Applying a site based crop model to estimate regional yields under current and changed climates

Ecological Modelling 131 (2000) 191 – 206 www.elsevier.com/locate/ecolmodel

Applying a site based crop model to estimate regional yields under current and changed climates Riitta A. Saarikko * Agricultural Research Centre of Finland, Plant Production Research, FIN-31600 Jokioinen, Finland Received 20 August 1999; received in revised form 17 February 2000; accepted 3 March 2000

Abstract A method of upscaling a site-based crop model to obtain regional and national results on spring wheat productivity under changing climate is presented. The model, CERES-Wheat, was calibrated and validated first at sites, and in the upscaling phase it was run across a regular 10×10 km grid over Finland. In the grid the model was run both for the present-day (1961–1996) climate and scenarios of future climate for 2050. Regional averages were computed for the years 1981–1996 to be comparable to yield observations in the farm yield statistics. CERES-Wheat does not consider crop stress caused by poor soil aeration under wet conditions; however, this was found to be crucial to obtain satisfactory simulation results at sites. The model was modified accordingly, and the new version labelled ‘CERESWet-Wheat’. The results indicate that CERES-Wet-Wheat was able to detect both at the site and regional scale yield variations dependent on climate, even though the approach did not consider variations in crop management, pests and diseases and soil dependent differences in initial conditions. A regional approach to estimate future yields is useful especially at northern latitudes where crop suitability is likely to shift under changing climate. However, in a regional assessment there are many uncertainties associated with the yield estimates, including uncertainties in projections of future climate, model errors and assumptions and observation errors. For this reason, the underlying assumptions need to be clear when the estimates in yield changes are interpreted. © 2000 Elsevier Science B.V. All rights reserved. Keywords: Suitability; Crop yield; Upscaling; Spring wheat; 2050 scenario; CERES-Wheat

1. Introduction Physiologically based crop simulation models are often regarded as embodying the current * Corresponding author. Present address: O8 verbyntie 14 S, FIN-02400 Kirkkonummi, Finland. Tel.: +358-9-2966085. E-mail address: [email protected] (R.A. Saarikko).

knowledge on crop environmental response, and they have been employed to estimate crop yields in applications including studies of future climate change (e.g. Rosenzweig and Iglesias, 1994) and future land use (e.g. Ro¨tter and van Diepen, 1994). Often the estimates are based on model runs at individual sites, but there are advantages to be gained in scaling up a model to obtain region, nation or continent wide estimates on

0304-3800/00/$ - see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S0304-3800(00)00257-X

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yields (e.g. Easterling et al., 1998; Downing et al., 2000). If modelled site estimates are averaged to obtain regional mean yields the procedure is likely to introduce aggregation error that depends on the degree of nonlinearity of the crop model functions as well as the density of sites in the region. Furthermore, in a climate change study the selection of single locations that are representative of present-day conditions may be inappropriate, if the projected future climate is likely to shift the suitability of a crop into new regions. This paper studies the possibilities of applying a site based crop model, for spring wheat (Triticum aesti6um), across Finland under the current climate and under future climate change. At present spring wheat production is constrained to the southern parts of Finland between latitudes 60 – 63°N, in some areas up to latitude 64°N (Mukula and Rantanen, 1989). Yields are quite low compared to more favourable regions in Europe, and the risk of obtaining poor quality for bread making is relatively high. With a climatic warming the thermal suitability of growing spring wheat is likely to shift northwards (Saarikko and Carter, 1996). At individual sites the effects of climatic warming on spring wheat growth have been investigated with experiments (e.g. Hakala, 1998) and regionally by applying empirical-statistical crop climate models (Kettunen et al., 1988). This paper builds on a study where a preliminary attempt was made to simulate regional barley yields with a physiologically based model (Carter et al., 1996). Here, the CERES-Wheat model (Tsuji et al., 1998) was employed since it has been studied at sites in Finland (Laurila, 1995) and applied in regional climate change studies elsewhere in the world (Rosenzweig and Iglesias, 1994; Brklacich et al., 1996). The CERES-Wheat model was first examined, calibrated and validated at selected sites in southern Finland. Second, an upscaling procedure was developed to enable application of the model with limited input data across Finland. Third, aggregation methods were developed to estimate annual regional crop production to enable comparison with measured crop statistics. In the fourth step regional and national crop yields were estimated under a set of climate conditions representing

both the current and 2050 climate. Finally, the study aimed to describe the main benefits and uncertainties attributable to the model, scenarios and methods.

