From rainfall to farm incomes transforming advice for Australian drought policy. II. Forecasting farm incomes

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1 CSIRO PUBLISHING Australian Journal of Agricultural Research, 27, 58, From rainfall to farm incomes transforming advice for Australian drought policy. II. Forecasting farm incomes Rohan Nelson A,D, Philip Kokic B, and Holger Meinke C A CSIRO Wealth from Oceans Flagship, GPO Box 284, Canberra, ACT 261, Australia, and formerly ABARE, GPO Box 1563, Canberra, ACT 261, Australia. B ABARE, GPO Box 1563, Canberra, ACT 261, Australia, and CSIRO Wealth from Oceans Flagship, GPO Box 284, Canberra, ACT 261, Australia. C Crop and Weed Ecology, Department of Plant Sciences, Wageningen University, PO Box 43, 67 AK Wageningen, The Netherlands. D Corresponding author. rohan.nelson@csiro.au Abstract. Australian drought policy is focussed on providing relief from the immediate effects of drought on farm incomes, while enhancing the longer term resilience of rural livelihoods. Despite the socioeconomic nature of these objectives, the information systems created to support the policy have focussed almost exclusively on biophysical measures of climate variability and its effects on agricultural production. In this paper, we demonstrate the ability of bioeconomic modelling to overcome the moral hazard and timing issues that have led to the dominance of these biophysical measures. The Agricultural Farm Income Risk Model (AgFIRM), developed and tested in a companion paper, is used to provide objective, model-based forecasts of annual farm incomes at the beginning of the financial year (July June). The model was then used to relate climate-induced income variability to the diversity of farm income sources, a practical measure of adaptive capacity that can be positively influenced by policy. Three timeless philosophical arguments are used to discuss the policy relevance of the bioeconomic modelling. These arguments are used to compare the value to decision makers of relatively imprecise, integrative information, with relatively precise, reductionist measures. We conclude that the evolution of bioeconomic modelling systems provides an opportunity to refocus the analytical support for Australian drought policy towards the rural livelihood effects that matter most to governments and rural communities. Introduction In Australia, drought can have a dramatic effect on farm incomes, with significant flow-on effects throughout the economy and society. As a consequence, Australian drought policy is focussed on providing relief from the immediate effects of drought on farm incomes, while enhancing the longer term resilience of rural livelihoods. Despite the socioeconomic nature of these policy interventions, the information systems used to support the policy have focussed almost exclusively on biophysical measures of climate variability and its effects on agricultural production. In an earlier paper (Meinke et al. 26), we referred to this misalignment between drought science and policy in Australia as a policy relevance gap, and highlighted a range of potential disciplinary and institutional causes. We suggested that institutional reform is required to realign research capacity and incentives to: (1) change the analytical focus of applied climate science; (2) merge climate variability and climate change research; (3) develop more holistic systems of risk management, which consider climate as part of the broader risk management strategies of rural communities; and (4) facilitate sharing of residual risks among governments, rural communities, and individuals. This paper explores the potential of bioeconomic modelling to address the first of these challenges, by refocussing of the analytical support for drought policy on the economic impacts of drought. The development and testing of the Agricultural Farm Income Risk Model (AgFIRM) was reported in the first paper in this series (Kokic et al. 27, this issue). In this earlier paper, we demonstrated how a simple farm income model could be made responsive to forecasts of crop and pasture growth from 2 agroecological models. This was made possible by the innovative use of multiple and M-quantile regression. Model testing demonstrated reliable forecasts of the direction of movement in Australian farm incomes at the beginning of the financial year (July June). The structure of the model, and the seasonal climate forecasting system used, meant that its predictive accuracy was greatest for forecasting the incomes of mixed cropping/livestock farms across the 12 regions of Australia s wheat sheep zone (Fig. 1). In this paper, we demonstrate how AgFIRM can be used to provide objective, model-based forecasts of annual farm incomes in July at the beginning of the financial year. We develop 3 simple examples of the kinds of information that the model can produce to support Australian drought policy including: (1) analyses of the effect of climate variability on farm incomes, using the full record of historical climate data; (2) objective, model-based seasonal forecasts of farm incomes at the beginning of the financial year (June July); and CSIRO /AR /7/114

