Abstract
A model was built using Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the relationship between the natural suitability index of rainfed maize and yield per hectare and percentage of production area lost for the state of Puebla. The data used to build the model presented inconsistencies. The data of the INEGIs 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 mark any distinctions of the loss. Even with data inconsistencies ANFIS produced a coherent output reviewed by experts and local studies. 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 and confirmed with the second model built adding soil degradation information.
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Acknowledgments
The present work was developed with the support of the Programa de Investigación en Cambio Climático (PINCC) of the Universidad Nacional Autónoma de México (UNAM) and the Consejo Nacional de Ciencia y Tecnología (Conacyt). We would like to thank Dr. Cecilia Conde, Dr. Alejandro Monterroso and M. Guillermo Rosales for their valuable inputs and serving as the experts to validate the model.
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Vermonden, A., Gay-García, C., Paz-Ortiz, I. (2015). Adaptive Neuro Fuzzy Inference System Used to Build Models with Uncertain Data: Study Case for Rainfed Maize in the State of Puebla (Mexico). In: Obaidat, M., Koziel, S., Kacprzyk, J., Leifsson, L., Ören, T. (eds) Simulation and Modeling Methodologies, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-11457-6_10
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DOI: https://doi.org/10.1007/978-3-319-11457-6_10
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