The value of selection in conventional breeding trials of cultivars destined for organic systems depends on the correlation between systems and relative heritability of key traits. Genotype by environment interactions is a common phenomenon in plant breeding trials. Thus, multienvironment testing to identify stable genotypes is a high priority for organic systems. In addition, This article is protected by copyright. All rights reserved. because organic systems have limited inputs to buffer the environment, they may have greater spatial heterogeneity which may be better accounted for by additional spatial blocking terms beyond traditional randomized complete block design. Over two years, we evaluated 100 hybrid and 40 inbred sweet corn genotypes in 11 trials in organic systems across 6 locations and evaluated the addition of augmented incomplete block and row-column design to estimate the performance of sweet corn genotypes. Hybrids differed in their performance for all tested traits. Inbred parents differed in per se performance and general combining ability for all traits. For the hybrid entries, modelling spatial factors beyond the replicated complete blocks improved the model fit for days to anthesis, plant height, ear height, husk protection, ear width and ear length. For inbred entries, modelling spatial factors beyond the replicated complete blocks improved \ model fit for plant height, ear height, tenderness, and ear width. Wricke's ecovalence (W 2 i) was a useful measure of stability, correlating reasonably well with two of the three stability statistics considered in this analysis. Based on Wricke's ecovalence, some inbred parents were more stable than others across tested environments in their combining ability for all traits.
Plant breeders need efficient systems to identify which inbreds to combine to create new hybrid cultivars. The North Carolina Design II (NC DII) is a useful mating design to evaluate the potential of hybrid varieties and their inbred parents. Genomic best linear unbiased prediction (GBLUP) models, either with or without the inclusion of a dominance term in the model, have been found to be an efficient method for using rich marker sets for prediction. This study used marker data and phenotypic data collected in 11 organic trials across 6 locations on 40 inbred sweet corn (Zea mays This article is protected by copyright. All rights reserved.L.) genotypes and 100 hybrid progenies formed from 4 disconnected NC DII mating blocks to predict performance of untested sweet corn hybrids. In 2017, validation trials of 24 previously untested hybrids were grown in five organic environments to assess the correlation between actual performance and the performance predicted by GBLUP or NC DII general combining abilities (GCAs).Five-fold cross-validation accuracy ranged from 0.29 to 0.82 for the GBLUP predictions based on additive effects alone, and from 0.70 to 0.91 for GBLUP predictions based on combined additive and dominance effects. For all traits except flavor, addition of dominance effects to the model increased the cross-validation accuracy. Correlations between values measured in the 2017 validation trials and values predicted from the 2015 and 2016 training trials ranged from 0.36 to 0.
Open-pollinated varieties provide a number of benefits for organic and smallholder farmers, allowing them to save seed, conduct on-farm selection, and maintain on-farm crop genetic diversity.
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