| |
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha : |
27/08/2020 |
|
Actualizado : |
27/08/2020 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Autor : |
MACEDO, F.; CHRISTENSEN, O. F.; ASTRUC, J.M.; AGUILAR, I.; MASUDA, Y.; LEGARRA, A. |
|
Afiliación : |
FERNANDO LIBER MACEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GenPhySE, Castanet Tolosan, France; OLE F. CHRISTENSEN, Center for Quantitative Genetics and Genomics, Tjele, Denmark; JEAN-MICHEL ASTRUC, Institut de l’Elevage, Castanet Tolosan, France; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; YUTAKA MASUDA, Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA; ANDRÉS LEGARRA, GenPhySE, INRAE, Castanet Tolosan, France. |
|
Título : |
Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. |
|
Fecha de publicación : |
2020 |
|
Fuente / Imprenta : |
Genetics, Selection, Evolution : GSE, 12 August 2020, Volume 52, Issue 1, Page 47. OPEN ACCESS. DOI: https://doi.org/10.1186/s12711-020-00567-1 |
|
ISSN : |
1297-9686 |
|
DOI : |
10.1186/s12711-020-00567-1 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: Received 03 March 2020; Accepted 04 August 2020; Published 12 August 2020. |
|
Contenido : |
Abstract
BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. CONCLUSIONS: The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years. MenosAbstract
BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of... Presentar Todo |
|
Palabras claves : |
Animal experiment; Animal model; Dairy sheep; Genetic marker. |
|
Asunto categoría : |
L10 Genética y mejoramiento animal |
|
URL : |
https://gsejournal.biomedcentral.com/track/pdf/10.1186/s12711-020-00567-1
|
|
Marc : |
LEADER 03420naa a2200265 a 4500
001 1061282
005 2020-08-27
008 2020 bl uuuu u00u1 u #d
022 $a1297-9686
024 7 $a10.1186/s12711-020-00567-1$2DOI
100 1 $aMACEDO, F.
245 $aBias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.$h[electronic resource]
260 $c2020
500 $aArticle history: Received 03 March 2020; Accepted 04 August 2020; Published 12 August 2020.
520 $aAbstract BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. CONCLUSIONS: The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years.
653 $aAnimal experiment
653 $aAnimal model
653 $aDairy sheep
653 $aGenetic marker
700 1 $aCHRISTENSEN, O. F.
700 1 $aASTRUC, J.M.
700 1 $aAGUILAR, I.
700 1 $aMASUDA, Y.
700 1 $aLEGARRA, A.
773 $tGenetics, Selection, Evolution : GSE, 12 August 2020, Volume 52, Issue 1, Page 47. OPEN ACCESS. DOI: https://doi.org/10.1186/s12711-020-00567-1
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha actual : |
04/03/2024 |
|
Actualizado : |
04/03/2024 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
|
Circulación / Nivel : |
Nacional - -- |
|
Autor : |
CAL, A.; PASTORINI, M.; TISCORNIA, G.; RIVAS-RIVERA, N.; GORGOGLIONE, A. |
|
Afiliación : |
ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCOS PASTORINI, Universidad de la República, Facultad de Ingeniería, Instituto de Computación (InCo), Montevideo, Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NOELIA RIVAS-RIVERA, Universidad de la República, Facultad de Ciencias, Instituto de Ecología y Ciencias Ambientales (IECA), Montevideo, Uruguay; ANGELA GORGOGLIONE, Universidad de la República, Facultad de Ingeniería, Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Montevideo, Uruguay. |
|
Título : |
Assessing dependence between land use/land cover and water quality: A comparison at a small and a large watershed in Uruguay. [Evaluación de la dependencia entre el uso/cobertura del suelo y la calidad del agua: comparación entre una cuenca pequeña y una grande en Uruguay.]. [Avaliação da dependência entre uso/cobertura do solo equalidade da água: comparação entre uma pequena e um agrande bacia no Uruguai.] |
|
Complemento del título : |
Advances in Water in Agroscience. Water quality and environmental sustainability. |
|
Fecha de publicación : |
2023 |
|
Fuente / Imprenta : |
Agrociencia Uruguay, 2023, Vol.27(NE1), e1192. https://doi.org/10.31285/AGRO.27.1192 -- OPEN ACCESS. |
|
ISSN : |
2730-5066 |
|
DOI : |
10.31285/AGRO.27.1192 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: Received 09 May 2023; Accepted 04 October 2023; Published 06 February 2024. -- Editor: Álvaro Otero, Instituto Nacional de Investigación Agropecuaria (INIA), Salto, Uruguay. -- Correspondence: Ángela Gorgoglione, agorgoglione@fing.edu.uy -- Funding: This work was supported by the National Research and Innovation Agency (ANII) [grant numbers: FSA_PI_2018_1_147713, SA_PI_2018_1_148628, FSA_PP_2018_1_147701]. -- The data set supporting the results of this study is partially publicly available. The water quality data for the San Salvador river basin can be found at https://www.ambiente.gub.uy/oan/ -- License: This work is licensed under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/ ) |
|
Contenido : |
