ORIGINAL PAPER

Investigating the impact of climate change on cropphenological events in Europe with a phenology model

Shaoxiu Ma & Galina Churkina & Kristina Trusilova

Received: 20 October 2010 /Revised: 10 July 2011 /Accepted: 11 July 2011 /Published online: 31 July 2011# ISB 2011

Abstract Predicting regional and global carbon and waterdynamics requires a realistic representation of vegetationphenology. Vegetation models including cropland modelsexist (e.g. LPJmL, Daycent, SIBcrop, ORCHIDEE-STICS,PIXGRO) but they have various limitations in predictingcropland phenological events and their responses to climatechange. Here, we investigate how leaf onset and offset days ofmajor European croplands responded to changes in climatefrom 1971 to 2000 using a newly developed phenologicalmodel, which solely relies on climate data. Net ecosystemexchange (NEE) data measured with eddy covariancetechnique at seven sites in Europe were used to adjust modelparameters for wheat, barley, and rapeseed. Observationaldata from the International Phenology Gardens were used tocorroborate modeled phenological responses to changes inclimate. Enhanced vegetation index (EVI) and a crop calendarwere explored as alternative predictors of leaf onset andharvest days, respectively, over a large spatial scale. In eachspatial model simulation, we assumed that all Europeancroplands were covered by only one crop type. Given this

assumption, the model estimated that the leaf onset days forwheat, barley, and rapeseed in Germany advanced by 1.6, 3.4,and 3.4 days per decade, respectively, during 1961–2000. Themajority of European croplands (71.4%) had an advancedmean leaf onset day for wheat, barley, and rapeseed (7.0%significant), whereas 28.6% of European croplands had adelayed leaf onset day (0.9% significant) during 1971–2000.The trend of advanced onset days estimated by the model issimilar to observations from the International PhenologyGardens in Europe. The developed phenological model can beintegrated into a large-scale ecosystem model to simulate thedynamics of phenological events at different temporal andspatial scales. Crop calendars and enhanced vegetation indexhave substantial uncertainties in predicting phenologicalevents of croplands. Caution should be exercised when usingthese data.

Keywords Phenology model . International phenologygardens . Crop calendar . Remote sensing

Introduction

Phenology, the study of the timing of recurring biologicalcycles and their connection to climate, has emerged as animportant focus for ecological research because phenologicalevents (leaf onset and offset days) are regarded as goodindicators of climate change (Schwartz 1999; Parmesan andYohe 2003; Menzel 2006). A number of studies onphenology reported an earlier budburst and a longer growingseason in response to climate change at the end of the 20thcentury, based on observation of phenological events(Menzel 2002; Chmielewski et al. 2004), satellite studies(Tucker et al. 2001; White et al. 2009), and climatologicalstudies (Linderholm et al. 2008; Thum et al. 2009).

S. Ma :G. Churkina :K. TrusilovaLeibniz Center for Agricultural Landscape Research,15374 Müncheberg, Germany

S. MaMax-Planck Institute for Biogeochemistry,07745 Jena, Germany

S. Ma (*)Key Laboratory of Desert and Desertification, Cold and AridRegion Environmental and Engineering Research Institute, CAS,Lanzhou 730000, Chinae-mail: shaoxiu.ma@zalf.de

G. ChurkinaInstitute of Geography, Humboldt University of Berlin,12489 Berlin, Germany

Int J Biometeorol (2012) 56:749–763DOI 10.1007/s00484-011-0478-6

The phenological events of plants impact the mass andenergy cycle of the biosphere. Plant vegetative cycles, such asthe timing and duration of foliation, determine exchangeperiods of carbon dioxide and water between the land surfaceand atmosphere, which has in turn an important influence onthe global carbon cycle (Keeling et al. 1996; White et al.1999; Jolly et al. 2005; Piao et al. 2007). Annual integratednet ecosystem exchange (NEE) of carbon dioxide is stronglyrelated to the length of the growing season (Goulden et al.1996; Baldocchi et al. 2005; Churkina et al. 2005). Inaddition to carbon cycle impacts, the patterns of canopydevelopment and senescence have also been linked toseasonal changes in surface resistance and roughness, aswell as the turbulent exchange of water and energy (Mooreet al. 1996; Sakai et al. 1997; Hollinger et al. 2004).

Phenological observations record changes in the pheno-logical events of plants and provide fundamental evidencefor scientific research. The phenological dates of floweringand leaf-out have traditionally been kept through fieldobservations, sometimes as an individual's hobby (Fitterand Fitter 2002), or by generations of people within thesame family, extending from 1736 to 1958 (Sparks andMenzel 2002; Richardson et al. 2007). The InternationalPhenology Gardens (IPG) were set up to record the time ofphenological events for different species and there are about90 IPG across Europe recording the phenological events ofdifferent plants (http://www.agrar.hu-berlin.de/struktur/institute/nptw/agrarmet/phaenologie/ipg). According to IPGrecords (Menzel 2000), there is an average trend in Europefor all springtime phases (leaf unfolding, May shoot andflowering of different species) to advance 2.1 days/decade,and for the autumn phase (leaf coloring and leaf fall) to bedelayed by 1.6 days/decade from 1959 to1996 in Europe. Thenearly Europe-wide warming in early spring (February–April)over 1969–1998 led to an earlier start of the growing seasonby 8 days (Chmielewski and Rozer 2001). These observationsprovide fundamental evidence for the shift of phenologicalevents in response to climate change. However, these on-siterecords cannot project the spatial pattern of phenologicalevents in response to environmental changes onto a regionalscale and cannot predict changes in phenological events inresponse to future climate changes.

