Crop yield must increase to keep pace with growing global demand. Past increases in crop production have rarely been attributable to an individual innovation but have occurred when technologies and practices combine to form improved farming systems. Inevitably this has involved synergy between genotypic and management improvements. We argue that research focused on developing synergistic systems that overcome clear production constraints will accelerate increases in yield. This offers the opportunity to better focus and multiply the impact of discipline-focused research. Here we use the rainfed grain production systems of south-eastern Australia as a case study of how transformational change in water productivity can be achieved with research focused on genotype × management synergies. In this region, rainfall is low and variable and has declined since 1990. Despite this, growers have maintained yields by implementing synergistic systems combining innovations in (i) soil water conservation, (ii) crop diversity, (iii) earlier sowing, and (iv) matching nitrogen fertilizer to water-limited demand. Further increases are emerging from synergies between genetic improvements to deliver flowering time stability, adjusted sowing times, and potential dual-purpose use. Collaboration between agronomists, physiologists, and crop breeders has led to development of commercial genotypes with stable flowering time that are in early phases of testing and adoption.

Farmers are increasingly demanding rapid, cost-effective, easy-to-use and non-destructive methods for monitoring changes in the physical and chemical characteristics of crops and plants from the early stages of crop development until harvest. Remote and proximal sensor tools have been used recently to monitor different aspects of cereal production (e.g., fertilization, crop diseases). Most of these tools are characterized as non-destructive, non-invasive and easy-to-use, and most of them are based in near-infrared (NIR) spectroscopy. This article reviews recent and potential applications for the use of proximal sensors based on NIR spectroscopy to monitor dry matter (DM), yield, nitrogen and diseases in different cereal crops.

The strigolactone (SL) plant hormone pathway inhibits tiller bud outgrowth. SLs also influence plant height, grain number, and grain size, but it is unclear how these traits are connected. To separate the effects of SLs on plant architecture, grain size, and yield, we tested SL barley mutant plants in a range of conditions and utilized exogenous hormone-related treatments. SL mutants consistently showed smaller average grain mass, even when tiller number was similar to that of the wild type. However, direct hormone treatments of developing grains caused a reduction in grain size, while inhibition of the pathway had an opposite effect. A direct effect of SL in the grain is consistent with the expression of specific SL-related genes across key stages of grain development. These findings suggest that SLs have a dual action by regulating vegetative stages to indirectly promote barley grain size, while directly repressing grain size later during reproduction. This raises important implications for increasing crop yield through manipulation of the SL pathway.

Summary: Rapid prediction and control of flowering time is essential for breeding crops resilient to changing climates. Current models often fail to predict flowering time in new cultivars because molecular models lack integration of environmental signals, while physiological models inadequately capture the interactions of vernalization, photoperiod and temperature. This leads to mischaracterized genotypes and inaccurate forecasts. A new Cereal Anthesis Molecular Phenology (CAMP) model was developed for wheat. It explicitly integrates the regulatory roles of three major ‘virtual’ flowering genes ( Vrn1 , Vrn2 , and Vrn3 ) with environmental cues. A novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration. CAMP predicted flowering time within 4–7 d across 64 genetically diverse wheat cultivars grown under contrasting environments. The leaf‐number phenotyping method reduced phenotyping time by more than 80%, offering a practical alternative to resource‐intensive field trials. Together, these advances enable accurate cultivar characterization and scalable prediction of flowering behaviour. CAMP enables the ability to predict flowering time directly from genotypic data (e.g. SNPs), eliminating the need for costly controlled‐environment experiments. This represents a step change in molecular–physiological modelling, supporting faster deployment of new cultivars and more effective design of wheat for future climates.