constraints and opportunities in applying seasonal climate forecasts in agriculture

CSIRO PUBLISHING

www.publish.csiro.au/journals/ajar Australian Journal of Agricultural Research, 2007, 58, 952–965

Constraints and opportunities in applying seasonal climateforecasts in agriculture

Andrew AshA,E, Peter McIntoshB, Brendan CullenA, Peter CarberryC, and Mark Stafford SmithD

ACSIRO Wealth from Oceans Flagship, 306 Carmody Rd, St Lucia, Qld 4067, Australia.BCSIRO Wealth from Oceans Flagship, GPO Box 1538, Hobart, Tas. 7001, Australia.CCSIRO Wealth from Oceans Flagship, 203 Tor St, Toowoomba, Qld 4350, Australia.DCSIRO Sustainable Ecosystems, GPO Box 284, Canberra, ACT 2601, Australia.ECorresponding author. Email: [email protected]

Abstract. Climate variability has an enormous impact on agricultural productivity, rural livelihoods, and economicsat farm, regional, and national scales. An every-day challenge facing farmers is to make management decisions in theface of this climate variability. Being able to minimise losses in droughts and take advantage of favourable seasons isthe promise of seasonal climate forecasts. The criteria for their adoption depends on what variables are forecast, theiraccuracy, the likely economic and/or natural resource benefits and how well they are communicated. In reviewing howcurrent seasonal climate forecasts meet these criteria, it is clear that they offer considerable potential to buffer the effectsof climate variability in agriculture, particularly in regions that have high levels of inter-annual rainfall variability andare strongly influenced by El Nino and La Nina events. However, the current skill, lead time, relevance to agriculturaldecisions, and communication techniques are not well enough advanced and/or integrated to lead to widespread confidenceand adoption by farmers. The current challenges are to continue to improve forecast reliability and to better communicatethe probabilistic outputs of seasonal climate forecasts to decision makers.

Additional keywords: ENSO, adoption, communication.

Introduction

The strongest driver of inter-annual variability in agriculturaloutput in many environments is climate variability. El NinoSouthern Oscillation (ENSO) alone can explain 15–35% ofglobal yield variation in wheat, oilseeds, and coarse grainsexperienced in the last 40 years (Ferris 1999). A recent exampleis the severe 2002 drought in Australia, which was estimated tohave reduced Australia’s agricultural output by 30% or $AUD8 billion, decreased gross domestic product (GDP) by 1.6%,and lowered employment by 70 000 jobs (Adams et al. 2002).Similarly the 1994–95 drought in Australia resulted in a declinein gross agricultural production of 9.6% or $AUD 4.8 billion,reduced GDP by 1.1%, and export volumes by 6.3% (Hoganet al. 1995). In contrast, in a good recovery year after a drought,the value of Australian farm production might increase by30–40% (White 2000). These variations in gross agriculturalproductivity due to climate are supported by recent work atthe farm scale, which showed that in the wheat–sheep zoneof Australia, farm cash income was simulated to be belowAUD $70 000 in poor seasons and around AUD $120 000 infavourable seasons (Nelson and Kokic 2004). In other parts ofthe world, both El Nino and La Nina can inflict similar economiccosts to agriculture. In India, a failure in the monsoon seasonresults in a 10–20% decline in total food grain production(Krishna Kumar et al. 2004). In the United States, El Ninoevents have been estimated to cost $USD 1.5–1.7 billion and LaNina events cost $USD 2.2–6.5 billion because of the variable

effects they have on different agricultural regions (Adamset al. 1999).

It is clear that climate variability has played a significant rolein shaping global agricultural production and will continue to doso. One of the challenges facing farmers is to make appropriatemanagement decisions in the face of this climate variability.Although a great deal of importance is placed on avoiding lossesin a drought year, it is even more important to make the most ofgood years. In commercial dryland farming regions, 70–80% offarm profit may be made in just 30% of years (Egan and Hammer1995).

One means of minimising losses in droughts and takingadvantage of favourable seasons is through the use of seasonalclimate forecasts. In the context of this paper, seasonal climateforecasts refer to prediction of climate for a period of 3–12months ahead. While the emphasis in this paper is on seasonalclimate forecasts it is important to remember that they are partof a complex global climate system that is composed of signalsin global sea-surface temperatures (SSTs) and atmosphericpressures at quasi-biennial (e.g. 2.5 years), inter-annual (e.g.3–7 years), quasi-decadal (e.g. 11–13 years), and inter-decadal(e.g. 15–20 years) time scales (Allan 2000) as well as generationtime scales of 20–30 and 60–80 years. In addition, long-termclimate variability is also influenced by natural forcings (solarvariability, volcanoes) and anthropogenic forcings (greenhousegas emissions, stratospheric ozone depletion, and sulfate

© CSIRO 2007 10.1071/AR06188 0004-9409/07/100952

Climate forecasts in agriculture Australian Journal of Agricultural Research 953

aerosols). Thus, an important criterion for evaluating thereliability of seasonal forecasts is the extent to which theresulting decision-making systems also address other time scalesassociated with the climate system.

Over the last 2 decades the relationship between oceanphenomena such as El Nino and La Nina and rainfall or cropyields has been well established in Australia (Nicholls 1985,1986), southern Africa (Cane et al. 1994), and other parts ofthe world. The potential to use this relationship in a predictiveway through seasonal climate forecasts was first demonstrated inAustralia by McBride and Nicholls (1983) who examined laggedcorrelations between district rainfall and various measures ofthe intensity of the Southern Oscillation Index (SOI). Clewettet al. (1988, 1990) and Stone and Auliciems (1992) took thisapproach further in terms of operational climate risk assessmentby dividing the SOI into 3 classes or 5 phases, respectively.Seasonal climate forecasts and their application have receivedconsiderable attention since then because of their significantpotential to buffer agricultural production against the effects ofclimate variability by allowing producers to minimise risk andmaximise opportunities.

However, despite the potential benefits that seasonal climateforecasts offer and the considerable effort that has gone intotheir promotion, especially in Australia, evidence of their benefitis not clear. Surveys in Australia show that 77% of farmerswere aware of seasonal climate forecasts but the percentageof farmers using forecasts was just 44% (Agriculture Fisheriesand Forestry Australia 2003). In broader natural resourcemanagement issues such as catchment water management,seasonal climate forecasts have not yet been used (Ritchieet al. 2004a). What are the constraints to realising greaterbenefits of seasonal climate forecasts in agriculture? Are theforecasts not skilful enough? Are the forecasts not predicting themost appropriate variable? Do forecast information structuresfail to adequately match management decision structures? Isthe difficulty in communicating forecast information and thebenefits of forecasts in a way that engenders confidence amongthe rural sector? Is the difficulty in quantitatively measuring thedirect benefit of using or not using a seasonal climate forecast?In this paper we attempt to address some of these issues, namelythose associated with forecast skill, application of forecasts, andtheir uptake by the farming community.