Numerical models are a good supplement to observa-tions for estimating the impact of climate change onphenological events over long temporal and large spatialscales. The phenology models for natural vegetation (Whiteet al. 1997; Botta et al. 2000; Jolly et al. 2005) have beendeveloped and tested with ground observation and remotesensing data for different plant functional types. Some ofthem were integrated into ecosystem models, such as theIBIS model (Foley et al. 1996; Kucharik et al. 2000), theBiome-BGC model (Thornton 1998), and the LPJ model(Bondeau et al. 2007).

A variety of algorithms have been developed to predictphenological events for managed ecosystems, especially forcrops. They can be classified into two different groups. Thefirst group includes detailed crop development informationorganized by planting day, such as seeding emergence,tassel initiation, silking, grain fill, and germination. Thesemodels have been integrated into detailed crop simulationmodels, such as the Sirius model (Jamieson et al. 1998;Lawless et al. 2005), the ARCWHEAT model (Travis et al.1988; Lawless et al. 2005), the STICS model (Brisson et al.2003), and the CERES model (Gungula et al. 2003). Thesemodels provide detailed information for site level manage-ment. The second group focuses on predicting crop plantingand harvesting days on a large scale. These phenologicalmodels have been integrated into ecosystem models, suchas the LPJmL (Bondeau et al. 2007), Daycent (Stehfest etal. 2007), SIBcrop (Lokupitiya et al. 2009), ORCHIDEE-STICS (Smith et al. 2010) and PIXGRO (Adiku et al.2006). The Daycent phenological model focuses onoptimizing crop productivity, and it does not reflect theimpact of climate on phenological events directly, and theLPJmL phenological model uses temperature and precipi-tation to predict planting day. The SIBcrop phenologicalmodel uses mean air temperature as a single indicator topredict the planting day of crops, although in reality, notonly temperature, but also moisture and photoperiod areidentified as the main controlling factors of plant phenology(Myneni et al. 1997; Chuine and Cour 1999; Schwartz andReiter 2000). Growing degree days (GDD) were used todescribe the crop growth process in the ORCHIDEE-STICS(Smith et al. 2010) and PIXGRO (Adiku et al. 2006)models. Planting and harvesting dates can shift over time,responding to changes in climate as well as changes intechnological and socio-economic factors (Kucharik 2006).Planting and harvesting decisions are often influenced byfactors that are much more complicated than those assumedin existing global models (Sacks et al. 2010). The existinglarge-scale, phenological models for croplands, e.g.,LPJmL, Daycent, SIBcrop, ORCHIDEE-STICS and PIX-GRO, have their own limitations in predicting phenologicalevents as described above.

New models or further development of existing modelsare needed to predict the phenology of crops on largetemporal and spatial scales. The aim of this study was toinvestigate changes in phenological events of crops inresponse to climate change by developing, calibrating andvalidating an existing crop phenology model. This modifiedmodel predicts leaf onset and offset days of crops and notplanting and harvesting dates, for the following tworeasons. First, the length of the growing season has directimpacts on the mass and energy exchange betweenatmosphere and biosphere. Ecosystem models address thenatural development process of crops not human decisions.

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Second, leaf onset and offset days are less dependent onmanagement decisions than planting and harvesting dates.

In this study, the general phenology model or growingseason index (GSI) (Jolly et al. 2005) for natural vegetationwas modified to predict leaf onset and offset days of crops.The modified model was calibrated using the observed leafonset and offset days detected from NEE measured with theeddy covariance technique at seven sites in Europe. Theability of remotely sensed data, EVI, and a crop calendar topredict crop phenological events was tested. The analysis oftrends in leaf onset and offset days of European croplandswas carried out based on the model simulation resultsduring 1971–2000.

Materials and methods

Model testing sites

The model was calibrated with observation data from eddycovariance measurement sites in Europe for different crops(Table 1). The eddy covariance measurement data are fromthe CarboEuropeIP database (http://gaia.agraria.unitus.it/DATABASE/carboeuropeip/mustlogin.aspx) for wheat, bar-ley, and rapeseed on seven observation sites, includingKlingerberg, Germany (DEKli), (Prescher et al. 2010);Gebesee, Germany (DEGeb), (Anthoni et al. 2004);Lonzée, Belgium (BELon), (Aubinet et al. 2009; Moureaux

et al. 2008); Auradé, France (FRAur) (Béziat et al. 2009);Grignon, France (FRGri), (Lamaud et al. 2009); Oensingen,Switzerland (CHOe2), (Dietiker et al. 2010); and Risbyholm,Denmark (DKRis), (Moors et al. 2010) (Fig. 1). Most ofthese sites applied rotation management during the carbonflux observation time (Table 1). The annual mean airtemperature ranged from 7°C to 18°C across these sitesand the annual mean precipitation stretched from 500 mm to1100 mm. Meteorological data, such as maximum temper-ature, minimum temperature, vapor pressure deficit (VPD),and radiation are available for each site from the CarboEur-opeIP database.

