Using simulation models to investigate the cumulative effects of sowing rate, sowing date and cultivar choice on weed competition

Abstract

With the increasing pressure on crop production from the evolution of herbicide resistance, farmers are increasingly adopting Integrated Weed Management (IWM) strategies to augment their weed control. These include measures to increase the competitiveness of the crop canopy such as increased sowing rate and the use of more competitive cultivars. While there are data on the relative impact of these non-chemical weed control methods assessed in isolation, there is uncertainty about their combined contribution, which may be hindering their adoption. In this article, the INTERCOM simulation model of crop/weed competition was used to examine the combined impact of crop density, sowing date and cultivar choice on the outcomes of competition between wheat (Triticum aestivum) and Alopecurus myosuroides. Alopecurus myosuroides is a problematic weed of cereal crops in North-Western Europe and the primary target for IWM in the UK because it has evolved resistance to a range of herbicides. The model was parameterised for two cultivars with contrasting competitive ability, and simulations run across 10 years at different crop densities and two sowing dates. The results suggest that sowing date, sowing density and cultivar choice largely work in a complementary fashion, allowing enhanced competitive ability against weeds when used in combination. However, the relative benefit of choosing a more competitive cultivar decreases at later sowing dates and higher crop densities. Modeling approaches could be further employed to examine the effectiveness of IWM, reducing the need for more expensive and cumbersome long-term in situ experimentation.

Keywords: Competition; Cultural weed control; INTERCOM; Suppression; Tolerance.

Fig. 1

Differences between two contrasting cultivars used in the in silico experiments for two traits: a) relative growth rate of green area (cm² cm⁻² day⁻¹) calculated using the daily mean temperature averaged over ten years and b) increase in plant height calculated using photothermal time, (- - -) KWS Santiago, (…) Duxford and (^___) A. myosuroides.

Fig. 2

Interaction of crop density (100–400 plants m⁻²) and sowing date (15th September to 14th November) calculated as the mean output for each combination of density × date using weather data from 2005 to 2014. In all scenarios, a weed density of 80 plants m⁻² was used and an emergence date for crop and weed of 7 and 10 days after sowing respectively.

Fig. 3

INTERCOM predictions using two contrasting cultivars showing impact of variable weather on a) percentage yield loss from years 2005–2014, and b) weed free wheat yield; the accumulated thermal time of each year is included as the dashed line. ■ = Duxford; □ = KWS Santiago. Crop density 300 plants m⁻², sown 20 September, A. myosuroides density 80 plants m⁻².

Fig. 4

The seed return per plant of A. myosuroides (approx. 80 plants m⁻² equiv.) when grown alongside one of two cultivars (275 plants m⁻² equiv.) across three years in a container-based experiment.  = Duxford; □ = KWS Santiago. Mean temperature in 2011–12 was 8.3 °C, in 2012–13 was 6.3 °C and in 2013–14 was 8.9 °C.

Fig. 5

The predicted percentage yield loss for () Duxford and (●) KWS Santiago when sown at a) different densities (with a sowing date of 20 September) and b) different sowing dates (with a crop density of 150 plants m⁻²). In both cases, weed density was 80 plants m⁻² and dates of emergence were 10 and 7 days after sowing for the crop and weed respectively.