A Fuzzy Decision Support System for irrigation and water conservation in agriculture

Since agriculture is the major water consumer, web services have been developed to provide the farmers with considerate irrigation suggestions. This study improves an existing irrigation web service, based on the IRRINET model, by describing a protocol for the field implementation of a fully automated irrigation system. We demonstrate a Fuzzy Decision Support System to improve the irrigation, given the information on the crop and site characteristics. It combines a predictive model of soil moisture and an inference system computing the most appropriate irrigation action to keep this above a prescribed "safe" level. Three crops were used for testing the system: corn, kiwi, and potato. This Fuzzy Decision Support System (FDSS) favourably compared with an existing agricultural model and data-base (IRRINET). The sensitivity of the FDSS was tested with random rainfall and also in this extended case the water saving was confirmed. Display Omitted We describe a Fuzzy Decision Support System to decide the irrigation based on soil moisture and rain forecast.Its rules are easily editable and can be specialized for each crop and agricultural condition.The system provided improved irrigation suggestions in terms of timing and water saving.

[1]  M. Rivington,et al.  Raising the bar? - The challenges of evaluating the outcomes of environmental modelling and software , 2011, Environ. Model. Softw..

[2]  Anthony J. Jakeman,et al.  A methodology for the design and development of integrated models for policy support , 2011, Environ. Model. Softw..

[3]  Z. Paydar,et al.  Evaluation of potential irrigation expansion using a spatial fuzzy multi-criteria decision framework , 2012, Environ. Model. Softw..

[4]  Michiel Blind,et al.  Perceived effectiveness of environmental decision support systems in participatory planning: Evidence from small groups of end-users , 2011, Environ. Model. Softw..

[5]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[6]  Jeffrey S. Reid,et al.  Water deficit effects on sweet corn. I. Water use, radiation use efficiency, growth, and yield , 2001 .

[7]  Koksal Aydinsakir,et al.  The influence of regular deficit irrigation applications on water use, yield, and quality components of two corn (Zea mays L.) genotypes , 2013 .

[8]  Gerrit Hoogenboom,et al.  Water use and water use efficiency of sweet corn under different weather conditions and soil moisture regimes. , 2009 .

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Jacques-Eric Bergez,et al.  IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management , 2010, Environ. Model. Softw..

[11]  Michael Winter,et al.  Valuing local knowledge as a source of expert data: Farmer engagement and the design of decision support systems , 2012, Environ. Model. Softw..

[12]  Craig A. Aumann,et al.  Constructing model credibility in the context of policy appraisal , 2011, Environ. Model. Softw..

[13]  János Abonyi,et al.  Fuzzy Model Identification for Control , 2003 .

[14]  Raffaele Giordano,et al.  An integrated modelling tool to evaluate the acceptability of irrigation constraint measures for groundwater protection , 2013, Environ. Model. Softw..

[15]  Robert Finger,et al.  Economic and environmental assessment of irrigation water policies: A bioeconomic simulation study , 2014, Environ. Model. Softw..

[16]  H. V. Nanjappa,et al.  Effect of phenophase based irrigation schedules on growth, yield and quality of baby corn (Zea mays L.) , 2011 .

[17]  Maarten S. Krol,et al.  Feedback mechanisms between water availability and water use in a semi-arid river basin: A spatially explicit multi-agent simulation approach , 2010, Environ. Model. Softw..

[18]  Alison McCarthy,et al.  Simulation of irrigation control strategies for cotton using Model Predictive Control within the VARIwise simulation framework , 2014 .

[19]  Luis S. Pereira,et al.  Partitioning evapotranspiration, yield prediction and economic returns of maize under various irrigation management strategies , 2014 .

[20]  Giorgio Guariso,et al.  Decision support systems for water management: The Lake Como case study☆ , 1985 .

[21]  Anthony J. Jakeman,et al.  Environmental decision support systems (EDSS) development - Challenges and best practices , 2011, Environ. Model. Softw..

[22]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[23]  Stefano Marsili-Libelli,et al.  Fuzzy modelling of the composting process , 2010, Environ. Model. Softw..

[24]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[25]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[26]  Roberto Genovesi,et al.  IRRINET: Large Scale DSS Application for On-farm Irrigation Scheduling , 2013 .

[27]  Chunlin Huang,et al.  A Decision Support System for irrigation water allocation along the middle reaches of the Heihe River Basin, Northwest China , 2013, Environ. Model. Softw..