Criteria for forecasts to be useful

For forecasts to be of value to agriculture they must meet severalcriteria (Hansen 2002; Meinke and Stone 2005), which include:

(a) being reasonably accurate and relevant to the scale ofapplication;

(b) forecasting the most appropriate variable for different typesof decisions in particular agricultural sectors, and havinglead times appropriate to the decision type;

(c) having economic benefit when applied in the context of thewhole agricultural system and its complexity, and wherepossible, enhancing sustainability;

(d) being unambiguously communicated and targetted tofarmers who have the capacity and willingness to changebehaviour, i.e. farmers must be willing to update theirclimate beliefs and overcome cognitive biases.

Forecast skill

Most forecasts used in agriculture are based on statisticalapproaches that use historical records of SSTs or sea-levelpressure, e.g. the SOI and the SOI phases (Stone and Auliciems1992). These climate indicators are statistically linked tovariables of interest, e.g. rainfall, or dynamic climate modelsthat are based on physical processes in the ocean and atmosphere,e.g. Walker circulation (Cane 2000). More recently there havealso been advances in combining statistical approaches, coupledocean–atmosphere models, and expert opinion in assimilatedor integrated forecasts (Bates et al. 2000; Berri et al. 2005;Coelho et al. 2006).

In regions strongly affected by ENSO such assouthern Africa, India, eastern Australia, and parts ofSouth America, forecasts tend to be dominated by statisticalapproaches that use ENSO phases (Stone et al. 1996; Meinkeand Hochman 2000; Meza and Wilks 2003). In areas whereENSO has less of an influence (e.g. Europe, temperate Asia)there is more rapid evolution towards dynamic coupledmodel approaches (Cantalaube and Terres 2005; Tippettet al. 2005) because statistical approaches offer little forecastskill. However, in the ENSO-affected regions of the worldthere is reasonable forecast skill associated with statisticalapproaches that predict rainfall in the following 3 months.This is highlighted in Fig. 1, which shows for Australiathe skill of the Bureau of Meteorology operational modelbased on EOF (empirical orthogonal functions) patterns ofPacific and Indian Ocean SSTs (Drosdowsky and Chambers1998; Chambers et al. 2002). The measure of accuracy inthese forecasts is ‘Percent Consistent’ or ‘Hit Rate’, whichrefers to the percentage of forecasts that were consistentwith the category later observed, i.e. 50% or less representsclimatology (no significant difference from the long-termaverage climate) in situations where there are 2 forecastcategories, while for tercile forecasts (3 categories)climatology is a hit rate of 33% in each category. It isrecognised that this simple measure of forecast skill largelydeals with reliability and does not address other aspectsof forecast quality including the magnitude of the shift indistribution (mean or median) or changes in dispersion orvariability (Potgieter et al. 2003). Another important aspect ofdetermining forecast skill is to ensure that forecasts, particularlymulti-variate statistical forecasts, are cross-validated to removeany biases or artificial skill (Drosdowsky and Chambers 1998;McIntosh et al. 2005).

The skill analysis reveals that the Bureau of Meteorologystatistical forecasts have most skill over eastern Australiaduring July–November, with the highest skill evident innorth-eastern Australia (Fig. 1). A formal analysis of theforecasts reveals that rainfall forecasts have performed betterthan climatology (Fawcett et al. 2005). However, for muchof Australia the skill levels remain low for much of the year,which is consistent with other assessments of temporal andspatial skill (Henry 2006), thus highlighting how care needsto be exercised in using and applying forecasts (Vizardet al. 2005).

In general, forecast skill in Australia using statistical seasonalclimate forecasts compares favourably with forecast skill over

954 Australian Journal of Agricultural Research A. Ash et al.

Australian Government

Bureau of Meteorology

75%

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Fig. 1. Spatial forecast accuracy of the Bureau of Meteorology 3-month seasonal rainfallforecast for the September–November period based on a 2-category forecast (above and belowmedian rainfall), where 50% represents climatology. Figure supplied by R. Fawcett, Bureau ofMeteorology, Melbourne.

South America (Berri et al. 2005) and Africa (Adejuwon andOdekunle 2004). Berri et al. (2005) found that in the 20–40◦Sregion of South America, east of the Andes, only 30% of theregion has forecast skill that is potentially useful and even thenwith only moderate levels of skill (55–65%).

Forecast skill of dynamic coupled models is generallyworse than that of statistical approaches (Folland et al. 2001;Moura and Hastenrath 2004), although coupled models aremore skilful than statistical forecasts in some instances (vanOldenborgh et al. 2005), especially in forecasting the occurrenceof extremes (Syktus et al. 2003). With the development ofassimilated and integrated forecasts that link dynamic modeland statistical approaches, there is evidence that forecastskill may be comparable with purely statistical approaches(Coelho et al. 2006).

A key impediment to forecast uptake in agriculture is theperceived lack of skill: 76% of respondents in a survey of over2500 farmers in Australia identified forecast reliability as therationale for not using seasonal climate forecasts (AgricultureFisheries and Forestry Australia 2003). The example forecastaccuracy map for Australia for the period September–November(Fig. 1) indicates that, for much of Australia, seasonal climateforecasts are only 50–60% ‘accurate’, where 50% accuracyrepresents climatology. So at what skill level do forecastsbecome unequivocally beneficial? The answer to this will verymuch depend on how, when, and where the forecasts are beingapplied. It has been shown in extensive beef environments thatas inter-annual rainfall variability increases so do the benefitsfrom regularly adjusting stock numbers in response to variableforage supplies (Ash et al. 2000). Similarly, where inter-annualvariability of rainfall or a production indicator such as crop yieldis high, then it is likely that forecasts of relatively lower skill will

be useful. However, where inter-annual variability in rainfall orcrop yield is low and seasons are reasonably reliable, forecastskill will need to be good to be of benefit.

