Journal of
Engineering and Computer Innovations

  • Abbreviation: J. Eng. Comput. Innov.
  • Language: English
  • ISSN: 2141-6508
  • DOI: 10.5897/JECI
  • Start Year: 2010
  • Published Articles: 32

Full Length Research Paper

Applying AquaCrop-OSPy to real-time irrigation

Peter G. Raeth
  • Peter G. Raeth
  • Creative Solutions, Beavercreek, Ohio, USA.
  • Google Scholar


  •  Received: 01 March 2023
  •  Accepted: 07 July 2023
  •  Published: 31 July 2023

References

Abdulhameed I (2016). How to know exactly the volume of water needed for example 7 mm irrigation rate. Department of Irrigation and Drainage Engineering, University of Anbar, Iraq, ResearchGate question response,

View

 

Abioye EA, Hensel O, Esau TJ, Elija O, Abidin MSZ, Ayobami AS, Yerima O, Nasirahmadi A (2022). Precision irrigation management using machine learning and digital farming solutions. AgriEngineering 4(1):70-103, 
Crossref

 

Abolafie-Rosenzweig R, Livneh B, Small EE, Kumar SV (2019). Soil moisture data assimilation to estimate irrigation water use. Journal of Advances in Modeling Earth Systems 11(11). 
Crossref

 

Allen RG, Pereira LS, Raes D, Smith M (1998). FAO Irrigation and Drainage Paper No. 56 - Crop Evapotranspiration. United Nations Report, 

 

Awawda J, Ishaq I (2023). IOT smart irrigation system for precision agriculture. Proceedings: Intelligent Sustainable Systems, pp. 335-346. 
Crossref

 

Brahmanand PS, Singh AK (2022). Precision irrigation water management - Current status, scope and challenges. Indian Journal of Fertilizers 18(4):372-380.

View

 

Caldwell TG, Cosh MH, Evett S, Edwards N, Hofman H, Illston BG, Meyers T, Skumanich M, Sutcliffe K (2022). In situ soil moisture sensors in undisturbed soils. Journal of Visualized Experiments. 
Crossref

 

Ccama IB, Santoro BF, Semino JO (2022). Model predictive control for precision irrigation of a quinoa crop. Open Chemistry, review article.

View

 

Chen P, Fedosejevs G, Tiscareno-Lopez M, Arnold JG (2006). Assessment of modis-evi, modis-ndvi and vegetation-ndvi composite data using agricultural measurements. Environmental Monitoring and Assessment 119:69-82. 
Crossref

 

Chowdhury S, Sen S, Janardhanan S (2022). Comparative analysis and calibration of low cost resistive and capacitive soil moisture sensor. Internal Report, Department of Electrical Engineering, Indian Institute of Technology, Delhi.

View

 

Darji P, Desai N, Bhavsar D, Pandya H (2023). Applications of remote sensing in agriculture. International Association of Biologicals and Computational Digest 2(1):108-117, 
Crossref

 

Delgoda D, Hector M, Syed KS, Malka NH (2015). Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AquaCrop model. Elsevier Press, Journal of Environmental Modelling and Software 78:40-53, 
Crossref

 

Delgoda D, Syed KS, Malano H, Halgamugeb MN (2016). Root zone soil moisture prediction models based on system identification: Formulation of the theory and validation using field and AquaCrop data. Elsevier Press. Journal of Agricultural Water Management, 163:344-353. 
Crossref

 

Food and Agriculture Organization of the United Nations (FAO) (2023). AquaCrop downloads and documentation.

View

 

Foster T, Brozovic N, Butler AP, Neale CMU, Raes D, Steduto P, Fereres, E, Hsiao TC (2016). AquaCrop-OS: An open source version of FAO's crop water productivity model. Elsevier Press, Journal of Agricultural Water Management 181:18-22. 
Crossref

 

Gundim A, Melo V, Coelho R, Silva J, Rocha M, Franca A, Conceicao A. (2023). Precision irrigation trends and perspectives. Rural Engineering 53(8). 
Crossref

 

Gurmiere SJ, Camporese M, Botto A, Lanfond JA, Paniconi C, Gallichand J, Rousseau AN (2020). Machine learning vs. physics-based modeling for real-time irrigation management. Frontiers in Water, Section on Water and Hydrocomplexity, 2. 
Crossref

 

Kassing R, Schutter B, Abraham E (2020). Optimal control for precision irrigation of a large-scale plantation. Wiley Agricultural Publications, Journal of Water Resources Research 56(10), 
Crossref

 

Kelly TD, Foster T (2021) AquaCrop-OSPy: Bridging the gap between research and practice in crop-water modeling. Elsevier Press, Journal of Agricultural Water Management, p. 254. 
Crossref

 

Kelly TD (2022). Assessing the Value of Improved Information and Management Strategies for Optimal Irrigation Scheduling. Dissertation, Department of Mechanical, Aerospace, and Civil Engineering, University of Manchester.

