MY SIRR: Minimalist agro-hYdrological model for Sustainable IRRigation management - Soil moisture and crop dynamics

The paper introduces a minimalist water-driven crop model for sustainable irrigation management using an eco-hydrological approach. Such model, called MY SIRR, uses a relatively small number of parameters and attempts to balance simplicity, accuracy, and robustness. MY SIRR is a quantitative tool to assess water requirements and agricultural production across different climates, soil types, crops, and irrigation strategies. The MY SIRR source code is published under copyleft license. The FOSS approach could lower the financial barriers of smallholders, especially in developing countries, in the utilization of tools for better decision-making on the strategies for short- and long-term water resource management.

[1]  Abraham Blum,et al.  Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress , 2009 .

[2]  Daniel P. Loucks,et al.  Water management: Current and future challenges and research directions , 2015 .

[3]  A. Porporato,et al.  From rainfed agriculture to stress-avoidance irrigation: II. Sustainability, crop yield, and profitability , 2011 .

[4]  Kazuo Kawano,et al.  Harvest index and evoluation of major food crop cultivars in the tropics , 1990, Euphytica.

[5]  J. T. Musick,et al.  Water-Yield Relationships for Irrigated and Dryland Wheat in the U.S. Southern Plains , 1994 .

[6]  T. Sinclair,et al.  Transpiration response of Arabidopsis, maize, and soybean to drying of artificial and mineral soil , 2007 .

[7]  P. Milly A minimalist probabilistic description of root zone soil water , 2001 .

[8]  Shaozhong Kang,et al.  Effects of limited irrigation on yield and water use efficiency of winter wheat in the Loess Plateau of China , 2002 .

[9]  D. Post,et al.  Detritus, trophic dynamics and biodiversity , 2004 .

[10]  Jan Adamowski,et al.  Collaborative Strategies for Sustainable EU Flood Risk Management: FOSS and Geospatial Tools - Challenges and Opportunities for Operative Risk Analysis , 2015, ISPRS Int. J. Geo Inf..

[11]  Sheng-Feng Kuo,et al.  Simulation and optimization model for irrigation planning and management , 2003 .

[12]  Anna Balenzano,et al.  Inter-comparison of hydrological model simulations with time series of SAR-derived soil moisture maps , 2012 .

[13]  I. Rodríguez‐Iturbe,et al.  Soil Water Balance and Ecosystem Response to Climate Change , 2004, The American Naturalist.

[14]  D. Raes,et al.  AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles , 2009 .

[15]  Salvatore Manfreda,et al.  On the importance of accurate depiction of infiltration processes on modelled soil moisture and vegetation water stress , 2009 .

[16]  D. C. Kincaid,et al.  On‐Farm System Design and Operation and Land Management , 2015 .

[17]  Luca Ridolfi,et al.  Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress: III. Vegetation water stress , 2001 .

[18]  A. Torrecillas,et al.  Response of apricot trees to deficit irrigation strategies , 2009, Irrigation Science.

[19]  D. Raes,et al.  Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas , 2009 .

[20]  Amilcare Porporato,et al.  From rainfed agriculture to stress-avoidance irrigation: I. A generalized irrigation scheme with stochastic soil moisture , 2011 .

[21]  Salvatore Manfreda,et al.  Space‐time modeling of soil moisture: Stochastic rainfall forcing with heterogeneous vegetation , 2006 .

[22]  A. Porporato,et al.  Ecohydrological modeling in agroecosystems: Examples and challenges , 2015 .

[23]  A. Porporato,et al.  Traditional and microirrigation with stochastic soil moisture , 2010 .

[24]  William A. Jury,et al.  The Emerging Global Water Crisis: Managing Scarcity and Conflict Between Water Users , 2007 .

[25]  A. Porporato,et al.  Ecohydrology of Agroecosystems: Quantitative Approaches Towards Sustainable Irrigation , 2014, Bulletin of Mathematical Biology.

[26]  Don R. Davison,et al.  Comparison of irrigation strategies for surface-irrigated corn in West Central Nebraska , 2006, Irrigation Science.

[27]  Jan Adamowski,et al.  READY: a web-based geographical information system for enhanced flood resilience through raising awareness in citizens , 2015 .

[28]  H. Lambers,et al.  Effect of soil drying on growth, biomass allocation and leaf gas exchange of two annual grass species , 1996, Plant and Soil.

[29]  J. Faci,et al.  Deficit irrigation in maize for reducing agricultural water use in a Mediterranean environment , 2009 .

[30]  Francesco Morari,et al.  Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors , 2009 .

[31]  Salvatore Manfreda The Water Management in the Present Century , 2013 .

[32]  Salvatore Manfreda,et al.  The olive tree: a paradigm for drought tolerance in Mediterranean climates , 2007 .

[33]  A. Torrecillas,et al.  Apricot tree response to withholding irrigation at different phenological periods. , 2000 .

[34]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[35]  D. Corwin,et al.  Application of Soil Electrical Conductivity to Precision Agriculture , 2003 .

[36]  Jay R. Lund,et al.  Modeling irrigated agricultural production and water use decisions under water supply uncertainty , 2005 .

[37]  Harry F. Blaney,et al.  Determining consumptive use and irrigation water requirements. , 1962 .

[38]  E. Fereres,et al.  Deficit irrigation for reducing agricultural water use. , 2006, Journal of experimental botany.

[39]  A. Porporato,et al.  Probabilistic description of crop development and irrigation water requirements with stochastic rainfall , 2013 .

[40]  V. Isham,et al.  Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation , 1999, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.