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Dissecting Maize Productivity:Ideotypes Associated with Grain Yield under Drought Stress and Well-watered Conditions

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Journal of Integrative Plant Biology 2012年第12期54卷 1007-1020页

作者： Jill E.Cairns Ciro Sanchez mateo vargas Raziel Ordoez Jose Luis Araus Global Maize Program International Maize and Wheat Improvement Center(CIMMYT)Mexico D.F.Mexico Bioinformatics and Statistics Unit International Maize and Wheat Improvement Center(CIMMYT)Mexico D.F.Mexico Unit of Plant Physiology Department of Plant BiologyUniversity of BarcelonaBarcelonaSpain

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.

关键词： Climate change drought stress ideotypes maize senescence spectral reflectance.

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META-R:A software to analyze data from multi-environment plant breeding trials

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The Crop Journal 2020年第5期8卷 745-756页

作者： Gregorio Alvarado Francisco M.Rodríguez Angela Pacheco Juan Burgueño JoséCrossa mateo vargas Paulino Pérez-Rodríguez Marco A.Lopez-Cruz Biometrics and Statistics Unit International Maize and Wheat Improvement Center(CIMMYT)MexicoDF 6-64106600Mexico Centro de Investigación en Matemáticas(CIMAT)A.C. Jalisco S/NCol.ValencianaGuanajuatoGto 40236023Mexico Universidad Autónoma Chapingo Carretera Federal México-Texcoco km 38.5TexcocoMéxico 6356230Mexico Colegio de Postgraduados Carretera México-Texcoco Km.36.5MontecilloTexcoco 6356231Mexico Department of Plant Soil and Microbial SciencesMichigan State University1066 Bogue StEast LansingMI 48824USA

META-R(multi-environment trial analysis in R)is a suite of R scripts linked by a graphical user interface(GUI)designed in Java *** 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 ***-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 *** parameters are very important in the selection of top performing genotypes in plant ***-R also computes the phenotypic and genetic correlations among environments and between traits,as well as their statistical *** 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 ***-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.

关键词： environments breeding variance

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META-R: A software to analyze data from multi-environment plant breeding trials

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The Crop Journal 2020年第5期

作者： Gregorio Alvarado Francisco M.Rodríguez Angela Pacheco Juan Burgueño JoséCrossa mateo vargas Paulino Pérez-Rodríguez Marco A.Lopez-Cruz

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