2. Material and methods

2.1. Crop model CERES-Wheat (ver. GECER960) computes crop growth and dry matter partitioning each day as a function of global radiation, minimum and maximum air temperature and precipitation (Ritchie et al., 1998; Tsuji et al., 1998). It simulates crop development from sowing to grain maturity through seven phases that describe crop phenology and morphology, which themselves have an effect on the production and partitioning of dry matter. Potential growth is proportional to the intercepted light, which depends on the prevailing leaf area and on light extinction in the canopy. When the daytime temperature is either below or above 18°C, the potential growth is reduced. Water and nitrogen availability can also limit crop growth both by reducing the potential production and affecting tillering and extension growth of leaves and stem. The effect of altered atmospheric CO2-concentration is estimated by scaling the potential growth and by adjusting the leaf stomatal resistance. CERES-wheat includes seven crop cultivar related input parameters. Four of them determine the phenological development and include the effects of vernalisation, response to photoperiod, phyllochron interval and a parameter defining the length of grain filling duration. Three parameters define ear and panicle growth, grain filling and grain number determination (Ritchie et al., 1998). To simulate water and nitrogen availability CERES-Wheat simulates a layered soil profile. The soil water amount increases as a result of precipitation and irrigation and it decreases through evaporation, root absorption, runoff and drainage. The water content at certain soil water tensions — wilting point, field drained upper limit and saturated water content — needs to be specified for each soil layer (Ritchie, 1998). Fertiliser

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applications and all major processes of the soil nitrogen balance are considered in the model, i.e. leaching, mineralisation, immobilisation, nitrification, denitrification and plant nitrogen uptake (Godwin and Singh, 1998).

2.2. Experimental data, model performance and sensiti6ity tests at sites

Fig. 1. Site used in calibration (Jokioinen) and sites used for independent testing of CERES-Wheat. For testing one yearly experiment was conducted at each site during 1985–1990, however, the results were missing at Kokema¨ki in 1990 and at Tuusula in 1989. Modelled phasic development was tested at all sites, but modelled grain yield only at six sites excluding Anjalankoski and Hauho. The five unshaded rural business districts were used in regional validation in 1992–1996.

Fig. 2. Mean May – August temperature and precipitation sum at the experimental sites used for testing the modelled yield prediction during 1985 – 1990. For comparison, average May– August temperatures and precipitations are shown at Jokioinen for the baseline mean (1961–1990) and for two scenarios (AERO and Ref) for year 2050.

To calibrate and test the performance of CERES-Wheat detailed measurements of cv. Polkka were available from an experiment that was conducted in 1994–1996 at Jokioinen (60°49%N, 23°30%E). Cv. Polkka was one commercially grown spring wheat cultivar in Finland during the first part of 1990s, and other experiments concerning elevated temperatures and enhanced levels of atmospheric carbon dioxide concentrations were conducted using this cultivar (Hakala, 1998). Data from the first 2 years (1994– 1995) were applied to calibrate the model parameters and to examine the model functions while the data from 1996 were used independently in model testing. The data included within season measurements of above ground biomass separated into leaves, stems and grains. Furthermore, dates of phenological events, fertiliser applications, and other management practises had been recorded (Carter and Saarikko, 1995; Saarikko et al., 1996). To examine the model validity at different locations, variety trials data on cv. Polkka were available from the period 1985–1990 (Fig. 1). Fig. 2 illustrates the weather conditions during different years at each experimental site. The variety trials data included final grain yield, fertiliser application, sowing density, and phenology. In all field experiments crop management followed local farm recommendations, and the crops were not irrigated. The Jokioinen 1994–1996 experiment was conducted on a clay loam soil while the variety trials were made on clay, clay loam and sandy soils. Daily weather observations were obtained for the corresponding periods at all experimental sites from the Finnish Meteorological Institute. In the model soil parameters were defined to approximate the three different soil types of the experiments (clay, clay loam, and fine sand). This

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Table 1 Estimated volumetric soil water content (cm3 cm−3) at the lower limit of plant water availabilility (LL), field drained upper limit (DUL) and saturation (SAT) for clay, clay loam, and fine sand Soil water status

Clay

Clay loam

Fine sand

LL DUL SAT

0.244 0.388 0.413

0.160 0.360 0.410

0.070 0.210 0.280

classification was coarse since no detailed measurements were available on the soil structure, the rooting and the water status during the season. Water retention was characterised by specifying volumetric soil water content at three different soil water status following approximately the values given in Karvonen and Varis (1992), (Table 1). The entire profiles (0 – 150 cm) were assumed to have a homogeneous structure. Rooting depth was assumed to be 120 cm while the majority of roots were defined to be in the layer 0 – 60 cm. At sowing the moisture content of the topsoil (0 – 15 cm) were assumed to be at 80% of the field drained upper limit and at field drained upper limit in the subsoil. Both water and nitrogen limited growth was simulated at each occasion. Model sensitivity to different weather variables and atmospheric CO2 was tested at Jokioinen using weather data from the years 1961 to 1990. Temperature, radiation, precipitation, and atmospheric CO2 content were changed in turn systematically. CO2 concentrations were varied at equal increments of 81 ppmv between 272 (approximating the preindustrial concentration) and 596 ppmv. This interval was chosen to encompass the levels selected to represent the baseline (353 ppmv) and 2050 scenarios (515 ppmv). The climate adjustments comprised annual temperature increases of between +1 and + 4°C and precipitation and radiation changes of between − 20 and + 20% relative to the baseline values.

