LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study

The efficient use of water in agriculture is one of the most significant agricultural challenges that modern technologies are helping to cope with through Irrigation Advisory Services (IAS) and Decision Support Systems (DSS). These last are considered powerful management instruments able to help farmers achieve the best efficiency in irrigation water use and to increase their incomes through obtaining the highest possible crop yield. In this context, within the project “An advanced low cost system for farm irrigation support – LCIS” (a joint Italian-Israeli RD IRRISAT®, remote sensing; W-Mod, simulation modelling of water balance in the soil-plant and atmosphere system), has been developed. These three LCIS-DSS tools have been evaluated, in terms of their ability to support the farmer in irrigation management, in a real applicative case study on maize grown on Andosols in a private farm in southern Italy in the 2018 season. The evaluation considered the predictive performance of the tools and also the pros and cons of their application, due their different spatial scale applicability, costs and complexity of use. The results have shown that all three approaches are able to realise the maximum obtainable maize production. However, the method based on in situ soil sensor (W-Tens) supplied 40% more water compared to the other two methods, whereas the IRRISAT® and W-Mod approaches represent the best solution in terms of irrigation water use efficiency (IWUE). Moreover, IRRISAT® has the advantage of being able to work without soil spatial information, although unlike W-Tens both the latter methods need a high level of user expertise and consequently support of external service providers. Integration between different tools represents an opportunity for improved water use efficiency in agriculture (e.g., field sensors and remote sensing).

[1]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[2]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

[3]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[4]  M. Jensen,et al.  Scheduling Irrigations Using Climate-Crop-Soil Data , 1970 .

[5]  Jochen Hemming,et al.  Root Zone Sensors for Irrigation Management in Intensive Agriculture , 2009, Sensors.

[6]  D. G. Rao,et al.  Consequences of future climate change and changing climate variability on maize yields in the midwestern United States , 2000 .

[7]  N. Rosenberg,et al.  Sensitivity of evapotranspiration in a wheat field, a forest, and a grassland to changes in climate and direct effects of carbon dioxide , 1989 .

[8]  J. Wallace,et al.  Evaporation from sparse crops‐an energy combination theory , 2007 .

[9]  J. Monteith,et al.  Principles of Environmental Physics , 2014 .

[10]  G. Buttafuoco,et al.  Complementary techniques to assess physical properties of a fine soil irrigated with saline water , 2012, Environmental Earth Sciences.

[11]  M. Raupach,et al.  Maximum conductances for evaporation from global vegetation types , 1995 .

[12]  Francesco Vuolo,et al.  Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy , 2018, Remote Sensing of Environment.

[13]  Stefano Marsili-Libelli,et al.  A Fuzzy Decision Support System for irrigation and water conservation in agriculture , 2015, Environ. Model. Softw..

[14]  Andrew P. Whitmore,et al.  Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring , 1987, The Journal of Agricultural Science.

[15]  Fabio Terribile,et al.  A Web-based spatial decision supporting system for land management and soil conservation , 2015 .

[16]  M. Rivington,et al.  Adaptation for crop agriculture to climate change in Cameroon: Turning on the heat , 2009 .

[17]  J. J. Neeteson,et al.  Response of potatoes to N fertilizer: Dynamic model , 1985, Plant and Soil.

[18]  A Calera Belmonte,et al.  GIS tools applied to the sustainable management of water resources: Application to the aquifer system 08-29 , 1999 .

[19]  J. Gardiol,et al.  Modelling evapotranspiration of corn (Zea mays) under different plant densities , 2003 .

[20]  A. P. Annan,et al.  Electromagnetic determination of soil water content: Measurements in coaxial transmission lines , 1980 .

[21]  M. Petersen,et al.  Comparison of two electromagnetic induction tools in salinity appraisals , 2001 .

[22]  Mercedes Valdés-Vela,et al.  Stem water potential estimation of drip-irrigated early-maturing peach trees under Mediterranean conditions , 2015, Comput. Electron. Agric..

[23]  G. Richard,et al.  From spatial-continuous electrical resistivity measurements to the soil hydraulic functioning at the field scale , 2009 .

[24]  L. Cockx,et al.  Comparing the EM38DD and DUALEM‐21S Sensors for Depth‐to‐Clay Mapping , 2009 .

[25]  Angus R. Simpson,et al.  A genetic algorithm for optimizing off-farm irrigation scheduling , 2001 .

[26]  Zailin Huo,et al.  Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model , 2016 .

[27]  Luciano Mateos,et al.  SIMIS: the FAO decision support system for irrigation scheme management , 2002 .

[28]  Alfonso Calera,et al.  Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users , 2017, Sensors.

[29]  R. Gebbers,et al.  Electrical conductivity mapping for precision farming , 2009 .

