Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine

Sadras, Víctor O.; Reynolds, M.P.; Vega, Abelardo J. de la; Petrie, Paul R.; Robinson, R.G.

doi:10.1016/j.fcr.2008.09.004

Cited by 128 publications

(97 citation statements)

References 37 publications

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“…However, the PP estimation method that was used in the current research returns results that are equivalent to those obtained by a common method to quantify PP, which involves models of phenotype vs. environment, where the parameter estimates of the functions are taken as a measure of plasticity [38].…”

Section: Estimation Of Phenotypic Plasticitymentioning

confidence: 99%

Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum aestivum L. Genotypes

Jaradat

2018

Agronomy

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Future challenges to the role of durum and bread wheat in global food security will be shaped by their potential to produce larger yields and better nutritional quality, while increasingly adapting to multiple biotic and abiotic stresses in the view of global climate change. There is a dearth of information on comparative assessment of phenotypic plasticity in both wheat species under long-term multiple abiotic stresses. Phenotypic plasticities of two durum and bread wheat genotypes were assessed under increasing abiotic and edaphic stresses for six years. Combinations of normal and reduced length of growing season and population density, with or without rotation, generated increasing levels of competition for resources and impacted phenotypic plasticity of several plant and yield attributes, including protein and micronutrients contents. All the phenotypic plasticity (PPs) estimates, except for the C:N ratio in both genotypes and grain protein content in T. aestivum genotype, were impacted by abiotic stresses during the second stress phase (PS II) compared with the first (PS I); whereas, covariate effects were limited to a few PPs (e.g., biomass, population density, fertile tillers, grain yield, and grain protein content). Discrimination between factor levels decreased from abiotic phases > growth stages > stress treatments and provided selection criteria of trait combinations that can be positively resilient under abiotic stress (e.g., spike harvest and fertility indices combined with biomass and grain yield in both genotypes). Validation and confirmatory factor models and multiway cluster analyses revealed major differences in phenotypic plasticities between wheat genotypes that can be attributed to differences in ploidy level, length of domestication history, or constitutive differences in resources allocation. Discriminant analyses helped to identify genotypic differences or similarities in the level of trait decoupling in relation to the strength of their correlation and heritability estimates. This information is useful in targeted improvement of traits directly contributing to micronutrient densities, yield components, and yield. New wheat ideotype(s) can be designed for larger grain yield potential under abiotic stress by manipulating yield components that affect kernels m −2 (e.g., number of tillers, number of florets per spikelet, and eventually spike fertility and harvest indices) without impacting nutrient densities and kernel weight, thus raising harvest index beyond its current maximum.

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Section: Estimation Of Phenotypic Plasticitymentioning

confidence: 99%

Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum aestivum L. Genotypes

Jaradat

2018

Agronomy

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“…One alternative to study this phenomenon is to evaluate the stability or phenotypic plasticity of the cultivars (Sadras et al, 2009). The use of FinlayWilkinson regression (Finlay & Wilkinson, 1963) is one way to evaluate yield stability and group cultivars into stable, low or high response cultivars.…”

Section: Performance Stabilitymentioning

confidence: 99%

“…Another approach used to study the GEI has been the use of stability index, including Finlay-Wilkinson regression models proposed by Finlay & Wilkinson (1963). This index, that determines the stability of different cultivars through various evaluation environments, has shown successful results in several species, including sunflower (Sadras et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

González-Barrios¹,

Castro²,

Pérez³

et al. 2018

Span. j. agric. res.

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Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.Additional keywords: genotype by environment interaction; multi-environment trials; sunflower; network efficiency; yield stability. Abbreviations used: CPD (critical percentage difference); GEI (genotype by environment interaction); GGE (genotype plus genotype by environment); LE (La Estanzuela); MET (multi-environment trial); NENSU (National evaluation network of sunflower cultivars of Uruguay); PLS (partial least squares); RCBD (randomized complete block design); YG (Young).

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“…The Donald's ideotype concept have been widely studied and reviewed for a variety of crops and traits, e.g. in plant canopy and leaf architecture modeling (Carvalho et al 1978), in ideotype-based breeding strategies for wheat with genotype×environment (G×E) covariances (Sedgley 1991, de la Vega et al 2002, in crop modeling studies (Boote et al 2001) and in phenotypic plasticity studies for wheat yields (Sadras et al 2009). According to Manuscript received April 2012 Sadras et al (2009) high yield and low plasticity for yield were coupled with early anthesis, long anthesis duration and low plasticity of post-anthesis development with wheat genotypes grown in Mexico.…”

mentioning

confidence: 99%

“…in plant canopy and leaf architecture modeling (Carvalho et al 1978), in ideotype-based breeding strategies for wheat with genotype×environment (G×E) covariances (Sedgley 1991, de la Vega et al 2002, in crop modeling studies (Boote et al 2001) and in phenotypic plasticity studies for wheat yields (Sadras et al 2009). According to Manuscript received April 2012 Sadras et al (2009) high yield and low plasticity for yield were coupled with early anthesis, long anthesis duration and low plasticity of post-anthesis development with wheat genotypes grown in Mexico. In Finland Peltonen et al (1993) applied the Cultivation value model (Weizensorten und Backqualität 1990) to estimate the cultivation scoring and ranking values with adaptation plasticity, cultivation certainty and baking quality components for current high yielding wheat genotypes.…”

mentioning

confidence: 99%

A comparative ideotype, yield component and cultivation value analysis for spring wheat adaptation in Finland

Laurila¹,

Mäkelä²,

Kleemola³

et al. 2012

AFSci

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In this study Mixed structural covariance, Path and Cultivation Value analyses and the CERES-Wheat crop model were used to evaluate vegetation and yield component variation affecting yield potential between different highlatitude (> 60° N lat.) and mid-European (< 60° N lat.) spring wheat (Triticum aestivum L.) genotypes currently cultivated in southern Finland. Path modeling results from this study suggest that especially grains/ear, harvest index (HI) and maximum 1000 kernel weight were significant factors defining the highest yield potential. Mixed and Cultivation value modeling results suggest that when compared with genotypes introduced for cultivation before 1990s, modern spring wheat genotypes have a significantly higher yielding capacity, current high yielding midEuropean genotypes even exceeding the 5 t ha -1 non-potential baseline yield level (y b ). Because of a forthcoming climate change, the new high yielding wheat genotypes have to adapt for elevated temperatures and atmospheric CO 2 growing conditions in northern latitudes. The optimized ideotype profiles derived from the generic high-latitude and mid-European genotypes are presented in the results. High-latitude and mid-European ideotype profiles with factors estimating the effects of concurrent elevated CO 2 and temperature levels with photoperiodical daylength effects can be utilized when designing future high yielding ideotypes adapted to future growing conditions. The CERES-Wheat ideotype modeling results imply, that with new high yielding mid-European ideotypes, the non-potential baseline yield (y b ) would be on average 5150 kg ha -1 level (+ 108 %) vs. new high-latitude ideotypes (y b 4770 kg ha -1 , 100%) grown under the elevated CO 2(700ppm) ×temperature (+3°C) growing conditions projected by the year 2100 climate change scenario in southern Finland.

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Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine

Cited by 128 publications

References 37 publications

Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum aestivum L. Genotypes

Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum aestivum L. Genotypes

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

A comparative ideotype, yield component and cultivation value analysis for spring wheat adaptation in Finland

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