Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

Niguss Solomon Hailegnaw; Haimanote K Bayabil; Mulatu Liyew Berihun; Fitsum Tilahun Teshome; Vakhtang Shelia; Fikadu Getachew

doi:10.1016/j.scitotenv.2023.169403

Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

Sci Total Environ. 2024 Feb 20:912:169403. doi: 10.1016/j.scitotenv.2023.169403. Epub 2023 Dec 16.

Authors

Niguss Solomon Hailegnaw¹, Haimanote K Bayabil², Mulatu Liyew Berihun³, Fitsum Tilahun Teshome¹, Vakhtang Shelia⁴, Fikadu Getachew⁵

Affiliations

¹ Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA.
² Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA. Electronic address: hbayabil@ufl.edu.
³ 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.
⁴ Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA.
⁵ 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.

PMID: 38110092
DOI: 10.1016/j.scitotenv.2023.169403

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.

Keywords: Agriculture; Florida; Irrigation; Sweet corn; Vegetable; Water resources.