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 (R 2 = 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 R 2 = 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. Electronic supplementary material The online version of this article (10.1186/s13007-019-0456-2) contains supplementary material, which is available to authorized users.

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.

Increasing the rate of genetic gain for dry matter (DM) yield in perennial ryegrass (Lolium perenne L.), which is a key source of nutrition for ruminants in temperate environments, is an important goal for breeders. Genomic selection (GS) is a strategy used to improve genetic gain by using molecular marker information to predict breeding values in selection candidates. An empirical assessment of GS for herbage accumulation (HA; proxy for DM yield) and days-to-heading (DTH) was completed by using existing genomic prediction models to conduct one cycle of divergent GS in four selection populations (Pop I G1 and G3; Pop III G1 and G3), for each trait. G1 populations were the offspring of the training set and G3 populations were two generations further on from that. The HA of the High GEBV selection group (SG) progenies, averaged across all four populations, was 28% higher (p < 0.05) than Low GEBV SGs when assessed in the target environment, while it did not differ significantly in a second environment. Divergence was greater in Pop I (43%-65%) than Pop III (10%-16%) and the selection response was higher in G1 than in G3. Divergent GS for DTH also produced significant (p < 0.05) differences between High and Low GEBV SGs in G1 populations (+6.3 to 9.1 days; 31%-61%) and smaller, non-significant (p > 0.05) responses in G3. This study shows that genomic prediction models, trained from a small, composite reference set, can be used to improve traits with contrasting genetic architectures in perennial ryegrass. The results highlight the importance of target environment selection for training models, as well as the influence of relatedness between the training set and selection populations.

Wake vortices produced by the lifting surfaces of large aircraft can have catastrophic effects on aircraft that follow too close behind. Many incidents have been blamed on wingtip vortices in the past several decades. Therefore, vortex detection is important for enhancing airport productivity by allowing adoptive spacing and for increasing the safety of all aircraft operating around the airport by alerting controllers that hazardous conditions may exist near the runways. Many methods have been developed for detecting wake vortices. However, there is a lack of a literature review to summarize all the methods and compare their advantages and drawbacks. Thus, the purpose of this paper is to review these technologies and to summarize their strengths and weaknesses. There are two main methods available in the literature: active and passive detection methods. Active detection methods include LIDAR (LIGHT Detection And Ranging), RADAR (Radio Detection and Ranging), and SODAR (Sonic Detection And Ranging). Passive detection methods include microphone systems, opto-acoustic systems, and ultrasonic detection of circulation. Although vortex detection methods are available, due to military and scientific usage, many researchers are still investigating new methods that are more effective.

Plant breeding has had, and continues to have, an important role in providing farmers with resilient pastures. Early breeding relied on improvement of ecotype populations and this was accelerated by crossing with selected introduced germplasm. The primary traits under selection have targeted speed of establishment, total and/or seasonal dry matter (DM) yield, nutritive value or feed quality, flowering time and reduced aftermath heading, disease resistance, persistence and seed yield. Continued improvement through plant breeding to meet environmental concerns and tolerances to both biotic and abiotic stresses will be achieved through ongoing plant introductions, exploiting heterosis, speed breeding, genomic selection, improvements in phenotyping, metabolomics, improved compatibility with beneficial microbes, and potentially the use of transgenic and gene editing technologies.