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Agricultural and Forest Meteorology 170 (2013) 183–194

Contents lists available at SciVerse ScienceDirect

Agricultural and Forest Meteorology

journa l homepage: www.e lsev ier .com/ locate /agr formet

ariation in the global-scale impacts of climate change on crop productivity dueo climate model uncertainty and adaptation

om Osbornea,b,∗, Gillian Roseb,c, Tim Wheelerb,c

National Centre for Atmospheric Science (NCAS), University of Reading, UKWalker Institute, University of Reading, UKDepartment of Agriculture, University of Reading, UK

r t i c l e i n f o

rticle history:eceived 31 March 2011eceived in revised form 11 June 2012ccepted 5 July 2012

eywords:limate changerop yieldrop modellingdaptationncertainty

a b s t r a c t

Crop production is inherently sensitive to fluctuations in weather and climate and is expected to beimpacted by climate change. To understand how this impact may vary across the globe many studieshave been conducted to determine the change in yield of several crops to expected changes in climate.Changes in climate are typically derived from a single to no more than a few General Circulation Models(GCMs). This study examines the uncertainty introduced to a crop impact assessment when 14 GCMs areused to determine future climate. The General Large Area Model for annual crops (GLAM) was appliedover a global domain to simulate the productivity of soybean and spring wheat under baseline climateconditions and under climate conditions consistent with the 2050s under the A1B SRES emissions scenarioas simulated by 14 GCMs.

Baseline yield simulations were evaluated against global country-level yield statistics to determinethe model’s ability to capture observed variability in production. The impact of climate change variedbetween crops, regions, and by GCM. The spread in yield projections due to GCM varied between nochange and a reduction of 50%. Without adaptation yield response was linearly related to the magnitudeof local temperature change. Therefore, impacts were greatest for countries at northernmost latitudes

where warming is predicted to be greatest. However, these countries also exhibited the greatest potentialfor adaptation to offset yield losses by shifting the crop growing season to a cooler part of the year and/orswitching crop variety to take advantage of an extended growing season. The relative magnitude ofimpacts as simulated by each GCM was not consistent across countries and between crops. It is important,therefore, for crop impact assessments to fully account for GCM uncertainty in estimating future climates

ssum

and to be explicit about a

. Introduction

Crop production is one of the most vulnerable sectors to cli-atic variability and change (Slingo et al., 2005). The potential

mpacts of climate change have been examined in many crop mod-lling scientific studies for several crops, for example groundnutsChallinor et al., 2005a,b) and rice (Matthews et al., 1997), andt many locations, for example Africa (Liu et al., 2008) and USA

Reilly et al., 2003). Most crop modelling studies create future cli-

ates by perturbing observed time series of weather with climatehange anomalies derived from the output of General Circulation

Abbreviations: GCM, General Circulation Model/Global Climate Model; GLAM,eneral Large Area Model for annual crops; SRES, Special Report on Emissions Sce-arios; TE, Transpiration Efficiency; RUE, Radiation Use Efficiency.∗ Corresponding author at: NCAS-Climate, University of Reading, Earley Gate,eading RG6 6BB, UK. Tel.: +44 0118 378 6592; fax: +44 0118 378 8316.

E-mail address: t.m.osborne@reading.ac.uk (T. Osborne).

168-1923/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agrformet.2012.07.006

ptions regarding adaptation.© 2012 Elsevier B.V. All rights reserved.

Models (GCMs), i.e. the difference between a model’s present-dayclimatology and the average climate of a future time period (e.g.2040–2059). An important aspect of any crop impact assessment,therefore, is the choice of GCM or GCMs.

The sources of uncertainty in projections of future climate (aver-ages of temperature and precipitation) at global and regional scaleshave been examined by Hawkins and Sutton (2009) who analysedthe output of 15 GCMs which participated in the World ClimateResearch Programme’s Coupled Model Intercomparison Projectphase 3 (CMIP3). They partitioned the variation of future climateto three factors: differences in the greenhouse gas emissions path-way (scenario uncertainty), differences due to the choice of GCM(model uncertainty), and differences due to the natural, or ‘inter-nal’ variability of the climate system due to long-term changes in,for example, ocean circulation. The relative contributions of each

source to total uncertainty of a prediction of future climate wasfound to vary dependent upon lead time, the choice of climate vari-able (e.g. temperature or rainfall), spatial scale of aggregation, andlocation (Hawkins and Sutton, 2009). The longer the lead time the

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84 T. Osborne et al. / Agricultural and

ore important uncertainty in greenhouse gas emissions becomes,hereas internal variability is more important at shorter lead times.awkins and Sutton (2009) showed that GCM uncertainty was theajor source of uncertainty in regional temperature projections at

ntermediate lead times (∼30–50 years). For rainfall, internal vari-bility is of greater importance, even at long lead times (Hawkinsnd Sutton, 2011). This appreciation of uncertainty in future climaterojections and its various sources is not yet adequately reflected inhe design of crop impact assessments. For example, in the study ofiu et al. (2008) which determined the expected impacts of climatehange by the 2030s on crop yields in Sub-Saharan Africa, future cli-ate scenarios were derived from a single climate model (HadCM3)

nder four different emissions scenarios. Given what is now knownollowing the study of Hawkins and Sutton (2009), it would haveeen more appropriate to sample climate change as simulated byour different GCMs under a single emissions scenario.

