ACIAR - Improving Postrainy Sorghum Varieties to Meet the Growing Grain and Fodder Demand in India – Phase 2
Postrainy sorghum is important for about 5 million households of India. Both grain and stover residues play an almost equally important role in the sorghum value chain, and the price of stover is linked to stover quality. Postrainy sorghum production is constrained by water limitation. The purpose of that project is therefore two-folds: (i) generate cultivars with higher productivity and quality under such limitation, (ii) generate knowledge to speed up the generation of improved cultivars for similar constraints across the world.
During the project’s first phase, the introgression of stay-green QTLs into senescent sorghum lines improved grain yield, stover yield, and stover quality traits, without trade-offs between these traits. There were also synergistic associations between stover productivity and stover quality. Introgression of these QTLs has then been initiated in the background of four farmer-preferred sorghum lines (M 35-1, Parbhani Moti=SPV1411, PhuleVasudha and CRS1) and need to be advanced by two more backcrosses before selection. Re-sequencing of introgression lines developed during the first phase, plus fine-mapping information on different stay-green QTLs and re-sequencing data for 40 diverse sorghum lines will provide a set of closely linked, if not gene-based, markers for precise selection of target genomic regions for the on-going breeding efforts.
A very solid ex-ante assessment (see Appendix) has shown the highly beneficial impacts of the proposed improved varieties, even in the most conservative scenario, for both the producer and the consumer. An important partner in the project, the Directorate of Sorghum Research (DSR), also has a good network of testing locations, including both DSR stations and regional stations of the State Agricultural Universities. DSR will undertake the seed release process of good materials during or beyond this phase.
From the first phase, the mechanisms underlying the expression of the stay-green phenotype have been identified: (i) improved water extraction; (ii) improved transpiration efficiency (TE); and (iii) reduced leaf area and large genetic variation for these traits has been identified. The second phase will then develop back cross nested association mapping (BCNAM) populations in the background of a few post-rainy season varieties with germplasm variants donors for these traits to harness their genetics and develop promising breeding materials. This anticipates an evolution from breeding “stay-green QTL” to breeding QTLs involved in the key underlying mechanisms, and/or combinations of these to optimize production.
Weather patterns in the sorghum production area show the existence of sub-zones with different stress severity patterns. Simulation of the effect of some of the underlying traits predicts an increase in yield and yield resilience (less frequency of crop failure), with the highest effects being specific to certain stress patterns. Crop simulation modeling will be expanded to West Africa to characterize stress patterns and prevalence and to guide the choice of key traits and/or management options for these different scenarios. The work is aligned with the CGIAR Research Program (CRP) on Dryland Cereals, and aims at a global integration of sorghum breeding efforts for water-limited environments, with short to mid-term support from ACIAR and long term support from the CRP.
Select best markers for each of the key QTLs to breed improved cultivars, further advance and select the best back cross lines in post-rainy season backgrounds for stay-green expression, agronomic/stover quality traits, and underlying traits, and initiate a “seed delivery” mechanism in the scope of multi-location testing and on-station demonstrations. (ii) Develop BCNAM populations using trait variants (for TE, water extraction capacity, altered canopy development) in farmer-adapted backgrounds for quick identification of promising entries and of mapping genomic regions, and link up/coordinate similar types of activities for West African regions (parameterization of key cultivars, weather scenario analysis, cross linkage on trait analysis, training of African scientists).
Pre-release postrainy season lines with improved grain and stover yield, high stover quality;
Seed delivery pipeline framework put in place (seed production and distribution system of DSR under ICAR project);
Additional pre-breeding lines coming from the BCNAM efforts;
Effective marker system/assays for efficient introgression of key regions, better knowledge of optimal QTL combinations, and genomic regions involved in key underlying mechanisms;
Sorghum breeding efforts integrated across regions and multi-institutions partners.
For us gems means GEMS, or G*E*M*S (genotype by environment by management by society) interactions, i.e. the fact that crop yields results from complex biophysical interactions while acceptance depends on farmer/consumer preferences. This complexity becomes an opportunity when it is cracked into components that can be analysed, understood, predicted, and then used to prioritise research investments to maximise return. This is what we do, and this is when GEMS become gems!
For us gems means GEMS, or G*E*M*S (genotype by environment by management by society) interactions, i.e. the fact that crop yields results from complex biophysical interactions while acceptance depends on farmer/consumer preferences. This complexity becomes an opportunity when it is cracked into components that can be analyzed, understood, predicted, and then used to prioritize research investments to maximise return. This is what we do, and this is when GEMS become gems!
A crop performs in different ways in different sites, years and agronomic managements. These are called genotype-by-environment-by management(G*E*M) interactions, and they are a main challenge for breeders and agronomists. There is one more layer of interaction, even more complex: the society (S). Farmers and consumers have different desires, needs, expectations, and a cultivar that fits one may not fit the other (G*E*M*S interactions). The puzzle is complex and challenging but if its components are understood, specific interventions can be undertaken.For instance, breeding for a particular genotype (G, with particular physiological characteristics), for a particular environment (E, with a particular kind of drought pattern that requires a specific adaptive trait), in a particular management practice (M, for instance a sowing density, or a N fertilizer treatment), and targeted to particular farmer/consumer (S, for instance a genotype that produces a lot of rich stover for cattle ranchers) is the need of the hour.