Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza

Radanielson, Ando Mariot; Gaydon, Donald S.; Li, Tao; Angeles, Olivyn; Roth, Christian

doi:10.1016/j.eja.2018.01.015

Cited by 75 publications

(49 citation statements)

References 70 publications

(101 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At least 6% of the world's land is affected by salinity [3], and the yield of many rice varieties can be reduced by up to 50% in response to 50 mM NaCl. Hence, efforts to increase the salt tolerance of rice would aid in increasing the use of marginal saline-alkaline land and improving crop production [4,5]. Rice is considered as most susceptible to salt stress in the seedling, reproductive, and 2-3 leaf stages [6].…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation a Major QTL from “Sea Rice 86” Seedlings Conferred Salt Tolerance

Yang

et al. 2020

Agronomy

View full text Add to dashboard Cite

Saline stress severely affects rice (Oryza sativa L.) growth and development and reduces crop yield. Therefore, developing salt-tolerant and high-yielding rice using quantitative trait loci (QTLs) and linkage markers is a priority for molecular breeding. Here, the indica rice Sea Rice 86 (SR86) seedlings showed higher tolerance than ordinary rice varieties in saline soil, and a dominant effect on salinity sensitivity was demonstrated by genetic analysis. We constructed bulked segregant analysis pools using F 2 populations from parents Dianjingyou 1 as the recipient and SR86 as the donor. We identified a 2.78 Mb region on chromosome 1 as the candidate region. Using simple sequence repeat markers and substitution analysis, we mapped the target region within 5.49 cM in the vicinity of markers RM8904-RM493. We speculated that this QTL, named qST1.1, might contribute significantly to the salt tolerance of SR86. The high salt tolerance of introgression lines obtained by marker assistant selection (MAS) confirmed that the qST1.1 region was associated with salinity tolerance. This newly-discovered QTL will be helpful for the analysis of the salt-tolerant mechanism of rice and breeding high-quality rice varieties using MAS.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification and Validation a Major QTL from “Sea Rice 86” Seedlings Conferred Salt Tolerance

Yang

et al. 2020

Agronomy

View full text Add to dashboard Cite

show abstract

“…APSIM-Oryza is a model for rice growth simulation, and it has been increasingly used in related studies because of the widely-accepted APSIM (Agricultural Production Systems sIMulator) platform (Amarasingha et al, 2015;Gaydon et al, 2012;Gaydon et al, 2017;Holzworth et al, 2014;Radanielson et al, 2018;Zhang et al, 2007). The crop growth process of APSIM-Oryza was borrowed from the Oryza2000 model (https://sites.google.com/a/irri.org/oryza2000/, Bouman et al, 2001;Bouman and Van Laar, 2006;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions

Liu

Zhu

et al. 2019

Science of The Total Environment

View full text Add to dashboard Cite

This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. Two output variables (i.e. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. T 30% and s of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g.RGRLMX, WGRMX and SPGF). In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Differences also exist for parameter sensitivity of early and late rice in the same site. The CO2 concentration did not have much influence on the results of sensitivity analysis. The range of parameter variation affected the stability of sensitivity analysis results, but the main s study. Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters.

show abstract

“…Relationships between model parameters and quantitative trait loci (Yin et al, 1999) or single nucleotide polymorphisms (Cooper et al, 2016) were instead used to predict the performance of genotypes starting from genetic information. Given the relevance of salt stress for agricultural production, different models for crop response to salt stress were developed (Ferrer-Alegre and Stockle, 1999;Karlberg et al, 2006, Radanielson et al, 2018. However, they mainly focus on the effect of the osmotic stress on water uptake, without explicitly considering the ionic effect of Na + , which instead is a key component of salt stress (Munns and Tester 2008;Faiyue et al, 2012), especially in rice (Negrão et al, 2011).…”

mentioning

confidence: 99%

Analysis and Modeling of Processes Involved with Salt Tolerance and Rice

et al. 2019

View full text Add to dashboard Cite

Salinity is a worldwide problem for rice (Oryza sativa L.) cultivation, and a number of breeding programs targeting increased salt tolerance are ongoing. A new trait‐based mathematical model for salt stress on rice was recently proposed, characterized by a high level of detail in the description of physiological mechanisms dealing with crop response to salinity. In this study, dedicated growth chamber experiments were performed where three rice cultivars with different degrees of tolerance were grown under different salinity levels. The aim was to improve the understanding of physiological mechanisms like Na+ uptake and sequestration in structural tissues, and to validate the model using new datasets where temporal dynamics in plant response to salt stress were analyzed. Model evaluation demonstrated strong agreement between measured and simulated dry weights of plant organs (e.g., R2 = 0.88–0.97 for aboveground biomass), [Na+] in plant tissues (R2 = 0.73–0.88), and green leaf area index (R2 = 0.71–0.99). These results demonstrate the reliability of the model and support its adoption within studies aimed at analyzing or predicting the response of different cultivars to temporal dynamics of Na+ concentration in soil and water.

show abstract

Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza

Cited by 75 publications

References 70 publications

Identification and Validation a Major QTL from “Sea Rice 86” Seedlings Conferred Salt Tolerance

Identification and Validation a Major QTL from “Sea Rice 86” Seedlings Conferred Salt Tolerance

Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions

Analysis and Modeling of Processes Involved with Salt Tolerance and Rice

Contact Info

Product

Resources

About