Several assessments of the impact of climate change on croproductivity at the global scale have been published. In the firstuch study, Rosenzweig and Parry (1994) estimated impacts underlimate change scenarios from three GCMs. Subsequently, Parryt al. (2004) utilised climate change scenarios derived from justsingle GCM but driven with greenhouse gas concentrations from

he four Special Report on Emissions Scenarios (SRES) emissionscenarios and reported results for the 2020s, 2050s and the 2080s.ore recently, the reports of Fischer (2009) and Nelson et al. (2009)

stimated impacts under climate change by 2050 using climatehange projections of two GCMs while that of Müller et al. (2010)ased their future climate impacts on the output of five GCMs andresented the results of the ensemble mean. Therefore, it is pos-ible that the uncertainty in future climate due to climate modelncertainty has not been adequately examined in these impactssessments. Statistical approaches have also been used to pre-ict the impact of climate change on crop yield. Because of theirelative simplicity and ease of application such approaches havenabled a fuller exploration of GCM uncertainty in the assess-ent of impacts. For example, Lobell et al. (2008) utilised the

utput of 21 GCMs from the CMIP3 ensemble. However, it is impor-ant to note that because of their basis on historical relationshipsith climate statistical approaches should be used with cautionhen projecting impacts at long lead times. For instance, Lobell

t al. (2008) only estimated impacts out to 2030 where predictedhanges in mean climate are of the same order as interannual vari-bility.

This study examines the importance of considering climatehange as simulated by many (14) GCMs for the estimation ofrop yield changes using a crop simulation model applied acrosshe global domain. We focus at a single time point in the future2050s) where it is expected that variation between climate modelss the dominant source of uncertainty in future climate projec-ions. The following section describes how the future climatecenarios were generated and how they were used to deriveuture crop yield across the global domain, results are presentedn Section 3, discussed in Section 4 and conclusions drawn inection 5.

. Methods

To determine the impact of climate change on crop yieldequires the comparison of crop yields simulated under future cli-ate conditions with yields simulated under present-day climatic

onditions. For this study the GLAM crop model was used to simu-

ate crop yields, the dataset of Mitchell and Jones (2005) was usedo represent present-day climate, and pattern-scaling techniquesere used to derive future climates. These aspects of experimentalesign are described in more detail in the following sub-sections.

Meteorology 170 (2013) 183–194

2.1. Crop yield simulation

Crop yield was simulated by the General Large Area Model forannual crops. GLAM was originally developed by Challinor et al.(2004) to simulate groundnuts in India. Subsequently, it has beenused in several studies examining the impacts of climate variabilityand change on crops, including the impact of climate change onwheat yields in China (Challinor et al., 2010), and groundnut yieldin India (Challinor et al., 2005a). For this study, two crops weresimulated; spring wheat and soybean. The parameterisation of Liet al. (2007) was used to simulate the response of spring wheat andthe model was further developed and parameterised to simulatesoybean.

GLAM determines the growth, development and yield of annualcrops by combining a soil water balance model with crop growthparameterisations. Between crop emergence and harvest the dailyincrease in crop biomass is determined using a Transpiration Effi-ciency (TE) concept whereby a crop specific TE (reduced underhigh vapour pressure deficit conditions) is used to convert croptranspiration to new biomass assimilated. Crop transpiration isdetermined from the potential evapotranspiration as determinedby the atmosphere, potentially extractable soil water and the leafarea index of the crop. This approach has been shown to workwell in the water-limited environments of the semi-arid Tropics forwhich GLAM was initially developed. Because of the global nature ofthis study, the Radiation Use Efficiency (RUE) concept was added,whereby a crop specific RUE is used to convert intercepted solarradiation to new biomass. For each day of the crop simulation thebiomass assimilated is the minimum of that associated with TE orRUE.

