Calibration and validation of the FAO AquaCrop water productivity model for cassava (Manihot esculenta Crantz)

Wellens, Joost; Raes, Dirk; Fereres, E.; Diels, Jan; Coppye, C.; Adiele, J.G.; Ezui, K.S.; Becerra, Luis Augusto; Selvaraj, Michael Gomez; Dercon, Gerd; Heng, Lee

doi:10.1016/j.agwat.2022.107491

Cited by 17 publications

(9 citation statements)

References 16 publications

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“…The validation performance value of AquaCrop on CC, BM, and SWC are shown in Table 9 and Figure 13,14,15,16,17,18,19,20,and 21. Based on the report suggested by Dirk et al [12], the obtained performance value on CC were in ranges of very good r ≥ 0.96, good (3.1% ≤ RMSE ≤ 7.7%), good and moderate good (8.2% ≤ CV (RMSE) ≤ 20.8%), very good (EF ≥ 0.91), and very good (d ≥ 0.98). The Aqua-Crop model validation performance on biomass were very Generally, the AquaCrop model performed well on CC and BM at all treatments.…”

Section: Statically Validation Performance 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 Aquacropmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…While most studies calibrated their models for yield only [40][41][42][43], some also calibrated for variables that are important to yield, including total biomass, LAI, and soil moisture. [44][45][46][47]. Since crop yield and many model outputs often depend on multiple model parameters [34], there is a need to calibrate all sensitive parameters, either separately or simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields

Amori,

Abimbola,

Franz

et al. 2024

Water

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Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, we calibrated and validated the Hybrid-Maize model using soil moisture and yield data from eight commercial production fields in two years. We used a new method for the calibration and multi-parameter optimization (MPO) based on kriging with modified criteria for selecting the parameter combinations. The soil moisture-related parameter combination (SM-PC3) improved simulations of soil water dynamics, but improvement in model performance is still required. The grain yield-related parameter combination significantly improved the yield simulation. We concluded that the calibrated model is good enough for irrigation water management at the field scale. Future studies should focus on improving the model performance in simulating total soil water (TSW) dynamics at different soil depths by including more soil water processes in a more dynamic manner.

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“…In arid and semi-arid regions, water resources are already scarce due to both climate change and human activities. The agricultural sector consumes more than 70% of freshwater, which makes it the largest user of water [1]. Seasonal management of irrigation water is the most important decision that farmers have to plan in order to preserve water resources and to improve crop water productivity.…”

Section: Introductionmentioning

confidence: 99%

Using AquaCrop for Irrigation and water productivity assessment of Table grapes in arid region of Mexico

Er-Raki,

Bouras,

Rodriguez

et al. 2024

E3S Web Conf.

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The aim of this work is to use the AquaCrop model for irrigation and water productivity assessment of Table grapes in arid region of Mexico during 2005 and 2006 cropping seasons. The irrigation efficiency was investigated by comparing the irrigation scheduling design used by the farmer to the AquaCrop model recommendations. Data analysis showed that the farmer irrigates almost every day, which results in the water content in the root zone always exceeding the soil moisture at field capacity (FC). This generates substantial losses of water through deep percolation. By using the AquaCrop model, the optimization of irrigation water scheduling in order to avoid both water stress and deep percolation was about 547 mm and 510 mm, which it is about half of what was applied by the farmer (1006 mm and 930 mm) during 2005 and 2006, respectively. This large difference, lost through deep percolation, reduces the water productivity (WP) by about 45%.

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Calibration and validation of the FAO AquaCrop water productivity model for cassava (Manihot esculenta Crantz)

Cited by 17 publications

References 16 publications

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

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

Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields

Using AquaCrop for Irrigation and water productivity assessment of Table grapes in arid region of Mexico

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