Crop Adaptation to Climate Change (Yadav/Crop Adaptation to Climate Change) || Downscaling Global Climatic Predictions to the Regional Level: A Case Study of Regional Effects of Climate

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BLBS082-1-2 BLBS082-Yadav July 12, 2011 12:41 Trim: 246mm X 189mm

Chapter 1.2

Downscaling Global Climatic Predictions tothe Regional Level: A Case Study of RegionalEffects of Climate Change on Wheat CropProduction in Victoria, AustraliaGarry O’Leary, Brendan Christy, Anna Weeks, James Nuttall, Penny Riffkin, Craig Beverly,and Glenn Fitzgerald

Introduction

Downscaling global climate change predictionsto the regional level requires both regional cli-matic and crop growth data. Crop simulationmodels provide robust and objective methodsto extrapolate likely crop response to climatechange over different landscapes and time pe-riods (Hansen and Jones 2000; Hoogenboom2000). The example taken in this chapter fromVictoria, Australia, demonstrates how such anapproach may be applied to inform possibleadaptation strategies with respect to known cropgrowth responses to local climatic variables. Thisis an approach that can be applied in other re-gional areas where sufficient climatic and cropdata is available.

A landscape scale analysis allows the ex-ploration of local adaptation strategies to cli-mate change. Such models must include func-tions to account for the effects of elevated CO2

and temperature that are expected to occur overthe coming decades. These models also mustaccount for shortages of water if crops in the

study area suffer or are likely to suffer signifi-cant water stress. Previous regional analyses ofclimate change in Australia have been made asa series of point-source analyses using represen-tative localities to describe the likely responseof wheat to future climate scenarios (Hammeret al. 1987; Wang and Connor 1996; Assenget al. 2004; Howden and Jones 2004; Howdenand Crimp 2005; Anwar et al. 2007; Crimp et al.2008). In Victoria, the few locations previouslymodeled are not considered representative of theregion (e.g., Birchip or Mildura), particularly forsouthern Victoria where future wheat productiv-ity and area of production might increase undera warmer and drier climate. This chapter showshow a landscape-scale crop model has performedagainst experimental data across the region, in-cluding elevated atmospheric CO2 data in a FreeAir Carbon dioxide Enrichment (FACE) exper-iment located at Horsham, Victoria, Australia,from 2007 (Mollah et al. 2009). The landscape-scale crop model was selected from the suite ofthe catchment analysis tools (CAT; from Beverlyet al. 2005). After satisfactory validation, we

Crop Adaptation to Climate Change, First Edition. Edited by Shyam S. Yadav, Robert J. Redden, Jerry L. Hatfield,Hermann Lotze-Campen and Anthony E. Hall.c© 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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DOWNSCALING GLOBAL CLIMATIC PREDICTIONS TO THE REGIONAL LEVEL 13

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applied the landscape-scale crop model to simu-late likely effects of climate change on crop pro-duction across the whole of Victoria to help in-form future policy on adaptive strategies neededin rural Victoria. Spring wheat is normally sownin autumn and winter to coincide with the winterdominant rainfall of the region. This typicallyresults in sowing from April to July in the north-ern region and from March to September in thesouthern region. Specifically, we examine howcrop phenology might need to be altered to max-imize yield in various production zones in Vic-toria and how farmers can use this knowledge tominimize the risks of uncertain climatic changeand variability.

Methods

IPCC scenarios

We chose to utilize the Intergovenmental Panelon Climate Change (IPCC) scenarios to definelikely climatic changes that are well understoodthroughout the world (IPCC 2001, 2007). Thiswas to provide a known reference for others tocompare our results to analyses done in otherlocations under the same or similar climate sce-nario. Prior to the IPCC publishing their sce-narios, many scientists proposed their own. Themost common were a mean temperature rise ofabout 2◦C and sometimes a doubled atmosphericCO2 concentration. There were numerous varia-tions, particularly in relation to rainfall (Whetton

et al. 2000). Sometimes, future rainfall changeswere considered so unreliable that the histori-cal rainfall patterns were considered a sufficientrepresentation of a climate changed future (Wangand Connor 1996).