Detecting leaf onset and offset days of croplandsfrom the daylight NEE

Leaf onset and offset days were detected from eddycovariance measurements of the NEE flux. The eddycovariance method provides direct and continuous infor-mation about local CO2 fluxes (Baldocchi 2003) and eddycovariance measurements were used as reference measurefor the duration of the growing season to which the othermethods were compared (Thum et al. 2009). In this study,to simplify the description, a positive sign of NEE indicatesa flux from the atmosphere into the ecosystem (i.e, netcarbon dioxide uptake). The fluxes were calculated at30 min intervals. Turbulence and storage of carbon dioxidewere calculated using standard CarboEurope software

Table 1 Site location and land cover information

Site name andsite ID

Latitude and longitude T P Years Crop S F I Sowing day Harvest day

Auradé -FRAur 43° 32'58'' N1°6'28''E 12.9 700 2004–2005 Rapeseed Direct 0 13-9-2004a 27-6-2005

2005–2006 Winter wheat Multiple M 0 1-10-2005a 2-6-2006

Gebesee -DEGeb 51° 6'00.36''N10°54'51.48''E 9.6 501 2003–2004 Rapeseed Tillage M 0 20-8-2003a 6-8-2004

2004–2005 Winter barley 16-9-2004a 19-7-2005

2006–2007 Winter wheat Tillage M 0 14-11-2006a 5-8-2007

Grignon -FRGri 48°51'N1°58'E 11.1 600 2005–2006 Winter wheat Tillage M 0 28-10-2005b 24-6-2006

2006–2007 Winter barley Tillage M+O 0 4-10-2006b 13-4-2007

Klingerberg -DEKli 50° 3'35.02"N13°31'20.58"E 7.0 850 2004–2005 Rapeseed Tillage M+O 0 21-8-2004a 9-9-2005

2005–2006 Winter wheat Tillage M 0 23-9-2005a 9-10-2006

Lonzée -BELon 50°33'08'' N4°44'42'' E 10.0 800 2004–2005 Winter wheat Tillage M 0 1-10-2004a 21-7-2005

2006–2007 Winter wheat Tillage M 0 16-9-2006a 8-7-2007

Oensingen -CHOe2 47°17'10.7'' N7°44'03.6'' E 9.0 1100 2004–2005 Winter barley Plowing M 0 29-9-2004a 14-7-2005

2006–2007 Winter wheat Plowing M 0 19-10-2006a 14-7-2007

Risbyholm -DKRis 55° 31' 49'' N12° 05' 50'' E 9.0 575 2003–2004 Winter wheat Plowing M 0 20-9-2003b 5-9-2004

2004–2005 Winter wheat Plowing M 0 15-10-2004b 13-8-2005

2005–2006 Winter wheat Plowing M 0 26-10-2005b 19-8-2006

2006–2007 Winter wheat Plowing M 0 No

S soil preparation methods (direct=without preparation, multiple=tillage and plowing), F fertilizer methods (M=mineral, O=Organic), I irrigationamount (mm), T annual mean temperature (°C), P annual sum precipitation (mm)a The sowing day and harvesting day are from the records of site observationb The sowing and harvesting days are taken from Moors et al. (2010)

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(Mauder et al. 2008). Integrated daylight NEE is the netecosystem exchange during the daytime when the solarshortwave, incoming radiation is positive. Integrated day-light NEE is a better proxy of photosynthesis than integrateddaily NEE because the latter also includes respiration atnight when there is no photosynthesis. Therefore, daylightNEE was selected to detect leaf onset and offset days.

Daylight NEE, smoothed with a ten-day window, was usedto retrieve leaf onset and offset days because NEE fluctuatesfrom day to day due to weather conditions. The start of thegrowing season was defined as the day when leaves take upmore carbon dioxide from the atmosphere than they releaseduring daylight. At the beginning of the growing season, thecarbon flux signal is weak and increases gradually over aperiod of one week to one month or even longer. During thistime, the measured NEE signal is highly variable anduncertain because measurement error and signals fromneighboring ecosystems increase this variability thus compli-cating the definition of the onset day. Based on the observedNEE, the carbon flux signal was found to be stable when NEEwas above 2 (gC m-2 day-1) (Fig. 2). Finally, the leaf onsetday of crops according to integrated daylight NEE wasdefined in two steps:

1. The day when integrated daylight NEE exceeded2 gC m-2 day-1 was chosen as a reference day based

on the smoothed daylight NEE. A moving average ofthe daylight NEE signal as described above was appliedto smooth the variability in the signal and to reduce thesensitivity of the onset day choice to extreme cases.

Fig. 1 Map of the observationalsites

Fig. 2 An example for the definition of leaf onset and offset days basedon net ecosystem exchange (NEE) and enhanced vegetation index (EVI)data for Lonzee, Belgium, in 2005. The two solid lines on the left-handside and the arrow illustrate the definition of the onset day based onNEE. ‘15’ indicates that the distance between the two solid lines is15 days. The dashed line on the left-hand side illustrates the definitionof the onset day based on EVI. The solid line on the right-hand sideillustrates the definition of the offset day according to NEE

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2. Leaf onset day was defined as occurring 15 days beforethe reference day. The 15 days were defined based onobserved NEE data as shown in Fig. 2.

