Abstract
Accurate prediction of irrigation requirements ensures that water is applied only when necessary, reducing wastage and conserving this precious resource. This study provides a probabilistic framework for determining the irrigation requirements of crops, referred to as the Irrigation Factor (IF). IF was calculated based on three indicators - soil moisture (SM), leaf area index (LAI), and evapotranspiration (ET). Irrigation requirement is determined based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation are calculated for each indicator by comparing the predicted and actual values in the historical time period, which are then used to calculate the error weights (normalized) of the three indicators for each month to also capture the seasonal variations. Third, we calculate the lower and upper limits by adding the error values (5th and 95th percentiles) to a simulated value. Using these values, we determine the mean, minimum, and maximum levels of irrigation requirement based on the levels’ threshold values. To determine the final levels of irrigation requirement at a daily time scale, we multiply the calculated levels (mean, minimum, and maximum) for each indicator by their respective weights. The outcome derived from the test case indicated that while certain variables exhibited no demand for water, there was a necessity for irrigation in other cases, and vice versa. This holistic approach to irrigation scheduling helps to ensure that plants receive adequate water while minimizing water wastage and promoting sustainability. It is especially valuable for agricultural operations, where optimizing water usage is essential economically and environmentally.
Highlights
Irrigation Factor (IF) was developed – a probabilistic framework to determine the irrigation requirements of crops.
IF was computed by combining three key indicators covering the soil moisture deficit, plant water stress, and atmospheric demand.
Combining multiple indicator variables arguably enhanced the robustness of the framework by overcoming the shortcomings within individual variables.
The probabilistic nature of the IF framework additionally provided crucial information about the mean, minimum, and maximum irrigation requirements, enabling more informed decision-making, particularly in uncertain scenarios.
The easy-to-use IF framework also captures seasonal variations in irrigation requirements, aiding more realistic decision-making.
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Data availability
The datasets and codes used and analyzed during the current study are available from the corresponding author upon reasonable request.
References
Adugna T, Xu W, Fan J (2022) Comparison of Random Forest and Support Vector Machine classifiers for Regional Land Cover Mapping using Coarse Resolution FY-3 C images. Remote Sens 14:574. https://doi.org/10.3390/rs14030574
Alibabaei K, Gaspar PD, Lima TM (2021) Crop yield estimation using deep learning based on Climate Big Data and Irrigation Scheduling. Energies 14:3004. https://doi.org/10.3390/en14113004
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment Part I : Model Development’ basin scale model called SWAT (Soil and Water speed and storage, advanced software debugging policy to meet the needs, and the management to the tank model 34:73–89
Breiman L (2001) Random forests. 5–32. https://doi.org/10.1023/A:1010933404324
Changnon D, Sandstrom M, Schaffer C (2003) Relating changes in agricultural practices to increasing dew points in extreme Chicago heat waves. Clim Res 24:243–254. https://doi.org/10.3354/cr024243
Davis SL, Dukes MD (2010) Irrigation scheduling performance by evapotranspiration-based controllers. Agric Water Manag 98:19–28. https://doi.org/10.1016/j.agwat.2010.07.006
Dong Y (2023) Irrigation scheduling methods: overview and recent advances. Irrig Drain - Recent Adv 1–16. https://doi.org/10.5772/intechopen.107386
Falamarzi Y, Palizdan N, Huang YF, Lee TS (2014) Agric Water Manag 140:26–36. https://doi.org/10.1016/j.agwat.2014.03.014. Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)
Farooque AA, Afzaal H, Abbas F, Bos M, Maqsood J, Wang X, Hussain N (2022) Forecasting daily evapotranspiration using artificial neural networks for sustainable irrigation scheduling. Irrig Sci 40:55–69. https://doi.org/10.1007/s00271-021-00751-1
Geological Survey US (2022) National Water Information System - web interface. Retrieved from https://waterdata.usgs.gov/nwis/ [Data set] 1–2
Girjesh GK, Kumaraswamy AS, Sreedhar S, Dinesh Kumar M, Vageesh TS, Rajashekarappa KS (2011) Heat unit utilization of kharif maize in transitional zone of Karnataka. J Agrometeorol 13:43–45. https://doi.org/10.54386/jam.v13i1.1332
Grömping U (2009) Variable Importance Assessment in Regression: Linear Regression versus Random Forest. Am Stat 63:308–319. https://doi.org/10.1198/tast.2009.08199
Gu Z, Qi Z, Burghate R, Yuan S, Jiao X, Xu J (2020) Irrigation scheduling approaches and applications: a review. J Irrig Drain Eng 146. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001464
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut JN (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049. https://doi.org/10.1002/qj.3803
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huffman RL, Fangmeier DD, Elliot WJ, Workman SR (2013) Irrigation Principles, in: Soil and Water Conservation Engineering Seventh Edition. American Society of Agricultural and Biological Engineers, pp. 351–373. https://doi.org/10.13031/swce.2013.15