2.3. The up-scaling procedure to estimate regional yields To produce regional and national estimates of spring wheat suitability and yield the CERES-

Wheat model was applied in a grid based analysis system (Carter and Saarikko, 1996). In the system Finland was covered by 3827 grid boxes that each had a size of 100 km2. At every grid box monthly means of minimum and maximum temperature, global radiation and the precipitation sum were available for the period 1961–1996. The gridded values were derived from stations by a kriging method, which can take external forcing, such as altitude and surface water cover into account in the interpolation (Henttonen, 1991; Vena¨la¨inen and Heikinheimo, 1997). To test the accuracy of the method interpolated values of weather variables were compared to the measurements at control stations, which were excluded from the analysis. Monthly temperature values were interpolated from  150 stations. For precipitation, the data network was denser (  500 stations), but the spatial variability of precipitation is much greater than for temperature (Henttonen, 1991). Global radiation was derived based on data from direct measurements of solar radiation (five stations) and indirectly, based on sunshine duration measurements (17 stations) or synoptic cloud observations (15 stations). The error of the baseline mean (1961–1990) monthly radiation was less than 5% for most months at any randomly selected grid box (Vena¨la¨inen and Heikinheimo, 1997). Because the crop model needs daily weather data as an input, daily temperature and radiation were derived from monthly means using a sine curve interpolation method (Brooks, 1943). Daily rainfall distribution was created using the observed frequency distribution (1961–1990) at Jokioinen site. The method of using interpolated daily weather as input to the crop model was tested at the Jokioinen site, where the daily precipitation distribution was derived from another site, Jyva¨skyla¨ (62°24%N, 25°41%E). The gridded database also included information on fields (% arable land at each grid box). Data on soil textural types (0–25 cm, percentage of arable land) were available for the 460 municipal districts in Finland (Ka¨ha¨ri et al., 1987). The 22 mineral soil types, given by municipality, were each allocated to one of three categories representing the generic soil profiles fine sand, clay

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loam, and clay soil. They were assumed to have the water retention characteristics described in Table 1. As spring wheat is recommended to be sown early in spring (Mukula and Rantanen, 1989), organic soil types were not considered, since they become warm and dry slowly in spring. Proportions of the three generic soil types were assigned to each grid box according to the municipality that covered the greatest proportion of the grid box in question. It should be noted that the generic soil types only illustrate some variability in yield results instead of trying to mimick full range of variability in the conditions of cultivated soils. Before the crop model was run, the favourable growing season was estimated and the suitability of the crop was studied preliminarily at each grid box. A grid box was classified as unsuitable if the proportion of arable land did not exceed 1% or if no mineral soil type was present. Otherwise the favourable growing season was determined as the period between sowing (assumed to occur when daily mean temperature exceeded + 8°C) and an autumn cut-off (defined as the date when daily mean temperature fell below +12°C), (Saarikko and Carter, 1996). Finally, if the simulated crop matured within the favourable growing time, the average yield was computed for a grid box taking into account the proportions of the three generic soil profiles. The crop was supplied with a moderate nitrogen input (100 kg ha − 1) at sowing.

2.4. Testing model performance at regional le6el Based on farm sample surveys, average yields were available for agricultural advisory districts during 1981–1991 and for rural business districts during 1992–1996 (Information Centre of the Ministry of Agriculture and Forestry, various dates). The crop model was run using the gridded weather 1981–1996 and yearly increasing atmospheric CO2 concentration from 341.9 ppmv in 1981 to 352.9 in 1996. The latter values are taken from the MAGICC model (Wigley and Raper, 1992; Hulme et al., 1995) and are very similar to those recorded at Barrow in Alaska (Keeling and Whorf, 1998). The modelled gridded yields were aggregated to obtain an average value for each

195

district and year in the main production area, which comprised eight districts during 1981–1991 and five during 1992–1996 (Fig. 1). It was assumed that spring wheat was grown in every grid box identified as containing suitable land. The modelled district averages were compared to the recorded values. Furthermore, an average value was computed across all the districts to obtain a modelled and observed annual average for the main production area of spring wheat in Finland.

