Performance Evaluation of Aquacrop Model Greenhouse Cherry Tomato with Plastic Film Mulch Under Various Water and Nitrogen Supplies

Cheng, Minghui; Wang, Haidong; Fan, Junliang; Xiang, Youzhen; Liu, Xiaoqiang; Liao, Zhenqi; Abdelghany, Ahmed Elsayed; Zhang, Fucang

doi:10.2139/ssrn.4175988

Cited by 1 publication

(9 citation statements)

References 39 publications

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“…The Aqua-Crop model validation performance on biomass were very Generally, the AquaCrop model performed well on CC and BM at all treatments. These results were agreed with the result conducted on tomato using mulching film system [28], who describes a remarkable match between both the measured and simulated CC as well as on BM on different irrigation treatments. The AquaCrop model was able to simulate CC, BM, and WP ET with acceptable accuracy under all irrigation levels at both calibration and validation seasons, but it was observed that the simulated results for I75V × 100R, I75F × 100R, I75R × 100R, and I50V × 100R were slightly better than for the FI application.…”

Section: Statically Validation Performance Of Aquacropsupporting

confidence: 90%

“…The AquaCrop model accurately simulated the SWC of the tomato, yet it consistently overestimated SWC across all irrigation treatments (EF ≤ 0.00). In a greenhouse experiment on cherry tomato with plastic film mulch [28], Cheng et al [28] also observed an overestimation of SWC. However, this result contradicts with the result of [39] on maize, [23,40] on Canola, who suggests the Aqua-Crop model performed well on SWC.…”

Section: Performance Evaluation Of Aquacropmentioning

confidence: 97%

“…The experimental design for staged wise deficit irrigation: 3 irrigation levels + 4 growth stages × 3 replications + 1 control = 39 plots and the experiment is designed in random complete block design (RCBD), as shown in Figure 2. The tomatoes were transplanted with a row and plant spacing of 40 cm [28]. Each plot has four furrows and 10 plants that are 4 m long and 1.6 m wide (6.4 m 2 ).…”

Section: Treatments Design Andmentioning

confidence: 99%

“…3 Normalized root mean square error CV ðRMSEÞ: [28], lettuce by Sabzian et al [37], and for cucumber by Khafajeh et al [38]. The AquaCrop model accurately simulated the SWC of the tomato, yet it consistently overestimated SWC across all irrigation treatments (EF ≤ 0.00).…”

Section: Performance Evaluation Of Aquacropmentioning

confidence: 99%

“…This practice has been carried out in different climatic and agronomic conditions on the yield and productivity of different crops in different areas, such as potato [15,17,18], maize [19,20], cassava [21], chickpea [22], canola [23], and tomato [24,25,26,27,28]. However, most of these studies have investigated the applicability of the AquaCrop model under reduced water application of independent of growth stages and also the model performance varied with variations of crop types, climate, locations, irrigation seasons [29], the soil texture affects the model performance [30], some reports were found in the literature concerning the calibration of this model for arid climates on tomato crop.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Statically Validation Performance Of Aquacropsupporting

confidence: 90%