Harnessing cold adaptation for postglacial colonisation: Galactinol synthase expression and raffinose accumulation in a polyploid and its progenitors.

Allotetraploid white clover (Trifolium repens) formed during the last glaciation through hybridisation of two European diploid progenitors from restricted niches: one coastal, the other alpine. Here, we examine which hybridisation-derived molecular events may have underpinned white clover's postglacial niche expansion. We compared the transcriptomic frost responses of white clovers (an inbred line and an alpine-adapted ecotype), extant descendants of its progenitor species and a resynthesised white clover neopolyploid to identify genes that were exclusively frost-induced in the alpine progenitor and its derived subgenomes. From these analyses we identified galactinol synthase, the rate-limiting enzyme in biosynthesis of the cryoprotectant raffinose, and found that the extant descendants of the alpine progenitor as well as the neopolyploid white clover rapidly accumulated significantly more galactinol and raffinose than the coastal progenitor under cold stress. The frost-induced galactinol synthase expression and rapid raffinose accumulation derived from the alpine progenitor likely provided an advantage during early postglacial colonisation for white clover compared to its coastal progenitor.

Responses to water stress extremes in diverse red clover germplasm accessions.

Red clover (Trifolium pratense L.), a key perennial pastoral species used globally, can strengthen pastural mixes to withstand increasingly disruptive weather patterns from climate change. Breeding selections can be refined for this purpose by obtaining an in-depth understanding of key functional traits. A replicated randomized complete block glasshouse pot trial was used to observe trait responses critical to plant performance under control (15% VMC), water deficit (5% VMC) and waterlogged conditions (50% VMC) in seven red clover populations and compared against white clover. Twelve morphological and physiological traits were identified as key contributors to the different plant coping mechanisms displayed. Under water deficit, the levels of all aboveground morphological traits decreased, highlighted by a 41% decrease in total dry matter and 50% decreases in both leaf number and leaf thickness compared to the control treatment. An increase in root to shoot ratio indicated a shift to prioritizing root maintenance by sacrificing shoot growth, a trait attributed to plant water deficit tolerance. Under waterlogging, a reduction in photosynthetic activity among red clover populations reduced several morphological traits including a 30% decrease in root dry mass and total dry matter, and a 34% decrease in leaf number. The importance of root morphology for waterlogging was highlighted with low performance of red clover: there was an 83% decrease in root dry mass compared to white clover which was able to maintain root dry mass and therefore plant performance. This study highlights the importance of germplasm evaluation across water stress extremes to identify traits for future breeding programs.

Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks.

Breeding higher yielding forage species is limited by current manual harvesting and visual scoring techniques used for measuring or estimation of biomass. Automation and remote sensing for high throughput phenotyping has been used in recent years as a viable solution to this bottleneck. Here, we focus on using RGB imaging and deep learning for white clover (Trifolium repens L.) and perennial ryegrass (Lolium perenne L.) yield estimation in a mixed sward. We present a new convolutional neural network (CNN) architecture designed for semantic segmentation of dense pasture and canopies with high occlusion to which we have named the local context network (LC-Net). On our testing data set we obtain a mean accuracy of 95.4% and a mean intersection over union of 81.3%, outperforming other methods we have found in the literature for segmenting clover from ryegrass. Comparing the clover/vegetation fraction for visual coverage and harvested dry-matter however showed little improvement from the segmentation accuracy gains. Further gains in biomass estimation accuracy may be achievable through combining RGB with complimentary information such as volumetric data from other sensors, which will form the basis of our future work.