We originally chose the IPCC scenarios ofA1Fi, A2, and B1 to reflect realistic possible fu-tures of a worst-case scenario (A1Fi), and themost conservative (B1) was chosen to providea boundary to compare potential gains or losses(Fig. 1.2.1). However, during our analyses, wedecided to consider only the A1Fi scenario be-cause it was clear that worldwide mitigation ef-fects expressed in A2 and B1 were unrealistic.Indeed, Australia has shelved an emissions trad-ing scheme until the end of 2013 for politicalreasons. Further, the present day (2010) actualatmospheric CO2 concentrations at Horsham is382 ppm, which is very close to the A1Fi scenario(391 ppm), and it is now possible that the futuremight be much more pessimistic than A1Fi asoriginally determined in 1990.

Downscaling from global to regionalVictoria, Australia

Daily crop simulation models that account forrainfall, temperature, solar radiation, evapora-tion, and CO2 can be employed to study thelikely effects of climate change on crop pro-duction; however, current global climatic modelsgenerally operate on an annual basis and must be



14 CROP ADAPTATION TO CLIMATE CHANGE

downscaled to meet the daily input requirementof the crop models. A useful method used inVictoria by the Commonwealth Scientific andIndustrial Research Organization (CSIRO) in-volves scaling historic daily climate sequencesby a derived mean-monthly spatial pattern of cli-mate change per degree of global warming (Sup-piah et al. 2001). The historical sequence weatherdata can provide a reference or base line for cur-rent nonclimate changed environment compar-isons to some future environment. The compar-isons, however, can be made in many ways andare somewhat dependent on the type of futuredata generated.

We have explored two methods for down-scaling global data. The first was the methodof Suppiah et al. (2001) as applied by Anwaret al. (2007) (Method A) to provide a continu-ous sequence that represents the nonstationerynature of climate change, i.e., increasing globalwarming trend each year applied to the histori-cal sequence. The historical sequence is the basisof future climate where autocorrelation is main-tained. The advantage of this method is that itprovides a realistic, smooth, and complete se-quence of weather data for analyses of an in-cremental nature over time. The disadvantage ofthe method is that stochastic sequences with thesame variance consistent with a specific climatechange scenario are not considered.

We used patterns of climate change per de-gree of global warming on a monthly basis

for four climate variables (rainfall, maximumand minimum temperature, and solar radia-tion) across Victoria (Hennessy et al. 2006).The pattern was applied to 70 years histori-cal data across Victoria (1935–2004) (obtainedfrom the Department of Environment and Re-source Management, Queensland as patch-pointdata, http://www.longpaddock.qld.gov.au/silo/).This was then used to create a 70-year futurescenario from 2003 to 2072 by Method A. Thismethod assumes that the identical sequence ofthe detrended historical data is applied to futureclimate, but the monthly means are amended toreflect the future climate scenarios. The choiceof historic year range was to ensure consistencywith leap-year generation.

To deal with stochastic options that providea more realistic analysis representative of mul-tiple seasons for any particular future year, e.g.,2030 or 2070, a sequence of years is neededto represent the future year (Method B). Weachieved this by applying the detrended histor-ical data referenced by the base year (1990) tothe particular year of interest by applying theglobal warming factor for that year to the wholedata sequence so that it is shifted vertically (inthis exaggerated maximum temperature exam-ple, Fig. 1.2.2). This implies that for each futureyear, we have a sequence of weather data havingthe same number of years as the historical data.This method allows the climate change effect tobe analyzed without confounding from atypical

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Fig. 1.2.2. Exaggerated example of the comparison of the historical (circles) and high global warming scenario (squares)daily maximum temperature for January (Method A). The detrended data from the base year has been amended by the globalwarming and pattern of change factors representative of the year 2030 (Method B).




individual years. Both methods assume that theautocorrelation that exists between years in thehistorical data is applied to all sequences appliedto any future years (Fig. 1.2.2). They, however,do not address extreme events that are believedto increase in intensity and frequency due to pro-gressive climate change.