While the level of forecast skill considered to be ‘useful’will vary among regions, farming systems, and individualperceptions of utility, there have been some attempts to quantifythe level of forecast skill required to ensure that adoptionoccurs. Ziervogel et al. (2005) demonstrated that, if forecast trustincreases with each forecast that is considered to be accurateand declines when forecasts are perceived to be inaccurate, thenwith a forecast accuracy of 60% it can take at least 15 years tobuild up a threshold level of trust that would lead to adoption.It is unlikely that most farmers would continue to experimentwith forecasts for 15 years, i.e. threshold trust levels need to beachieved considerably sooner. From this it could be concludedthat forecasts need to be at least 65–70% accurate to achievelong-term trust and adoption. This is consistent with otherstudies (e.g. Jochec et al. 2001; Leith 2006) that have shownan accuracy of 70–80% is required and that the forecasts wouldhave to be proven for a 4–5 year period before they wouldbe adopted.

Adoption of forecasts may also be influenced by the fact thatforecast skill varies over longer time periods (Power et al. 1999),within seasons, and spatially (Maia and Meinke 2006; Maia et al.2007). For example, there can be periods of 5 years or morewhere forecast accuracy is well below 60% and this poses seriouschallenges in achieving adoption or in maintaining trust for thosewho are using forecasts (Fig. 2). For much of eastern Australiathese periods of low forecast skill coincide fairly closely with thepositive (warm) phase of the Inter-decadal Pacific Oscillation(IPO) (Power et al. 1999). McKeon et al. (2004) have shownthat these phases of the IPO can be used in conjunction with the


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Fig. 2. Temporal variation in forecast skill (based on correlation (r) values) for north-easternQueensland, using a sliding 10-year correlation. Results are based on the experimental SST statisticalforecasting system of McIntosh et al. (2005).

SOI to better discriminate year types than using the SOI alone.However, while the IPO index can be used to explain historicalinteractions with ENSO on rainfall variability there is still debateas to whether it can be used in a predictive capacity (Meinke et al.2005). For example, White et al. (2003) demonstrated that quasi-decadal and bi-decadal signals could explain a high proportion ofhistorical variability in several regions of Australia’s rangelands,but their attempt to develop a forecasting system was equivocal(White et al. 2004). While the predictability of inter-decadalocean patterns is still being researched, experimental forecastsystems that link both ENSO and inter-decadal signals havebeen developed (e.g. SPOTA-1, Day et al. 2000; Crimp and Day2003). In addition, information on inter-decadal variability hasbeen combined with probabilities of ENSO to form compositeforecasts with longer lead times than just seasonal forecasts(Henry et al. 2004). Composite forecasts are becoming morecommon and also offer a means of combining output from GCMsand statistical forecasting approaches. It is likely that compositeforecasts will become more common (Goddard et al. 2003).

In addition to variation in skill at the inter-decadal time scalethere is also a considerable amount of variation in skill withina year (Fig. 3). Seasonal climate forecast skill is generally lowin the autumn period because this is a time of rapid change inocean currents and temperatures in the equatorial region and hasbecome known in Australia as the autumn predictability barrier.Low predictability at this time limits the usefulness of forecastsaround the planting time of winter crops. However, as skillimproves through the winter (Maia et al. 2006), managementinputs to crops can be altered in response to forecasts.

Given the suggestion that forecasts of 70% accuracy arerequired for wide adoption, this does raise the question ofwhether seasonal climate forecast accuracies of 70–80% areachievable. Clearly there are limits to predictability due tochaos in the dynamics of ocean–atmosphere interactions, butit is not clear how close we already are to the limits ofpredictability. It is highly possible that statistical forecasts,

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Fig. 3. Intra-annual variation in forecast skill (cross-validated r values)of predicted rainfall in the following 3-month period, using global SSTcorrelations (McIntosh et al. 2005).

which are based on ENSO phases, are already approaching thelimit of their predictability and, given that climate change isaffecting the oceans and the atmosphere and their interaction, theskill of statistical forecasts could in fact decline (Meinke et al.2004). However, there is still much that we do not understandabout the Southern and Indian Oceans, their interaction withENSO (Meyers et al. 2007), and the combined influenceon Australian climate. It is possible that improved seasonalpredictability can be achieved as our knowledge of oceansand of atmospheric mechanisms improves. It is likely that thisimproved understanding will best be represented in dynamiccoupled ocean–atmosphere models which, although still low inskill in seasonal prediction, offer the best path towards improvedforecasts in the longer term (Goddard et al. 2003). It is usefulto reflect on how dynamic weather forecasts have improvedsubstantially over the years. These improvements have occurredincrementally and it would be hoped that similar incremental


improvements will be achieved in coupled ocean–atmospheremodels.

Relevance of forecasts to agriculture

Most operational seasonal climate forecasts predict rainfall ortemperature for the 3-month season ahead. However, this maynot be the most relevant variable to be predicting for primaryproduction and natural resource management needs. Forecasts ofcrop or pasture growth or of soil water or irrigation storage maybe more important. While total rainfall in a season is generallycorrelated with crop or pasture growth, the distribution of thatrainfall within the season can be just as or more important thanthe total amount. This was the case in 1997 when a single rainfallevent at the right time during the El Nino-related drought inAustralia ‘saved’ the Queensland wheat crop, resulting in only aslightly below-average wheat yield for those regions. Integratingboth the total amount and distribution of rainfall in a simplebiological index such as plant growth days can provide a moreuseful forecast variable (McIntosh et al. 2005). In addition, therecan be more skill in forecasting some measure of plant or pasturegrowth or crop yield than in forecasting rainfall (Cobon 1999;Keogh et al. 2004; McIntosh et al. 2005; Meinke and Stone2005). It is also likely that pasture or crop growth forecasts canbe more easily integrated into farmers’ responses to seasonalclimate risk than crude rainfall probabilities (Leith 2006). Aswell as knowing the length of the growing season, knowing thebreak or the end of the rainy season can also be more importantthan the total amount of rainfall (Ingram et al. 2002; Lusenoet al. 2003; Keogh et al. 2005).

Another issue associated with 3-month seasonal forecastsis that they often do not extend over the full crop or pasture

growing period at the time when they are issued. For example,the most appropriate time to make a decision on stocking ratesin response to a forecast in the northern Australian pastoralindustry is during the dry season from May to September inpreparation for the next wet season (October–March). So formany agricultural enterprises a forecast period of longer than3 months is required and often with a lead time of 1–6 months(Luseno et al. 2003; McIntosh et al. 2005). Clearly, more effortneeds to be put into not only improving forecast skill but makingsure that the forecasts are targetted to user needs in terms of leadtime and forecast period.