2.5. National yield estimates under the current and possible future climate CERES-Wheat was run across the Finnish grid initially by applying the baseline weather (1961– 1990). Subsequently, it was run by applying the baseline conditions modified in three different ways. First, the weather and CO2 concentration were altered according to a number of arbitrary adjustments to test modelled yield sensitivity. Second, in order to evaluate how modelled wheat yields are affected by multi-decadal natural climatic variability as represented by a global climate model (GCM), anomalies from the 240-year mean of eight non-overlapping 30-year periods from the Hadley Centre HadCM2 model (Johns et al., 1997) control simulation (labelled NOISE scenarios — Barrow et al., 2000) were applied as adjustments to the baseline climate. Finally, the climate was adjusted according to two scenarios of future climate based on GCM transient outputs for the period centred on 2050: the first HadCM2 simulation for a greenhouse gas-induced radiative forcing approximating the IS92a emissions scenario developed by the Intergovernmental Panel for Climate Change (Leggett et al., 1992), and the first HadCM2 greenhouse gas+sulphate aerosols simulation for an IS92a-type forcing (AERO scenario — Barrow et al., 2000). Both scenarios assumed a future atmospheric CO2 concentration of 515 ppmv based on the model of Wigley and Raper (1992), while the concentration during the baseline period was fixed at 353 ppmv. All GCMbased patterns of climate change were interpolated linearly from the original GCM resolution ( 280×210 km) to the Finnish 10 × 10 km2 grid. Temperature and precipitation adjustments

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to the baseline climate were made by adding absolute differences (°C and mm day − 1) and solar radiation adjustments by multiplying ratios. Radiation changes were converted from GCM estimates of cloudiness change by applying empirical relationships between cloudiness and radiation (K. Niemi, 1996, personal communication), weighted according to the observed seasonal cycle of cloudiness based on monthly mean data for 1961 –1990 from 107 sites in Finland (Carter et al., 2000). In this study, as in earlier works (e.g. Saarikko and Carter, 1996), the regional long-term crop suitability was determined by applying an arbitrary limit of 80% probability of crop ripening, i.e. crop failure in no more than 2 years per decade. For all studied climatic conditions an average yield and coefficient of variation (CV) representing the annual variability were computed both for the region of estimated baseline suitability and for the entire region of suitability (providing the simulated national yield).

3. Results

3.1. Model performance at the site scale 3.1.1. Model calibration and a change to the model functions Because the CERES-Wheat model was originally developed for dry conditions, crop stress caused by poor soil aeration is not considered (Ritchie, 1998). However, poor aeration can reduce crop growth during wet periods on poorly structured soils in Finland. During the early growing period of the 2 years used in model calibration, 1994 and 1995, the moisture conditions were different. In 1995 the weather was cool and topsoil moisture was high just after sowing and seedling emergence. As a result, CERESWheat was not able to simulate the crop growth satisfactorily, especially when applying the set of parameter values that was appropriate from the 1994 model calibration run. Consequently, the model was modified to match the crop measurements in 1995 more precisely. It was assumed that the potential water uptake from any soil layer

starts to decline if the gas filled porosity drops below an anaerobiosis point of 0.07 cm3 cm − 3 (Karvonen and Varis, 1992). A fixed anaerobiosis point at which oxygen deficiency restricts the growth is very difficult to define, and the litterature shows that plant roots can grow well in very low gas-filled porosities (Karvonen and Varis, 1992). However, in this study the water uptake was assumed to decline in a linear way below the anaerobiosis point and being zero in a water saturated soil layer. Fig. 3 illustrates the modelled and observed accumulation of above ground biomass and grain in 1994, 1995 and also in 1996, which functioned as an independent validation year. The modified model version (identified as CERES-Wet-Wheat) gave a lower estimate of growth and grain yield than observed in 1996. When crop growth and development were studied it was found that CERES-Wheat failed to simulate crop morphological development precisely, regardless of the input parameters. According to field observations spring wheat consistently produced seven leaves on the main stem. On the other hand, although the model simulated the timing of leaf appearance well, it produced two to three extra leaves after the final observed leaf had appeared. This had implications for the partitioning of biomass between different plant organs, tillering and leaf area. However, with respect to the estimated grain yield it is important that the simulated stem and spike weight are correct at the time when the stem elongation ceases. With a suitable set of input parameters the model was able to fulfil this demand, and the internal structure of the model was not changed here.

3.1.2. Testing model performance at sites in southern Finland To verify the applicability of the crop model, estimates of both CERES-Wheat and CERESWet-Wheat were compared against independent crop data (cv. Polkka) from a few sites during 1985–1990 (cf. Fig. 1). Two aspects of the simulated results were studied: predictions of crop phenology and of grain yield. The phenology was simulated with a reasonable accuracy; the root mean square difference (RMSD) between the observed and simulated dates of grain maturity was

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Fig. 3. Simulated and measured spring wheat productivity (cv. Polkka) at Jokioinen in 1994 – 1996. The measurements from 1994–1995 were used to calibrate the model parameters, while the year 1996 results serve as an independent test of model validity.

