PREDICTIVE MODELING OF RICE GERMINATION SENSITIVITY TO SALINITY USING LITERATURE-DERIVED DATA
Main Article Content
Authors
A. Samat
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
A. Yesseneyev
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
M. Bralina
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
D. Talgatbekova
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
A. Kurmanbayeva
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
Zh. Bekshin
Republican Collection of Microorganisms, Astana, 010000, Kazakhstan
Zh. Tekebayeva
Republican Collection of Microorganisms, Astana, 010000, Kazakhstan
A. Tuyakbayeva
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University,
A. Bekturova
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
A. Temirkhanov
Republican Collection of Microorganisms, Astana, 010000, Kazakhstan
Zh. Nurbekova
Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
Abstract
Soil salinity remained a major constraint on crop establishment, and rice (Oryza sativa L.) was recognized as particularly sensitive during germination. The present study aimed to quantify the concentration-dependent response of rice germination to NaCl stress using a literature-derived dataset and regression-based modeling. Published data on rice germination under different NaCl concentrations were compiled from peer-reviewed studies identified through Google Scholar. Germination percentage was used as the primary response variable, and linear regression as well as polynomial regression models of degree 2 and degree 3 were implemented in Python using the scikit-learn library. The compiled dataset showed uneven representation across salinity treatments, with concentrations of 75, 125, and 150 mM being reported more requently than several low and intermediate–high levels. Overall, germination declined with increasing NaCl concentration, although moderate-range variability was observed in the aggregated data. Regression analysis showed that all models captured the general negative trend, while their predictive behavior differed according to the evaluation metric. The second-degree polynomial model produced lower mean absolute error, whereas linear-type models showed stronger performance for error variance-related metrics in the current dataset. The study demonstrated that regression-based approaches were useful for integrating fragmented salinity experiments and for approximating the germination response of rice across tested and intermediate NaCl levels.
Keywords
rice germination,, Oryza sativa L.,, salinity stress,, NaCl,, dose-response modeling,, polynomial regression,, computational biology
Article Details
References
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