Phenomics analysis of drought responses in Miscanthus collected from different geographical locations

Abstract

Miscanthus is a genus of C4 perennial grasses capable of high biomass potential even in temperate regions making it an ideal industrial crop for the renewable supply of energy and chemicals. Yield is strongly linked to water availability, and many environments have limited water supply where otherwise irradiation and temperature are favourable. A total of 47 Miscanthus genotypes, diverse regarding collection site and genotype, were screened in a high-throughput phenomics facility under drought to generate high-quality time-course data for biomass accumulation and water use. Plants were subjected to three treatments: a watered control, mild drought (20% of field capacity) and a severe drought (water completely withdrawn). Daily visual spectrum images were calibrated to harvested biomass and used to assess biomass accumulation over the experiment. Image analyses were used to determine growth and senescence as functions of time and treatment, plant survival and to relate responses to geographical data. An accurate prediction of plant biomass (R 2 = 0.92***) was made by comparing actual harvested biomass and projected shoot area. Dynamic responses in senescence between the multiple genotypes under the three treatments demonstrated stay-green and senescence responses were not associated with species. Microclimate/geographical modelling indicated that origin of genotype was associated with drought tolerance and this helped explain the different responses within the same species. Water-use efficiency (WUE), the amount of dry biomass accumulated per kg of water, correlated with summer rainfall. Phenomic analysis of drought responses was shown to have the potential to improve the selection of breeding candidates in Miscanthus and has identified interesting Miscanthus genotypes combining high biomass and high WUE for further characterization.

Breeding progress and preparedness for mass‐scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar

“…Extensive environmental measurements of soil and climate, combined with growth monitoring, are being used to understand abiotic stresses (Nunn et al, ; Van der Weijde, Huxley, et al, ) and develop genotype‐specific scenarios similar to those reported earlier in Hastings et al (). Phenomics experiments on drought tolerance have been conducted on wild and improved germplasm (Malinowska, Donnison, & Robson, ; Van der Weijde, Huxley, et al, ). Recently produced interspecific hybrids displaying exceptional yield under drought (~30% greater than control Mxg ) in field trials in Poland and Moldova are being further studied in detail in the phenomics and genomics facility at Aberystwyth to better understand gene–trait associations which can be fed back into breeding.…”

Breeding Strategies to Improve Miscanthus as a Sustainable Source of Biomass for Bioenergy and Biorenewable Products

“…A growth model was developed and refined that takes meteorological and soil data as input and estimates yield at a spatial resolution of 1 ha. This model was largely developed and parameterised with data from field trials growing M × g. Controlled environments and field experiments have demonstrated that M × g is sensitive to drought stress in particular, but also chilling stress and that accessions that yield more under abiotic stress have been identified [23,24]. Thus there is identified genetic potential for improvements over the current commercial standard in particular stressed environments.…”

Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data

“…Leveraging economies of scale and using standardised procedures, high-throughput phenotyping screens addresses these challenges and have been applied in biological screening of chemical compound libraries, agricultural evaluation of crop plants, genome-wide CRISPR-based mutagenic cell line screens and multi-centre phenotypic screening of mutated model organisms [6][7][8][9][10][11][12][13] . The continuous generation of large volumes of data introduces new challenges affecting automated approaches to statistical analysis that have to scale with increasing data and address the underlying complexity inherent in large projects [14][15][16][17] .…”