The length of the crop growing season (i.e. from emergenceto harvest) is dependent on the accumulation of thermal time.Daily thermal time is the amount by which daily mean tem-perature (T) exceeds a crop-specific base temperature (Tb) upto an optimum temperature (To), above which the amount ofthermal time then reduces until the temperature reaches the max-imum (Tm) at which thermal time is zero. Therefore, thermal timeis greatest, and rate of development fastest, when T = To. Eachgrowth stage (e.g. emergence to flowering) is defined by a pre-scribed amount of thermal time (TT1–4). Therefore, the sum overall growth stages gives the total thermal time requirement for thatcrop. For soybean, development is also sensitive to photoperiod.Therefore, an additional dependence on photoperiod was addedwhereby the rate of development due to thermal time is reducedwhen the photoperiod is greater than a defined critical period.The reduction factor is calculated each day up to flowering as:RPE = 1.0 − (PHOT − CRITPP) * PPSE, where PHOT is the photoperiod(h), CRITPP is the critical photoperiod, and PPSE is the sensitivity.Photoperiod sensitivity was not included in the spring wheat ver-sion of GLAM developed by Li et al. (2007) because its sensitivityis smaller than that of winter wheat (Prasil et al., 2004). However,there is evidence that the development of some varieties of springwheat is sensitive to photoperiod (Ewert and Pleijel, 1999). Theimplication of this assumption for the results is addressed in Sec-tion 4. The relevant model parameters and their values used in thisstudy are given in Table 1. In this study, to represent the array ofvarieties existing for each crop three varieties are simulated. Thevarieties are differentiated in their thermal time requirements and,in the case of soybean, their sensitivity to photoperiod. The valuesof parameters that vary between varieties are shown in Table 1. Thevalues chosen do not correspond directly to know varieties but areconsidered as representative of long, medium and short duration

varieties.

Final yield is the product of biomass at the end of the growingseason and harvest index. Harvest index increases at a prescribedrate during the later growth stages to represent partitioning of

T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194 185

Table 1Values of key crop model parameters for soybean and spring wheat simulations. For explanation of the parameters see Section 2.1.

Parameter Units Soybean Spring wheat

TE g m−2 1.8 4.0RUE g m−2 1.4 1.4Tcrit

◦C 31 28Tzero

◦C 36 36Tb

◦C 7 1a

To◦C 28 22a

Tm◦C 45 35

Short Medium Long Short Medium LongTT1

◦Cd 346 410 477 1000 1150 1300TT2

◦Cd 285 314 380 110 150 190TT3

◦Cd 342 295 19 330 435 540TT4

◦Cd 413 546 865 80 90 100

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ivflgidcpmtboFttct2GtTi

CRITPP h 14.48 13.25PPSE 0.188 0.289

a For the first growth stage, Tb = 0 and To = 23 ◦C.

iomass to the harvested organs (e.g. grains). High tempera-ure stress at flowering was included by reducing the prescribedate of increase of harvest index by a reduction factor whichas calculated as the mean over the flowering growth stage of:− [(T − Tcrit)/(Tzero − Tcrit)], where T is the mean daily tempera-

ure, Tcrit is the temperature above which harvest index is reducednd Tzero is the temperature above which seed set is entirelyborted. The values of Tcrit and Tzero used in this study are given inable 1. The groundnut version of GLAM includes a more complexarameterisation of the effect of high temperatures on flower-

ng, developed by Challinor et al. (2005b). The simple formulationescribed above was introduced to reduce the length of model inte-rations and the number of parameters required to run the model.

In the future crops will grow under elevated concentrations ofO2 which is expected to enhance growth. To represent this effect inLAM, in all future climate simulations TE and RUE were increased

rom their baseline value by 21 and 18% for soybean and springheat, respectively, assuming a CO2 concentration of 330 ppm in

he baseline period and 532 ppm in the 2050s. These increases wereerived from the increases in yield observed on the Free Air CO2nrichment experiments reported by Long et al. (2006) and com-ares to values of 17 and 19% for C3 crops in the crop models CERESnd EPIC, respectively, as reported in Tubiello et al. (2007) for anncrease in CO2 concentrations from 330 to 550 ppm.

The Yield Gap Parameter (YGP) in GLAM is a parameter cal-brated on observed yield and is used to represent the spatialariation in crop management which leads to deviation of actualarm yields from potential levels. When GLAM is run at the regionalevel, YGP is calibrated against observed yield data. However, thelobal nature of the integrations in this study meant that such cal-bration was unfeasible; instead country-level values of YGP wereetermined before simulation. To determine yield gap requires theomparison of climatic potential yields and actual yields. Climaticotential yield was determined using the simple FAO potential yieldodel driven with observed country- and growing season-average

emperature and cloud cover for the years 1988–92. The differenceetween simulated potential yield and the maximum observedver 1988–92, as reported in the statistics obtained from the UNood and Agriculture Organisation (FAO), provided and estimate forhe yield gap of that country. This approach therefore assumed thathe yield gap is constant within countries but could vary betweenountries. Alternative approaches could have been to use either ofhe recently published analysis of global yield gaps (Licker et al.,010; Neumann et al., 2010) or to simulate potential yields with

LAM where YGP = 1 to derive a potential yield and then to compare

his to the country-level observations of FAO to determine the YGP.he approach taken in this study was chosen because a degree ofndependence from the FAO data was desirable for later evaluation,

11.91 No photoperiod sensitivity0.340

and neither of the aforementioned datasets were available for usewhen this work was undertaken.

