Stay-green Drought Adaptation Trait Enhances Sorghum Production in Sub-tropical Australia, Central-Western India and Sub-Saharan Africa

Drought during grain filling is a common challenge for sorghum production in north-eastern Australia, central-western India, and sub-Saharan Africa. We show that the stay-green drought adaptation trait enhances sorghum grain yield under post-anthesis drought in these three regions. A positive relationship between stay-green and yield was generally found in breeding trials in north-eastern Australia that sampled 1668 unique hybrid combinations and 23 environments. Physiological studies in Australia also found that introgressing four individual stay-green (Stg1–4) quantitative trait loci (QTLs) into a senescent background reduced water demand before flowering and hence increased water supply during grain filling, resulting in higher grain yield relative to the senescent control. Studies in India found that various Stg QTLs affected both transpiration and transpiration efficiency, although these effects depended on the interaction between genetic background (S35 and R16) and individual QTLs. The yield variation unexplained by harvest index was related to transpiration efficiency in S35 (R2 = 0.29) and R16 (R2 = 0.72), and was related to total water extracted in S35 (R2 = 0.41) but not in R16. Finally, sixty-eight stay-green enriched lines were evaluated in six countries in sub-Saharan Africa during the 2013/14 season. Analysis of the data from Kenya indicates that stay-green and grain size were positively correlated at two sites: Kiboko (high yielding, r2=0.25) and Masongaleni (low yielding, r2=0.37). Together, these studies suggest that stay-green is a beneficial trait for sorghum production in the semi-arid tropics and is a consequence of traits altering the plant water budget.

Genetic diversity of stay-green sorghums and their derivatives revealed by microsatellites

The genetic variability of 28 sorghum genotypes of known senescence phenotype was investigated using 66 SSR markers well-distributed across the sorghum genome. The genotypes of a number of lines from breeding programmes for stay-green were also determined. This included lines selected phenotypically for stay-green and also RSG 03123, a marker-assisted backcross progeny of R16 (recurrent parent) and B35 (stay-green donor). A total of 419 alleles were detected with a mean of 6.2 per locus. The number of alleles ranged from one for Xtxp94 to 14 for Xtxp88. Chromosome SBI-10 had the highest mean number of alleles (8.33), while SBI-05 had the lowest (4.17). The PIC values obtained ranged from zero to 0.89 in Xtxp94 and Xtxp88, respectively, with a mean of 0.68. On a chromosome basis, mean PIC values were highest in SBI-10 (0.81) and lowest in SBI-05 (0.53). Most of the alleles from B35 in RSG 03123 were found on chromosomes SBI-01, SBI-02 and SBI-03, confirming the successful introgression of quantitative trait loci associated with stay-green from B35 into the senescent background R16. However, the alternative stay-green genetic sources were found to be distinct based on either all the SSRs employed or using only those associated with the stay-green trait in B35. Therefore, the physiological and biochemical basis of each stay-green source should be evaluated in order to enhance the understanding of the functioning of the trait in the various backgrounds. These genetic sources of stay-green could provide a valuable resource for improving this trait in sorghum breeding programmes.

Optimised wheat root architecture for increased yield and yield stability in the face of a changing climate.

This project provided information, selection techniques and strategies to facilitate the development of high-yielding, stay-green wheat varieties for Australian growers through: a) Improved understanding of the relationships between seminal root traits and other root- and shoot-related traits in determining high-yielding, stay-green phenotypes. b). Molecular markers and rapid phenotypic screening methods that allow selection in breeding programs and identification of genetic regions controlling favourable traits. c). Identification of traits leading to high-yielding, stay-green phenotypes for particular target populations of environments using computer simulation studies.

Sorghum Crop Modeling and Its Utility in Agronomy and Breeding

Crop models are simplified mathematical representations of the interacting biological and environmental components of the dynamic soil–plant–environment system. Sorghum crop modeling has evolved in parallel with crop modeling capability in general, since its origins in the 1960s and 1970s. Here we briefly review the trajectory in sorghum crop modeling leading to the development of advanced models. We then (i) overview the structure and function of the sorghum model in the Agricultural Production System sIMulator (APSIM) to exemplify advanced modeling concepts that suit both agronomic and breeding applications, (ii) review an example of use of sorghum modeling in supporting agronomic management decisions, (iii) review an example of the use of sorghum modeling in plant breeding, and (iv) consider implications for future roles of sorghum crop modeling. Modeling and simulation provide an avenue to explore consequences of crop management decision options in situations confronted with risks associated with seasonal climate uncertainties. Here we consider the possibility of manipulating planting configuration and density in sorghum as a means to manipulate the productivity–risk trade-off. A simulation analysis of decision options is presented and avenues for its use with decision-makers discussed. Modeling and simulation also provide opportunities to improve breeding efficiency by either dissecting complex traits to more amenable targets for genetics and breeding, or by trait evaluation via phenotypic prediction in target production regions to help prioritize effort and assess breeding strategies. Here we consider studies on the stay-green trait in sorghum, which confers yield advantage in water-limited situations, to exemplify both aspects. The possible future roles of sorghum modeling in agronomy and breeding are discussed as are opportunities related to their synergistic interaction. The potential to add significant value to the revolution in plant breeding associated with genomic technologies is identified as the new modeling frontier.