Another factor to consider in assessing the relevance ofseasonal climate forecasts to agriculture is the ability of farmingpractices to buffer some of the effects of climate variability.For example, cropping soils with high clay contents and waterstorage capacities of 100–300 mm plant-available water content(e.g. the Vertosols of north-eastern Australia) can significantlymoderate the reliance on in-season rainfall (Dalgliesh and Foale1998) and thus the importance of seasonal forecasts in someseasons. A measured full profile of soil water provides far greaterassurance to a decision maker than a probabilistic forecast offuture rainfall, and so farmers around Australia are investingin soil monitoring as a preferred predictor of likely seasonaloutcomes (Carberry et al. 2002; Foale et al. 2004). Combininginformation on soil resources, which can be readily measured,with probabilistic seasonal forecasts, provides advantages thateither one alone is unlikely to match (Carberry et al. 2000).

A summary of forecast lead and forecasting periodrequirements for a range of Australian agricultural industries andregions is shown in Table 1. This table of forecast requirementswas based on surveys and a workshop involving farmers,

Table 1. Summary of forecast requirements for several agricultural regions and industries based on a survey and workshop involving farmers,agribusiness, agricultural researchers, and agricultural extension officers

In the context of this table, forecast lead time refers to the period between the date at which the forecast is made (or desired decision date on which a forecastwould be useful) and the start of the period being forecast, and forecast period is the actual period over which the forecast runs

Agricultural enterprise Key decision Forecast lead Forecasttime period

Northern Australian rangelands(beef, summer-dominant rain)

Stocking rate decisions in May (1st round muster) and September(2nd round muster) in relation to pasture growth in the followingwet season Nov.–Mar.

May: 6 months Sept.:2 months

5 months

Southern Australian rangelands(sheep/wool, winter-dominantrain)

Stocking rate decisions in April/May in relation to pasture growthin following winter June–Aug and following summer Dec.–Mar.

For winter: 1 monthFor summer:8 months

3 months4 months

Winter-dominant rainfall(wheat/pulses/canola)

Decisions on varieties to plant, fertiliser inputs and planting densityin April for the length of the crop season. Decisions on inputs inthe middle of the crop-growing period (June/July)

1 month 0 months 4–6 months3 months

Summer-dominant rainfall (wheat,sorghum, cotton)

Decision to plant in imminent season (April for winter crop andOct. for summer crop) or deferral planting to subsequent season,plus choice of crop and variety and adjustment of inputs inrelation to rainfall and temperatures

April: 1 month Oct.:1 month

4–6 months4–6 months

Sugar production in irrigation areasin NE Australia

Decisions about whether to use irrigation water supplies inSept.–Dec. in relation to rainfall occurrence in followingsummer Jan.–Mar.

Oct.: 3 months 3 months

Sugar mill planning in rainfedtropical sugar systems

Mill planning decisions in Nov.–Dec. about harvest periodconditions (mainly wetness) in the following June–Dec.,especially late in the harvest in Nov.–Dec.

Nov.: 7 months 6 months

Agribusiness in cereal-growingregions

Grain storage, transport and fertiliser supply decisions in Nov inrelation to next year’s winter grain production

Nov.: 6 months 4–6 months


agricultural researchers, and agricultural extension officers. Thesort of forecast information required by farmers in other partsof the world is not dissimilar to Australia; e.g. in Mexico, smallscale farmers were seeking information on the date of onset ofthe rainy season, quality of the rainy season (wetter or drierthan ‘normal’), date of end of the rainy season, frequency andtiming of extreme weather events, spatial distribution of rainfall,number and timing of hurricanes, and interpretation of all thisinformation in terms of which crops and varieties to plant (Eakin2000). The types of decisions listed in Table 1 reflect prospectivechanges in management if forecasts are adopted. There is alsoa range of other less significant management decisions thatwould benefit from forecasts. In many cases these decisions carrylittle down-side risk of an inaccurate forecast (e.g. in a pastoralsituation, cleaning out dams early in the year in response to aseasonal forecast of an above-average rainfall year), and cantherefore be used without exposure even where forecast skillis modest.

Forecast schemes rarely consider the spatial reliability of theforecast in relation to the spatial scale at which the informationmay be used. For example, a forecast may provide a much higherskill at a regional scale averaged over many weather stationsthan it does at an individual station. On the one hand this willresult in the information being less useful for decisions relatedto the state of an individual farm, but may still be useful for on-farm decisions responding to regional conditions. For example,pastoralists might not change stock numbers on-farm because oftheir own expected feed conditions, but they may choose to sellstock earlier because of the higher probability that many farmsin the region will be drought stricken and will therefore ‘flood’the market with stock later in the season. This type of analysisof both types of decision and spatial reliability of the forecast isalso needed to improve relevance to decision makers.

Evaluating the benefits of forecasts

Even when forecasts are of reasonable skill and are available atcritical times, they are only of value if they can be incorporatedinto the complex decision-making matrix that characterisesagricultural management. Robust approaches to assessing theeconomic and environmental value of forecasts in farming andgrazing systems are required if we are to reliably estimatethe benefits of climate forecasts. Currently there are 2 generalapproaches to assessing the value of forecasts. The first approachinvolves incorporating forecasts into farming or agriculturalsystems models that are linked to economic analyses at the

farm, regional, or even national scale. In this simulationapproach, farm management or agricultural trade decisions andinputs are modified in response to a forecast and the modeloutput compared with scenarios where forecasts are not used.This modelling approach relies on a good understanding ofthe farming or agricultural system and simulation of modifiedfarming or trade practices that are consistent with real-worldfarming decisions. Confidence in this method of valuingforecasts is greater if a participatory action research approachis adopted where simulations are based on and tested with jointresearch–practitioner understanding of the farming system inquestion (McCown 2001; Carberry et al. 2002).

The second approach examines retrospectively howindividual farmers or groups of farmers have responded toforecasts and then the benefits (or losses) that accrued fromchanging management practices in response to the forecasts areestimated. This approach uses case studies rather than modelsand has the advantage of being based on real-world responses toforecasts. It can vary from qualitative surveys of how forecastswere used (e.g. McNew et al. 1991) to more formal interviewsto assess forecast benefit (e.g. Sonka et al. 1992) to moreempirical approaches that try to measure changes in plantings,fertiliser use, and crop output in response to forecasts. These 2different approaches have been applied to several regions andagricultural industries around the world and some examples arenow discussed. The advantage of the first approach is that the useof forecasts can be evaluated as a management strategy in whichthe benefits are calculated over the long term (enterprise lifetime)rather than as a consequence of a single tactical decision.Furthermore, the evaluation using the second approach can leadto incorrect conclusions given the probabilistic nature of climateforecasting.