2.2. Set-up of global simulations

For the crop simulation, information on which grid cells to runthe crop model and at what date to begin the simulation wererequired. These ancillary variables were determined simultane-ously by a climatic suitability algorithm based upon the thermaltime requirements of growing season length of GLAM. Three virtualvarieties of each crop were examined which differed in their ther-mal time requirements and photoperiod sensitivity (see Table 1).The suitability algorithm searched the climate time-series at eachgrid cell for a suitable growing period, defined as a period of timeduring which the crop variety would reach maturity within anacceptable range of duration and with sufficient accumulated rain-fall. The algorithm is applied to a 30 year time series of climate tocapture interannual variability. Monthly rainfall totals were usedto determine accumulated rainfall and daily temperatures (linearlyinterpolated from monthly means) were used for the thermal timecalculation. If suitable growing periods were not found for everyyear then that grid cell was not deemed climatically suitable forcrop simulation. For suitable grid cells, the median starting dateover the 30 years was then used for the planting date in GLAM.

The algorithm was first applied to the baseline (1961–1990)climate data of Mitchell and Jones (2005) which is at 0.5◦ spa-tial resolution. Fig. 1 shows the global distribution of grid cellsdeemed suitable for the simulation of soybean, compared to thedistribution of rain-fed and irrigated soybean from the MIRCA2000observed cultivated area dataset (Portmann et al., 2010). There arelarge areas in the humid tropics where the suitability algorithmhas deemed the climate suitable but MIRCA2000 does identify itas cultivated, there are many grid cells where climatic suitabil-ity overlaps with MIRCA2000, and areas at high latitudes whereclimate is deemed unsuitable by the algorithm but MIRCA2000suggests that soybean is cultivated. Because MIRCA200 providescultivated area at each grid cell it is possible to calculate how muchof the global MIRCA2000 area is covered by our climatic suitabil-ity. For rain-fed (irrigated) cultivation 79% (78%) of the MIRCA2000area overlaps with the climatically suitable grid cells. Fig. 2 showsthe global distribution of suitable grid cells for spring wheat com-pared to the observational dataset for wheat which is the combinedarea for spring and winter wheat. The algorithm captures grid cellswhich cover 54 (55%) of the rain-fed (irrigated) cultivated area. The

algorithm does not cover the entire MIRCA cultivated area. Onelikely explanation for this is that the use of just three varieties isnot sufficient, and for wheat the comparison is not entirely fair asthe MIRCA data will include regions of winter wheat production. It

186 T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194

Fig. 1. Global distribution of grid cells determined to be climatically suitable for simulation of soybean (blue), grid cells containing actual soybean cultivated area in theM ntaina s. (Fot

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IRCA2000 dataset (red), and grid cells which are both climatically suitable and cond irrigated (b) production. NB. Climatically suitable extent is the same in both mapo the web version of the article.)

s also possible that the time difference between the climate datased (1961–90) and that of the observations (2000) could give riseo some of the anomaly. For example, the global area of soybeanwheat) production has increased from 24 (205) to 74 (215) mil-ion hectares between 1961 and 2000 (FAO). Also, the MIRCA datas derived from observations provided at spatial resolution muchoarser than the final 0.5◦ resolution. It is likely that for many partsf the world data is applied to all tgrid cells within a spatial unitithout consideration for climatic suitability. In which case, the

uitability algorithm may be more believable than the details ofIRCA data.The planting date determined for soybean was compared to

he month of planting given in the MIRCA data set. The plantingates for spring wheat were not compared due to the combina-ion of spring and winter wheat in the MIRCA product. It wasound that the planting date of 37% of the climatically suitable gridells were within 1 month of the MIRCA estimate. This level ofgreement is less than that reported by Waha et al. (2011) whoound that at least 50% of their simulated planting dates across

global domain were within 1 month of the MIRCA values. Theajority of the remainder in this study were found to be earlier

han the MIRCA estimate which is consistent with the iterativeature of the methodology which searches through the calendarear for a suitable cropping window and then stops when one isound.

ig. 2. As Fig. 1 but for spring wheat. NB: MIRCA2000 data for wheat includes both springhe reader is referred to the web version of the article.)

actual production (yellow). MIRCA2000 splits cultivated area between rain-fed (a)r interpretation of the references to color in this figure legend, the reader is referred

The suitability algorithm was then applied to the multiple futureclimate scenarios (see Section 2.3). This allows the area and theplanting date to change under the changed climate. For the cropsimulation both the baseline and future climatic suitability andplanting dates are used for all three varieties. Therefore, two adap-tation scenarios were investigated. Firstly, the variety at each gridcell was fixed to that which yielded the highest under the baselineclimate but the planting date was allowed to change (Altered Sow-ing Date). Secondly, the planting date was altered and the choice ofvariety was allowed to switch if an alternative gave higher yields(Altered Sowing Date and Variety Switch). It is important to notethat the planting date was not optimised on yield, but determinedbefore the crop simulation. Under future climates the extent ofsuitability changed, commonly an expansion northwards due towarmer temperatures, but for the comparison baseline and futurecountry-level yields these new grid cells were not included.

