Setting the Scene

In the tropics and sub-tropics, CV is a major source of agricultural production variability (e.g., Hammer et al., 1987; Dilley, 2000). Although most dramatic at the farm level, this effect of CV is apparent throughout entire economies and can even affect macroeconomic indicators such as international wheat prices, employment statistics or currency exchange rates (Chapman et al., 2000b; White, 2000a). In October 2002, media reports in Australia attributed half of the reported inflation rate to the effects of the El-Nino-induced drought.

Since humans began farming, climate variability has influenced people's experiences, which in turn resulted in agricultural systems that are somewhat resilient, i.e., systems that are capable of absorbing some of that variability without immediate disastrous results. An example is dryland winter and summer cropping in the northeastern region of Australia where water is stored in the heavy clay soils over fallow periods. This water is then used by the next crop grown and acts as a buffer against possible low, in-season rain (similar practices exist throughout the semiarid tropics and sub-tropics). Other examples are the wheat/pasture rotations in Southern Australia that remain productive even under adverse climatic conditions (i.e., prolonged droughts or water-logging).

Issues that we have not addressed here are the longer-term implications of CC on enterprise profitability. For instance, some horticultural enterprises, where the set-up cost is substantial and returns are realised on longer time scales, are extremely susceptible to CC. Producers making decisions on introducing such enterprises need to consider not only CV, but also the combined impact of CC and CV.

Although current systems have been developed to cope with the variable climate, they are not necessarily optimally adapted. For instance, relying on fixed fallow lengths can leave fields prone to erosion and drainage below the root zone. Production systems developed during a run of wetter seasons may not be as resilient in drier seasons. Peanut production in Southern Queensland, Australia, for instance, started during the above average summer rainfall conditions of the 1950-1970s but resulted in unrealistically high yield expectations for the changed climate patterns of the 1980s and 1990s (Meinke and Hammer, 1995).

It is important to acknowledge that, although important (and sometimes even dominant), CV is only one of many risk factors impacting on agriculture. From a decision-maker's perspective, it is the consequences of CV and/or CC on possible management responses that are of interest, rather than CV or CC per se. This highlights that forecasts must be appropriately 'contextualised' before they can positively influence decision-making. In other words, causes, choices and consequences must be clearly outlined and quantified (Hayman, 2001). This requires effective cross-disciplinary research and we argue here that such contextualised delivery can only be achieved through an integrated, systems analytical approach (cf. Hammer, 2000). While forecasts in terms of a seasonal rainfall or temperature outlook are often followed with interest, they ultimately may not result in subsequent action (i.e., changed practice). However, the same forecast provided, for instance, in terms of crop or pasture yields has the potential for much stronger impact by influencing the decision-making via the quantification and discussion of decision options (Hayman, 2001).

The picture gets even murkier when we consider that particularly policy decisions relating to agriculture are not made in isolation from other topics such as issues relating to markets, political environment and lobby groups. In this paper, we deliberately excluded this aspect and concentrated entirely on issues within the realm of agricultural production systems. We refer readers interested in these broader societal issues to publications by Buizer et al. (2000), Agrawala and Broad (2002), Agrawala et al. (2001) and IRI (2000).

At the highest level, the problem appears simple: it is about better risk management and our ability to produce food and fibre products economically and in a socially and environmentally desirable fashion.

This is where the complexity starts and differences based on socio-economic conditions emerge: effective risk management in developed countries is about profitability and the tensions associated with economic production and consequent social and environmental risks (i.e., sustainability). In developing countries, sus-tainability issues are often, from a farmer's perspective, secondary and only deserve attention after the most pressing needs for survival are met. We need to break the vicious cycle of poverty and hunger before we can successfully promote sustainable development in these countries.

Profitably applying climate information requires the identification of the key decision points in agricultural production systems. We must not assume that these decision points are the same in developing and developed countries. The prime need from climate forecasting in developing countries may simply be disaster management, i.e., to protect the system against the extremes of CV. For example, in Indo-China the fundamental issue related to CV is the capacity to protect local people, to protect natural ecosystems, and to protect national economies. The current ability to provide protection against the impacts of El Niño and La Niña in many Southeast Asian countries remains limited. This is because both effective climate forecasting systems and linkages to decision systems for this region are still in various stages of development (CERED, 2000; Hansen, 2002). There is a fundamental and urgent need to capitalise on our climate knowledge. We may achieve this by translating that knowledge into meaningful, locally relevant decision options.

Decision makers, who must prepare for the range of possible outcomes, often use conservative risk management strategies that reduce negative impacts of climatic extremes. In favourable seasons, however, this can be at the expense of reduced productivity and profitability, inefficient use of resources, and accelerated natural resource degradation (e.g., under-investment in soil fertility inputs or soil conservation measures). Improvements in our understanding of interactions between the atmosphere and sea and land surfaces, advances in modelling the global climate, and substantial investment in monitoring the tropical oceans now provide some degree of predictability of climate fluctuations months in advance in many parts of the world (Goddard et al., 2001).

Broad and Agrawala (2000) showed how climate forecasting can contribute to elevating vulnerability but caution against seeing it as a panacea for solving future food crises. Clearly, 'risk management' must be seen within the context of the actual risk posed (individual survival, economic and environmental risks) and requires a clear analysis of all contributing factors. The role of climate and climate-related risk management tools must then be established and the chosen strategies must take this into account appropriately. This also requires a careful analysis and understanding of the existing policy framework. Often policies have been developed with the aim to alleviate consequences of high climate variability. Such policies (e.g., income subsidies) can act as disincentives for the adoption of better climate-related risk management strategies.

This fact needs to be considered when evaluating the potential of climate forecasting.

Even under homogenous socio-economic conditions, the requirements ofpoten-tial users of climate forecast information will differ. Firstly, as the title of this paper already implies, there are at least two major stakeholder groups, namely those involved in agricultural planning (mostly policy makers, regulators and large agribusinesses including financial institutions) and those involved directly in agricultural production, i.e., farmers, farm managers, some rural businesses and consultants. Information needs for these groups differ, but even more importantly, their needs vary with on-going changes in socio-economic and market conditions. Tactical as well as strategic decisions must be made all the time and while climate-related information might be highly relevant for some of these decisions, it will be irrelevant for others. Hence, it is no surprise that we generally fail to measure a notable impact of non-contextualised climate forecasts.

When climate forecasting is discussed there is often an implicit assumption that perfect knowledge of, for instance, future rainfall would change the way agriculture is practised. This assumption is rarely challenged, but it touches on two issues that are fundamental when considering the value of climate information in decisionmaking:

The first issue is the notion that such 'perfect knowledge' might be - at least theoretically - achievable. Although we still have much to learn about the underlying physical processes, we now appreciate that climate has many chaotic and non-deterministic features, which will prevent us from ever achieving complete certainty in climate forecasting. Any deterministic forecasting system is therefore either wrong or misleading and should not be endorsed (Meinke et al., 2003).

The second issue is the implicit assumption that a forecast will be useful and lead to improved outcomes. Although many examples can be found where this is the case, others show either negative outcomes or identify decisions that are insensitive to such information. Several conditions must be met before a seasonal forecast will result in an improved outcome: a forecast

• must honestly convey the inherent uncertainty (i.e., the information must be presented in a probabilistic form);

• must be relevant (neither trivial nor obvious; timely);

• must be able to be 'tracked' in terms of how well the forecasts are representing the actual climate;

• must be inclusive of provision of histories of previous forecasts (Pulwarty and Redmond, 1997);

• the information content must be applied (see also Glantz, 1977; Lamb, 1981; Nicholls, 2000).

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