Holger Meinke¹, Graeme Hammer¹, Roger Stone², Scott Power³, Rob Allan⁴, Andries Potgieter² and Mark Howden⁵

Climatic variability in Australia occurs at widely varying time scales. Better knowledge of such variability combined with probabilistic forecasting capabilities is valuable for agricultural decision making. A range of current research projects aim to integrate the ever increasing understanding of sources of climatic variability. Agricultural simulation models can help to add value to this improved understanding of climate variability and are used to objectively assess management options. Decisions based on such tools ranges from short-term, tactical crop management options to policy decisions about future land use.

1. Introduction

Climate, and in particular rainfall variability and its interaction with land management has shaped Australian agriculture since the beginning of white settlement over 200 years ago. In less than a century European settlers had transformed much of Australia’s natural landscape. Extreme climate events combined with factors such as overgrazing resulted in major long-term resource degradation (McKeon et al., 1990). High rainfall variability is also the major source of dryland yield fluctuations (Hammer et al., 1987).

Although most dramatic at the farm level, the impact of climatic variability is apparent throughout the entire Australian economy and can even affect macroeconomic indicators such as international wheat prices (Chapman et al., 2000), employment or the exchange rate (White, 2000). To remain economically viable in an internationally competitive market, Australian farmers have to devise management options that can produce long-term, sustainable profits in such a variable environment. This requires a sound understanding of the sources of rainfall variability, their degree of predictability and objective tools to assess management options in agronomic, economic and environmental terms. It also requires that issues of temporal and spatial scale are addressed explicitly.

In this paper we will show how a targeted forecasting approach at a range of temporal scales combined with simulation modelling can facilitate better understanding of this climate variability in cropping systems and help to reduce risks and capitalise on the up-sides of the climate seesaws. Similar approaches are being applied to grazing systems (eg. Carter et al., 2000).

2. Climate varies at a range of scales

Research and experience over recent decades has shown that the El Niño - Southern Oscillation phenomenon (ENSO) plays a critical role in explaining rainfall variability. However, it is not the only source of variability. There are a range of other climate phenomena varying at a wide range of time scales and our understanding of the underlaying processes is increasing rapidly. A quick scan of the literature reveals the considerable research effort currently being invested in order to better understand these phenomena. These range from work on high frequency phenomena such as the Madden-Julian Oscillation (also known as the ‘30 to 50 day wave’), to ENSO related information (eg. SOI or SST based forecasting systems), to decadal and multidecadal rainfall variability and finally to greenhouse related changes in climate patterns (eg. Stone et al., 1996; Power et al., 1999; Timmermann et al., 1999; Tourre and Kushnir, 1999; White, 1999; Allan, 2000). Our enhanced understanding of the causes of this variability and our increasing ability to predict these cycles and their likely consequences has made ‘managing for climate variability’ an important feature of Australian farming system.

In managing agricultural systems, farmers make decisions that are influenced by many factors. While economic returns are of primary importance, decisions are also based on perceived risk of economic loss, weed and disease control, the risk of soil degradation, and lifestyle. Most management decisions have to fit within a whole farm strategic plan such that many decisions are planned months ahead and their consequences seen months afterwards. This requirement for a certain lead-time between deciding on a course of action and realising its results is a characteristic of managing cropping and grazing systems (Carberry et al., 2000; Carter et al., 2000).

Decisions that could benefit from such targeted forecasts are made at a range of temporal scales. These range from tactical decisions regarding the scheduling of planting or harvest operations to policy decisions regarding land use allocation that are outside the control of individual producers (eg. grazing systems vs cropping systems). Table 1 gives a few examples of these types of decisions at similar time scales to those seen in climatic patterns.

Analysing agricultural systems and their alternative management options experimentally and in real time is generally not feasible because of the length of time and amount of resources required. Alternatively, well-tested simulation approaches offer a time and cost-efficient alternative to experimentation on the physical system and results can be obtained in hours or days rather than years or decades. This provides the capacity to assess a large a number of combinations. Today simulation analyses have become a legitimate means of evaluating policy and resource management issues (eg., Nelson et al. 1998; Howden et al. 1999), but they also provide valuable information for on-farm decision making (Carberry et al., 2000; Meinke and Hochman, 2000).

Decision making in agriculture happens at many levels and involves a wide range of possible users. To provide these clients with the most appropriate tools for decision making requires a clear focus on their specific requirements and needs. Although farmers are one obvious client group they are not necessarily the ones most responsive to a forecast. This responsiveness depends very much on the socio-economic and political circumstances, local infrastructure and the agricultural system in question. To identify clearly clients and their decision points it is helpful to classify them according to geographic scale and information needs. Such conceptual framework assists in identifying the information needs of decision makers, it also assists in selecting the most appropriate and efficient tools to use. Although modelling approaches are frequently the tools of choice, the type of model required will differ depending geographic scale, required inputs and information needs.

Some specific examples of the value for decision making from such research are:

1. cotton growers in Queensland, many of whom are now scheduling the timing of their cotton harvests based on the expected passing of the next 30 to 50 day wave;
2. farmers in northeastern Australia who use ENSO-based information to tailor their rotations and crop management based on local conditions at the time and rainfall probabilities for the coming months (Meinke and Hochman, 2000);
3. bulk handling and marketing agencies who require accurate regional commodity forecasts to assist them in storage and transport logistics and export sales well before harvest (Hammer et al., 2000);
4. government agencies who require objective assessments of the impact and severity of climate variability on production (eg. Keating and Meinke, 1998) and
5. policy makers who require impact assessments of greenhouse scenarios for input into international treaty negotiations (eg. Howden et al., 1999).

Other applications are currently under development and will incorporate climatic patterns associated with, for instance, the latitude of the subtropical ridge (Stone, 2000, pers. com.), the Antarctic Circumpolar Wave (White, 2000) and decadal and multi-decadal climate signals (Meinke et al., 2000)

This paper draws on work from a wide range of current research projects funded by DPI, CSIRO, LWRRDC, GRDC, CRDC, the Australian Greenhouse Office, START and the APN. These contributions are gratefully acknowledged.