Predicting yield and stability analysis of wheat under different crop management systems across agro-ecosystems in India
The objectives of the study were as follows: 1) to evaluate the GxExM for wheat genotypes; 2) to predict yield performance and identify high stable wheat genotypes in different management practices; and 3) to make genotype-specific management and high performing genotype recommendations within and across agro-ecological regions. A diverse set of twenty-one genotypes was evaluated over three years (2012, 2013 and 2014) under two levels of crop management practices (CT and ZT) across three agro-ecological regions (BR, MP and PB) of India in replicated trials. Data were analyzed with SASGxE and RGxE programs using SAS and R programming languages, respectively. Across and within a location(s), the pattern of GxExM and GxMxY interactions (respectively) among univariate and multivariate stability statistics, grouping of genotypes in divisive clusters and estimates (with a prediction interval) of genotype varied in management practice CT and ZT. Across locations, the genotypes “Munal” and “HD-2967” were the best performers and high stable in CT and ZT, respectively. Genotypes “HD-2824” and “DPW-621-50”, and “Munal” may serve as diverse parents for developing high quality, climate smart, locally adapted genotypes for BR in CT and ZT, respectively. Genotypes “HD-2932”, “BAZ” and “JW-3288”, and “GW-322” and “HD-2967” are suitable for developing locally adapted stress tolerant genotypes for MP in management practices CT and ZT, respectively. Relatively small GxM and GxExM interactions in PB preclude in making definitive conclusions.
| Main Authors: | , , , , , , , , |
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| Format: | Article biblioteca |
| Language: | English |
| Published: |
Scientific Research Publishing Inc.
2017
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Stability Analysis, GxExM, BLUP, Forest Plots, GGE Biplot, Univariate Stability Statistics, WHEAT, YIELD FACTORS, GENETIC STABILITY, STATISTICAL METHODS, FIELD EXPERIMENTATION, GENOTYPE ENVIRONMENT INTERACTION, BEST LINEAR UNBIASED PREDICTOR, |
| Online Access: | http://hdl.handle.net/10883/19182 |
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