Economic balance in control

Due to the sporadic nature of stem rot it is uneconomical to apply fungicides routinely, although to be effective they need to be applied before the plant becomes infected. In Australia, growers are advised to consider the current price of both chemical and canola to determine the viability of Sclerotinia control before applying a fungicide (Hind-Lanoiselet and Lewington 2004, and Hind-Lanoiselet et al.

2005). A table is used to help determine the level of Sclerotinia infection that would justify a fungicide application (Table 16.1).

Note: Net returns from Sclerotinia control for each fungicide are based on a 2 t/ha potential yield and chemical and application costs of $82/ha for Rovral, and a rule of thumb that yield loss = 0.5 (disease incidence).

tte data in the table show that:

• A yield loss of 10% to 15% would be required to break even and justify using the fungicide

• A 10% yield loss would represent 20% stem rot in the crop

• A 15% yield loss would represent 30% stem rot in the crop, a high disease level tte RustMan support tool can be profitably used before the rust is seen so that farmers can make early decisions (Gordon Murray, personal communication,

2006). In reality the software is typically not used until the disease has taken hold at which point effective management may not be possible, ttis delay is exacerbated by the limited stock and thus appreciable waiting time for fungicide. To improve this situation RustMan needs to be used early on in the assessment and to facilitate this, relevant output information needs to be effectively communicated through one of the public access web sites or by electronic mail.

Towards the Future

Agriculture in Australia has shown considerable capacity to meet challenges through farm management practice, appropriate crops and cultivar selection, technologies to increase water use efficiency, and pest control. Global warming, however, poses a much greater and broader challenge than those previously experienced and current financial resources may be inadequate. Dissention about the potential outcomes of global warming is problematic. While the agricultural impacts of drought and floods of specific duration and intensity can be estimated, some parts of Australia may, in response to global warming, experience improved conditions as a result of longer growing seasons, fewer frosts, higher rainfall (northern Australia) and increased atmospheric carbon dioxide (Australian Greenhouse Office 2006).

A future trend in climate prediction is likely to be in the area of macroclimate forecasting, with medium range weather forecasts (3-10 days) being increasingly

Table 16.1. Returns from the use of Rovral fungicide for Sclerotinia control

Rovral yield loss On Farn Price Canola ($/tonne) % yield (t/ha) at 2t/ha -

Table 16.1. Returns from the use of Rovral fungicide for Sclerotinia control

Rovral yield loss On Farn Price Canola ($/tonne) % yield (t/ha) at 2t/ha -

















































































































used in operational farm management decisions, ^ere is increasing capacity to integrate seasonal climate forecasts, medium range weather forecasts and historical climate information, to enhance the availability and accuracy of data to be included in proactive decision making. In crop protection there have been simultaneous advances in describing the relationships between plant diseases and crop microclimate, such as those relating to field temperature and leaf wetness.

Further research is, however, urgently required to explore relationships between macroclimate (climate of a region) and microclimate (climate immediately within and surrounding a plant canopy), and this will require improved collection of microclimate and local disease incidence data. Recent work in Australia suggests the value of such information in risk and opportunity-management decision making (Wallace and Huda 2005; Huda et al. 2004).

Epidemiology and risk assessment will undoubtedly play an increasing role in anticipating the complex interaction between climate and disease. In its broadest sense, epidemiology is "the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems" (Last 2001). While originally developed as the science of disease control in human populations (demos being Greek for "the people"), epidemiological approaches are today fundamental to disease control in the agricultural sector.

A basic concept in traditional epidemiology is the Host-Agent-Environment (HAE) disease model which in its simplest form is represented by a triangle as shown in Figure 16.4.

^e model proposes that for a disease to exist, all three co-factors must be present. In the case of the fungal diseases discussed in this paper, these factors include:

• Host (crop) factors: plant species and variety, time of planting, crop rotation, general crop hygiene, coexistence of other pathologies.

• Agent factors: mould types present, load and viability of infectious forms, state of spore activation.

• Environment factors: climate (including macroclimate, microclimate and seasonal change, weather immediately following fungicide or pesticide application,

hail damage and relative humidity), insect damage, sunlight duration, presence of atmospheric gases including carbon dioxide, and acid-forming gases, soil factors (macronutrients, micronutrients, salinity and iron sulfides), irrigation factors (volumetric and qualitative, application technique, leaf runoff, soil pooling, nutrient residue on leaf), availability, type and application of fungicide, and agricultural practice.

Some plant epidemiologists have suggested the addition of a fourth factor, time, to the model (forming a "disease pyramid") to take into account the temporal process of disease development (Stevens 1960; Van der Plank 1975). tte authors, however, view time not as a single, independent variable but as integral to each of the three base variables, because of the need to consider distinct and complex time-series when developing probabilistic risk-factor distributions for a range of polycyclic processes in risk modeling (Zadoks and Schein 1979).

Epidemiology is not only a study system but one committed to the management of problems, tte term "coping strategies" in this chapter title relate to an Australian commitment to view crop disease control not only as a theoretical field but as a field of endeavor aimed at securing sustainable regional economic, social, ecological and health outcomes, tte centrality of integrated plant production and pest control in achieving food security with limited environmental impacts has been clearly identified by major international organizations (Food and Agriculture Organisation 2005; Unnevehr and Hirschhorn 2000).

Good risk assessment alone is powerless to bring about change unless operating within a framework for sound and intersectoral risk management, ttis is particularly true where a project must bring together a number of countries in collaborative effort to ensure effective regional risk management.

At a recent workshop in Hyderabad it was proposed that an integrative model proposed by Derry et al. (2006) be used to guide an Asia Pacific Network research project into Asia-Pacific regional climate and disease risk management (Figure 16.5).

tte model facilitates early identification of climatic hazard or change likely to impact on agricultural security in terms of epidemiological realities (stage 1). Proactive risk assessment (stage 2) incorporates the consideration of existing climate/crop-disease models and the possible development of downscaled models on the basis of local epidemiological records and observed agricultural practice. In practical terms this stage is already under development in Australia, with models relating to disease frequency and impact being investigated. Risk assessment information communicated to government and farming organizations enables the fine-tuning of policy (stage 3), to encourage proactive and cost-effective epidemiological and economic interventions (stage 4). Examples are the application of fungicide during a period of expected high humidity with suitable temperature range for mycotic growth, or the avoidance of fungicidal leaf treatments prior to a period of predicted rainfall, when wash-off can occur. Developing systems for monitoring changes in crop health status following intervention (stage 5) provides feedback for the further fine-tuning of policy and interventions.

It should be noted that the overall process is a cyclic one, with a potential starting point at any one of the five loci, ttus there is no "correct" place to start, and

all meaningful work can be "banked" at a relevant point within the conceptual framework. Effective intersectoral and multi-staged communication of risk lies at the hub of the model, which will involve the development of communication pathways and a common dialogue between scientists, managers and communities, tte Hyderabad workshop was seen to provide opportunities for such collaboration on a regional level.

In terms of the model some envisaged policy-related strategies are:

• tte assistance of agricultural development by anticipating short-term climatic variations, in order to improve economic yield, and hence security relating to food supply with positive outcomes on socioeconomic conditions and population health

• tte provision of a suitable framework for policy modification in the anticipation of important, short-term climatic change, enabling the incorporation of proactive intervention in agricultural practice

• tte exploration of new approaches to managing crop diseases and the application of pesticides and herbicides to ensure economic use, and prevent overuse, as an important component in human health and aquatic ecosystem protection

• tte encouragement of multilateral agricultural risk communication and dialogue between all stakeholders in the agrometeorological process

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