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Physiological causes of transplantation shock on rice growth inhibition and delayed heading.

ABSTRACT: Transplanting is an important rice cultivation method; however, transplanting shock commonly affects grain yield, and the mechanisms underlying the inhibition of growth, development, and delayed heading caused by transplanting shock have not yet been clearly elucidated. Here, we investigated the effects of seedling age, temperature, and root damage during transplanting on growth, development, and time to heading, both under artificially controlled and natural day length. Additionally, we investigated the impact of seedling root growth space and the potential mitigating effects of residual seed nutrients on young transplanted seedlings. The delay in heading in transplanted versus directly seeded plants was affected more by growth inhibition during the seedling period than by root damage during transplanting. However, root damage had an effect on the inhibition of leaf and tiller development, and the ratio of leaves to tillers increased because tiller development was inhibited more by transplanting shock compared with leaf development. Based on these findings, we propose factors reflecting the delay in growth due to transplanting shock that should be included for more accurate rice phenology modeling and suggest advantageous seeding conditions and transplanting methods for improved rice cultivation and yield in response to climate change.

SUBMITTER: Lee H

PROVIDER: S-EPMC8376942 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F2 segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.

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Physiological causes of transplantation shock on rice growth inhibition and delayed heading.

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