The future climate change projections basedon the IPCC scenarios indicate significantchange for Victoria, Australia. Expectations ofwarmer and drier conditions with rising atmo-spheric CO2 concentrations are predicted forwestern Victoria (Whetton et al. 2000). Cropsimulation models can be used to determine thelikely crop response to these changed climateconditions, although they first need to be vali-dated to ensure they adequately predict responseto elevated CO2 levels.

The generation of climate changed datafor spatiotemporal modeling

The Catchment Analysis Toolkit (CAT) climatechange module, CATCLIM, was developed aspart of the Victorian Climate Change AdaptationProgramme (VCCAP) to generate future climatepatterns to assess the likely impacts of climatechange on cropping systems in Victoria. CAT-CLIM is a user interface that sits within theCAT modeling framework and enables flexible,repeatable, batch methods to generate future cli-mate data (Fig. 1.2.3). Both methods (A and B)of downscaling have been incorporated into asoftware package interfaced to our CAT model(Weeks et al. 2010). The package generates thedaily sequence of data that the user specifies.We have made a database that contains Methods

Fig. 1.2.3. CAT interface showing rainfall pattern of change for the month of May, with CATCLIM module opened.




A and B for all years to 2070 for all climatestations in Victoria for B1, A2, and A1Fi IPCCscenarios.

The crop simulation model

The crop model of choice was the LandscapeCAT model (Beverly et al. 2005). This is adaily time step model that simulates water bal-ance for a crop, including crop transpiration anddeep drainage. It comprises submodels that sim-ulate crop phenology, above-ground biomass,and grain yield accumulation. The yield modelis based on the OLEARY & CONNOR model(O’Leary and Connor 1996a, 1996b) that allowspreanthesis assimilation partitioning to grain andpartitioning to grain during the grain filling stageon a daily basis unlike its parent, the PER-FECT wheat model (Littleboy et al. 1992) thatwas originally designed for tropical environ-ments and does not perform well with respectto grain yield simulation in southern Australia.The model simulates a catchment water balancein a three-dimensional mode that allows analy-ses of the fate of water in the root zone (0–2 m),aquifers, and streams. The model requires sig-nificant soil parameters such as lower and up-per extractable water limits, bulk density, pH,and initial soil water and mineral and organiccarbon and nitrogen. A spatial database com-prising these data is interpolated across the land-scape with respective weather data used as in-put to apply the model to a 100 × 100 m gridacross Victoria. Initially, this was applied only tosouthwestern Victoria but extended to the wholeState so we could compare the results againstprevious studies in other regions and so thatthe methodology could be applied state-wide atshort notice.

The model, however, needed to respond toatmospheric CO2 levels. Functions from theCROPSYST model that proportionally adjustradiation use efficiency (RUE) and transpira-tion efficiency (TE) were used (Stockle andNelson 2001) with simplifying assumptions ofaerodynamic resistance of crop canopy of

300 s m−1 and canopy resistance of 36 s m−1 at350 ppm CO2.

RUECC = (−1.7/(350 × (1 − 1.7))× CO2 × 1.7)/(−1.7/(350× (1 − 1.7)) × CO2 + 1.7)

(1.2.1)TECC = RUECC/{[δ + (γ

× (36 + 300)/300)] /[δ+ γ × (((36 × CO2)/(350× RUECC) + 300)/300]}

(1.2.2)

where RUECC is the proportional adjustmentmade to RUE (g/MJ) such that RUECC = 1 isequivalent to present day RUE; TECC is the pro-portional adjustment made to TE (kg/ha/mm)such that TECC = 1 is equivalent to presentday TE; CO2 is the atmospheric CO2 con-centration (ppm), delta (kPa/◦C), and gammakPa/◦C) are the psychrometric constant and slopeof saturated vapor pressure–temperature curve,respectively.

These functions were first tested in theOLEARY & CONNOR wheat crop model be-cause that model had been well validated inthe Mallee and Wimmera districts of Victoriaand the public domain code made amendmentseasy. Both CAT and the OLEARY and CON-NOR models account for shortages of water andnitrogen supplies.