Studies have been conducted in a large number of countriesto determine the net benefit of using forecasts at the farm scale.Most of these studies show that the economic benefits are smallwhen all input costs and returns are calculated and down-siderisks of erroneous decisions are incorporated properly (Table 2).Typical benefits in cropping systems at the farm scale are 1–3%of gross margin or net benefit in the USA, 2–15% in SouthAmerica, but a much wider and potentially larger range ofbenefits (3–50%) has been reported in Australia. A key resultfrom the work of Meza and Wilks (2003) in South America,who examined a range of crops and at a range of locations,was that the value of the forecast increases as the agriculturalsystem becomes more susceptible to climate variability. This

Table 2. Summary of benefits of forecasting for a range of farming and grazing systems across 4 continents

Country Agricultural Forecast type Variable % Benefit Referencesystem

South Africa Grazing Perfect knowledge of ENSO Income 10 Thornton et al. (2004)NE Australia Grazing SST forecast, SOI forecast Profit 27, 15 McIntosh et al. (2005)Chile Potatoes Imperfect forecasts of SSTs Net benefit 1 Meza and Wilks (2003)

in Nino regionChile Spring wheat, potato, Perfect forecasts of SSTs Net benefit 5–10, 3–9, 15 Meza and Wilks (2003)

sugarbeet in Nino regionSE USA, Argentina Multi-crop, multi-crop Perfect knowledge of ENSO Gross margin 2 Jones et al. (2000)Moree, Australia Winter wheat, summer cotton SOI or SST forecast Gross margin 3, 44–71 McIntosh et al. (2005)Dalby, Australia Cotton–sorghum system SOI forecast, SST forecast Gross margin 14, 19 Carberry et al. (2000)


is consistent with the results of Ash et al. (2000) whodemonstrated that the value of forecasts in semi-arid pastoralsystems increased as the coefficient of variation in inter-annualrainfall increased.

The value of forecasts in grazing lands in northern Australiaand southern Africa seems to be greater than for crops (Cobon1999; Thornton et al. 2004; McIntosh et al. 2005) but this maysimply reflect greater forecast skill in the regions where thesegrazing studies were conducted. Grazing systems that involvebreeding herds can retain the signal of a change in stockingrate in response to a forecast for up to 10 years after thestocking-rate change (Boone et al. 2004). This point highlightsthe lag and long-term feedback effects that exist in extensivepastoral systems, which can make it difficult to demonstrateand communicate the benefits of forecasts in these systems.In contrast, in cropping systems the value of the forecast for aparticular crop is generally known within a few months of cropgrowth, although if forecasts are being used to alter croppingsystems (cropping sequence, fallow period, etc.) it may be farmore complex to determine the long-term benefits of a forecaston productivity.

The ability of farmers to respond to a forecast in terms ofon-farm crop or grazing management can be limited because ofimmediate operational constraints or because the managementdecision for the next 3–6 months in response to a forecast needsto be balanced with the longer term implications of that decision.For example, in extensive grazing systems based on breedingoperations the optimal change in stocking rate in response to aforecast is usually only 10–30% of base-line numbers (StaffordSmith et al. 2000; Thornton et al. 2004) because larger changesin stock numbers can affect longer term herd dynamics andprofitability. Similarly, in cropping systems the opportunities toalter management may be restricted to a few options, e.g. plantingdate, planting density, fertiliser, and other direct inputs. Largerbenefits from forecasts may accrue from using the forecastinformation to alter marketing strategies, although this can alsopotentially have negative effects on returns if significant numbersof farmers take the same approach (Jagtap et al. 2002).

Another issue in using simulation models to evaluateforecasts is that they usually adopt a prescriptive response tothe forecasts, e.g. ‘increase N fertiliser by 50 kg/ha in all yearsforecast to be above average’ or ‘decrease planting density inforecast poor years’. As a consequence, the models probablyoverestimate the benefits of forecasts because management forone reason or another cannot respond optimally from year to year.They are also limited by how much the model captures the real-world variation in the system; e.g. the agronomic models mayonly account for 70–80% of the variation in crop yield or pasturegrowth. On the other hand the dynamics of a farming system aresuch that forecasts may add enormous benefit in the few yearsthat are critical to farm profitability or viability but which cannotbe adequately captured in a simulation model. In short, it is verydifficult for simulation models to capture the complexities ofthe system where there are interactions among multiple decisionsand various actors. An additional constraint of farm or enterprisescale models in assessing the value of climate forecasts is thatthey usually do not take account of the distributional effectsof improved forecasts where some groups might benefit at theexpense of other groups (Stern and Easterling 1999). This can

occur through the interaction of forecast response by a largenumber of farmers and its influence on prices, or by largeroperators being in a better position of being able to absorbfixed costs associated with responding to forecasts comparedwith small operators. Also, many modelling approaches treatfarm-scale economics fairly simplistically (e.g. gross marginanalysis), and ignore other sources of income that can have aninfluence on the way forecast information is used and valued. Itis clear that better systems approaches to evaluating forecastsare needed and these need to be undertaken not just in thecontext of management responses to seasonal climate forecastsbut rather are a part of broader approaches to improving overallrisk management (Meinke and Stone 2005).

One aspect of the effect of climate forecasts on farmeconomics that is consistent across several studies is thatincome variability is usually greatly increased as a result of theapplication of seasonal climate forecasts. Although increasedvariability usually implies increased risk, it is often associatedwith an increase in income in forecast good seasons in croppingsystems, while in extensive grazing systems it is often associatedwith increased income through sale of animals in forecastpoor years. These changes in income profile can be mitigatedin a variety of complex ways by taxation policy (such asfarm management deposits and so-called ‘income averaging’provisions for farmers in Australia; Stafford Smith 2003; Martinet al. 2005). Thus, taxation implications need to be takeninto account in determining the overall economic benefits offorecasting.

In evaluating forecasts it is also important to determine howrisk alters in response to using the forecast (Carberry et al. 2000;Hammer et al. 2000). Despite engaging in a livelihood that isinherently risky, farmers are often risk averse when it comesto management decisions. The trade-off between additionalincome and risk to that income is important and, generally, asincome increases in response to seasonal climate forecasts sodoes financial risk (Carberry et al. 2000; Ritchie et al. 2004b).However, environmental risk (e.g. soil loss) may remain aboutthe same (Ash et al. 2000) or even decrease (Hammer et al.2000) through use of seasonal climate forecasts.

