Search

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (Ac) and electron transport-limited (Aj) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C3 wheat and C4 sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing Ac alone generate more consistent but smaller yield gains across all water and nitrogen environments, Aj enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both Ac and Aj generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research.

Main shoot total leaf number (TLN) is a key determinant of plant leaf area and crop adaptation. Environmental factors other than photoperiod can affect TLN in sorghum, implying that leaf appearance rate (LAR) and development rate can differ in response to temperature. The objectives of this study were to determine if (1) temperature effects on TLN can be explained as a consequence of differences in temperature responses across phenological processes and (2) genotypic differences in these responses can be linked to agroecological adaptation.Nineteen sorghum genotypes were sown on twelve dates at two locations in Ethiopia with contrasting altitude, creating temperature differences independent of photoperiod. TLN and temperature were recorded in all experiments and LAR in six sowing dates.Eleven of the genotypes showed a temperature effect on TLN, which was associated with a significantly higher base temperature (Tbase) for LAR than for pre-anthesis development rate (DR). In contrast, genotypes with no effect of temperature on TLN had similar Tbase for LAR and DR. Across genotypes, Tbase for LAR and DR were highly correlated, but genotypes with low Tbase had the greatest difference in Tbase between the two processes. Genotypic differences were associated with racial grouping.Genotypic and racial differences in responses of phenological processes to temperature, in particular in Tbase, can affect specific adaptation to agroecological zones, as these differences can affect TLN in response to temperature and hence canopy size and the duration of the pre-anthesis period. These can both affect the amount of water used and radiation intercepted pre-anthesis. A multi-disciplinary approach is required to identify genotype × environment × management combinations that can best capture the ensuing specific adaptation.

Developing sorghum genotypes adapted to different light environments requires understanding of a plant’s ability to capture light, determined through leaf angle specifically. This study dissected the genetic basis of leaf angle in three-year field trials at two sites, using a sorghum diversity panel (729 accessions). A wide range of variation in leaf angle with medium heritability was observed. Leaf angle explained 36% variation in canopy light extinction coefficient, highlighting the extent to which variation in leaf angle influences light interception at whole canopy level. This study also found the sorghum races of Guinea and Durra consistently having the largest and smallest leaf angle, respectively, highlighting the potential role of leaf angle in adaptation to distinct environments. The genome-wide association study detected 33 QTL associated with leaf angle. Strong synteny was observed with previously detected leaf angle QTL in maize (70%) and rice (40%) within 10 cM, among which the overlap was significantly enriched according to Chi-square tests, suggesting a highly consistent genetic control in grasses. A priori leaf angle candidate genes identified in maize and rice were found to be enriched within a 1-cM window around the sorghum leaf angle QTL. Additionally, protein domain analysis identified the WD40 protein domain as being enriched within a 1-cM window around the QTL. These outcomes show that there is sufficient heritability and natural variation in the angle of upper leaves in sorghum which may be exploited to change light interception and optimise crop canopies for different contexts.

In cereal crops, average leaf angle of the canopy affects the total amount of radiation that is intercepted and how effectively light is distributed through the canopy. Selecting for more erect leaves in maize and rice has allowed higher density plantings and has been associated with significant productivity gains in those crops. The effects of leaf angle on crop productivity depend on how it influences within-canopy light distribution and the plants’ efficiency in using that light to produce biomass. Canopy light distribution is influenced by agronomic interventions such as growing at different locations, sowing on different dates, using different plant densities (leaf area index). For sorghum, which may be grown across a large range of latitudes and plant population densities, the effects of leaf angle manipulations have not previously been studied. We used the APSIM-DCaPST sorghum model to simulate the effects of more erectophile canopies on yield for two locations with different latitudes and planting dates, assuming low-and high- intensity agronomies. The simulations showed that more erect leaves would result in yield benefits in most years at both of those locations. Contrary to the situation in maize, the simulated yield gains were greatest in the low-yielding rather than the high-yielding years. Interestingly also, the simulated productivity gains in erectophile sorghum canopies were not due to enhanced light penetration as suggested in maize, but rather through effects on extent of radiation interception and its implications for early crop growth and crop water balance through the crop life cycle.

