Usefulness of cultivar-level calibration of AquaCrop for vegetables depends on the crop and data availability

Frontiers in Plant Science

Plant Science

Sabien Pollet¹  Tim De Cuypere¹  Kris Verheyen²  Pieter De Frenne²  Peter Lootens³  Tom De Swaef³  Willem Coudron⁴  Anne Gobin⁵  Charlotte Boeckaert⁶ 

[1] Department of Outdoor Horticulture And Precision Agriculture, Inagro, Rumbeke-Beitem, Belgium;Forest & Nature Lab, Department of Environment, Ghent University, Gontrode, Belgium;Plant Science Unit, Research Institute for Agriculture, Fisheries and Food (ILVO), Melle, Belgium;Plant Science Unit, Research Institute for Agriculture, Fisheries and Food (ILVO), Melle, Belgium;Forest & Nature Lab, Department of Environment, Ghent University, Gontrode, Belgium;Remote Sensing, Flemish Institute for Technological Research (VITO), Mol, Belgium;Department of Earth and Environmental Sciences, Faculty of Bioscience Engineering, KU Leuven, Leuven, Belgium;Vlaams Kenniscentrum Water (VLAKWA), Flemish Institute for Technological Research (VITO), Kortrijk, Belgium;

关键词: Bayesian calibration;    crop model;    decision support tool;    spinach;    cauliflower;    sensitivity analyis;   

DOI  :  10.3389/fpls.2023.1094677

 received in 2022-11-10, accepted in 2023-02-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

As a result of climate change, climatic extremes are expected to increase. For high-value crops like vegetables, irrigation is a potentially economically viable adaptation measure in western Europe. To optimally schedule irrigation, decision support systems based on crop models like AquaCrop are increasingly used by farmers. High value vegetable crops like cauliflower or spinach are grown in two distinct growth cycles per year and, additionally, have a high turnover rate of new varieties. To successfully deploy the AquaCrop model in a decision support system, it requires a robust calibration. However, it is not known whether parameters can be conserved over both growth periods, nor whether a cultivar dependent model calibration is always required. Furthermore, when data are collected from farmers’ fields, there are constraints in data availability and uncertainty. We collected data from commercial cauliflower and spinach fields in Belgium in 2019, 2020 and 2021 during different growing periods and of different cultivars. With the use of a Bayesian calibration, we confirmed the need for a condition or cultivar specific calibration for cauliflower, while for spinach, splitting the data per cultivar or pooling the data together did not improve uncertainty on the model simulations. However, due to uncertainties arising from field specific soil and weather conditions, or measurement errors from calibration data, real time field specific adjustments are advised to simulations when using AquaCrop as decision support tool. Remotely sensed or in situ ground data may be invaluable information to reduce uncertainty on model simulations.

【 授权许可】

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Copyright © 2023 Coudron, De Frenne, Verheyen, Gobin, Boeckaert, De Cuypere, Lootens, Pollet and De Swaef