |
ISSN : |
1844-8135 |
DOI : |
10.15287/afr.2021.2073 |
Idioma : |
Inglés |
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. |
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 |
Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
22/11/2023 |
Actualizado : |
22/11/2023 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
CIAPPESONI, G.; MARQUES, C. B.; NAVAJAS, E.; PERAZA, P.; CARRACELAS, B.; VERA, B.; VAN LIER, E.; DE BARBIERI, I.; SALADA, S.; MONZALVO, C.; CASTELLS, D. |
Afiliación : |
CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAMILA BALCONI MARQUES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO PERAZA DOS SANTOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EMERITA BEATRIZ CARRACELAS MARQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BRENDA VERA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; E. VAN LIER, Departamento de Producción Animal y Pasturas Facultad de Agronomía Universidad de la República Avda. Garzón 780, Montevideo 129 00, Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; S. SALADA, Centro de Investigación y Experimentación Dr Alejandro Gallinal Secretariado Uruguayo de la Lana Ruta 7 km 140, Cerro Colorado, Florida 94000, Uruguay; CARLOS ENRIQUE MONZALVO CAMPAÑA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; D. CASTELLS, Centro de Investigación y Experimentación Dr Alejandro Gallinal Secretariado Uruguayo de la Lana Ruta 7 km 140, Cerro Colorado, Florida 94000, Uruguay. |
Título : |
Breeding for sheep parasite resistance in extensive production systems in Uruguay: From phenotype to genotype. |
Complemento del título : |
Advances in biotechnologies for improving livestock breeding and feeding. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
In: Viljoen, G., Garcia Podesta, M. & Boettcher, P. (eds). 2023. International Symposium on Sustainable Animal Production and Health - Current status and way forward. Vienna, Austria, 28 June to 2 July 2021. Rome, FAO. Pp.224-236. https://doi.org/978-92-5-137052-0 |
ISBN : |
978-92-5-137052-0 |
DOI : |
https://doi.org/978-92-5-137052-0 |
Idioma : |
Inglés |
Contenido : |
ABSTRACT.- Gastrointestinal parasites (GIP) are one of the main causes of economic losses for sheep farmers worldwide. The need for alternative control measures comes from increasingly critical anthelmintic resistance. One alternative is to include genetic resistance to GIP in breeding programmes, by selecting for worm faecal egg count (FEC). Using this selection criterion since 1994, Uruguay has included genetic resistance to GIP in the genetic evaluation of Australian Merino and Corriedale breeds. Although FEC has been the most used selection criterion to evaluate resistant animals, data recording is time-consuming and costly and requires a nematode infection challenge. Selecting parasite resistance without the need for nematode challenge would be a less expensive alternative approach without compromising the wellbeing of the animals. Moreover, other indicator traits such as packed cell volume (PCV), FAMACHA© score, body condition score (BCS) could be included to increase genetic improvement. This paper describes the current selection programmes for GIP-resistant sheep, data recording, new criteria evaluation, selection nuclei, development and use of molecular tools, projects, as well as further approaches to enhance and improve genetic progress in Uruguay. Current databases enabled various estimations and demonstrated that genetic progress can be achieved. We can highlight the following results: (i)
FEC heritability values ranging from 0.15 to 0.21; (ii) high genetic correlation between FEC in ewes at spring rise and FEC in lambs at post-weaning (0.81 ± 0.11); (iii) genetic selection by FEC is effective in different environments (low or high worm environments) and the genetic correlation
between environments is high (0.87 ± 0.04); (iv) there is a moderate favorable genetic correlation between FEC and FAMACHA©; (v) the Corriedale susceptible line had up to 3.3 times higher average of FEC than resistant line; (vi) INIA Corriedales showed better genetic merit for twinning rate, greasy fleece weight, fibre diameter, and body weight at shearing in comparison with the resistant line of SUL; and (vii) in Australian Merino, it has been possible to generate heavier progeny producing more and finer wool, and also more resistant to GIP. Moreover, generating