There have been several efforts to assess the benefits offorecasts at regional, national, and international scales. At theregional scale, Jones et al. (2000) showed that, with a perfectforecast of ENSO, regional benefit would be in the order of 2%of gross margin. Potgieter et al. (2002) and Nelson and Kokic(2004) showed that regional wheat yields and farm incomes atthe regional scale, respectively, could be statistically classifiedaccording to ENSO state. However, in the case of regional wheatyields the discriminatory power of ENSO was not very strong(Potgieter et al. 2002). One of the potential effects of a largenumber of farmers using forecasts at a regional scale relates toeffects on prices received. If a large proportion of producers whoare all involved in the same commodity change managementin response to a forecast, then market forces can erode muchof the benefit, especially where there is an inelastic supply–demand curve. This has been demonstrated for the tomato-growing region in Florida in the United States, which supplies35% of the US market (Jagtap et al. 2002).

At the national scale, Solow et al. (1998) examined thebenefits that could be obtained from forecasts for a range


of crops including wheat, barley, corn, cotton, hay, potatoes,rice, sorghum, soybeans, and tomatoes. Collectively, these cropsrepresent 90% of agricultural acreage in the US and 80% offarm-gate value. The estimated national value of forecasts was$USD 240M, $USD 266M, and $USD 323M for forecasts oflow, moderate, and high skill, respectively, which represents1–2% of the total value of agricultural production. Surprisingly,despite the large effort in Australia to quantify the benefits ofseasonal climate forecasts, very little of this analysis has beendirected at the national scale. Based on the skill analysis inFig. 1 it is assumed that most forecast skill occurs in easternAustralia, east of a line that extends from Broome through toAdelaide. This is a conservative approach as the farm incomeanalysis of Nelson and Kokic (2004) indicates some quite gooddiscrimination in farm incomes in south-west Western Australiadue to type of season. Gross value of agricultural productionin Australia in 2005 was $AUD 36 billion (ABARE 2005) andabout $AUD 30 billion of this production occurs in the easternhalf of Australia, with net farm income of about $AUD 6 billion.Based on the analyses of benefits in Table 2, we assume a benefitof forecasts that represents 3–10% of gross margin ($AUD180M–600M). Based on adoption rates of 40–50% (AgricultureFisheries and Forestry Australia 2003), estimated benefit toAustralian agriculture, in net farm income terms, is then in theorder of $AUD 70M – 300M per annum.

At the international scale, Hill et al. (2004) examined thevalue of forecasts for world wheat trade, which for this analysisincluded Australia, Canada, and the USA and incorporatedthe value of forecasts across the value chain by examiningproduction, storage, and trade. As a result of using forecasts,production benefits were just positive for Australia, 2.5% upfor Canada, and down by 0.6% in the USA. Consistent withfarm-scale studies reported above, variability in production wasincreased by the use of forecasts. Wheat prices were not greatlyaffected by the use of forecasts, possibly because of the verymodest change in wheat production as a result of applyingforecasts. In another international analysis including storageand trade, Hallstrom (2004) showed that climate forecasts canreduce expected prices and increase price variability. Tradewas essential to realising the benefits of climate forecasts, withgreatest increase in welfare occurring in markets that were bothspatially and temporally integrated.

Most of the results reported above used statistical seasonalclimate forecasts and there are few published studies that clearlyshow the benefits of forecasts using coupled ocean–atmospheremodels. Hansen and Indege (2004) used rainfall predictions froma General Circulation Model (GCM) statistically down-scaledto a subregion and then linked to a crop model. Although theauthors expressed some cause for optimism using this approach,even the very small benefit the GCMs indicated may have beenoverstated because observed rather than predicted SSTs wereused in the forecast GCM runs.

An interesting insight from this examination of the literaturerelating to simulating the economic benefits of seasonal climateforecasts is the surprising number of studies that only evaluatethe benefit of forecasts assuming perfect knowledge of the typeof season ahead, e.g. La Nina, El Nino, or neutral phases ofENSO, rather than the benefits of using operational forecastsor hindcasts. Despite this shortcoming of many studies in how

they evaluate forecasts, appears that most benefit accrues withrelatively modest skill, and having perfect knowledge of whetherit will be an above-average or below-average season confers onlya moderate additional economic benefit (McIntosh et al. 2005).

There are very few studies that have retrospectively examinedhow farmers have responded to forecasts as a means ofdetermining their value in a real-world context. The advantage ofthis approach is that it also factors in adoption levels of forecasts.Phillips et al. (2002) examined farmers’ management responsesto the issue of an El Nino forecast in 1997 and a La Nina in thefollowing year, 1998, in Zimbabwe. Interestingly, 95% of thepopulation had heard the forecast and 84% placed faith in itsaccuracy. Area of crops planted was down ∼20% in 1997–98,although there was considerable regional variation because someareas did receive good planting rain and the 1997–98 El Ninowas not severe in rainfall effect in southern Africa and Australia,due to the anomalously warm sea-surface conditions in theIndian Ocean (Harrison 2005; Potgieter et al. 2005). Area ofcrop planted in 1998 was higher than average but highly variablefrom region to region, with the drier regions showing the largestincrease in area planted in response to the La Nina forecast.The authors used this response in farming system behaviour toexamine longer term benefits if these strategies had been appliedover a longer time period. Their analysis revealed that over a15-year period, crop production would have increased by 10% iffarmers had been responding to forecasts. There are surprisinglyfew examples of where this approach has been applied anddocumented, possibly because application of forecasts is stillin its infancy.

This retrospective approach also allows for better analysisof the benefits and disadvantages of applying seasonal climateforecasts. One example of this was provided by Glantz (1982)who provided the costs of legal claims brought by farmers, whohad received a forecast of a water shortage and had initiatedmanagement actions to avoid potential losses, but the forecastproved to be inaccurate. This sort of outcome raises manyissues for the way forecasts are communicated (usually in aprobabilistic way) and how they are acted on and assessed byfarmers (often in a deterministic way), and this will be dealtwith in the next section of the paper.