2 Forecasting farm incomes Australian Journal of Agricultural Research 15 Fig. 1. Australian broadacre regions in the wheat sheep zone. (3) establishing a quantitative link between climate-related income variability and one of the key drivers of resilience that can be influenced by policy, the diversity of on- and off-farm income sources. Analytical support for Australian drought policy Australia s national drought policy has been described in detail by DAFF (26), while its history and development has been comprehensively reviewed by Botterill (25), with early history reviewed by James (1973). In policy development since the early 199s, drought has been considered a natural characteristic of Australia s variable and changing climate, with successful management of climate risk recognised as a definitive characteristic of farming excellence (e.g. Blackadder 25). With this focus on self-reliance, the rationale for providing financial support to farmers is to ensure that farmers with longterm prospects for viability will not be forced to leave the land due to short-term adverse events that are beyond their ability to manage (DAFF 26). The policy has multiple objectives that include [adapted from Drought Policy Task Force 1997, cited in Botterill (25)]: (1) encouraging self-reliant approaches to managing climate variability; (2) protecting the natural resource base during times of extreme climate stress; (3) ensuring adequate welfare support for farm families commensurate with that available to other Australians; (4) ensuring that the policy does not impede structural adjustment in the farm sector; and (5) a high level of awareness and understanding of drought and drought policy. Although self-reliance has been the focus of Australian drought policy since the early 199s (Drought Policy Review Task Force 199), it also provides for short-term relief from exceptional climatic events. Exceptional circumstances relief includes income support, interest-rate subsidies, and income smoothing mechanisms to farmers in regions that are drought declared. To qualify for this support, applications must demonstrate that climate conditions (DAFF 26): were rare (a 1 in 2 25 year event) and severe; resulted in a severe downturn in farm income over a prolonged period (e.g. more than 12 months) for a significant number of farmers in a region or industry; and were not predictable or part of a process of structural adjustment (the policy does not cover downturns in commodity prices). Despite the clear focus of these policies on farm incomes, the analytical support provided to policy advisers for assessing exceptional circumstances applications has focussed almost exclusively on measures of variability in rainfall, temperature, soil moisture, and plant growth of the type described by Laughlin and Clark (2) and Clark et al. (2). This type of analysis focusses on proving that climatic conditions (rather than income variability) represent a 1-in-2 year event. At the beginning of 27, this biophysical emphasis still remains in the newly developed National Agricultural Monitoring System (NAMS) ( nearly 1 years after this divergence between drought science and policy in Australia was first pointed out (Thompson and Powell 1998). Meinke et al. (26) refer to this misalignment of drought science and policy in Australia as a policy relevance gap, highlighting a range of potential disciplinary and institutional causes that need to be addressed. The bioeconomic modelling capacity described in this paper challenges 5 of the key reasons why the analytical support for exceptional circumstances determinations in Australia has focussed on biophysical rather than socioeconomic measures of climate impacts and adaptation. The first key issue is whether biophysical measures of climate variability are in some way more objective and therefore more useful to decision makers than alternative methods of assessing drought (White et al. 25). A key point of contention has been the extent to which survey-based estimates of farm incomes collected during droughts are subject to response bias. The earliest forecasts of farm incomes currently available in Australia have been based on phone surveys with farmers around October each year, when drought conditions have already emerged. Consequently, it has been possible that many of the farmers interviewed at this time of year may be eligible for, and actively seeking, drought assistance. This potentially creates a moral hazard. In this context, moral hazard refers to the possible disincentives that assistance programs may introduce for farmers to manage climate risk effectively, or to accurately report its effect on farm incomes. A second key reason has been an inability to disentangle the effect of climate variability on farm incomes from other sources of variability such as prices and management skill. Without a capacity to distinguish between sources of income variability, policies directed towards reducing the effect of climate risk on farm incomes may inadvertently reduce incentives to better manage other sources of risk. The criteria for exceptional circumstances assistance in Australia recognise that there may be little or no case for government intervention to reduce

