Modelling plants across scales of biological organisation for guiding crop improvement.

Grain yield improvement in globally important staple crops is critical in the coming decades if production is to keep pace with growing demand; so there is increasing interest in understanding and manipulating plant growth and developmental traits for better crop productivity. However, this is confounded by complex cross-scale feedback regulations and a limited ability to evaluate the consequences of manipulation on crop production. Plant/crop modelling could hold the key to deepening our understanding of dynamic trait–crop–environment interactions and predictive capabilities for supporting genetic manipulation. Using photosynthesis and crop growth as an example, this review summarises past and present experimental and modelling work, bringing about a model-guided crop improvement thrust, encompassing research into: (1) advancing cross-scale plant/crop modelling that connects across biological scales of organisation using a trait dissection–integration modelling principle; (2) improving the reliability of predicted molecular–trait–crop–environment system dynamics with experimental validation; and (3) innovative model application in synergy with cross-scale experimentation to evaluate G × M × E and predict yield outcomes of genetic intervention (or lack of it) for strategising further molecular and breeding efforts. The possible future roles of cross-scale plant/crop modelling in maximising crop improvement are discussed. Significant improvements in the grain yield of globally important staple crops will be required in the coming decades, prompting genetic manipulation of plants for gains. But this path is impeded by limited understanding of molecular- and crop-scale interactions and insufficient field testing. To overcome these challenges, a model-guided crop improvement strategy can be employed, which capitalise on emerging cross-scale plant/crop models for unpacking complex agronomic traits and evaluating genetic manipulation targets, adding significant value to yield improvement efforts. [ABSTRACT FROM AUTHOR]