Improving Knowledge and Practices in Mango Supply Chains

Mangoes consigned to domestic markets suffered from fruit quality problems from 1997 to 2000. A high incidence of disease breakdown and green-ripe fruit resulted in loss of confidence by marketers, and reduced profits for everyone from grower to retailer. The ‘Better Mangoes’ project was initiated to identify where, and why quality was being lost, and to use this information to improve the knowledge and practices of supply chain businesses.

A spatially explicit multi-competitor coexistence model of penaeid (shrimp) distribution on the Australian Great Barrier Reef

A spatially explicit multi-competitor coexistence model was developed for meta-populations of prawns (shrimp) occupying habitat patches across the Great Barrier Reef, where dispersal was localised and dispersal rates varied between species. Prawns were modelled as individuals moving to and from patches or cells according to pre-set decision rules. The landscape was simulated as a matrix of cells with each cell having a spatially explicit survival index for each species. Mixed species prawn assemblages moved over this simplified spatially explicit landscape. A low level of chronic random environmental disturbance was assumed (cyclone and tropical storm damage) with additional acute spatially confined disturbance due to commercial trawling, modelled as an increase in mortality affecting inter-specific competition. The general form of the results was for increased disturbance to favour good-colonising "generalist" species at the expense of good-competitor "specialists". Increasing fishing mortality (local patch extinctions) combined with poor colonising ability resulted in low equilibrium abundance for even the best competitor, while in the same circumstances the poorest competitor but best coloniser could have the highest equilibrium abundance. This mimics the switch from high-value prawn species to lower-value prawn species as trawl effort increases, reflected in historic catch and effort logbook data and reported anecdotaly from the north Queensland trawl fleet. To match the observed distribution and behaviour of prawn assemblages, a combination inter-species competition, a spatially explicit landscape, and a defined pattern of disturbance (trawling) was required. Modelling this combination could simulate not only general trends in spatial distribution of each of prawn species but also localised concentrations observed in the survey data

Improving knowledge and practices in fresh produce chains

A successful supply chain must delivery the right product, value and satisfaction to the end customer, and profitability for its participants. Critical to getting the product right is the practices used to produce and maintain product quality through the supply chain from production to sale to the end customer. This paper describes the approach used by a R&D team to add value to supply chains through improving knowledge and practices. The desired outcome is better produce quality for consumers and more control and less wastage for chain participants. The team worked with specific supply chains to identify areas for improvement and to develop, test and implement improved practices. The knowledge gained was communicated to the industry to gain wider adoption of results. Three conditions were identified as critical for practice change - motivation, knowledge, and capacity for change. For improvement in practices to occur, a business must be motivated and have the knowledge and capacity to improve. Two case studies of working with Australian supply chains (mango and melons) are presented to illustrate our participatory methodology. A key activity is monitoring produce quality and handling practices and conditions to demonstrate to participants the points where quality deterioration occurs in the supply chain. This participatory approach is successful because working with supply chain participants generates knowledge and solutions to real problems. It enables the participants to observe the effect of handling practices and conditions on produce quality, gain knowledge and assess the benefits of improvements. Where existing knowledge is not present, research is conducted to fill the knowledge gaps. IV International Conference on Managing Quality in Chains - The Integrated View on Fruits and Vegetables Quality

A generic design environment for the rural industry knowledge acquisition

This paper describes a new knowledge acquisition method using a generic design environment where context-sensitive knowledge is used to build specific DSS for rural business. Although standard knowledge acquisition methods have been applied in rural business applications, uptake remains low and familiar weaknesses such as obsolescence and brittleness apply. We describe a decision support system (DSS) building environment where contextual factors relevant to the end users are directly taken into consideration. This "end user enabled design environment" (EUEDE) engages both domain experts in creating an expert knowledge base and business operators/end users (such as farmers) in using this knowledge for building their specific DSS. We document the knowledge organisation for the problem domain, namely a dairy industry application. This development involved a case-study research approach used to explore dairy operational knowledge. In this system end users can tailor their decision-making requirements using their own judgement to build specific DSSs. In a specific end user's farming context, each specific DSS provides expert suggestions to assist farmers in improving their farming practice. The paper also shows the environment's generic capability.

From inferential statistics to climate knowledge

Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty.

Actionable climate knowledge: From analysis to synthesis

The traditional reductionist approach to science has a tendency to create 'islands of knowledge in a sea of ignorance', with a much stronger focus on analysis of scientific inputs rather than synthesis of socially relevant outcomes. This might be the principal reason why intended end users of climate information generally fail to embrace what the climate science community has to offer. The translation of climate information into real-life action requires 3 essential components: salience (the perceived relevance of the information), credibility (the perceived technical quality of the information) and legitimacy (the perceived objectivity of the process by which the information is shared). We explore each of these components using 3 case studies focused on dryland cropping in Australia, India and Brazil. In regards to 'salience' we discuss the challenge for climate science to be 'policy-relevant', using Australian drought policy as an example. In a village in southern India 'credibility' was gained through engagement between scientists and risk managers with the aim of building social capital, achieved only at high cost to science institutions. Finally, in Brazil we found that 'legitimacy' is a fragile, yet renewable resource that needs to be part of the package for successful climate applications; legitimacy can be easily eroded but is difficult to recover. We conclude that climate risk management requires holistic solutions derived from cross-disciplinary and participatory, user-oriented research. Approaches that combine climate, agroecological and socioeconomic models provide the scientific capabilities for establishment of 'borderless' institutions without disciplinary constraints. Such institutions could provide the necessary support and flexibility to deliver the social benefits of climate science across diverse contexts. Our case studies show that this type of solution is already being applied, and suggest that the climate science community attempt to address existing institutional constraints, which still impede climate risk management.

