ORCID Profile
0000-0001-5486-6625
Current Organisation
CSIRO
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Publisher: Elsevier BV
Date: 05-2019
DOI: 10.1016/J.PLANTSCI.2018.06.017
Abstract: Improved genetic, genomic and statistical technologies have increased the capacity to enrich breeding populations for key alleles underpinning adaptation and continued genetic gain. In turn, directed genomic selection together with increased heritability will reduce genetic variance to narrow the genetic base in many crop breeding programs. Diverse genetic resources (GR), including wild and weedy relatives, landraces and reconstituted synthetics, have potential to contribute novel alleles for key traits. Targeted trait identification may also identify genetic ersity in addressing new challenges including the need for modified root architecture, greater nutrient-use efficiency, and adaptation to warmer air and soil temperatures forecast with climate change. Yet while core collections and other GR sources have historically been invaluable for major gene control of disease and subsoil constraints, the mining of genetically (and phenotypically) complex traits in GR remains a significant challenge owing to reduced fertility, limited seed quantities and poor adaptation through linkage drag with undesirable alleles. High-throughput field phenomics (HTFP) offers the opportunity to capture phenotypically complex variation underpinning adaptation in traditional phenotypic selection or statistics-based breeding programs. Targeted HTFP will permit the reliable phenotyping of greater numbers of GR-derived breeding lines using smaller plot sizes and at earlier stages of population development to reduce the duration of breeding cycles and the loss of potentially important alleles with linkage drag. Two key opportunities are highlighted for use of HTFP in selection among GR-derived wheat breeding lines for greater biomass and stomatal conductance through canopy temperature.
Publisher: MDPI AG
Date: 10-07-2014
Publisher: Springer Science and Business Media LLC
Date: 12-2015
Publisher: Frontiers Media SA
Date: 06-12-2016
Publisher: Frontiers Media SA
Date: 09-07-2019
Publisher: CSIRO Publishing
Date: 2005
DOI: 10.1071/SR04116
Abstract: Soil structural properties of swelling/shrinking soils play an important role in assessing hydraulic properties. However, the effect of shrinkage/swelling processes on structure formation and strength especially in saline–sodic soils, such as a Typic Chromexert, has not yet been clarified. In this study, we investigate the changes in the shrinkage pattern after applying saline sewage water and use a 3-parameter sigmoidal curve model to fit its shrinkage data. Our aims were to determine the overall effect of sewage water application on soil structure and shrinkage processes after applying saline–sodic water and to evaluate soil shrinkage behaviour through parameters in relation to soil properties. Three plots within the FILTER Project, which were irrigated for summer and winter irrigated cropping with around 1000 m3/ha every 2 weeks with different saline sewage effluent concentrations for years, were s led from the top horizon to 1.00 m depth. The exchangeable sodium percentage is greatly decreased due to the application of low salt concentration except in the deep horizon. Soil structural properties such as aggregate strength and hydraulic properties are improved, especially in topsoil horizon. The stabilised soil structure reduces the volume change of structural shrinkage. Three parameters of the shrinkage model, defined as α, m, and n, present different physical meanings in relation to soil structure. Parameters α and m have similar functions, both a significantly exponential relationship with aggregate strength and a linear relationship with structural and residual shrinkages, whereas parameter n has a significantly linear relationship with aggregate strength and with the slope of the proportional shrinkage. The relation between parameters of the model and shrinkage behaviour facilitates the prediction of changes in pore water and soil structure and will be a useful tool for modelling water flow in non-rigid soils.
Publisher: Wiley
Date: 26-04-2019
DOI: 10.1111/NPH.15817
Abstract: Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 2021
Abstract: Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few ex les demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders’ needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.