At the end of the growing season, the carbon flux signaldecreased rapidly at the observation sites. The offset daywas defined as the day when the ten-day moving average ofdaylight NEE became negative.

Eleven observation samples were available for winterwheat. Seven were used to calibrate the model, and fourwere used to validate the model. Three samples wereavailable for barley and rapeseed each, and all of them wereused to calibrate the model.

Phenological model for croplands

The GSI model (Jolly et al. 2005) combines temperature,VPD, and photoperiod to predict onset and offset days ofnatural vegetation. To predict the leaf onset day of crops, theoriginal model was modified according to the crop physiology.Temperature (Jame et al. 1998), moisture (Boonjung andFukai 1996), and photoperiod (Keatinge et al. 1998) wereidentified as the main climate factors of the growing season.Accumulated daylight temperature (ADT) calculated fromthe leaf onset day was used to define the offset day.

Leaf onset day

In the GSI model, minimum temperature was replaced bymean temperature to define the temperature index for thecrop phenology model because mean air temperature wasregarded as a main control factor triggering the growingseason (Chmielewski and Koen 2000; Mitchell and Hulme2002). The product of mean temperature, VPD, andphotoperiod index forms a combined index (growingseason length index, iGSI). The leaf onset day of cropswas defined according to the combined index. Thefunctions of the model are described in detail below.

Mean temperature indicator After seven consecutive dayswith the daily mean temperature above the threshold valueand without water and photoperiod limitations, crop leavesare big enough to take up more carbon dioxide than theyemit through respiration. The temperature will not dropbelow the crop specific threshold during the growingseason except under some extreme weather conditions,such as hail. A similar method was used in other studies(Chmielewski and Koen 2000; Mitchell and Hulme 2002;Lokupitiya et al. 2009). The mean temperature index wasdefined as follows:

iT ¼ ¼ 1; if T >¼ threshold¼ 0; if T < threshold

�ð1Þ

iT daily indicator for mean temperature (only two values 0or 1). This binary iT value was different from the original(continuous linear variation between 0 and 1)temperature index in the original GSI model. The firstseven consecutive 1 values of iT were replaced with 0values. All 1 values for the days prior to these first sevenconsecutive days were also replaced with 0 values.

T the observed daily mean air temperature

The crop-specific threshold values (Table 2) wereadopted from the literature (Chmielewski and Koen 2000;Mitchell and Hulme 2002) and adjusted manually throughcross-validation between the onset day estimated by themodel and detected from NEE flux to reach a minimumabsolute difference.

VPD and photoperiod indicators The algorithm for calcu-lating the VPD indicator (iVPD) and photoperiod indicator(iPhoto) were taken directly from the GSI model (Jolly etal. 2005). The crop-specific threshold values for VPD andphotoperiod (Table 2) were adopted from the work of Jollyet al. (2005) and adjusted manually through cross-validation between the onset day estimated by the modeland detected from NEE flux as described above.

iPhoto ¼0; if Photo � PhotoMin

Photo� PhotoMin

PhotoMax � PhotoMin

1; if Photo � PhotoMax

8>><>>:

; if PhotoMax > Photo > PhotoMin

ð2ÞPhotoMin minimum threshold of photoperiodPhotoMax maximum threshold of photoperiodiPhoto photoperiod index

iVPD ¼0; if VPD � VPDMax

1� VPD�VPDMinVPDMax�VPDMin

; if VPDMax > VPD > VPDMin

1; if VPD � VPDMin

8<:

ð3ÞVPDMin minimum threshold of VPDVPDMax maximum threshold of VPDiVPD VPD index

The product of individual daily indicators of iT, iVPD,and iPhoto forms a single metric. The iGSI is a dailyindicator which represents climatic limitation on foliarcanopy development. iGSI is continuous but varies between1 (active) and 0 (inactive). The daily metric iGSI wascalculated as:

iGSI ¼ iT � iVPD� iPhoto ð4Þ

iGSI the daily index, a unitless indicatoriT the mean air temperature indicatoriVPD the VPD indicatoriPhoto the photoperiod indicator

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To trigger the growing season, the product of thephotoperiod index, VPD index and temperature indexneeds to be greater than 0.2, indicating that the photoperiodis not too long, and the VPD is not too low. The meantemperature needs to be above the threshold for at least7 days. The threshold value of iGSI was estimatedmanually by cross-validation between the onset dayestimated by the model and detected from NEE flux toreach a minimum absolute difference.

Leaf offset day

In this study, the leaf offset day marks the end of the cropgrowing season. Leaf offset day is defined as the day whenthe ecosystem's carbon emission is faster than the grossassimilation by photosynthesis. The maturity day refers tothe day when fruit formation is completed, and most of theleaves are yellow. Offset day is similar to maturity day.Harvest day is the day of the year when farmers gathermature crops from the fields. Normally, the harvest day islater than the offset and maturity days.