2.3. Derivation of future climates

The pattern-scaling approach of ClimGen (Osborn, 2009) wasused to determine future climates. The patterns are maps of regres-

sion coefficients relating change in local variable (e.g. temperature,precipitation) with change in global mean temperature. To deter-mine the local climate at some point in the future (in this case2050 under the A1B SRES scenario) these patterns were scaled by

and winter wheat. (For interpretation of the references to color in this figure legend,

T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194 187

Table 2The 14 General Circulation Models (GCMs) for which future climates were deter-mined and their global mean temperature increase from 1961–1990 to 2050 underthe SRES A1B scenario. NB. Results are from the MAGICC simple climate model tunedto each GCM.

GCM Global warmingby 2050 (◦C)

GCM Global warmingby 2050 (◦C)

CCSM30 1.60 GISS MODELER 1.56CCMA CGCM31 2.06 UKMO HadCM3 2.21CSIRO mk30 1.53 UKMO HadGEM 1.79ECHAM5 2.44 IPSL CM4 2.18GFDL CM20 1.71 CCSR MIROC HI 2.95

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Table 3Summary statistics of model skill for the top 15 soybean producing nations in thebaseline simulation.

Country % globalproduction

Correlation Ratio ofvariance

NormalisedRMSE

Brazil 30.8 0.51** 0.88 0.32USA 29.9 0.35* 1.38 1.91Argentina 17.1 0.49** 0.96 0.75China 10.2 −0.15 1.48 0.71India 5.5 0.38* 0.59 0.40Paraguay 3.5 −0.27 0.58 0.50Bolivia 0.5 0.01 0.30 1.19Indonesia 0.3 0.26 1.13 3.22Italy 0.3 −0.05 1.73 1.56Korea, DPR 0.2 0.10 3.34 1.59Viet Nam 0.2 0.03 2.15 1.29South Africa 0.2 0.22 0.75 0.25Canada 0.2 0.16 1.53 0.64Korea 0.1 −0.09 5.32 1.98Myanmar 0.1 −0.04 1.53 0.39

* Significant correlation at 5% significance level.** Significant correlations at 1% significance level.

significant (p < 0.05) correlations between observed and simulatedyields were found for just three countries (Australia, Argentina,Hungary). One possible explanation for the low bias of the modelis the inclusion of winter wheat in the FAO statistics. Winter wheat

GFDL CM21 1.58 CCSR MIROC MED 2.31GISS MODELEH 1.81 NCAR PCM1 1.42

he amount of global warming of each respective GCM by 2050nder the SRES A1B scenario as predicted by the MAGICC simplelimate model tuned to each GCM’s original simulation. Climatehange anomalies were applied to the 1961–1990 observed climateime series of Mitchell and Jones (2005). Therefore, the interannualariability of present-day climate was preserved. To run the cropodel the monthly time series were downscaled to daily resolution

sing the weather generator of Gosling and Arnell (2011). There-ore, future regional climate varied between GCMs because of theirifferent response of global mean temperature to the greenhouseas forcing under the A1B scenario by 2050 (i.e. each GCM’s cli-ate sensitivity) and the simulated changes to regional climate

i.e. the pattern of climate change). Table 2 lists all 14 GCMs andheir projected global warming by 2050 from 1961 to 90 climateonditions.

.4. Aggregation of 0.5◦ GLAM output to country scale

GLAM outputs a yield value for each of 29 years at each grid celleemed suitable for cultivation (blue and yellow shaded areas inigs. 1 and 2). For evaluation against FAO statistics, country-levelield values were calculated by multiplying the simulated yield atach grid cell by the observed cultivated area of MIRCA2000 (sepa-ately for rain-fed and irrigated simulations), then summing over allrid cells within each country to determine total production, andnally dividing this total production (rain-fed + irrigated) by theotal area (rain-fed + irrigated). Grid cells at which a single, com-lete growing season was not simulated for each year of the 29ears were discarded from the analysis. Only grid cells commono both current and future climate simulations were used for theggregation.

. Results

.1. Evaluation of baseline climate simulations

To assess the skill of the crop model when applied across thelobal domain, yield output of the baseline climate simulations,ggregated to country-level, were compared to reported country-evel crop yield data of the FAO. For each of the top 30 producingountries, the average yield over the 1981–1990 period, and thetandard deviation of yields over the 1961–90 period were com-uted. The reported data of the FAO were linearly de-trended andhifted to 1990 yield levels. Fig. 3 shows that the soybean versionf the model was able to capture the observed variability of meanield between countries. Some of this skill, however, will be dueo the use of a spatially varying Yield Gap Parameter which was

tself determined using the FAO data. Therefore, further valida-ion of the model can be provided by comparing the simulated andbserved variability (standard deviation) of yield. Fig. 3b shows thathe spatial variation in the standard deviation of yield was not well

Fig. 3. Comparison of simulated and observed mean (a) and standard deviation (b)of country-level soybean yields (t ha−1) of the top 30 producing countries in thebaseline simulation. Size of circle is proportional to harvested area of each country.

captured by the model. However, when the observed and simulatedtime-series of yield for each country were correlated (Table 3), themodel exhibited significant skill (p < 0.05) in four out of the top fiveproducing countries for four out of the top five producing countries(Brazil, USA, Argentina, India) providing some confidence in theability of GLAM to simulate the response of soybean yield to climaticvariations. For spring wheat the spatial variation across the globe inboth the mean and standard deviation of yield was well captured bythe model with evidence of an underestimation of yield for severalcountries (Fig. 4). At an individual country level, Table 4 shows that

Fig. 4. Comparison of simulated and observed mean (a) and standard deviation (b)of country-level wheat yields (t ha−1) the top 30 producing countries in the base-line simulation. Size of circle is proportional to harvested area of each country. NB.Observations include both spring and winter sown wheat.

