Now showing items 1-10 of 37
User's guide for spatial analysis of field variety trials using ASREML
This manual describes an approach to the spatial analysis of field experiments based on the software package AS residual maximum likelihood (ASREML; Gilmour et al. 1999). It describes common sources of spatial variation ...
The AMMI analysis and graphing the biplot
With the objective of facilitating researchers of CIMMYT and National Programs to compute the AMMI model and the biplot of multi-environment trials, three programs were written in SAS for computing the AMMI analysis and ...
Genomic prediction in CIMMYT maize and wheat breeding programs
(Springer Nature, 2014)
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending ...
Analysis and interpretation of interactions in agricultural research
(American Society of Agronomy, 2015)
When reporting on well-conducted research, a characteristic of a complete and proper manuscript is one that includes analyses and interpretations of all interactions. Our purpose is to show how to analyze and interpret ...
META: A suite of sas programs to analyze multienvironment breeding trials
(American Society of Agronomy, 2013)
Multi-environment trials (METs) enable the evaluation of the same genotypes in a variety of environments and management conditions. We present here META (Multi Environment Trial Analysis), a suite of 31 SAS programs that ...
Sashaydiall: A SAS program for hayman’s diallel analysis
(Crop Science Society of America (CSSA), 2018)
Different methods of diallel crossing are commonly used in plant breeding. The diallel cross analysis method proposed by Hayman is particularly useful because it provides information, among others, on additive and dominance ...
Extending the marker x environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat
(Crop Science Society of America (CSSA), 2016)
The marker ´ environment interaction (M´E) genomic model can be used to generate predictions for untested individuals and identify genomic regions in which effects are stable across environments and others that show ...
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, ...
Modelación de la interacción genotipo x ambiente en rendimiento de hibridos de maiz blanco en ambientes múltiples
(Sociedad Mexicana de Fitogenética, 2015)
Los programas de fitomejoramiento enfocados a la obtención de genotipos con mayor rendimiento y estables en una amplia gama de condiciones ambientales enfrentan factores ambientales que enmascaran el potencial de los ...
Analysis and answers to frequently asked questions in quantitative trait locus mapping
(Institute of Crop Sciences, 2010)
QTL mapping is an important step in gene fine mapping, map-based cloning, and the efficient use of gene information in molecular breeding. Questions are frequently met and asked in the application of QTL mapping in practical ...