Modelling evolution and management of glyphosate resistance in Amaranthus palmeri

Abstract

Neve P, Norsworthy JK, Smith KL & Zelaya IA (2011). Modelling evolution and management of glyphosate resistance in Amaranthus palmeri. Weed Research51, 99–112. Summary A population‐based model was developed to simulate the evolution of glyphosate resistance in populations of Amaranthus palmeri. Model parameters were derived from published and unpublished sources, and the model was implemented using previously established principles and methods. Sensitivity analyses indicated that the model was sensitive to variations in population size, mutation rate and seed bank dynamics. A distribution was assigned to these parameters and Monte Carlo type simulations were performed. Simulation results are therefore derived from a range of possible input parameters, enabling the risk of resistance evolution to be assessed when parameter values were unknown, uncertain or variable. In the ‘worst‐case’ of five annual glyphosate applications in continuous glyphosate resistant cotton, evolution of glyphosate resistance was predicted in 39% of populations after 5 years and in c. 60% of populations after 10 years. These results are consistent with observations of the timescale for evolution of glyphosate resistance in A. palmeri in the field. The main drivers for glyphosate resistance evolution were selection pressure and population size, the greatest risks being associated with the largest A. palmeri populations. Risks of resistance were reduced when one of the five glyphosate applications was replaced by another mode of action with identical efficacy. However, not all glyphosate applications exerted the same selection pressure. Application of a soil residual herbicide at the time of crop sowing can provide control of A. palmeri well into the growing season and significantly reduced the rate and risk of glyphosate resistance evolution.

An individual‐based model of seed‐ and rhizome‐propagated perennial plant species and sustainable management of Sorghum halepense in soybean production systems in Argentina

“…The effect of ecological, evolutionary, anthropogenic, and economic factors can be evaluated in these models, and accordingly weed management strategies can be optimized before they are recommended to growers. (Lindsay et al, 2017;Liu et al, 2017;Neve, Norsworthy, Smith, & Zelaya, 2011). Past modeling efforts have focused primarily on annual weed species, such as L. rigidum (Monjardino, Pannell, & Powles, 2003), A. myosuroides (Colbach, Chauvel, Darmency, Délye, & Corre, 2016), and Amaranthus spp.…”

Herbicide Program Approaches for Managing Glyphosate-Resistant Palmer Amaranth (Amaranthus palmeri) and Waterhemp (Amaranthus tuberculatusandAmaranthus rudis) in Future Soybean-Trait Technologies

“…If the mutation rate for glyphosate-resistance alleles is set at 5 3 10 À9 (five per one billion individuals) (Neve et al 2011), only 4,000 plants producing 250,000 seeds plant À1 are required to result in five of those seeds possessing resistance to glyphosate or a herbicide from another site of action with a similar mutation rate. Considering there were over 35 million ha planted in soybean in the United States in 2014 (USDA-NASS 2015), prolific seed producers, such as Amaranthus spp., are a serious threat for evolving resistance to any herbicide that is frequently used in production fields over a large geographical area.…”

Optimal weed population control using nonlinear programming

“…Several proposed strategy to reduce the risk of herbicide resistance evolution use herbicide rotation, sequences and mixture, these had been investigated in Diggle et al (2003) and Neve et al (2011). Our modeling analysis has shown that the continuous use of a single herbicide application for long period of time increases the selection of resistant byotype, thus the optimal control of weed can contribute to reduce the herbicide resistance.…”

“…There has been an increasing interest in modeling the resistance evolution in weed populations (Maxwell et al 1990;Diggle et al 2003;Gressel 2009;Neve et al 2011).…”