PPO-inhibiting herbicides are widely used to manage weeds in different cropping systems, yet resistance evolution threatens their long-term efficacy. Here, we investigated the molecular basis of resistance to PPO-inhibiting herbicides in Bassia scoparia biotypes collected from four locations in North Dakota, USA. Greenhouse dose–response assays revealed high levels of resistance to saflufenacil and carfentrazone-ethyl, while fomesafen retained full efficacy across all biotypes. Resistant plants did not show increased copy number or elevated expression of PPO1 or PPO2 . Sequencing of survivor plants revealed conserved PPO2 sequences, but consistent target-site substitutions at position F454 in PPO1, including F454I, F454L, and F454V. In vitro enzyme assays demonstrated that these substitutions impair PPO1 sensitivity to saflufenacil and carfentrazone-ethyl, but not to fomesafen. Ectopic expression of B. scoparia PPO1 F454 mutant variants in Arabidopsis thaliana conferred tolerance to saflufenacil and carfentrazone-ethyl, but not to fomesafen, supporting greenhouse and in vitro results. Molecular modeling indicated that the conformational flexibility and interaction profile of fomesafen enables it to maintain binding to mutated PPO1 variants, in contrast to the more rigid structures of saflufenacil and carfentrazone-ethyl. A yeast-based complementation system further confirmed that F454 substitutions decrease herbicide sensitivity. In addition, developmental profiling showed distinct expression patterns of PPO1 and PPO2 during early growth stages in B. scoparia and Amaranthus spp., highlighting isoform-specific roles. Together, these findings represent the first reported PPO1 target-site mutations in a broadleaf weed species as a key mechanism of resistance and highlight that fomesafen is effective to control resistant B. scoparia populations. ### Competing Interest Statement The authors have declared no competing interest.

Diversified cropping systems in the semi-arid Canadian prairies comprise major cereal, oilseed, and pulse crops. Further diversification with minor oilseeds, such as oriental (Brassica juncea), industrial (Brassica carinata L.), and yellow (Sinapis alba L.) mustard, camelina [Camelina sativa (L.) Crantz], and flax (Linum usitatissimum L.) could enhance productivity and sustainability. This 5-year (2018-2022) study, conducted at four sites (Brooks and Lethbridge, Alberta, and Scott and Swift Current, Saskatchewan), compared system productivity, expressed as annualized canola (Brassica napus L.) equivalent yield (CEY), and nitrogen use efficiency (NUE) of 14 cropping sequences, and examined the effect of stubbles, particularly minor oilseeds, on subsequent crop yields and the influence of soil and environmental driving factors. Averaged across sites, CEY was significantly higher for cropping sequences with minor oilseeds and pulses compared to continuous spring wheat (Triticum aestivum L.) or wheat with fallow. However, there were no significant differences in CEY among sequences with oilseeds and between sequences with oilseeds and field pea (Pisum sativum L.) versus lentil (Lens culinaris Medikus). While CEY and NUE were similar between sequences with minor oilseeds and canola, NUE (grain yield based) was significantly higher for the conventional (spring wheat, canola, and pulse) than diversified sequence with minor oilseeds. Furthermore, the minor oilseeds had no adverse effect on subsequent pulse grain yields. Growing season and July mean air temperature most significantly impacted productivity and NUE. Our findings reveal that diversifying existing cropping systems with minor oilseeds can sustain productivity while enhancing NUE in the semi-arid Canadian prairies.

