A physiological framework to explain genetic and environmental regulation of tillering in sorghum

Tillering determines the plant size of sorghum (Sorghum bicolor) and an understanding of its regulation is important to match genotypes to prevalent growing conditions in target production environments. The aim of this study was to determine the physiological and environmental regulation of variability in tillering among sorghum genotypes, and to develop a framework for this regulation. * Diverse sorghum genotypes were grown in three experiments with contrasting temperature, radiation and plant density to create variation in tillering. Data on phenology, tillering, and leaf and plant size were collected. A carbohydrate supply/demand (S/D) index that incorporated environmental and genotypic parameters was developed to represent the effects of assimilate availability on tillering. Genotypic differences in tillering not explained by this index were defined as propensity to tiller (PTT) and probably represented hormonal effects. * Genotypic variation in tillering was associated with differences in leaf width, stem diameter and PTT. The S/D index captured most of the environmental effects on tillering and PTT most of the genotypic effects. * A framework that captures genetic and environmental regulation of tillering through assimilate availability and PTT was developed, and provides a basis for the development of a model that connects genetic control of tillering to its phenotypic consequences.

Drought adaptation of stay-green sorghum is associated with canopy development, leaf anatomy, root growth, and water uptake

Stay-green sorghum plants exhibit greener leaves and stems during the grain-filling period under water-limited conditions compared with their senescent counterparts, resulting in increased grain yield, grain mass, and lodging resistance. Stay-green has been mapped to a number of key chromosomal regions, including Stg1, Stg2, Stg3, and Stg4, but the functions of these individual quantitative trait loci (QTLs) remain unclear. The objective of this study was to show how positive effects of Stg QTLs on grain yield under drought can be explained as emergent consequences of their effects on temporal and spatial water-use patterns that result from changes in leaf-area dynamics. A set of four Stg near-isogenic lines (NILs) and their recurrent parent were grown in a range of field and semicontrolled experiments in southeast Queensland, Australia. These studies showed that the four Stg QTLs regulate canopy size by: (1) reducing tillering via increased size of lower leaves, (2) constraining the size of the upper leaves; and (3) in some cases, decreasing the number of leaves per culm. In addition, they variously affect leaf anatomy and root growth. The multiple pathways by which Stg QTLs modulate canopy development can result in considerable developmental plasticity. The reduction in canopy size associated with Stg QTLs reduced pre-flowering water demand, thereby increasing water availability during grain filling and, ultimately, grain yield. The generic physiological mechanisms underlying the stay-green trait suggest that similar Stg QTLs could enhance post-anthesis drought adaptation in other major cereals such as maize, wheat, and rice.

APSIM – Evolution towards a new generation of agricultural systems simulation

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.

Stay-green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns

* Stay-green is an integrated drought adaptation trait characterized by a distinct green leaf phenotype during grain filling under terminal drought. We used sorghum (Sorghum bicolor), a repository of drought adaptation mechanisms, to elucidate the physiological and genetic mechanisms underpinning stay-green. * Near-isogenic sorghum lines (cv RTx7000) were characterized in a series of field and managed-environment trials (seven experiments and 14 environments) to determine the influence of four individual stay-green (Stg1–4) quantitative trait loci (QTLs) on canopy development, water use and grain yield under post-anthesis drought. * The Stg QTL decreased tillering and the size of upper leaves, which reduced canopy size at anthesis. This reduction in transpirational leaf area conserved soil water before anthesis for use during grain filling. Increased water uptake during grain filling of Stg near-isogenic lines (NILs) relative to RTx7000 resulted in higher post-anthesis biomass production, grain number and yield. Importantly, there was no consistent yield penalty associated with the Stg QTL in the irrigated control. * These results establish a link between the role of the Stg QTL in modifying canopy development and the subsequent impact on crop water use patterns and grain yield under terminal drought.

Crop design for specific adaptation in variable dryland production environments

Climatic variability in dryland production environments (E) generates variable yield and crop production risks. Optimal combinations of genotype (G) and management (M) depend strongly on E and thus vary among sites and seasons. Traditional crop improvement seeks broadly adapted genotypes to give best average performance under a standard management regime across the entire production region, with some subsequent manipulation of management regionally in response to average local environmental conditions. This process does not search the full spectrum of potential G × M × E combinations forming the adaptation landscape. Here we examine the potential value (relative to the conventional, broad adaptation approach) of exploiting specific adaptation arising from G × M × E. We present an in-silico analysis for sorghum production in Australia using the APSIM sorghum model. Crop design (G × M) is optimised for subsets of locations within the production region (specific adaptation) and is compared with the optimum G across all environments with locally modified M (broad adaptation). We find that geographic subregions that have frequencies of major environment types substantially different from that for the entire production region show greatest advantage for specific adaptation. Although the specific adaptation approach confers yield and production risk advantages at industry scale, even greater benefits should be achievable with better predictors of environment-type likelihood than that conferred by location alone.

Sorghum genotypes differ in high temperature responses for seed set

Significant genotypic differences in tolerance of pollen germination and seed set to high temperatures have been shown in sorghum. However, it is unclear whether differences were associated with variation in either the threshold temperature above which reproductive processes are affected, or in the tolerance to increased temperature above that threshold. The objectives of this study were to (a) dissect known differences in heat tolerance for a range of sorghum genotypes into differences in the threshold temperature and tolerance to increased temperatures, (b) determine whether poor seed set under high temperatures can be compensated by increased seed mass, and (c) identify whether genotypic differences in heat tolerance in a controlled environment facility (CEF) can be reproduced in field conditions. Twenty genotypes were grown in a CEF under four day/night temperatures (31.9/21.0 °C, 32.8/21.0 °C, 36.1/21.0 °C, and 38.0/21.0 °C), and a subset of six genotypes was grown in the field under four different temperature regimes around anthesis. The novelty of the findings in this study related to differences in responsiveness to high temperature—genotypic differences in seed set percentage were found for both the threshold temperature and the tolerance to increased maximum temperature above that threshold. Further, the response of seed set to high temperature in the field study was well correlated to that in the CEF (R2 = 0.69), although the slope was significantly less than unity, indicating that heat stress effects may have been diluted under the variable field conditions. Poor seed set was not compensated by increased seed mass in either CEF or field environments. Grain yield was thus closely related to seed set percentage. This result demonstrates the potential for development of a low-cost field screening method to identify high-temperature tolerant varieties that could deliver sustainable yields under future warmer climates.

Heat stress effects on grain sorghum productivity-biology and modelling

Heat stress can cause sterility in sorghum and the anticipated increased frequency of high temperature events implies increasing risk to sorghum productivity in Australia. Here we summarise our research on specific varietal attributes associated with heat stress tolerance in sorghum and evaluate how they might affect yield outcomes in production environments by a crop simulation analysis. We have recently conducted a range of controlled environment and field experiments to study the physiology and genetics of high temperature effects on growth and development of sorghum. Sorghum seed set was reduced by high temperature effects (>36-38oC) on pollen germination around flowering, but genotypes differed in their tolerance to high temperature stress. Effects were quantified in a manner that enabled their incorporation into the APSIM sorghum crop model. Simulation analysis indicated that risk of high temperature damage and yield loss depended on sowing date, and variety. While climate trends will exacerbate high temperature effects, avoidance by crop management and genetic tolerance seems possible.

Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.