Multi-scale modelling predicts plant stem bending behaviour in response to wind to inform lodging resistance.

Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.

Mapping crown rust resistance at multiple time points in elite oat germplasm.

Crown rust, caused by Puccinia coronata f. sp. avenae Erikss., is the most important disease impacting cultivated oat (Avena sativa L.). Genetic resistance is the most desirable management strategy. The genetic architecture of crown rust resistance is not fully understood, and previous mapping investigations have mostly ignored temporal variation. A collection of elite oat lines sourced from oat breeding programs in the American Upper Midwest and Canada was genotyped using a high-density genotyping-by-sequencing system and evaluated for crown rust disease severity at multiple time points throughout the growing season in three disease nursery environments. Genome-wide association mapping was conducted for disease severity on each observation date of each trial, area under the disease progress curve for each trial, heading date for each trial, and area under the disease progress curve in a multi-environment model. Crown rust resistance quantitative trait loci (QTL) were detected on linkage groups Mrg05, Mrg12, Mrg15, Mrg18, Mrg20, and Mrg33. None of these QTL were coincident with a days-to-heading QTL detected on Mrg02. Only the QTL detected on Mrg15 was detected in multiple mapping models. The QTL on Mrg05, Mrg12, Mrg18, Mrg20, and Mrg33 were detected on only a single observation date and were not detected on observations just days before and after. This result uncovers the importance of temporal variation in mapping experiments which is usually ignored. It is possible that high density temporal data could be used to more precisely characterize the nature of plant resistance in other systems.

Quantifying cereal crop movement through hemispherical video analysis of agricultural plots.

BACKGROUND: Violent movement of crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly detrimental to cool-season cereals such as oat, barley, and wheat; contributing to yield and economic losses. Phenotyping the movement of cereal crops in real-time could aid in the breeding and selecting of lodging resistant cereals. Since no methods exist to quantify dynamic, real time plant responses in an agricultural setting, we devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system. RESULTS: We present both the image analysis method for identifying predefined regions of a 2D field design as they appear on 360° field of view video, as well as a signal processing pipeline to quantify movement from time varying color signals from plot canopies within these predefined field regions. We detected significant differences in the natural frequency and amplitude of plant movement from video of 16 cereal cultivars planted in a randomized complete block design on five different windy days. Natural frequencies quantified by this method averaged 1.37 Hz, while over 2.5-fold differences in amplitude within similar frequency ranges were detected across the 16 cereal cultivars. CONCLUSIONS: This method is sensitive enough to systematically differentiate small frequency and amplitude differences in cultivar movement, and shows promise for investigating the physiological basis for differences in cereal movement and lodging resistance. The relative accuracy of the plot demarcation protocol suggests it could be used for other high-throughput phenotyping applications that require both high image resolution and a large field of view.