ORIGINAL RESEARCH
The Assessment of AquaCrop Model in Predicting Rice Genotypes Grain and Biological Yield under Water Management Conditions
Sh Roushani 1  
,   M. H. Rashed Mohassel 2  
,   Reza Sadrabadi Haghighi 1  
,   E. Amiri 3  
 
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1
Department of Agricultural Science, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2
Department of Agronomy, Faculty of Agriculture, Ferdowsi University of Mashhad Iran, Mashhad, Iran
3
Department of Water Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
CORRESPONDING AUTHOR
Reza Sadrabadi Haghighi   

Agricultural Science Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran, Iran
Submission date: 2020-02-23
Final revision date: 2020-08-02
Acceptance date: 2020-08-02
Online publication date: 2021-01-29
Publication date: 2021-03-08
 
Pol. J. Environ. Stud. 2021;30(3):2283–2291
 
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ABSTRACT
The present study was conducted to assess the ability of AquaCrop model in predicting genotypes of rice genotypes grain and biological yield in water management on Rice Research Institute of Iran, for two consecutive years of 2017 and 2018. The type of experiment was split plot based on randomized complete block design with three replications per treatment. The main plot included of irrigation treatment at four levels, (flood irrigation, 5, 8 and 11 days intervals) and sub plot, included of Ali kazemi, Dorfak and Bahar rice genotypes. Evaluation simulated and measured grain yield and biological yield by adjusted coefficient of correlation and by absolute and normalized root mean square errors (RMSEn). The results indicated, the RMSEn for predicting the amounts of grain yield in validation and calibration phases for Ali kazemi, Dorfak and Bahar was assessed in the range of 6 to 8 and 8 to 9 percent respectively and the RMSEn for predicting amounts of biological yield in validation and calibration for rice genotypes was assessed in the range of 3 to 13 and 7 to 15 % respectively. The results showed that the AquaCrop model had acceptable accuracy in predicting grain yield and biological yield of the crop.
eISSN:2083-5906
ISSN:1230-1485