Conventional breeding techniques have been integral to the development of many agronomically important traits in numerous crops. The adoption of modern biotechnology approaches further advanced and refined trait development and introduction beyond the scope possible through conventional breeding. However, crop yields continue to be challenged by abiotic and biotic factors that require the development of traits that are more genetically complex than can be addressed through conventional breeding or traditional genetic engineering. Therefore, more advanced trait development approaches are required to maintain and improve yields and production efficiency, especially as climate change accelerates the incidence of biotic and abiotic challenges to food and fibre crops. Synthetic biology (SynBio) encompasses approaches that design and construct new biological elements (e.g., enzymes, genetic circuits, cells) or redesign existing biological systems to build new and improved functions. SynBio ‘upgrades’ the potential of genetic engineering, which involves the transfer of single genes from one organism to another. This technology can enable the introduction of multiple genes in a single transgenic event, either derived from a foreign organism or synthetically generated. It can also enable the assembly of novel genomes from the ground up from a set of standardised genetic parts, which can then be transferred into the target cell or organism. New opportunities to advance breeding applications through exploiting SynBio technology include the introduction of new genes of known function, artificially creating genetic variation, topical applications of small RNAs as pesticides and potentially speeding up the production of new cultivars with elite traits. This review will draw upon case studies to demonstrate the potential application of SynBio to improve crop productivity and resistance to various challenges. Here, we outline specific solutions to challenges including fungal diseases, insect pests, heat and drought stress and nutrient acquisition in a range of important crops using the SynBio toolkit.

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program’s commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there has not been a study to evaluate GS prediction models that may be used for predicting cotton breeding lines across multiple environments. In this study, we evaluated the performance of Bayes Ridge Regression, BayesA, BayesB, BayesC and Reproducing Kernel Hilbert Spaces regression models. We then extended the single-site GS model to accommodate genotype × environment interaction (G×E) in order to assess the merits of multi- over single-environment models in a practical breeding and selection context in cotton, a crop for which this has not previously been evaluated. Our study was based on a population of 215 upland cotton (Gossypium hirsutum) breeding lines which were evaluated for fiber length and strength at multiple locations in Australia and genotyped with 13,330 single nucleotide polymorphic (SNP) markers. BayesB, which assumes unique variance for each marker and a proportion of markers to have large effects, while most other markers have zero effect, was the preferred model. GS accuracy for fiber length based on a single-site model varied across sites, ranging from 0.27 to 0.77 (mean = 0.38), while that of fiber strength ranged from 0.19 to 0.58 (mean = 0.35) using randomly selected sub-populations as the training population. Prediction accuracies from the M×E model were higher than those for single-site and across-site models, with an average accuracy of 0.71 and 0.59 for fiber length and strength, respectively. The use of the M×E model could therefore identify which breeding lines have effects that are stable across environments and which ones are responsible for G×E and so reduce the amount of phenotypic screening required in cotton breeding programs to identify adaptable genotypes.