Agriculture, Ecosystems and Environment 82 2000 213–228
Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat in Denmark
J.E. Olesen
a,∗
, P.K. Bøcher
b
, T. Jensen
b
a
Department of Crop Physiology and Soil Science, Danish Institute of Agricultural Sciences, PO Box 50, DK-8830 Tjele, Denmark
b
Department of Agricultural Systems, Danish Institute of Agricultural Sciences, PO Box 50, DK-8830 Tjele, Denmark
Abstract
Crop growth models are essentially site-based, and use of such models for assessing regional productivity of crops requires methods for aggregating over space. Different method for aggregating simulated county and national crop yields for winter
wheat Triticum aestivum L. in Denmark were tested using a crop simulation model CLIMCROP, which was run with and without irrigation for a range of soil types and climatic conditions. The aggregated county or national yield was calculated by
summing simulated yield of each category multiplied by the area, they represent. Ten different combinations of scales of climate and soil data were used. The wheat area was distributed between the different soil types using either a uniform distribution or a
distribution that gave preference to soils with high water-holding capacity. The simulated results were compared with Danish county and national yield statistics for winter wheat from the period 1971–1997. There was, in general, a poor relationship
between simulated and observed yields when the observed yields had been detrended to remove the technology effect. A larger fraction of the inter-annual variability was captured by the model on the loamy soils compared with the sandy soils.
The model was able to capture most of the spatial variation in observed yields, except at the coarsest resolutions of the soil data. The finest resolution of soil and climate data gave a better fit of simulated to observed spatial autocorrelation in yield.
The results indicate that upscaling of simulated productivity of crops for Danish conditions requires a spatial resolution of soil data of 10 × 10 km
2
or finer. A single climate station may be sufficient if only national yields are estimated, but more stations are required, if regional yields are to be estimated. Consideration should also be given to the distribution of crop area
on the different soil types. © 2000 Elsevier Science B.V. All rights reserved.
Keywords: Climate change; Climate sensitivity; Land use; Crop model; Spatial scale; Yield forecasting
1. Introduction
Many impact studies have analysed the effect of changes of climatic variables and of atmospheric CO
2
concentration on crop production. Most studies have used simulation models applied at individual sites
e.g., Semenov et al., 1996; Brown and Rosenberg, 1997 or in grid boxes across smaller or larger areas
∗
Corresponding author. Tel.: +45-89991659; fax: +45-89991619.
E-mail address: jorgene.olesenagrsci.dk J.E. Olesen.
e.g., Harrison and Butterfield, 1996; Davies et al., 1997; Dhakhwa et al., 1997. These crop models are
designed to be run at specific sites with specific soil and climate characteristics.
Current crop models have been shown to only ex- plain a small proportion of the variation in winter
wheat Triticum aestivum L. yields in UK and Den- mark Landau et al., 1998; Olesen et al., 2000. The
weather in UK and Denmark is generally favourable for cereal crops, and observed regional yields can be
as high as 9 Mg ha
− 1
. The yield variation is there- fore not mainly caused by direct effects of weather on
0167-880900 – see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 8 0 9 0 0 0 0 2 2 7 - 9
214 J.E. Olesen et al. Agriculture, Ecosystems and Environment 82 2000 213–228
crop physiology, but by secondary effects such as the presence and effects of weeds, pests, diseases, lodging
and anaerobic soil conditions Jamieson et al., 1999. These effects are not handled by current crop models,
and some of them interact strongly with both weather and crop management.
The area grown with winter wheat in Denmark increased sevenfold from 1971 to 1997. The increase
in wheat area occurred at the expense of the area with spring barley Hordeum vulgare L.. The in-
crease in winter wheat area can largely be attributed to higher-yielding varieties Silvey, 1994 and the in-
troduction of effective fungicides for disease control Orson, 1995. The relative increase in wheat area
was largest in the counties with sandy soils, indicating that winter wheat has expanded onto lighter soils with
lower soil water-holding capacities. This change in land allocation may have influenced the response of
national yields to weather, because of different yield responses on different soil types Wassenaar et al.,
1999; Olesen et al., 2000.
Only little attention has been given to the problem of scaling simulated crop production across areas
of contrasting soils and climate LeDuc and Holt, 1987; Easterling et al., 1998; Wassenaar et al., 1999.
This upscaling is necessary in order to obtain esti- mates of crop production at aggregated regional or
national levels. The aggregated yield is the weighted sum of yields obtained under different climatic, soils
and management conditions. The interaction between these factors and the correlation of yields over space
affect the aggregated yield variability. Simulated site yields in Denmark have been shown to respond dif-
ferently to climatic variation on different soil types Olesen et al., 2000, which would make the re-
sponse of aggregated yield strongly dependent on the soil × climate interaction. Other factors operating at
higher scales may, however, be linked with soils and climate variation, and influence actual aggregated
yields, e.g., farm types and land use restrictions. The optimal scales of climate and soil data for estimat-
ing county and national yields can thus not be easily deduced.
The purpose of this study was to examine the effects of different scales of climate and soil data on simulated
yield of winter wheat on regional and national scales in Denmark, and to compare these simulated aggregated
yields with observed yields.
2. Materials and methods
