By Tammy Jones
Integrated weed management (IWM) has long been recommended as the sustainable solution for effectively controlling weeds. Even before herbicide resistance was widespread, the recommendation was to utilize cultural, mechanical and biological strategies to enhance the impact of herbicides. How many times can it be said that crop rotation, varying seeding dates, optimizing seeding rates and row spacing, and utilizing tillage are important to a sustainable production system? Unfortunately, the weed scientists making these types of recommendations have rarely moved the needle without substantiated proof.
The limitations to providing proof have been considerable. Complex recommendations require complex investigations across many environments and involve more time, land and capital resources than most weed science research programs can afford. The base model of IWM recommendations focused on a single weed species’ biology, the impact of tillage systems or the long-term impact of crop rotation. For instance, the impact of weed density on yield has been observed for many important weeds, including wild oat, green foxtail and kochia. A summary of numerous kochia studies (as described by Friesen et al. in The Biology of Canadian Weeds) showed a range from 10 to 73 per cent yield loss of spring wheat depending on weed density, competitive ability of the population and other factors that were not quantified in the research. This demonstrated the need to control kochia, but didn’t account for other weeds in the field or how to enhance the competitiveness of the crop to minimize the weed’s impact.
A poster about a long-term crop rotation study at Lethbridge recently showed how weed and cropping system studies have gained a few bells and whistles. The poster, presented at the Canadian Weed Science Society meeting in Vancouver in February 2025, provided the conclusion that a complex crop rotation, involving both annual and perennial crops and using cattle manure to supply phosphorous, could ensure that none of the four weed species became prevalent.
Unpacking that, the non-replicated century-long study was modified in 1989 and divided into three replicates for better ability to determine treatment effects, allowing the authors to look at cropping systems that involved annual, perennial or a crop rotation including both life cycles along with a comparison of different phosphorus fertilizer sources. The poster focused on four weeds which were impacted by the cultural management interactions of crop rotation and fertilizer source: lamb’s-quarter, kochia, stinkweed (winter annual) and green foxtail. While lamb’s-quarter was favoured by annual crops and manure applications, kochia was surprisingly adapted to perennial rotations and green foxtail preferred to grow when there was no phosphorus fertilizer applied. The study demonstrated that farmers can impact a weed population by altering their fertility choices in a cropping system to favour the crop.
But was this integrated enough? The study does not account for every variable within a field to manage all weeds, and reducing phosphorus to manage green foxtail may not be sustainable. Which begs the question, is “cropping systems” research the best path forward for assessing the sustainability of an integrated weed management plan? And how can a weed scientist develop a robust set of experiments to measure the variables within a field and extrapolate to a whole farm or even a growing region?
With that in mind, weed scientists at the University of Manitoba and the University of Saskatchewan have collaborated to develop a framework to improve long-term weed management recommendations by bringing together four pillars of research. The framework, as outlined by Benaragama et al. in the journal of Agricultural Systems (2024), strives to allow for cropping systems comparisons, while adding in the ability to generalize across different growing regions and predict future weed dynamics.
The first pillar proposed refers to cropping system functionality. This research includes everything previously studied in cropping systems: the impact of tillage, herbicides, crop sequences, various cultural practices and soil fertility, but adds in the concept of a “competition gradient” for resources like light, water or fertility; a ranking system for the importance of various components of the cropping system; and changes the focus of the statistical analysis to identify real differences.
The second pillar focuses on the weed traits as impacted by the cropping system, using similar measures as would be used in crop phenotyping and acknowledging that current management plans do not anticipate the evolutionary processes that lead to weeds being able to survive the management strategy.
Weed persistence is the third focus of the framework, with a nod to the weed seedbank as an indicator, but also studying the impact of environment and weed management systems for short-term effects to the mother plant and the long-term evolution of the plant to survive and produce seeds that also survive.
Finally, the framework suggests that future weed scientists will also need to understand the impact of time on weed populations. To say that this gets complicated would be a gargantuan understatement.
What seems interesting and possibly deflating about this futuristic model of weed science is that the contemporary, influential weed scientists who developed the framework encouraged more research, more long-term studies and the incorporation of evolutionary biology, remote sensing and improved statistical knowledge into weed science curriculums to “produce better weed scientists for the future.” This desire to build a better weed scientist is commendable for the farmer of the future, but if we exhaust the tools available today waiting for the solutions, there will be no need to build a better weed scientist because we might have already built the better weed, which makes producing crops impossible. Imperfect recommendations and possible solutions seem much more desirable than perfect and impossible.
