Biophysical characteristics of maize pollen: Variability during emission and consequences on cross-pollination risks

Marceau, Alexis; Saint‐Jean, Sébastien; Loubet, Benjamin; Foueillassar, Xavier; Huber, Laurent

doi:10.1016/j.fcr.2011.11.006

Cited by 10 publications

(14 citation statements)

References 28 publications

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“…These profiles show also the quality of the model parameterization for interaction 111. Although the inferred pollen rates are higher than the measured ones, these results are similar to those found by Jarosz et al (2005) and Marceau et al (2012). inferred pollen deposition rates, the calculated values are mostly within the range of acceptable model performance for interactions 110 and 111 corresponding to the fact that the model includes no deposition on leaves, no resuspension, and taking (or not) into account horizontal pollen fluctuations.…”

Section: Resultssupporting

confidence: 84%

“…The SMOP model has been evaluated in many experimental studies with various hybrids, for example, studies conducted in France at Montargis and Grignon in 2000-2002, 2008and 2009(Jarosz et al, 2004, 2005Marceau et al, 2011Marceau et al, , 2012. During these experiments, airborne concentration and deposition rates of maize pollen were measured at several locations within and downwind from various maize fields.…”

Section: Discussionmentioning

confidence: 99%

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Modelling and inference of maize pollen emission rate with a Lagrangian dispersal model using Monte Carlo method

Souhar¹,

Marceau²,

Loubet³

2020

J. Agric. Sci.

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This work explores the uncertainty of the inferred maize pollen emission rate using measurements and simulations of pollen dispersion at Grignon in France. Measurements were obtained via deposition of pollen on the ground in a canopy gap; simulations were conducted using the two-dimensional Lagrangian Stochastic Mechanistic mOdel for Pollen dispersion and deposition (SMOP). First, a quantitative evaluation of the model's performance was conducted using a global sensitivity analysis to analyse the convergence behaviour of the results and scatter diagrams. Then, a qualitative study was conducted to infer the pollen emission rate and calibrate the methodology against experimental data for several sets of variable values. The analysis showed that predicted and observed values were in good agreement and the calculated statistical indices were mostly within the range of acceptable model performance. Furthermore, it was revealed that the mean settling velocity and vertical leaf area index are the main variables affecting pollen deposition in the canopy gap. Finally, an estimated pollen emission rate was obtained according to a restricted setting, where the model studied includes no deposition on leaves, no resuspension and with horizontal pollen fluctuations either taken into account or not. The estimated pollen emission rate obtained was nearly identical to the measured quantity. In conclusion, the findings of the current study show that the described methodology could be an interesting approach for accurate prediction of maize pollen deposition and emission rates and may be appropriate for other pollen types.

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Section: Resultssupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Modelling and inference of maize pollen emission rate with a Lagrangian dispersal model using Monte Carlo method

Souhar¹,

Marceau²,

Loubet³

2020

J. Agric. Sci.

Self Cite

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“…Among all available approaches, physical-based models are arguably the most accurate, as they use a theoretical model of aerial particle dispersal, which is the main process implied in PMGF (Dupont et al, 2006;Jarosz et al, 2004). However, they are also extremely laborious to develop, due to the need to model each biological and physical process (Marceau et al, , 2012Viner et al, 2010;Chamecki et al, 2011). Recently, the biophysical approach was validated with success on individual experimental fields by Dietiker et al (2011) and Astini et al (2009) for hybrid and seed production fields, respectively.…”

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confidence: 99%

Updated Empirical Model of Genetically Modified Maize Grain Production Practices to Achieve European Union Labeling Thresholds

Marceau

Gustafson

Brants³

et al. 2013

Crop Science

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An updated empirical approach is proposed for specifying coexistence requirements for genetically modified (GM) maize (Zea mays L.) production to ensure compliance with the 0.9% labeling threshold for food and feed in the European Union. The model improves on a previously published (Gustafson et al., 2006) empirical model by adding recent data sources to supplement the original database and including the following additional cases: (i) more than one GM maize source field adjacent to the conventional or organic field, (ii) the possibility of so‐called “stacked” varieties with more than one GM trait, and (iii) lower pollen shed in the non‐GM receptor field. These additional factors lead to the possibility for somewhat wider combinations of isolation distance and border rows than required in the original version of the empirical model. For instance, in the very conservative case of a 1‐ha square non‐GM maize field surrounded on all four sides by homozygous GM maize with 12 m isolation (the effective isolation distance for a single GM field), non‐GM border rows of 12 m are required to be 95% confident of gene flow less than 0.9% in the non‐GM field (with adventitious presence of 0.3%). Stacked traits of higher GM mass fraction and receptor fields of lower pollen shed would require a greater number of border rows to comply with the 0.9% threshold, and an updated extension to the model is provided to quantify these effects.

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“…Aylor et al (2003) reported that depending on the VPD, the water contents of maize pollen could change from being fully hydrated to being nearly dehydrated in 1-4 hour. When maize pollen were exposed to drying condition, the shape of pollen were changed from prolate spheroid to prismatic, and it led to decreased pollen viability (Aylor et al, 2003;Marceau et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Responses of spikelet fertility to air, spikelet, and panicle temperatures and vapor pressure deficit in rice

Jung

Lee

2015

J. Crop Sci. Biotechnol.

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Along with the rapid temperature rise anticipated in the future, high temperatureinduced spikelet sterility is expected to expose a serious problem in rice production.High relative humidity (RH) or small vapor pressure deficit (VPD) that suppresses transpirational cooling of rice panicle is reported to increase high temperatureinduced spikelet sterility.-1- The spikelet fertility showed wide range of variation from 97.3% to 4.6% depending on temperature treatments, years, and cultivars. The standardized partial ridge regression revealed that not only air temperature but also VPD was negatively associated with spikelet fertility. The spikelet fertility was well fitted to logistic equations not only of air temperature, internal temperature of spikelet, and surface temperature of panicle but also of VPD. The model performances showed no clear differences among temperatures employed to the model equations, while the bi-logistic equation model employing air temperature and VPD as independent variables showed the best performance.Our result was contrary to the previous reports that the increase of VPD (low humidity) reduced high temperature-induced spikelet sterility by increasing -2-transpirational cooling of panicle. However, it was inferred that increased VPD under high temperature conditions would accelerate desiccation of anther or pollen during flowering and result in the decline of pollen viability and germination, leading to lower spikelet fertility. Further detailed study is needed to verify VPD effects on spikelet fertility under high temperature conditions.

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Biophysical characteristics of maize pollen: Variability during emission and consequences on cross-pollination risks

Cited by 10 publications

References 28 publications

Modelling and inference of maize pollen emission rate with a Lagrangian dispersal model using Monte Carlo method

Modelling and inference of maize pollen emission rate with a Lagrangian dispersal model using Monte Carlo method

Updated Empirical Model of Genetically Modified Maize Grain Production Practices to Achieve European Union Labeling Thresholds

Responses of spikelet fertility to air, spikelet, and panicle temperatures and vapor pressure deficit in rice

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