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Modelling the Geographical Origin of Rice Cultivation in Asia Using the Rice Archaeological Database.

Silva F, Stevens CJ, Weisskopf A, Castillo C, Qin L, Bevan A, Fuller DQ - PLoS ONE (2015)

Bottom Line: We have compiled an extensive database of archaeological evidence for rice across Asia, including 400 sites from mainland East Asia, Southeast Asia and South Asia.This dataset is used to compare several models for the geographical origins of rice cultivation and infer the most likely region(s) for its origins and subsequent outward diffusion.The model that best fits all available archaeological evidence is a dual origin model with two centres for the cultivation and dispersal of rice focused on the Middle Yangtze and the Lower Yangtze valleys.

View Article: PubMed Central - PubMed

Affiliation: University College London, Institute of Archaeology, London, United Kingdom.

ABSTRACT
We have compiled an extensive database of archaeological evidence for rice across Asia, including 400 sites from mainland East Asia, Southeast Asia and South Asia. This dataset is used to compare several models for the geographical origins of rice cultivation and infer the most likely region(s) for its origins and subsequent outward diffusion. The approach is based on regression modelling wherein goodness of fit is obtained from power law quantile regressions of the archaeologically inferred age versus a least-cost distance from the putative origin(s). The Fast Marching method is used to estimate the least-cost distances based on simple geographical features. The origin region that best fits the archaeobotanical data is also compared to other hypothetical geographical origins derived from the literature, including from genetics, archaeology and historical linguistics. The model that best fits all available archaeological evidence is a dual origin model with two centres for the cultivation and dispersal of rice focused on the Middle Yangtze and the Lower Yangtze valleys.

No MeSH data available.


Related in: MedlinePlus

Number of times the real dispersal source was selected by different fitness indices over a range of underlying correlation coefficients.The R2adj curve (in yellow) is exactly the same, and therefore hidden behind, the AIC OLS curve (in black).
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pone.0137024.g003: Number of times the real dispersal source was selected by different fitness indices over a range of underlying correlation coefficients.The R2adj curve (in yellow) is exactly the same, and therefore hidden behind, the AIC OLS curve (in black).

Mentions: A comparison of the efficiency of each index, where the frequency with which each index picked the real source, is shown, for varying levels of noise in the dataset (represented by the correlation coefficient in the horizontal axis), in Fig 3. A correlation coefficient close to zero means that the dataset has high levels of noise and is heteroscedastic. This figure is in every way similar for both the linear and log-log/power law dispersal scenarios.


Modelling the Geographical Origin of Rice Cultivation in Asia Using the Rice Archaeological Database.

Silva F, Stevens CJ, Weisskopf A, Castillo C, Qin L, Bevan A, Fuller DQ - PLoS ONE (2015)

Number of times the real dispersal source was selected by different fitness indices over a range of underlying correlation coefficients.The R2adj curve (in yellow) is exactly the same, and therefore hidden behind, the AIC OLS curve (in black).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556484&req=5

pone.0137024.g003: Number of times the real dispersal source was selected by different fitness indices over a range of underlying correlation coefficients.The R2adj curve (in yellow) is exactly the same, and therefore hidden behind, the AIC OLS curve (in black).
Mentions: A comparison of the efficiency of each index, where the frequency with which each index picked the real source, is shown, for varying levels of noise in the dataset (represented by the correlation coefficient in the horizontal axis), in Fig 3. A correlation coefficient close to zero means that the dataset has high levels of noise and is heteroscedastic. This figure is in every way similar for both the linear and log-log/power law dispersal scenarios.

Bottom Line: We have compiled an extensive database of archaeological evidence for rice across Asia, including 400 sites from mainland East Asia, Southeast Asia and South Asia.This dataset is used to compare several models for the geographical origins of rice cultivation and infer the most likely region(s) for its origins and subsequent outward diffusion.The model that best fits all available archaeological evidence is a dual origin model with two centres for the cultivation and dispersal of rice focused on the Middle Yangtze and the Lower Yangtze valleys.

View Article: PubMed Central - PubMed

Affiliation: University College London, Institute of Archaeology, London, United Kingdom.

ABSTRACT
We have compiled an extensive database of archaeological evidence for rice across Asia, including 400 sites from mainland East Asia, Southeast Asia and South Asia. This dataset is used to compare several models for the geographical origins of rice cultivation and infer the most likely region(s) for its origins and subsequent outward diffusion. The approach is based on regression modelling wherein goodness of fit is obtained from power law quantile regressions of the archaeologically inferred age versus a least-cost distance from the putative origin(s). The Fast Marching method is used to estimate the least-cost distances based on simple geographical features. The origin region that best fits the archaeobotanical data is also compared to other hypothetical geographical origins derived from the literature, including from genetics, archaeology and historical linguistics. The model that best fits all available archaeological evidence is a dual origin model with two centres for the cultivation and dispersal of rice focused on the Middle Yangtze and the Lower Yangtze valleys.

No MeSH data available.


Related in: MedlinePlus