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Biology subjects

Ewers, B. E.

Publications and source records attributed to Ewers, B. E..

2 recordsLinked to original sources

Reduced leaf numbers due to shade avoidance allows sugar beet to maintain biomass under drought stress

The physical environment is complex even in managed agroecosystems, and plants often experience multiple stressors such as light competition and drought simultaneously. Phenotypic responses to multiple stressors may exhibit tradeoffs or constraints, such that harvestable yield is reduced In this study, we investigate how sugar beets respond to combinations of neighbor presence, resource competition, and water stress, and test the hypothesis that shade avoidance cues from neighboring vegetation may cause yield loss similar to, or even greater than, resource depletion by neighboring vegetation. Sugar beets were grown in 19L pails surrounded by Kentucky bluegrass (Poa pratiensis). Competition treatments included "shade avoidance only" where B. vulgaris leaves were exposed to reflected light from neighbors without shading or root competition, "shade avoidance plus root interaction" where crop and competitor roots interacted, and "no competition" as a control. Sugar beets in each competition treatment were subjected to 3 levels of irrigation: 100% of estimated evapotranspiration (ET), 80% ET and 60% ET. When grown without drought stress (100% ET), the shade avoidance treatment had 16% less root biomass compared to the no competition control (P = 0.007); however, under the most severe drought treatment (60% ET) root biomass was similar between the shade avoidance and no competition treatments (P = 0.48). Under 60% ET, plants in the shade avoidance only treatment produced 15% fewer leaves (P < 0.001) but 14% greater leaf area (P = 0.049) compared to the no competition control. The reduced number of leaves produced due to shade avoidance resulted in less structural tissue to be maintained under drought stress, and thus provides a potential benefit to shade avoidance responses that are not directly related to avoiding shade.

plant biology↗

A framework for genomics-informed ecophysiological modeling in plants

Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first-principles based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intra-specific variation. We develop a new canopy growth sub-model for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a Recombinant Inbred Line population, and explore model performance on an in silico population under non-stressed and mild water stressed conditions. We find evidence that the updated whole plant model has capacity to distill genotype by environment interaction (G x E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of un-phenotyped, genetically-related individuals under un-tested environmental scenarios.

plant biology↗