The first major task in classifying the likely effects of climate change on landuse involved developing and comparing present and future land use scenarios. Neural networks like those employed in Task 1 were trained to model the relationship between climate (temperature and rainfall), soil characteristics (permeability, texture, fertility, parent material), biomass, elevation, population densities, other socio-economic variables, and agricultural landuse. Having trained the neural networks to classify contemporary landuse they were applied to classify future landuse using predicted values of the input variables. The predictions and forecasts of landuse were then translated into land degradation terms using fuzzy statements about landuse changes. Further analysing the difference between contemporary and future classifications and comparing with ESA definitions in Medalus case study areas is covered in Task 3. Various future scenarios can be simulated by directly changing forecasts of the inputs. The first run is unlikely to produce results which enable the precise location of ESA areas to be determined although this become increasingly likely as the data inputs and modelling procedure improves in subsequent models. It is hoped that the first set of results make sense at a country scale and could be used directly to help allocate EU funds to mitigate land degradation, but it would be better to wait for later model outputs as the information becomes more reliable.
The first essential step in developing the SPS involved acquiring input data for now and future forecasts.
Data from the above sources was manipulated using ArcInfo and ArcView
into 1DM raster grid inputs for the SPS.
Dominant landuse was classed into 4 categories; arable, trees and orchards, waste and others. Neural networks were then trained to classify each landuse seperately based on the various physical and socio-economic inputs described below. Having trained to predict contemporary landuse the neural networks were applied to generate landuse forecasts using forecast values of the predictor variables. The difference between predicted contemporary and forecast agricultural landuse was then translated into land degradation terms using fuzzy inference. inputs y seperately, then combine the independent models in a Multi-Criteria Evaluation (MCE) framework using fuzzy logic. The first landuse category selected was arable. Dominant arable landuse occurs on a large proportion of cultivated land which is usually of the highest quality.
Maps of observed, predicted and forecast agricultural
EU CAP subsides may contribute to landuse decisions which encouraging short term economic reasoning and result in land degradation. It maybe that an increasingly large number of farmers have become unlikely to pass on working farms to their children. There maybe a general lifestyle change driving agricultural abandonment, where there are less people maintaining the land it is more likely to degrade? Whilst this change takes place by in funding his children through college/university which requires money in the short term. Whilst his children are away the farmer may no longer be capable of looking after the entire farm properly and may opt for a subsidised crop and hope land degredation impacts never really take effect.) It maybe that farming decisions which decrease land degredation risk can be encouraged with a form of set a side as an option to inappropriate subsidised landuse and that land help from local support groups could return the land to a more natural stable state capable of being farmed at some point in the future.
There are essentially two main types of land degradation: that which is related to physical land deterioration, reduction in biomass and loss of soil; and, that which results in less profit or revenue being generated by agriculture. Although the two are closely related they are quite different. To fit in with the rest of the Medalus work here the focus is on the dynamics of landuse change which result in a switch in land from being agriculture capable to being agriculture incapable. I would argue that in this case the focus is on the physical land deterioration type of land degradation.