A heat unit, expressed as growing degree days (GDD), isfrequently used to describe the timing of biologicalprocesses (McMaster and Wilhelm 1997). Plant develop-ment stages based on GDD are described in detailed cropsimulation models. The daylight temperature, however,accounts for both the day length and temperature duringthe day. Both variables are directly related to carbonassimilation and plant growth. Solantie (2004) discussedthe advantages of using daylight temperature for predictingnot only evapotranspiration, but also carbon assimilation aswell as the start/end of the growing season in detail. Inaddition, a recent study pointed out that the basic helix-loop-helix transcription factor (SPATULA) integrates timeof day and temperature to control vegetative growth rate(Sidaway-Lee et al. 2010), shedding light on the molecularmechanisms linking daylight temperature and the develop-ment of plants. The ADT was used to define leaf offset day,and the daylight mean temperature (Tdaylight) was calculatedaccording to (Hungerford et al. 1989) as follows:

Tdaylight ¼ Tmean þ A» Tmax � Tmeanð Þ ð5Þ

Tmean daily mean temperatureTmax daily maximum temperatureA weight coefficient

A general coefficient value (A=0.45) was used in thisstudy. The ADT was calculated as follows:

ADT ¼Xn

onsetday

Tdaylight if Tdaylight > 0 ð6Þ

The leaf offset day is triggered when the ADT exceedsthe crop specific threshold temperature. The thresholdvalues for different crops are listed in Table 2. They wereadjusted manually through cross-validation between theoffset day estimated by the model and detected from NEEflux to reach a minimum absolute difference.

Corroboration of modeled leaf onset and offset dayswith remote sensing data

Remote sensing data can provide information about vegeta-tion coverage on large temporal and spatial scales and, thus,these data enable the monitoring of land cover changes.Remote sensing data, such as the normalized differencevegetation index (NDVI) and EVI, have been used to detectphenological events (White et al. 1997; Botta et al. 2000;Jolly et al. 2005). EVI has an advantage over NDVI becauseEVI includes a blue band, which allows residual atmosphericcontamination and weight to be taken into account,compensating for the variable soil background reflectance(Churkina et al. 2005). In this study, EVI was selected tomonitor phenological events because EVI corresponds moreclosely to carbon flux data than NDVI. The time resolutionof EVI data is 16 days, and the spatial resolution is 250 m.Site locations and land cover information are listed inTable 1. Advanced statistical EVI data were taken fromMODIS land subset database (http://daac.ornl.gov/cgi-bin/MODIS/GR_col5_1/time.advanced.pl/). The value of EVIwas replaced with the mean value of the two adjacent pointswhen the flag of EVI was less than 0.9.

A threshold value of 0.3 (White et al. 2009) was used todetect the leaf onset day of crops based on EVI. Linearinterpolation was used to obtain daily time step from the16-day interval EVI data.

Table 2 Parameter values for modeled crops

Cropname

Thresholdtemperature (T, °C)

Threshold accumulated daylighttemperature (ADT, °C)

Maximumphotoperiod (h)

Minimumphotoperiod (h)

MaximumVPD (Pa)

MinimumVPD (Pa)

Wheat 2 1900 12 10 4100 900

Barley 0 1700 11 9 4100 900

Rapeseed 0 1900 11 9 4100 900

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Model simulations for European croplands

Leaf onset and offset days were estimated with the modifiedphenology model for European croplands for 1961–2000.Model simulations were driven by daylight temperatures,daily mean temperature, VPD, and photoperiod from themodified climate research unit (MCRU) dataset (Mitchelland Jones 2005; Chen et al. 2009). The map of Europeancroplands was obtained from the land cover datasetcompiled for the CarboEuropeIP project (Vetter et al.2008). The pixel resolution is 0.25×0.25 degrees. Weperformed model simulations for pixels where the C3 cropcoverage exceeded 50%. The assumption for the modelsimulations was that a whole pixel was covered with onecrop type. Model simulations were run for different croptypes separately.

Analysis of trends in modeled leaf onset and offset daysof European croplands

The trend analysis for modeled leaf onset and offset daywas carried out for German croplands for 1961–2000 and

European croplands for 1971–2000 because we wantedto compare the simulated trend with the IPG's observedtrend. Linear trends for the simulated leaf onset andoffset day of each crop were calculated pixel by pixel andtested for statistical significance. The estimated trendswere then compared with observation trends from theIPG.

Corroboration of modeled leaf offset with harvest daysfrom a crop calendar

A crop calendar is a tabular depiction of plant developmentstages such as planting day and germination. In this study,we compared the harvest days extracted from a cropcalendar with the leaf offset day estimated by the model.Crop calendar maps for different crops with a 5 minutespatial resolution were downloaded from the web (http://www.sage.wisc.edu/download/sacks/crop_calendar.html).The maps were aggregated into 0.25 degree resolution witha moving window in order to match the model output, andcrop calendar maps for Europe were extracted from theseaggregated maps.

Fig. 3 Assessment of model performance. The performance of themodel in predicting the onset day compared to onset day detectedfrom net ecosystem exchange (NEE) for calibration, and fromenhanced vegetation index (EVI), respectively. NEE represents the

onset day that was detected from NEE data. EVI represents the onsetday that was detected from EVI. The model represents the onset daythat was estimated by the present model. The observed samples havebeen used to calibrate the threshold value of the model parameters

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This crop calendar contains the mean planting andharvesting dates for the 1990s and early 2000s (Sacks etal. 2010). The average leaf offset days for Europeancroplands during 1991–2000 estimated by the model wereselected for comparison with the harvest days from the cropcalendar. The weighted average of the difference betweenaverage offset day (model) and the harvest day (calendar),where each grid cell is weighted by the actual coverage areaof the given crop, were calculated on pixel and countrylevel for European croplands.