Sorghum, a genetically diverse C(4) cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V (cmax)), phosphoenolpyruvate (PEP) carboxylation (V (pmax)), and electron transport (J (max)), quantified using a C(4) photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V (cmax) (R (2) = 0.83), V (pmax) (R (2) = 0.93), J (max) (R (2) = 0.76), SLN (R (2) = 0.82), and LMA (R (2) = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V (cmax), V (pmax), J (max), SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V (cmax) and two QTL for J (max). Candidate genes within 200 kb of the V (cmax) QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J (max) QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.

The stay-green trait is recognised as a key drought adaptation mechanism in cereals worldwide. Stay-green sorghum plants exhibit delayed senescence of leaves and stems, leading to prolonged growth, a reduced risk of lodging and higher grain yield under end-of-season drought stress. More than 45 quantitative trait loci (QTL) associated with stay-green have been identified, including two major QTL (Stg1 and Stg2). However, none of the contributing genes that regulate functional stay-green are known. Here we show that the PIN FORMED family of auxin efflux carriers induce some of the causal mechanisms driving the stay-green phenotype in sorghum, with SbPIN4 and SbPIN2 located in Stg1 and Stg2, respectively. We found that 9 of 11 sorghum PIN genes aligned with known stay-green QTL. In transgenic studies, we demonstrated that PIN genes located within the Stg1 (SbPIN4), Stg2 (SbPIN2) and Stg3b (SbPIN1) QTL regions acted pleiotropically to modulate canopy development, root architecture and panicle growth in sorghum, with SbPIN1, SbPIN2 and SbPIN4 differentially expressed in various organs relative to the non stay-green control. The emergent consequence of such modifications in canopy and root architecture is a stay-green phenotype. Crop simulation modelling shows that the SbPIN2 phenotype can increase grain yield under drought.

The efficiency with which sorghum crops use intercepted radiation (RUE) affects their maximum productivity. RUE in turn, is affected by crop canopy characteristics, as well as leaf biochemical parameters determining maximum leaf photosynthetic rates. These traits are notoriously slow to measure and have previously not been amenable to selection in breeding programs. We used hyperspectral sensors attached to a tractor to estimate leaf and canopy traits associated with RUE in large sorghum field trials. Using Partial Least Square Regression we developed models to predict leaf biochemical photosynthetic parameters known to limit C4 photosynthesis, such as maximum Rubisco and PEP carboxylation rates (Vcmax and Vpmax, respectively) and maximum electron transport rate (Jmax) from the canopy hyperspectral reflectance. We also used various vegetation indices to predict canopy characteristics such as light interception and biomass which allowed us to calculate canopy radiation use efficiency for each trial plot. This is the first time that parameters related to photosynthetic efficiency in sorghum have been estimated for so many different genotypes, showing that significant and heritable variation exists. The predicted values were also used for Genome Wide Association studies to identify the genomic regions associated with canopy RUE and hence improved productivity. Sorghum being such a great genetic model, this may also help elucidate the genetic mechanisms underpinning photosynthetic efficiency and RUE in other cereals.

Sorghum is an important food and feed crop globally. Strategies to optimise its yield performance are needed to help meet rising demands in changing future environments. Typically, sorghum is grown as a dryland crop, with water availability being a major limitation for yield. Here we present a new tool, CropGen, for optimising sorghum design and yield performance. CropGen connects the APSIM sorghum model with an evolutionary optimisation algorithm to enable iterative exploration of genotype × agronomy combinations to identify optimal strategies based on a number of objectives, including grain yield, water use, and crop failure risk over diverse production environments. Initial model testing was undertaken using single year simulations both with and without irrigation to explore tillering × maturity combinations and quantify their yield and water use. In the irrigated scenario, optimal yields were achieved with high tillering and long maturity sorghum designs, whereas lower tillering and shorter maturity designs were identified for the non-irrigated scenario. These findings are consistent with known effects of water availability, crop growth, and yield interactions. CropGen was then applied to optimise tillering × maturity designs for Dalby, a key sorghum production region in Australia, by quantifying the yield, water use, and crop failure risks. The nuanced understanding of crop performance over diverse environments generated by CropGen is key in identifying optimal strategies for guiding research and plant breeding efforts to maximise crop improvement.