Replicate allocation to improve selection efficiency in the early stages of a potato breeding scheme

Paget, M. F.; Alspach, P. A.; Anderson, John A.; Genet, R. A.; Braam, W. F.; Apiolaza, Luis A.

doi:10.1007/s10681-017-2004-3

Cited by 8 publications

(12 citation statements)

References 34 publications

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“…Furthermore, Lorenz (2013) recommends increasing population sizes rather than replication when simulating resource allocation strategies for genomic selection. Additionally, Paget et al (2017) found that full replications at early stages of evaluation and the use of PREP designs evaluated over several locations improves selection efficiency, and McCann et al (2010) suggests that evaluation over several locations and years is more efficient than increasing replications within a single location. Furthermore, Endelman et al (2014) found that it is more efficient to use unbalanced designs spread across locations than testing all entries in one location.…”

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confidence: 99%

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

2019

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The efficient use of testing resources is one of the key factors for successful plant breeding programs. Controlling micro‐ and macro‐environmental variability is an effective way of improving the testing efficiency and the selection of superior genotypes. Common experimental designs in genotypic testing usually use replicated or augmented experiments at each location, but they are balanced across locations. Some studies suggest that the increase in population size even at the expense of balanced experiments might be beneficial if genotype × environment interaction (GEI) is modeled. The objective of this study was to compare strategies for micro and macro‐environmental variability control that include GEI information to optimize resource allocation in multi‐environment trials (METs). Six experimental designs combined with four spatial correction models were compared for efficiency under three experimental sizes using simulations under a real yield variability map. Additionally, six resource allocation strategies were evaluated in terms of accuracy and the expected response to selection. The α‐lattice (ALPHA) experimental design was the best one at controlling micro‐environmental variability. The moderate mega‐environmental design (MED) strategy had the largest response to selection. This strategy uses historical mega‐environments (MEs) to unbalance genotypic testing within MEs while modeling GEI. The MED was the best resource allocation strategy and could potentially increase selection response up to 43% in breeding programs when genotypes are evaluated in METs.

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confidence: 99%

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

2019

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“…Hence, selection for productivity using nonreplicated breeding trials seems to be ineffective, even when considering the best breeding clones from the previous year assessment; i.e., in T 2 . Trial heterogeneity in early-stage potato breeding trials calls for the use of augmented (Federer 1956) or p-rep designs (Paget et al 2017) and spatial data analysis (Kempton et al 1994) when using non-replicated plots, and pedigree-based BLUPs for selection of promising bred-germplasm in T 1 and T 2 . As indicated by Slater et al (2014), BLUPs that use pedigree results in increased Δ G when having low H 2 in potato.…”

Section: Discussionmentioning

confidence: 99%

Optimizing multi-environment testing in potato breeding: using heritability estimates to determine number of replications, sites, and years for field trials

et al. 2023

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Multi-environment trials (METs) of potato breeding clones and cultivars allow to precisely determine their performance across testing sites over years. However, these METs may be affected by the genotype × environment interaction (GEI) as noted in tuber yield. Furthermore, trials are replicated several times to optimize the predictive value of the data collected because knowledge on spatial and temporal variability of testing environments is often lacking. Hence, the objectives of this research were to use components of variance from METs to estimate broad sense heritability (H2) based on best linear unbiased predictors and use these estimates to determine the optimum number of sites, years, and replications for testing potato breeding clones along with cultivars. The data were taken from METs in southern and northern Sweden comprising up to 256 breeding clones and cultivars that underwent testing using a simple lattice design of 10-plant plots across three sites over 2 years. Percentage starch in the tuber flesh had the largest H2 in each testing environment (0.850–0.976) or across testing environments (0.905–0.921). Total tuber weight per plot also exhibited high H2 (0.720–0.919) in each testing environment or across them (0.726–0.852), despite a significant GEI. Reducing sugar content in the tuber flesh had the lowest, but still medium H2 (0.426–0.883 in each testing environment; 0.718–0.818 across testing environments). The H2 estimates were smaller when their variance components were disaggregated by year and site, instead of lumping them as environments. Simulating H2 with genetic, site, year, site × year, genetic × site, genetic × year, genetic × site × year, and residual variance components led to establish that two replicates at each of two sites in 2-year trials will suffice for testing tuber yield, starch and reducing sugars. This article provides a methodology to optimize the number of testing size and years for METs of potato breeding materials, as well as tabulated information for choosing the appropriate number of trials in same target population of environments.

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“…Hence, selection for productivity using nonreplicated breeding trials seems to be ineffective, even when considering the best breeding clones from the previous year assessment; i.e., in T2. Trial heterogeneity in early-stage potato breeding trials calls for the use of augmented (Federer 1956) or p-rep designs (Paget et al 2017) and spatial data analysis (Kempton et al 1994) when using non-replicated plots, and pedigree-based BLUPs for selection of promising bred-germplasm in T1 and T2. As indicated by Slater et al (2014),…”

Section: Discussionmentioning

confidence: 99%

Optimizing multi-environment testing in potato breeding: using heritability estimates to determine number of replications, sites, and years for field trials

Ortíz¹,

Reslow²,

Huicho

et al. 2022

Preprint

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Multi-environment trials (METs) of potato breeding clones and cultivars allow to precisely determine their genetic values. However, these METs may be affected by the genotype ´ environment interaction (GEI) as noted in tuber yield. Hence, the objectives of this research were to use components of variance from METs to estimate broad sense heritability (H2) based on best linear unbiased predictors and use these estimates to determine the optimum number of sites, years, and replications for testing potato breeding clones along with cultivars. The data were taken from METs in southern and northern Sweden comprising up to 256 breeding clones and cultivars that underwent testing using a simple lattice design of 10-plant plots across three sites over two years. Tuber flesh’s starch had the largest H2 in each (0.850–0.976) or across (0.905–0.921) testing environments. Total tuber weight per plot also exhibited high H2 (0.720 – 0.919) in each testing environment or across them (0.726–0.852), despite a significant GEI. Reducing sugar content in the tuber flesh had the lowest, but still medium H2 (0.426–0.883 in each testing environment; 0.718–0.818 across testing environments). The H2 estimates were smaller when their variance components were disaggregated by year and site, instead of lumping them as environments. Simulating H2 with genetic, site, year, site ´ year, genetic ´ site, genetic ´ year, genetic ´ site ´ year, and residual variance components led to establish that two replicates at each of two sites in two-year trials will suffice for METs in potato when testing, in the target population of environments, breeding clones along with cultivars for tuber yield, tuber flesh’s starch and reducing sugars in the tuber flesh.

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Replicate allocation to improve selection efficiency in the early stages of a potato breeding scheme

Cited by 8 publications

References 34 publications

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

Optimizing multi-environment testing in potato breeding: using heritability estimates to determine number of replications, sites, and years for field trials

Optimizing multi-environment testing in potato breeding: using heritability estimates to determine number of replications, sites, and years for field trials

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