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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: Simultech 2013 - Proceedings Of The 3rd International Conference On Simulation And Modeling Methodologies, Technologies And Applications Título del capítulo: Using andaptive neuro fuzzy inference system to build models with uncertain data for rainfed maize: Study case in the state of puebla (Mexico)
Adaptive neuro-fuzzy inference system; ANFIS; Data inconsistencies; Me-xico; Neuro-fuzzy inference systems; Production area; Suitability index; Uncertain datas; Fuzzy logic; Agriculture
Resumen:
Using the methodology of Adaptive Neuro Fuzzy Inference System (ANFIS) a model to determine the relationship suitability index with the yields per hectare and the percentage of production area lost of rainfed maize for the state of Puebla was built. The data used to build the model presented inconsistencies. The data of the INEGI's land use map presented more municipalities without rainfed maize agriculture than the database of SAGARPA. Also the SAGARPA data, in terms of the percentage of production area lost, do not show any distinctions between the loss due to climate, pests, or simply that the farmer did not plant the total area that was declared, or had not harvested all the area declared. Even with data inconsistencies ANFIS produced a coherent output reviewed by experts. The model shows that higher the percentage of production area lost and high yields the higher the suitability index is. According to local studies this is due to the high degradation of the soils.
