We present a method to visually score 10 root architectural traits of the root crown of an adult maize plant in the field in a few minutes. Phenotypic profiling of three recombinant inbred line (RIL) populations of maize (Zea mays L.; B73xMo17, Oh43xW64a, Ny821xH99) was conducted in 2008 in a silt loam soil in Pennsylvania and in a sandy soil in Wisconsin, and again in 2009 in Pennsylvania. Numbers, angles and branching pattern of crown and brace roots were assessed visually at flowering. Depending on the soil type in which plants were grown, sample processing took from three (sand) to 8 min (silt-loam). Visual measurement of the root crown required 2 min per sample irrespective of the environment. Visual scoring of root crowns gave a reliable estimation of values for root architectural traits as indicated by high correlations between measured and visually scored trait values for numbers (r 2 =0.46-0.97), angles (r 2 =0.66-0.76), and branching (r 2 =0.54-0.88) of brace and crown roots. Based on the visual evaluation of root crown traits it was possible to discriminate between populations. RILs derived from the cross NY821 x H99 generally had the greatest number of roots, the highest branching density and the most shallow root angles, while inbred lines from the cross between OH43 x W64a generally had the steepest root angles. The ranking of genotypes remained the same across environments, emphasizing the suitability of the method to evaluate genotypes across environments. Scoring of brace roots was better correlated with the actual measurements compared to crown roots. The visual evaluation of root architecture will be a valuable tool in tailoring crop root systems to specific environments.

Landraces (traditional varieties) of domesticated species preserve useful genetic variation, yet they remain untapped due to the genetic linkage between the few useful alleles and hundreds of undesirable alleles. We integrated two approaches to characterize the diversity of 4,471 maize landraces. First, we mapped genomic regions controlling latitudinal and altitudinal adaptation and identified 1,498 genes. Second, we used F-one association mapping (FOAM) to map the genes that control flowering time, across 22 environments, and identified 1,005 genes. In total, we found that 61.4% of the single-nucleotide polymorphisms (SNPs) associated with altitude were also associated with flowering time. More than half of the SNPs associated with altitude were within large structural variants (inversions, centromeres and pericentromeric regions). The combined mapping results indicate that although floral regulatory network genes contribute substantially to field variation, over 90% of the contributing genes probably have indirect effects. Our dual strategy can be used to harness the landrace diversity of plants and animals.

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The objective of this study was to develop a phenotyping platform for the non-destructive, digital measurement of early root growth of axile and lateral roots and to evaluate its suitability for identifying maize (Zea mays L.) genotypes with contrasting root development. The system was designed to capture images of the root system within minutes and to batch process them automatically. For system establishment, roots of the inbred line Ac7729/TZSRW were grown until nine days after germination on the surface of a blotting paper in pouches. An A4 scanner was used for image acquisition followed by digital image analysis. Image processing was optimized to enhance the separation between the roots and the background and to remove image noise. Based on the root length in diameter-class distribution (RLDD), small-diameter lateral roots and large-diameter axile roots were separated. Root systems were scanned daily to model the growth dynamics of these root types. While the axile roots exhibited an almost linear growth, total lateral root length increased exponentially. Given the determined exponential growth, it was demonstrated that two plants, germinated one day apart but with the same growth rates differed in root length by 100%. From the growth rates we were able to identify contrasting genotypes from 236 recombinant inbred lines (RILs) of the CML444 x SC-Malawi cross. Differences in the growth of lateral roots of two selected RILs were due to differences in the final length and linear density of the primary lateral roots, as proven by the manual reanalysis of the digital images. The high throughput makes the phenotyping platform attractive for routine genetic studies and other screening purposes.

Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best‐performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single‐time‐point data.