Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-18T02:04:33.308Z Has data issue: false hasContentIssue false
  • English
  • Français

Three-dimensional prediction of maize pollen dispersal andcross-pollination, and the effects of windbreaks

Published online by Cambridge University Press:  13 August 2010

Tomoki Ushiyama ,
Mingyuan Du ,
Satoshi Inoue ,
Hiroyuki Shibaike ,
Seiichiro Yonemura ,
Shigeto Kawashima  and
Katsuki Amano
Tomoki Ushiyama*
Affiliation:
The National Institute for Agro-Environmental Sciences, Tsukuba Ibaraki, Japan
Mingyuan Du
Affiliation:
The National Institute for Agro-Environmental Sciences, Tsukuba Ibaraki, Japan
Satoshi Inoue
Affiliation:
The National Institute for Agro-Environmental Sciences, Tsukuba Ibaraki, Japan
Hiroyuki Shibaike
Affiliation:
The National Institute for Agro-Environmental Sciences, Tsukuba Ibaraki, Japan
Seiichiro Yonemura
Affiliation:
The National Institute for Agro-Environmental Sciences, Tsukuba Ibaraki, Japan
Shigeto Kawashima
Affiliation:
Graduate School of Agriculture, Kyoto University, Japan
Katsuki Amano
Affiliation:
The National Center for Seeds and Seedlings, Japan
*
* Corresponding author:[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

With the extensive adoption of transgenic crops, an understanding of transgene flow isessential to manage gene flow to non-GM crops. Thus, a flexible and accurate numericalmodel is required to assess gene flow through pollen dispersal. A three-dimensionalatmospheric model combined with a diffusion transport model would be a useful tool forpredicting pollen dispersal since it would be flexible enough to incorporate the effectsof factors such as the spatial arrangement of crop combinations, land use, topography,windbreaks, and buildings. We applied such a model to field measurements of gene flowbetween two adjacent maize (Zea mays) cultivars, with suppression effectsdue to windbreaks, in an experimental cornfield in Japan. This combined model reproducedthe measured cross-pollination distribution quite well in the case of maize plots withplant windbreaks slightly taller than the maize and without windbreaks, but the modelunderestimated the effect of a 6-m-tall windbreak net beyond 25 m from the donor pollensource on cross-pollination. The underestimation was most probably due to the problem ofassimilated wind data. The model showed that the 6-m-tall windbreak and the plant windbreak suppressed average cross-pollination rate by about 60% and 30%, respectively.Half-tall and coarser mesh windbreak net suppressed cross-pollination rates by 40% byreducing the swirl of donor pollen by reduced wind speed.

Keywords

pollen dispersalnumerical predictioncross-pollinationmaizewindbreak
Type
Research Article
Information
Environmental Biosafety Research , Volume 8 , Issue 4 , October 2009 , pp. 183 - 202
Copyright
© ISBR, EDP Sciences, 2010

