Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes

Todorović, Mladen; Albrizio, Rossella; Životić, Ljubomir; Saab, Marie-Therese Abi; Stöckle, Claudio O.; Steduto, Pasquale

doi:10.2134/agronj2008.0166s

Cited by 209 publications

(124 citation statements)

References 48 publications

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“…In this sense, AquaCrop was been configured and tested in corn [12], and also subject to validation under irrigated and water deficit conditions [13]; parameterized and tested in irrigated and rainfed cotton [14]; compared with other crop models to estimate sunflower growth under different water regimes [15]; and to evaluate the quinoa (Chenopodium quinoa Willd.) yield response to water availability [16].…”

Section: Aquacrop Modelmentioning

confidence: 99%

Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions

Casa¹,

Ovando²,

Bressanini³

et al. 2013

ACS

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AquaCrop model estimates the crop productivity decrease in response to water stress, determining the biomass (B) based on water productivity (WP) and accumulated transpiration (ΣTr); and the yield (Y) is calculated according to B and the harvest index (HI). AquaCrop was evaluated considering different WP values for 2010 late growing season to simulate crop yield of potato (Solanum tuberosum L.) cv. Spunta, in a commercial production field of 9 ha located in the green belt of Cordoba city (31˚30'S, 64˚08'W, 402 m asl), while monitoring in 2009 was used to verify the model. Canopy cover estimation by AquaCrop was adjusted using observed field data obtained from vertical digital photographs acquired at 2.5 m height. WP values of 15.8 and 31.6 (for C 3 and C 4 species, respectively) and two intermediate values 21 and 26.3 g·m −2 were considered to evaluate the model performance. While linear function between observed tuber yields and estimated by AquaCrop had always a correlation coefficient greater than 0.94 (p < 0.001), using WP = 26.3 and WP =31.6 g·m −2 presented overestimation, whereas with 15.8 g·m −2 had an opposite behavior, while WP = 21 g·m −2 was the value that produced the lowest estimation error. In addition, soil moisture from this estimated value of WP was highly correlated with measured water content in different areas of production field. The verification test shows that while the model slightly underestimates canopy cover, biomass was overestimated. After setting the coefficients of canopy cover development, the AquaCrop crop model estimated adequately potato yield for high production values that are less affected by lack of water, but in both years showed a tendency to overestimate the lowest yields, as was observed for other crops. Meanwhile, the dispersion between the observed and estimated yield was higher in the verification test because the sampling this year was more random.

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Section: Aquacrop Modelmentioning

confidence: 99%

Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions

Casa¹,

Ovando²,

Bressanini³

et al. 2013

ACS

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“…This model has successfully simulated crop growth and yield as influenced by varying soil moisture environments for crops like sunflower [4], bambara groundnut [5], and winter wheat [6]. Farahani et al [3] and Geerts et al [7] suggest that this model maintains a good balance between robustness and accuracy, and a noteworthy feature of the model compared to other cereal crop growth models is the simplicity it offers its users; it does not require advanced skill for its calibration or operation and does not require a large number of input parameters [8].…”

Section: Introductionmentioning

confidence: 99%

“…The relatively small number of input data describes the soil-crop-atmosphere environment in which the crop develops, most of which can be derived by simple methods. AquaCrop simulates crop growth and yield based on the water-driven growth model that relies on the conservative behavior of biomass per unit transpiration relationship [4,9]. This fundamental principle contributed to the simplistic structure of the model, having a greater applicability in space and time, as the model features "conservative input parameters" that transcend geographical location and cultivar [9,10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of FAO AquaCrop Model for Simulating Maize Growth and Productivity under Deficit Irrigation in a Tropical Environment

Greaves

Wang

2016

Water

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Abstract:Crop simulation models have a pivotal role to play in evaluating irrigation management strategies for improving agricultural water use. The objective of this study was to test and validate the AquaCrop model for maize under deficit irrigation management. Field observations from three experiments consisting of four treatments were used to evaluate model performance in simulating canopy cover (CC), biomass (B), yield (Y), crop evapotranspiration (ET c ), and water use efficiency (WUE). Statistics for root mean square error, model efficiency (E), and index of agreement for B and CC suggest that the model prediction is good under non-stressed and moderate stress environments. Prediction of final B and Y under these conditions was acceptable, as indicated by the high coefficient of determination and deviations <10%. In severely stressed conditions, low E and deviations >11% for B and 9% for Y indicate a reduction in the model reliability. Simulated ET c and WUE deviation from observed values were within the range of 9.5% to 22.2% and 6.0% to 32.2%, respectively, suggesting that AquaCrop prediction of these variables is fair, becoming unsatisfactory as plant water stress intensifies. AquaCrop can be reliably used for evaluating the effectiveness of proposed irrigation management strategies for maize; however, the limitations should be kept in mind when interpreting the results in severely stressed conditions.

