Changes in climate variability with reference to land quality and agriculture in Scotland

Int J Biometeorol. 2015 Jun;59(6):717-32. doi: 10.1007/s00484-014-0882-9. Epub 2014 Aug 7.

Abstract

Classification and mapping of land capability represents an established format for summarising spatial information on land quality and land-use potential. By convention, this information incorporates bioclimatic constraints through the use of a long-term average. However, climate change means that land capability classification should also have a dynamic temporal component. Using an analysis based upon Land Capability for Agriculture in Scotland, it is shown that this dynamism not only involves the long-term average but also shorter term spatiotemporal patterns, particularly through changes in interannual variability. Interannual and interdecadal variations occur both in the likelihood of land being in prime condition (top three capability class divisions) and in class volatility from year to year. These changing patterns are most apparent in relation to the west-east climatic gradient which is mainly a function of precipitation regime and soil moisture. Analysis is also extended into the future using climate results for the 2050s from a weather generator which show a complex interaction between climate interannual variability and different soil types for land quality. In some locations, variability of land capability is more likely to decrease because the variable climatic constraints are relaxed and the dominant constraint becomes intrinsic soil properties. Elsewhere, climatic constraints will continue to be influential. Changing climate variability has important implications for land-use planning and agricultural management because it modifies local risk profiles in combination with the current trend towards agricultural intensification and specialisation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture / statistics & numerical data*
  • Climate
  • Climate Change / statistics & numerical data*
  • Computer Simulation
  • Conservation of Natural Resources / statistics & numerical data
  • Crops, Agricultural / supply & distribution*
  • Ecosystem*
  • Geography
  • Models, Statistical*
  • Scotland
  • Spatio-Temporal Analysis