High-throughput phenotyping of nitrogen and biomass partitioning of central European wheat cultivars and breeding lines during grain filling in different climatic zones
Final Report Abstract
This study verifies our hypothesis that spectral high-throughput approach is suitable for phenotyping of traits of plant dry matter translocation and assimilation and nitrogen translocation and N uptake during grain filling stages of winter wheat. To our knowledge, this belongs to the first reports. The models deriving from spectral measurements to predict the traits of plant dry matter translocation and assimilation and nitrogen translocation and N uptake during grain filling stages of winter wheat can be well validated in independent field experiments with similar growth conditions to those of the calibration models, whereas for validations in independent field studies under contrasting conditions, calibration models should be still further tested and developed. Non-imaging techniques, i.e. non-destructive spectral sensing techniques, are still more reliable and can be applied for high-throughput phenotyping in field compared to imaging methods. Compared with aerial-based imaging techniques, ground-based imaging techniques is still more reliable to assess early vigor, leaf senescence and grain yield of winter wheat. Terrestrially developed spectral algorithms can potentially be transferred to satellite based sensing which will be useful in agriculture to predict relevant traits of nitrogen uptake and grain yield. Based on the current study, it seems that high-throughput phenotyping is progressing faster than genomic analysis as verified in a large population study.
Publications
- (2016) High-throughput phenotyping of wheat and barley plants grown in single or few rows in small plots using active and passive spectral proximal sensing. Sensors. 16. 11. 1860
Barmeier, G; Schmidhalter, U.
(See online at https://doi.org/10.3390/s16111860) - (2016) Referencing laser and ultrasonic height measurements of barley cultivars by using a herbometre as standard. Crop and Pasture Science. 1215-1222
Barmeier, G., Mistele, B., Schmidhalter, U.
(See online at https://doi.org/10.1071/CP16238) - (2017) Digital counts of maize plants by unmanned aerial vehicles (UAVs). Remote Sensing. 9. 6. 544
Gnädinger, F.; Schmidhalter, U.
(See online at https://doi.org/10.3390/rs9060544) - (2017) Evaluation of yield and drought using active and passive spectral sensing systems at the reproductive stage in wheat. Frontiers in Plant Science. 8
Becker, E.; Schmidhalter, U.
(See online at https://doi.org/10.3389/fpls.2017.00379) - (2017) High-throughput field phenotyping of leaves, leaf sheaths, culms and ears of spring barley cultivars at anthesis and dough ripeness. Frontiers in Plant Science. 8
Barmeier, G.; Schmidhalter, U.
(See online at https://doi.org/10.3389/fpls.2017.01920) - (2017) Mid-season prediction of grain yield and protein content of spring barley cultivars using high-throughput spectral sensing. European Journal of Agronomy. 90. 108-116
Barmeier, G.; Hofer, K.; Schmidhalter, U.
(See online at https://doi.org/10.1016/j.eja.2017.07.005) - (2017) Spektrale Erfassung von Merkmalen der Stickstoffeffizienz bei Winterweizen. VDLUFA Schriftenreihe 74, 63-70
Prey, L.; Hu, Y.; Schmidhalter U.
