Results Directory UMM :Data Elmu:jurnal:A:Agriculture, Ecosystems and Environment:Vol82.Issue1-3.Dec2000:

J.E. Olesen et al. Agriculture, Ecosystems and Environment 82 2000 213–228 221 Fig. 4. Time trend of observed d and simulated s grain yield for winter wheat for two counties Ringkøbing and Storstrøm. The simulated yields were calculated using scale 6GP with a uniform distribution of winter wheat area within each county.

3. Results

There were large differences in observed and simulated responses between the counties as illus- trated in Fig. 4. The observed yield trend for win- ter wheat was much steeper for Storstrøm county 0.15 Mg ha − 1 yr − 1 than for Ringkøbing county 0.10 Mg ha − 1 yr − 1 . The yield level was highest in Storstrøm county, giving an annual yield increase of 2.0 in Storstrøm county and 1.7 in Ringkøbing county. Storstrøm county is dominated by loamy soils, whereas Ringkøbing county is dominated by sandy soils. The simulated yield trends showed tendencies of increasing yields, but these were in most cases not significant. The effect of the different scales of soil and climate data on mean and standard deviation of simulated na- tional grain yield and irrigation demand is shown in Table 4. Mean simulated, detrended yield was about 1 Mg ha − 1 higher than the observed for an even wheat area distribution and about 1.5 Mg ha − 1 higher than observed for a soil type dependent distribution. The standard deviation of simulated, detrended yield was higher than the observed value, but decreased when the number of climate stations was increased from 1 to 6. Decreasing the spatial resolution of the soil data 222 J.E. Olesen et al. Agriculture, Ecosystems and Environment 82 2000 213–228 Table 4 Measured and simulated national winter wheat grain yield and irrigation amounts for different scales of climate and soil data and two different methods of distributing wheat area within each county a Scale of input data Uniform distribution Soil type-dependent distribution Mean yield Mg ha − 1 S.D. of yield Mg ha − 1 Irrigation Mm 3 yr Mean yield Mg ha − 1 S.D. of yield Mg ha − 1 Irrigation Mm 3 yr Measured 6.57 0.62 6.57 0.62 1OP 7.61 0.89 20.3 8.39 0.90 10.4 1RP 7.61 0.90 20.2 8.38 0.90 10.7 1GP 7.35 0.88 20.0 8.11 0.94 11.6 6OP 7.51 0.77 20.5 8.25 0.77 11.7 6RP 7.59 0.76 19.9 8.31 0.77 10.9 6GP 7.61 0.77 20.5 8.34 0.78 11.3 1OG 7.54 0.88 16.4 7.91 0.88 10.2 1OC 7.44 0.90 15.3 7.38 0.90 10.3 1RG 7.54 0.89 16.3 7.91 0.89 10.4 1RC 7.44 0.91 15.5 7.38 0.91 10.6 a The notation for scale of input data is explained in Table 1. Both observed and simulated yields were detrended and adjusted to 1990 level using a linear technology trend. considerably decreased mean simulated grain yield and irrigation demand. Simulated irrigation demand decreased by 25–50 when changing from the uni- form to the soil type-dependent distribution of the wheat area. Table 5 shows the analysis of county yields using the statistical model in Eq. 4. The slopes of the re- sponse of observed to simulated yields were in the range 0.18–0.30. The values increased slightly when using six versus only one climate station, and were lowest at the coarsest resolution of soil data. The two Table 5 Analysis of the relation between observed and simulated county yields using the statistical model in Eq. 4 for different scales of climate and soil data and two different methods of distributing wheat area within each county a Scale of input data Uniform distribution Soil type-dependent distribution β δ σ 2 G σ 2 E β δ σ 2 G σ 2 E 1OP 0.098 ∗∗∗ 0.25 ∗∗∗ 0.074 0.349 0.105 ∗∗∗ 