Effect of micro-propagation on the health status of strawberry planting material for commercial production of strawberry runners for Queensland

Plant tissue culture has been used for a number of years to produce micropropagated strawberry plants for planting into runner growing beds in the Stanthorpe (Queensland) and Bothwell (Tasmania) regions. This process has allowed the rapid release of new cultivars from the LAWS (Late Autumn, Winter, Spring) breeding program into the current runner production system. Micro-propagation in vitro allows plants to be produced during the autumn and winter months, when mother plants would normally be in a fruit production phase in the field in Queensland. The plants produced are of a high health status when they are planted. The subsequent arrival and build up of various diseases in the runner fields are closely monitored. Using tissue culture for the first generation reduces the time the plants spend in the field by twelve months, reducing disease incidence. To date, any disease outbreak has been successfully managed using early detection and rapid response methods.

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.

Micropropagation of vegetatively propagated crops: accelerating release of new cultivars and providing an important source of clean planting material

Micropropagation is unequalled for the rapid clonal propagation of improved cultivars from several Australian breeding programmes. This has been particularly true of the pineapple breeding programme, but it has also found an important role in the strawberry breeding programme where high-health mother stock is of paramount concern. In the banana and ginger industries, while access to new cultivars has been of importance, micropropagation has been adopted by the industry to ensure that planting materials are free from serious pests and diseases. Bananas can be used as planting material as early as the first generation ex vitro and is responsible for the establishment of laboratories and nurseries specializing in the production of pathogen-tested plants. The ginger industry, on the other hand, has used micropropagated plants as a source of disease and pest-free stock to establish a clean 'seed' scheme based on the production of conventional planting material.

ACIAR Producing virus-free sweetpotato planting material in Papua New Guinea

The aim of this small research activity (SRA) is to provide a foundation for establishing a national 'clean seed system' for sweetpotato in Papua New Guinea.

Growing healthy sweetpotato: best practices for producing planting material

Sweetpotato is a major food crop in Papua New Guinea, with about 2.9 million tonnes grown each year. But sweetpotato is prone to pests and diseases, particularly viruses, which can significantly reduce yields. Because there are no varieties known to be resistant to viruses, the next best solution is to produce planting material that is free from infection, and to make this readily available to growers. This manual is aimed at researchers and technicians, and describes how to test for sweetpotato viruses and to keep vines free from infection. The methods described should help locals in PNG and other Pacific nations produce disease-free planting material for sweetpotato and other root and tuber crops.