The process for implementation of a Quality Management System within a multi-functional cereal laboratory

The intent of this study was to design, document and implement a Quality Management System (QMS) into a laboratory that incorporated both research and development (R&D) and routine analytical activities. In addition, it was necessary for the QMS to be easily and efficiently maintained to: (a) provide documented evidence that would validate the system's compliance with a certifiable standard, (b) fit the purpose of the laboratory, (c) accommodate prevailing government policies and standards, and (d) promote positive outcomes for the laboratory through documentation and verification of the procedures and methodologies implemented. Initially, a matrix was developed that documented the standards' requirements and the necessary steps to be made to meet those requirements. The matrix provided a check mechanism on the progression of the system's development. In addition, it was later utilised in the Quality Manual as a reference tool for the location of full procedures documented elsewhere in the system. The necessary documentation to build and monitor the system consisted of a series of manuals along with forms that provided auditable evidence of the workings of the QMS. Quality Management (QM), in one form or another, has been in existence since the early 1900's. However, the question still remains: is it a good thing or just a bugbear? Many of the older style systems failed because they were designed by non-users, fiercely regulatory, restrictive and generally deemed to be an imposition. It is now considered important to foster a sense of ownership of the system by the people who use the system. The system's design must be tailored to best fit the purpose of the operations of the facility if maximum benefits to the organisation are to be gained.

Designing better on-farm research in Australia using a participatory workshop process

There are renewed calls for end-user participation and the integration of local knowledge in agricultural research. In Australia, the response has included an increased emphasis on participatory on-farm research with farmers and commercial agronomists that tests accepted principals to answer practical local farming questions. However, this pursuit of greater relevance has often led to compromises in research designs, unclear results and frustration amongst farmers, commercial agronomists and Research Development and Extension (RDE) agency researchers. This paper reports on a series of pre-season planning workshops from `Doing successful on-farm research', a workshop-based initiative that provides guidelines and a series of interactive activities to plan better participatory on-farm research. The workshop approach helps people design on-farm research that is appropriate to their own needs and local conditions. It assists them to clearly identify their issues, develop specific research questions and decide the best approach to answer those questions with the appropriate rigour for their own situations. These `Doing successful on-farm research' workshops address four potential deficiencies in on-farm research and farming systems RDE more generally in Australia: (1) variable participation of scientists and farmers in on-farm research; (2) the lack of clear guidelines for effective participatory practice and on-farm research; (3) limited support for on-farm research beyond the intensive investigations conducted by RDE agencies and (4) limited support for industry and farmers to contextualise information and research outcomes for specific individual circumstances and faster adaptation of technology. This may be a valuable contribution to balancing the demands for both relevance and rigour in on-farm research in Australia. In "Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.

A collaborative design to adaptively manage for landscape sustainability in north Australia: lessons from a decade of cooperative research

Approaches to manage for the sustainable use of natural and cultural resources in a landscape can have many different designs. One design is adaptive collaborative landscape management (ACLM) where research providers and users work closely together on projects to develop resources while adaptively managing to sustain or maintain landscapes in the long term. We propose that collaborative projects are more useful for achieving outcomes than integrative projects where participants merely join their separate contributions. To foster collaborative research projects to adaptively manage landscapes in northern Australia, a Tropical Savannas Cooperative Research Centre (TSCRC) was established in 1995. The TSCRC is a joint venture of major organizations involved in research and land management. This paper is our perspective on the four most important 'lessons learned' after using a ACLM-type approach for over 10 y. We learnt that collaboration (working in combination) not necessarily integration (combining parts into a whole) achieved sustainable outcomes. We found that integration across culturally diverse perspectives seldom achieved sustainable solutions because it devalued the position of the less empowered participants. In addition, positive outcomes were achieved when participants developed trust and respect for each other by embracing and respecting their differences and by sharing unifying concepts such as savanna health. Another lesson learned was that a collaborative organization must act as an honest broker by resisting advocacy of one view point over another. Finally, we recognized the importance of strongly investing in communication and networking so that people could adaptively learn from one another's experiences, understand each other's challenges and respect each other's choices. Our experience confirms the usefulness of the ACLM approach and highlights its role in the process of sustaining healthy landscapes.

ShadowBoxer and LocusEater: programs to optimise experimental design and multiplexing strategies for genetic mark-recapture

Genetic mark–recapture requires efficient methods of uniquely identifying individuals. 'Shadows' (individuals with the same genotype at the selected loci) become more likely with increasing sample size, and bias harvest rate estimates. Finding loci is costly, but better loci reduce analysis costs and improve power. Optimal microsatellite panels minimize shadows, but panel design is a complex optimization process. locuseater and shadowboxer permit power and cost analysis of this process and automate some aspects, by simulating the entire experiment from panel design to harvest rate estimation.

A Practitioner-Oriented Decision Support Process for Forestry Pest Management

The article discusses a new decision support process for forestry pest management. Over the past few years, DSS have been introduced for forestry pest management, providing forest growers with advice in areas such as selecting the most suitable pesticide and relevant treatment. Most of the initiatives process knowledge from various domains for providing support for specific decision making problems. However, very few studies have identified the requirements of developing a combined process model in which all relevant practitioners can contribute and share knowledge for effective decision making; such an approach would need to include the decision makers’ perspective along with other relevant attributes such as the problem context and relevant policies. We outline a decision support process for forestry pest management, based on the design science research paradigm, in which a focus group technique has application to acquire both expert and practical knowledge in order to construct the DSS solution.

Crop design for specific adaptation in variable dryland production environments

Climatic variability in dryland production environments (E) generates variable yield and crop production risks. Optimal combinations of genotype (G) and management (M) depend strongly on E and thus vary among sites and seasons. Traditional crop improvement seeks broadly adapted genotypes to give best average performance under a standard management regime across the entire production region, with some subsequent manipulation of management regionally in response to average local environmental conditions. This process does not search the full spectrum of potential G × M × E combinations forming the adaptation landscape. Here we examine the potential value (relative to the conventional, broad adaptation approach) of exploiting specific adaptation arising from G × M × E. We present an in-silico analysis for sorghum production in Australia using the APSIM sorghum model. Crop design (G × M) is optimised for subsets of locations within the production region (specific adaptation) and is compared with the optimum G across all environments with locally modified M (broad adaptation). We find that geographic subregions that have frequencies of major environment types substantially different from that for the entire production region show greatest advantage for specific adaptation. Although the specific adaptation approach confers yield and production risk advantages at industry scale, even greater benefits should be achievable with better predictors of environment-type likelihood than that conferred by location alone.