The increasing prevalence of herbicide-resistant weeds in western Canada continues to reduce crop yields, increase herbicide expenditures, and threaten farm profitability. Knowledge-based integrated weed management strategies are necessary to mitigate further evolution and spread of herbicide-resistant weeds. Identification and differentiation of herbicide-resistant and -susceptible weeds can inform and appropriately target these strategies. However, traditional methods to identify herbicideresistant weeds are time-consuming, labor intensive and mosty require mature weed seeds. To improve the efficiency of herbicide-resistant weed identification, we utilized hyperspectral imaging integrated with machine learning to discriminate herbicide-resistant and -susceptible weed biotypes. Greenhouse and field experiments were performed at Lethbridge and Lacombe Research and Development Centres (Alberta, Canada) to evaluate resistance in kochia (Bassia scoparia (L.) A.J. Scott) to glyphosate (Group 9) and fluroxypyr (Group 4), and in wild oat (Avena fatua L.) to fenoxaprop (Group 1) and imazamox (Group 2). Hyperspectral data (400-1000 nm, 204 bands) were classified using quadratic discriminant analysis. In kochia, accuracies ranged from 70.9% to 90.4% for glyphosate and 72.9%-92.7% for fluroxypyr, when models were evaluated before and after herbicide treatment. In contrast, wild oat resistance was assessed after treatment only, with quadratic discriminant analysis achieving 87.9% accuracy for fenoxaprop and 75.1% for imazamox. These results demonstrate that hyperspectral can detect herbicide-resistant-related spectral differences before herbicide treatment and visible herbicide symptomology, allowing for earlier and more precise intervention. This study highlights the potential of hyperspectral imaging combined with machine learning as a high-throughput, non-invasive approach for detecting herbicide-resistant weeds. Overall, the findings support the integration of advanced digital technologies into sustainable weed management practices.

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023-2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using similar to 520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R-2 = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R-2 = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R-2 = 0.77, RMSE = 0.54% for MSI; R-2 = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions.

Herbicide-resistant weeds are a growing concern for Manitoba farmers. Continued monitoring is essential to understand how to mitigate and manage this increasing threat to crop production. A randomized-stratified preharvest survey of 155 annualcropped fields was conducted in 2022 to determine the distribution, frequency, and impact of herbicide-resistant weeds in Manitoba. Mature seeds were collected from all visible uncontrolled weed patches and tested for resistance to acetyl-CoA carboxylase (ACCase)-, acetolactate synthase (ALS), and 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS)-inhibiting herbicides using whole-plant bioassays. Overall, 584 weed seed samples were collected, representing 44 different species. Uncontrolled herbicide-resistant weeds occupied 72% of the fields surveyed and about 1.5 million ha of cropland across the province. Herbicide-resistant weeds were estimated to cost Manitoba farmers $77 million annually in reduced crop yields, quality, and increased weed management expenditures. Compared to the previous 2016 survey, a trend toward increasing ALS inhibitorresistant broadleaf weeds and decreasing herbicide-resistant grasses was observed, with some exceptions. Eleven herbicideresistant weed species were documented, four of which were not observed in the 2016 survey; including ALS inhibitor-resistant powellii S. Watson), and spiny sowthistle (Sonchus asper (L.) Hill). Manitoba farms are increasingly affected by wild oat (Avena fatua L.) and kochia (Bassia scoparia (L.) A.J. Scott) with resistance to multiple herbicide sites-of-action, leaving few alternatives for chemical management. The increasing impact of herbicide-resistant weeds in Manitoba emphasizes the critical need for adoption of integrated weed management where nonchemical tactics augment contemporary herbicidal weed control.

Despite the potential to improve nutrient cycling, weed suppression, and system resilience, mixed-species cover crops remain underutilized in organic irrigated systems. This study evaluated the influence of cover crop diversity and associated weeds on biomass production (both cover crops and weeds), as well as carbon (C) and nitrogen (N) acquisition, and weed suppression. Three summer cover crop-carrot rotation cycles (2018-2019, 2019-2020, and 2021-2022) were established on a clay-textured soil in a 50-ha organic pivot-irrigated field in Alberta. Cover crops included a polyculture (POLY) and monoculture of mustard (MUST; white [Sinapsis alba L.] and brown [Brassica juncea (L.) Czern.]), buckwheat (BWHT, Fagopyrum esculentum Moench), faba bean (FABA; Vicia faba L.), and a no-cover control (CONT). Cover crop performance varied by species and year. POLY consistently produced the highest biomass (up to 2.79 Mg ha-1) and, along with BWHT, achieved the greatest weed suppression (62%-72%), while FABA and MUST were least effective. FABA had the highest N concentration (27-32 g kg-1) and lowest C:N ratios (13-16), whereas BWHT had the highest C:N ratios (21-36). C and N uptake were generally greater in POLY (up to 1.20 Mg C ha-1; 74 kg N ha-1) and lowest in FABA or MUST, with POLY and BWHT accounting for the largest share of total biomass nutrient uptake (53%-69%). Overall, these findings demonstrate that cover crop performance is highly context-dependent, with POLY and BWHT offering more consistent benefits in biomass production, weed suppression, and nutrient acquisition, and highlight the complementary role of weeds in nutrient cycling.