6.6 days. Grain yield was simulated better with CERES-Wet-Wheat than with the original model; the RMSD between the measured and simulated values were 890 and 1340 kg ha − 1, respectively. Modelled yields (CERES-Wet-Wheat) are plotted against field measurements in Fig. 4. The average measured grain yield across the sites and years was 3400 kg ha − 1 and the modelled one with the modified model 3720 kg ha − 1. CERES-Wheat performed especially poorly in the cool and wet year 1987 overestimating the yield, on average, by 2530 kg ha − 1, while the modified model limited the growth, and the respective average error dropped to 1110 kg ha − 1. Bearing in mind that many assumptions and simplifications were made concerning the soil types and initial conditions, which were not measured in the field experiments, the modified model appeared capable of detecting some of the annual yield variation across sites in Finland.

values, the simulated yield was 11–26% higher (depending on soil type) than that obtained by using observed daily weather (Table 2). This increase was mainly due to the derived temperature, followed by radiation. When daily values of temperature are interpolated from monthly averages, the extreme high and low daily temperatures sometimes observed in daily weather are not repeated. As a result, the daytime temperature is closer to the modelled growth optimum of 18°C

3.2. Model sensiti6ity analysis 3.2.1. Model errors due to weather inputs Site comparisons at Jokioinen showed that when the input weather was derived from monthly

Fig. 4. Comparison of the measured grain yields with the modelled ones by CERES-Wet-Wheat for six sites in southern Finland during 1985 – 1990, (R 2 =0.37). The 1:1 line is also shown. Soil type was clay loam at Pa¨lka¨ne, Salo and Tuusula; clay at Jokioinen and Mietoinen and fine sand at Kokema¨ki.

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Table 2 Comparison of spring wheat yields at Jokioinen modelled using observed and derived daily temperature (T), radiation (R), and precipitation (P) as input. Derived relative to observed data

T+R

T+P

P+R

T+P+R

Mean difference (%) Clay Sand Loam

+23 +18 +11

+14 +5 +7

+8 0 +2

+26 +16 +11

during a greater number of days in the derived data set than in the daily observations. Furthermore, the yield variability was greater when the yield was simulated with the observed climate than with the derived data. To conclude, while on average the derived weather data give higher yields compared with yields obtained with daily weather measurements, the errors differ not only between soil types but also between different years. For this reason, the yields simulated with monthly climate data can not be empirically corrected, e.g. with the values given in Table 2. To compare, the overestimation of spring wheat yields was also found by Nonhebel (1994) when the yields were computed with 10-day averages rather than daily weather data.

3.2.2. Modelled yield sensiti6ity to CO2 and climate changes Under the baseline climate (1961 – 1990) the simulated crop matured successfully in 21 years out of 30 at Jokioinen. Consequently, to give a comparable measure the 21 suitable years were used to study modelled physiological responses to environmental changes. The clay loam soil provided the highest yields, while the lowest yields and greatest yield variations were found on the clay soil. On the clay profile the crop appeared to be susceptible to stress caused by excess soil water, while drought was the major concern on the sandy soil. Fig. 5 illustrates the sensitivity of the grain yield to changes in temperature and atmospheric CO2 concentration over the 21 suitable years at Jokioinen. Overall, the temperature and CO2 effects on yield appeared to dominate over precipitation and radiation effects across the tested range

of climate changes. Increasing temperature decreased the average yield, and the decrease was greatest on the sandy soil because of the drought risk. The coefficient of variation (CV) declined with increasing temperatures on clay, remained approximately unchanged on clay loam but increased rapidly on the sand. There was a positive response of yield to increasing CO2, the lowest response, on average, being on the clay loam soil where the yields were least limited by excess or deficient soil water content. For an increase in CO2 concentration from the baseline (1990) value of 353 to 515 ppmv assumed for the 2050s under the REF and AERO scenarios (see above), the yields rose, on average, by 9.6% on the clay loam, 14.9% on the sand and 16.4% on the clay soil. Without water and nitrogen limitation the yield increase would be about 12%. On the clay soil the yield CV rose with increasing CO2, while the CV was almost unaffected on the two other soil types. Simulations for combinations of CO2 and temperature change indicate that the positive effect on mean yield at Jokioinen of a CO2 increase from 353 to 515 ppmv is almost entirely offset by a temperature increase of 2°C (Fig. 5). Changes in precipitation only had a minor effect on yield. On clay and clay loam the average yields were highest with the lowest amount of precipitation. Relative to this, a 20% increase in precipitation lowered yields, on average, by 5.7% on clay and 6.4% on clay loam, and increased the CV. The effect on sand was opposite, with an increase of 8.2% between the lowest (− 20% decrease) and highest (+ 20% increase) precipitation scenario and a corresponding decline in the CV.