0.22 ∗∗∗ 0.154 0.344 1RP 0.099 ∗∗∗ 0.25 ∗∗∗ 0.076 0.344 0.106 ∗∗∗ 0.23 ∗∗∗ 0.157 0.339 1GP 0.111 ∗∗∗ 0.24 ∗∗∗ 0.092 0.348 0.118 ∗∗∗ 0.22 ∗∗∗ 0.173 0.339 6OP 0.102 ∗∗∗ 0.27 ∗∗∗ 0.078 0.347 0.110 ∗∗∗ 0.24 ∗∗∗ 0.170 0.343 6RP 0.102 ∗∗∗ 0.30 ∗∗∗ 0.070 0.342 0.111 ∗∗∗ 0.26 ∗∗∗ 0.163 0.339 6GP 0.101 ∗∗∗ 0.28 ∗∗∗ 0.074 0.348 0.110 ∗∗∗ 0.25 ∗∗∗ 0.163 0.342 1OG 0.098 ∗∗∗ 0.25 ∗∗∗ 0.046 0.348 0.100 ∗∗∗ 0.27 ∗∗∗ 0.038 0.347 1OC 0.101 ∗∗∗ 0.18 ∗∗∗ 0.136 0.348 0.101 ∗∗∗ 0.18 ∗∗∗ 0.132 0.346 1RG 0.099 ∗∗∗ 0.26 ∗∗∗ 0.047 0.344 0.101 ∗∗∗ 0.27 ∗∗∗ 0.042 0.342 1RC 0.102 ∗∗∗ 0.19 ∗∗∗ 0.139 0.342 0.102 ∗∗∗ 0.19 ∗∗∗ 0.134 0.341 a The notation for scale of input data is explained in Table 1. β and δ are the slopes of effects of year and simulated yields, respectively. σ 2 G and σ 2 E are the error variances associated with counties and residuals, respectively. ∗ p0.05; ∗∗ p0.01; ∗∗∗ p0.001. methods of distributing wheat area did not affect the slopes. The error variance σ 2 E were largely unaf- fected by resolution of the climate and soil data, but consistently slightly lower error variances were ob- tained for the soil type-dependent compared with the uniform distribution of wheat area. The error variance associated with county differences σ 2 G were almost constant within each scale of soil data, and consi- derably higher variances were obtained for the soil type-dependent compared with the uniform distribu- tion, but only at the finest spatial resolution of soil data. J.E. Olesen et al. Agriculture, Ecosystems and Environment 82 2000 213–228 223 Table 6 Coefficient of determination R 2 for linear regression of simulated on measured Danish county and national yields from 1971 to 1997 for different scales of climate and soil data and two different methods of distributing wheat area within each county a Scale of input data Uniform distribution Soil type-dependent distribution National yield County yield County residuals National yield County yield County residuals 1OP 0.12 0.36 ∗∗∗ 0.08 ∗∗∗ 0.14 0.31 ∗∗∗ 0.08 ∗∗∗ 1RP 0.13 0.37 ∗∗∗ 0.09 ∗∗∗ 0.15 ∗ 0.31 ∗∗∗ 0.09 ∗∗∗ 1GP 0.14 0.39 ∗∗∗ 0.08 ∗∗∗ 0.16 ∗ 0.32 ∗∗∗ 0.09 ∗∗∗ 6OP 0.13 0.36 ∗∗∗ 0.09 ∗∗∗ 0.14 0.30 ∗∗∗ 0.08 ∗∗∗ 6RP 0.15 ∗ 0.37 ∗∗∗ 0.10 ∗∗∗ 0.16 ∗ 0.31 ∗∗∗ 0.09 ∗∗∗ 6GP 0.13 0.36 ∗∗∗ 0.08 ∗∗∗ 0.15 ∗ 0.31 ∗∗∗ 0.09 ∗∗∗ 1OG 0.12 0.38 ∗∗∗ 0.08 ∗∗∗ 0.13 0.39 ∗∗∗ 0.09 ∗∗∗ 1OC 0.12 0.25 ∗∗∗ 0.08 ∗∗∗ 0.12 0.25 ∗∗∗ 0.08 ∗∗∗ 1RG 0.13 0.39 ∗∗∗ 0.09 ∗∗∗ 0.14 0.40 ∗∗∗ 0.10 ∗∗∗ 1RC 0.13 0.25 ∗∗∗ 0.09 ∗∗∗ 0.13 0.26 ∗∗∗ 0.10 ∗∗∗ a The notation for scale of input data is explained in Table 1. The significance level is based on an F-test of the slope in the regression. Both observed and simulated yields were detrended and adjusted to 1990 level using a linear technology trend. The linear regression was performed for detrended national yields, detrended county yields and for residuals obtained from linear regression of yields on year for each county. ∗ p0.05; ∗∗ p0.01; ∗∗∗ p0.001. The effect of using different scales of climate and soil data on the ability of the model to explain inter-annual variability in yield is shown in Table 6. The coefficients of determination for both national yield and for county residuals were low and largely unaffected by scale of climate or soil data. The coefficient of determination was higher for county compared with national detrended yields Fig. 5. Simulated mean grain yield versus measured mean grain yield scaled to 1990 level for each county. The simulated yields were calculated using scale 6GP for either a uniform distribution of wheat area or a soil type-dependent distribution of wheat