In Vitro Conditions for a Successful Biolistics transformation System of Pineapples (ananas comosus merr.), in 'Contributing to a Sustainable Future'

In order to develop an efficient and reliable biolistics transformation system for pineapples parameters need to be optimised for growth, survival and development of explants pre- and post transformation. We have optimised in vitro conditions for culture media for the various stages of plant and callus initiation and development, and for effective selection of putative transgenic material. Shoot multiplication and proliferation is best on medium containing MS basic nutrients and vitamins with the addition of 0.1 mg/L myo-inositol, 20 g/L sucrose, 2.5 mg/L BAP and 3 g/L Phytagel, followed by transfer to basic MS medium for further development. Callus production on leaf base explants is best on MS nutrients and vitamins, to which 10 mg/L of BAP and NAA each was added. Optimum explant age for bombardment is 17-35 week old callus, while a pre-bombardment osmoticum treatment in the medium is not required. By comparing several antibiotics as selective agent, it has been established that a two-step selection of 2 fortnightly sub-cultures on 50 μg/mL of geneticin in the culture medium, followed by monthly sub-cultures on 100 μg/mL geneticin is optimal for survival of transgenic callus. Shoot regeneration from callus cultures is optimal on medium containing MS nutrients and vitamins, 5% coconut water and 400 mg/L casein hydrolysate. Plants can be readily regenerated and multiplied from transgenic callus through organogenesis. Rooting of shoots does not require any additional plant hormones to the medium. A transformation efficiency of 1 – 3.5% can be achieved, depending on the gene construct applied.

The Introduction of Transgenes to Control Blackheart in Pineapple: Biolistics Vs Agrobacterium Transformation, in 'Contributing to a Sustainable Future'

Techniques for the introduction of transgenes to control blackheart by particle bombardment and Agrobacterium co-transformation have been developed for pineapple cv. Smooth Cayenne. Polyphenol oxidase (PPO) is the enzyme responsible for blackheart development in pineapple fruit following chilling injury. Sense, anti-sense and hairpin constructs were used as a means to suppress PPO expression in plants. Average transformation efficiency for biolistics was approximately 1% and for Agrobacterium was approximately 1.5%. These results were considered acceptable given the high regeneration potential of between 80-90% from callus cultures. Southern blot analysis revealed stable integration of transgenes with lower copy number found in plants transformed with Agrobacterium compared to those transformed by biolistics. Over 5000 plants from 55 transgenic lines are now undergoing field evaluation in Australia

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

The climate change risk management matrix for the grazing industry of northern Australia

The complexity, variability and vastness of the northern Australian rangelands make it difficult to assess the risks associated with climate change. In this paper we present a methodology to help industry and primary producers assess risks associated with climate change and to assess the effectiveness of adaptation options in managing those risks. Our assessment involved three steps. Initially, the impacts and adaptation responses were documented in matrices by ‘experts’ (rangeland and climate scientists). Then, a modified risk management framework was used to develop risk management matrices that identified important impacts, areas of greatest vulnerability (combination of potential impact and adaptive capacity) and priority areas for action at the industry level. The process was easy to implement and useful for arranging and analysing large amounts of information (both complex and interacting). Lastly, regional extension officers (after minimal ‘climate literacy’ training) could build on existing knowledge provided here and implement the risk management process in workshops with rangeland land managers. Their participation is likely to identify relevant and robust adaptive responses that are most likely to be included in regional and property management decisions. The process developed here for the grazing industry could be modified and used in other industries and sectors. By 2030, some areas of northern Australia will experience more droughts and lower summer rainfall. This poses a serious threat to the rangelands. Although the impacts and adaptive responses will vary between ecological and geographic systems, climate change is expected to have noticeable detrimental effects: reduced pasture growth and surface water availability; increased competition from woody vegetation; decreased production per head (beef and wool) and gross margin; and adverse impacts on biodiversity. Further research and development is needed to identify the most vulnerable regions, and to inform policy in time to facilitate transitional change and enable land managers to implement those changes.