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Autor: Carlos Jara
Idea de nación en la República Liberal : Colombia 1930-1940
Carlos Mario Jaramillo Ramírez (2021)
Capítulo de libro
Sivakumar Sukumaran Jose Crossa Carlos Jara Marta Lopes Matthew Paul Reynolds (2016)
Increases in genetic gains in grain yield can be accelerated through genomic selection (GS). In the present study seven genomic prediction models under two cross validation scenarios were evaluated on the Wheat Association Mapping Initiative population of 287 advanced elite lines phenotyped for grain yield (GY), thousand grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 environments (year location combinations) in major wheat producing countries in 2010 and 2011. The seven genomic prediction models tested herein: four of them (model 1 (L+E), model 2 (L+E+G), model 3 (L+E+A) , and model 4 (L+E+A+G )) with main effects (lines (L), environme nts (E), genetic relationship matrix (G), and pedigree derived matrix (A) and three of them (model 5 (L+E+A+AE), model 6 (L+E+G+GE), and model 7 (L+E+G+A+AE+GE)) with interaction effects between A×E, G×E, and both together with main effects. Moreover, two cross validation (CV) schemes were applied: (1) predicting lines’ performance at untested sites (CV1) and (2) predicting the lines’ performance at some sites with the performance from other sites (CV2). The genomic prediction models with interaction terms, models 6 and 7 had the highest prediction accuracy on average for CV1 for GY (0.31), GN (0.30), and model 5 for TTF (0.26). Models 3 and 7 2, were the best model for GW (0.45 each) under CV1 scenario. For CV2, the prediction accuracy was generally high for the model with interaction terms models 5, 6, and 7 for GY (0.39), model 5 and 7 for GN (0.43. For GW and TTF models prediction accuracy were similar. Results indicated genomic selection can be used to predict genotype by environment (G×E) interaction in multi environment trials to select varieties for release as well as for accelerated breeding.
Dataset
Sivakumar Sukumaran Jose Crossa Carlos Jara Marta Lopes Matthew Paul Reynolds (2016)
Increases in genetic gains in grain yield can be accelerated through genomic selection (GS). In the present study seven genomic prediction models under two cross validation scenarios were evaluated on the Wheat Association Mapping Initiative population of 287 advanced elite lines phenotyped for grain yield (GY), thousand grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 environments (year location combinations) in major wheat producing countries in 2010 and 2011. The seven genomic prediction models tested herein: four of them (model 1 (L+E), model 2 (L+E+G), model 3 (L+E+A) , and model 4 (L+E+A+G )) with main effects (lines (L), environme nts (E), genetic relationship matrix (G), and pedigree derived matrix (A) and three of them (model 5 (L+E+A+AE), model 6 (L+E+G+GE), and model 7 (L+E+G+A+AE+GE)) with interaction effects between A×E, G×E, and both together with main effects. Moreover, two cross validation (CV) schemes were applied: (1) predicting lines’ performance at untested sites (CV1) and (2) predicting the lines’ performance at some sites with the performance from other sites (CV2). The genomic prediction models with interaction terms, models 6 and 7 had the highest prediction accuracy on average for CV1 for GY (0.31), GN (0.30), and model 5 for TTF (0.26). Models 3 and 7 2, were the best model for GW (0.45 each) under CV1 scenario. For CV2, the prediction accuracy was generally high for the model with interaction terms models 5, 6, and 7 for GY (0.39), model 5 and 7 for GN (0.43. For GW and TTF models prediction accuracy were similar. Results indicated genomic selection can be used to predict genotype by environment (G×E) interaction in multi environment trials to select varieties for release as well as for accelerated breeding.
Dataset
SOFIA ESPERANZA GARRIDO HOYOS ARIOSTO AGUILAR CHAVEZ CARLOS DIAZ DELGADO MARTIN ENRIQUE JARA MARINI María del Pilar Saldaña Fabela MARIA ANTONIETA GOMEZ BALANDRA HECTOR DAVID CAMACHO GONZALEZ MARIA VICENTA ESTELLER ALBERICH (2019)
El presente constituye el informe final del segundo año de actividades del proyecto. En dicho informe se muestra el inventario preliminar sobre la contaminación del río Yaqui, también se exponen los preliminares del modelo de dispersión y transporte de los contaminantes a través del río hasta el Golfo de California y, finalmente, el estudio de la posible bioacumulación y/o biomagnificación de los contaminantes en la cadena trófica del ecosistema acuático. El desarrollo de este trabajo y los resultados obtenidos podrán ser considerados como de referencia por los tomadores de decisiones para la definición de nuevos y/o de mejoras de los criterios de evaluación y monitoreo medioambiental en las cuencas hidrológicas. La contaminación difusa presente en la zona de estudio se atribuye al transporte y transformación de desechos presentes e incorporados de manera no puntual, esto nos muestra un panorama de acumulación de contaminantes, como resultado del estudio de la dispersión y transporte de los contaminantes inorgánicos-orgánicos presentes en la cuenca del río Yaqui.
Documento de trabajo
Cuencas Contaminación del agua Impacto ambiental Prevención y mitigación INGENIERÍA Y TECNOLOGÍA
Sofía Garrido Hoyos Ariosto Aguilar MARIA VICENTA ESTELLER ALBERICH CARLOS DIAZ DELGADO MARTIN ENRIQUE JARA MARINI María del Pilar Saldaña Fabela Maria Antonieta Gomez Balandra Ruben Morales Héctor Camacho MARTHA AVILES FLORES (2018)
El presente constituye el informe final del primer año de actividades del proyecto, en él se muestra el inventario preliminar sobre la contaminación del río Yaqui, también se exponen los preliminares del modelo de dispersión y transporte de los contaminantes a través del río hasta el Golfo de California, y finalmente el estudio de la posible bioacumulación y/o biomagnificación de los contaminantes en la cadena trófica del ecosistema acuático. La finalidad de elaborar un inventario de la contaminación es identificar y determinar la posible bioacumulación y/o biomagnificación de los contaminantes en la cadena trófica del ecosistema, evaluado y monitoreando el impacto de contaminantes, para proponer medidas de mitigación que contrarrestar daños ambientales y de salud pública.
Documento de trabajo
Cuencas Contaminación del agua Impacto ambiental Prevención y mitigación BIOLOGÍA Y QUÍMICA