Bootstrap simulations for evaluating the model estimation of the extent of cross-pollination in maize at the field-scale level

PLoS One. 2021 May 19;16(5):e0249700. doi: 10.1371/journal.pone.0249700. eCollection 2021.

Abstract

With the recent advent of genetic engineering, numerous genetically modified (GM) crops have been developed, and field planting has been initiated. In open-environment cultivation, the cross-pollination (CP) of GM crops with wild relatives, conventional crops, and organic crops can occur. This exchange of genetic material results in the gene flow phenomenon. Consequently, studies of gene flow among GM crops have primarily focused on the extent of CP between the pollen source plot and the adjacent recipient field. In the present study, Black Pearl Waxy Corn (a variety of purple glutinous maize) was used to simulate a GM-maize pollen source. The pollen recipient was Tainan No. 23 Corn (a variety of white glutinous maize). The CP rate (%) was calculated according to the xenia effect on kernel color. We assessed the suitability of common empirical models of pollen-mediated gene flow (PMGF) for GM maize, and the field border (FB) effect of the model was considered for small-scale farming systems in Asia. Field-scale data were used to construct an optimal model for maize PMGF in the maize-producing areas of Chiayi County, southern Taiwan (R.O.C). Moreover, each model was verified through simulation and by using the 95% percentile bootstrap confidence interval length. According to the results, a model incorporating both the distance from the source and the FB can have optimal fitting and predictive abilities.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Crop Production / methods
  • Ecosystem
  • Gene Flow
  • Hybridization, Genetic*
  • Models, Genetic*
  • Pollination*
  • Zea mays / genetics*
  • Zea mays / physiology

Associated data

  • figshare/10.6084/m9.figshare.13370756

Grants and funding

The Ministry of Science and Technology provided funding for the publication fee in the form of a grant awarded to BJK, through Pervasive AI Research (PAIR) Labs, Taiwan (108-2634-F-005-003), and the Innovation and Development Center of Sustainable Agriculture, from The Featured Areas Research Center Program, within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. No additional external funding was received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.