Carrot (Daucus carota) is an important crop grown in Canada and globally. Fresh market carrots have strict cosmetic requirements to command full value at “Grade A” and are frequently downgraded for irregular shape, size, or pest damage. Organic farming presents challenges for nutrient management, soil health and pest control, which may be mitigated with cover cropping. A 3-year field experiment was conducted on a commercial organic farm to 1) test the effects of six preceding-year cover crop treatments compared to a weedy fallow control on carrot yield and quality, wireworm damage, reasons for downgrading, and populations of plant parasitic nematodes, and 2) characterize within-farm spatiotemperal variability in production to identify strategies to improve and stabilize economic return. Carrot yield (42–55 Mg ha−1), quality (39–92

Management practices and cultivars for canola ( Brassica napus L.) have evolved for seeding and harvest management systems including the adoption of straight‐cutting (S/C) over windrowing. We explored how manipulations to seeding rate, pod shatter reduction hybrid, and harvest method alter canola seed yield and quality. An experiment was conducted at five locations across the Canadian Prairies between 2018 and 2022, consisting of two pod shatter reduction hybrids with contrasting growth phenology sown at densities of 60, 120, and 180 seeds m −2 , and subjected to either windrowing at 60% and 90% seed color change (SCC), or S/C at 10% and 5% seed moisture. Irrespective of hybrid choice or harvest management, densities of 120 and 180 seeds m −2 provided high and stable yield relative to 60 seeds m −2 . Seed losses were minimal for both hybrids, but the late‐maturing cultivar expressed higher seed yield and oil concentration. Straight‐cutting at 10% seed moisture achieved the highest yields for both hybrids, but delays in S/C timing reduced its advantage over windrowing at 90% SCC. Yield components such as seed number and seed weight on secondary branches became critical to achieve high yields at lower seeding densities when environmental stress was low. While reducing seeding densities to cut costs can be tempting, the highest and most stable yields were achieved with a late‐maturing hybrid, sown at 120 seeds m −2 and managed with S/C at harvest. This study provides insights into how seeding density and harvest method interact to affect canola yield within a genetic × environment × management framework.

Estimating the nitrogen (N) content of crops is crucial for determining key indicators such as nitrogen use efficiency (NUE). Traditionally, most methods for assessing N content have been destructive, time-consuming, and labor-intensive. In this study, we present a non-destructive approach using unmanned aerial vehicle (UAV) multispectral imagery to estimate crop nitrogen content at various growth stages. Multispectral drone data were collected over canola and wheat fields at three growth stages across two experimental sites in Alberta, Canada, over two growing seasons (2023-2024). Simultaneously, leaf tissue samples were gathered from different nitrogen treatment levels, each replicated four times. Multiple machine learning (ML) models were developed and tested to predict plant nitrogen uptake. Our findings indicate that multispectral imagery can estimate N content in canola with a root mean square error (RMSE) ranging from 0.38 to 0.71 and a coefficient of determination (R-2) between 0.77 and 0.92. For wheat, the RMSE values ranged from 0.33 to 0.68, with R-2 values between 0.5 and 0.89. The models showed good transferability across both study sites and two years, suggesting the feasibility of scaling N-content estimation to broader areas. Overall, our results highlight the strong potential of UAV-based multispectral imaging as a reliable, non-invasive tool for estimating nitrogen-related parameters, including plant Nuptake and NUE.

