Phenotyping of plants in competitive but controlled environments: a study of drought response in transgenic wheat.

In recent years, the interest in new technologies for wheat improvement has increased greatly. To screen genetically modified germplasm in conditions more realistic for a field situation we developed a phenotyping platform where transgenic wheat and barley are grown in competition. In this study, we used the platform to (1) test selected promoter and gene combinations for their capacity to increase drought tolerance, (2) test the function and power of our platform to screen the performance of transgenic plants growing in competition, and (3) develop and test an imaging and analysis process as a means of obtaining additional, non-destructive data on plant growth throughout the whole growth cycle instead of relying solely on destructive sampling at the end of the season. The results showed that several transgenic lines under well watered conditions had higher biomass and/or grain weight than the wild-type control but the advantage was significant in one case only. None of the transgenics seemed to show any grain weight advantage under drought stress and only two lines had a substantially but not significantly higher biomass weight than the wild type. However, their evaluation under drought stress was disadvantaged by their delayed flowering date, which increased the drought stress they experienced in comparison to the wild type. Continuous imaging during the season provided additional and non-destructive phenotyping information on the canopy development of mini-plots in our phenotyping platform. A correlation analysis of daily canopy coverage data with harvest metrics showed that the best predictive value from canopy coverage data for harvest metrics was achieved with observations from around heading/flowering to early ripening whereas early season observations had only a limited diagnostic value. The result that the biomass/leaf development in the early growth phase has little correlation with biomass or grain yield data questions imaging approaches concentrating only on the early development stage.

[1]  S. Haefele,et al.  Climate ready rice: Augmenting drought tolerance with best management practices , 2016 .

[2]  Jan U.H. Eitel,et al.  Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality , 2016 .

[3]  Hamid Laga,et al.  RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues , 2015, PloS one.

[4]  P. Langridge,et al.  Constitutive overexpression of the TaNF-YB4 gene in transgenic wheat significantly improves grain yield , 2015, Journal of experimental botany.

[5]  Anuj Srivastava,et al.  Landmark-free statistical analysis of the shape of plant leaves. , 2014, Journal of Theoretical Biology.

[6]  M. Tester,et al.  High-Throughput Phenotyping to Detect Drought Tolerance QTL in Wild Barley Introgression Lines , 2014, PloS one.

[7]  E. Sadoulet,et al.  Flood-tolerant rice reduces yield variability and raises expected yield, differentially benefitting socially disadvantaged groups , 2013, Scientific Reports.

[8]  Peter Langridge,et al.  Optimization of TaDREB3 gene expression in transgenic barley using cold-inducible promoters. , 2013, Plant biotechnology journal.

[9]  P. Langridge,et al.  Complex Regulation by Apetala2 Domain-Containing Transcription Factors Revealed through Analysis of the Stress-Responsive TdCor410b Promoter from Durum Wheat , 2013, PloS one.

[10]  Hendrik Poorter,et al.  Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. , 2012, Functional plant biology : FPB.

[11]  Jinhai Cai,et al.  Novel Image Segmentation Based on Machine Learning and Its Application to Plant Analysis , 2011 .

[12]  B. Mistele,et al.  Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars , 2011 .

[13]  M. Tester,et al.  Genetic analysis of abiotic stress tolerance in crops. , 2011, Current opinion in plant biology.

[14]  P. Langridge,et al.  Improvement of stress tolerance of wheat and barley by modulation of expression of DREB/CBF factors. , 2011, Plant biotechnology journal.

[15]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[16]  Vm Dunbabin,et al.  Simulating the role of rooting traits in crop-weed competition , 2007 .

[17]  J. Passioura.  Viewpoint: The perils of pot experiments. , 2006, Functional plant biology : FPB.

[18]  William R. Raun,et al.  Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation , 2006 .

[19]  John B. Solie,et al.  Late-season Prediction Of Wheat Grain Yield And Grain Protein , 2003 .

[20]  J. Araus,et al.  Plant breeding and drought in C3 cereals: what should we breed for? , 2002, Annals of botany.

[21]  S. S. Sidhu,et al.  The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat , 1991, Experimental Agriculture.

[22]  J. Kleman,et al.  Influence of different nitrogen and irrigation treatments on the spectral reflectance of barley , 1987 .

[23]  Z. Plaut.  The effect of soil moisture tension and nitrogen supply on nitrate reduction and accumulation in wheat seedlings , 1973, Plant and Soil.

[24]  P. Langridge,et al.  The TaDREB3 transgene transferred by conventional crossings to different genetic backgrounds of bread wheat improves drought tolerance. , 2016, Plant biotechnology journal.

[25]  G. Iskakova,et al.  Biolistic transformation of wheat with centrophenoxine as a synthetic auxin. , 2014, Methods in molecular biology.

[26]  Xi Chen,et al.  Evaluation of seven function-known candidate genes for their effects on improving drought resistance of transgenic rice under field conditions. , 2009, Molecular plant.

[27]  John B. Solie,et al.  In‐Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance , 2001 .

[28]  M. Nicolas,et al.  Comparison of Sudden Heat Stress With Gradual Exposure to High Temperature During Grain Filling in Two Wheat Varieties Differing in Heat Tolerance. I. Grain Growth , 1995 .

[29]  N. Otsu.  A Threshold Selection Method from Gray-Level Histograms , 1979, IEEE Trans. Syst. Man Cybern..