188 T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194

Table 4Summary statistics of model skill for the top 15 spring wheat producing nations inthe baseline simulation.

Country % globalproduction

Correlation Ratio ofvariance

NormalisedRMSE

China 30.0 −0.03 0.56 1.66USA 9.9 0.28 0.67 2.50Russia 4.9 No observationsGermany 4.9 0.06 1.90 2.30Argentina 4.8 0.43** 0.69 0.60Poland 4.6 0.07 2.79 0.90Australia 4.5 0.64** 0.66 0.22India 4.3 0.00 0.86 3.46Canada 4.3 0.31 0.79 1.02Ukraine 3.8 No observationsFrance 2.1 0.17 1.20 1.92Romania 1.9 0.31 0.69 1.40United Kingdom 1.7 0.19 1.07 1.05Hungary 1.6 0.34* 0.94 1.97Brazil 1.6 0.17 0.80 0.53

NB. Observations include both spring and winter sown wheat.

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Fig. 5. Impact of climate change on soybean yield for the top 15 producing countriesof the baseline simulation. Each circle represents the impact simulated with a singleclimate model change pattern under No Adaptation (black circles), Altered SowingDate (blue circles), and Altered Sowing Date and Variety Switch (red circles). (For

* Significant correlation at 5% significance level.** Significant correlations at 1% significance level.

s the more common form of cultivated wheat in many countries,articularly at higher latitudes where cooler temperatures satisfyhe crop’s vernalisation requirements. Because it grows for longer,inter wheat often produces higher yields than spring varieties;

herefore, its absence from this study will likely have led to thenderestimation of yield in some countries. Therefore, it is impor-ant to emphasise that the results of spring wheat in this studyhould not be taken as indicative of wheat more generally. Rather,he spring wheat simulations are included as an alternative to soy-ean. Clearly, a more complete assessment of the impacts of climatehange on wheat productivity should include both types of wheat.

.2. Impacts of climate change

The impact of climate change on crop yield was initially deter-ined for each GCM future climate, and each adaptation scenario,

t the level of each individual grid cell. Example maps of this anal-sis are provided in Figs. A1 and A2 for soybean and spring wheat,espectively. Whilst the global maps provide an indication of theeographic details involved in the simulations it is not easy to com-are between GCMs, crops, and adaptation scenarios. Therefore, theuture climate yield simulations were aggregated to country levelccording to the method described in Section 2.4. Global productionalues were also calculated by summing the national values. Thempact of climate change on the global production of soybean andpring wheat is shown in Table 5. Impact varies between crops, withdaptation scenario and is dependent on the choice of GCM. Con-idering the full range of GCMs and adaptation scenarios the impact

n soybean production ranges from −43 to +10%, while for springheat the impact is more severe ranging from −52% to +1%. For soy-

ean, the range in impact due to the choice of GCM is equivalent to

able 5imulated impact (% change from baseline) of climate change by 2050 on globalroduction of soybean and spring wheat under three adaptation scenarios. Valueshown for the minimum, mean and maximum of the range simulated in the GCMnsemble.

Soybean Spring wheat

Min Mean Max Min Mean Max

No Adaptation −43 −30 −14 −52 −33 −14Altered Sow −30 −19 −7 −46 −26 −7Altered Sow and Variety Switch −15 −4 10 −39 −17 1

interpretation of the references to color in this figure legend, the reader is referredto the web version of the article.)

the variability in impact due to adaptation. For spring wheat, therange over GCMs is larger than that for the adaptation scenarios.

Fig. 5 shows the simulated change in country-wide soybeanyield plotted as the ratio of yield in the future to that simu-lated under the climate of 1961–90. In general, the climate changescenarios have a negative impact on yield. However, there is con-siderable variability between countries, between climate models,and between adaptation scenarios. Without adaptation impacts aremostly negative with the largest decreases in Canada and Italy andthe smallest changes in India. Climate model uncertainty is largefor all countries with the exception of Indonesia. However, thelarge magnitude of impact ensures that the signal-to-noise ratio(mean/standard deviation) is less than −1 for all countries, exceptIndia, in the No Adaptation and Altered Sowing Date scenarios. TheAltered Sowing Date and Variety Switch scenario offset the neg-ative impacts for several countries and lead to a 70% increases inyield in one GCM for the DPR of Korea. In some instances the rangeof impact simulated across the different climate model patterns isreduced under the adaptation scenarios. For example, for Argentinathe signal-to-noise ratio increases from −2.75 to −0.9.

