Breeding for Grass Seed Yield

Boelt, Birte; Studer, Bruno

doi:10.1007/978-1-4419-0760-8_7

Cited by 40 publications

(46 citation statements)

References 34 publications

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“…Nevertheless, Y 1 was the most important and effective component that contributed to Z significantly (P \ 0.0001) as the coefficients were 0.3769, 0.6295, and 0.4418 in the 3 years, respectively. This finding is in accordance with previous reports in fescues (Boelt and Studer 2010;Wang et al 2005), Russian wildrye (Sun et al 2005;Wang et al 2010), zoysiagrass (Ma et al 2004), bermudagrass (Wu et al 2008), crested wheatgrass (Taghizadeh et al 2008), gramineous plants (Canode 1980;Hampton and Fairey 1998), alfalfa (Rashidi et al 2009), and white clover Hampton and Fairey 1997;Meints et al 2001;Lopes and Franke 2009). Additionally, path analysis uncovered the relationships between the components and the yield that agreed with analogous results (Golparvar and Ghasemi-Pirbalouti 2009;Lopes and Franke 2009;Ozturk and Ada 2009;Sodavadiya et al 2009).…”

Section: Resultssupporting

confidence: 93%

“…A low heritability value for seed yield has been reported in tall fescue (Festuca arundinacea Schreber) (Canode 1980), perennial ryegrass (Hampton and Fairey 1998) and in red fescue (Festuca rubra L.) (Boelt and Studer 2010); however, the heritability of seed weight was somewhat higher than that of seed yield in perennial ryegrass , white clover (Trifolium repens) (Lopes and Franke 2009) and in red fescue (F. rubra L.) (Boelt and Studer 2010). Our data suggest that selection based on Y 1 and Y 2 is expected to be important for the improvement of seed yield in smooth bromegrass.…”

Section: Resultsmentioning

confidence: 97%

“…Seed yield is a quantitative character that is largely influenced by the environment and, hence, has low heritability (Bliss et al 1973;Ofori 1996;Boelt and Studer 2010;Firincioglu et al 2010;Wang et al 2010). Therefore, the response to direct selection for seed yield may be unpredictable unless there is good control for environmental variation.…”

Section: Introductionmentioning

confidence: 99%

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Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

et al. 2011

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Based on multi-factor orthogonally designed field experimental plots, the correlations among yield components as well as their direct and indirect effects on the seed yield of Bromus inermis L. cv. 'Carlton' were investigated. The seed yield parameters fertile tillers/m 2 (Y 1 ), spikelets/fertile tiller (Y 2 ), florets/spikelet (Y 3 ), seed number/spikelet (Y 4 ), seed weight (Y 5 ), and seed yield (Z) were determined by hand in the field for the years 2003-2005. Via ridge regression analysis, a steady algorithmic model of seed yield with its five components was found that could closely estimate the seed yield. The component Y 1 had the largest correlation coefficient with Z, followed by Y 2 . The contributions of the five components to the seed yield in decreasing order are Y 3 . The inter-correlation among the components Y 1 to Y 5 and Z exhibited significance but Y 1 was not correlated with Y 3 . Therefore, direct selection for large Y 1 , Y 2 , and Y 4 would be an effective means of selection for high seed yield in the grass.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

et al. 2011

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“…1 where N may have been limiting (Table 3). Greater tillering early in the growing season could lead to more fertile tillers, greater vegetative biomass, and lower HI which was observed during the 2010 growing season in our study (Table 3) (Cookson et al, 2000;Boelt and Studer, 2010). In Exp.…”

Section: Discussionmentioning

confidence: 53%

“…2 (Tables 3 and 5). Greater N availability early in spring with the single standard NAM vs. the three-split NAM may have resulted in increased early season tillering and growth leading to greater seed yield and vegetative biomass production, and lower 2010 HI (Table 3) (Cookson et al, 2000;Boelt and Studer, 2010). 1 may have occurred because no N was applied to the site in the fall following seeding, resulting in Table 9.…”

Section: Discussionmentioning

confidence: 99%

A Split Application Approach to Nitrogen and Growth Regulator Management for Perennial Ryegrass Seed Production

2013

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Experiments were conducted during 2009 and 2010 in first year perennial ryegrass (Lolium perenne L.) fields under limiting or adequate spring N. Three N application methods (single, two‐split, and three‐split), five growth regulators (split and single applications of prohexadione Ca or trinexapac ethyl) and two spring N rates (56 or 100 kg N ha−1) were evaluated in a split‐split plot design. Under adequate spring N, all N application methods resulted in similar seed yields but when spring N was limiting, a single application of N resulted in up to 14% greater yield vs. the three‐split. The three‐split N application improved relative chlorophyll index (RCI) 6 to 20% late in the harvest season and reduced stem rust (Puccinia graminis Pers. subsp. graminicola) area under the disease progress curve (AUDPC) by 18 to 39% vs. the single N application. Other effects of three‐split N applications observed in 2010 were 8 to 19% less biomass, 2 to 3% greater harvest index (HI), 4% greater seedling vigor, and 25% less lodging. The split prohexadione Ca treatment gave the most consistent results and when vegetative growth was greatest it resulted in a 36% increase in seed yield, a 5.5% increase in HI, 14 cm shorter plants, 70% less lodging, 30% greater RCI, 13% lower stem rust incidence, and 3% lower stem rust severity. Development of a model to optimize rates and timings for split N and growth regulator applications based on growth stage, soil N availability, and growing degree days (GDD) could improve efficacy of split applications.

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Influence of water deficit and defoliation managements on post‐drought recovery and persistence of smooth bromegrass

et al. 2020

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In arid and semiarid regions, successful recovery from drought may be of more economic significance than plant productivity during a dry season. This research was conducted to investigate the effects of pre-drought irrigation and defoliation managements (seed and forage managements) on post-drought recovery of diverse smooth bromegrass (Bromus inermis Leyss.) genotypes, and to identify growth traits associated with persistence and recovery as indices for selecting superior genotypes. Thirty-six genotypes were field evaluated in two separate experiments of seed and forage managements during 2013-2015 under well-watered and deficit irrigation regimes. In the fourth year (2016), irrigation was withheld from both moisture regimes for two months (almost complete senescence) and then plants were re-watered to find the effect of prolonged drought stress on post-drought recovery. Pre-drought irrigation increased persistence of genotypes relative to the well-watered treatment in the seed management experiment. In the seed management trial, genotypes had greater recovery under deficit irrigation when compared to forage management counterpart. There was no significant difference between seed and forage management experiments under the well-watered regime in this respect. Positive correlations were observed between recovery after prolonged drought and total aerial biomass, persistence, and winter growth vigor under both irrigation regimes. Superior genotypes were identified as having more productivity, better recovery, higher persistence, and drought tolerance.

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Breeding for Grass Seed Yield

Cited by 40 publications

References 34 publications

Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

A Split Application Approach to Nitrogen and Growth Regulator Management for Perennial Ryegrass Seed Production

Influence of water deficit and defoliation managements on post‐drought recovery and persistence of smooth bromegrass

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