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Hailegnaw NS, Bayabil HK, Berihun ML, Teshome FT, Shelia V, Getachew F. Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management. Sci Total Environ 2024; 912:169403. [PMID: 38110092 DOI: 10.1016/j.scitotenv.2023.169403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023]
Abstract
The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used. Moreover, the effect of ETo model selection on sweet corn irrigation water use was investigated by integrating simulated ETo data from empirical and ML models using the Decision Support System for Agrotechnology Transfer (DSSAT) model at two locations (Citra and Homestead) in Florida. Furthermore, a linear bias correction calibration technique was employed to improve the performance of empirical models. Results were consistent in that the NN and RF models outperformed the empirical models. The empirical models tended to underestimate and overestimate small and high daily ETo values, respectively, with the HS model exhibiting the least accuracy. However, calibrated PT and TU models performed comparably to the ML models. Results also revealed that using an inappropriate ETo model could lead to over-irrigation by up to 54 mm during a single crop season. Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance. In areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed. It is important to highlight that distinct plant species exhibit varying transpiration characteristics and, consequently, have different water requirements. These differences play a pivotal role in shaping the overall impact of ETo models on crop water use.
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Affiliation(s)
- Niguss Solomon Hailegnaw
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA
| | - Haimanote K Bayabil
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA.
| | - Mulatu Liyew Berihun
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA; Faculty of Civil and Water Resources Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia
| | - Fitsum Tilahun Teshome
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA
| | - Vakhtang Shelia
- Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | - Fikadu Getachew
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA; Division of Basin Management and Modeling, St. Johns River Water Management District, Palatka, FL, USA
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Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW, Singh U, White JW, Asseng S, Lizaso JI, Moreno LP, Pavan W, Ogoshi R, Hunt LA, Tsuji GY, Jones JW. The DSSAT crop modeling ecosystem. Advances in crop modelling for a sustainable agriculture 2019. [DOI: 10.19103/as.2019.0061.10] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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