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Evaluating the Comprehensive Performance of Herbaceous Peonies at low latitudes by the Integration of Long-running Quantitative Observation and Multi-Criteria Decision Making Approach.

Jiaping ZhangXiaobin WangDong ZhangShuai QiuJianfen WeiJuan GuoDan-Qing LiYi-Ping Xia
Published in: Scientific reports (2019)
Enlarging the planting area of economic plants, such as the "Southward Planting of Herbaceous Peony" (Paeonia lactiflora. Pall), is significant for improving people's lives. Peony is globally known as an ornamental because of gorgeous flowers and is mainly cultivated in the temperate regions with relatively cool and dry climates in the Northern Hemisphere. Promoting the landscape application of peony to the lower latitude regions is difficult because of the hot-humid climate. In this study, 29 northern peony cultivars and a unique Chinese southern peony, 'Hang Baishao', were introduced to Hangzhou, located in the central subtropics. Annual growth cycles, resistances and dormancy durations were measured, and crossbreeding between the southern and northern peonies was performed for six years, from 2012 to 2017. Based on data collected from the long-running quantitative observation (LQO), a multi-criteria decision making (MCDM) system was established to evaluate the comprehensive planting performance of these 30 cultivars in the central subtropics. 'Qihua Lushuang', 'Hang Baishao' and 'Meiju' were highly recommended, while 'Zhuguang' and 'Qiaoling' were scarcely recommended for the Hangzhou landscape. This study highlights the dependability and comprehensiveness of integrating the LQO and MCDM approaches for evaluating the introduction performance of ornamental plants.
Keyphrases
  • decision making
  • high resolution
  • high intensity
  • machine learning
  • mass spectrometry
  • artificial intelligence
  • big data