Publisher: Frontiers Media SA
Date: 27-02-2018
Publisher: CSIRO Publishing
Date: 2012
DOI: 10.1071/FP12180
Abstract: Field evaluation of germplasm for performance under water and heat stress is challenging. Field environments are variable and unpredictable, and genotype × environment interactions are difficult to interpret if environments are not well characterised. Numerous traits, genes and quantitative trait loci have been proposed for improving performance but few have been used in variety development. This reflects the limited capacity of commercial breeding companies to screen for these traits and the absence of validation in field environments relevant to breeding companies, and because little is known about the economic benefit of selecting one particular trait over another. The value of the proposed traits or genes is commonly not demonstrated in genetic backgrounds of value to breeding companies. To overcome this disconnection between physiological trait breeding and uptake by breeding companies, three field sites representing the main environment types encountered across the Australian wheatbelt were selected to form a set of managed environment facilities (MEFs). Each MEF manages soil moisture stress through irrigation, and the effects of heat stress through variable sowing dates. Field trials are monitored continuously for weather variables and changes in soil water and canopy temperature in selected probe genotypes, which aids in decisions guiding irrigation scheduling and s ling times. Protocols have been standardised for an essential core set of measurements so that phenotyping yield and other traits are consistent across sites and seasons. MEFs enable assessment of a large number of traits across multiple genetic backgrounds in relevant environments, determine relative trait value, and facilitate delivery of promising germplasm and high value traits into commercial breeding programs.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 2020
Abstract: Highly repeatable, nondestructive, and high-throughput measures of above-ground biomass (AGB) and crop growth rate (CGR) are important for wheat improvement programs. This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). Across three field experiments, contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture, several concurrent measurements of LiDAR and AGB were made from jointing to anthesis. Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant, ranging from 0.31 ( P 0.05 ) to 0.86 ( P 0.0001 ), providing confidence in the LiDAR indices as effective surrogates for AGB. The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB, particularly under water limitation. The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments. However, across all experiments, the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB, except for the 3DPI in the water-limited environment. In our experiments, the repeatability of either LiDAR index was consistently higher than that of AGB, both at discrete time points and when CGR was calculated. These findings provide promising support for the reliable use of ground-based LiDAR, as a surrogate measure of AGB and CGR, for screening germplasm in research and wheat breeding.
Publisher: Springer Science and Business Media LLC
Date: 23-11-2012
Publisher: Springer Science and Business Media LLC
Date: 19-05-2013
Publisher: Oxford University Press (OUP)
Date: 30-09-2019
DOI: 10.1093/JXB/ERZ439
Abstract: One way to increase yield potential in wheat is screening for natural variation in photosynthesis. This study uses measured and modelled physiological parameters to explore genotypic ersity in photosynthetic capacity (Pc, Rubisco carboxylation capacity per unit leaf area at 25 °C) and efficiency (Peff, Pc per unit of leaf nitrogen) in wheat in relation to fertilizer, plant stage, and environment. Four experiments (Aus1, Aus2, Aus3, and Mex1) were carried out with erse wheat collections to investigate genetic variation for Rubisco capacity (Vcmax25), electron transport rate (J), CO2 assimilation rate, stomatal conductance, and complementary plant functional traits: leaf nitrogen, leaf dry mass per unit area, and SPAD. Genotypes for Aus1 and Aus2 were grown in the glasshouse with two fertilizer levels. Genotypes for Aus3 and Mex1 experiments were grown in the field in Australia and Mexico, respectively. Results showed that Vcmax25 derived from gas exchange measurements is a robust parameter that does not depend on stomatal conductance and was positively correlated with Rubisco content measured in vitro. There was significant genotypic variation in most of the experiments for Pc and Peff. Heritability of Pc reached 0.7 and 0.9 for SPAD. Genotypic variation and heritability of traits show that there is scope for these traits to be used in pre-breeding programmes to improve photosynthesis with the ultimate objective of raising yield potential.
Publisher: Oxford University Press (OUP)
Date: 09-2016
DOI: 10.1093/JXB/ERW301
Publisher: Wiley
Date: 06-2021
DOI: 10.1111/ANZS.12336
Abstract: High‐throughput phenomics data are being collected in both the laboratory and the field. The data are often collected at many time points and there may be spatial variation in the laboratory or field that impacts on the growth of the plants, and that may influence the traits of interest. Modelling the genetic effects is of primary interest in such studies, but these effects might be biased if non‐genetic effects present in the experiment are ignored. With data that are collected both in time and space, there may be a need to jointly model these multi‐dimensional non‐genetic effects. Thus both modelling of genetic effects over time and non‐genetic effects over time and space in a one‐stage analysis is considered. An experiment that involves field phenomics data with four dimensions, two in space and two in time, provides the vehicle to examine the models. Factor analytic (FA) models are often used for genetic effects for different environments to provide reliable estimates of genetic variances and correlations. As the time dimension defines the environments, FA models are examined for the phenomics data. Reduced rank tensor smoothing splines are presented as a possible approach for modelling the spatio‐temporal effects, although an additional term is included for heterogeneity over the two time dimensions. This approach is feasible, although very time‐consuming. The process of model selection for the genetic effects is presented including tests, information criteria and diagnostics. Comparisons of more simplistic models are made with the reduced rank tensor spline. This also shows the interplay between the genetic and residual models in model selection.
Publisher: IEEE
Date: 2010
Publisher: CSIRO
Date: 2006
Publisher: CSIRO
Date: 2020
DOI: 10.25919/A30X-RX96
Location: Australia
No related grants have been discovered for David Deery.