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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Z. Z. Jahufer ◽  
Sai Krishna Arojju ◽  
Marty J. Faville ◽  
Kioumars Ghamkhar ◽  
Dongwen Luo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Dry Matter ◽  
New Technologies ◽  
Stochastic Modelling ◽  
Phenotypic Selection ◽  
Breeding Strategy ◽  
Yield Data ◽  
The Impact

AbstractIncreasing 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.

scholarly journals Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs

2020 ◽  
Vol 10 (10) ◽  
pp. 3783-3795
Author(s):  
Hadi Esfandyari ◽  
Dario Fè ◽  
Biructawit Bekele Tessema ◽  
Lucas L. Janss ◽  
Just Jensen
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Breeding Program ◽  
Breeding Programs ◽  
Traditional Program ◽  
Breeding Schemes ◽  
Estimated Breeding Values

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in substantially larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on genomic estimated breeding values. Also, GS allows for reduction in waiting time for the availability of the superior genetic materials from previous cycles, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

scholarly journals Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs

2020 ◽  
Author(s):  
Hadi Esfandyari ◽  
Dario Fè ◽  
Biructawit Bekele Tessema ◽  
Lucas L. Janss ◽  
Just Jensen
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Breeding Program ◽  
Breeding Programs ◽  
Traditional Program ◽  
Breeding Schemes ◽  
Efficient Selection

AbstractGenomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in a significantly larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on GEBV. Also, GS allows for reduction in cycle time, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

scholarly journals A multi-part strategy for introgression of exotic germplasm into elite plant breeding programs using genomic selection

2022 ◽  
Author(s):  
Irene S. Breider ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Steve Thorn ◽  
Manish K. Pandey ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Improvement ◽  
Breeding Strategy ◽  
Breeding Program ◽  
Breeding Programs ◽  
Polygenic Inheritance ◽  
Polygenic Traits

Abstract Some of the most economically important traits in plant breeding show highly polygenic inheritance. Genetic variation is a key determinant of the rates of genetic improvement in selective breeding programs. Rapid progress in genetic improvement comes at the cost of a rapid loss of genetic variation. Germplasm available through expired Plant Variety Protection (exPVP) lines is a potential resource of variation previously lost in elite breeding programs. Introgression for polygenic traits is challenging, as many genes have a small effect on the trait of interest. Here we propose a way to overcome these challenges with a multi-part pre-breeding program that has feedback pathways to optimise recurrent genomic selection. The multi-part breeding program consists of three components, namely a bridging component, population improvement, and product development. Parameters influencing the multi-part program were optimised with the use of a grid search. Haploblock effect and origin were investigated. Results showed that the introgression of exPVP germplasm using an optimised multi-part breeding strategy resulted in 1.53 times higher genetic gain compared to a two-part breeding program. Higher gain was achieved through reducing the performance gap between exPVP and elite germplasm and breaking down linkage drag. Both first and subsequent introgression events showed to be successful. In conclusion, the multi-part breeding strategy has a potential to improve long-term genetic gain for polygenic traits and therefore, potential to contribute to global food security.

Effect of defoliation frequency and summer irrigation on survival of perennial (Lolium perenne) and biennial (Lolium multiflorum) ryegrass in the subtropics

1997 ◽  
Vol 37 (5) ◽  
pp. 537 ◽  
Author(s):  
D. J. Donaghy ◽  
J. M. Scott ◽  
W. J. Fulkerson
Keyword(s):  
Lolium Perenne ◽  
Perennial Ryegrass ◽  
Dry Matter ◽  
Seed Set ◽  
Seedling Recruitment ◽  
Early Summer ◽  
Pasture Production ◽  
Second Year ◽  
The Impact ◽  
Tropical Grass