Kochia [Bassia scoparia (L.) A.J. Scott] is a problematic tumbleweed that has been reported resistant to five herbicide sites-of-action. Recently, protoporphyrinogen oxidase (PPO) inhibitor-resistant kochia was documented in Saskatchewan (2021) and North Dakota (2022). However, its frequency and distribution remain unknown. Screening with saflufenacil (50 g ai ha-1) revealed PPO inhibitor resistance in two of 14 grower-submitted samples, but not in 882 samples from randomized-stratified surveys of Manitoba (2018), Saskatchewan (2019), and Alberta (2021). To-date, PPO inhibitor-resistant kochia has been confirmed in two municipalities in western Canada; the Rural Municipality of Newcombe in Saskatchewan, and Forty Mile County in Alberta.

Soybean (Glycine max (L.) Merr.) is a sub-tropical crop which thrives in warm soils. In the Northern Great Plains, early spring seeding exposes soybean to cooler soil temperatures. Slower early-season development under cooler conditions may reduce competitiveness with adapted cool-season summer annual weeds, increasing yield loss risk from interference. Knowledge of critical temperature below which growth and development stops, referred to as base temperature (Tb), could help breeders develop soybean cultivars that can extend their roots into cooler soils in early spring and compete better with weeds. Our objective was to determine the base temperature for root elongation (TbRE) of 10 divergent, commercial soybean cultivars grown in Manitoba. Seeds of each cultivar were incubated at 25 °C for 3 days to germinate. Germinated seedlings were transferred to growth pouches. The growth pouches were placed in four growth chambers each set to a different temperature (i.e., 15, 20, 25, and 30 °C). The experiment was repeated three times. Root images were captured at the time of and every 2 days after transferring germinated seeds to the growth pouches. The x-intercept method was used to determine TbRE. There was a relatively large range in TbRE among the 10 soybean cultivars ranging from 8.1 to 13.2 °C and it was not related to the cultivar’s maturity grouping. Cultivars with lower TbRE are expected to be able to explore and occupy greater soil volume under cooler conditions. These observed result warrants further investigation in the field.

Accurately estimating nitrogen (N) content in crops is essential for assessing nitrogen use efficiency (NUE), a critical parameter for optimizing fertilizer management and improving crop productivity. Traditional methods for N estimation are often destructive, time-consuming, and labor-intensive, making them impractical for large-scale applications. This study presents a non-invasive approach using unmanned aerial vehicle (UAV)-based hyperspectral imaging to estimate N content in canola and wheat at different growth stages. Hyperspectral images were collected at three growth stages during an experimental field trial conducted in Lethbridge, Alberta, Canada. Concurrently, leaf tissue samples were gathered from seven nitrogen treatment levels, each replicated four times, to serve as ground truth data. Several machine learning (ML) models were developed and evaluated to predict plant N-uptake. The results demonstrated that hyperspectral imaging could estimate N content with high accuracy. For canola, the root mean square error (RMSE) ranged from 0.51 to 0.65, with R2 values between 0.73 and 0.84. For wheat, the RMSE ranged from 0.28 to 0.49, with R2 values between 0.75 and 0.92. These findings highlight the potential of UAV-based hyperspectral imaging, combined with ML models, as a powerful and efficient tool for estimating N-uptake. This approach offers significant benefits for precision agriculture, enabling sustainable nitrogen management and improving crop productivity.

In the context of canola (Brassica napus L.)-winter wheat (Triticum aestivum L.) rotational systems, the timing of canola stubble availability and effective weed management play a crucial role in the production of a subsequent winter wheat phase. This study, conducted from 2018 to 2022 across the Canadian prairies, applied a genotype x environment x management framework to examine how manipulations to canola harvest management can help optimize winter wheat production. The factorial treatment structure included two canola hybrids (early- and late-maturing), three canola harvest management systems (early-timing and conventional windrowing at 40 % and 60 % seed color change, respectively, and straight-cutting at 10 % seed moisture), and three weed management treatments (pre-harvest herbicide for canola, pre-plant herbicide for winter wheat, and pre-harvest+pre-plant herbicides). Windrowing and pre-harvest herbicides were completed simultaneously by retrofitting the swather with an onboard sprayer. Across all 16 site-years, winter wheat planted after a late-maturing canola hybrid demonstrated comparable performance to that after early-maturing canola. However, delaying canola harvest reduced winter wheat yields. Conventional windrowing in conjunction with pre-harvest herbicide or preharvest+pre-plant herbicides improved winter wheat yields and enhanced weed management, while maintaining canola seed quality, as no herbicide residues were detected in the harvested seed. Our previous research indicated that in-crop herbicide applications are unnecessary due to the high competitiveness of winter wheat against weeds. This research reaffirms in-crop herbicides could be eliminated and underscores the competitiveness and sustainability that a winter wheat phase offers when integrated in Canadian Prairie cropping systems.