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The effect of increased radiation was to enhance average yields and to reduce the CV on clay and clay loam. A decrease in radiation also led to a decline in yield on sand, but because of its effect in reducing water loss by evapotranspiration this decline was less than for the other soil types. On the other hand, when radiation was increased by 20% from the baseline values, the yield on sand was also reduced and the CV was increased, due to more frequent and severe episodes of water stress.

3.3. Model performance at the regional and national scale 3.3.1. Simulated yields under the baseline climate: regional model 6alidation To evaluate the performance of CERES-Wet-

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Wheat regionally, average annual spring wheat yields were computed from the gridded values for each agricultural administrative district in southern Finland. The simulated regional average yields were higher than the recorded ones. Depending on the district the RMSD values ranged from 1350 to 1970 kg ha − 1 during 1981–1991 and from 1190 to 1500 kg ha − 1 during 1992–1996. This overestimation is probably because the model assumed optimal crop management, which is less justified at aggregate farm level than under the experimental conditions for which the model was calibrated (Russell and van Gardingen, 1997). The year 1987 was excluded from the analysis as the modelled crop failed to ripen across the whole country. Though yields were recorded in 1987, they were approximately half of the level recorded in subse-

Fig. 5. Sensitivity of modelled wheat yield to changes in temperature and CO2 concentration relative to baseline climate (1961 – 1990) and CO2 level (353 ppmv).

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Fig. 6. Comparison of modelled to observed aggregate yields of spring wheat over southern Finland, 1981 – 1996.

quent years, and only 15% of the harvested spring wheat was considered to be of good quality, compared with over 90% in most years (Information Centre of the Ministry of Agriculture and Forestry, 1990). When the district yields are aggregated to one annual average value the observed series displays an upward trend (of 66 kg ha − 1 per year), whilst the modelled series shows a slight downward trend (Fig. 6). The positive observed trend could be related to several factors: (i) introduction of higher yielding cultivars and/or improved crop husbandry, (ii) a sharp contraction (by about one half) in cropped area to the most favourable (higher yielding) regions after 1990, related to land set-aside policy and (iii) improved conditions for crop growth. However, the negative trend in modelled yield, which occurs in spite of the beneficial physiological effects of an assumed increase in atmospheric CO2 concentration, suggests that the climate may have changed in a manner detrimental to yields of this wheat cultivar over the period. A positive relationship (R 2 =0.28) was found between the modelled and observed yields during the full period. However, when the period 1981 –1991 is considered, the relationship is much stronger (R 2 = 0.67), indicating good model performance in detecting inter-annual variations (Fig. 6). The poorer performance over the longer period may be related, in part, to the contraction in cultivated area to more productive land after 1990.

3.4. National mean yields: sensiti6ity analysis and 6ariation under simulated climate with no greenhouse gas forcing Table 3 shows the average yield response when a number of arbitrary adjustments were applied to the baseline climate to test the sensitivity of national mean yields to climate change. Modelled potential unconstrained yield with no water or nutrient limitations was 19% higher than the estimated rainfed yield, while the yield CV was approximately unchanged. In line with the site sensitivity results, the changes in precipitation amount (9 20%) had little impact on the attainable rainfed yield. CO2 increase from 353 to 515 ppmv enhanced the average yield by 14%. Also, while a temperature increase of +2°C increased the regional yield slightly (3%), an increase of 4°C decreased it clearly (11%) (see bottom of Table 3, to compare with the REF scenario). However, yield reliability in the area of baseline suitability improved under these two temperature scenarios (CV declined to around one-third) and the area of suitability expanded with elevated temperature (into 1446 and 2076 additional grid boxes, respectively). In order to evaluate how modelled wheat yields are affected by multi-decadal natural climatic variability as represented by a climate model, anomalies from the 240-year mean of eight nonoverlapping 30-year periods from the HadCM2

Table 3 Comparison of modelled national spring wheat yields for the baseline climate (1961–1990) with yields under alternative scenarios of present-day and future climate for grid boxes designated as suitable under the baseline climate (successful ripening in ]80% of years)a Scenario type

Acronyms

Suitable areab

May–August climate relative to BASE T (°C)

BASE 1961–90d POTENTIALd

0/0.90f 0

Sensitivity

CO2 =515 P−20%d P+20%d

0 0 0

Hadley Centre (HC) 240-year control: Modelled natural variability for eight non-overlapping 30-year periods

NOISE1d NOISE2d

0 −0.4

NOISE3d NOISE4d NOISE5d NOISE6d NOISE7d NOISE8d

0.3 0 −0.1 0 −0.1 0.2

Mean

S.D.

