Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield

2000 ◽  
Vol 51 (2) ◽  
pp. 197 ◽  
Author(s):  
S. C. Chapman ◽  
M. Cooper ◽  
D. G. Butler ◽  
R. G. Henzell
Keyword(s):  
Variance Components ◽  
Genetic Correlations ◽  
Target Population ◽  
Breeding Strategies ◽  
Random Set ◽  
Crop Water ◽  
Genotype By Environment ◽  
Eastern Australia ◽  
North Eastern ◽  
Seasonal Weather

Past sorghum hybrid trials in north-eastern Australia have detected substantial genotype by environment (G×E) interactions for yield in sampling a variable target population of environments (TPE) that is affected by spatial and seasonal differences in crop water supply. Three datasets, comprising yields of commercial and final stage experimental hybrids and covering 9–17 years (Y) and up to 30 locations (L), were analysed to quantify variance components for trial error, genotypic (σ2g), and G×E (σ2gl, σ;2gy, and σ2gly) interaction effects. Whereas trial means varied 2–3-fold across seasons, a greater range was estimated for variance components of trial error (range of 0.05–0.5), G (0–>0.3), and G×L interaction (0.05–>1.0). There was substantial seasonal variation in the ratio of σ2g to (σ2g +σ2gl), and in two datasets, 73% of the seasonal σ2gl was due to poor genetic correlations among locations. This implies that any given set of hybrids in a random set of locations would be ranked differently from season to season. Analysis of locations over years detected 90% of the total G×E interaction as G×L×Y, rather than G×L or G×Y, although this was reduced by accounting for genotype maturity. To achieve repeatabilities of >80%, trials would need to be conducted over at least 5 years and 20 locations per year. The variable and unpredictable nature of much of the G×E interaction in the region implies that broad adaptation to different water regimes is required, unless prior knowledge of the seasonal weather can be used to choose ‘narrowly adapted’ cultivars. With current approaches, a large sample of environments is needed to identify such hybrids, and testing across locations and years is equally important. Alternative breeding strategies based on classifying environment types are discussed.

Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments

2002 ◽  
Vol 53 (4) ◽  
pp. 379 ◽  
Author(s):  
Scott C. Chapman ◽  
Mark Cooper ◽  
Graeme L. Hammer
Keyword(s):  
Cropping Systems ◽  
Target Population ◽  
Genotype By Environment Interaction ◽  
Expected Improvement ◽  
Stress Index ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Systems Model ◽  
Water Stress Index ◽  
Eastern Australia

Multi-environment trials (METs) used to evaluate breeding lines vary in the number of years that they sample. We used a cropping systems model to simulate the target population of environments (TPE) for 6 locations over 108 years for 54 ‘near-isolines’ of sorghum in north-eastern Australia. For a single reference genotype, each of 547 trials was clustered into 1 of 3 ‘drought environment types’ (DETs) based on a seasonal water stress index. Within sequential METs of 2 years duration, the frequencies of these drought patterns often differed substantially from those derived for the entire TPE. This was reflected in variation in the mean yield of the reference genotype. For the TPE and for 2-year METs, restricted maximum likelihood methods were used to estimate components of genotypic and genotype by environment variance. These also varied substantially, although not in direct correlation with frequency of occurrence of different DETs over a 2-year period. Combined analysis over different numbers of seasons demonstrated the expected improvement in the correlation between MET estimates of genotype performance and the overall genotype averages as the number of seasons in the MET was increased.