3 16 Australian Journal of Agricultural Research R. Nelson et al. income risk arising from sources such as variability in prices or management skill (White et al. 25). A third key reason for the dominance of rainfall over income analysis relates to the timing of farm income data. Preliminary estimates of farm incomes for the July June financial year have only become available towards the end of the calendar year when the main winter cropping season is concluding. This is long after drought conditions have become obvious in May and June via antecedent soil moisture conditions and seasonal climate forecasts for the coming winter season (Hammer et al. 1996; Meinke and Stone 25; Potgieter et al. 25). There is currently no capacity to provide earlier forecasts of the likely effect of expected seasonal conditions on farm incomes. Fourth, when the survey-based estimates of farm incomes have become available around December each year, the methods that can be used to assess whether the income criteria for exceptional circumstance relief are met have been constrained by a limited time series of historical farm income data. Judgements about whether a downturn in income meets the 1 in 2 year criterion for assistance have been made against the variability of farm incomes since , smoothed using Monte Carlo analysis (ABARE 22). This is a relatively short time series against which to assess the range of climate variability likely to be experienced by Australian farmers. Much longer term information on climate variability is available from historical climate records for most populated areas of Australia from the late 18s onwards. Furthermore, Monte Carlo analysis also does not in itself provide a means of isolating the multiple sources of variability in farm incomes. A fifth reason is a misconception over the extent to which biophysical measures of climate variability can inform policy design and implementation. Biophysical measures of climate variability and its effects on soil water, plant growth, and agricultural production provide information on the exposure of rural communities to climate variability. Not only is policy largely powerless to influence the effect of climate variability on these biophysical measures of exposure to climate risk, they also bear little or no relation to more holistic measures of the key policy outcome: the adaptive capacity of rural communities (Nelson et al. 25; Meinke et al. 26). The adaptive capacity or resilience of rural livelihoods can be enhanced through policies that increase the diversity of both farm and non-farm assets and activities from which rural livelihoods are derived, and the flexibility to switch between them (Ellis 2). This is particularly the case when non-farm sources of income less directly affected by climate are available. This approach has been used to analyse the adaptive capacity of rural communities in both developing (Ellis and Freeman 25) and developed nations, including Australia (Nelson et al. 25; Meinke et al. 26). Policies with potential to increase the diversity of farm and non-farm income sources include investment in production, transport, and marketing infrastructure, education and training, regional development, and policies that affect the cost and availability of rural credit (Anderson 23). Method The AgFIRM model developed and tested in an earlier paper (Kokic et al. 27, this issue) was designed to provide objective model-based forecasts of farm incomes at the beginning of the financial year in early July. Here we demonstrate the ability of AgFIRM to: (1) isolate and analyse the effect of climate variability on regional crop and mixed crop/livestock farm incomes using long-term historical climate records; (2) use seasonal climate forecasting to provide objective modelbased forecasts of farm incomes at the beginning of the financial year; and (3) quantify relationships between the diversity of farm income sources and the effect of climate variability on farm incomes. Two alternative base periods were used to calibrate the econometric farm income model (FIM) in AgFIRM for these applications. Data in the base period define the management and enterprise mix of the typical farm in each region, and the relationship between crop and pasture growth and farm incomes. Other parameters adjust output given expected commodity prices (see Kokic et al. 27, this issue, for details). For each application, the model was used to simulate farm incomes 13 times using simulated crop yields and pasture growth for each year from 19 1 to The simulations show what average incomes would have been across each region if the climate for each year between 19 1 and 22 3 had been experienced with the farming systems that existed during the base periods. This isolates the variability of farm incomes due to climate by holding agronomic conditions, prices, farming technology, and management as they were in the base period. Box plots were used as a convenient means to summarise differences in the distributions of simulated farm incomes over the 13 years for each region. For the box plots used in this paper, the median is shown as the solid line in the middle of the box, while the mean is displayed as an X. The upper and lower borders of the box show the upper and lower quartiles of the distribution. Departing from convention, the whiskers of the box plot show the 95th and 5th percentiles of the distribution, consistent with the 1-in-2 year criterion used in Australian drought policy. Effect of climate variability on farm incomes In the first application, AgFIRM was used to distinguish the long-term effect of climate variability on farm incomes from other sources of risk for the 12 regions of Australia s wheat sheep zone (Fig. 1). To demonstrate this capability, average farm financial data for the 1 years from to 2 1 were used to calibrate the FIM. This was done to reduce price and production variability from sources other than climate, which from time to time heavily affect individual regions, enabling a more effective longer term comparison between regions. To compare the variability of farm incomes with different median incomes, the income distributions for each region were standardised by subtracting the median for all 13 years from each percentile of farm income, and then dividing the difference by the median to create a percentage. The regions were then ranked in order of ascending interquartile range across all 13 years of the simulation.