CV

Significancec

0 0

528 528

3895 4654

1441 1664

37 36

++

0 −20 20

528 528 528

4449 3835 3845

1639 1428 1444

37 37 38

++ 0 0

−2.2 −4.8

646 132

3931 3835

1411 1509

36 39

+ 0

−9.2 −4.4 5.4 4.3 −6.9 17.8

929 447 275 384 405 787

4105 3838 3844 3850 3780 4041

1082 1491 1493 1423 1488 1357

26 39 39 37 39 34

0 − 0 0 −− ++

REF variability

IAV PRESENTd

0/1.48f

0

54

3663

1688

46

Scenario 2050 Sensitivity

REFe T+2°Cd T+4°Cd

1.7 2.0 4.0

8 0 0

1874 1974 2604

4656 4023 3477

602 492 518

13 12 15

−− −−

Scenario 2050

AEROe

2.0

0.8

1928

4809

568

12

+

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Baseline Unconstrained

P (%)

Wheat yields (kg ha−1), CV (%) and statistical comparison

a

Statistical comparisons above the bold horizontal line are with the BASE scenario; below the line with the REF scenario. Number of 100 km2 grid boxes in which modelled crop ripens in ]80% years. c Paired t-test: −−/++ =PB0.01; −/+ =PB0.05; 0 =P]0.05=no significant difference. d CO2 = 353 ppmv. e 515 ppmv f National mean/absolute SD (Jyva¨skyla¨). b

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control simulation (NOISE scenarios) were applied as adjustments to the observed climate and yields estimated across the grid. For a range of 30-year mean May– August temperature anomalies relative to the 240-year mean of − 0.4 to + 0.3°C, the area of suitability varied between 132 and 929 grid boxes, and national mean yields varied from −3 to +5% relative to the baseline value, some of these differences statistically significant (Table 3). This result indicates that significant multi-decadal variations in mean yield can occur simply as a result of natural variations in the climate, without any greenhouse gas-induced forcing. Such ‘noise’ in the long-term climatic record complicates the detection of a greenhouse gas ‘signal’, both in the climate itself and in the response of crop yields (Hulme et al., 1999). However, it should be noted that this interpretation is based on the premise that the climate model control simulation provides an accurate description of natural climatic variability. This assumption is difficult to test due to the absence of long-term climatic observations that are unaffected by greenhouse gas forcing, although recent analyses of paleoclimatic records for the past millenium indicate broad similarities with GCM control run temperatures (Jones et al., 1998). In addition, a comparison was made of the inter-annual variability of modelled wheat yields based on observed 1961 – 1990 baseline climate data with the yield variability for the same long term mean climate but with inter-annual variability based on the HadCM2 REF simulation (IAV

PRESENT scenario). The HadCM2 temperature and precipitation variability was greater than that observed, resulting in a higher standard deviation of modelled national mean yield. Another effect of the higher variability in the modelled climate was to decrease the reliability of crop ripening (due to a higher frequency of cool years), hence reducing the area of suitability to only 54 grid boxes (Table 3).

3.5. National yield under projected future climate The effects of future climate change on spring wheat yields were examined for two scenarios (REF and AERO) representing the climate of 2050 based on climate model outputs (Table 3). Comparisons between the scenarios were made for the region of baseline suitability. Because this defines the area in which the crop ripens in at least 80% of years, it implies that in some years and over some grid boxes in the suitable region, there are instances of zero yield and the distribution of yields during the 30 simulated years is non-normal. For this reason, median rather than mean yields were estimated for mapping purposes, along with a related measure of variability (the median based index of variability). Results of wheat simulations under the baseline and the two scenarios are shown in Figs. 7 and 8. Under both scenarios the area of suitability expands northwards (into 1346 and 1400 additional grid boxes for REF and AERO, respectively),

Fig. 7. Simulated 30-year mean spring wheat yields: (a) baseline 1961 – 1990; (b) REF scenario for 2050; and (c) AERO scenario for 2050.

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Fig. 8. Median based index of variability for simulated spring wheat yields: (a) baseline 1961 – 1990; (b) REF scenario for 2050; and (c) AERO scenario for 2050.

mean yields increase in most (Fig. 7b) or all (Fig. 7c) suitable grid boxes and yield variability decreases markedly (Table 3 and Fig. 8). Under AERO, the nationally-averaged mean May – August warming is slightly greater than under REF (which accounts for the larger expansion of suitability), while precipitation remains similar to the baseline and less than under REF (Table 3). This appears to be beneficial for yields, because nationally the mean yield is significantly greater under AERO than REF and yields are slightly more reliable (lower CV in Table 3). The reason for these differences is not clear, but is probably related to the beneficial effects of warmer, drier conditions applied to wet baseline years under AERO outweighing the detrimental effects of this scenario in warm dry baseline years. Under REF, the 8% increase in precipitation (Table 3) may constrain the benefits of higher temperatures and elevated CO2 in wet years.