In addition to the direct profitability benefits, wise applicationof seasonal climate forecasts has the potential to substantiallyreduce the need for government drought assistance (StaffordSmith 2003). The Australian Government spent $AUD 411Mbetween 2001 and 2004 on welfare and business-related droughtassistance as part of the Exceptional Circumstances Policy(Drought Review Panel 2004). The Australian Governmentcommitted a further $AUD 1.25 billion in support forregions deemed to be in Exceptional Circumstances during2004–06 (Prime Ministerial Media Release, 30 May 2005[www.pm.gov.au/news/media Releases/media Release1405.html]), only a small proportion of which went to supportingstrategic actions as opposed to tactical relief. In addition to thedirect Commonwealth Government assistance provided throughExceptional Circumstances there are additional Commonwealthmeasures such as Farm Management Deposits (FMD), whichallow farmers to deposit pre-tax income (currently $AUD 2.5billion in FMD scheme) and to draw on this deposit in timesof need such as drought (usually paying less tax at this stage).


There is also a range of State measures such as low-interestloans, grants, and fodder and livestock freight subsidies, whichmay total over $AUD 50M per annum on a national basis.So total average expenditure in drought assistance measuresprobably exceeds $AUD 200M per annum and so there is goodopportunity to lessen the need for this expenditure throughuse of seasonal climate forecasts. Until 1989, Australia hadtreated drought as a natural disaster and provided substantialrelief payments in response to each drought event. However, anew National Drought Policy released in 1992 adopted a policyof drought preparedness rather than drought relief for all butthe worst droughts (Botterill 2003). This policy of droughtpreparedness is still endorsed by farmers today and indeed thereis support for a further move away from business aid measures(Drought Review Panel 2004), as there is a view among farmersthat many of the relief measures simply support undesirablefarm business practices. Seasonal climate forecasts could aid inthis increased drive towards self-reliant approaches to managingclimate variability.

Communication and adoption of seasonal climateforecasts

A real constraint in valuing forecasts is the assumption that,given a shift in probabilities associated with a forecast,landholders or farmers will behave in an economically rationalmanner. However, the reality is that farmers tend not toaccept objective probabilities (even if they are communicatedwell) but rather formulate their own beliefs about uncertainoutcomes and therefore deal in subjective probabilities. Asmentioned above, this is often at odds with the way the value ofseasonal climate forecasts is determined in simulation modelswhere decisions based on climate information are incorporatedinto farm management independently of other influences. Inreality, farmers and land managers, when they understand theclimatological skill in the forecast provided, must then integratethat information with a range of other, often higher orderinfluences on decision making. New approaches that incorporatea more complex, systems approach to decision making areneeded in assessing the value of forecasts (Stern and Easterling1999; Meinke and Stone 2005).

Bayesian belief networks are one potential approach tobetter evaluating the real benefit of seasonal forecasts in farmmanagement. Existing beliefs and probabilities can be updatedwith the addition of new information in the form of climateforecasts. In many situations where it is somewhat ambiguous,as can be the case with climate forecasts, the new informationis processed critically and subjectively or at worst is misreadand used to justify existing hypotheses or beliefs. This washighlighted in a study that examined the interaction betweenforecast presentation, the understanding of the forecast, and theattitude towards forecasts (McCrea et al. 2005). This study foundthat the understanding of the forecast was a more importantfactor in the use of forecasts than the presentation format, butonly when the attitude to the forecasts was good.

Bayesian approaches allow the influence of new informationon changed behaviours to be more realistically assessed, but stillin a quantitative way. In the area of seasonal climate forecasting,Bayesian approaches have been used with pastoralists in eastern

Africa who have long-held indigenous forecasting approachesand beliefs (Lybbert et al. 2004). In that study, pastoralists whoreceived climate science-based computer-generated forecastsupdated their seasonal rainfall expectations, in most casesoptimistically, i.e. they placed more value on forecasts ofhigher than average rainfall than on forecasts of below expectedrainfall. Interestingly, even though pastoralists were capable ofprocessing the climate forecasts and updating their beliefs, theirmanagement may have altered only marginally in response toforecasts because of the way they had developed approaches tocope with extreme climate variability ex post rather than ex ante(Luseno et al. 2003).

This example highlights the problem of adoption of forecastseven when they appear to be climatologically useful. TheAfrican pastoral example above highlighted a lack of adoptionof forecasts because of inherent flexibility in the managementsystem that allows herders to cope with extreme temporalvariability by exploiting spatial variability. In the arid rangelandsof Australia, which also experience high year-to-year rainfallvariability, there is some evidence that management systemshave developed to cope with this variability. Nelson et al.(2005) assessed the vulnerability of Australian farm householdsand developed a vulnerability index based on 12 componentindicators measuring human, social, natural, physical, andfinancial capital. They found that farming in a harsh environmentdid not necessarily lead to a high score in their vulnerabilityanalysis: ‘Grazing based operations in central Australia witha high frequency of extreme pasture growth conditions do nottend to rate highly on the vulnerability index. This indicates thatappropriate farming systems can effectively manage the risksassociated with a highly variable, low rainfall climate so long asthey have adequate scale.’

In cropping systems where soils are capable of storingsignificant soil moisture, farmers can use temporal variabilityto their advantage by using rainfall from previous months inthe form of stored soil moisture to overcome shortfalls in in-crop rainfall during the crop growth season (Carberry et al.2002; Foale et al. 2004). However, in many cropping and grazingenvironments this sort of flexibility cannot be incorporated sothere is more potential for adoption of forecasts in those systems.However, adoption will still be problematic if there are notsuitable options to adjust behaviour to the predicted conditions(Podesta et al. 2002). For example, in Zimbabwe, limitedcapacity to obtain alternative seed or fertiliser to take advantageof forecasts limits adoption (Phillips et al. 2002). Likewise, in awinter-wheat cropping system in eastern Australia, where morereliable forecasts only become available after planting, cropmanagement options to use forecasts may be limited to adjustingfertiliser application during crop growth. Adoption is likely tobe greater where the forecast is available well ahead of the morecritical farming decisions.

In addition to problems associated with incorporatingforecasts into existing belief and farm-management systemsthere is an ongoing impediment to the use of forecasts simplybecause of the way they are communicated. This area of forecastcommunication has not received nearly as much effort asresearch initiatives on valuing forecasts (White 2000; Podestaet al. 2002; Leith 2006), although the issue has been wellrecognised by climatologists (Nicholls 1999). Most forecasts are


communicated in a probabilistic way rather than a deterministicway so that the uncertainty in the forecasts can be expressedto end-users (Murphy 1993). However, most end-users havedifficulty in interpreting probabilistic forecasts in a way thatcan be used as input to a deterministic management decision,particularly when it is in the context of including climateinformation in a complex matrix of decisions. This problem isexacerbated when the wording attached to forecasts is interpretedin a different way from the way it is intended, e.g. a forecastsaying that ‘dry conditions are likely’ implies to a forecasterthat dry conditions are more probable than wet conditions but toan end-user like a farmer it is interpreted as a more deterministic‘almost certainly dry’ scenario (Nicholls 2000).