We tested both these models for their perfor-mance against observed data from field experi-ments at Walpeup, Horsham, and Hamilton. Oneyear of data (2007) from the Horsham FACE ex-periment was available to test performance un-der elevated CO2 (550 ppm to represent the year2050 under the A1Fi scenario). In addition to theelevated CO2, the agronomic treatments of irri-gation, N fertilization, and time of sowing werealso available in the FACE experiment. Two fur-ther years of FACE data will be available later tocomplete a more comprehensive analysis despiteproviding an initial satisfactory validation. Othercrop models are currently being tested.




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Phenological Development

Figure 1.2.4 shows the performance of the CATwheat model against observed phenological dataacross Victoria. A range of cultivar types is rep-resented by thermal sums providing a modestmean root mean squared error of 5.2 days forthe prediction of anthesis date. The phenologicalmodel of CAT employs thermal time methodol-ogy adjusted for photoperiod via a sowing timecurve, based on the photosensitive behavior ofthe representative cultivar types. As such, it doesnot account for vernalization per se because de-pendence on this will be problematic in warmingclimates.

Biomass and grain yield accumulation

The range of early and mid-season cultivartypes grown from the drier north to the wettersouthern region, with and without elevated CO2,provided performance typical of these models(Fig. 1.2.5). The response to elevated CO2

was within or close to the experimental error(data not shown), and while our analyses areincomplete, the performance of the model showssufficient explanatory behavior with respect toelevated CO2 such that when applied elsewhere

across the landscape, the response is believed tobe realistic within the error range of the modelof around 0.74 t/ha for grain yield. Publishedmean errors for these types of simulation modelsare typically around 0.75 t/ha.

Results

Likely response of wheat crop yieldto climate change across Victoria

We applied the IPCC extreme climate changescenario (A1Fi) using Method B, employing68 years of data representing that scenario at2050 across the whole of the available crop-ping and pasture land in Victoria. We simulatedwheat yield with the CAT model from 12 timesof sowing (one per month from January to De-cember) for three crop types (short-, mid-, andlong-season types) under the CCAM Mark 2 andMark 3 downscaled climate data (Fig. 1.2.6).

Can current wheat varieties be adaptedto new climates in Victoria?

The initial impact of climate change on cropgrowth in Victoria is generally expected to bepositive with elevated temperature and CO2 levelenhancing growth to a level that more than




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Fig. 1.2.5. Comparison of simulated wheat biomass at anthesis (a) and grain yield (b) from a rangeof crops from the Mallee (�), Wimmera (©, �, and �), and Western Districts (♦). Data from the 2007Australian AGFACE experiment shows the ambient CO2 (�), and elevated CO2 (�) environments. The 1: 1relationship is the dashed line.

compensates for the lower growing season rain-fall. Eventually, as the climate continues to be-come drier and hotter, crop growth will reacha threshold and then decline. This report showssome preliminary results as to when this thresh-old may be reached for wheat in Victoria.

The future climate change projections basedon IPCC scenarios indicate significant changes

for Victoria. Expectations of warmer and drierconditions with rising atmospheric CO2 concen-trations are predicted for western Victoria. Sig-nificant differences occurred between the CCAMMark 2 and Mark 3 climate data, reflecting someuncertainty in an IPCC A1Fi climate outlook.Despite this uncertainty, clear trends are evident.For the more arid zones, sowing a faster maturing




Sowing: May 1 CCAM Mark 2 Sowing: May 1 CCAM Mark 3

Sowing: June 1 CCAM Mark 2

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Fig. 1.2.6. Effect of IPCC scenario A1Fi downscaled by two models (CCAM Mark 2 and CCAM Mark 3) on percentagechange in mean wheat grain yield from current climate examining the impact of sowing time on a fast-developing croptype (a), medium-developing type (b), and slow-developing type (c).