ABSTRACT.- Changes in land use/land cover (LULC) directly or indirectly affect water quality in watercourses and impoundments. Sustainable management strategies aimed to enhance ecosystem health and community well-being require an accurate water-quality evaluation. This study looks into the correlation between temporal changes in LULC, represented by selected landscape variables (land cover area and proportion, patch density, Euclidean nearest-neighbor distance, mean shape index, and Shannon index), and water quality variables (nitrate, total phosphorus, and total suspended solids) at catchment scale. To compare the watershed-size influence, this analysis was performed at two different spatial scales represented by two Uruguayan basins of different sizes, San Salvador (3,118 km2) and Del Tala (160 km2). Partial Least Squares and Random Forest unsupervised machine-learning models were employed for this analysis. By exploiting a non-model-biased method based on game theory (SHAP), the LULC characteristics were quantified and ranked based on their level of importance in the water-quality evaluation. The main outcomes of this study proved that patch density is one of the most influencing metrics in both watersheds and for both models. Agricultural land use is the most critical one at both catchments and agricultural with a forage crop land uses are the most important ones for both algorithms. Furthermore, it is possible to state that the adopted techniques are valuable tools that can provide an adequate overview of the water‐quality behavior in space and time and the correlations between water-quality variables and LULC. .-.-.-.-.-.-.-.-.-.-.-. RESUMEN.- Los cambios en el uso del suelo y la cobertura del suelo (LULC) afectan directa o indirectamente la calidad del agua en cursos de agua y embalses. Las estrategias de gestión sostenible destinadas a mejorar la salud del ecosistema y el bienestar de la comunidad requieren una evaluación precisa de la calidad del agua. Este estudio analiza la correlación entre los cambios temporales en LULC, representados por variables de paisaje seleccionadas (área y proporción de cobertura del suelo, densidad de parches, distancia euclidiana al vecino más cercano, índice de forma promedio e índice de Shannon), y las variables de calidad del agua (nitrato, fósforo total y sólidos suspendidos totales) a nivel de cuenca. Para comparar la influencia del tamaño de la cuenca, este análisis se realizó a dos escalas espaciales diferentes representadas por dos cuencas uruguayas de diferentes tamaños, San Salvador (3118 km2) y Del Tala (160 km2). Se emplearon modelos de aprendizaje automático no supervisados de Mínimos Cuadrados Parciales y Bosque Aleatorio para este análisis. Al aprovechar un método no sesgado basado en teoría de juegos (SHAP), las características de LULC se cuantificaron y clasificaron según su nivel de importancia en la evaluación de la calidad del agua. Los principales resultados de este estudio demostraron que la densidad de parches es una de las métricas más influyentes en ambas cuencas y para ambos modelos. El uso agrícola del suelo es crítico en ambas cuencas, y los usos agrícolas con cultivos forrajeros son los más importantes para ambos algoritmos. Además, es posible afirmar que las técnicas adoptadas son herramientas valiosas que pueden proporcionar una visión adecuada del comportamiento de la calidad del agua en el espacio y el tiempo, así como las correlaciones entre las variables de calidad del agua y LULC. .-.-.-.-.-.-.-.-.-.-.-. RESUMO.- Alterações no uso do solo/cobertura do solo (LULC) afetam diretamente ou indiretamente a qualidade da água em cursos d'água e reservatórios. Estratégias de gestão sustentável voltadas para melhorar a saúde do ecossistema e o bem-estar da comunidade requerem uma avaliação precisa da qualidade da água. Este estudo examina a correlação entre mudanças temporais no LULC, representadas por variáveis de paisagem selecionadas (área e proporção de cobertura do solo, densidade de manchas, distância euclidiana até o vizinho mais próximo, índice de forma média e índice de Shannon), e variáveis de qualidade da água (nitrato, fósforo total e sólidos suspensos totais) em escala de bacia hidrográfica. Para comparar a influência do tamanho da bacia hidrográfica, essa análise foi realizada em duas escalas espaciais diferentes, representadas por duas bacias uruguaias de tamanhos diferentes, San Salvador (3118 km2) e Del Tala (160 km2). Modelos de aprendizado de máquina não supervisionados de Mínimos Quadrados Parciais e Floresta Alea-tória foram empregados para essa análise. Ao explorar um método não enviesado pelo modelo baseado na teoria dos jogos (SHAP), as características de LULC foram quantificadas e classificadas com base em seu nível de importância na avaliação da qualidade da água. Os principais resultados deste estudo mostraram que a densidade de manchas é uma das métricas mais influentes em ambas as bacias hidrográficas e para ambos os modelos. O uso agrícola da terra é crítico em ambas as bacias hidrográficas, e o uso agrícola com cultivo forrageiro é o mais importante para ambos os algoritmos. Além disso, é possível afirmar que as técnicas adotadas são ferramentas valiosas que podem fornecer uma visão adequada do comportamento da qualidade da água no espaço e no tempo e das correlações entre as variáveis de qualidade da água e LULC. @2023 Agrociencia Uruguay MenosABSTRACT.