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

Published online by Cambridge University Press:  01 October 2020

Otmane Souhar Open the ORCID record for Otmane Souhar [Opens in a new window] ,
Alexis Marceau  and
Benjamin Loubet Open the ORCID record for Benjamin Loubet [Opens in a new window]
Otmane Souhar*
Affiliation:
LIMA Laboratory, University of Chouaib Doukkali, BP. 20, 24000 El Jadida, Morocco
Alexis Marceau
Affiliation:
National Institute of Agronomic Research, UMR EcoSys INRAE-AgroParisTech, 78850, Thiverval-Grignon, France
Benjamin Loubet
Affiliation:
National Institute of Agronomic Research, UMR EcoSys INRAE-AgroParisTech, 78850, Thiverval-Grignon, France
*
Author for correspondence: Otmane Souhar, E-mail: [email protected]
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Abstract

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.

Keywords

Lagrangian stochastic approachMonte Carlo methodpollen dispersion and depositionsensitivity analysis
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 158 , Issue 5 , July 2020 , pp. 383 - 395
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Ahuja, S and Kumar, A (1996) Evaluation of MESOPUFF-II SOx transport and deposition in the Great Lakes Region. AWMA Speciality Conference on Atmospheric Deposition to the Great Lakes, V IP-72, October 28–30, pp. 283–299.Google Scholar
Arritt, RW, Clark, CA, Goggi, AS, Sanchez, HL, Westgate, ME and Riese, JM (2007) Lagrangian numerical simulations of canopy air flow effects on maize pollen dispersal. Field Crops Research 102, 151–162.CrossRefGoogle Scholar
Astini, JP, Fonseca, JP, Clark, C, Lizaso, J, Grass, L, Westgate, M and Arritt, R (2009) Predicting out crossing in maize hybrid seed production. Agronomy Journal 101, 373–380.CrossRefGoogle Scholar
Aylor, DE (2002) Settling speed of corn (Zea mays) pollen. Journal of Aerosol Science 33, 1601–1607.CrossRefGoogle Scholar
Aylor, DE (2003) Rate of dehydration of corn (Zea mays L.) pollen in the air. Journal of Experimental Botany 54, 2307–2312.CrossRefGoogle ScholarPubMed
Aylor, DE, Schultes, NP and Shields, EJ (2003) An aerobiological framework for assessing cross-pollination in maize. Agricultural and Forest Meteorology 119, 111–129.CrossRefGoogle Scholar
Aylor, DE, Boehm, MT and Shields, EJ (2006) Quantifying aerial concentrations of maize pollen in the atmospheric surface layer using remote-piloted airplanes and Lagrangian stochastic modelling. Journal of Applied Meteorology and Climatology 45, 1003–1015.CrossRefGoogle Scholar
Baklanov, A, Smith Korsholm, U, Nuterman, R, Mahura, A, Nielsen, KP, Sass, BH, Rasmussen, A, Zakey, A, Kaas, E, Kurganskiy, A, Sørensen, B and González-Aparicio, I (2017) Enviro-HIRLAM online integrated meteorology-chemistry modelling system: strategy, methodology, developments and applications. Geoscientific Model Development 10, 2971–2999.CrossRefGoogle Scholar
Chang, JC and Hanna, SR (2004) Air quality model performance evaluation. Journal of Meteorology and Atmospheric Physics 87, 167–196.Google Scholar
Di-Giovanni, F, Kevan, PG and Nasr, ME (1995) The variability in settling velocities of some pollen and spores. Grana 34, 39–44.CrossRefGoogle Scholar
Dietiker, D, Stamp, P and Eugster, W (2011) Predicting seed admixture in maize combining flowering characteristics and a Lagrangian stochastic dispersion model. Field Crops Research 121, 256–267.CrossRefGoogle Scholar
Drouet, JL (2003) MODICA And MODANCA: modelling the three-dimensional shoot structure of graminaceous crops from two methods of plant description. Field Crops Research 83, 215–222.CrossRefGoogle Scholar
Dupont, S, Brunet, Y and Jarosz, N (2006) Eulerian modelling of pollen dispersal over heterogeneous vegetation canopies. Agricultural and Forest Meteorology 141, 82–104.CrossRefGoogle Scholar
