Genetic variation in heading dates and phenological parameters of Myanmar rice

ABSTRACT

Genetic variation in heading dates is essential for developing a rational rice cultivation program, optimizing irrigation practices, and advancing plant breeding in year-round irrigated environments. In this study, we investigated the heading dates of eight rice varieties with varying degrees of photoperiod sensitivity under natural field conditions by conducting year-round periodic sowing in 2019 in Nay Pyi Taw, located in the central region of Myanmar. We elucidated genetic differences in critical day length by observing the longest day length at 30 days before heading, which is likely to be the starting point for floral induction. We analyzed two phenological models: the conventional developmental rate (DVR) model and a modified model considering the critical day length. These analyses aimed to uncover the genetic differences in phenological parameters: temperature sensitivity, photoperiod sensitivity, and earliness among rice varieties. Incorporating the critical day length into the DVR model significantly improved its accuracy in predicting heading dates, particularly for photoperiod-sensitive rice varieties. The parameters derived from the 2019 data proved effective for predicting heading dates in 2018, especially for photoperiod-sensitive varieties. The genetic variation in critical day length and model parameters could be valuable for adapting rice cultivars to different seasons and determining yield and agronomic practices for varietal development programs. These findings contribute to a deeper understanding of rice phenology and the genetic basis for photoperiod sensitivity in Myanmar rice.

GRAPHICAL ABSTRACT

KEYWORDS:

• Photoperiod sensitivity
• Days to heading
• Critical day length
• Developmental rate model
• Oryza sativa

Disclosure statement

No potential conflict of interest was reported by the authors.

Authors’ contributions

AY and YY conceptualized and administered the study. MMH, KTW, and OMS conducted experiments in Myanmar. AO collected and analyzed the climate data. MMH, YY, and HY gathered the data and performed statistical analyses. The R script was developed by YY. Acceleration and R package construction for the GitHub repository were performed by TF. The manuscript was initially written by MMH and YY and reviewed by MMH and YY.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1343943X.2024.2308336

Data availability statement

The data used to support the findings of this study have been included in this article.

Additional information

Funding

This research was supported by the Science and Technology Research Partnership for Sustainable Development (SATREPS) in collaboration with the Japan Science and Technology Agency (JST, JPMJSA1706) and the Japan International Cooperation Agency (JICA).