META-R(multi-environment trial analysis in R)is a suite of R scripts linked by a graphical user interface(GUI)designed in Java language.The objective of META-R is to accurately analyze multi-environment plant breeding...META-R(multi-environment trial analysis in R)is a suite of R scripts linked by a graphical user interface(GUI)designed in Java language.The objective of META-R is to accurately analyze multi-environment plant breeding trials(METs)by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design(RCBD)and the alpha-lattice/lattice designs.META-R simultaneously estimates the best linear and unbiased estimators(BLUEs)and the best linear and unbiased predictors(BLUPs).Additionally,it computes the variance-covariance parameters,as well as some statistical and genetic parameters such as the least significant difference(LSD)at 5%significance,the coefficient of variation in percentage(CV),the genetic variance,and the broad-sense heritability.These parameters are very important in the selection of top performing genotypes in plant breeding.META-R also computes the phenotypic and genetic correlations among environments and between traits,as well as their statistical significance.The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram(dendrogram).Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits.META-R performs multi-environment analyses by using the residual maximum likelihood(REML)method;these analyses can be done by environment,across environments by grouping factors(stress conditions,nitrogen content,etc.)and across environments;the analyses across environments can be done with a pre-defined degree of heritability.展开更多
To increase maize （Zea mays L.） yields in drought-prone environments and offset predicted maize yield losses under future climates, the development of improved breeding pipelines using a multi-disciplinary approach ...To increase maize （Zea mays L.） yields in drought-prone environments and offset predicted maize yield losses under future climates, the development of improved breeding pipelines using a multi-disciplinary approach is essential. Elucidating key growth processes will provide opportunities to improve drought breeding progress through the identification of key phenotypic traits, ideotypes, and donors. In this study, we tested a large set of tropical and subtropical maize inbreds and single cross hybrids under reproductive stage drought stress and well-watered conditions. Patterns of biomass production, senescence, and plant water status were measured throughout the crop cycle. Under drought stress, early biomass production prior to anthesis was important for inbred yield, while delayed senescence was important for hybrid yield. Under well-watered conditions, the ability to maintain a high biomass throughout the growing cycle was crucial for inbred yield, while a stay-green pattern was important for hybrid yield. While new quantitative phenotyping tools such as spectral reflectance （Normalized Difference Vegetation Index, NDVI） allowed for the characterization of growth and senescence patterns as well as yield, qualitative measurements of canopy senescence were also found to be associated with grain yield.展开更多
基金We are grateful for the financial support provided by the Bill&Melinda Gates Foundation and CIMMYT's CGIAR CRP(MAIZE and WHEAT),as well as the USAID Projects(Cornell University and Kansas State University)that generated the CIMMYT wheat data analyzed in this study.We acknowledge the financial support provided by the Foundation for Research Levy on Agricultural Products(FFL)and the Agricultural Agreement Research Fund(JA)in Norway through NFR grant 267806.
文摘META-R(multi-environment trial analysis in R)is a suite of R scripts linked by a graphical user interface(GUI)designed in Java language.The objective of META-R is to accurately analyze multi-environment plant breeding trials(METs)by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design(RCBD)and the alpha-lattice/lattice designs.META-R simultaneously estimates the best linear and unbiased estimators(BLUEs)and the best linear and unbiased predictors(BLUPs).Additionally,it computes the variance-covariance parameters,as well as some statistical and genetic parameters such as the least significant difference(LSD)at 5%significance,the coefficient of variation in percentage(CV),the genetic variance,and the broad-sense heritability.These parameters are very important in the selection of top performing genotypes in plant breeding.META-R also computes the phenotypic and genetic correlations among environments and between traits,as well as their statistical significance.The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram(dendrogram).Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits.META-R performs multi-environment analyses by using the residual maximum likelihood(REML)method;these analyses can be done by environment,across environments by grouping factors(stress conditions,nitrogen content,etc.)and across environments;the analyses across environments can be done with a pre-defined degree of heritability.
基金supported in part by the Bill and Melinda Gates Foundation under the Drought Tolerant Maize for Africa projectBMZ under the Precision phenotyping for improving drought stress tolerant maize for southern Asia and eastern Africa projectthe MAIZE Global Alliance for Improving Food Security and the Livelihoods of the Resource-poor in the Developing World research program of the Consultative Group on International Agricultural Research(CGIAR)
文摘To increase maize （Zea mays L.） yields in drought-prone environments and offset predicted maize yield losses under future climates, the development of improved breeding pipelines using a multi-disciplinary approach is essential. Elucidating key growth processes will provide opportunities to improve drought breeding progress through the identification of key phenotypic traits, ideotypes, and donors. In this study, we tested a large set of tropical and subtropical maize inbreds and single cross hybrids under reproductive stage drought stress and well-watered conditions. Patterns of biomass production, senescence, and plant water status were measured throughout the crop cycle. Under drought stress, early biomass production prior to anthesis was important for inbred yield, while delayed senescence was important for hybrid yield. Under well-watered conditions, the ability to maintain a high biomass throughout the growing cycle was crucial for inbred yield, while a stay-green pattern was important for hybrid yield. While new quantitative phenotyping tools such as spectral reflectance （Normalized Difference Vegetation Index, NDVI） allowed for the characterization of growth and senescence patterns as well as yield, qualitative measurements of canopy senescence were also found to be associated with grain yield.