Dyer, AJ (1974) A review of flux-profile relationships. Boundary-Layer Meteorology 7, 363–371.CrossRefGoogle Scholar
Flesch, TK, Wilson, JD and Yee, E (1995) Backward-time Lagrangian stochastic dispersion models and their application to estimate gaseous emissions. Journal of Applied Meteorology 34, 1320–1332.2.0.CO;2>CrossRefGoogle Scholar
Garratt, JR (1992) The Atmospheric Boundary Layer. Cambridge, UK: Cambridge University Press.Google Scholar
Gash, JHC (1986) Observation of turbulence downwind of a forest-heath interface. Boundary-Layer Meteorology 36, 227–237.CrossRefGoogle Scholar
Gregory, PH (1973) Microbiology of the Atmosphere. New York, Toronto: John Wiley & Sons.Google Scholar
Hall, AJ, Vilella, F, Trapani, N and Chimenti, C (1982) The effect of water stress and genotype on the dynamics of pollen shedding and silking in maize. Field Crops Research 5, 349–363.CrossRefGoogle Scholar
Hanna, SR, Chang, JC and Strimaitis, DG (1993) Hazardous gas model evaluation with field observations. Atmospheric Environment 27A, 2265–2285.CrossRefGoogle Scholar
Heisler, GM and DeWalle, DR (1988) Effects of windbreak structure on wind flow. Agriculture, Ecosystem and Environment 18, 41–69.CrossRefGoogle Scholar
Helton, JC, Johnson, JD, Sallaberry, CJ and Storlie, CB (2006) Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering and System Safety 91, 1175–1209.CrossRefGoogle Scholar
Iman, RL (1992) Uncertainty and sensitivity analysis for computer modeling applications. Reliability technology 28, 153–168.Google Scholar
Janssen, PHM (1994) Assessing sensitivities and uncertainties in models: a critical evaluation. Proceedings of the 75th Anniversary Conference of WAU, April 5–7, Wageningen, The Netherlands, pp. 344–361.CrossRefGoogle Scholar
Janssen, PHM, Heuberger, PSC and Sanders, R (1992) UNCSAM 1.1: A Software Package for Sensitivity and Uncertainty Analysis. National Institute of Public Health and Environmental Protection, Bilthoven, The Netherlands. Report No. 959101004.Google Scholar
Jarosz, N, Loubet, B, Durand, B, McCartney, A, Foueillassar, X and Huber, L (2003) Field measurements of airborne concentration and deposition rate of maize pollen. Agricultural and Forest Meteorology 119, 37–51.CrossRefGoogle Scholar
Jarosz, N, Loubet, B and Huber, L (2004) Modelling airborne concentration and deposition rate of maize pollen. Atmospheric Environment 38, 5555–5566.CrossRefGoogle Scholar
Jarosz, N, Loubet, B, Durand, B, Foueillassar, X and Huber, L (2005) Variations in maize pollen emission and deposition in relation to microclimate. Environmental Science and Technology 39, 4377–4384.CrossRefGoogle Scholar
Kaimal, JC and Finnigan, JJ (1994) Atmospheric Boundary Layer Flows Their Structure and Measurement. Oxford: Oxford University Press.CrossRefGoogle Scholar
Klein, EK, Lavigne, C, Foueillassar, X, Gouyon, PH and Laredo, C (2003) Corn pollen dispersal: quasi-mechanistic models and field experiments. Ecological Monographs 73, 131–150.CrossRefGoogle Scholar
Kumar, A, Luo, J and Bennett, G (1993) Statistical evaluation of lower flammability distance using four hazardous release models. Process Safety Progress 12, 1–11.CrossRefGoogle Scholar
Lizaso, JI, Westgate, ME, Batchelor, WD and Fonseca, A (2003) Predicting potential kernel set in maize from simple flowering characteristics. Crop Science 43, 892–903.CrossRefGoogle Scholar
Loubet, B (2000) Modélisation du dépôt sec d'ammoniac atmosphérique à proximité des sources (PhD thesis). Paul Sabatier University, Toulouse, France.Google Scholar
Loubet, B, Cellier, P, Milford, C and Sutton, MA (2006) A coupled dispersion and exchange model for short-range dry deposition of atmospheric ammonia. Quarterly Journal of the Royal Meteorological Society 132, 1733–1763.CrossRefGoogle Scholar