View

 

Katz L, Ben-Gal A, Litaor MI, Naor A, Peres M, Avira P, Alchanatis V, Cohen Y (2022). A spatiotemporal decision support protocol based on thermal imagery for variable rate drip irrigation of a peach orchard. Irrigation Science 41(21):1-19, 
Crossref

 

Katz L, Ben-Gal A, Litaor MI, Naor A, Peeters A, Goldshtein E, Ligor G, Keisar O, Rosenfeld SM, Alchanatis V, Cohen Y (2023). How sensitive is thermal image-based orchard water status estimation to canopy extraction quality. Remote Sensing, v. 15, 
Crossref

 

Liang Z, Liu X, Xiong J, Xiao J (2020) Water allocation and integrative management of precision irrigation. Water 12(11), 
Crossref

 

Luong TT, Vorobevskii I, Kronenberg R, Jacob F, Peters A, Petzoid R, Andreae H (2023). Toward reliable model-based soil moisture estimates for forest managers. Meteorologische Zeitschrift.

View

 

Lyu J, Jiang Y, Xu C, Liu Y, Su Z, Liu J, He J (2022). Multi-objective winter wheat irrigation strategies optimization based on coupling AquaCrop-OSPy and NSGA-III: A case study in Yangling, China. Elsevier Press, Science of the Total Environment 843(4). 
Crossref

 

Martin DL, Gilley JR (1993). Irrigation Water Requirements. National Engineering Handbook, Part 623, Section 15, Irrigation.

View

 

Mather PM. (1999) Computer Processing of Remotely-Sensed Images, 2nd Edition. West Sussex, England: John Wiley & Sons, p. 120,

View

 

Netafim (2022) Corn production using subsurface drip irrigation. System Manual,

View

 

Norizan MS, Wayayok A, Abdullah AF, Mahadi MR, Karim YA (2021). Spatial variations in water-holding capacity as evidence of the need for precision irrigation. Water 13(16). 
Crossref

 

Plascak I, Jurisic M, Radocaj D, Vujic M, Zimmer D. (2021). An overview of precision irrigation systems used in agriculture. Technical Gazette 15(4). 
Crossref

 

Samreen T, Ahmad M. Baig M, Kanwal S, Nazir M, Muntaha S (2022). Remote sensing in precision agriculture for irrigation management. Environmental Sciences 23(1).

View

 

Schwamback D, Persson M, Berndtsson R, Bertotto LE, Kobayashi A, Wendland E (2023). Automated low-cost soil moisture sensors: Trade-off between cost and accuracy. Sensors 23(5). 
Crossref

 

Sharma PK, Kumar D, Srivastava HS, Patel P (2018). Assessment of different methods for soil moisture estimation. Remote Sensing and GIS 9(1):57-73.

View

 

Steven MD (1998). The sensitivity of the OSAVI vegetation index to observational parameters. Remote Sensing of Environment 63:49-60. 
Crossref

 

Thorp K, Calleja S, Pauli W, Thompson A, Elshikha D (2022). Agronomic outcomes of precision irrigation management technologies with varying complexity. Transactions of the American Society of Agricultural and Biological Engineers 65(1):135-150. 
Crossref

 

Zhang J, Guan K, Peng B, Jiang C, Zhou W, Yang Y, Pan M, Franz TE, Heeren DM, Rudnick DR, Abimbola O, Kimm H, Caylor K, Good S, Khanna M, Gates J, Cai Y (2021). Challenges and opportunities in precision irrigation decision-support systems for center pivots. Environmental Research Letters 16(5). 
Crossref

 

Zhang T, Su J, Liu C, Chen WH (2019) Integration of calibration and forcing methods for predicting timely crop states by using aquacrop-os model. 2nd UK-RAS Robotics and Autonomous Systems Conference, Loughborough, UK. 
Crossref