4 Forecasting farm incomes Australian Journal of Agricultural Research 17 Forecasting farm incomes In the second application, the potential for AgFIRM to provide forecasts of farm incomes relevant to Australian drought policy is demonstrated by hindcasting the very different seasons experienced by Australian farmers in 21 2 and 22 3 (see Figs 4a and 5a below). Australia s rainfall variability is strongly influenced by the dynamics of the El Niño/Southern Oscillation (ENSO) phenomena, and the Southern Oscillation Index (SOI) is a convenient method of indexing the state of the ENSO system. Prolonged periods of negative (positive) SOI values are often indicative of El Niño (La Niña) type climatic conditions that generally result in decreased (increased) rainfall probabilities over much of Australia (McBride and Nicholls 1983; Stone et al. 1996). The summer of 21 2 was a neutral SOI period followed by a favourable season with near-record crop and pasture production and high incomes. In contrast, 22 3 was classified as a negative SOI year and followed by severe drought, with the effect of low crop and pasture production on farm incomes partially offset by relatively high commodity prices. For this hindcasting application, the model was calibrated using 2 1 and 21 2 as consecutive base years, and run using forecasts of crop yields, pasture growth, and prices available at the beginning of the forecast years. Maps showing the probability of exceeding median farm incomes are presented, along with maps showing the probability of incomes exceeding the 5th percentile (1-in-2) criterion used for exceptional circumstances determinations. The operation of the AgFIRM model in forecast mode was described and tested in the first paper in this series (Kokic et al. 27, this issue). To forecast farm incomes, each of the distributions of farm income were subdivided into 3 groups of historical analogue years using the Southern Oscillation Index (SOI) phase forecasting system of Stone et al. (1996). Each year of simulated farm income was classified as positive/rising (negative/falling) if the SOI phases were consistently positive or rapidly rising (consistently negative or rapidly falling) at the end of both May and June. All other years were defined as SOI neutral. This results in a stricter classification of year types than the use of an SOI phase at the end of a single month, with 26 years classified as positive/rising, 14 years negative/falling, and 63 years considered SOI neutral. Diversity of farm incomes In a third application, AgFIRM was used to compare the effect of climate variability on farm incomes in each region with an index of the diversity of farm income sources. For this analysis, an income diversity index was derived from 7 sources of income, including income from beef, lamb, wool, wheat, other winter crops, summer crops, and non-farm sources. The value of this index ranges from 1 to 7, with the maximum value achieved only in the unlikely event that each of the 7 income sources contributes an equal share of total income. An average diversity index for each cropping region was calculated for the 1 years between and 2 1, using ABARE s farm survey data. A similar index was previously applied to Australian broadacre farms by Kokic et al. (2). Results Effect of climate variability on farm incomes The effect of climate variability on regional crop farm incomes can be compared using the distribution of simulated farm incomes relative to the long-term median for each region, and displayed using box plots (Fig. 2). Ranking the regions of the wheat sheep zone in order of ascending interquartile range shows in which regions farm incomes are most affected by climate variability. Regions with significant income variability due to climate variability include the Mallee (221), the Eyre Peninsula (421), and the northern and eastern wheatbelt of Western Australia (522). Crop yields and pasture growth in these regions fluctuate with low and uncertain rainfall, and sandy soils provide little stored soil moisture to sustain crop growth when in-crop rainfall is low. Climate variability also has a significant effect on farm incomes across the central western slopes and plains of New South Wales (122), due to inconsistent rainfall and low soil moisture storage in the western districts of this region. Regions with the least income variability due to climate include the central and southern wheatbelt of Western Australia (521) and the south-eastern cropping regions stretching from the Yorke Peninsula in South Australia (422) through central Victoria (223) to the Riverina in southern New South Wales (123) (Fig. 2). All of these regions rely on relatively low but consistent winter rainfall to support crop production. While seasonal rainfall variability can be high across Queensland s eastern Darling Downs (321), its effect on farm incomes is reduced by soils with high moisture-storage capacity. Forecasting farm incomes We compared the relationship between forecast seasonal conditions and the effect of climate variability on Australian crop and mixed crop/livestock farm incomes across all regions (Fig. 3a c). In general, the interquartile range of farm incomes was low in years when the SOI was positive or rising at the end of May and June, with a 75% probability of exceeding the longterm median in most regions (Fig. 3a). In contrast, incomes are much more variable in years with a negative or falling SOI, with a 75% chance of falling below the long-term median in most regions (Fig. 3b). In years with a neutral SOI, the interquartile Deviation from median (%) All Region Fig. 2. The relative variability of mixed crop livestock farm incomes simulated using AgFIRM for 13 years, using historical climate data from 19 1 to 22 3, for the regions of Australia s wheat sheep zone shown in Fig. 1. The results have been standardised by subtracting the median for all 13 years from each percentile of farm income, and then dividing the difference by the median to create a percentage.