Loubet, B, Jarosz, N, Saint-Jean, S and Huber, L (2007) A method for measuring the settling velocity distribution of large biotic particles. Aerobiologia 23, 159–169.CrossRefGoogle Scholar
Marceau, A, Loubet, B, Andrieu, B, Durand, D, Foueillassar, X and Huber, L (2011) Modelling diurnal and seasonal patterns of maize pollen emission in relation to meteorological factors. Agricultural and Forest Meteorology 151, 11–21.CrossRefGoogle Scholar
Marceau, A, Saint-Jean, S, Loubet, B and Huber, L (2012) Biophysical characteristics of maize pollen: variability during emission and consequences on cross-polination risks. Field Crops Research 127, 51–63.CrossRefGoogle Scholar
McCartney, HA and Lacey, ME (1991) Wind dispersal of pollen from crops of oilseed rape (Brassica napus L.). Journal of Aerosol Science 22, 467–477.CrossRefGoogle Scholar
McKay, MD, Beckmann, RJ and Conover, WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245.Google Scholar
Paulson, CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology 9, 857–861.2.0.CO;2>CrossRefGoogle Scholar
Raupach, MR (1989) Applying Lagrangian fluid-mechanics to infer scalar source distributions from concentration profiles in plant canopies. Agricultural and Forest Meteorology 47, 85–108.CrossRefGoogle Scholar
Raupach, MR, Finnigan, JJ and Brunet, Y (1996) Coherent eddies and turbulence in vegetation canopies: the mixing-layer analogy. Boundary-Layer Meteorology 78, 351–382.CrossRefGoogle Scholar
Raynor, GS, Eugene, CO and Janet, VH (1972) Dispersion and deposition of corn pollen from experimental sources. Agronomy Journal 64, 420–427.CrossRefGoogle Scholar
Reynolds, AM (2000) Prediction of particle deposition on to rough surfaces. Agricultural and Forest Meteorology 104, 107–118.CrossRefGoogle Scholar
Saltelli, A, Chan, K and Scott, EM. (2000 a) Probability and Statistics series. In Sensitivity Analysis. John Wiley and Sons.Google Scholar
Saltelli, A, Tarantola, S and Compolongo, F (2000 b) Sensitivity analysis as an ingredient of modelling. Statistical Science 15, 377–395.Google Scholar
Scott, RK (1970) The effect of weather on the concentration of pollen within sugar-beet seed crops. Annals of Applied Biology 66, 119–127.CrossRefGoogle Scholar
Snyder, WH and Lumley, JL (1971) Some measurements of particle velocity autocorrelation functions in a turbulent flow. Journal of Fluid Mechanics 48, 41–71.CrossRefGoogle Scholar
Sofiev, M, Siljamo, P, Ranta, H, Linkosalo, T, Jaeger, S, Rasmussen, A, Rantio-Lehtimaki, A, Severova, E and Kukkonen, J (2013) A numerical model of birch pollen emission and dispersion in the atmosphere. International Journal of Biometeorology 57, 45–58.CrossRefGoogle ScholarPubMed
Torimaru, T, Wennstrom, U, Lindgren, D and Wang, XR (2012) Effects of male fecundity, interindividual distance and anisotropic pollen dispersal on mating success in a Scots pine (Pinus sylvestris) seed orchard. Heredity 108, 312–321.CrossRefGoogle Scholar
Treu, R and Emberlin, J (2000) Pollen dispersal in the crops maize, oilseed rape, potatoes, sugar beet and wheat. In a report for the Soil Association from the National Pollen Research Unit (Worcester, University College Worcester).Google Scholar
Zhang, R, Duhl, T, Salam, MT, House, JM, Flagan, RC, Avol, EL, Gilliland, FD, Guenther, A, Chung, SH, Lamb, BK and VanReken, TM (2014) Development of a regional-scale pollen emission and transport modeling framework for investigating the impact of climate change on allergic airway disease. Biogeosciences 11, 1461–1478.CrossRefGoogle Scholar
Zink, K, Kaufmann, P, Petitpierre, B, Broennimann, O, Guisan, A, Gentilini, E and Rotach, MW (2017) Numerical ragweed pollen forecasts using different source maps: a comparison for France. International Journal of Biometeorology 61, 23–33.CrossRefGoogle ScholarPubMed