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type (e.g., cv. Silverstar) on the June 1 resultedin the best outcome with some small gains andlosses predicted. Sowing earlier or later resultedin yield losses of around 10–20%. In the wet-ter areas of southern Victoria, consistent gains inyield of around 10–20% should occur in all cul-tivar types (e.g., Silverstar, Chara, or Mackellar)but only from later sowing times (July to August)(Fig. 1.2.6).

The highest yields, however, in southernVictoria occurred for cv. Mackellar sown inJuly under the present climate with potential forgreater yields if sown in August under the A1Fiscenario (CCAM Mark 2 or Mark 3) (Fig. 1.2.7).

Once the impact of climate change on produc-tivity is determined, the models are then usedto explore how the agricultural enterprises canchange to better adapt to future climate stresses.For the cropping data shown above, the model-

ing has explored how changing practices (e.g.,sowing time) can overcome the predicted yieldlosses in Victoria (Fig. 1.2.8). Areas shown indi-cate the parts of the landscape where crop yieldstart to decrease in spite of changing manage-ment practices.

Discussion

Our analyses show very intriguing results. Fromprevious simulation analyses in the Mallee re-gion, significant declines in mean wheat grainyield have been reported of around 20% (P.Grace, unpublished data; Anwar et al. 2007),which is consistent with our landscape analy-sis in that region where declines are expected torange from 10% to 20%. However, significantgains in yield are expected in much of the wet-ter regions in the medium term. However, by

Sowing: July 1 Historic Sowing: July 1 2050

Sowing: August 1 Historic Sowing: August 1 2050

Fig. 1.2.7. The effect of sowing time on wheat yield (cv. Mackellar) under historic and CSIRO CCAM Mark 3 A1Fi climatechange scenario. Note the increase in area for the 3000–4000 kg/ha band under the climate changed scenario for both July andAugust sowings and the beneficial effect of delaying sowing until August compared to the present climate optimum.




Crop yield still increasing by year 2070

Decreasing crop yield by year 2070



Decreasing crop yield by year 2015Kilometers

0 98 196

Fig. 1.2.8. Area where crop yield (slow-season type) can no longer adapt to climate change and starts to decrease in yieldunder the A1Fi CSIRO Mark 3 scenario. Note the southern areas (black) where yields are still increasing by 2070.

2070, with temperature rise of between 2◦C and3◦C, yield declines are expected over much ofVictoria. One particular problem is the variancebetween the climate models. We only tested twoAustralian models (CSIRO Mark 2 and 3) anddid not have the latest CSIRO Mark 3.5 modeldata available, but that is expected to show sim-ilar spatial patterns to Mark 3.0. This variancebetween the climate models means that we can-not rely on the maps as an authority in a spatialcontext because if the patterns are different, thenthe crop response will be correspondingly differ-ent. In reality, the crop models are more accuratethan the climate models and the uncertainty ofthe input climate data limit the conclusions toa regional aspect rather than the 100 × 100 mgrid cell analyses that we pursued. The problemof uncertainty of climate predictions has beenpreviously noted to the extent that no specific in-centives for farmers were proposed in some earlyanalyses (Lottkowitz 1989).

From a policy point of view, however, we canmake suggestions as to the directions needed inmanaging the expected climate change in Victo-ria with respect to the grains industry. One par-ticular question deals with adapting crop culti-vars to a future climate. The expected responseof spring wheat to possible future climates inAustralia ranges from positive (0% to 20%) to

negative (20% to 0%). Some studies have re-ported an expectation of little change in grainyield or where gains are indicated, they are ex-pected to be negated by other factors (Poweret al. 2004). Reasons for such diverse conclu-sions are varied but include spatial variance insoil types, agronomy, and the climate itself. Ofthese, climate is probably the largest factor af-fecting the yield expectation in any locality inAustralia.