The impact of climate change on spring wheat yield is shownin Fig. 6. Compared to soybean the impact of climate change variesmore widely between countries with a greater frequency of positiveimpacts under climate change. The impact of adaptation can belarge (e.g. Russia) or small (e.g. Brazil) and the spread of impactdue to climate model uncertainty also varies between countries. ForChina under No Adaptation impact varies from +5 to −15% while inPoland it is between no change and a reduction of 45%. Under theNo Adaptation scenario the signal-to-noise ratio is greater than 1or less than −1 for all countries, except Brazil. Similarly to soybean,the adaptation scenarios can either offset some of the yield lossesor even lead to increases in yield under future climate conditions.

For both crops the impact of climate change in the absenceof adaptation was found to be strongly related to the change ingrowing season temperature (Fig. 7a and d). The strong relation-ship between temperature and yield change is primarily due to theeffect of temperature on crop growing season duration. Up to the

optimum temperature for development, warming leads to a fasteraccumulation of thermal time and hence a shorter growing seasonlength. All else being equal this will lead to lower yields as there

T. Osborne et al. / Agricultural and Forest

iietWhs

mgattvlap

FA

Fig. 6. As Fig. 7 but for spring wheat.

s less time for biomass accumulation to occur. Because warm-ng is predicted to be largest in the northern high latitudes thisxplains why the simulated impact on crop yield (without adapta-ion) is greatest for countries such as Canada, Russia, and Ukraine.

hile tropical countries with warmer climates (e.g. Indonesia)ave smaller impacts without adaptation as they experience amaller temperature change.

Once adaptation is considered, this simple picture becomesore complicated. Allowing sowing date to change can shift the

rowing season to a cooler time of the year leading to a smallermount of warming (compared to baseline) being experienced byhe crop than when the sowing date is fixed (Fig. 7b and e). Whenhe variety is also allowed to change then the benefits of culti-

ating a crop variety with longer thermal time requirements (i.e.onger growing season) can be realised and negative yield impactsre reduced or yield increases are observed (Fig. 7c and f). Theotential for shifting varieties is greater for cooler countries where

ig. 7. Relative change in yield against absolute change in growing season temperatureltered Sowing Date (b, e), and Altered Sowing Date and Variety Switch (c, f). Each circle

Meteorology 170 (2013) 183–194 189

long duration varieties are not suitable in the baseline climate butbecome suitable under climate change.

Because the impact of climate change at the country-level canvary significantly depending on the choice of GCM it could berecommended that impacts assessments utilise climate change sce-narios from as many GCMs as possible. However, the computationalrequirements involved may limit modellers to a sub-sample. Toexamine whether the choice of particular GCMs might bias suchapproaches a ranking exercise was performed on the 14 GCMs overthe top 30 producing countries for each crop such that a GCM whichconsistently leads to the lowest (highest) yields in the future wouldbe given a ranking of 14 (1). Fig. 8 shows that under No Adap-tation several GCMs produce consistently higher or lower yieldscompared to the rest of the ensemble. For example, for both soy-bean and spring wheat the changes in climate derived from NCARPCM often leads to the highest yields in the future, while climatechange simulated by MIROCH often leads to the lowest yields inthe future. Once adaptation is considered, however, the spread inimpact ranking for each GCM widens but the relative ordering ofhigh to low impacts models is still evident. It is also worth not-ing that the order of GCMs is similar between soybean and springwheat. To examine whether this consistent discrimination betweenGCMs is related to differences in the response of global tempera-ture to increased greenhouse gases (i.e. climate sensitivity) or toregional patterns of change, the ranking of GCMs’ global warmingby 2050 from Table 1 was correlated with their ranking in Fig. 8. Itwas found that ranking of global warming explained 7% (r = 0.27)of the ranking of soybean yield and 22% (r = 0.47) of spring wheat.Therefore, a larger part of the variation of yield due to GCMs is dueto how local climate changes with global warming (i.e. the patternof climate change) rather than the magnitude of global warmingitself.

4. Discussion

The strong relationship between temperature change and yieldimpact suggests that the variation in impact due to climate model

for soybean (a–c) and spring wheat (d–f) simulations with No Adaptation (a, d),represents a single country-GCM combination.

