Using landraces for broadening the genetic base of elite maize germplasm is hampered by heterogeneity and high genetic load. Production of DH line libraries can help to overcome these problems. Landraces of maize (Zea mays L.) represent a huge reservoir of genetic diversity largely untapped by breeders. Genetic heterogeneity and a high genetic load hamper their use in hybrid breeding. Production of doubled haploid line libraries (DHL) by the in vivo haploid induction method promises to overcome these problems. To test this hypothesis, we compared the line per se performance of 389 doubled haploid (DH) lines across six DHL produced from European flint landraces with that of four flint founder lines (FFL) and 53 elite flint lines (EFL) for 16 agronomic traits evaluated in four locations. The genotypic variance ([Formula: see text]) within DHL was generally much larger than that among DHL and exceeded also [Formula: see text] of the EFL. For most traits, the means and [Formula: see text] differed considerably among the DHL, resulting in different expected selection gains. Mean grain yield of the EFL was 25 and 62% higher than for the FFL and DHL, respectively, indicating considerable breeding progress in the EFL and a remnant genetic load in the DHL. Usefulness of the best 20% lines was for individual DHL comparable to the EFL and grain yield (GY) in the top lines from both groups was similar. Our results corroborate the tremendous potential of landraces for broadening the narrow genetic base of elite germplasm. To make best use of these "gold reserves", we propose a multi-stage selection approach with optimal allocation of resources to (1) choose the most promising landraces for DHL production and (2) identify the top DH lines for further breeding.

A plethora of maize (Zea mays L.) landraces is stored in gene banks worldwide. However, information about their value in breeding is scarce and strategies for identifying the most promising landraces in prebreeding are largely lacking. This study was conducted to (i) evaluate the testcross performance of 70 European flint landraces in combination with two elite dent testers and compare these results with the performance of modern hybrids for important agronomic traits, (ii) estimate the genetic variances among landraces and trait correlations for these two testcross series as well as the correlation between them, and (iii) devise a testing scheme for assessing the breeding potential of a large number of landraces for hybrid breeding. Grain yield of the landrace testcrosses was on average about 26% lower than modern hybrids. Genotypic variances among landrace testcrosses were significant for all traits, and genetic correlations were moderate to high for most trait combinations in both testcross series. Thus, it seems promising to tap this huge genetic reserve for enlarging the genetic base of the elite flint germplasm pool in Central Europe. Since the genetic correlation between the two testcross series exceeded 0.74 for all traits, we recommend assessing the breeding potential of landraces for broadening existing heterotic groups by evaluating their testcross performance in combination with one or two elite single‐cross tester(s) from the opposite heterotic pool. Subsequently, doubled haploid lines from a few of the most promising landraces could be produced to exploit the large genetic variation within landraces to the full extent.

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High‐throughput (HT) precision phenotyping of agronomic traits is important for well‐founded, rapid selection decisions in plant breeding. This applies especially to nondestructive measurement of single‐seed oil content, for which an HT platform has recently become available. The objectives of this study were (i) to evaluate the suitability of this HT platform for measuring seed mass, oil mass, and oil content in various oil crops, (ii) to determine the accuracy and repeatability of the measurements, and (iii) to discuss technical adjustments required for specific crops. Seeds of canola (Brassica napus L.), castor bean (Ricinus communis L.), cotton (Gossypium hirsutum L), jatropha (Jatropha curcas L.), maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and sunflower (Helianthus annuus L.) were measured repeatedly using a randomized complete block design. Additionally, the oil content of bulks of seeds from two crops was determined by wet chemistry analysis. Repeatability of all three traits recorded via the HT platform generally exceeded 98% in all seven crops. Oil content of bulks determined by wet chemistry analysis was almost perfectly correlated (R2 > 99.9%) with the mean of nuclear magnetic resonance (NMR) measurements of single seeds from these bulks. To warrant precise results and smooth operation, yielding an average throughput of ∼600 seeds h−1, technical modifications in certain modules of the HT platform are required to accommodate the size, geometry, and oil content of seeds from different crops. In conclusion, the HT platform demonstrated high repeatability and accuracy of measurements, which opens up several fields of application in plant breeding.

Thousands of maize landraces are stored in seed banks worldwide. Doubled-haploid libraries (DHL) produced from landraces harness their rich genetic diversity for future breeding. We investigated the prospects of genomic prediction (GP) for line per se performance in DHL from six European landraces and 53 elite flint (EF) lines by comparing four scenarios: GP within a single library (sL); GP between pairs of libraries (LwL); and GP among combined libraries, either including (cLi) or excluding (cLe) lines from the training set (TS) that belong to the same DHL as the prediction set. For scenario sL, with N = 50 lines in the TS, the prediction accuracy (r) among seven agronomic traits varied from 20.53 to 0.57 for the DHL and reached up to 0.74 for the EF lines. For LwL, r was close to zero for all DHL and traits. Whereas scenario cLi showed improved r values compared to sL, r for cLe remained at the low level observed for LwL. Forecasting r with deterministic equations yielded inflated values compared to empirical estimates of r for the DHL, but conserved the ranking. In conclusion, GP is promising within DHL, but large TS sizes (N . 100) are needed to achieve decent prediction accuracy because LD between QTL and markers is the primary source of information that can be exploited by GP. Since production of DHL from landraces is expensive, we recommend GP only for very large DHL produced from a few highly preselected landraces.