Two undisputed climatic factors that will havelarge effects on crop production are the risingglobal temperatures and atmospheric CO2 con-centrations. The extent to which these are mod-erated by rainfall in nonirrigated production sys-tems is another source of uncertainty with greaterresponse to elevated CO2 under dry conditionsreported in some locations (e.g., Reyenga et al.1999), but not all (e.g., Asseng et al. 2004). Un-der the present climate, the adaptation strategyof Australian crop scientists is to design cropsthat can be sown early enough to produce a largeenough biomass by the time of flowering. Theiraim is to breed plants that can set a sufficientlylarge number of grains outside the period wherewinter and spring frosts destroy those grains. Theproblem, however, is that delayed flowering willthreaten grain filling because of the inevitableterminal drought. The aim is to maximize grain




yield while minimizing environmental stressto crops.

Under future climates, how might our presentwheat cultivars need to be altered to maximizegrain yield? The present genetic variability inwheat (see Chapter 6, this book) appears to besufficient to cope with the climatic changes rep-resented by the IPCC A1Fi scenario but only toabout 2050, as the threshold temperature toler-ance is reached.

The most critical climatic factor is the increas-ing temperatures. While mean temperature underA1Fi appears manageable, fluctuations above thenormal coping range for crops is expected to in-duce more frequent catastrophic crop failures.Crops are most sensitive to high temperaturesduring pollen formation and fertilization, whichusually occurs between September and Octo-ber in Victoria. Currently, no contemporary cropmodels simulate the effect that increasing tem-perature has on the biophysical processes duringgrain set due largely to the poor knowledge ofhow to model such behavior at a field or land-scape scale (Kirby et al. 1999; Wheeler et al.2000). While our simulations do not explicitlyaccount for such failures, they should be consid-ered the best case scenario rather than the worstcase despite the simulated declining yields be-yond 2050 for much of Victoria.

We expect that if mean temperatures rise by3◦C in western Victoria by 2100 as estimatedby CSIRO’s latest modeling (CSIRO Mark3.5), then crop failures would be more thenorm and there would be limited capacity forour wheat genetic resources to prevent suchfailure. However, in the context of moderatetemperature rise of about 2◦C, we expect thatwheat crop phenological development can bere-engineered toward slower developing springtypes to maximize grain yield under futureclimate scenarios across Victoria.

Conclusions

Overall, scaled down detailed modeling is re-quired to put global climate change predictions in

a local context. Agronomic options, such as dateof sowing, and genetic options, such as varietalmaturity type, are significant factors in adjust-ment to climate change. Further local knowledgewith validation of crop modeling is required tomeaningfully examine climate change scenariosat the local level both for policy making and toassist farmers in choosing adjustment strategies.

Specifically, for our example region of study,reduced rainfall in the drier regions is the bigchallenge—reduced rainfall in the high rainfallzone (500–800 mm pa) is less problematic. In thesemiarid zone of the region, yield loss (10% to20%) is expected in North West Victoria if cropssown outside the June sowing time. For the highrainfall zone in the south west, yield gains (10%to 20%) are expected.

Adaptation in the high rainfall zone is maxi-mized by slower developing types and later timeof sowing with yields still increasing by 2070in South West Victoria. However, yields are ex-pected to decline in most regions by 2070.

Farmers are already adapting to climatechange/variability in Victoria. These strategiesinclude sowing early and late, minimum tillage,stubble retention, opportunity cropping, com-puter models of climate and weather (SouthernOscillation Index, seasonal outlooks, and radar),and computer models of crops and strategies.Lower risk options include cereals, long fallow,hay and spring crops, and sheep.

In summary, because adaptation is crucial andthere are great benefits still to be gained, thefollowing directions point to high benefit/costratio research and development:

� Adapted cultivars (e.g., slow-developingtypes);

� Adapted agronomic practices (e.g., low costprecision agriculture and reduced tillage);

� More efficient water and fertilizer use (e.g.,remote and proximal sensing);

� Focus on productivity, profitability, and prod-uct quality;

� Better understanding of weather/crop ex-tremes;




� Better understanding of disease and pest be-havior (biosecurity); and

� Stronger partnerships among growers, indus-try, and government.

Acknowledgments

We thank the Victorian Department of PrimaryIndustries through the Victorian Government’sClimate Change Adaptation Programme andthe Australian Grains Research and Develop-ment Corporation who funded much of thisproject work.

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