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“…Crop models currently used for simulating sunflower yield in response to various environments are either: (i) generic (a single mode for multiple species): STICS (Brisson et al, 2003), CropSyst (Stöckle et al, 2003;Todorovic et al, 2009;Moriondo et al, 2011), EPIC/EPIC-Phase (Kiniry et al, 1992;Cabelguenne et al, 1999), AquaCrop (Raes et al, 2009;Todorovic et al, 2009), AqYield (Constantin et al, 2015), WOFOST (Todorovic et al, 2009) or (ii) specific to sunflower crop: Oilcrop-Sun (Villalobos et al, 1996), QSUN (APSIMsunflower) (Chapman et al, 1993;Zeng et al, 2016), SUNFLO (Casadebaig et al, 2011).…”

Section: Crop Models For Exploring the Impacts Of Climate Changementioning

confidence: 99%

Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe

Debaeke¹,

Casadebaig²,

Flénet

et al. 2017

OCL

108

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-Climate change is characterized by higher temperatures, elevated atmospheric CO 2 concentrations, extreme climatic hazards, and less water available for agriculture. Sunflower, a springsown crop often cultivated in southern and eastern regions of Europe, could be more vulnerable to the direct effect of heat stress at anthesis and drought during its growing cycle, both factors resulting in severe yield loss, oil content decrease, and fatty acid alterations. Adaptations through breeding (earliness, stress tolerance), crop management (planting dates), and shifting of growing areas could be developed, assessed and combined to partly cope with these negative impacts. New cultivation opportunities could be expected in northern parts of Europe where sunflower is not grown presently and where it could usefully contribute to diversify cereal-based cropping systems. In addition, sunflower crop could participate to the mitigation solution as a low greenhouse gas emitter compared to cereals and oilseed rape. Sunflower crop models should be revised to account for these emerging environmental factors in order to reduce the uncertainties in yield and oil predictions. The future of sunflower in Europe is probably related to its potential adaptation to climate change but also to its competitiveness and attractiveness for food and energy.Keywords: CO 2 / temperature / crop model / biotic stress / water deficit Résumé -Culture du tournesol et changement climatique : vulnérabilité, adaptation et potentiel d'atténuation via des études de cas en Europe. Le changement climatique se caractérise par des températures élevées, de plus fortes concentrations atmosphériques en CO 2 , des risques climatiques extrêmes et moins d'eau disponible pour l'agriculture. Le tournesol, culture semée au printemps dans le sud et l'est de l'Europe, pourrait être plus exposé à l'avenir aux fortes températures et à un déficit hydrique marqué dès la floraison, avec pour conséquences des pertes de rendement, une diminution de la teneur en huile et une altération de la composition en acides gras. Des adaptations sont possibles à court et moyen terme par la sélection (précocité, tolérance aux stress), la conduite de culture (date de semis) et le déplacement des zones de production, permettant de faire face en partie aux impacts négatifs attendus. Ainsi le tournesol pourrait être cultivé plus au nord participant utilement à la nécessaire diversification des bassins céréaliers. En outre, la culture de tournesol étant faiblement émettrice de gaz à effet de serre par rapport aux céréales ou au colza pourrait contribuer davantage à la solution climatique apportée par l'agriculture. Les modèles de culture devraient être revus pour mieux tenir compte de ces facteurs environnementaux émergents si l'on veut réduire les incertitudes dans les prédictions de rendement et de teneur en huile. L'avenir du tournesol en Europe est probablement lié à son potentiel d'adaptation au changement climatique, mais dépendra aussi de sa compétitivité et de son attractivité en t...

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Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes

Cited by 209 publications

References 48 publications

Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions

Aquacrop Model Calibration in Potato and Its Use to Estimate Yield Variability under Field Conditions

Assessment of FAO AquaCrop Model for Simulating Maize Growth and Productivity under Deficit Irrigation in a Tropical Environment

Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe

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