The offset day is about two weeks earlier than theharvest day for winter wheat, barley, and rapeseed. Thisresult is based on observation data, which was communi-cated with observers through e-mail and in-person ortelephone interviews. This result is also supported byexperts (Martin Wattenbach, personal communication,January, 14, 2011; Werner Eugster, e-mail communication,January, 08, 2011) who are involved in analyzing the NEEflux data used in the present study.

Results and discussion

Model calibration and corroboration

Comparison with carbon flux observations

The leaf onset and offset day estimated by the modelreached a close agreement with the leaf onset and offset daydetected from NEE flux according to linear regressionanalysis (Figs. 3, 4 and 5). The linear regression coefficientbetween the onset and offset days estimated by the model

and those observed from NEE were close to one. Theabsolute mean differences between the onset days estimatedby the model and from NEE data for wheat, barley, andrapeseed were 6, 2.7 and 2.3 days, respectively, based oncalibration samples (Fig. 3). The absolute mean differencesbetween the offset days as estimated by the model and asdetermined from NEE were 4.7, 4.7 and 5.3 days for wheat,barley, and rapeseed, respectively, based on calibrationsamples (Fig. 4). The absolute mean difference between theestimated and observed onset and offset days for wheat

Fig. 5 Assessment of the model performance: The figure shows theperformance of the model in predicting onset and offset dayscompared to estimates from net ecosystem exchange (NEE) for wheat.NEE represents the onset and offset days that were detected from NEEdata, and Model represents the onset and offset days that wereestimated by the present model. The observed samples were not usedfor calibrating the threshold values of the model parameters

Fig. 4 Assessment of the model performance. Performance of themodel in predicting the offset day compared to the offset day detectedfrom net ecosystem exchange (NEE). NEE represents the offset day

that was detected from NEE data. Model represents the offset day thatwas estimated by the model. The observed samples have been used tocalibrate the threshold value of the model parameters

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based on the validation data are 5 and 10 days, respectively(Fig. 5). The observed samples of winter wheat were welldistributed among the observation sites Fig. 1, so that thecalibrated parameters provided a good representation forthat species. More observation data are needed to achievemore reliable, generalized parameter values of the modelfor barley and rapeseed because only three observationsamples were available for these crops in this study.

Comparison with remote sensing observations

Compared to the leaf onset day as determined from themodel simulation and from NEE, the leaf onset daydetected from EVI was earlier than NEE for wheat,barley, and rapeseed (Fig. 3). The difference can beattributed to the following reasons. First, the definition of

leaf onset day according to NEE and EVI is different.According to EVI, the leaf onset day is defined as the daywhen leaves open. In contrast, leaf onset derived from theNEE data was defined by the net carbon balance. Inspringtime, the remotely sensed leaf onset day should beearlier than the leaf onset day detected from NEE becausethe increase of theoretical, light-saturated rate of canopyphotosynthesis tends to lag behind changes in canopygreenness, as evaluated from webcam and radiometric data(Richardson et al. 2007). Second, the detection ofphenological events from remote sensing data is asubjective process. It is difficult to define the absolutebeginning or end of the growing season from satelliteobservation in a uniform way (White et al. 1997; 2009). Inaddition, EVI can be contaminated by the signals fromneighboring land use elements. At the same time, the

Wheat

Barley Barley

Rapeseed Rapeseed

Wheat

Fig. 6 Difference between the mean offset days estimated by themodel and the harvest days from the crop calendar data for wheat,barley, and rapeseed. The mean offset day (day) estimated by themodel is the average of 1991–2000. The panels on the left-hand siderepresent the difference in days calculated for each pixel. The panels

on the right-hand side show the weighted difference (in days) at thecountry level. Negative values indicate that the offset day as estimatedfrom the model is earlier than the harvest day from the crop calendar,whereas, positive values indicate that the modeled offset day followsthe harvest day

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detection of phenological events from NEE data also is asubjective process.

The offset day detected from EVI (not shown) was muchlater (from one day up to two months) than the offset daydetected from NEE (Fig. 2). The large difference betweenthe two might be attributed to crop residues left in the fieldwhich can be detected by remote sensors.

Comparison with the crop calendar

The weighted mean differences between offset days during1991–2000 from the model simulations and harvest days fromthe crop calendar were calculated for wheat, barley, andrapeseed. The weighted mean difference for wheat, barley, andrapeseed were −5.1, 7.3, and −1.4 days for European crop-lands, respectively (Fig. 6). Thus, the model predicted anearlier offset day for wheat and rapeseed and a later offset dayfor barley relative to the harvest day from the crop calendar.

The weightedmean difference between the estimated offsetday and the harvest day for wheat on a country level isreasonable for the majority of countries in central Europetaking into account the two-week delay in harvest day relativeto offset day. The estimated offset day for wheat preceded theharvest day from the crop calendar for the major countries incentral Europe, such as Germany (−20.1 days), France(−25.6 days), Belgium (−19.3 days), Luxembourg(−24.1 days), Moldova (−20.2 days), and the Netherlands(−22.6 days). The estimated offset day for barley and rapeseedin central Europe were later by about one week compared toharvest day from the crop calendar (Fig. 6).

