Modelling annual grass weed seed dispersal in winter wheat, when influenced by hedges and directional wind

Gayle. J. Somerville, Bo Melander, Per Kudsk and Solvejg K Mathiassen

Ecological Modelling, 2019, vol. 410, issue C, -

Abstract: Accurate predictions of weed seed dispersal are important for spatial modelling of weed population dynamics. However there are few multi-year data sets available, and they are seldom analysed well. Here we gathered together and re-examined population growth and seed spread data from three grass weed species over 3 years. Using a new seed spread function in the simulation model SOMER it was possible to accurately parametrise population growth and seed spread to replicate the field data of Apera spica-venti (APESV) and Bromus sterilis (BROST). In contrast, the greatest increases in Alopecurus myosuroides (ALOMY) numbers occurred in the direction of machinery movement, which could not be predicted using this function. A probability-sum, exponential-type function gave an excellent fit to the field data for APESV and BROST, with changes in seed spread distances due to species differences, wind, and ‘shading’ from the hedge, accommodated by alterations in the scale, shape, and wind dispersal parameters. Here we describe a method of parametrising the probability function from within a stochastic simulation. Working within the simulation allowed empirical population size data sets to be successfully parameterised, without mathematical integration. This function is fully explained with stochastic simulated examples, and fitted to various two-dimensional longitudinal data sets. Enough detail to enable its parametrisation (including simplification) within a majority of spatial stochastic population models is included.

Keywords: Spatial; Population models; Simulation; Crop weeds; Two-dimension (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:410:y:2019:i:c:10

DOI: 10.1016/j.ecolmodel.2019.108729

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