Usefulness of cultivar-level calibration of AquaCrop for vegetables depends on the crop and data availability
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P. Lootens | A. Gobin | Anne Gobin | K. Verheyen | C. Boeckaert | P. De Frenne | T. De Swaef | T. De Cuypere | S. Pollet | Willem Coudron
[1] A. Gobin,et al. Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium , 2022, Water.
[2] P. Lootens,et al. Data collection design for calibration of crop models using practical identifiability analysis , 2021, Computers and Electronics in Agriculture.
[3] Qi Jing,et al. The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise , 2021, Environ. Model. Softw..
[4] G. Hoogenboom,et al. Comparison of three calibration methods for modeling rice phenology , 2020 .
[5] R. Jiang,et al. Evaluating the Effect of Groundwater Table on Summer Maize Growth Using the AquaCrop Model , 2019, Environmental Modeling & Assessment.
[6] Roel Van Hoolst,et al. Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data , 2019, Agricultural Water Management.
[7] Taru Palosuo,et al. Towards improved calibration of crop models – Where are we now and where should we go? , 2018 .
[8] Pierre Defourny,et al. Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach , 2017, Comput. Electron. Agric..
[9] James W. Jones,et al. Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice , 2017 .
[10] Daniel Wallach,et al. Estimating uncertainty in crop model predictions: Current situation and future prospects , 2017 .
[11] M. Trnka,et al. Variability in the water footprint of arable crop production across European regions , 2017 .
[12] M. Trnka,et al. Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat , 2016 .
[13] Luke Owen,et al. Exploring the role of smartphone technology for citizen science in agriculture , 2016, Agronomy for Sustainable Development.
[14] D. Lobell,et al. A meta-analysis of crop yield under climate change and adaptation , 2014 .
[15] Dirk Raes,et al. Economic assessment at farm level of the implementation of deficit irrigation for quinoa production in the Southern Bolivian Altiplano. , 2013 .
[16] M. Singh,et al. Performance evaluation of AquaCrop model for maize crop in a semi-arid environment , 2012 .
[17] Daniel Wallach,et al. Crop Model Calibration: A Statistical Perspective , 2011 .
[18] D. Raes,et al. Using AquaCrop to derive deficit irrigation schedules , 2010 .
[19] Sanford Weisberg,et al. An R Companion to Applied Regression , 2010 .
[20] Karline Soetaert,et al. Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME , 2010 .
[21] D. Raes,et al. AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize , 2009 .
[22] D. Raes,et al. AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles , 2009 .
[23] D. Raes,et al. AquaCrop — The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description , 2009 .
[24] Andrea Saltelli,et al. An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..
[25] Max D. Morris,et al. Factorial sampling plans for preliminary computational experiments , 1991 .
[26] Jinya Su,et al. State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter , 2021, Comput. Electron. Agric..
[27] Patrick Willems,et al. Global sensitivity analysis of yield output from the water productivity model , 2014, Environ. Model. Softw..
[28] M. Powell. The BOBYQA algorithm for bound constrained optimization without derivatives , 2009 .
[29] Wyn L. Price,et al. A Controlled Random Search Procedure for Global Optimisation , 1977, Comput. J..