- Changes in land use/land cover (LULC) directly or indirectly affect water quality in watercourses and impoundments. Sustainable management strategies aimed to enhance ecosystem health and community well-being require an accurate water-quality evaluation. This study looks into the correlation between temporal changes in LULC, represented by selected landscape variables (land cover area and proportion, patch density, Euclidean nearest-neighbor distance, mean shape index, and Shannon index), and water quality variables (nitrate, total phosphorus, and total suspended solids) at catchment scale. To compare the watershed-size influence, this analysis was performed at two different spatial scales represented by two Uruguayan basins of different sizes, San Salvador (3,118 km2) and Del Tala (160 km2). Partial Least Squares and Random Forest unsupervised machine-learning models were employed for this analysis. By exploiting a non-model-biased method based on game theory (SHAP), the LULC characteristics were quantified and ranked based on their level of importance in the water-quality evaluation. The main outcomes of this study proved that patch density is one of the most influencing metrics in both watersheds and for both models. Agricultural land use is the most critical one at both catchments and agricultural with a forage crop land uses are the most important ones for both algorithms. Furthermore, it is possible to state that the adopted techniques are valuable tools tha... Presentar Todo |
|
Palabras claves : |
Aprendizado não supervisionado; Aprendizaje no supervisado; Calidad del agua; Características relevantes; Feature importance; Land use/land cover; Qualidade da água; SISTEMAS DE INFORMACIÓN Y TRANSFORMACIÓN DIGITAL - INIA; Unsupervised learning; Uso/cobertura del suelo; Uso/cobertura do solo; Water quality. |
|
Asunto categoría : |
P01 Conservación de la naturaleza y recursos de La tierra |
|
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17516/1/2730-5066-1192.pdf
|
|
Marc : |
LEADER 07773naa a2200349 a 4500
001 1064492
005 2024-03-04
008 2023 bl uuuu u00u1 u #d
022 $a2730-5066
024 7 $a10.31285/AGRO.27.1192$2DOI
100 1 $aCAL, A.
245 $aAssessing dependence between land use/land cover and water quality$bA comparison at a small and a large watershed in Uruguay. [Evaluación de la dependencia entre el uso/cobertura del suelo y la calidad del agua: comparación entre una cuenca pequeña y una grande en Uruguay.]. [Avaliação da dependência entre uso/cobertura do solo equalidade da água: comparação entre uma pequena e um agrande bacia no Uruguai.]$h[electronic resource]
260 $c2023
500 $aArticle history: Received 09 May 2023; Accepted 04 October 2023; Published 06 February 2024. -- Editor: Álvaro Otero, Instituto Nacional de Investigación Agropecuaria (INIA), Salto, Uruguay. -- Correspondence: Ángela Gorgoglione, agorgoglione@fing.edu.uy -- Funding: This work was supported by the National Research and Innovation Agency (ANII) [grant numbers: FSA_PI_2018_1_147713, SA_PI_2018_1_148628, FSA_PP_2018_1_147701]. -- The data set supporting the results of this study is partially publicly available. The water quality data for the San Salvador river basin can be found at https://www.ambiente.gub.uy/oan/ -- License: This work is licensed under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/ )
520 $aABSTRACT.- Changes in land use/land cover (LULC) directly or indirectly affect water quality in watercourses and impoundments. Sustainable management strategies aimed to enhance ecosystem health and community well-being require an accurate water-quality evaluation. This study looks into the correlation between temporal changes in LULC, represented by selected landscape variables (land cover area and proportion, patch density, Euclidean nearest-neighbor distance, mean shape index, and Shannon index), and water quality variables (nitrate, total phosphorus, and total suspended solids) at catchment scale. To compare the watershed-size influence, this analysis was performed at two different spatial scales represented by two Uruguayan basins of different sizes, San Salvador (3,118 km2) and Del Tala (160 km2). Partial Least Squares and Random Forest unsupervised machine-learning models were employed for this analysis. By exploiting a non-model-biased method based on game theory (SHAP), the LULC characteristics were quantified and ranked based on their level of importance in the water-quality evaluation. The main outcomes of this study proved that patch density is one of the most influencing metrics in both watersheds and for both models. Agricultural land use is the most critical one at both catchments and agricultural with a forage crop land uses are the most important ones for both algorithms. Furthermore, it is possible to state that the adopted techniques are valuable tools that can provide an adequate overview of the water‐quality behavior in space and time and the correlations between water-quality variables and LULC. .