190 T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194

F = mosw ety (cA

cutmcfciB

Fg(St

ig. 8. Box plots of the distribution of ranks for each GCM (where 1 = least and 14heat (b, d) under No Adaptation (a, b) and Altered Sowing Date and Switch Varidaptation simulations for each crop, respectively.

hoice is primarily determined by the strength of warming as sim-lated by each model. This apparent dominance of changes inemperature over other variables, such as precipitation, for deter-

ining crop impact has been noted previously but with statisticalrop response models (Lobell and Burke, 2008). To examine this

urther crop yield as simulated under the 14 GCM-derived futurelimates was regressed against two climatic variables: mean grow-ng season temperature and mean growing season rainfall per day.y comparing the r2 of the multiple linear regression with those

ig. 9. Fraction of variation in future rain-fed soybean yield explained by meanrowing season temperature (black bars), mean growing season rainfall per daywhite bars), and their interaction (grey bars). (a) No Adaptation and (b) Alteredowing Date and Variety Switch. For calculation see text. Values are plotted addi-ively.

t severe impact) over the top 30 producing countries for soybean (a, c) and spring, d) adaptation scenarios. GCMs plotted in order of average ranking under the No

determined by correlation between yield and each variable inde-pendently the relative contribution of each in explaining futureyield variability was determined. Figs. 9 and 10 show the resultsfor soybean and spring wheat, respectively. Overall, as expectedfrom the results in Fig. 7a and d, growing season temperature wasfound to explain the largest proportion of the variation of impactbetween GCMs under No Adaptation. However, important excep-tions include spring wheat in the USA, and soybean in India. With

adaptation, the ability to explain the variation in future yield withinthe GCM ensemble by variation in seasonal climate variables isreduced for many countries.

Fig. 10. As Fig. 9 but for spring wheat.

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T. Osborne et al. / Agricultural and

Figs. 5 and 6 show that the potential for adaptation predom-nantly exists in cooler countries where the varieties with theargest thermal time requirements were not suitable for cultiva-ion under baseline climate conditions but became suitable underlimate change. However, this result is dependent on the numberf varieties examined. Clearly, more than three varieties of soybeannd spring wheat exist. For example, Thornton et al. (2010) exam-ned the response of 2711 varieties of spring wheat to increases in

ean temperature. Previous global modelling studies have exam-ned just a single variety (Nelson et al., 2009), while others havellowed parameter values associated with variety (i.e. thermal timeequirements) to vary continuously across the globe as a func-ion of climate (Müller et al., 2010). This paper falls between thesewo cases in simulating the response of three varieties separately,nabling the demonstration of the potential impact of switchingariety in the future. Additionally, the results for spring wheatre subject to the assumption that development is not sensitiveo photoperiod. It is not easy to judge the extent to which theimulations would change if photoperiod were included becausehe effects of photoperiod on time to flowering are intrinsicallyinked to those of temperature (Craufurd and Wheeler, 2009), but itould affect both the distribution of varieties under baseline climateonditions and the potential for switching varieties under climatehange.

. Conclusions

In the simulations without consideration of adaptation, theagnitude of impact was strongly related to the change in grow-

ng season temperature due to the associated reduction in growingeason duration. For many countries altering the crop planting daten the future reduced the magnitude of the warming thereby less-ning the impacts of climate change. Subsequently switching to aonger duration variety offset yield losses further and even led to

ncreased yields in the future compared to the baseline for someountries. Conclusions from modelling studies are always depen-ent on the assumptions made and this study is no exception. Inhis study the identified potential of adaptation is subject to the

Meteorology 170 (2013) 183–194 191

caveat that only three varieties were considered and that greaterpotential may exist if more were considered.

The variation of future crop yields across the GCM ensemblewas, for most countries due to the range in projected tempera-ture, rather than rainfall. However, considering adaptation reducedthe importance of temperature changes and, in some instances,increased the importance of rainfall uncertainty. It was also shownthat the ranking of impacts simulated under climate change fromeach GCM was consistent across countries and the two crops exam-ined. This has major implications for the interpretation of paststudies which have utilised one or two GCMS, and the design offuture impact assessment. For example, if only a limited sampleof GCMs are to be utilised than it would seem sensible to includeNCAR PCM1 and MIROCH, the GCMs with the highest and low-est future yields, respectively. Following adaptation, the relativeranking was maintained, on average, but was less consistent acrosscountries.

The applicability of these results to other crops requires furtherinvestigation. They also need to be placed in the context of othersources of uncertainty that were not examined such as: choice ofcrop model and the processes within the crop model (e.g. strengthof CO2 fertilisation), and the method of future climate scenario gen-eration (Hawkins et al., 2012, this issue). This study focused on asingle future time period, therefore, it remains to be shown how theimportance of GCM uncertainty for crop impact assessment varieswith lead time.

Acknowledgments

We would like to acknowledge the support of the Natu-ral Environment Research Council for funding the QUEST-GSIproject and NCAS-Climate. We would also like to thankDEFRA for funding of the Hadley Centre contract T12412H50455. We would also like to acknowledge the two anony-mous reviewers whose comments greatly improved the

manuscript.

Appendix A.

192 T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194

Fig. A1. Grid cell level yield change (%) for soybean under Altered Sowing Date and Variety Switch adaptation scenario, for all 14 GCM future climates and the ensemblemean.

T. Osborne et al. / Agricultural and Forest Meteorology 170 (2013) 183–194 193

1 but f

R

C

C

Fig. A2. As Fig. A

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