The following factors might partially explain why theestimated offset day for barley and rapeseed lagged behindthe harvest day. First, the simplified phenological model didnot consider the impact of human decisions. In reality, theharvest day depends more on management decisions thanon climate conditions, and there is no accurate way todistinguish the offset day from the harvest day (personalcommunication Wilfried Mirschel, December 15, 2010).Second, the model parameters identified through cross-calibration for barley and rapeseed may not well representthese crops over a large area due to the scarcity ofobservation data (three samples each). As a consequence,more datasets are necessary to calibrate the model param-eters. Third, the crop calendar data include two limitations.The first limitation is that the data do not capture thegeographic variability. Most observations are specified foran entire country or for a fairly large sub-national unit(Sacks et al. 2010), which sometimes cover differentclimatic zones. The second limitation is that the cropcalendar does not capture any changes over time (Sacks etal. 2010). In reality, planting and harvesting dates will varyover time in response to changes in climate as well astechnological and socio-economic factors.

Model application

The trend in leaf onset day in Germany

Leaf onset days of crops have shifted in response toclimate change in Germany during the last few decades.The model simulation showed that the weighted mean ofleaf onset days have advanced 1.6, 3.4, and 3.4 days perdecade during the 1961–2000 period for wheat, barley,and rapeseed, respectively (Fig. 7). According to IPGobservations, the beginning of the growing season inGermany has advanced on average by 2.3 days per decadeduring 1961–2000 (p<0.05) (Fig. 7). The beginning ofthe growing season according to IPG is indicated by theaverage date of leaf unfolding for white birch, wildcherry, mountain ash, and alpine currant (Rötzer andChmielewski 2001; Chmielewski et al. 2004). Observa-tions for rye have shown an advancement of 2.9 days perdecade during the same period (Chmielewski et al. 2004)(Fig. 7).

The leaf onset trend estimated by the model is similar tothe observed mean trend of the leaf onset day from the IPG.The trend estimated by the model did not pass thesignificance check (p≤0.05). This finding can be explainedby the variation of climate from year to year which leads tothe variations of estimated leaf onset day. The mean airtemperature for February, March, and April showed indeedlarge variations between 1961 and 2000 (Fig. 8). As aresult, the simulated leaf onset days changed from year toyear with the climate but were not statistically significantfor the overall trend.

Fig. 7 Comparison between observed and simulated trends of leafonset day in Germany for 1961–2000. The black bars show trends inthe observations from the International Phenological Gardens (IPG)(Chmielewski et al. 2004). The grey bars represent the weighted meantrends of leaf onset day as calculated with present model, which didnot pass a significance check. BG represents the average date of leafunfolding of white birch, wild cherry, mountain ash, and alpine currant

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The trend in leaf onset day in Europe

Crop leaf onset and offset days shifted in response toclimate change in Europe. The simulation results show that71.4% of European croplands had an advanced leaf onsetday for wheat, barley, and rapeseed (7% significantly), and28.6% of European croplands had a delayed leaf onset day(0.9% significantly) during 1971–2000 (Table 3, Fig. 9).The observations of the IPG show that 78% of the samplesof all leafing, flowering, and fruiting records advanced(30% significantly), and only 3% were significantlydelayed (Menzel 2006).

The difference between the observed onset day from theIPG data and the onset day estimated by the model mightbe partially explained by the climate's inter-annual climatevariability. The climate in the assessed period showed largeinter-annual variation in Europe. The mean air temperatureof February, March, and April fluctuated a lot from year toyear and caused the estimated leaf onset day to change fromyear to year (Fig. 8).

The advantages and limitations of the modified phenologymodel

Advantages

The modified phenology model combines the advantages ofa phenology models for natural vegetation and crops. Theoriginal phenology model GSI (Jolly et al. 2005) integrated

temperature, moisture, and photoperiod to simulate thedevelopment of vegetation, which improved the applicabil-ity of the model under different climate conditions andallowed controlling climatic factors to shift or co-limitedboth temporally and spatially. The same method wasadopted in the modified phenology model here except thatthe minimum temperature in the original model wasreplaced by daily mean temperature. The daily meantemperature was converted to the temperature index in themodified model to replace the original temperature indexfor minimum temperature.

This model is independent of any particular application,and therefore, it could be incorporated into larger modelingapplications. It will be integrated into the ecosystem modelBiome-BGC to simulate crop phenology. Daily tempera-ture, VPD, and photoperiod are the only input data requiredand these data are available on different spatial and timescales (e.g., the E-OBS gridded dataset of the ENSEM-BLES project [http://eca.knmi.nl/download/ensembles/ensembles.php] and the ECMWF ERA Interim [http://www.ecmwf.int/products/data/archive/descriptions/ei/index.html]).

