Objective
STREAM 1:
Objective 1: (Sharpe) Use successional and reconciliation ecology principles to manage kochia.
We will collaborate with non-profits (Redberry Biosphere Region, Ducks Unlimited Canada) to evaluate an agroecosystem diversification approach via marginal areas conversion or discrete patch areas to perennial forages for kochia management. We will collaborate with the Bridge to Land-Water-Sky Living Labs project as the diversification approach is compatible and complimentary to their (and our) ongoing work and occurring in a similar locale.
Objective 2: (Tidemann) Identify within year management tactics for wild oat and kochia with the best return on investment.
Identify which integrated weed management tactics have the most impact on wild oat and kochia populations in a single year to identify a ‘starter tactic’ or ‘starter package’ for producers for adoption of integrated weed management. Often, we present complex, layered strategies which can lead to overwhelm and inactivity. This project would provide a stepping stone for which tactics to incorporate for the biggest return on investment, and which layer best combined with seeding rate. This will be investigated through use of multi-site, replicated over years, plot studies. This would provide a potential strategy for working farmers from herbicide reliance to full integrated weed management systems using a step-wise adoption approach through the most valuable tactics, and is complementary to projects investigating fully integrated strategies.
Objective 3: (Geddes) Determine the long-term impact of crop rotation and crop life cycle diversity on multiple herbicide-resistant kochia after 12 years (years 9-12).
A small-plot field experiment located at Lethbridge, AB will be continued through years 9-12 to determine the long-term impact of crop rotation and crop life cycle diversity on multiple herbicide-resistant kochia populations.
Objective 4: (Geddes) Develop an integrated strategy to quickly control multiple herbicide-resistant kochia outbreaks.
A two-year small-plot experiment will be conducted at Lethbridge, AB and Scott, SK to determine the impact of crop competition (fallow control vs. spring wheat vs. winter wheat vs. fall rye), tillage (with vs. without), and a residual preemergence herbicide (with vs. without sulfentrazone) on herbicide-resistant kochia populations.
Objective 5: (Geddes) Understand the interaction of fenoxaprop and pinoxaden for management of Group 1-resistant and susceptible wild oat.
Whole-plant dose-response bioassays will be used to evaluate the interaction of fenoxaprop and pinoxaden for management of Group 1-resistant and susceptible wild oat populations, and understand the implications for selection of Group 1 resistance in wild oat.
Objective 6: (McKenzie-Gopsill) Evaluate cultural and mechanical practices for suppressing wild oat and kochia in specialty crops in rain-fed and irrigated systems.
Evaluate cultural and mechanical practices including changes to seeding rate and method and the use of living cover crop mulches as well as in-season mechanical weeding for suppression of wild oat and kochia in specialty crops including potato, flax, and hemp under rain-fed and irrigated systems.
Objective 7: (Smith) Characterize chemical composition and degree of variability of wild oat and kochia surface wax and identify gene targets for wax manipulation.
The activity will generate new knowledge of wax chemistry, wax distribution and genetic variability for the two species using modern analytical methods. This could be applied to aid in choice of development timing of herbicide applications, and the selection of appropriate detergents and adjuvants for better herbicide adherence and penetration, thus ensuring that applications are optimally tailored for weed variety and growing conditions. The identification of genes involved in wax biosynthesis, and particularly its regulation, would provide potential targets for control utilizing RNA interference technologies.
Objective 8: (Robinson) Evaluate options for RNAi applications for wild oat in-crop and non-selective pre-seeding timings.
We will leverage ongoing research on RNAi development for kochia management and pipelines developed for FHB management. While RNAi is typically sought after for selective management, identifying options for nonselective control for all vegetation at the pre-seeding timing would provide additional options for both kochia and wild oat. Additionally, selective control of wild oat within targeted crops will be evaluated.
STREAM 2:
Objective 9: (Zhang) Metabolite/protein profiling to understand the mechanisms of auxinic-resistant kochia and triallate-resistant wild oat.
This activity will involve the targeted quantification of representative herbicides (triallate, pyroxasulfone, fluroxypyr, dicamba) in plants to understand their translocation mechanisms. Using untargeted profiling of weed metabolites/proteins in treated and untreated, resistant and sensitive kochia and wild oat lines by UHPLC-ESI MS, to identify metabolic/proteomic differences related to Non-Target Site Resistance (NTSR). Additional targeted metabolite monitoring will be conducted using stable-isotope labeled herbicides (commercially available), or enzyme inhibitors, to monitor herbicide metabolic mechanisms in weeds and uncover the fate of herbicides in resistant plants. High throughput screening method will be developed to monitor potential NTSR metabolite biomarkers.
Objective 10: (Geddes) Understand the current status and impact of triallate-resistant wild oat across the Canadian Prairies and whether it is related to pyroxasulfone resistance.
