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Small scale method for estimation of genetic coefficients of photoperiod-insensitive rice using generalized likelihood uncertainty estimation
Author(s)
Date Issued
March 1, 2023
Type
Article
Abstract
Importance of the work: Genetic coefficients are important parameters for simulations of rice yield performance in crop growth models. Most genetic coefficients (GCs) are obtained from large experiments. Objectives: To estimate the GCs of seven photoperiod-insensitive rice cultivars for four planting dates in a pot experiment. Materials & Methods: Input data (soil, weather, management, plant parameters) were collected and used to calibrate the GCs of seven rice cultivars using the GLUE estimator in the DSSAT version 4.7 package. The data were collected from four planting dates: 1) 23 Nov 2019; 2) 23 Dec 2019; 3) 23 Jan 2020; and 4) 23 Feb 2020. The data from planting dates 1, 3 and 4 were used for calibration of the GCs, whereas the data from planting date 2 were used for evaluation of the GCs. Results: Good prediction qualities of the model for most cultivars were indicated for days to anthesis and days to physiological maturity; however, there were poor prediction qualities for almost all cultivars for their biomass and grain weight. Main finding: This information should be useful for further investigations of GCs in rice. Although the results were contrary to the initial hypothesis, the method showed promise for further use in rice modeling research if the method can be improved by experimental management, the use of suitable reference plants for each cultivar and running the model for an appropriate cycle. It was possible to obtain some reliable GCs from small-scale experiments, so the experiment should be improved to obtain better results. Further investigations should focus on the optimum scale and weather data specific to experimental sites.
Citation
Agriculture and Natural Resources, 57(2), 211-222, 2023