5 18 Australian Journal of Agricultural Research R. Nelson et al. 4 (a) Deviation from median (%) (b) 4 (c) All Region Fig. 3. Distributions of simulated farm incomes for each region by year type defined using the SOI. (a) Consistently positive or rapidly rising SOI (26 years). (b) Consistently negative or rapidly falling SOI (14 years). (c) Neutral SOI (63 years). range is somewhere in-between the 2 extremes for most regions, centred around the median for all 13 years (Fig. 3c). AgFIRM hindcast a 7% probability of exceeding median farm incomes across most regions of eastern Australia in 21 2 (Fig. 4b), and a <4% probability of exceeding median incomes in 22 3 (Fig. 5b). While this strongly reflects the direction of movement in crop farm incomes across eastern Australia in these years, high commodity prices moderated the effects of lower production in 22 3 (Figs 4a, 5a).The ability of the model to predict the direction of movement in farm incomes for these 2 years was lower in Western Australia, where seasonal conditions are not as strongly related to the SOI. For eastern Australia, the model predicted that the probability of incomes falling below the 5th percentile in 21 2 was <1% (Fig. 4c). In contrast, in 22 3 the probability of farm incomes falling below the 5th percentile was forecast to be significantly higher at 1 5% (Fig. 5c). The first paper in this series (Kokic et al. 27, this issue) revealed that smoothing during parameterisation of the model reduces its ability to forecast extreme values of farm incomes more precisely. Diversity of farm incomes A comparison of climate-related income variability and the diversity of income sources revealed a distinct negative correlation. Cropping regions with relatively high (low) variability of farm incomes due to climate variability also tend to have relatively low (high) diversity of income sources (Fig. 6a, b). Regions with moderate to high income variability due to climate, such as the Mallee (221), the Eyre Peninsula (421), and the northern and eastern wheatbelt of Western Australia (522), also tend to have a low diversity of incomes sources. Farm households in these regions are likely to be less resilient to climate variability and change because of fewer

6 Forecasting farm incomes Australian Journal of Agricultural Research 19 (a) (a) Percentile Percentile (b) (b) Probability (%) Probability (%) (c) (c) Probability (%) Probability (%) Fig. 4. Actual v. forecast ranking of farm incomes in (a) The percentile rank of observed income in 21 2 relative to all observed values from to (b) The probability of exceeding median income for 21 2 for cropping and mixed crop livestock farms simulated over 13 years using AgFIRM. (c) The probability of farm incomes falling below the 5th percentile for 21 2 for cropping and mixed crop livestock farms simulated over 13 years using AgFIRM. Fig. 5. Actual v. forecast ranking of farm incomes in (a) The percentile rank of observed income in 22 3 relative to all observed values from to (b) The probability of exceeding median income for 22 3 for cropping and mixed crop livestock farms simulated over 13 years using AgFIRM. (c) The probability of farm incomes falling below the 5th percentile for 22 3 for cropping and mixed crop livestock farms simulated over 13 years using AgFIRM. opportunities to substitute between income sources in response to a variable climate. Regions with low income variability due to climate include the central and southern wheatbelt of Western Australia (521), the Yorke Peninsula (422), central Victoria (223), and the Riverina (123). With the exception of the Yorke Peninsula, each of these regions also tends to have a moderate to high diversity of income sources. The correlation between regions with high (low) income variability and low (high) diversity of income sources was confirmed using a Spearman rank correlation test. The Spearman rank correlation coefficient between the interquartile range of farm incomes and the diversity of income sources across the 12 cropping regions of Australia s wheat sheep zone was 45%. Even with only 12 sample points, this finding was statistically significant at a 1% level. A similar correlation ( 4%) was

7 11 Australian Journal of Agricultural Research R. Nelson et al. (a) (b) Fig. 6. (a) Inter-quartile range of simulated farm incomes over 13 years, using the 1 years to 2 1 as a base. (b) Average diversity index of farm household income over the 1-year period to 2 1. derived by Kokic et al. (2) from an earlier version of this model at the individual-farm scale, with a very high level of statistical significance (<.1%). Discussion The results show that bioeconomic modelling with AgFIRM has potential to overcome the moral hazard and timing issues that have led to the dominance of historical rainfall and temperature analysis in the analytical support provided for Australian drought policy. The bioeconomic model overcomes the risk of moral hazard by using objective model-based forecasts of crop and pasture growth to forecast farm incomes. Its design also isolates the production-related effects of climate variability on farm incomes from other sources of risk such as price and management skill. The use of ENSO-based seasonal climate forecasting techniques enables probabilistic forecasts of regional farm incomes to be produced at the beginning of the financial year, several months earlier than is currently possible. The use of historical climate data ( 1 years) to simulate the effect of climate variability on farm incomes provides a much longer term view of the range of variability than that