-.-.-.-.-.-.-.-.-.-.-. RESUMEN.- Los cambios en el uso del suelo y la cobertura del suelo (LULC) afectan directa o indirectamente la calidad del agua en cursos de agua y embalses. Las estrategias de gestión sostenible destinadas a mejorar la salud del ecosistema y el bienestar de la comunidad requieren una evaluación precisa de la calidad del agua. Este estudio analiza la correlación entre los cambios temporales en LULC, representados por variables de paisaje seleccionadas (área y proporción de cobertura del suelo, densidad de parches, distancia euclidiana al vecino más cercano, índice de forma promedio e índice de Shannon), y las variables de calidad del agua (nitrato, fósforo total y sólidos suspendidos totales) a nivel de cuenca. Para comparar la influencia del tamaño de la cuenca, este análisis se realizó a dos escalas espaciales diferentes representadas por dos cuencas uruguayas de diferentes tamaños, San Salvador (3118 km2) y Del Tala (160 km2). Se emplearon modelos de aprendizaje automático no supervisados de Mínimos Cuadrados Parciales y Bosque Aleatorio para este análisis. Al aprovechar un método no sesgado basado en teoría de juegos (SHAP), las características de LULC se cuantificaron y clasificaron según su nivel de importancia en la evaluación de la calidad del agua. Los principales resultados de este estudio demostraron que la densidad de parches es una de las métricas más influyentes en ambas cuencas y para ambos modelos. El uso agrícola del suelo es crítico en ambas cuencas, y los usos agrícolas con cultivos forrajeros son los más importantes para ambos algoritmos. Además, es posible afirmar que las técnicas adoptadas son herramientas valiosas que pueden proporcionar una visión adecuada del comportamiento de la calidad del agua en el espacio y el tiempo, así como las correlaciones entre las variables de calidad del agua y LULC. .-.-.-.-.-.-.-.-.-.-.-. RESUMO.- Alterações no uso do solo/cobertura do solo (LULC) afetam diretamente ou indiretamente a qualidade da água em cursos d'água e reservatórios. Estratégias de gestão sustentável voltadas para melhorar a saúde do ecossistema e o bem-estar da comunidade requerem uma avaliação precisa da qualidade da água. Este estudo examina a correlação entre mudanças temporais no LULC, representadas por variáveis de paisagem selecionadas (área e proporção de cobertura do solo, densidade de manchas, distância euclidiana até o vizinho mais próximo, índice de forma média e índice de Shannon), e variáveis de qualidade da água (nitrato, fósforo total e sólidos suspensos totais) em escala de bacia hidrográfica. Para comparar a influência do tamanho da bacia hidrográfica, essa análise foi realizada em duas escalas espaciais diferentes, representadas por duas bacias uruguaias de tamanhos diferentes, San Salvador (3118 km2) e Del Tala (160 km2). Modelos de aprendizado de máquina não supervisionados de Mínimos Quadrados Parciais e Floresta Alea-tória foram empregados para essa análise. Ao explorar um método não enviesado pelo modelo baseado na teoria dos jogos (SHAP), as características de LULC foram quantificadas e classificadas com base em seu nível de importância na avaliação da qualidade da água. Os principais resultados deste estudo mostraram que a densidade de manchas é uma das métricas mais influentes em ambas as bacias hidrográficas e para ambos os modelos. O uso agrícola da terra é crítico em ambas as bacias hidrográficas, e o uso agrícola com cultivo forrageiro é o mais importante para ambos os algoritmos. Além disso, é possível afirmar que as técnicas adotadas são ferramentas valiosas que podem fornecer uma visão adequada do comportamento da qualidade da água no espaço e no tempo e das correlações entre as variáveis de qualidade da água e LULC. @2023 Agrociencia Uruguay
653 $aAprendizado não supervisionado
653 $aAprendizaje no supervisado
653 $aCalidad del agua
653 $aCaracterísticas relevantes
653 $aFeature importance
653 $aLand use/land cover
653 $aQualidade da água
653 $aSISTEMAS DE INFORMACIÓN Y TRANSFORMACIÓN DIGITAL - INIA
653 $aUnsupervised learning
653 $aUso/cobertura del suelo
653 $aUso/cobertura do solo
653 $aWater quality
700 1 $aPASTORINI, M.
700 1 $aTISCORNIA, G.
700 1 $aRIVAS-RIVERA, N.
700 1 $aGORGOGLIONE, A.
773 $tAgrociencia Uruguay, 2023, Vol.27(NE1), e1192. https://doi.org/10.31285/AGRO.27.1192 -- OPEN ACCESS.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
| Expresión de búsqueda válido. Check! |
|
|