Summary. The present study investigated, in a subtropical environment, the timing of defoliation treatments in spring and summer irrigation management on the survival of perennial (Lolium perenne cv. Yatsyn) and biennial (L. multiflorum cv. Noble) ryegrass in a mixed ryegrass–white clover (Trifolium repens) pasture over the first summer, and seedling recruitment the following autumn. Defoliation options were related to various ryegrass plant development stages such as the number of leaves per tiller attained during regrowth, stem elongation and seed set. The criterion for timing of frequent defoliation was 1 leaf/tiller regrowth and infrequent defoliation 3 leaves/tiller. Both pasture types were defoliated either frequently or infrequently at specific times from sowing to mid summer. Half the plots were irrigated from 30 November to 6 April while the remaining plots were not irrigated over this period. There was no survival of biennial ryegrass plants into autumn of the second year and pasture production was entirely from seedling recruitment of seed set in the previous spring. The maximum seedling recruitment (plant population 89% of spring in establishment year) was achieved by infrequent defoliation in mid spring and then cessation of defoliation until mid summer to allow plants to set seed. However, this resulted in a production loss of 3094 kg dry matter/ha of ryegrass and clover. In contrast, production of perennial ryegrass in the second year was reliant almost exclusively on individual ryegrass plants surviving the summer, as there was little seed set and virtually no seedling recruitment. There would appear to be 2 contrasting defoliation requirements to optimise perennial ryegrass persistence. Infrequent defoliation from sowing to early spring (22 March–2 September) and frequent defoliation in early summer (19 November–3 February) resulted in maximum plant survival and minimum tropical grass incursion. Frequent, compared with infrequent, defoliation up to 2 September decreased root dry matter in February by 45% to 1.66 g dry matter/plant. However in early summer, frequent defoliation maximised survival, presumably by reducing shading by tropical grasses, and preventing a closed canopy which encourages ‘rust’ infestation of the ryegrass. Irrigation of ryegrass over summer, in situations likely to become waterlogged, will only be of benefit in dry years and if scheduling is frequent enough to benefit ryegrass rather than tropical grass. These results highlight the importance of maintaining an infrequent defoliation interval to maximise persistence of perennial ryegrass in the subtropics. More frequent defoliation may be necessary in late spring/early summer to reduce the impact of leaf rust.

scholarly journals Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Zibei Lin ◽  
Noel O. I. Cogan ◽  
Luke W. Pembleton ◽  
German C. Spangenberg ◽  
John W. Forster ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Breeding Program ◽  
Commercial Breeding

Effects of spring grazing on dryland perennial ryegrass/white clover dairy pastures. 1. Pasture accumulation rates, dry matter consumed yield, and nutritive characteristics

2006 ◽  
Vol 57 (5) ◽  
pp. 543 ◽  
Author(s):  
F. R. McKenzie ◽  
J. L. Jacobs ◽  
G. Kearney
Keyword(s):  
Perennial Ryegrass ◽  
White Clover ◽  
Dry Matter ◽  
Grazing Intensity ◽  
Water Soluble ◽  
Accumulation Rates ◽  
Leaf Stage ◽  
Rate Of Increase ◽  
The Impact ◽  
Over Time