A systematic approach to biological control agent exploration and prioritisation for prickly acacia (Acacia nilotica ssp. indica)

Agent selection for prickly acacia has been largely dictated by logistics and host specificity. Given that detailed ecological information is available on this species in Australia, we propose that it is possible to select agents based on agent efficacy and desired impact on prickly acacia demography. We propose to use the 'plant genotype' and 'climatic' similarities as filters to identify areas for future agent exploration; and plant response to herbivory and field host range as 'predictive' filters for agent prioritisation. Adopting such a systematic method that incorporates knowledge from plant population ecology and plant-herbivore interactions makes agent selection decisions explicit and allow more rigorous evaluations of agent performance and better understanding of success and failure of agents in weed biological control.

A review of current knowledge of the weedy species Themeda quadrivalvis (grader grass)

This review of grader grass (Themeda quadrivalvis) attempts to collate current knowledge and identify knowledge gaps that may require further research. Grader grass is a tropical annual grass native to India that is now spread throughout many of the tropical regions of the world. In Australia, it has spread rapidly since its introduction in the 1930s and is now naturalised in the tropical areas of Queensland, the Northern Territory and Western Australia and extends south along the east coast to northern New South Wales. It is a vigorous grass with limited palatability, that is capable of invading native and improved pastures, cropping land and protected areas such as state and national parks. Grader grass can form dense monocultures that reduce biodiversity, decrease animal productivity and increase the fire hazard in the seasonally dry tropics. Control options are based on herbicides, grazing management and slashing, while overgrazing appears to favour grader grass. The effect of fire on grader grass is inconclusive and needs to be defined. Little is known about the biology and impacts of grader grass in agricultural and protected ecosystems in Australia. In particular, information is needed on soil seed bank longevity, seed production, germination and growth, which would allow the development of management strategies to control this weedy grass.

Refining the process of agent selection through understanding plant demography and plant response to herbivory

Understanding plant demography and plant response to herbivory is critical to the selection of effective weed biological control agents. We adopt the metaphor of 'filters' to suggest how agent prioritisation may be improved to narrow our choices down to those likely to be most effective in achieving the desired weed management outcome. Models can serve to capture our level of knowledge (or ignorance) about our study system and we illustrate how one type of modelling approach (matrix models) may be useful in identifying the weak link in a plant life cycle by using a hypothetical and an actual weed example (Parkinsonia aculeata). Once the vulnerable stage has been identified we propose that studying plant response to herbivory (simulated and/or actual) can help identify the guilds of herbivores to which a plant is most likely to succumb. Taking only potentially effective agents through the filter of host specificity may improve the chances of releasing safe and effective agents. The methods we outline may not always lead us definitively to the successful agent(s), but such an empirical, data-driven approach will make the basis for agent selection explicit and serve as testable hypotheses once agents are released.

Effects of externally supplied protein on root morphology and biomass allocation in Arabidopsis

Growth, morphogenesis and function of roots are influenced by the concentration and form of nutrients present in soils, including low molecular mass inorganic N (IN, ammonium, nitrate) and organic N (ON, e.g. amino acids). Proteins, ON of high molecular mass, are prevalent in soils but their possible effects on roots have received little attention. Here, we investigated how externally supplied protein of a size typical of soluble soil proteins influences root development of axenically grown Arabidopsis. Addition of low to intermediate concentrations of protein (bovine serum albumen, BSA) to IN-replete growth medium increased root dry weight, root length and thickness, and root hair length. Supply of higher BSA concentrations inhibited root development. These effects were independent of total N concentrations in the growth medium. The possible involvement of phytohormones was investigated using Arabidopsis with defective auxin (tir1-1 and axr2-1) and ethylene (ein2-1) responses. That no phenotype was observed suggests a signalling pathway is operating independent of auxin and ethylene responses. This study expands the knowledge on N form-explicit responses to demonstrate that ON of high molecular mass elicits specific responses.

Herbicide resistance modelling: Past, present and future

Computer simulation modelling is an essential aid in building an integrated understanding of how different factors interact to affect the evolutionary and population dynamics of herbicide resistance, and thus in helping to predict and manage how agricultural systems will be affected. In this review, we first discuss why computer simulation modelling is such an important tool and framework for dealing with herbicide resistance. We then explain what questions related to herbicide resistance have been addressed to date using simulation modelling, and discuss the modelling approaches that have been used, focusing first on the earlier, more general approaches, and then on some newer, more innovative approaches. We then consider how these approaches could be further developed in the future, by drawing on modelling techniques that are already employed in other areas, such as individual-based and spatially explicit modelling approaches, as well as the possibility of better representing genetics, competition and economics, and finally the questions and issues of importance to herbicide resistance research and management that could be addressed using these new approaches are discussed. We conclude that it is necessary to proceed with caution when increasing the complexity of models by adding new details, but, with appropriate care, more detailed models will make it possible to integrate more current knowledge in order better to understand, predict and ultimately manage the evolution of herbicide resistance. © 2014 Society of Chemical Industry.