A5057812476- Genetics and Plant Breeding
- Genetic Mapping and Diversity in Plants and Animals
- Wheat and Barley Genetics and Pathology
- Genetic and phenotypic traits in livestock
- Crop Yield and Soil Fertility
- Plant Disease Resistance and Genetics
- Food composition and properties
- Spectroscopy and Chemometric Analyses
- Agriculture, Soil, Plant Science
- Soybean genetics and cultivation
- Yeasts and Rust Fungi Studies
- Seed and Plant Biochemistry
- Plant nutrient uptake and metabolism
- Mycotoxins in Agriculture and Food
- Bioenergy crop production and management
- Greenhouse Technology and Climate Control
- Sugarcane Cultivation and Processing
- Berry genetics and cultivation research
- Plant Stress Responses and Tolerance
- Plant pathogens and resistance mechanisms
- Genetic and Environmental Crop Studies
- Phytoestrogen effects and research
- Banana Cultivation and Research
- Plant Micronutrient Interactions and Effects
- Agronomic Practices and Intercropping Systems
Agriculture and Agri-Food Canada
2015-2024
Ottawa Research and Development Centre
2007-2024
Wuhan University
2024
Zhongnan Hospital of Wuhan University
2024
National Association of Friendship Centres
2023
Reid Health
2017
Agriculture Environmental Renewal Canada (Canada)
2016
University of Guelph
1998-2003
Institute of Crop Science
2002
Northwest A&F University
1995-1998
Cultivar evaluation and mega-environment identification are among the most important objectives of multi-environment trials (MET). Although measured yield is a combined result effects genotype (G), environment (E), × interaction (GE), only G GE relevant to cultivar identification. This paper presents GGE (i.e., + GE) biplot, which constructed by first two symmetrically scaled principal components (PC1 PC2) derived from singular value decomposition environment-centered MET data. The biplot...
Biplot analysis has evolved into an important statistical tool in plant breeding and agricultural research. Here we review the basic principles of biplot recent developments its application analyzing multi-environment trail (MET) data, with aim providing a working guide for breeders, agronomists, other scientists on interpretation. The is divided four sections. first section complete but succinct description analysis. second detailed treatment genotype by environment data. It addresses...
ABSTRACT The use of genotype main effect (G) plus genotype‐by‐environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi‐environment trial (MET) data. Recently, however, its legitimacy was questioned a proponent Additive Main Effect Multiplicative Interaction (AMMI) analysis. objectives this review are: (i) to compare GGE AMMI on three aspects data (GED) analysis, namely...
Plant breeding trials produce quantities of data and finding the useful information within that has historically been a major challenge plant breeding. A recently developed graphical summary, called GGEbiplot, can aid in exploration. GGEbiplot is Windows application performs biplot analysis two‐way assume an entry × tester structure. analyzes outputs results as image, it also produces interactive show data. It allows visualization from various perspectives. multienvironment trial set, which...
Superior crop cultivars must be identified through multi‐environment trials (MET) and on the basis of multiple traits. The objectives this paper were to describe two types biplots, GGE biplot GT biplot, which graphically display genotype by environment data trait data, respectively, hence facilitate cultivar evaluation MET Genotype main effect plus interaction (GGE) analysis soybean [ Glycine max (L.) Merr.] yield for 2800 heat unit area Ontario in period 1994–1999 revealed yearly crossover...
Multienvironment trials (MET) are conducted every year for all major crops throughout the world, and best use of information contained in MET data cultivar evaluation recommendation has been an important issue plant breeding agricultural research. A genotype main effect plus × environment interaction (GGE) biplot based on allows visualizing (i) which‐won‐where pattern MET, (ii) interrelationship among test environments, (iii) ranking genotypes both mean performance stability. Correct...
Genotype selection based on multiple traits is a key issue in plant breeding; it has been dependent setting subjective weight for each trait index and truncation point independent culling, the weights points can be highly subjective. In this paper we proposed demonstrated novel approach genotype traits, by yield*trait (GYT) biplot, where "trait" any breeding objective other than yield; may an agronomic trait, grain quality, processing or nutritional quality disease resistance. The GYT biplot...
SA genotype main effect plus × environment interaction (GGE) biplot graphically displays the genotypic (G) and (GE) of multienvironment trial (MET) data facilitates visual evaluation both genotypes environments. This paper compares merits two types GGE biplots in MET analysis. The first type is constructed by least squares solution sites regression model (SREG 2 ), with principal components as primary secondary effects, respectively. second Man‐del's for component extracted from residual M+1...
