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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.

Late-maturity alpha-amylase (LMA) is a key concern for Australia’s wheat industry because affected grain may not meet receival standards or market specifications, resulting in significant economic losses for producers and industry. The risk of LMA incidence across Australia’s wheatbelt is not well understood; therefore, a predictive model was developed to help to characterise likely LMA incidence. Preliminary development work is presented here based on diagnostic simulations for estimating the likelihood of experiencing environmental conditions similar to a potential triggering criterion currently used to phenotype wheat lines in a semi-controlled environment. Simulation inputs included crop phenology and long-term weather data (1901–2016) for >1750 stations across Australia’s wheatbelt. Frequency estimates for the likelihood of target conditions on a yearly basis were derived from scenarios using either: (i) weather-driven sowing dates each year and three reference maturity types, mimicking traditional cropping practices; or (ii) monthly fixed sowing dates for each year. Putative-risk ‘footprint’ maps were then generated at regional shire scale to highlight regions with a low (<33%), moderate (33–66%) or high (>66%) likelihood of experiencing temperatures similar to a cool-shock regime occurring in the field. Results suggested low risks for wheat regions across Queensland and relatively low risks for most regions across New South Wales, except for earlier planting with quick-maturing varieties. However, for fixed sowing dates of 1 May and 1 June and varying maturity types, the combined footprints for moderate-risk and high-risk categories ranged from 34% to 99% of the broad wheat region for South Australia, from 12% to 97% for Victoria, and from 9% to 59% for Western Australia. A further research component aims to conduct a field validation to improve quantification of the range of LMA triggering conditions; this would improve the predictive LMA framework and could assist industry with future decision-making based on a quantifiable LMA field risk.

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying effectively how the seasonal growth and yield dynamics of target crops might be affected over a wide range of environments is limited. Using a state-of-the-art cross-scale model we predicted crop-level impacts of a broad list of promising photosynthesis manipulation strategies for C3 wheat and C4 sorghum. The manipulation targets have varying effects on the enzyme-limited (Ac) and electron transport-limited (Aj) rates of photosynthesis. In the top decile of seasonal outcomes, yield gains with the list of manipulations were predicted to be modest, ranging between 0 and 8%, depending on the crop type and manipulation. To achieve the higher yield gains, large increases in both Ac and Aj are needed. This could likely be achieved by stacking Rubisco function and electron transport chain enhancements or installing a full CO2 concentrating system. However, photosynthetic enhancement influences the timing and severity of water and nitrogen stress on the crop, confounding yield outcomes. Strategies enhancing Ac alone offers more consistent but smaller yield gains across environments, Aj enhancement alone offers higher gains but is undesirable in less favourable environments. Understanding and quantifying complex cross-scale interactions between photosynthesis and crop yield will challenge and stimulate photosynthesis and crop research.

The cover image is based on the Original Article A cross‐scale analysis to understand and quantify the effects of photosynthetic enhancement on crop growth and yield across environments by Alex Wu et al., https://doi.org/10.1111/pce.14453.

For twenty four years, the Agricultural Production Systems sIMulator (APSIM) has grown from a farming systems framework used by a small number of people, into a large collection of models used by many thousands of modellers internationally. The software consists of many hundreds of thousands of lines of code in 6 different programming languages. The models are connected to each other using a 'common modelling protocol'. This infrastructure has successfully integrated a diverse range of models but isn't capable of easily meeting the challenges outlined above. For these reasons, the APSIM Initiative has begun developing a next generation of APSIM (dubbed APSIM Next Gen) that is written from scratch and designed to 'run anywhere'. This paper explores each of the design decisions outlined above and discusses why the decision was made to 'start from scratch'.

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