The primary maize (Zea mays L.) production areas are in temperate regions throughout the world and this is where most maize breeding is focused. Important but lower yielding maize growing regions such as the sub-tropics experience unique challenges, the greatest of which are drought stress and aflatoxin contamination. Here we used a diversity panel consisting of 346 maize inbred lines originating in temperate, sub-tropical and tropical areas testcrossed to stiff-stalk line Tx714 to investigate these traits. Testcross hybrids were evaluated under irrigated and non-irrigated trials for yield, plant height, ear height, days to anthesis, days to silking and other agronomic traits. Irrigated trials were also inoculated with Aspergillus flavus and evaluated for aflatoxin content. Diverse maize testcrosses out-yielded commercial checks in most trials, which indicated the potential for genetic diversity to improve sub-tropical breeding programs. To identify genomic regions associated with yield, aflatoxin resistance and other important agronomic traits, a genome wide association analysis was performed. Using 60,000 SNPs, this study found 10 quantitative trait variants for grain yield, plant and ear height, and flowering time after stringent multiple test corrections, and after fitting different models. Three of these variants explained 5–10% of the variation in grain yield under both water conditions. Multiple identified SNPs co-localized with previously reported QTL, which narrows the possible location of causal polymorphisms. Novel significant SNPs were also identified. This study demonstrated the potential to use genome wide association studies to identify major variants of quantitative and complex traits such as yield under drought that are still segregating between elite inbred lines.

High-throughput phenotyping technologies, which can generate large volumes of data at low costs, may be used to indirectly predict yield. We explore this concept, using high-throughput phenotype information from Fourier transformed near-infrared reflectance spectroscopy (NIRS) of harvested kernels to predict parental grain yield in maize (Zea mays L.), and demonstrate a proof of concept for phenomic-based models in maize breeding. A dataset of 2,563 whole-kernel samples from a diversity panel of 346 hybrid testcrosses were scanned on a plot basis using NIRS. Scans consisted of 3,076 wavenumbers (bands) in the range of 4,000-10,000 cm −1. Corresponding grain yield for each sample was used to train phenomic prediction and selection models using three types of statistical learning: (a) partial least square regression (PLSR), (b) NIRS best linear unbiased predictor (NIRS BLUP), and (c) functional regression. Our results found that NIRS data were a useful tool to predict maize grain yield and showed promising results for evaluating genetically independent breeding populations. All model types were successful; functional regression followed by the PLSR model resulted in the best predictions. Pearson's correlations between predicted and observed grain yields exceeded .7 in many cases within random cross validation. Abbreviations: AF, aflatoxin; BLUE, best linear unbiased estimator; BLUP, best linear unbiased predictor; CV, cross validation; CV0, predicting one environment using data from all other environments; CV1, 20% of the hybrids are predicted by the remaining 80% of hybrids (five-fold), within each environment; CV2, predicting across environments, where hybrids are seen in some environments but predicted in others (mimics sparse testing); G × E, genotype by environment; G-BLUP, genomic best linear unbiased predictor; GEM, germplasm enhancement of maize lines; GWAS, genome-wide association study; LM, simple linear model; NIRS, near-infrared reflectance spectroscopy; NIRS BLUP, NIRS-based best linear unbiased predictor; PLSR, partial least squares regression; RMSEP, root mean square error of prediction; SERAT, southeast regional aflatoxin trial; UAS, unoccupied aerial systems; WS, water stress, unirrigated treatment; WW, well-watered, irrigated treatment. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The growing demand for food with limited arable land available necessitates that the yield of major food crops continues to increase over time. Advances in marker technology, predictive statistics, and breeding methodology have allowed for continued increases in crop performance through genetic improvement. However, one major bottleneck is the generation time of plants, which is biologically limited and has not been improved since the introduction of doubled haploid technology. In this opinion article, we propose to implement in vitro nurseries, which could substantially shorten generation time through rapid cycles of meiosis and mitosis. This could prove a useful tool for speeding up future breeding programs with the aim of sustainable food production.

A new fluorescence-based method for inbred haploid differentiation in maize kernels was developed by utilizing the R1-nj colour marker in combination with fluorescence microspectroscopy and imaging. Seven inbred lines with varying R1-nj expression were used in this study. The fluorescence response of the diploid kernels at the embryonic dye spot was shown to simultaneously exhibit lower intensity and occur at a higher wavelength than the fluorescence of the dye-lacking haploid embryos. Intensity and area thresholds were applied to fluorescence images to sort the haploids from mixed sample populations, and sorting efficiencies of greater than 80% were achieved in all seven inbred lines (with values greater than 90% for five lines). The potential for highthroughput sorting when fluorescence imaging is combined with existing technologies for seed handling as well as high sorting efficiency may make fluorescence a viable and promising alternative to current sorting methods for some inbred lines.Key words: maize -haploid -diploid -fluorescencesortingThe development of new hybrids of maize (Zea mays L.) with desirable traits (e.g. high yield, pest tolerance) is of considerable importance to the global economy, with the 2014 worldwide production estimated at 1.02 billion metric tons (FAO 2014). In modern commercial maize breeding programmes, new hybrids are generated by crossing homozygous inbred lines that have been produced via doubled haploid (DH) line development (Murigneux et al. 1993, R€ ober et al. 2005, Prasanna 2012). Traditional inbred line development via manual self-pollination requires 6-8 generations to produce a highly homozygous line, whereas DH line development only requires 2-3 generations for a 100% homozygous line. This reduction in line development time allows breeders not only to produce more inbred lines in less time, but also to react to new selection targets quickly. In the DH process, the haploid kernels need to be collected from the mixed population as only the haploids are suitable for the next generation of inbreeding.Most methods for haploid kernel selection rely on pioneering work by Nanda and Chase, who used purple embryo marker (PEM) stock as the male parent to produce the anthocyanin marker R1-Navajo (R1-nj) in the progeny kernels (Nanda and Chase 1966). The dominantly inherited R1-nj marker causes a xenia effect resulting in pigmentation of the embryo and on the cap of the aleurone of the seed. As is the case in many angiosperms, sexual reproduction occurs through the double fertilization process where fusion of one sperm and the egg constitutes the union of genetic information that produces the diploid embryo, while union of the second sperm and the central cells develops into triploid endosperm. The DH system takes advantage of the phenomenon of haploid induction. In this case, it is known as maternal haploid induction because haploid embryos contain the cytoplasm of the female (donor) parent and the inducer line is the male in the cross. On average, 10% of double fertilizati...

The use of doubled haploids (DHs) in maize has become ubiquitous in maize breeding programmes as it allows breeders to go from cross to evaluation in as little as 2 years. Two important aspects of the in vivo DH system used in maize are as follows: (i) the identification of haploid progeny and (ii) doubling of the haploid genome to produce fertile inbred lines. This study is focused on the first step. Currently, identification of maize haploid progeny is performed manually using the R1‐nj seed colour marker. This is a labour‐intensive and time‐consuming process; a method for automated sorting of haploids would increase the efficiency of DH line development. In this study, six inbred lines were crossed with the maternal haploid inducer ‘RWS/RWK‐76’ and a sample of seed was sorted manually for each line. Using the VideometerLab 3 system, spectral imaging techniques were applied to discriminate between haploids and hybrids. Using DNA markers to confirm the haploid/diploid state of the tested seed, for the majority of genotypes haploid identification was possible with over 50% accuracy.