Fuzzy logic based global assessment of the marginality of agricultural land use

Abstract

In order to assess the agricultural potential of regions and to evaluate the risk of environmental degradation due to agriculture, we define a marginality index for agricultural land use which is based on available global data sets and takes into account various environmental conditions. Influencing factors taken into consideration are the general climatic conditions for plant growth, the soil fertility, the soil moisture availability, the precipitation uncertainty and the erosion risk due to the steepness of slopes. Comparison of our marginality index with present global assessments of agricultural land use shows that about 30% of agricultural land currently in use can be identified as marginal, in other words as having a disposition towards overuse and potential environmental degradation. The validity of our approach is strengthened by the fact that agriculturally used areas identified as marginal are described as vulnerable in the literature. The risk of degradation wlth a further intensification of agriculture on existing crop land and the potential for the cultivation of previously unused land can be identified using our fuzzy logic technique.

Influence of Indian Ocean zonal circulation on variability and predictability of Ethiopia highlands vegetation

“…Semi arid parts of Africa bordering the Congo monsoon, such as the Sahel and Zambezi, are characterised by high rainfall variability and low yield agriculture that contribute to food insecurity (Cassel-Gintz et al, 1997). To better understand how summer rainfall affects crop production at state-level, past work has compared time series of farm yields, rain-gauge and satellite data (Tucker, 1979;Eklundh, 1998;Richard and Poccard, 1998;Al-Bakri and Suleiman, 2004;Budde et al, 2004).…”

GIS-based assessment of the threat to world forests by patterns of non-sustainable civilisation nature interaction

“…They were generated in a manner similar to the Overexploitation Syndrome. For details regarding the analysis for the Sahel Syndrome refer to Cassel-Gintz et al (1997), Schellnhuber et al (1997), PetschelHeld et al (1999) and Lüdeke et al (1999). The Dust Bowl Syndrome is described in greater detail in WBGU (2000).…”

“…In addition, suitable fuzzy operators are used for the combination of the various disposition elements. For a detailed introduction to the fuzzy logic methods and concepts utilised here, refer to Cassel-Gintz et al (1997).…”

“…Mathematical concepts of fuzzy logic (fuzzy sets, fuzzy operators) are used in a GIS environment to incorporate qualitative and fuzzy statements with quantitative information (Zadeh, 1965;Böhme, 1993;Cassel-Gintz et al, 1997). These concepts allow us to operate with these kinds of information and to draw valid conclusions.…”

Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic

“…The fuzzy logic approach is certainly not the only methodology suitable for this purpose. It is chosen because of its conceptual simplicity and generality, as has been demonstrated repeatedly in multiple fields, including medicine (Sproule et al, 1997;Wirsam and Uthus, 1996), agriculture (Cassel-Gintz et al, 1997), and biotechnology (Kennedy and Spooner, 1996;Mukherjee et al, 1998;). Compared with other approximation techniques (e.g., piecewise linear approximation, splines, etc.…”