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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

Scatterplot of the dataset for the best-fitting model L7.The quantile interpolation of the 10th-percentile of the data (blue line) shows that the distribution follows a power law rule. Sites earlier than the model are circled and identified (see the text for related discussion).
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pone.0137024.g009: Scatterplot of the dataset for the best-fitting model L7.The quantile interpolation of the 10th-percentile of the data (blue line) shows that the distribution follows a power law rule. Sites earlier than the model are circled and identified (see the text for related discussion).

Mentions: Finally, one can look at the scatterplot for the best-fitting model in search of outliers and different signals that might help direct future archaeological sampling as well as identify biogeographical features that impacted the spread of rice outside of the Yangtze valley (Fig 9). Since we are interested in origins and the dispersal of rice farming in Asia we can define outliers as those sites that are older than predicted by the best-fitting model, given by Eq 1 below. There are only a handful of these in temperate regions: Jiahu and Baligang in the Henan province of China, and Gahyeon-ri, Seongjeo and Daechon-ri in South Korea (grey shaded in Fig 9). The first two might indicate a third early centre of innovation between the Yangtze and Yellow rivers or trade between this region and the Yangtze valley. Whereas the Korean sites might indicate that rice arrived by crossing the Yellow sea, something that was not included in any of the above models and that would shorten the cost distances between these sites and the origins of the dispersal. The reported date of these sites may also be spurious, as these are not directly-dated rice remains. Other critical reviews, for example, have suggested that rice may be intrusive and younger at Daechon-ri and mis-dated at the other sites [76]. In either case, they are only a handful of sites in each region and further archaeological sampling is necessary in order to test and further refine these ideas. It should also be noted that the arrival time for rice in Japan, predicted by the model is significantly earlier (ca. 2,000–1,500 BC) than the generally accepted terminal or post-Jomon arrival (ca. 1,000–800 BC) [35,77]. This suggests that factors of cultural choice may have resisted rice in the Japanese islands, despite millennia of plant management and some domestications by Jomon societies [77]. In tropical regions, namely in northern India, we find several sites with possibly cultivated rice that can predate model L7 by more than a millennium (as much as 3,000 years for the case of Lahuradewa). This might correspond to an independent episode of domestication of proto-Indica rice and its subsequent spread up the Ganges valley [17,34]. However, the Early Holocene evidence from Lahuradewa in India, remains unclear as to whether this material was actually cultivated and was part of an independent domestication trajectory [27,78], and further work is needed.


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)

Scatterplot of the dataset for the best-fitting model L7.The quantile interpolation of the 10th-percentile of the data (blue line) shows that the distribution follows a power law rule. Sites earlier than the model are circled and identified (see the text for related discussion).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0137024.g009: Scatterplot of the dataset for the best-fitting model L7.The quantile interpolation of the 10th-percentile of the data (blue line) shows that the distribution follows a power law rule. Sites earlier than the model are circled and identified (see the text for related discussion).
Mentions: Finally, one can look at the scatterplot for the best-fitting model in search of outliers and different signals that might help direct future archaeological sampling as well as identify biogeographical features that impacted the spread of rice outside of the Yangtze valley (Fig 9). Since we are interested in origins and the dispersal of rice farming in Asia we can define outliers as those sites that are older than predicted by the best-fitting model, given by Eq 1 below. There are only a handful of these in temperate regions: Jiahu and Baligang in the Henan province of China, and Gahyeon-ri, Seongjeo and Daechon-ri in South Korea (grey shaded in Fig 9). The first two might indicate a third early centre of innovation between the Yangtze and Yellow rivers or trade between this region and the Yangtze valley. Whereas the Korean sites might indicate that rice arrived by crossing the Yellow sea, something that was not included in any of the above models and that would shorten the cost distances between these sites and the origins of the dispersal. The reported date of these sites may also be spurious, as these are not directly-dated rice remains. Other critical reviews, for example, have suggested that rice may be intrusive and younger at Daechon-ri and mis-dated at the other sites [76]. In either case, they are only a handful of sites in each region and further archaeological sampling is necessary in order to test and further refine these ideas. It should also be noted that the arrival time for rice in Japan, predicted by the model is significantly earlier (ca. 2,000–1,500 BC) than the generally accepted terminal or post-Jomon arrival (ca. 1,000–800 BC) [35,77]. This suggests that factors of cultural choice may have resisted rice in the Japanese islands, despite millennia of plant management and some domestications by Jomon societies [77]. In tropical regions, namely in northern India, we find several sites with possibly cultivated rice that can predate model L7 by more than a millennium (as much as 3,000 years for the case of Lahuradewa). This might correspond to an independent episode of domestication of proto-Indica rice and its subsequent spread up the Ganges valley [17,34]. However, the Early Holocene evidence from Lahuradewa in India, remains unclear as to whether this material was actually cultivated and was part of an independent domestication trajectory [27,78], and further work is needed.

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