[30]  Michael D. Dukes,et al.  Tomato yield, biomass accumulation, root distribution and irrigation water use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling , 2009 .

[31]  P. Miller,et al.  Adaptation of Pulse Crops to the Changing Climate of the Northern Great Plains , 2007 .

[32]  A. Thomson,et al.  Climate Impacts on Agriculture: Implications for Crop Production , 2011 .

[33]  M. Mori,et al.  Yields and quality of biomasses and grain in Cynara cardunculus L. grown in southern Italy, as affected by genotype and environmental conditions , 2017 .

[34]  W. James Shuttleworth,et al.  Evaporation Models in Hydrology , 1991 .

[35]  M. Menenti,et al.  Climate change, effective water use for irrigation and adaptability of maize: A case study in southern Italy , 2014 .

[36]  Wenchao Wang,et al.  Web-based decision support system for canal irrigation management , 2017, Comput. Electron. Agric..

[37]  Todd V. Elliott,et al.  WISE: a web-linked and producer oriented program for irrigation scheduling , 2001 .

[38]  J. Bouma,et al.  Climate Change Effects on the Suitability of an Agricultural Area to Maize Cultivation: Application of a New Hybrid Land Evaluation System , 2015 .

[39]  Basile,et al.  Identifying Optimal Irrigation Water Needs at District Scale by Using A Physically Based Agro-Hydrological Model , 2019, Water.

[40]  Joop G Kroes,et al.  SWAP Version 3.2. Theory description and user manual , 2008 .

[41]  Quanjiu Wang,et al.  Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners , 2015, PloS one.

[42]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[43]  K. Loague,et al.  Statistical and graphical methods for evaluating solute transport models: Overview and application , 1991 .

[44]  N. Rosenberg,et al.  Sensitivity of some potential evapotranspiration estimation methods to climate change , 1993 .

[45]  Jesús Martínez del Rincón,et al.  A decision support system for managing irrigation in agriculture , 2016, Comput. Electron. Agric..

[46]  J. Marsal,et al.  Use of CropSyst as a decision support system for scheduling regulated deficit irrigation in a pear orchard , 2011, Irrigation Science.

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

[48]  S. Baxter,et al.  World Reference Base for Soil Resources. World Soil Resources Report 103. Rome: Food and Agriculture Organization of the United Nations (2006), pp. 132, US$22.00 (paperback). ISBN 92-5-10511-4 , 2007, Experimental Agriculture.

[49]  Yanjun Shen,et al.  Web-based irrigation decision support system with limited inputs for farmers , 2018, Agricultural Water Management.

[50]  Marco Acutis,et al.  SWAP, CropSyst and MACRO comparison in two contrasting soils cropped with maize in Northern Italy. , 2010 .

[51]  Claire D'Este,et al.  Development of an intelligent environmental knowledge system for sustainable agricultural decision support , 2014, Environ. Model. Softw..

[52]  Raffaele Casa,et al.  Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods , 2013 .

[53]  Jeffrey J. McDonnell,et al.  Assessment of multi-frequency electromagnetic induction for determining soil moisture patterns at the hillslope scale. , 2009 .

[54]  J. Wösten,et al.  Development and use of a database of hydraulic properties of European soils , 1999 .

[55]  Wim G.M. Bastiaanssen,et al.  Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize , 2004 .

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

[57]  Edward M. Barnes,et al.  Cotton irrigation scheduling using a crop growth model and FAO-56 methods: Field and simulation studies , 2017 .

[58]  Allan Leck Jensen,et al.  Pl@nteInfo® : a web-based system for personalised decision support in crop management , 2000 .

[59]  Joe T. Ritchie,et al.  Model for predicting evaporation from a row crop with incomplete cover , 1972 .

[60]  Mercedes Valdés-Vela,et al.  Soft computing applied to stem water potential estimation: A fuzzy rule based approach , 2015, Comput. Electron. Agric..

[61]  Nicholas Dercas,et al.  Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems , 2015 .

[62]  S. Kuo,et al.  Decision support for irrigation project planning using a genetic algorithm , 2000 .

[63]  Marvin E. Jensen,et al.  Evapotranspiration and irrigation water requirements : a manual , 1990 .

[64]  Guido D'Urso,et al.  Current Status and Perspectives for the Estimation of Crop Water Requirements from Earth Observation , 2010 .

[65]  Biagio Bianchi,et al.  Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia , 2015 .

[66]  Michael D. Dukes,et al.  Precision of soil moisture sensor irrigation controllers under field conditions , 2010 .

[67]  J. Bouma,et al.  The role of soils in the analysis of potential agricultural production: A case study in Lebanon , 2017 .