contained in historical records of farm incomes ( 3 years). Bioeconomic modelling with AgFIRM also enables a relationship to be established between the effect of climate variability on farm incomes and policy-relevant measures of the diversity of farm income sources. In its current form, the AgFIRM bioeconomic modelling system produces regional forecasts of the direction of movement in annual farm incomes at the beginning of the financial year. Its potential usefulness for supporting drought policy depends partly on future scope to improve the model, discussed below. However, its usefulness also depends on correctly identifying the potential value of imperfect, probabilistic forecast information for managing uncertainty in complex social ecological systems. We argue that less precise systemslevel information can provide more confidence to decision makers than more precise information about individual-system components. As quipped by George Box, a pioneer statistician in the areas of quality control, time series analysis, Bayesian analysis, and experimental design: All models are wrong. Some are useful. (Box 1979, p. 22). Can bioeconomic systems models be relatively imprecise but still useful to decision makers? We have identified 3 timeless philosophical reasons why integrative information provided by bioeconomic models is likely to provide more confidence in implementing drought policy than traditional biophysical measures. The first is that the apparent precision provided by scientific measures of individual sources of risk does not necessarily inform important dimensions of uncertainty surrounding the emergent properties of complex systems. This is because risk can only be precisely quantified in relatively simple, closed systems with transparent and measurable links between cause and effect (Berstein 1996). As pointed out by Berstein (1996), in the same way that a die only and always has 6 sides, and a roulette wheel 36 numbers, relatively closed natural and human systems are more amenable to measuring the probability of expected outcomes. That is, the possible outcome of future events can be understood by observing the past frequency of cause and effect. Knight (1921) and Keynes (1921) pointed out that cause and effect relationships become considerably more difficult to define as the systems under analysis become more complex and open. In the extreme, as for example with larger socioeconomic systems, there may be no scientific basis on which to form any calculable probability whatever. We simply do not know! (Keynes 1937, p. 214). On this scale, the agricultural systems on which Australian drought policy is focussed, are intermediate in complexity. Effective decision support requires causal understanding of the relationship between climate and plant growth to be viewed in the context of behavioural responses within farm management more generally, including multiples sources of risk. The fact that a holistic evaluation of multiple sources of risk is generally less precise than a more reductionist quantification of a single source of risk (e.g. rainfall) is a logical consequence of the analysis. According to Walker and Marchau (23), this means that policy processes have to accept that fuzzy answers may be the best expression of expertise; [while] scientists will have to learn that the identification of the fuzzy borderline between knowledge and ignorance may be the sign of real competence (p. 2). In systems of intermediate complexity, quantitative scientific measurement is likely to only ever be partly available to support

8 Forecasting farm incomes Australian Journal of Agricultural Research 111 policy decision making, resulting in islands of understanding in oceans of ignorance (Lowe 22). This is a point often missed by drought scientists passionate about their research, and policy advisers seeking an objective basis for allocating drought assistance. The partial ability of climate science to resolve uncertainty in agriculture by no means diminishes its value or the value of scientific process more generally. Quite the opposite is the case: it increases the relevance and the credibility of science as one of many bases for decision making (Walker and Marchau 23). A second philosophical reason why imprecise, integrative information is likely to provide more confidence in decision making than precise, reductionist information, is the subjective way in which quantitative information is used in decision making. In 1738, Daniel Bernoulli showed that knowledge of the mathematical probability and likely consequences of events is subjectively assessed quite differently by different individuals (Bernoulli 1738). This concept remains one of the key principles of modern risk management in agriculture (Hardaker et al. 24). The subjective assessment of risk by individuals depends critically on economic concepts of value: the magnitude of expected losses or gains relative to what the decision maker already has, often expressed in terms of wealth. Farm incomes provide a direct and integrative measure of value by which individuals can subjectively assess the effects of climate variability. Biophysical measures such as rainfall and temperature are inputs to agriculture, and the causal link to economic value in agricultural systems can be difficult to identify (Kokic et al. 27, this issue). A third philosophical argument in support of integrative analytical systems for Australian drought policy is the question of contextual framing for decision support. Kahneman and Tversky (2) showed that outcomes of decision making under uncertainty depend