Comparing yield trial locations based on their elicited expressions of genetic variance among soybean cultivars

Ashworth, Amanda J.; Knapp, Victoria; Allen, Fred L.; Saxton, Arnold M.

doi:10.1002/csc2.20066

Cited by 3 publications

(12 citation statements)

References 15 publications

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“…Different developmental stages responded distinctly to annual weather effects, considering each MG had varying responses to years, which was also observed by Ashworth et al. (2020) with different MG of soybeans. Location‐induced effects were likely owing to different MGs experiencing soil moisture and temperature (day and nighttime) at varying maturity stages, especially tasseling, silking, and grain fill.…”

Section: Resultssupporting

confidence: 77%

“…In a previous study, Ashworth et al. (2020) found that four of the five locations were needed and one location could be eliminated without compromising cultivar yield trial information for five soybean MG tests.…”

Section: Resultsmentioning

confidence: 99%

“…Yield data within each location–year–MG combination were analyzed with an all random effects model using Prox MIXED in SAS version 9.4:

yiel {normald}_{i j k} = μ + Re {normalp}_{i} + IB {((), Rep)}_{i j} + Cul {normalt}_{k} + e_{i j k}

where individual plot yields were from the i th replication, j th incomplete block, and k th cultivar (Ashworth et al., 2020). The cultivars entered in each MG and year were considered to be random, as representative lines from a reference or conceptual population of advanced generation inbred lines that are continually being developed by public and private corn breeding programs.…”

Section: Methodsmentioning

confidence: 99%

“…The biplot display of two‐way data (e.g., genotypes and environments; locations and years) was presented by Gabriel (1971) and has since been refined (Yan, 2001; Yan & Kang, 2003) and utilized by numerous researchers for selection of best cultivars and used to identify test locations with high discriminating ability into mega‐environments in crops such as soybean (Ashworth et al., 2020; Yan & Rajcan, 2002). An additional study by Miŝević and Dumanović (1989) evaluated the predictive potential of corn yield trials based on correlations between location means and the overall mean and found that average‐yielding locations had higher estimators of prediction than lower‐yielding environments.…”

Section: Introductionmentioning

confidence: 99%

“…Multigene traits, such as yield, have a proven genotype × environment interaction (G×E), particularly across diverse environments and across genotypes (e.g., cultivars and breeding lines; Ashworth, Knapp, Allen, & Saxton, 2020). Therefore, multienvironment trials (i.e., location–year combinations) are important in order to obtain estimates of yield performance of different genotypes across a target population of environments or a geographic production region (Comstock & Moll, 1963; Dia et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of discriminatory effects of corn yield test locations based on their genetic variation expression among hybrids

et al. 2020

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It is important for yield test sites to elicit differences among genotypes, minimize redundancy of performance information, and generate a genotype × environment interaction representative of the targeted region. The objective of this study was to use corn (Zea mays L.) variety trial data from three relative maturity group (MG) tests (early, medium, and late) at five Tennessee locations spanning 14 yr to compare test location uniqueness and redundancy via genetic variance estimates and cluster analysis. Principle component analyses allowed for location comparisons of genetic variance expression among cultivars in different MG tests. Individual location–test–year genetic variance estimates ranged from <1 to 584 (Mg ha−1)2. Years and locations affected the magnitude of genetic variance, with years largely driving genetic expression within MGs in these environments. However, the late MG consistently had the greatest variance estimates. Across 14 yr, there was minimum duplicative information provided across the five yield test sites. Consequently, all locations were needed and none could be eliminated without compromising cultivar yield trial information for the three MG tests. Correlations between mean yields and genetic variance showed a weak relationship across locations (environments), years, and MG tests (r = .021); thus, high‐yielding environments did not correspond with increased genetic variance expression. These types of analyses are useful for evaluating the discriminating ability and duplicative or correlated expression of genetic information across locations for robust variety trials within and across maturity groups and targeted regions.

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Section: Resultssupporting

confidence: 77%

Section: Resultsmentioning

confidence: 99%

“…Yield data within each location–year–MG combination were analyzed with an all random effects model using Prox MIXED in SAS version 9.4:

yiel {normald}_{i j k} = μ + Re {normalp}_{i} + IB {((), Rep)}_{i j} + Cul {normalt}_{k} + e_{i j k}

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of discriminatory effects of corn yield test locations based on their genetic variation expression among hybrids

et al. 2020

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A regression approach to identify discriminating locations

Vemireddy

Ólafsson

2023

Crop Science

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Selection of planting locations is an important issue in plant breeding and may significantly impact the probability of correctly identifying the desired genotype based on noisy data. This is especially true for early‐stage experimentation where a set of experimental genotypes is observed in very few locations. This study aims to identify locations that are most helpful for discriminating the main effects of two or more genotypes. A regression‐based model is proposed that characterizes the sensitivity of location to the genotype main effect, and locations with significant positive sensitivity are termed discriminative. Comparing or ranking genotypes using these locations results in exaggerated expected differences between genotype main effects, allowing for more accurate comparisons in the presence of noise. Based on the proposed model, an expression for the probability of making correct genotype selection is derived given a set of locations with known location sensitivities. The probability of correct selection is then used to characterize an optimal set of location that maximizes the probability of selecting the genotype with better main effect. These locations are independent of the genotypes being compared and only depend on the number of locations being considered and the sensitivities of these locations to genotype effects. The theoretical implications of the model are validated using data obtained from a commercial soybean breeding program. The results show that by using discriminating locations correct selection is made 76% of the time, versus 67% for all locations and only 63% of the time for non‐discriminating locations.

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Using Partial Least Squares and Regression to Interpret Temperature and Precipitation Effects on Maize and Soybean Genetic Variance Expression

Ashworth,

Allen,

Saxton

2023

Agronomy

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Partial least squares (PLS) is a statistical technique that can evaluate the association of large numbers of external environmental variables with biological responses. PLS is a good method for analyzing the relative importance of variables and compressing the data for regression analyses. The objective of this study was to use PLS and regression analyses on soybean (Glycine max L.) and maize (Zea mays L.) variety trial results for five (soybean) or three (maize) maturity group (MG) tests, at five Tennessee locations spanning 14 years, in order to determine the environmental effects (weekly minimum and maximum air temperature and precipitation) on the expression of yield genetic variance (Vg). Overall, PLS excelled at identifying combinations of weather variables to develop models with high R2 values (41–59%) relative to the regression analysis (R2 = 34–44%), but they did not address the effects of specific variables as in regression analysis. In both maize and soybean, differences in genetic variance occurred among MG tests and locations. Overall, precipitation was the driving variable for maize Vg, indicating maize is more sensitive to rain events during the growing season than soybean, i.e., with each cm of precipitation, maize Vg increased by 11.38–23.78 (Mg ha−1)2. The results suggest that ensuring adequate water, particularly during weeks 3 and 6, is critical for maize Vg, regardless of the MG test and location. Genetically modified soybean cultivars responded similarly to conventional cultivars, suggesting no Vg response differences due to the glyphosate tolerance trait. These results have important implications for irrigation timing for the maximum expression of genetic differences in maize and soybean cultivars, particularly for management planning during future stochastic weather events.

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Comparing yield trial locations based on their elicited expressions of genetic variance among soybean cultivars

Cited by 3 publications

References 15 publications

Comparison of discriminatory effects of corn yield test locations based on their genetic variation expression among hybrids

Comparison of discriminatory effects of corn yield test locations based on their genetic variation expression among hybrids

A regression approach to identify discriminating locations

Using Partial Least Squares and Regression to Interpret Temperature and Precipitation Effects on Maize and Soybean Genetic Variance Expression

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