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cje ›› 2012, Vol. 31 ›› Issue (11): 2943-2949.

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Research advances in weed community and its adaptive evolution in croplands as affected by long-term fertilization.

WAN Kai-yuan1, PAN Jun-feng1,2, TAO Yong1, TANG Lei-lei1, CHENG Chuan-peng1, CHEN Fang1**   

  1. (1Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; 2College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)
  • Online:2012-11-10 Published:2012-11-10

Abstract: Weeds can compete with crops for resources, resulting in the yield reduction of crops, while play an indispensable role in maintaining the ecological balance of cropland and its sustainable utilization. The application of chemical fertilizers not only greatly accelerated the evolution of soil fertility, but also directly affected the weed growth, its community succession, and genetic evolution in croplands. Therefore, how to scientifically constitute a nutrient management strategy in cropland has become a realistic issue in modern agriculture. This paper comprehensively reviewed the related literatures, and summarized the effects of longterm fertilization regimes on the weed species, community structure, and genetic evolution. Through thoroughly analyzing the weaknesses of the current weed management, it was suggested that the weed control in croplands should be balanced with weed biodiversity, and the research on the long-term fertilization regimes affecting weed genetic diversity and its molecular ecological adaptability should be strengthened, which would have significance in assessing the genetic diversity of weeds under selective fertilization pressure, the potential of weed survival and evolution, and the molecular mechanisms of weed adaptive evolution, so as to supply a broader sense for the comprehensive management of weeds in croplands.

Key words: aquatic vegetation, estimation of scale parameter, supervised classification, mini-UAV image, visible vegetation index, object-oriented image classification.