Whole-plant bioassays will be used to conduct a survey of triallate resistance in wild oat populations across the Canadian Prairies. This will be the first survey of triallate-resistant wild oat since 2007-2009. Key populations will be used to evaluate the relationship between triallate and pyroxasulfone resistance.
Objective 11 a: (Nketia/Bais/Benaragama/Gulden) Develop site-specific approaches to detect HR weed escapes during the growing season across different crop types.
This objective focuses on developing robust, site-specific, and scalable tools to detect kochia and wild oat under varying densities and distributions within three major crops: wheat, canola, and lentils. This will enable us to detect weed escapes after herbicide application using UAV-based platforms. We will investigate: (a) the feasibility of training a single deep learning (DL) model to detect both kochia and wild oat across different densities, distributions, and crop backgrounds. (B) Early detection of HR populations using the spectral changes in HR weeds following herbicide application, and (C) determine the optimal time window and spatial resolution during the crop growth stages for post-herbicide application
Objective 11 b: (Nketia/Shirtliffe) Extend and refine satellite-based monitoring for large-scale HR weed patch detection across different crop types at the field scale.
Leveraging progress made under ADF 20190343, this objective will advance the scalability of satellite-based methodologies previously developed solely for Kochia by extending them to include Wild Oat detection. The precision HR weed detection solution will target both Kochia and wild oat using high spatio-temporal resolution imagery at the within-field scale. We will use a deep learning-based pipeline that enhances existing solutions by harmonizing optical (Landsat-8, Sentinel-2) and radar (RADARSAT, Sentinel-1) platforms to produce a super-resolution spectral imagery (1–3m). This super-resolution imagery, coupled with deep learning techniques, will allow for within-field HR weeds detection at near-real-time and support extrapolation to regional scales.
Objective 11 c: (Benaragama/Nketia/Gulden) Test and compare the efficacy of site-specific weed management strategies based on the developed AI solutions.
The weed detection models developed in objective 16a and 16b will be used to test their efficacy in site-specific weed management. Here, we will deploy spot spraying options such as an agriculture spray drone and or ground sprayers that support camera, prescription maps, and sensor integration to apply herbicides for weed escapes. The operational feasibility of integrating spot-spraying or other alternative precision spraying into commercial farm management practices will be evaluated. This will allow targeted herbicide treatments to detect HR weed patches (both small and large). Additionally, it will enable the comparison of the effectiveness of precision spot spraying with conventional broadcast herbicide application methods.
Objective 12: (Sharpe/Brackenridge) Develop an ecological-based, agro-ecosystem decision support system to help growers reduce herbicide-resistance risk due to kochia and wild oat.
We will develop a user-friendly support system for growers to understand how their management choices will affect wild oat and kochia in a simulated environment. A similar approach has been undertaken for ryegrass in Australia (RIM). The model takes inspiration from other ecological-based models like the Decision Support System for Agrotechnology Transfer (DSSAT), which simulate crop growth based on environmental and management inputs, but with the emphasis on the survival, reproduction, escape, and resistance selection of these weeds instead of yield-based outputs for the crops. We will work to integrate field imagery and detection models to estimate and use actual weed densities as inputs. The system will use a time-series approach with crop rotation and weed ecology to model the response of these weeds over time, costs of management, and risks to production from resistance evolution. We will leverage the vast amount of previous, ongoing, and proposed research on wild oat and kochia management to parameterize the models.
Objective 13: (Konkin) To investigate mutation rates in the kochia genome.
Characterize the rate and location of mutations in the kochia genome in order to improve management strategies including 1) estimate herbicide resistance risk due to standing variation for objective 12, 2) informing the design of RNA interference guides, and 3) helping predict the lifespan of herbicide.
Project Description
Herbicide-resistant kochia and wild oat are among the top threats to sustainable crop production in western Canada. Integrated pest management (IPM) is an important strategy but the chemical back-bone is breaking due to resistance evolution. Cultural strategies must be tested and promoted to supplement the loss of chemical strategies, but particular strategies are species-specific for wild oat and kochia, as these weeds have very diverging ecologies. The detection of herbicide-resistant biotypes and their distribution in the field are critical information to inform grower strategies but detection tests for triallate-resistant wild oat and auxinic-resistant kochia biotypes are not developed, nor is season-long remote sensing detection of both species in the field currently viable. Furthermore, there currently does not exist a system to synergize and deliver Prairie-focused research to growers in a format which is easily disseminated and used. The project aims to: 1) advance and study cultural and mechanical integrated weed management strategies in a variety of cropping systems, 2) advance bioassays for triallate-resistant wild oat, tissue testing assays for triallate-resistant wild oat and auxinic-resistant kochia, and enhance in-field remote sensing capabilities for wild oat and kochia, and 3) develop a lightweight, user-friendly decision support system tool based on weed ecology, weed management strategies, and known resistance risk to help growers identify and test strategies via simulation.
