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Forecasting rice latitude adaptation through a daylength-sensing-based environment adaptation simulator


Global climate change necessitates crop varieties with good environmental adaptability. As a proxy for climate adaptation, crop breeders could select for adaptability to different latitudes, but the lengthy procedures for that slow development. Here, we combined molecular technologies with a streamlined in-house screening method to facilitate rapid selection for latitude adaptation. We established the daylength-sensing-based environment adaptation simulator (DEAS) to assess rice latitude adaptation status via the transcriptional dynamics of florigen genes at different latitudes. The DEAS predicted the florigen expression profiles in rice varieties with high accuracy. Furthermore, the DEAS showed potential for application in different crops. Incorporating the DEAS into conventional breeding programmes would help to develop cultivars for climate adaptation.

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Fig. 1: Allele information of photoperiodic genes in indica hybrid rice varieties.
Fig. 2: Number of Hd1 and DTH8 allelic combinations within combined ecological classes.
Fig. 3: Using DEAS step 1 to detect rice daylength-sensing processes.
Fig. 4: DEAS step 2 couples environmental adaptation and daylength sensing.
Fig. 5: Categorical manner for evaluating the DEAS inference accuracy.
Fig. 6: Evaluation of the DEAS inference accuracy using field data.
Fig. 7: Application of the DEAS to soybean.
Fig. 8: Working model for how daylength sensing mediates rice latitude adaptation.

Data availability

Information about indica hybrid rice varieties grown in East Asia, including days to heading and geographic distribution, was obtained from the China Rice Data Center ( Daylength data for different latitudes were collected using the Rise and Set Times app developed by S. Vdovenko ( The haplotype information of flowering-time genes for sterile and restorer lines can be obtained from MBKBASE ( Hd3a and RFT1 expression data for a rice paddy field at Tsukuba, Japan, grown under normal agricultural conditions can be obtained from

Code availability

In this study, we did not generate any custom code. All mathematical algorithms can be implemented through MATLAB (MathWorks; licence of Xiamen University) and R. All maps were drawn using libraries mapdata, maptools and ggplot2 in R software.


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This work was supported by the National Key R&D Program of China (2017YFA0506100), the National Natural Science Foundation (31671378) and the Fundamental Research Funds for the Central Universities (20720170068 and 20720190085). We thank Y. Liu (South China Agricultural University) for providing the pYLCRISPR/Cas9-MTmono vectors. We thank the breeder L. Wang (Sichuan Agricultural University) for providing the indica hybrid rice seeds. We thank X. Wang Deng (Peking University), H. Wang (South China Agricultural University) and F. Kong (Guangzhou University) for reading and commenting on the manuscript. We thank Y. Cui (Xiamen University), Z. Zeng (Sichuan Agricultural University), Y. Wang (Sichuan Agricultural University), W. Hu (Xiamen University) and X. Liu (Xiamen University) for their technical assistance.

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X.O. designed the research; L.Q., Q.W., X.W., G.Z., Z.S., J.H., H.W., W.T., Q.L., J.R., J.X., C.L., Y.L., S.L., R.H., X.C., C.Z., M.L., X.H., S.L. and X.O. performed the research; L.Q., P.Q., Z.C., Z.L., H.H., J.H., X.W., C.L. and X.O. analysed the data; X.O. wrote the paper.

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Correspondence to Xinhao Ouyang.

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Qiu, L., Wu, Q., Wang, X. et al. Forecasting rice latitude adaptation through a daylength-sensing-based environment adaptation simulator. Nat Food 2, 348–362 (2021).

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