Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China

Hui Ran, Shaozhong Kang, Fusheng Li, Taisheng Du, Ling Tong, Sien Li, Risheng Ding and Xiaotao Zhang

Agricultural Water Management, 2018, vol. 203, issue C, 438-450

Abstract: A crop model is a powerful tool for developing an irrigation schedule and simulating crop yield. In this study, both an AquaCrop model using recommended default parameters and a parameterized AquaCrop model were used to simulate the growth of maize for seed production under plastic film-mulch. The model variables that were parameterized include canopy cover (CC), aboveground biomass, yield (Y) and soil water content (SWC). Data from field experiments, which included 23 irrigation treatments on four varieties of maize for seed production, were collected in an arid region of Northwest China from 2012 to 2015. The results from both the default AquaCrop model and the parameterized model were compared with the field data. The parameterized model performed much better than the default model. Overall it predicted CC well for most irrigation treatments, with determination coefficient (R2) and normalized root mean square error (NRMSE) of 0.818 and 19.3%, respectively. However, the model was rather sensitive to water stress during the vegetative stage and insensitive to water stress during the senescence stage, resulting in underestimation and overestimation of CC during these stages. As for biomass accumulation process, R2 and NRMSE were 0.929 and 19.1% for all treatments, respectively. The parameterized model estimated biomass accurately in the early and middle stages of growth, but generally overestimated biomass at the mature stage, giving a slightly decreased accuracy of final biomass (B) simulation. The parameterized AquaCrop model simulated B and Y values with errors of less than 5% of measured values for 4 and 7 treatments out of 23 treatments, respectively. There were of less than 15% for 12 and 13 treatments out of 23, of less than 30% for 19 and 16 treatments out of 23, and greater than 30% for 4 and 7 treatments out of 23, respectively. The model gave reasonable estimates of SWC with R2 and NRMSE of 0.736 and 15.2%, respectively, but tended to overestimate it for most irrigation treatments. Simulation of the variation of WP* in the growth period, and the differences of HI under different water stress conditions, might be improved in the AquaCrop model.

Keywords: AquaCrop model; Water stress; Water productivity; Yield forecast; Crop growth model (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377418300866
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:203:y:2018:i:c:p:438-450

DOI: 10.1016/j.agwat.2018.01.030

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().