A 3-year experiment (September 1999–August 2002) was conducted in south-western Victoria to determine the impact of spring grazing on pasture accumulation rates, dry matter (DM) consumed yield (estimate of DM yield), and pasture nutritive characteristics [metabolisable energy (ME), crude protein (CP), neutral detergent fibre (NDF), and water-soluble carbohydrates (WSC)] of a perennial ryegrass (Lolium perenne L.)–white clover (Trifolium repens L.) pasture. Spring grazing treatments, applied annually from September to November, were based on ryegrass leaf development stage with high (HF), medium (MF), and low (LF) grazing frequency being 2-, 3-, and 4-leaf stage, respectively, and post-grazing height as the grazing intensity with high (HI), medium (MI), and low (LI) grazing intensity being 3, 5, and 8 cm, respectively. Five combinations were used: HFHI, LFHI, MFMI, HFLI, and LFLI. A sixth treatment, rapid grazing (RG), maintained pasture between 1500 and 1800 kg DM/ha by grazing weekly during spring, and a seventh and eighth treatment, simulating forage conservation for early-cut silage (lock-up for 6–7 weeks; SIL) and late-cut hay (lock-up for 11–12 weeks; HAY), were also included. For the remainder of the year, all plots were grazed at the perennial ryegrass 3-leaf stage of growth, or when pasture mass had reached 2800 kg DM/ha, and grazed to a residual height of 5 cm. On average, pasture accumulation rates ranged from <5 (February–March) to 100–110 kg DM/ha.day (September–October). Overall, SIL resulted in a lower accumulation rate than all other treatments. High spring grazing frequency (including RG) treatments led to more grazing events than medium and low spring grazing frequency treatments. In Years 1, 2, and 3, DM consumed ranged from 9.7 (HAY) to 16.3 (RG), 4.2 (HAY) to 10.1 (HFHI), and 7.3 (SIL) to 10.9 t DM/ha.year (HAY), respectively. HAY resulted in a lower pasture ME content than SIL, HFHI, and LFHI spring grazing, and LFLI spring grazing resulted in a lower pasture ME content than all other treatments except HAY. HFHI grazing resulted in an increase in ME content over time, whereas the rate of increase in ME content over time was higher for LFLI spring grazing than for HAY, RG, and HFLI spring grazing. For all treatments, average pasture ME content ranged from 9.4 (January–February) to 11.4 MJ/kg DM (September). HAY resulted in a lower CP content than all treatments except LFLI grazing. RG resulted in no change in CP content over time. For all treatments, average pasture CP content ranged from a low of 11–14 (January–February) to a high of 24–28% DM (August–September). LFLI grazing resulted in a higher NDF content than HFHI, LFHI, MFMI, and HFLI grazing, while RG resulted in a lower NDF content than LFHI, MFMI, and HFLI. For all treatments, average pasture NDF content ranged from a low of 48–55 (August–September) to a high of 58–62% DM (January–February). All treatments resulted in an increase in pasture WSC content over time. The results demonstrate that frequent and intense grazing management (e.g. HFHI and RG) during spring is important in maintaining high pasture DM yields. Results also indicate positive pasture nutritive characteristic (ME, CP, and NDF) gains with more frequent spring grazing than with infrequent spring grazing. No treatment effect was observed for WSC content.

scholarly journals Modeling Illustrates That Genomic Selection Provides New Opportunities for Intercrop Breeding

2021 ◽  
Vol 12 ◽  
Author(s):  
Jon Bančič ◽  
Christian R. Werner ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Damaris A. Odeny ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Correlation ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Phenotypic Selection ◽  
Breeding Program ◽  
Weed Suppression ◽  
Selection Strategy ◽  
Agricultural Practice ◽  
Breeding Programs

Intercrop breeding programs using genomic selection can produce faster genetic gain than intercrop breeding programs using phenotypic selection. Intercropping is an agricultural practice in which two or more component crops are grown together. It can lead to enhanced soil structure and fertility, improved weed suppression, and better control of pests and diseases. Especially in subsistence agriculture, intercropping has great potential to optimize farming and increase profitability. However, breeding for intercrop varieties is complex as it requires simultaneous improvement of two or more component crops that combine well in the field. We hypothesize that genomic selection can significantly simplify and accelerate the process of breeding crops for intercropping. Therefore, we used stochastic simulation to compare four different intercrop breeding programs implementing genomic selection and an intercrop breeding program entirely based on phenotypic selection. We assumed three different levels of genetic correlation between monocrop grain yield and intercrop grain yield to investigate how the different breeding strategies are impacted by this factor. We found that all four simulated breeding programs using genomic selection produced significantly more intercrop genetic gain than the phenotypic selection program regardless of the genetic correlation with monocrop yield. We suggest a genomic selection strategy which combines monocrop and intercrop trait information to predict general intercropping ability to increase selection accuracy in the early stages of a breeding program and to minimize the generation interval.