An understanding of the causes genotype × environment (GE) interaction can help identify traits that contribute to better cultivar performance and environments facilitate evaluation. Through subjecting environment‐centered yield a multi‐environment trial data singular value decomposition, portion variation is relevant evaluation partitioned into noncrossover crossover GE interaction, quantified by first two principal components (PC), respectively. Each PC set genotypic scores multiplied...
Superior crop cultivars must be identified through multi-environment trials (MET) and on the basis of multiple traits. The objectives this paper were to describe two types biplots, GGE biplot GT biplot, which graphically display genotype by environment data trait data, respectively, hence facilitate cultivar evaluation MET Genotype main effect plus interaction (GGE) analysis soybean [Glycine max (L.) Merr.] yield for 2800 heat unit area Ontario in period 1994–1999 revealed yearly crossover...
Multienvironment trials (MET) generate two types of two‐way data: genotype × environment data for a target trait and in individual or across environments. These can be visually analyzed by GGE biplot biplot, respectively. This paper describes third type the covariate‐effect illustrates its tandem use with other biplots to achieve fuller understanding MET data. The is generated on basis an explanatory table consisting correlation coefficients between (e.g., yield) each traits displays...
Multienvironment trials (MET) are conducted every year for all major crops throughout the world, and best use of information contained in MET data cultivar evaluation recommendation has been an important issue plant breeding agricultural research. A genotype main effect plus × environment interaction (GGE) biplot based on allows visualizing (i) which-won-where pattern MET, (ii) interrelationship among test environments, (iii) ranking genotypes both mean performance stability. Correct...
Test environment evaluation has become an increasingly important issue in plant breeding. In the context of indirect selection, a test can be characterized by two parameters: heritability and its genetic correlation with target environment. GGE biplot analysis, is similarly discrimination power similarity other environments. This paper investigates relationships between biplots based on different data scaling methods theory introduces heritability-adjusted (HA) biplot. We demonstrate that...
Diallel crosses have been used in genetic research to determine the inheritance of important traits among a set genotypes and identify superior parents for hybrid or cultivar development. Conventional diallel analysis is limited partitioning total variation data into general combining ability (GCA) each genotype specific (SCA) cross. In this paper we formulate biplot approach graphical analysis. The constructed by first two principal components (PCs) derived from subjecting tester-centered...
Breeding line selection, either for potential varieties or useful parents, must be based on multiple breeding objectives (or traits). Varieties cannot have any major defects, while parents outstanding levels in at least one trait. Due to undesirable associations among objectives, it is difficult accomplish both tasks (variety selection and parent selection) through a single strategy. Additional complication results when program different end‐uses such that high low of trait are desirable....
The oat ( Avena sativa L.) breeding program at the Eastern Cereal and Oilseed Research Centre of Agriculture & Agri‐Food Canada has responsibility to breed new cultivars for producers in eastern Canada, which includes Ontario, Quebec, Atlantic provinces. A 3‐yr multilocation test was conducted understand genotype × location interaction patterns relationships among locations Canada. + environment biplot analysis yield data revealed three distinct mega‐environments Canada: (i) northern...
ABSTRACT The success of a plant breeding program depends on many factors; one crucial factor is the selection suitable and testing locations. A test location must be discriminating so that genetic differences among genotypes can easily observed, it representative target environments selected have desired adaptation, its representation environment should also repeatable in 1 yr will superior performance future years. Using yield data 2006 through 2010 Quebec Oat Registration Recommendation...
Genotype by environment interaction (GE) is a reality in plant breeding and crop production, has to be dealt with. There are but two viable options deal with GE: utilize it or avoid it, depending on whether repeatable. Repeatable GE can selected for (utilized) whereas unrepeatable against (avoided). To involves identifying repeatable GE, dividing the target region into subregions megaenvironments (ME) based pattern, selecting within ME. By definition, ME avoided. test sufficient number of...
A physically anchored consensus map is foundational to modern genomics research; however, construction of such a in oat (Avena sativa L., 2n = 6x 42) has been hindered by the size and complexity genome, scarcity robust molecular markers, lack aneuploid stocks. Resources developed this study include modified SNP discovery method for complex genomes, diverse set novel chromosome-deficient anchoring strategy. These resources were applied build first complete, physically-anchored hexaploid oat....
ABSTRACT Mega‐environment analysis and test location evaluation are two important issues for effective crop variety through multilocation trials. These must be done based on multiyear variety‐trial data, which usually highly unbalanced. This paper presents a new graphical approach conducting mega‐environment utilizing unbalanced trial data. It consists of three steps: (i) generating G (genotypic main effect) plus GE (genotype × environment interaction), or GGE, biplot using missing‐value...