Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments

Yang Lu, Tendai P. Chibarabada, Matthew McCabe, Gabriëlle J.M. De Lannoy, Justin Sheffield

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


The application of crop models towards improved local scale prediction and precision management requires the identification and description of the major factors influencing model performance. Such efforts are particularly important for dryland areas which face rapid population growth and increasing constraints on water supplies. In this study, a global sensitivity analysis on crop yield and transpiration was performed for 49 parameters in the FAO-AquaCrop model (version 6.0) across three dryland farming areas with different climatic conditions. The Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method were used to evaluate the parameter sensitivities of several staple crops (maize, soybean or winter wheat) under dry, normal and wet scenarios. Results suggest that parameter sensitivities vary with the target model output (e.g., yield, transpiration) and the wetness condition. By synthesizing parameter sensitivities under different scenarios, the key parameters affecting model performance under both high and low water stress were identified for the three crops. Overall, factors relevant to root development tended to have large impacts under high water stress, while those controlling maximum canopy cover and senescence were more influential under low water stress. Parameter sensitivities were also shown to be stage-dependent from a day-by-day analysis of canopy cover and biomass simulations. Subsequent comparison with AquaCrop version 5.0 suggests that AquaCrop version 6.0 is less sensitive to uncertainties in soil properties.
Original languageEnglish (US)
Pages (from-to)108182
JournalField Crops Research
StatePublished - May 25 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-11-21
Acknowledged KAUST grant number(s): OSR-2017-CRG6
Acknowledgements: This work was partly funded through the ‘A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model’ project, supported by the King Abdullah University of Science and Technology, grant number OSR-2017-CRG6, and through the ‘Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BRECcIA)’ project, which is supported by UK Research and Innovation as part of the Global Challenges Research Fund, grant number NE/P021093/1. Matthew McCabe was funded by KAUST. G. De Lannoy was funded by EU project SHui GA 773903. The authors thank Dr. Francesca Pianosi from University of Bristol for her help in performing the sensitivity analysis.

ASJC Scopus subject areas

  • Soil Science
  • Agronomy and Crop Science


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