ORIGINAL PAPER
Optimizing wheat yield and water use efficiency using AquaCrop model calibration and validation in various irrigation and tillage systems under climate change
1
College of Engineering, Al-Qasim Green University, Iraq
2
College of Agriculture, Al-Qasim Green University, Iraq
3
College of Environmental Sciences, Al-Qasim Green University, Iraq
4
Medical Laboratory Techniques Department , College of Health and Medical Techniques,, Al-Mustaqbal University, Iraq
5
Department of Environmental Sciences,, COMSATS University Islamabad, Pakistan
6
Faculty of Agronomy and Veterinary Sciences, Environment and Natural Resources department, Lebanese University, Lebanon
7
College of Agricultural Engineering Sciences, Baghdad University, Iraq
8
College of Agriculture, University of Wasit, Wasit, Iraq
These authors had equal contribution to this work
Submission date: 2024-07-22
Final revision date: 2024-08-24
Acceptance date: 2024-11-11
Online publication date: 2024-11-11
Publication date: 2024-11-11
Soil Sci. Ann., 2024, 75(3)195823
KEYWORDS
ABSTRACT
Modeling achievable yield under water-limiting environments is an imperative goal in arid, semi-arid, and drought-prone locations. This study aimed to calibrate the AquaCrop model simulation based on experience gained at al-Hashimiya District, 20 km east of Babylon, Iraq. Wheat (Triticum aestivum) in 2016–2017 and 2017–2018 was grown using deficit irrigation systems (surface and sprinkler irrigation) and different tillage methods (deep, moderate, and zero tillage). A simulation was conducted on the systems, using climate data for a period of 10 years and specific data on the study area, crops grown, and irrigation types. Then, statistical analysis was conducted with the root-mean-square error (RMSE), which refers to compatibility between the results and predictions of the measured values. For 2016–2017 and 2017–2018, RMSE values were 0.764 and 0.643 for biomass, 0.473 and 0.419 for dry matter, 0.141 and 0.154 for water productivity, and 1.59 and 0.946 for harvest index. The R2 values were 0.917 and 0.956 for dry matter, 0.982 and 0.946 for biomass, 0.869 and 0.849 for harvest index, and 0.923 and 0.943 for water productivity from 2016–2017 and 2017–2018. Mean bias error (MBE) and mean absolute error (MAE) were also derived with comparable results. The model results indicated that there is an explicit agreement with the simulated values of the wheat crop. The AquaCrop model can be used to estimate water productivity, dry matter, and biomass to improve agricultural water management and as an input into future research of alternate forms of irrigation.
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