RESUMO
Spatial system dynamics (SSD) models are typically implemented by linking stock variables to raster grids while the use of object representations of human artefacts such as buildings or ownership has been limited. This limitation is addressed by this article, which demonstrates the use of object representations in SSD. The objects are parcels of land that are attributed to grassland farms. The model simulates structural change in agriculture, i.e., change in the size of farms. The aim of the model is to reveal relations between structural change, farmland fragmentation and variable farmland quality. Results show that fragmented farms tend to become consolidated by structural change, whereas consolidated initial conditions result in a significant increase of fragmentation. Consolidation is reinforced by a dynamic land market and high transportation costs. The example demonstrates the capabilities of the object-based approach for integrating object geometries (parcel shapes) and relations between objects (distances between parcels) dynamically in SSD.
RESUMO
Agricultural production fulfills economic, ecological and structural functions. Despite technological advances, agricultural production remains sensitive to climate variations. In central Europe, climate change is predicted to bring more rainfall in winter, less rainfall in summer, and increased drought risk among other effects. Grassland agriculture, which is the dominant land use in Alpine regions, may be significantly affected by these climatic changes in the future. Motivated by this issue, the susceptibility of grassland yields to weather variations in Austria is empirical evaluated as a case study. The major objective of this study is to derive spatially distributed indications for climate change exposure by assessing the impacts of weather variations on past yield. It is assumed that reduced water supply during summer constitutes a threat to grassland productivity in regions that are warmer and drier already today. On the contrary, increased spring temperatures may improve grassland productivity in cooler regions like Alpine valleys, since the earlier snow melt leads to an extension of the growth period. Regression analyses are used for evaluating the relation between yearly yields and spring temperatures or water supply in summer, respectively. Water supply is thereby expressed by aggregated precipitation sums and the Climatic Water Balance (CWB). Input data are a meteorological time series as well as yearly yields available for 25 years between 1970 and 2010 and 99 districts in Austria. Yearly yields show a significant (P < 0.05) and positive dependency on water supply in summer for the eastern Austrian lowlands. The combination of temperature in spring and CWB in summer is only significant for six districts in the east of Austria. The positive impact of higher spring temperatures could not be verified. Generally, the regression coefficients are not very high, which indicates that temperature and water supply do not fully describe grassland productivity. Projected climate change may increasingly constitute a risk to yield reliability in the east of the country. That in turn, requires consideration in agricultural development plans and a quantification of these impacts from a social-economic perspective.
