Optimizing Irrigation Water Use Efficiency for Tomato and Maize Fields across Italy Combining Remote Sensing Data and the AquaCrop Model

Corbari, Chiara; Charfi, Imen Ben; Mancini, Marco

doi:10.3390/hydrology8010039

Cited by 18 publications

(9 citation statements)

References 57 publications

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“…Rinaldi et al [22] also found weaker relative RMSE results for dry yields compared to total dry matter. The modelled fresh yield of tomato was comparable to the observed one with an error of 9% in the calibration process [37]. We found lower errors in final yields, from 1.2% to 5.5%, depending on the water supply.…”

Section: Simulation Of Biomass and Yield In The Growing Seasonsupporting

confidence: 77%

Performance Evaluation of AquaCrop Model in Processing Tomato Biomass, Fruit Yield and Water Stress Indicator Modelling

Takács

Csengeri

Pék

et al. 2021

Water

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A three-year long experiment was conducted on open-field tomato with different levels of water shortage stress. Three different water supply levels were set in 2017 and four levels for 2018 and 2019. Biomass and yield data were collected, along with leaf-temperature-based stress measurements on plants. These were used for calibration and validation of the AquaCrop model. The validation gave various results of biomass and yield simulation during the growing season. The largest errors in the prediction occurred in the middle of the growing seasons, but the simulation became more accurate at harvest in general. The prediction of final biomass and yields were good according to the model evaluation indicators. The relative root mean square error (nRMSE) was 12.1 and 13.6% for biomass and yield prediction, respectively. The modeling efficiency (EF) was 0.96 (biomass) and 0.99 (yield), and Willmott’s index of agreement (d) was 0.99 for both predicted parameters at harvest. The lowest nRMSE (4.17) was found in the simulation of final yields of 2018 (the calibration year). The best accuracy of the validation year was reached under mild stress treatment. No high correlation was found between the simulated and measured stress indicators. However, increasing and decreasing trends could be followed especially in the severely stressed treatments.

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Section: Simulation Of Biomass and Yield In The Growing Seasonsupporting

confidence: 77%

Performance Evaluation of AquaCrop Model in Processing Tomato Biomass, Fruit Yield and Water Stress Indicator Modelling

Takács

Csengeri

Pék

et al. 2021

Water

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“…This shows that applying stage deficit irrigation improves yield and water productivity compared to FI [4]. This result is also confirmed by [22,24,47,48,49,50]; the best WP was observed at the lowest irrigation depth.…”

Section: Statically Validation Performance Of Aquacropsupporting

confidence: 57%

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

Yersaw,

Ebstu,

Areru

et al. 2024

Advances in Agriculture

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Crop modeling is a powerful tool for predicting yield and water productivity. The aim of the study was to calibrate and validate the AquaCrop model for tomato under staged deficit drip irrigation in Ethiopia. The AquaCrop model was calibrated and validated by using the observed data of canopy cover, biomass, dry yield, and soil water content. The results showed that the model was accurate in predicting canopy cover, biomass, and dry yield under different water levels. The overall performance in simulating canopy cover of AquaCrop, biomass, and soil water content showed a good match between measured and simulated data. The calibration results indicated good performance on canopy cover with 0.96 ≤ r ≤ 1.00, 2.9% ≤ RMSE ≤ 8.0%, 7.5% ≤ CV (RMSE) ≤ 21.1%, 0.91 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. On biomass, the model calibration values were 0.99 ≤ r ≤ 1.00, 0.3 t/ha ≤ RMSE ≤ 0.8 t/ha, 8.3% ≤ CV (RMSE) ≤ 25.2%, 0.92 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. Soil water content displayed poor performance on calibration, with performance values of 0.54 ≤ r ≤ 0.82, 3.6 mm ≤ RMSE ≤ 24.4 mm, 1.30% ≤ CV (RMSE) ≤ 9.00%, −4.18 ≤ EF ≤ 0.00, and 0.66 ≤ d ≤ 0.81. The validation results demonstrated that the model performed well on canopy cover with 0.96 ≤ r ≤ 1.00, 3.1% ≤ RMSE ≤ 7.7%, 8.2% ≤ CV (RMSE) ≤ 20.8%, EF ≥ 0.91, and d ≥ 0.9. The model validation correctly predicted biomass with r ≥ 0.98, 0.3 t/ha ≤ RMSE ≤ 0.7 t/ha, 8.9% ≤ CV (RMSE) ≤ 21.8%, EF ≥ 0.94, and d ≥ 0.98. The model validation poorly performed in forecasting soil water content, 0.20 ≤ r ≤ 0.80, 10.2 t/ha ≤ RMSE ≤ 24.0 t/ha, 3.5% ≤ CV (RMSE) ≤ 9.0%, −22.76 ≤ EF ≤ 0.63, and 0.40 ≤ d ≤ 0.87. The AquaCrop model is easy to use, requires less input data, and has high simulation accuracy, making it a useful tool for predicting crop yields and water productivity under staged deficit irrigation in areas with limited data.

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“…Additionally, studies have shown that the model accurately simulates the yields and water requirements of various crops grown worldwide [15][16][17][18][19]. AquaCrop can also be used effectively to predict crop water requirements by assimilating the canopy cover estimated from Sentinel-2 imagery [20][21][22][23], and to evaluate the effects of optimized irrigation management on the minimization of percolation losses and maximization of crop yield for different soil types [24].…”

Section: Introductionmentioning

confidence: 99%

AquaCrop Model Performance in Yield, Biomass, and Water Requirement Simulations of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

et al. 2023

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The application of crop growth simulation and water management models will become increasingly important in the future. They can be used to predict yield reductions due to water scarcity and allocate water to ensure profitable crop production. The objective of this research was to calibrate the AquaCrop model for common bean (Faseolus vulgaris L.) grown in temperate climates and to test whether the model can be used for different irrigation strategies to achieve high yield productivity. The model was calibrated using data obtained from two years of experimental research in the Serbian territory of the Syrmia region. There were three sowing periods/plots: I—mid April, II—end of May/beginning of June, and III—third decade of June/beginning of July; and three levels of irrigation/subplots: full irrigation (F) providing 100% of crop evapotranspiration (ETc), mild deficit irrigation (R) at 80% of ETc, and moderate deficit irrigation (S) at 60% of ETc. The results show that the AquaCrop model accurately predicts common bean yield, biomass, canopy cover, and water requirements. The statistical indices of the calibrated dataset, coefficient of determination (R2), normalized root mean square error (NRMSE), mean bias error (MBE), and Willmott agreement index (d) for yield and biomass were: 0.91, 0.99; 6.9%, 11.4%; −0.046, 1.186 and 0.9, 0.89, respectively. When testing three irrigation strategies, the model accurately predicted irrigation requirements for the full and two deficit irrigation strategies, with only 29 mm, 32 mm, and 34 mm more water than was applied for the Fs, Rs, and Ss irrigation strategy, respectively. The AquaCrop model performed well in predicting irrigated yield and can be used to estimate the yield of common bean for different sowing periods and irrigation strategies.

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Optimizing Irrigation Water Use Efficiency for Tomato and Maize Fields across Italy Combining Remote Sensing Data and the AquaCrop Model

Cited by 18 publications

References 57 publications

Performance Evaluation of AquaCrop Model in Processing Tomato Biomass, Fruit Yield and Water Stress Indicator Modelling

Performance Evaluation of AquaCrop Model in Processing Tomato Biomass, Fruit Yield and Water Stress Indicator Modelling

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

AquaCrop Model Performance in Yield, Biomass, and Water Requirement Simulations of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

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