No abstract

Motivation Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. Availability and implementation More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

High-throughput image-phenotyping promises to accelerate the rate of genetic improvement in plant breeding through varietal selections informed by longitudinal growth models. To facilitate routine analyses and to drive breeding decisions, data integration is critical for effective management of germplasm, field experiment design, phenotyping, tissue sampling, genotyping, aerial-phenotyping campaigns, image files, and geo-spatial information. To this end, ImageBreed provides a software solution for end-to-end image-based phenotyping integrated into the Breedbase plant breeding system. ImageBreed provides open-source orthophotomosaic construction for raw image captures from standard color cameras and from the MicaSense Red-Edge multispectral camera. Additionally, previously assembled orthophotomosaic raster images can be uploaded. Orthophotomosaic images allow for streamlined extraction of plot-polygon images; however, ImageBreed plot-polygon images can also be extracted directly from raw aerial image captures. A web-database interface streamlines assignment of plot-polygon images from the orthophotomosaic or raw aerial-captures to the field experiment design. Image processes spanning Fourier-transform filtering, thresholding, and vegetation index masking are applied to reduce noise in extracted phenotypes. Summary-statistic phenotypic values are extracted for every observed plot-polygon image using a structured ontology. Plot-polygon images are queryable against genotypic, phenotypic, and experimental design information for training of machine learning models and Abbreviations: API, application programming interface; CNN, convolutional neural network; FT-HPF, Fourier transform high-pass filter; FT-LPF, Fourier transform low-pass filter; ND, Chado Natural Diversity Database Schema; NDRE, normalized difference red-edge vegetation index; NDVI, normalized difference vegetation index; TGI, triangular greenness index; UAV, unoccupied aerial vehicle; VARI, visible atmospherically resistant index. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

No abstract

No abstract

Visible and near-infrared spectroscopy (Vis-NIRS) is a promising tool for increasing phenotyping throughput in plant-breeding programs, but existing analysis software packages are not optimized for a breeding context. Additionally, commercial software options are often outside of budget constraints for some breeding and research programs. To that end, we developed an open-source R package, waves, for the streamlined analysis of spectral data with several cross-validation schemes to assess prediction accuracy. waves is compatible with a wide range of spectrometer models and performs visualization, filtering, aggregation, cross-validation set formation, model training, and prediction functions for the association of Vis-NIR spectra with reference measurements. Furthermore, we have integrated this package into the Breedbase family of open-source databases, expanding the analysis capabilities of this growing digital ecosystem to a number of crop species. Taken together, the standalone and Breedbase versions of waves enhance the accessibility of tools for the analysis of spectral data during the plant breeding process.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

SummaryCassava is an important staple crop in sub‐Saharan Africa, due to its high productivity even on nutrient poor soils. The metabolic characteristics underlying this high productivity are poorly understood including the mode of photosynthesis, reasons for the high rate of photosynthesis, the extent of source/sink limitation, the impact of environment, and the extent of variation between cultivars. Six commercial African cassava cultivars were grown in a greenhouse in Erlangen, Germany, and in the field in Ibadan, Nigeria. Source leaves, sink leaves, stems and storage roots were harvested during storage root bulking and analyzed for sugars, organic acids, amino acids, phosphorylated intermediates, minerals, starch, protein, activities of enzymes in central metabolism and yield traits. High ratios of RuBisCO:phosphoenolpyruvate carboxylase activity support a C3 mode of photosynthesis. The high rate of photosynthesis is likely to be attributed to high activities of enzymes in the Calvin–Benson cycle and pathways for sucrose and starch synthesis. Nevertheless, source limitation is indicated because root yield traits correlated with metabolic traits in leaves rather than in the stem or storage roots. This situation was especially so in greenhouse‐grown plants, where irradiance will have been low. In the field, plants produced more storage roots. This was associated with higher AGPase activity and lower sucrose in the roots, indicating that feedforward loops enhanced sink capacity in the high light and low nitrogen environment in the field. Overall, these results indicated that carbon assimilation rate, the K battery, root starch synthesis, trehalose, and chlorogenic acid accumulation are potential target traits for genetic improvement.

Key message Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Abstract Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.