References

Angevin, F, Klein, EK, Choimet, C, Gauffreteau, A, Lavigne, C, Messean, A, Meynard, JM (2008) Modeling impacts of cropping systems and climate on maize cross-pollination in agricultural landscapes: The MAPD model. Europ. J. Agronomy 28: 471–484 CrossRefGoogle Scholar
Aylor, DE (2002) Settling speed of corn (Zea mays) pollen. J. Aerosol Sci. 33: 1601–1607 CrossRefGoogle Scholar
Aylor, DE, Schultes, NP, Shields, EJ (2003) An aerobiological framework for assessing cross-pollination in maize. Agric. For. Meteorol. 119: 111–129 CrossRefGoogle Scholar
Aylor, D, Boehm, MT, Shields, EJ (2006) Quantifying areal concentration of maize pollen in the atmospheric surface layer using remote-piloted airplanes and Lagrangian stochastic modeling. J. Appl. Meteor. 45: 1003–1015 CrossRefGoogle Scholar
Dupont, S, Brunet, Y, Jarosz, N (2006) Eulerian modeling of pollen dispersal over heterogeneous vegetation canopies. Agric. For. Meteorol. 141: 82–104 CrossRefGoogle Scholar
Dyer, AJ, Hicks, BB (1970) Flux-gradient relationship in the constant flux layer. Quart. J. Roy. Meteor. Soc. 96: 715–512 CrossRefGoogle Scholar
Helbig, N, Vogel, B, Vogel, H, Fiedler, F (2004) Numerical modeling of pollen dispersion on the regional scale. Aerobiologia 3: 3–19 CrossRefGoogle Scholar
Hirt, CW, Cook, JL (1972) Calculating three-dimensional flows around structures and over rough terrain. J. Comput. Physics 10: 324 CrossRefGoogle Scholar
Jarosz, N, Loubet, B, Huber, L (2004) Modelling airborne concentration and deposition rate of maize pollen. Atmos. Environ. 38: 5555–5566 CrossRefGoogle Scholar
Jarosz, N, Loubet, B, Durand, B, Foueillassar, X, Huber, L (2005) Variations in maize pollen emission and deposition in relation to microclimate. Environ. Sci. Technol. 29: 4377–4384 CrossRefGoogle Scholar
Jia, S, Wang, F, Sui, L, Yuan, Q, Liu, W, Liao, Y, Li, S, Jin, W, Peng, H (2007) Transgene flow to hybrid rice and its male-sterile lines. Transgenic Res. 16: 491–501 CrossRefGoogle ScholarPubMed
Kawashima, S, Fujita, T, Matsuo, K, Shibaike, H (2004) Development of an automatic corn pollen monitor. Jpn. J. Palynol. 50: 5–14 Google Scholar
Kawashima, S, Matsuo, K, Shibaike, H, Takahashi, Y, Inoue, S, Yonemura, S, Du, M (2007) Effects of biological and meteorological conditions on inter-annual variation in hybrid percentage of maize. Jpn. J. Palynol . 53: 9–17 Google Scholar
Klein, EK, Lavigne, C, Foueillassar, X, Gouyon, P, Laredo, C (2003) Corn pollen dispersal: quasi-mechanistic models and field experiments. Ecol. Monogr. 73: 131–150 CrossRefGoogle Scholar
Kuparinen, A (2006) Mechanistic models for wind dispersal. Trends Plant Sci. 11: 296–301 CrossRefGoogle Scholar
Kuparinen, A, Markkanen, T, Riikonen, H, Vesala, T (2007) Modelling air-mediated dispersal of spores, pollen, and seeds in forested areas. Ecol. Model. 208: 177–188 CrossRefGoogle Scholar
Loos, C, Seppelt, R, Meier-Bethke, S, Schiemann, J, Richter, O (2003) Spatially explicit modeling of transgenic maize pollen dispersal and cross-pollination. J. Theor. Biol. 225: 241–255 CrossRefGoogle Scholar
Mellor, GI, Yamad, T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys. 20: 851–875 CrossRefGoogle Scholar
Messean A, Angevin F, Gomez-Barbero M, Menrad K, Rodriguez-Cerezo E (2006) New case studies on the coexistence of GM and non-GM crops in European agriculture, Technical Report EUR No: 22102 EN
Nathan, R, Schurr, FM, Spiegel, O, Steinitz, O, Trakhtenbrot, A, Tsoar, A (2008) Mechanisms of long-distance seed dispersal. Trends. Ecol. Evol. 23: 638–647 CrossRefGoogle ScholarPubMed
Okubo, A, Levin, SA (1989) A theoretical framework for data analysis of wind dispersal of seeds and pollen. Ecology 70: 329–338 CrossRefGoogle Scholar
Pasken, R, Pietrowicz, JA (2005) Using dispersion and mesoscale meteorological models to forecast pollen concentrations. Atmos. Environ. 39: 7689–7701 CrossRefGoogle Scholar
Rognli, O, Nilsson, N-O, Nurminiemi, M (2000) Effects of distance and pollen competition on gene flow in the wind-pollinated grass Festuca pratensis Huds. Heredity 85: 550–560 CrossRefGoogle ScholarPubMed
Schueler, S, Schlünzen, KH (2006) Modeling of oak pollen dispersal on the landscape level with a mesoscale atmospheric model. Environ. Model Assess. 11: 179–194 CrossRefGoogle Scholar
Stauffer, DR, Seaman, NL (1990) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev. 118: 1250–1277 2.0.CO;2>CrossRefGoogle Scholar
Uchijima, Z (1961) On characteristics of heat balance of water layer under paddy plant cover. Bull. Nat. Inst. Agric. Sci. A: 243–263 Google Scholar
Ushiyama, T, Inoue, S, Shibaike, H (2009) Measurements of wind suppression effects of windbreak net using a wind tunnel for the purpose of applying numerical simulations. J. Agric. Meteorol. 65: 273–281 CrossRefGoogle Scholar
Wang, H, Takle, ES (1995) A numerical simulation of boundary-layer flows near shelterbelts. Boundary-Layer Meteorol. 75: 141–173 CrossRefGoogle Scholar
Wang, TY, Chen, HB, Reboud, X, Darmency, H (1997) Pollen-mediated gene flow in an autogamous crop: Foxtail millet (Setaria italica). Plant Breed. 116: 579–583 CrossRefGoogle Scholar
Wilson, JD, Sawford, BL (1996) Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Boundary-Layer Meteorol. 78: 191–210 CrossRefGoogle Scholar
Yamada, T (1981) A numerical simulation of nocturnal drainage flow. J. Meteor. Soc. Jpn. 59: 108–122 CrossRefGoogle Scholar
Yamada, T (1982) A numerical model study of turbulent airflow in and above a forest canopy. J. Meteor. Soc. Jpn. 60: 439–454 CrossRefGoogle Scholar
Yamada, T (2000) Numerical simulations of airflow and tracer transport in the southwestern United States. J. Appl. Metor. 39: 399–411 2.0.CO;2>CrossRefGoogle Scholar
Yamada, T, Bunker, S (1988) Development of a nested grid, second moment turbulence closure model and application to the 1982 ASCOT Brush Creek data simulation. J. Appl. Meteor. 27: 562–578 2.0.CO;2>CrossRefGoogle Scholar
Yamada, T, Bunker, S, Moss, M (1992) Numerical simulations of atmospheric transport and diffusion over coastal complex terrain. J. Appl. Meteor. 31: 565–578 2.0.CO;2>CrossRefGoogle Scholar
Yao, K, Hu, N, Chen, W, Li, R, Yuan, Q, Wang, F, Qian, Q, Jia, S (2008) Establishment of a rice transgene flow model for predicting maximum distances of gene flow in southern China. New Phytol. 180: 217–228 CrossRefGoogle ScholarPubMed
Zar JH (1984) Biostatistical analysis, second edition, Prentice Hall, London, pp 493