Fuzzy knowledge-based model for soil condition assessment in Argentinean cropping systems

A knowledge-based system (KBS) for assessing soil condition in agroecosystems is presented. The KBS was built through expert opinion elicitation and available scientific data using fuzzy logic. The system is structured into three main elements: (1) input variables that represent the physical domain of soil condition assessment and are related to environmental and crop management conditions; (2) primary modules that describe the fuzzy nature of the soil indicators and; (3) secondary modules that represent the elicited knowledge on soil condition assessment from an expert panel. The application of the KBS on data on crop fields from Inland Pampa (Argentina) indicated that soil nitrogen depletion poses a hazard for soil health as no crop was able to accomplish more than 50% of the sustainability criteria elicited for soil nitrogen extraction from the system. Conversely, soil carbon and physical conditions exhibited values closer to the desirable scenarios elicited by the fuzzy if-then rules, with values of 0.84, 0.71 and 0.74 for maize, soybean and wheat, respectively, where higher indicator values reflect better soil condition assessment. No significant differences were observed in the overall soil degradation module between crops, with values of 0.64 for maize and wheat and 0.67 for soybean. The KBS developed in this work provided an alternative modeling tool for assessing agroecosystem condition when knowledge regarding long-term assessment is imprecise and uncertain.