Case Studies
Dec 6, 2021

Performance of WOFOST Model for Simulating Maize Growth, Leaf Area Index, Biomass, Grain Yield, Yield Gap, and Soil Water under Irrigation and Rainfed Conditions

Publication: Journal of Irrigation and Drainage Engineering
Volume 148, Issue 2

Abstract

The World Food Studies (WOFOST) model’s performance in simulating different field maize (Zea mays L.) growth and productivity variables was evaluated using 6 years of field experimental data that were measured from the 2005–2010 maize growing seasons in south central Nebraska. Irrigation levels were rainfed (no irrigation), limited irrigation [50% full irrigation (FIT), 60% FIT, and 75% FIT] and full irrigation conditions. Different maize growth and developmental periods and the entire growing seasons were evaluated by comparing simulated leaf area index (LAI), aboveground biomass, grain yield, and soil water content (SWC). When the data for all growing seasons were pooled, the values of RMS error (RMSE) and normalized RMSE (NRMSE) between simulated and observed days to flowering were 3.7 and 4 days, respectively, which were considered very accurate. When all growing seasons’ data were combined, the values of RMSE and NRMSE between simulated and observed days to maturity were 7.5 and 5 days, respectively. There was acceptable agreement between predicted and observed LAI (RMSEn=13%24%, and R2=0.800.95) and aboveground biomass (RMSEn=9%25%, and R2=0.910.99). During the validation, there was no significant difference between the simulated and observed LAI values (P>0.05). The model simulated the rainfed and irrigated aboveground biomass reasonably well. There were no significant differences between model-estimated and measured grain yield. The Pe of grain yields across the 2005–2010 growing seasons ranged from 18% to 31%. The RMSE between the observed and simulated grain yield ranged between 1.02 and 2.05 ton ha1 and NRMSE ranged from 7% to 17%. The largest difference between yield potential and measured grain yield (yield gap) was observed in the rainfed treatment (9.97 ton ha1) and the least gap was in the FIT (3.75 ton ha1). The model’s performance in simulating SWC was considered to be poor to moderate, with a wide range R2 values between the treatments, from 0.10 to 0.82. The mean difference between observed and simulated SWC ranged from 0.2 to 0.4  m3  m3. Although overall, the WOFOST model’s performance was considered to be good for some of the variables (i.e., plant phenology, LAI, and grain yield), its performance in simulating other variables (i.e., SWC) was marginal under these experimental conditions. Its performance declined substantially under water-limiting and rainfed conditions. Further research that identifies potential reasons for the poor performance and determining potential solutions to improve the model’s prediction accuracy is needed.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This study is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, i.e., Suat Irmak’s Hatch Project, under Project No. NEB-21-155.

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Go to Journal of Irrigation and Drainage Engineering
Journal of Irrigation and Drainage Engineering
Volume 148Issue 2February 2022

History

Received: Jan 31, 2021
Accepted: Sep 20, 2021
Published online: Dec 6, 2021
Published in print: Feb 1, 2022
Discussion open until: May 6, 2022

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Authors

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Professor, Dept. of Water Engineering, Lahijan Branch, Islamic Azad Univ., Lahijan 4416939515, Iran (corresponding author). ORCID: https://orcid.org/0000-0002-5057-6759. Email: [email protected]
Suat Irmak, M.ASCE
Professor and Department Head, Dept. Agricultural and Biological Engineering, The Pennsylvania State (Penn State) Univ., University Park, PA 16802.
Hadis Yaghouti
Graduate Research Assistant, Dept. of Soil, Science and Research Branch, Islamic Azad Univ., Tehran 1477893955, Iran.

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  • Growth Indexes and Yield Prediction of Summer Maize in China Based on Supervised Machine Learning Method, Agronomy, 10.3390/agronomy13010132, 13, 1, (132), (2022).

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