reference populations for molecular studies and selection nuclei is also very important. All strategies described in this study aim at improving the genetic resistance of sheep to GIP. MenosABSTRACT.- Gastrointestinal parasites (GIP) are one of the main causes of economic losses for sheep farmers worldwide. The need for alternative control measures comes from increasingly critical anthelmintic resistance. One alternative is to include genetic resistance to GIP in breeding programmes, by selecting for worm faecal egg count (FEC). Using this selection criterion since 1994, Uruguay has included genetic resistance to GIP in the genetic evaluation of Australian Merino and Corriedale breeds. Although FEC has been the most used selection criterion to evaluate resistant animals, data recording is time-consuming and costly and requires a nematode infection challenge. Selecting parasite resistance without the need for nematode challenge would be a less expensive alternative approach without compromising the wellbeing of the animals. Moreover, other indicator traits such as packed cell volume (PCV), FAMACHA© score, body condition score (BCS) could be included to increase genetic improvement. This paper describes the current selection programmes for GIP-resistant sheep, data recording, new criteria evaluation, selection nuclei, development and use of molecular tools, projects, as well as further approaches to enhance and improve genetic progress in Uruguay. Current databases enabled various estimations and demonstrated that genetic progress can be achieved. We can highlight the following results: (i)
FEC heritability values ranging from 0.15 to 0.21; (ii) high genetic corr... Presentar Todo |
Palabras claves : |
FEC; Haemonchus contortus; SISTEMA GANADERO EXTENSIVO - INIA. |
Thesagro : |
CORRIEDALE; MERINO; SNP. |
Asunto categoría : |
L10 Genética y mejoramiento animal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17421/1/Ciappesoni-et.al-2023-FAO-cc2530en.pdf
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Marc : |
LEADER 03655naa a2200337 a 4500 001 1064377 005 2023-11-22 008 2023 bl uuuu u00u1 u #d 020 $a978-92-5-137052-0 024 7 $ahttps://doi.org/978-92-5-137052-0$2DOI 100 1 $aCIAPPESONI, G. 245 $aBreeding for sheep parasite resistance in extensive production systems in Uruguay$bFrom phenotype to genotype.$h[electronic resource] 260 $c2023 520 $aABSTRACT.- Gastrointestinal parasites (GIP) are one of the main causes of economic losses for sheep farmers worldwide. The need for alternative control measures comes from increasingly critical anthelmintic resistance. One alternative is to include genetic resistance to GIP in breeding programmes, by selecting for worm faecal egg count (FEC). Using this selection criterion since 1994, Uruguay has included genetic resistance to GIP in the genetic evaluation of Australian Merino and Corriedale breeds. Although FEC has been the most used selection criterion to evaluate resistant animals, data recording is time-consuming and costly and requires a nematode infection challenge. Selecting parasite resistance without the need for nematode challenge would be a less expensive alternative approach without compromising the wellbeing of the animals. Moreover, other indicator traits such as packed cell volume (PCV), FAMACHA© score, body condition score (BCS) could be included to increase genetic improvement. This paper describes the current selection programmes for GIP-resistant sheep, data recording, new criteria evaluation, selection nuclei, development and use of molecular tools, projects, as well as further approaches to enhance and improve genetic progress in Uruguay. Current databases enabled various estimations and demonstrated that genetic progress can be achieved. We can highlight the following results: (i) FEC heritability values ranging from 0.15 to 0.21; (ii) high genetic correlation between FEC in ewes at spring rise and FEC in lambs at post-weaning (0.81 ± 0.11); (iii) genetic selection by FEC is effective in different environments (low or high worm environments) and the genetic correlation between environments is high (0.87 ± 0.04); (iv) there is a moderate favorable genetic correlation between FEC and FAMACHA©; (v) the Corriedale susceptible line had up to 3.3 times higher average of FEC than resistant line; (vi) INIA Corriedales showed better genetic merit for twinning rate, greasy fleece weight, fibre diameter, and body weight at shearing in comparison with the resistant line of SUL; and (vii) in Australian Merino, it has been possible to generate heavier progeny producing more and finer wool, and also more resistant to GIP. Moreover, generating reference populations for molecular studies and selection nuclei is also very important. All strategies described in this study aim at improving the genetic resistance of sheep to GIP. 650 $aCORRIEDALE 650 $aMERINO 650 $aSNP 653 $aFEC 653 $aHaemonchus contortus 653 $aSISTEMA GANADERO EXTENSIVO - INIA 700 1 $aMARQUES, C. B. 700 1 $aNAVAJAS, E. 700 1 $aPERAZA, P. 700 1 $aCARRACELAS, B. 700 1 $aVERA, B. 700 1 $aVAN LIER, E. 700 1 $aDE BARBIERI, I. 700 1 $aSALADA, S. 700 1 $aMONZALVO, C. 700 1 $aCASTELLS, D. 773 $tIn: Viljoen, G., Garcia Podesta, M. & Boettcher, P. (eds). 2023. International Symposium on Sustainable Animal Production and Health - Current status and way forward. Vienna, Austria, 28 June to 2 July 2021. Rome, FAO. Pp.224-236. https://doi.org/978-92-5-137052-0
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