Limitations

The modified crop phenology model has a number oflimitations. First, the model considers the impact of climateon onset and offset day, but it does not consider the impactof human activity. The onset day depends more on the

Table 3 Observed and simulated trends of leaf onset day in Europe for 1971–2000 from model simulations and the International PhenologicalGardens (Menzel 2006)

Observed leaf onset dayfor the IPG

Simulated onset day(wheat)

Simulated onset day(barley)

Simulated onset day(rapeseed)

Simulated onset day(average)

Advancedratio %

Delayedratio %

Advancedratio %

Delayedratio %

Advancedratio %

Delayedratio %

Advancedratio %

Delayedratio %

Advancedratio %

Delayedratio %

78 22 72.9 27.1 70.7 29.3 70.7 29.3 71.4 28.6

30a 3a 10.6a 1.6a 5.2a 0.5a 5.2a 0.5a 7.0a 0.9a

Advanced ratio means the percent of cropland area which has an advanced onset day in the total cropland areaa Indicates that the numbers passed significant check (p=0.05)

Germany EuropeFig. 8 Trends in mean air tem-perature for March, April, andMay in Germany and Europe.The solid line shows the varia-tion of mean air temperaturefrom year to year and the dashedline shows the mean temperaturetrend. The mean temperaturetrend is 0.4°C per decade (p=0.051) for Germany and 0.5°Cper decade (p=0.055) forEurope

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sowing date than on meteorological conditions for springcrops, e.g. maize and rice (C. Kersebaum, personalcommunication, July 2010). Sowing and harvest dates can

change over time due to changes in climate as well astechnological and socio-economic factors (Kucharik 2006).The sowing days of summer crops depend more on the

WheatLeaf onset

Leaf offset Leaf offset

Leaf offset Leaf offset

Leaf onset

Leaf onset Leaf onset

Barley

Fig. 9 Trends of leaf onset and offset days of wheat, barley, andrapeseed for the 1971–2000 period in Europe. The left-hand sidepanels show the trend without a significance check whereas, in theright-hand side panels, only the trends, which passed the significance

check (p=0.05) were displayed In the color legend. A negativenumber of the legend represents advanced leaf onset or offset day(day/per decade) whereas a positive number represents delayed leafonset or offset days (day/per decade)

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harvest day of former crops than on climatic factors.Detailed data from crop fields are needed to explore therelative roles of biotic and human factors in determiningsowing dates of summer crops. Second, the model usesidentical parameters for the same crop species and does notaccount for variability in crop genotypes. If the model wasto be used for small regions with known different genotypesof the same crop, then it might need to be adjusted. Thegenotypes of crops may change from region to region dueto the adaptation of crops to climate conditions (Muchowand Carberry 1989; Lindquist and Mortensen 1999) anddifferent crop genotypes have different dependencies onmeteorological conditions (Carberry et al. 1989; Birch et al.2003). Third, the model's prediction accuracy for the offsetday was limited by the rough approximation of daylighttemperature. A fixed coefficient was used to estimatedaylight temperature according to Eq. 5 in this study. Infact, the coefficient in Eq. 5 should be re-calibrated whenthe equation is applied to a new region (Hungerford et al.1989; Huld et al. 2006) and the model's prediction abilityfor the offset day can be improved by a more realisticapproximation of daylight temperature, potentially.

Conclusions

A new model was introduced based on the modified GSImodel index. However, more observation data is needed to

parameterize the model, especially for barley and rapeseed.According to the model estimation and observations of theIPG, crop phenology events have changed in response toclimate change in Europe. The mean leaf onset day ofwheat, barley, and rapeseed in Germany has advanced about3 days per decade during 1961–2000. During 1971–2000,71.4% of European croplands had an advanced onset dayfor wheat, barley, and rapeseed. These trends weresimulated by the model solely considering the impact ofclimate changes as input driver.

The modified model performed well compared withobservations at site and regional levels. The modified cropphenology model integrates three climate indicators togetherto predict leaf onset day. The indicators can co-limit and shiftone limit to another so that the model can be used withoutspecial regional or climatic condition limits. The phenologicalmodel can be integrated into a large-scale ecosystem model tosimulate the dynamics of crop phenological events on largetemporal and spatial scales.

Our analysis also shows that the crop calendar andremotely sensed data, which provide large-scale pheno-logical data, should be used with caution. The cropcalendar has limitations in its representation of thegeographic variability for large countries and does notaccount for changes over time (e.g. in response toclimate or technology changes). Remote sensing canmonitor the main development process of a crop, but itcannot identify the growing season of crops precisely due

Rapeseed

Leaf onset

Leaf offset Leaf offset

Leaf onset

Fig. 9 (continued)

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to the mixed vegetation types in one pixel, plant residualleft after the harvest on the ground, and the signals fromneighboring land use elements.

Acknowledgements We thank Bernard Heinesch, CorinnaRebmann, Eric Ceschia, Christian Bernhofer, Quentin Laffineur, EnzoMagliulo, Marc Aubinet, Nina Buchmann, Olivier Zurfluh, PierreBéziat, Pierre Cellier, Paul di Tommasi, Werner Eugster, WernerKutsch, Thomas Grünwald, and Eric Larmanou, for sharing theirmeasurement data. We also thank Christian Kersebaum for helpfulcomments on an earlier version of this manuscript. We thank twoanonymous reviewers for constructive comments. We thank ArthurGessler for improving the grammar and style of the manuscript.

Financial support A Ph.D. scholarship is provided to Shaoxiu Maby the Max-Planck Society (MPG) and the Chinese Academy ofSciences (CAS) through a joint doctoral program and the Leibniz-Centre for Agricultural Landscape Research (ZALF).

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