An improved reference genome for Trifolium subterraneum L. provides insight into molecular diversity and intra-specific phylogeny.

Subterranean clover (Trifolium subterraneum L., Ts) is a geocarpic, self-fertile annual forage legume with a compact diploid genome (n = x = 8, 544 Mb/1C). Its resilience and climate adaptivity have made it an economically important species in Mediterranean and temperate zones. Using the cultivar Daliak, we generated higher resolution sequence data, created a new genome assembly TSUd_3.0, and conducted molecular diversity analysis for copy number variant (CNV) and single-nucleotide polymorphism (SNP) among 36 cultivars. TSUd_3.0 substantively improves prior genome assemblies with new Hi-C and long-read sequence data, covering 531 Mb, containing 41,979 annotated genes and generating a 94.4% BUSCO score. Comparative genomic analysis among select members of the tribe Trifolieae indicated TSUd 3.0 corrects six assembly-error inversion/duplications and confirmed phylogenetic relationships. Its synteny with T. pratense, T. repens, Medicago truncatula and Lotus japonicus genomes were assessed, with the more distantly related T. repens and M. truncatula showing higher levels of co-linearity with Ts than between Ts and its close relative T. pratense. Resequencing of 36 cultivars discovered 7,789,537 SNPs subsequently used for genomic diversity assessment and sequence-based clustering. Heterozygosity estimates ranged from 1% to 21% within the 36 cultivars and may be influenced by admixture. Phylogenetic analysis supported subspecific genetic structure, although it indicates four or five groups, rather than the three recognized subspecies. Furthermore, there were incidences where cultivars characterized as belonging to a particular subspecies clustered with another subspecies when using genomic data. These outcomes suggest that further investigation of Ts sub-specific classification using molecular and morpho-physiological data is needed to clarify these relationships. This upgraded reference genome, complemented with comprehensive sequence diversity analysis of 36 cultivars, provides a platform for future gene functional analysis of key traits, and genome-based breeding strategies for climate adaptation and agronomic performance. Pangenome analysis, more in-depth intra-specific phylogenomic analysis using the Ts core collection, and functional genetic and genomic studies are needed to further augment knowledge of Trifolium genomes.

Prospects for Trifolium Improvement Through Germplasm Characterisation and Pre-breeding in New Zealand and Beyond.

Trifolium is the most used pastoral legume genus in temperate grassland systems, and a common feature in meadows and open space areas in cities and parks. Breeding of Trifolium spp. for pastoral production has been going on for over a century. However, the breeding targets have changed over the decades in response to different environmental and production pressures. Relatively small gains have been made in Trifolium breeding progress. Trifolium breeding programmes aim to maintain a broad genetic base to maximise variation. New Zealand is a global hub in Trifolium breeding, utilising exotic germplasm imported by the Margot Forde Germplasm Centre. This article describes the history of Trifolium breeding in New Zealand as well as the role and past successes of utilising genebanks in forage breeding. The impact of germplasm characterisation and evaluation in breeding programmes is also discussed. The history and challenges of Trifolium breeding and its effect on genetic gain can be used to inform future pre-breeding decisions in this genus, as well as being a model for other forage legumes.

Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

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.

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning.

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70-100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.).

BACKGROUND: In-field measurement of yield and growth rate in pasture species is imprecise and costly, limiting scientific and commercial application. Our study proposed a LiDAR-based mobile platform for non-invasive vegetative biomass and growth rate estimation in perennial ryegrass (Lolium perenne L.). This included design and build of the platform, development of an algorithm for volumetric estimation, and field validation of the system. The LiDAR-based volumetric estimates were compared against fresh weight and dry weight data across different ages of plants, seasons, stages of regrowth, sites, and row configurations. RESULTS: The project had three phases, the last one comprising four experiments. Phase 1: a LiDAR-based, field-ready prototype mobile platform for perennial ryegrassrecognition in single row plots was developed. Phase 2: real-time volumetric data capture, modelling and analysis software were developed and integrated and the resultant algorithm was validated in the field. Phase 3. LiDAR Volume data were collected via the LiDAR platform and field-validated in four experiments. Expt.1: single-row plots of cultivars and experimental diploid breeding populations were scanned in the southern hemisphere spring for biomass estimation. Significant (P < 0.001) correlations were observed between LiDAR Volume and both fresh and dry weight data from 360 individual plots (R2 = 0.89 and 0.86 respectively). Expt 2: recurrent scanning of single row plots over long time intervals of a few weeks was conducted, and growth was estimated over an 83 day period. Expt 3: recurrent scanning of single-row plots over nine short time intervals of 2 to 5 days was conducted, and growth rate was observed over a 26 day period. Expt 4: recurrent scanning of paired-row plots over an annual cycle of repeated growth and defoliation was conducted, showing an overall mean correlation of LiDAR Volume and fresh weight of R2 = 0.79 for 1008 observations made across seven different harvests between March and December 2018. CONCLUSIONS: Here we report development and validation of LiDAR-based volumetric estimation as an efficient and effective tool for measuring fresh weight, dry weight and growth rate in single and paired-row plots of perennial ryegrass for the first time, with a consistently high level of accuracy. This development offers precise, non-destructive and cost-effective estimation of these economic traits in the field for ryegrass and potentially other pasture grasses in the future, based on the platform and algorithm developed for ryegrass.

Predicting the quality of ryegrass using hyperspectral imaging.

BACKGROUND: The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550-1700 nm wavelengths were obtained from 185 accessions of ryegrass (Lolium perenne), which were also analysed for 13 forage quality attributes. RESULTS: Medium to high predictive power was observed for the HSI models of total sugars (R2 validation of 0.58), high molecular weight sugars (R2 validation of 0.63), %Ash (R2 validation of 0.50) and %Nitrogen (R2 validation of 0.70). Significant HSI models with low R2 validation of 0.1-0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated. CONCLUSION: This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.

Review: New sensors and data-driven approaches-A path to next generation phenomics.

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

Camelina (Camelina sativa (L.) Crantz) as an alternative oilseed: molecular and ecogeographic analyses.

Camelina (Camelina sativa (L.) Crantz) is an oilseed known for its potential as a low-input biofuel feedstock and its high levels of beneficial fatty acids. We investigated the role of geographical origin in genetic variation and fatty acid content, expecting to find significant variability among 53 accessions and a link between ecogeography and both origin and key oil traits. Amplified fragment length polymorphism (AFLP) fingerprinting revealed high levels of diversity within the 53 accessions. Even though sampling was relatively biased towards the Russian-Ukrainian area, this region was identified as a genetic diversity hotspot and possible centre of origin for camelina. The accessions were categorized by principal coordinate analysis using molecular marker data, enabling identification of links between geographical distribution and these categories. The influence of geographic location on four canola oil quality measures in camelina was evaluated using a geographic information system. These measures were (1) more than 30% alpha-linolenic acid, (2) less than 3% erucic acid, (3) less than 10% saturated fatty acids, and (4) a ratio of alpha-linolenic to linoleic acid greater than 1. The results clearly confirm that camelina oil quality characteristics are strongly influenced by environmental factors. The unprecedented high genetic diversity in this group of accessions offers an excellent opportunity to investigate valuable genes for successful adaptation of camelina to specific ecogeographical conditions such as drought.