The agronomic and environmental benefits of diversified cropping systems have been well documented in the Canadian prairies. However, little is known about the profitability of diversified rotations with oilseeds, cereals, legumes, and specialty crops. This study consisted of two 5-year (2018-2022) experiments carried out at four sites in Saskatchewan and Alberta. Treatments were arranged in a randomized complete block design with four replicates. Net return (NR) was defined as total revenue minus total costs. Results showed diversified sequences with Oriental mustard, red lentil, yellow field pea and yellow mustard had higher NR than continuous wheat and wheat with chemical fallow sequences. Moreover, sequences diversified with quinoa, yellow mustard, field pea and wheat showed high NR across all sites. Wheat after chemical fallow in the wheat with chemical fallow sequence (wheat-wheat-chemical fallow-wheat-wheat) had high NR; however, this did not compensate for the loss of NR in the chemical fallow phase, resulting in the lowest NR. The inclusion of industrial, oriental, and yellow mustard in sequences with wheat and field pea decreased nitrogen cost by 30% compared to a continuous wheat sequence, concluding that such sequences not only improved NRs but also showed a significant reduction in nitrogen requirement costs.

Many farmers rely on herbicides as desiccants to dry crops before harvest and aid in harvest efficiency. Optimizing desiccant selection, application rates, and timing is critical for maintaining lentil yield and quality while minimizing herbicide residues. This study utilizes unmanned aerial vehicle-based hyperspectral imaging to assess desiccant efficacy in lentil crops across field trials at Saskatoon (2019, 2020) and Lethbridge (2023) research farms in Canada. Five conventional herbicide treatments, two application rates of an organic desiccant, and an untreated control were evaluated. Desiccation progress was monitored using visual dry-down ratings and plant moisture content at 0, 3-4, 7, 10, 14, and 24 days after treatment. Spectral data were collected using an aerial hyperspectral system and analyzed with partial least squares regression and support vector regression models to predict desiccation response. Year-wise regression analyses yielded R2 values of 47%–68% for visual ratings and 27%–63% for moisture predictions, while combined analyses showed R2 values of 65% and 67%, respectively, with support vector regression consistently outperforming partial least squares regression. The area under the desiccation progress curve for visual ratings, moisture, and normalized difference vegetation index quantified desiccant performance. Ammonium nonanoate (32%) exhibited the fastest desiccation, occasionally surpassing conventional herbicides. These results highlight the potential of unmanned aerial vehicle-based hyperspectral and machine learning for evaluating desiccation efficiency and plant moisture content. This approach advances precision crop management and enhances desiccant screening methodologies in modern agriculture.

