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Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios
Author(s) -
Zhang Chao,
Liu Jiangui,
Dong Taifeng,
Shang Jiali,
Tang Min,
Zhao Lili,
Cai Huanjie
Publication year - 2019
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2019.04.0305
Subject(s) - irrigation , mean squared error , yield (engineering) , leaf area index , agronomy , biomass (ecology) , crop simulation model , mathematics , crop yield , growing season , crop , environmental science , field experiment , grain yield , statistics , biology , materials science , metallurgy
Winter wheat ( Triticum aestivum L. ) is one of the major grain crops grown in Guanzhong Plain, China. Rapid and accurate crop growth and yield estimation are important for agricultural monitoring and policy decision‐making. Crop model simulation is an effective way to provide objective growth and yield forecast. In this study, we investigated the applicability of the Simple Algorithm for Yield Estimate (SAFY) model for estimating winter wheat dry shoot biomass ( M S,d ) and grain yield, using two growing seasons field data from different irrigation scenarios. Results showed that the leaf area index (LAI) could be reasonably well simulated, with a minimum root mean square estimate (RMSE) of 0.11. The water stress intensity of each irrigation scenario was well accounted for through stress factor of effective radiation use efficiency. Good accuracy were achieved for M S,d simulation in both calibration (RMSE = 0.054–0.183 kg m −2 ) and validation (RMSE = 0.146 kg m −2 ) datasets, but showed a general overestimation during later growth stages. The grain yield was well estimated with a relative error of 1.9% to 16.7% in 2013 to 2014 and 0.1% to 16.7% in 2014 to 2015. The comparison between estimated and measured yield of all irrigation scenarios were robust in both seasons, with the minimum RMSE and MRE of 35.0 g m −2 and 4.7%, respectively. This work demonstrates the potential of a simple crop model for estimating biomass and yield only by calibrate the LAI without further in situ data, and prefigures assimilation application with remote sensing data in future. Core Ideas The SAFY model was evaluated in different irrigation scenarios. The variation of LAI was highly sensitive to leaf partition parameters. SAFY effectively simulated wheat biomass and yield under various irrigation scenarios. A general overestimation on biomass was noted during later growth stages.