scholarly journals Validation of perennial ryegrass cultivar Forage Value Index rankings using independent trial data

2017 ◽  
Vol 79 ◽  
pp. 97-101
Author(s):  
C.I. Ludemann ◽  
C.M. Wims ◽  
D.F. Chapman
Keyword(s):  
Perennial Ryegrass ◽  
Dry Matter ◽  
Yield Data ◽  
Star Rating ◽  
Variety Trial ◽  
Operating Profit ◽  
Breeding Selection ◽  
Using Data ◽  
Forage Value

Abstract The current DairyNZ Forage Value Index (FVI) categorises ryegrass cultivar-endophyte combinations into five, 'star rating' groups for dry matter (DM) yield using data from the National Forage Variety Trial (NFVT) system. However, variability in performance of cultivars between trials raises the question of how cultivars with different star ratings perform against each other under different conditions. The validity of the FVI star rating categories for perennial ryegrass was assessed using cultivar DM yield data from two independent trials outside the NFVT system and under dairy cow grazing with white clover. Results from the trials were used in Monte Carlo simulations to provide a probabilistic determination of the likelihood of high FVI rated cultivars outperforming the low FVI rated cultivars. Results indicate selecting high FVI (5 star) perennial ryegrass cultivars over lower FVI (3 star) cultivars deliver greater contributions to dairy operating profit in over 94% of the simulated iterations for the Waikato and Canterbury. Keywords: Forage Value Index, Lolium perenne, plant breeding, selection, cultivars

Combining seasonal yield, silage dry matter yield, quality and persistency in an economic index to assist perennial ryegrass variety selection

2016 ◽  
Vol 155 (4) ◽  
pp. 556-568 ◽  
Author(s):  
M. O'DONOVAN ◽  
N. MCHUGH ◽  
M. MCEVOY ◽  
D. GROGAN ◽  
L. SHALLOO
Keyword(s):  
Perennial Ryegrass ◽  
Dry Matter ◽  
Production Systems ◽  
Decision Making Process ◽  
Economic Potential ◽  
Yield Data ◽  
Economic Values ◽  
Variety Selection ◽  
Yield Quality ◽  
Yield Threshold

SUMMARYA total economic merit index (Pasture Profit Index, PPI) for perennial ryegrass variety selection was developed to rank perennial ryegrass varieties (Lolium perenneL.) based on their economic potential for grass-based ruminant production systems. The key traits of importance identified were: spring, mid-season (April 11–August 10) and autumn dry matter (DM) yield, first and second cut silage DM yield, grass quality April to July (inclusive) and sward persistency. Variety persistency was quantified by determining the ground score (GS) change across years, which was associated with a yield threshold which triggered sward replacement. Each one-unit decline in GS was associated with a 1683 kg loss in DM yield. Data generated in the Irish recommended list trials for value for cultivation and use were analysed to quantify the relative performance of each variety for each of the aforementioned traits. A previously developed methodology to generate economic values was used with updated price assumptions to develop economic values, which were applied to the analysed performance data of individual varieties. These data were used to estimate the total economic merit of each variety. Thirty-nine varieties were ranked on total economic merit with the highest performing variety (Cv111) generating €213 per ha/year compared withCv201, which was the lowest ranking variety generating −€31 per ha/year. Use of the PPI provides information to end users in relation to the economic merit of one variety over another, facilitating a more informed decision-making process at farm level.

scholarly journals Divergent Genomic Selection for Herbage Accumulation and Days-To-Heading in Perennial Ryegrass

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 340
Author(s):  
Marty Faville ◽  
Mingshu Cao ◽  
Jana Schmidt ◽  
Douglas Ryan ◽  
Siva Ganesh ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Selection Response ◽  
Training Set ◽  
Days To Heading ◽  
Selection For ◽  
Target Environment

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.

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