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Evaluation of ORYZA2000 for Simulating Rice Growth of Different Genotypes at Two Latitudes

文献类型： 外文期刊

作者： Cao, Bingshuai 1 ; Hua, Shan 1 ; Ma, Yuntao 1 ; Li, Baoguo 1 ; Sun, Chuanqing 3 ;

作者机构： 1.China Agr Univ, Coll Resources & Environm, Key Lab Plant Soil Interact, Yuan Ming Yuan West Rd 2, Beijing 100193, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Digital Agr, Agr Model & IOT Lab, Sheng Middle Rd 298, Hangzhou 310021, Zhejiang, Peoples R China

3.China Agr Univ, Natl Ctr Evaluat Agr Wild Plants Rice, Dep Plant Genet & Breeding, Yuan Ming Yuan West Rd 2, Beijing 100193, Peoples R China

期刊名称：AGRONOMY JOURNAL （ 影响因子：2.24； 五年影响因子：2.829 ）

ISSN： 0002-1962

年卷期： 2017 年 109 卷 6 期

页码：

收录情况： SCI

摘要： Light distribution and light use efficiency in a cereal canopy are affected by the plant architecture of different genotypes as well as solar altitude at contrasting latitudes. This study was conducted to determine whether the spatiotemporal distribution of solar radiation in a rice (Oryza sativa L.) canopy could be accurately quantified using the ORYZA2000 model. We calibrated and evaluated ORYZA2000 for three rice varieties with contrasting aerial architecture (in terms of, e.g., growth habit, plant heights, and number of tillers), using data from two field experiments performed in China at two markedly different latitudes: in Beijing (ca. 40 degrees N) and Sanya (ca. 18 degrees N, approximately 2500 km South of Beijing). A trial-and-error approach was applied to calibrate the extinction coefficient for leaves (KDF), which reflects the degree of mutual shading by leaves in a canopy, and the results revealed that the simulated biomass and leaf area index (LAI) simulated by ORYZA2000 were sensitive to the aerial architecture and latitudinal variations in KDF. Comparison of simulated and measured organ biomass and LAI indicated that the predictive performance of ORYZA2000 was sufficiently accurate for simulating the biomass of the genotypes included in this study, but simulated LAI was generally underestimated. Our findings provide essential information for future research as well as model improvements that can be implemented in modeling studies.

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