critically on how the problem is framed. Their work confirmed the universal adage of asking the right question in order to get the right answer. Analytical support for Australian drought policy is currently answering questions like has it rained? or have pastures grown?, when the policy question is what effect have climate conditions had on farm incomes and the welfare of rural communities?. The bioeconomic modelling system we have presented in this paper, along with advances in seasonal climate forecasting, could answer questions like what is the likely effect that expected climate conditions will have on farm incomes in the coming year?. According to the famous statistician John Tukey, often considered to be the father of modern exploratory data analysis: Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise (Tukey 1962, p ). Opportunities to improve the predictive accuracy of AgFIRM were discussed in the first paper in this series (Kokic et al. 27, this issue). This application has highlighted several opportunities to enhance the design and application of the model to support policy. For example, an advantage of AgFIRM is its capacity to isolate climate-related variability in farm incomes from other sources of variability, particularly prices and management skill. Future developments could include modelling the combined effect of market and climate risk on farm incomes, integrating research on the effect of climate variability and change on global trade and commodity prices. Future versions of the model could be enhanced to provide higher spatial resolution to better support drought policy. The current version of the model produces forecasts of farm incomes for broad agricultural regions that contain multiple local-government areas (shires). This is a limitation for drought policy because exceptional circumstances claims are often made at a shire scale. This could be overcome in an operational version of AgFIRM because the underlying econometric model was developed for individual farms. The model was applied to broader regions in this paper because this is the scale at which the most statistically significant relationships between farm incomes and crop and pasture growth were obtained during model development and testing (Kokic et al. 27, this issue). In future versions, parameters of the model calibrated at a regional scale could be applied to farm-level models, enabling the results to be presented at any spatial aggregation. Over time, the spatial resolution of bioeconomic modelling systems could be enhanced by increasing the sampling intensity of the farm survey data used to calibrate the model, so that it better matches the spatial resolution of the agroecological models used in the analysis. Conclusions Overcoming the moral hazard and timing issues associated with income forecasts brings into question the dominance of historical rainfall analysis in the analytical support provided for drought policy. Income variability is a much more appropriate and relevant measure of the capacity of rural communities to adapt to the effects of climate variability. Historical rainfall and temperature analyses provide some insights into the exposure of rural communities to climate risk, but few insights into their ability to cope with it. To our knowledge, there are currently no reliable policy options for making it rain, or changing the temperatures that rural communities experience, at least in the short-term. In contrast, we have shown that bioeconomic models can be used to establish a direct relationship between the effect of climate variability on farm incomes, and the diversity of farm income sources. This means that building the resilience of rural livelihoods by increasing the diversity of farm income sources is likely to be an effective and measurable objective for drought policy. The results of this research demonstrate the technical feasibility of refocussing the analytical support for drought policy in Australia on the intended livelihood outcomes of the policy. The policies themselves are designed to enhance the selfreliance of rural communities, yet approaches for analysing the adaptive capacity of rural communities are poorly developed. We have demonstrated that models that integrate the biophysical and socioeconomic effects of climate on rural livelihoods can be used to turn seasonal climate forecasts into relevant and timely policy advice. The evolution of this technology provides an opportunity to refocus the data, tools, and institutions that comprise the analytical support for Australian drought policy towards the rural livelihood effects that matter most to governments and rural communities.

9 112 Australian Journal of Agricultural Research R. Nelson et al. Acknowledgments The research carried out in this project was partly funded by the Grains Research and Development Corporation, building on earlier research for the Managing Climate Variability Research and Development Program. The vision and support of these two agencies are gratefully acknowledged. Some of the key concepts explored in this research were conceived years before it commenced, in discussions with Graeme Hammer and Roger Stone. The authors gratefully acknowledge the assistance of Vernon Topp, Lisa Elliston, and Peter Martin in helping to refine earlier versions of this paper. For their generous collaboration the authors gratefully acknowledge the efforts of Andries Potgieter, Dorine Bruget, John Carter, and Beverley Henry. The insightful comments of an anonymous referee are gratefully acknowledged. 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