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

2005 ◽  
Vol 56 (9) ◽  
pp. 947 ◽  
Author(s):  
Graeme L. Hammer ◽  
Scott Chapman ◽  
Erik van Oosterom ◽  
Dean W. Podlich
Keyword(s):  
Complex Traits ◽  
Crop Improvement ◽  
Target Population ◽  
Breeding Strategies ◽  
Stay Green ◽  
Gene Effects ◽  
Component Trait ◽  
Phenotypic Complexity ◽  
Eastern Australia ◽  
Genetic Systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits—phenology, osmotic adjustment, transpiration efficiency, stay-green—and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location–season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments

2000 ◽  
Vol 51 (2) ◽  
pp. 223 ◽  
Author(s):  
S. C. Chapman ◽  
G. L. Hammer ◽  
D. G. Butler ◽  
M. Cooper
Keyword(s):  
Soil Type ◽  
Target Population ◽  
Soil Types ◽  
Stress Index ◽  
Variable Environments ◽  
Eastern Australia ◽  
Variable Nature ◽  
North Eastern ◽  
Farm Profit ◽  
The Difference

The variable nature of rainfall in north-eastern Australia confounds the process of selecting sorghum hybrids that are broadly adapted. This paper uses a crop simulation model to characterise the drought environment types (ET) that occur in the target population of environments (TPE) for dryland sorghum. Seventy seasons (1921–1990) of simulations of the yield of a sorghum genotype and the associated within-season sequence of a stress index were conducted for a small TPE of 6 locations and also for a large TPE of 211 locations that attempted to represent the entire sorghum region. Previously, using the small dataset of 6 locations, pattern analysis enabled us to group seasonal stress indices from each trial into major ETs: ‘low terminal stress’ (ET1), severe terminal stress (ET2), and intermediate mid-season/terminal stress (ET3) in the ratio 33 : 38 : 29. When the dataset was broken into a sequence of 16 multi-environment trials (METs), each of 3 years and 6 locations, the ratios of ET1 : ET2 : ET3 differed greatly among METs, i.e. any single MET was not randomly sampling the TPE. Hence, for any MET, the average yield (GVu) was not the same as the overall mean of the entire 70-year dataset. If the trial yields were weighted according to the ratio of ET1 : ET2 : ET3 in the overall TPE, then GVw (s.d. = 0.13) for a single MET was much closer to the overall mean than was GVu (0.38). For different METs, the values of GVw were up to 30% higher or 15% lower than GVu. Across METs, the difference between GVu and GVw was positively correlated (r = 0.88, n = 16, P < 0.05) with the frequency of ET1 (‘low terminal stress’) encountered within the MET and negatively correlated (r = −0.82) with the frequency of ET2. The value of weighting was confirmed by its ability to verify that two simulated genotypes had the same mean yield over many trials, even though they differed in their specific adaptation to the different ETs. The large TPE consisted of more than 15 000 simulations and was classified in 2 stages (within/among locations), repeated for each of 3 soil types. In years in which the simulation sowing criteria were met, the ratios of ET1 : ET2 : ET3 were about 4:2:4, 4:5:1, and 6:3:1 in the shallow, intermediate, and deep soils, respectively. Hence, over all soil types and locations, the sorghum TPE for northern Australia consists of at least 30% each of low terminal stress (ET1) or severe terminal stress (ET2) and these environment types need to be sampled. The incidence and nature of the ‘intermediate midseason/terminal stress’ environment type (ET3) varies with soil type and location. Weighting genotype performance should improve the precision of the estimate of its broadly adapted value, and be of practical use in breeding programs in these variable environments. Although the ‘boundary conditions’ of the TPE are not yet resolved, this paper also shows that simulation and pattern analyses can be used to determine the structure of the abiotic TPE. Taking other factors into account (e.g. soil type distribution, shire production levels, and farm profit), selection trials could be weighted to improve selection for narrow or broad adaptation, depending on the purpose of the breeding program.

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of Pinus taeda L. in the Southern USA

2020 ◽  
Author(s):  
Edwin Lauer ◽  
Andrew Sims ◽  
Steven McKeand ◽  
Fikret Isik
Keyword(s):  
Genetic Variation ◽  
Pinus Taeda ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Breeding Program ◽  
Model Fit ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Progeny Tests ◽  
Pinus Taeda L