Jimenez A-F, Cardenas P-F, Canales A, Jimenez F, Portacio A (2020) A survey on intelligent agents and multi-agents for irrigation scheduling. Comput Electron Agric 176:105474. https://doi.org/10.1016/j.compag.2020.105474
Jones HG (2004) Irrigation scheduling: advantages and pitfalls of plant-based methods. J Exp Bot 55:2427–2436. https://doi.org/10.1093/jxb/erh213
Kratzert F, Nearing G, Addor N, Erickson T, Gauch M, Gilon O, Gudmundsson L, Hassidim A, Klotz D, Nevo S, Shalev G, Matias Y (2023) Sci Data 10:1–11. https://doi.org/10.1038/s41597-023-01975-w. Caravan - A global community dataset for large-sample hydrology
Lees T, Reece S, Kratzert F, Klotz D, Gauch M, De Bruijn J, Kumar Sahu R, Greve P, Slater L, Dadson SJ (2022) Hydrological concept formation inside long short-term memory (LSTM) networks. Hydrol Earth Syst Sci 26:3079–3101. https://doi.org/10.5194/hess-26-3079-2022
Lopes SO, Fontes FACC, Pereira RMS, De Pinho M, Gonçalves AM (2016) Optimal control Applied to an Irrigation Planning Problem. Math Probl Eng 2016. https://doi.org/10.1155/2016/5076879
Ma L, Ascough JC, Ahuja IILR, Shaffer MJ, Hanson JD, Rojas KW, Root zone water quality model sensitivity analysis using monte carlo simulation (2000) Trans ASAE 43:883–895. https://doi.org/10.13031/2013.2984
Manjunathth GPT, & SBCYT, a.S., S (2013) Study on Water requirement of Maize (Zea mays L.) using CROPWAT Model in Northern Transitional Zone of Karnataka. J Environ Sci Comput Sci Eng Technol 2:105–113
Martens B, Miralles DG, Lievens H, Van Der Schalie R, De Jeu RAM, Fernández-Prieto D, Beck HE, Dorigo WA, Verhoest NEC (2017) GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10:1903–1925. https://doi.org/10.5194/gmd-10-1903-2017
Morison JIL, Lawlor DW (1999) Interactions between increasing CO 2 concentration and temperature on plant growth. Plant Cell Environ 22:659–682. https://doi.org/10.1046/j.1365-3040.1999.00443.x
Nagappan M, Gopalakrishnan V, Alagappan M (2020) Prediction of reference evapotranspiration for irrigation scheduling using machine learning. Hydrol Sci J 65:2669–2677. https://doi.org/10.1080/02626667.2020.1830996
Nebraska Department of Agriculture (2021) Agriculture Facts about Nebraska
Padilla-Díaz CM, Rodriguez-Dominguez CM, Hernandez-Santana V, Perez-Martin A, Fernández JE (2016) Scheduling regulated deficit irrigation in a hedgerow olive orchard from leaf turgor pressure related measurements. Agric Water Manag 164:28–37. https://doi.org/10.1016/j.agwat.2015.08.002
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O (2011) Scikit-learn: machine learning in Python. Scikit-learn Mach. Learn Python 12:2825–2830
Pereira LS, Allen RG, Smith M, Raes D (2015) Crop evapotranspiration estimation with FAO56: past and future. Agric Water Manag 147:4–20. https://doi.org/10.1016/j.agwat.2014.07.031
Price CJ (1980) The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation. Water Resour Res 16:787–795
Romero M, Luo Y, Su B, Fuentes S (2018) Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Comput Electron Agric 147:109–117. https://doi.org/10.1016/j.compag.2018.02.013
Roy T, Serrat-Capdevila A, Gupta H, Valdes J (2017) A platform for probabilistic Multimodel and Multiproduct Streamflow forecasting. Water Resour Res 53:376–399. https://doi.org/10.1002/2016WR019752
Siebert S, Burke J, Faures JM, Frenken K, Hoogeveen J, Döll P, Portmann FT (2010) Groundwater use for irrigation – a global inventory. Hydrol Earth Syst Sci 14:1863–1880. https://doi.org/10.5194/hess-14-1863-2010
Sun C, Ren L (2014) Assessing crop yield and crop water productivity and optimizing irrigation scheduling of winter wheat and summer maize in the Haihe plain using SWAT model. Hydrol Process 28:2478–2498. https://doi.org/10.1002/hyp.9759
Sutanudjaja EH, van Beek LPH, de Jong SM, van Geer FC, Bierkens MFP (2014) Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data. Water Resour Res 50:687–705. https://doi.org/10.1002/2013WR013807
Viani F (2016) Experimental validation of a wireless system for the irrigation management in smart farming applications. Microw Opt Technol Lett 58:2186–2189. https://doi.org/10.1002/mop.30000
Wang Y, Zhang, Chengfu, Meng FR, Bourque CPA, Zhang C (2020) Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China). PLoS ONE 15:1–15. https://doi.org/10.1371/journal.pone.0233525
Xiang Z, Yan J, Demir I (2020) A rainfall-runoff model with LSTM-Based sequence-to-sequence learning. Water Resour Res 56:1–17. https://doi.org/10.1029/2019WR025326
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Idea and Conceptualization: Tirthankar Roy, Shivendra Srivastava, and Nishant Kumar. Formal Analysis: Shivendra Srivastava and Nishant Kumar. Writeup: Shivendra Srivastava. Visualization: Shivendra Srivastava. Editing and/or Revision: Shivendra Srivastava, Nishant Kumar, Arindam Malakar, Sruti Das Chowdhury, Chittranjan Ray, and Tirthankar Roy. Supervision: Tirthankar Roy.
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Srivastava, S., Kumar, N., Malakar, A. et al. A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling. Water Resour Manage 38, 1639–1653 (2024). https://doi.org/10.1007/s11269-024-03746-7
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DOI: https://doi.org/10.1007/s11269-024-03746-7