4. Discussion This study developed some methods to upscale a site-based crop model to regional level. The results demonstrate that a rigorously calibrated and tested site-based model is also capable of detecting yield responses to climatic variations at regional level in Finland under the observed climate. However, at farm level yields can vary considerably depending on crop management, e.g., pest and disease control, soil conditions and

sowing date. Physiologically based crop models like CERES-Wheat are not developed for the purpose of mimicking the great variation in a field ecosystem. Because the climate signal can easily be detected even in the regional yield estimates, the results imply that under northern European conditions weather is a more dominant factor determining yield level than, for example, in the UK, where the weather signal easily disappears within the noise of management practises and pest and disease effects (Landau et al., 1998; Jamieson et al., 1999). However, as the climate warms, these confounding effects may become more important at high latitudes too, progressively obscuring the climate signal — a possible factor of uncertainty to consider when interpreting the results of this study. Regional yield predictions offer a number of advantages over the more conventional site-based analysis. Firstly, the grid-based approach can consider crop suitability. For instance, large areas of Finland under the present-day climate are classified as unsuitable for spring wheat production because the crop fails to ripen in a significant proportion of years. As the climate warms, so the suitable area expands, but this area cannot easily be represented on the basis of site information — a more systematic geographical approach is required. Secondly, the regional approach can expose key non-linearities that may not be apparent from a site-based analysis. For example, the response of crop yields to climate change at different locations can differ in magnitude or even in

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sign (e.g. see Fig. 7). A site-based analysis is unlikely to represent the diversity in crop response exhibited for local combinations and gradients of climate and soil. However, a regional model application means that the climatic input needs to be interpolated (here from monthly averages) thus giving a greater uncertainty at individual sites compared to model estimates with daily weather observations. Thirdly, the upscaling method permits a meaningful attempt at regional aggregation of yields. With estimates of both average yields and yield distributions it is possible to map the regional level and reliability of crop yield, information that might be of value for activities such as the agricultural extension service, crop insurance, irrigation planning and plant breeding. However, the map based results should be interpreted with caution, since this study has only shown results on the modelled climate – crop interaction. A number of uncertainty sources can be listed in this study: deficiencies of the crop model, little input data to calibrate and validate the model and finally, deficiencies in the climatic input and uncertainties in the climate change scenarios. Furthermore, the crop model relied on numerous assumptions about initial soil conditions, soil profile and sowing date. These details, required by a physiologically based crop model like CERES-Wheat, cannot easily be gathered comprehensively or estimated objectively. To compare, in a study where crop productivity was computed with WOFOST model for regions of the European Union, the average ratio of modelled to actual yield was 0.61 varying 0.14 – 0.95 for individual regions (de Koning and van Diepen, 1993; Russell and van Gardingen, 1997). It would be of interest to conduct a more detailed risk-analysis of yields as well as an evaluation of model uncertainty at regional scale. In this way, the mapped results could be displayed along with confidence intervals as was demonstrated in an earlier study of spring wheat suitability with a linear model of crop phasic development (Saarikko and Carter, 1996). Complex crop models include a number of non-linear relationships, and changes in crop yield risk have not been widely investigated. A little error in the theory of the model or poorly estimated parame-

ter value can result in a large error in the predicted yield. For this reason, a complex model may not be the best solution for assessing crop yields across large areas, where the final yield is the main output of the model. Hence, in upscaling exercises in the future, attention should be directed to risk and uncertainty, and that is more easily assessed with less complex models than CERES-Wheat. On the other hand, such simplified models should still be capable of representing the major processes to enable extrapolation outside current environmental conditions. It is this ability to extrapolate that distinguishes process-oriented models from the conventional statistical modelling approach.

Acknowledgements I am grateful to a number of colleagues for supplying advice, models and data: the scientists of the CLIVARA collaborative project, especially Dr Timothy Carter, for the climate scenarios and advice on regional model upscaling, Ms Marjo Pihala for managing the Jokioinen field experiments, and other colleagues at the Agricultural Research Centre of Finland for providing data on official variety trials. I also thank Dr Brian Baer, who supplied the latest computer code for the CERES-Wheat crop model, the Finnish Meteorological Institute for providing working facilities, climatological data and interpolation routines, and other government bodies for providing information used in the analysis system. This work was funded by the Environment Programme of the European Commission (contract number ENV4CT95-0154), Agricultural Reserarch Centre of Finland and the Academy of Finland.

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