Abstract Genetic parameters were estimated using a five-series multienvironment trial of Pinus taeda L. in the southern USA. There were 324 half-sib families planted in five test series across 37 locations. A set of six variance/covariance matrices for the genotype-by-environment (G × E) effect for tree height and diameter were compared on the basis of model fit. In single-series analysis, extended factor analytical models provided generally superior model fit to simpler models for both traits; however, in the combined-series analysis, diameter was optimally modeled using simpler variance/covariance structures. A three-way compound term for modeling G × E interactions among and within series yielded substantial improvements in terms of model fit and standard errors of predictions. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some G × E interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range. Latent factor regression revealed a small number of parents with large factor scores that changed ranks significantly between southern and northern environments. Study Implications Multienvironmental progeny tests of loblolly pine (Pinus taeda L.) were established over 10 years in the southern United States to understand the genetic variation for the traits of economic importance. There was substantial genetic variation between open-pollinated families, suggesting that family selection would be efficient in the breeding program. Genotype-by-environment interactions were negligible among sites in the deployment region but became larger between sites at the extremes of the distribution. The data from these trials are invaluable in informing the breeding program about the genetic merit of selection candidates and their potential interaction with the environment. These results can be used to guide deployment decisions in the southern USA, helping landowners match germplasm with geography to achieve optimal financial returns and conservation outcomes.

Freshwater Cyanobacteria of North-Eastern Australia: 2. Chroococcales

Phytotaxa ◽  
2013 ◽  
Vol 133 (1) ◽  
pp. 1 ◽  
Author(s):  
GLENN B. MCGREGOR
Keyword(s):  
Light Microscopy ◽  
Natural Populations ◽  
Northern Territory ◽  
Detailed Account ◽  
Ecological Data ◽  
Eastern Australia ◽  
North Eastern ◽  
Rivers And Streams ◽  
Original Illustrations ◽  
Freshwater Habitats

This volume provides the first detailed account of the Chroococcales of north-eastern Australia. It provides keys, morphological and ecological data for 6 families, 33 genera and 112 species, and photomicrographs and original illustrations to enable the identification of natural populations based on stable and recognizable characters observable with the aid of light microscopy. Distributional data are based on extensive surveys at 270 sites representing the major freshwater habitats including rivers and streams, palustrine and lacustrine wetlands, thermal springs, and man-made reservoirs in Queensland and the Northern Territory as well as a review of the Australian phycological literature. 

Some physical and chemical features of two upland crater lakes in tropical north-eastern Australia

1999 ◽  
Vol 50 (2) ◽  
pp. 159 ◽  
Author(s):  
D. Walker
Keyword(s):  
Thermal Stability ◽  
Meteorological Data ◽  
Euphotic Zone ◽  
Open Water ◽  
Crater Lakes ◽  
Ground Temperatures ◽  
Eastern Australia ◽  
North Eastern ◽  
Physical And Chemical ◽  
Continuous Deposition

Lakes Barrine and Eacham, ~1.0 and 0.5 km2 area, 67 and 63 m depth respectively, lie at ~740 m a.s.l., ~17°S in north-eastern Australia. Seasonal changes in their volumes modelled from meteorological data correspond well with observations at Eacham. Temperature profiles through 6 years show summer stratification with a metalimnion at 20–30 m; in winter, near isothermy is usually attained. At Barrine, thermal stability varies between winter and summer (<500 and >4000 g-cm cm-2 respectively). Mixing is related to low ground temperatures during periods of generally low thermal stability; exceptionally it penetrates to >60 m. Oxygen saturation decreases from the surface to ~20% at the base of the euphotic zone (15–21 m) but oxygen is carried lower by mixing after which anoxia commonly rises to ~40 m. At Barrine, Fe-reducing redox (<200 mV) usually occurs below 50 m, but during mixing this boundary falls to within 1 m of the mud–water interface. The Barrine solution is dilute (total dissolved solids 55–58 mg L-1), and that of Eacham is more so. A concentrated monimolimnion has developed in the lowermost 2–3 m at Barrine but not at Eacham. Sedimentation at the middle of each lake results from the continuous deposition of open-water products punctuated by the redistribution of coarser detritus from the ‘shallows’ at times of deep mixing. The resultant laminations are preserved only at Barrine, protected by the chemical stability of the monimolimnion.

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