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High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications. [ABSTRACT FROM AUTHOR]

Nutritive value (NV) of forage is too time consuming and expensive to measure routinely in targeted breeding programs. Non-destructive spectroscopy has the potential to quickly and cheaply measure NV but requires an intermediate modelling step to interpret the spectral data. A novel machine learning technique for forage analysis, Cubist, was used to analyse canopy spectra to predict seven NV parameters, including dry matter (DM), acid detergent fibre (ADF), ash, neutral detergent fibre (NDF), in vivo dry matter digestibility (IVDMD), water soluble carbohydrates (WSC), and crude protein (CP). Perennial ryegrass (Lolium perenne) was used as the test crop. Independent validation of the developed models revealed prediction capabilities with R2 values and Lin's concordance values reported between 0.49 and 0.82, and 0.68 and 0.89, respectively. Informative wavelengths for the creation of predictive models were identified for the seven NV parameters. These wavelengths included regions of the electromagnetic spectrum that are usually excluded due to high background variation, however, they contain important information and utilising them to obtain meaningful signals within the background variation is an advantage for accurate models. Non-destructive field spectroscopy along with the predictive models was deployed infield to measure NV of individual ryegrass plants. A significant reduction in labour was observed. The associated increase in speed and reduction of cost makes targeting NV in commercial breeding programs now feasible. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Forage plants underpin the livestock industry. Selective breeding, including polyploidization, where genome size is increased by whole genome duplication, changes the productivity and stress tolerance of new varieties. We conducted a growth chamber experiment to investigate the likely responses of Lolium perenne L. to drought, testing four diploid and four tetraploid varieties. We simulated projected spring and summer temperatures for the South-West of England in 2080, applying three projected rainfall scenarios, which varied in drought severity. Drought caused a reduction in productivity, but there was substantial variation between varieties (up to 82%), with the optimal variety changing depending on drought severity. Across three harvests, productivity declined by 43% and 27% (dry biomass) for the severe and likely drought scenarios, respectively. In the final harvest, tetraploids exhibited a greater biomass under severe drought, whereas diploids had a greater biomass under the current rainfall and likely drought scenarios. Longer stomata were observed in tetraploids; however, stomatal conductance was not significantly different between ploidy levels. Trait selection will be important for future drought adaptation. Local climate projections will need to be consulted when selecting L. perenne varieties to tolerate the spatially variable reductions in future rainfall. [ABSTRACT FROM AUTHOR]