ABOUT US

New environmental and pests difficulties are forcing plant breeders to check for new genes especially in old landraces. GRDC has developed a technology that uses germplasm collection site to predict for adaptive traits. This approach has led to the discovery of previously undiscovered genes and useful variations of known genes.

Period of Implementation

Jan 1, 2014 - Jun 30, 2019
Total Budget

USD 757,765.00

OUR IMPACT

Goals

The project will improve the efficiency of genetic resource utilization and shorten the timeline associated with novel gene discovery for crops of importance to both the Australian grain industry and resource-poor farmers globally.

Objectives

The project wants to improve higher yielding better adapted varieties containing genes introgressed from germplasm identified and delivered from genebank using enhanced FIGS methodologies.

Problems and Needs Analysis

With the looming spectre of climate change, the projects aims to discover previously undiscovered genes and useful variations of known genes for resistance to serious pests and diseases. This can be possible thanks to the use of FIGS which quantify and determine relathionship between collection site and the presence of specific traits.

Intervention Strategy(ies)

It start with identification of 5 target traits for bread and durum wheat in accordance with Australian grain industry needs. Develop, fine-tune, test and validate prediction algorithms for selected traits. Annually identified subsets of barley, durum and bread wheat for one of the five traits identified before. Then identify 5 traits for each of lentil, chick pea, faba bean and field peas according to a priority list of key of PBA breeders in Australia. Assemble relevant data for the selected traits and develop a prediction algorithm. Annually identified for one of the 5 traits identified before. Delivery to Australia of FIGS set material. Identify and develop a collaborative framework to create a user friendly software application.

Impact Pathway

The project will improve the efficiency and shorten the timeline associated with novel gene discovery for crops of importance to both the Australian grain industry and resource-poor farmers globally. It will do this by improving upon currently developed FIGS methodology and by identifying and delivering tailor made subsets of germplasm to pre-breeders and breeders who are looking for new sources of adaptive traits (biotic and abiotic). This multi-task process, will allow the discovery of previously undiscovered genes and useful variations of known genes for resistance to serious pests and diseases with improved higher yielding and better adapted varieties. The impact of this project will not only be realized in Australia but also globally as the approach becomes industry standard for the genebank community thereby improving the overall efficiency of genetic resource utilization, which is of increased significance in the context of global challenges posed by climate change to agro-ecosystems world wide.

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Sustainable Development Goals Contribution

WHERE WE WORK

Seasonal and climatic variation of weighted VPD for transpiration estimation

Author(s): Michel Edmond Ghanem | Zakaria Kehel | Hélène Marrou | Thomas R. Sinclair

Date: 2020-02-02 | Type: Journal Article

Predictive Characterization of ICARDA Genebank Barley Accessions sing FIGS and Machine Learning

Author(s): Zainab Azough | Zainab Azough | Zakaria Kehel | Aziza Benomar | Mostafa Bellafkih | Ahmed Amri

Date: 2019-07-24 | Type: Conference Paper

Quantifying the relationship between adaptive traits and agro-climatic conditions

Author(s): Teklezgi Mehari Gebre

Date: 2018-12-01 | Type: Thesis

2017 ICARDA Barley FIGS Set for Frost Tolerance

Author(s): Zakaria Kehel | Kenneth Street

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

2017 ICARDA Barley Net Form and Spot Blotch FIGS Subset

Author(s): Zakaria Kehel | Kenneth Street | Ahmed Amri

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

2017 ICARDA Bread Wheat Crown Rot FIGS Subset

Author(s): Zakaria Kehel | Kenneth Street

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

2017 ICARDA Durum Wheat Crown Rot FIGS Subset

Author(s): Zakaria Kehel | Kenneth Street

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

2017 ICARDA Faba Bean Acid Soils FIGS Subset

Author(s): Zakaria Kehel | Kenneth Street

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

2017 ICARDA Chickpea Chilling Tolerance FIGS Subset

Author(s): Zakaria Kehel | Kenneth Street

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

2017 ICARDA Lentil Reproductive Heat Tolerance FIGS Subset

Author(s): Zakaria Kehel | Kenneth Street

Date: 2017-12-14 | Type: Dataset - Sub-type(s): Experimental data

NEWS & EVENTS