In any case, the way forecasts are delivered poses problemsfor end-users; forecasts such as a ‘50% chance of abovemedian rainfall’ lead to considerable confusion. This wasclearly demonstrated by Keogh et al. (2005) who showedthat only 20% of pastoralists correctly interpreted a forecastthat stated there was a ‘70% probability of receiving abovemedian rainfall’. Clearly, better approaches to communicatingprobabilistic forecasts are needed. McCrea et al. (2005) showedthat probabilistic forecasts are better accepted when presentedin a frequency format rather than as single-event probabilities,while the use of techniques such as ‘chocolate wheels’(a spinning wheel divided into pies or categories of equal(climatology) or different (forecast) sizes where, with each spinof the wheel, the pointer can end up in any category) hasproved to be effective in communicating the probabilities ofboth wet and dry outcomes in an easy to understand graphicalform (Hayman 2000). Thought also needs to be given tocommunicating forecasts in a way that is useful for applicationin an agricultural context rather than the current approach ofcommunication in terms of climatological skill (White 2000;Leith 2006). For example, probabilistic forecasts of pasturegrowth are routinely made for the grazing lands of Queenslandbased on past growing conditions and future rainfall probabilities(Carter et al. 2000). Even when forecasts are well formulatedthere can be impediments to their interpretation and use becauseof cognitive biases that occur as a result of simplifying decisionmaking via simple rules of thumb or heuristics (Nicholls 2000).

Most forecasts are provided at a regional scale and thereis good evidence that forecasts need to be localised and putin the context of local climate conditions (Austen et al. 2002;Jagtap et al. 2002). A good example of this need for locallyexplicit forecasts has been demonstrated in Argentina throughan analysis of adoption of forecasts following the widelycommunicated La Nina event of 1998–99, which followeda strong El Nino event in 1997–98 (Podesta et al. 2002).Fifty-eight per cent of farmers did not change their managementin response to the forecast, with a large number not believingthe forecasts applied well to their subregion. There is also goodevidence that farmers will have more confidence in forecastsif they are communicated from a trusted ‘expert’ (Jagtapet al. 2002; Luseno et al. 2003) rather than from an institutionor agency.

Clearly, the dissemination and communication of forecastsis an area that needs considerably more effort. One of thechallenges in improving communication of forecasts is thatthere are now a plethora of forecasting systems and methods

of communicating forecasts as a result of both internet accessand climate institutions/agencies providing worldwide forecastsat a regional scale. This makes it difficult for new forecaststo be ‘seen and heard’ and probably highlights the need forforecasts to be better targetted to user needs and to be localisedif they are to have a significant effect. This will requirebetter partnerships between agencies that have responsibilityfor climate research and meteorological services and agri-industries, including agribusiness, extension agents and farmers.

Notwithstanding the issues identified above specificallyregarding seasonal climate forecasts and their use, thecommunication and adoption of information and tools targettedat influencing farmer decision making under uncertainty havebeen attempted in many guises for over 20 years with generallyunflattering results (McCown 2001, 2002; McCown et al. 2002).McCown (2002) argues that unless farm decisions are relativelysimple and structured, or are driven by external regulatorydemands or readily devolved to a consultant, then formaldecision-support information and tools dealing with climatevariability are unlikely to be used by farmers. Probabilisticseasonal climate forecasts undoubtedly fall outside thesesimple categories and thus their use more likely requiresMcCown’s (2002) fourth possibility whereby farmer learningand development are sought and result from active facilitation.In Australia, Carberry et al. (2002) report an example ofsuccessful intervention whereby farmers and their advisersbenefited from tools such as soil resource monitoring, seasonalclimate forecasts, and simulation modelling. In response tothis accumulated experience, Hunt et al. (2006) describe thecurrent effort in Australia to deliver on-line paddock-specificyield forecasts for wheat, based on seasonal climate forecasts(www.yieldprophet.com.au).

Conclusions

Given the large influence that inter-annual rainfall variabilityhas on agriculture in Australia and the key role that oceantemperatures have in driving this variability, there would appearto be considerable potential to capture value from seasonalforecasts. This is especially true for regions that are stronglyaffected by ENSO, which is a moderately predictable large-scale ocean–atmosphere phenomenon. However, the currentskill of forecasts in most regions is quite low and the relevanceof forecasts to agricultural decisions and the way they arecommunicated does not encourage widespread confidence andadoption.

To improve success in the use of forecasts we suggest thatefforts should be better targetted to (a) regions where forecastskill is currently useful or is likely to improve to useful levels,(b) the farming systems and mix of enterprises that are amenableto incorporation of seasonal forecasts, and (c) specific farmingdecisions that have the lowest down-side risks in relation tothe benefits that can be achieved from forecasts. A first stepin this targetting of future efforts in seasonal forecasts is tobetter understand climate drivers at a regional scale and to betterquantify the likely limits to predictability. In parallel we need tounderstand how forecasts can be better incorporated into farmingdecisions, not just in a technical sense but also in terms of theadaptive capacity of farmers and farming communities.


In pursuing these efforts we need to recognise that, whileseasonal climate forecasts have much to offer, they are nota panacea for coping with climate variability. It is importantthat care be exercised in demonstrating and communicatingthe value of forecasts so that there is confidence and trustin both the climate science and in how it is being used toinform decision making. Central to this approach is ensuring thatforecasts are placed in the context of complex farming systemswhere climate decisions are just one variable in a matrix ofmany decisions.

Acknowledgments

The authors acknowledge the funding provided by Land and WaterAustralia’s Climate Variability in Agriculture Program, which assistedin the undertaking of this work. Greg McKeon, Steven Crimp, HolgerMeinke, Mark Howden, and two anonymous reviewers provided many usefulcomments that have greatly improved the manuscript. Robert Fawcett at theBureau of Meteorology in Melbourne kindly supplied Fig. 1.

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Manuscript received 14 June 2006, accepted 13 June 2007

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constraints and opportunities in applying seasonal climate forecasts in agriculture

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