Key message: A simple and rapid speed breeding system was developed for short-day crops that enables up to five generations per year using LED lighting systems that allow very specific adjustments regarding light intensity and quality. Plant breeding is a key element for future agricultural production that needs to cope with a growing human population and climate change. However, the process of developing suitable cultivars is time-consuming, not least because of the long generation times of crops. Recently, speed breeding has been introduced for long-day crops, but a similar protocol for short-day crops is lacking to date. In this study, we present a speed breeding protocol based on light-emitting diodes (LEDs) that allow to modify light quality, and exemplarily demonstrate its effectiveness for the short-day crops soybean (Glycine max), rice (Oryza sativa) and amaranth (Amaranthus spp.). Adjusting the photoperiod to 10 h and using a blue-light enriched, far-red-deprived light spectrum facilitated the growth of short and sturdy soybean plants that flowered ~ 23 days after sowing and matured within 77 days, thus allowing up to five generations per year. In rice and amaranth, flowering was achieved ~ 60 and ~ 35 days after sowing, respectively. Interestingly, the use of far-red light advanced flowering by 10 and 20 days in some amaranth and rice genotypes, respectively, but had no impact on flowering in soybeans, highlighting the importance of light quality for speed breeding protocols. Taken together, our short-day crops’ speed breeding protocol enables several generations per year using crop-specific LED-based lighting regimes, without the need of tissue culture tools such as embryo rescue. Moreover, this approach can be readily applied to a multi-storey 96-cell tray-based system to integrate speed breeding with genomics, toward a higher improvement rate in breeding.

Copy number variation was found to be a frequent type of DNA polymorphism in the human genome often associated with diseases but its importance in crops and the effects on agronomic traits are still largely unknown. Here, we employed a large worldwide panel of 1110 winter wheat varieties to assess the frequency and the geographic distribution of copy number variants at the Photoperiod-B1 (Ppd-B1) and the Vernalization-A1 (Vrn-A1) loci as well as their effects on flowering time under field conditions. We identified a novel four copy variant of Vrn-A1 and based on the phylogenetic relationships among the lines show that the higher copy variants at both loci are likely to have arisen independently multiple times. In addition, we found that the frequency of the different copy number variants at both loci reflects the environmental conditions in the varieties’ region of origin and based on multi-location field trials show that Ppd-B1 copy number has a substantial effect on the fine-tuning of flowering time. In conclusion, our results show the importance of copy number variation at Ppd-B1 and Vrn-A1 for the global adaptation of wheat making it a key factor for wheat success in a broad range of environments and in a wider context substantiate the significant role of copy number variation in crops.

The efficiency of breeding programs depends on the ability to screen large numbers of individuals. For complex traits like yield, this can be assisted by genomic selection, which is based on estimating breeding values with genome‐wide marker data. Here, we evaluate phenomic prediction, which, similar to its genomic counterpart, aims to predict the performance of untested individuals but using near‐infrared spectroscopy (NIRS) data. In a large panel of 944 soybean [ Glycine max (L.) Merr.] recombinant inbred lines phenotyped for seed yield, thousand‐seed weight, and plant height at three locations, we demonstrate that the phenomic predictive abilities are high and comparable with those obtained by genomic prediction. We found that ridge regression best linear unbiased prediction performs well for phenomic prediction and that the number of wavelengths can be reduced without a decrease in predictive ability. For prediction at different locations, NIRS data from a single location can be used. However, NIRS data from different environments, like years, should be connected by common genotypes in training and prediction sets. Phenomic prediction appears to be less susceptible to relatedness between individuals in training and prediction sets than genomic prediction, as generally half‐sib but also unrelated families achieved high predictive abilities. Moreover, for the same training set sizes phenomic prediction resulted in higher predictive abilities compared to genomic prediction. Phenomic prediction can be applied at different stages in a breeding program, and collectively our results highlight the potential of this approach to increase genetic gain in plant breeding.

Sorghum (Sorghum bicolor L. Moench) productivity is severely impeded by low phosphorus (P) and aluminum (Al) toxic soils in sub-Saharan Africa and especially West Africa (WA). Improving productivity of this staple crop under these harsh conditions is crucial to improve food security and farmer’s incomes in WA. This is the first study to examine the genetics underlying sorghum adaptation to phosphorus limitation in a wide range of WA growing conditions. A set of 187 diverse sorghum genotypes were grown in 29 –P and + P field experiments from 2006-2012 in three WA countries. Sorghum grain yield performance under –P and + P conditions was highly correlated (r = 0.85***). Significant genotype-by-phosphorus interaction was detected but with small magnitude compared to the genotype variance component. We observed high genetic diversity within our panel, with rapid linkage disequilibrium decay, confirming recent sequence based studies in sorghum. Using genome wide association mapping based on 220 934 SNPs we identified one genomic region on chromosome 3 that was highly associated to grain yield production. A major Al-tolerance gene in sorghum, SbMATE, was collocated in this region and SbMATE specific SNPs showed very high associations to grain yield production, especially under –P conditions, explaining up to 16% of the genotypic variance. The results suggest that SbMATE has a possible pleiotropic role in providing tolerance to two of the most serious abiotic stresses for sorghum in WA, Al toxicity and P deficiency. The identified SNPs can help accelerate breeding for increased sorghum productivity under unfavorable soil conditions and contribute to assuring food security in WA.