area within each county. Table 6. This reflects the fact that simulated and measured mean wheat yields varied between counties Fig. 5. There was a slightly more linear relation- ship between simulated and measured yield for the uniform distribution of wheat area. The coefficient of determination for the county residuals varied strongly between counties when calculated on a county basis. Fig. 6 shows that higher 224 J.E. Olesen et al. Agriculture, Ecosystems and Environment 82 2000 213–228 Fig. 6. Coefficient of determination R 2 of linear regressions of simulated on measured detrended Danish county yields for each county plotted versus weighted soil water holding capacity of the county for two scales; 1OP and 6GP. The wheat area was distributed evenly on all soil types within each county. The lines show the linear regressions of R 2 on soil water-holding capacity. Fig. 7. Comparison of observed and simulated correlation of detrended winter wheat yields between counties for four different scales of climate and soil data using a uniform distribution of wheat area within each county. J.E. Olesen et al. Agriculture, Ecosystems and Environment 82 2000 213–228 225 Table 7 Regression of simulated correlation on observed correlation bet- ween detrended yields in different counties for different scales of climate and soil data and two different methods of distributing wheat area a Scale of input data Uniform distribution Soil type-dependent distribution Slope R 2 Slope R 2 1OP 0.11 0.32 ∗∗∗ 0.07 0.09 ∗∗∗ 1RP 0.14 0.35 ∗∗∗ 0.09 0.14 ∗∗∗ 1GP 0.16 0.33 ∗∗∗ 0.15 0.27 ∗∗∗ 6OP 0.21 0.11 ∗∗∗ 0.20 0.08 ∗∗∗ 6RP 0.17 0.09 ∗∗∗ 0.18 0.08 ∗∗∗ 6GP 0.22 0.23 ∗∗∗ 0.17 0.11 ∗∗∗ 1OG 0.17 0.35 ∗∗∗ 0.15 0.24 ∗∗∗ 1OC − 0.24 0.08 ∗∗ − 0.24 0.07 ∗∗ 1RG 0.22 0.46 ∗∗∗ 0.21 0.37 ∗∗∗ 1RC − 0.16 0.05 ∗∗ − 0.15 0.03 ∗ a The notation for scale of input data is explained in Table 1. Both the slope and coefficient of determination R 2 of the regres- sions are shown. ∗ p0.05; ∗∗ p0.01; ∗∗∗ p0.001. coefficients of determination were obtained for coun- ties with high soil water capacities. This was espe- cially the case when six climate stations were used for the upscaling. The simulated spatial autocorrelation was in most cases much higher than the observed autocorrelation Fig. 7. Increasing the number of climate stations from one to six or using the much larger network of precipitation stations reduced the simulated autocorre- lations. This was reflected in the slope of the line relat- ing simulated to observed spatial correlation Table 7, but not necessarily in the coefficient of determination of that relationship. The use of dominant soil types on the county scale also reduced the simulation correla- tion, but the slope of the relationship was reversed. Table 8 shows the slopes of linear regressions of simulated and observed detrended county yields on Table 8 Slope of regression of simulated or measured detrended yield on temperature or precipitation for the periods April–July and October–July Climate variable Measured yield Simulated yield Temperature, April–July ◦ C 0.32 ∗∗∗ 0.25 ∗ Temperature, October–July ◦ C 0.32 ∗∗∗ 0.16 ∗ Precipitation, April–July mm − 0.004 ∗∗∗ − 0.001 Precipitation, October–July mm − 0.002 ∗∗∗ − 0.003 ∗∗∗ ∗ p0.05; ∗∗ p0.01; ∗∗∗ p0.001. temperature and precipitation for either the whole growing season October–July or the main growing season April–July. The signs of the response of both simulated and observed yield to temperature and precipitation were identical, but the significance of the response was higher for observed yields.

4. Discussion