Kochia [Bassia scoparia (L.) A.J. Scott] is an invasive tumbleweed in the North American Great Plains that is difficult to manage in croplands and ruderal areas due to widespread resistance to up to four herbicide sites of action, including auxin mimics (Herbicide Resistance Action Committee [HRAC] Group 4) and inhibitors of acetolactate synthase (HRAC Group 2), photosystem II (HRAC Group 5), and 5-enolpyruvylshikimate-3-phosphate synthase (HRAC Group 9). Poor B. scoparia control with protoporphyrinogen oxidase (PPO)-inhibiting (HRAC Group 14) herbicides was noted in a brown mustard [Brassica juncea (L.) Czern.] field near Kindersley, SK, Canada, in 2021. Similar observations were made in a sunflower (Helianthus annuus L.) field near Mandan, ND, USA, and in research plots near Minot, ND, USA, in 2022. Whole-plant dose-response experiments were conducted to determine whether these B. scoparia accessions were resistant to the PPO-inhibiting herbicides saflufenacil and carfentrazone and the level of resistance observed. All three B. scoparia accessions were highly resistant to foliar-applied saflufenacil and carfentrazone compared with two locally relevant susceptible accessions. The Kindersley accession exhibited 57- to 87-fold resistance to saflufenacil and 97- to 121-fold resistance to carfentrazone based on biomass dry weight at 21 d after treatment (DAT). Similarly, the Mandan accession exhibited 204- to 321-fold resistance to saflufenacil and 111- to 330-fold resistance to carfentrazone, while the Minot accession exhibited 45- to 71-fold resistance to saflufenacil and 88- to 264-fold resistance to carfentrazone. Substantial differences in visible control at 7 and 21/28 DAT were also observed between the putative-resistant and susceptible accessions. This study represents the first confirmations of PPO inhibitor-resistant B. scoparia globally and the fifth herbicide site of action to which B. scoparia has evolved resistance. It also documents this issue present at three locations in the Northern Great Plains region that occur up to 790 km apart and on both sides of the Canada/U.S. border.

From the early days of agricultural production in the 1800s through to the present day, farmers, agronomists, and other motivated people have worked to improve crop production through pest management and surveillance, selection of crop genotypes and agronomic innovations such as reduced and zero-tillage. These essential contributions also helped raise awareness of the practical problems that farmers have faced, of the potential solutions to those problems, and the problems that remain to be solved. In many cases, farmers have organized their efforts to support research to address agricultural challenges through commodity organizations who actively fund research, raise awareness of science, and encourage participation in activities such as pest monitoring and on-farm research trials. This review highlights some of the important contributions of Canadian community scientists. The future of a biovigilance approach to crop production depends on the continued participation of agricultural community members.

AbstractHerbicides have been placed in global Herbicide Resistance Action Committee (HRAC) herbicide groups based on their sites of action (e.g., acetolactate synthase–inhibiting herbicides are grouped in HRAC Group 2). A major driving force for this classification system is that growers have been encouraged to rotate or mix herbicides from different HRAC groups to delay the evolution of herbicide-resistant weeds, because in theory, all active ingredients within a herbicide group physiologically affect weeds similarly. Although herbicide resistance in weeds has been studied for decades, recent research on the biochemical and molecular basis for resistance has demonstrated that patterns of cross-resistance are usually quite complicated and much more complex than merely stating, for example, a certain weed population is Group 2-resistant. The objective of this review article is to highlight and describe the intricacies associated with the magnitude of herbicide resistance and cross-resistance patterns that have resulted from myriad target-site and non–target site resistance mechanisms in weeds, as well as environmental and application timing influences. Our hope is this review will provide opportunities for students, growers, agronomists, ag retailers, regulatory personnel, and research scientists to better understand and realize that herbicide resistance in weeds is far more complicated than previously considered when based solely on HRAC groups. Furthermore, a comprehensive understanding of cross-resistance patterns among weed species and populations may assist in managing herbicide-resistant biotypes in the short term by providing growers with previously unconsidered effective control options. This knowledge may also inform agrochemical company efforts aimed at developing new resistance-breaking chemistries and herbicide mixtures. However, in the long term, nonchemical management strategies, including cultural, mechanical, and biological weed management tactics, must also be implemented to prevent or delay increasingly problematic issues with weed resistance to current and future herbicides.

The evolution and spread of herbicide resistance among the weed community has increased interest in alternative weed management strategies such as harvest weed seed control. Western Canadian producers have begun adopting physical impact mills as an additional weed management strategy. A survey of early adopters of physical impact mill technology in Canada was conducted to better understand the motivations behind producers adopting, initial experiences, and research needs. Ten producers responded to the survey, accounting for 18 out of an estimated 30 impact mills in use in Canada, believed to be located primarily in the Canadian Prairies. These producers were mainly from larger farms (>4000 ha), equipped the majority of their combines (75% average) and used the mills in essentially all crops grown. The majority of respondents were located in Saskatchewan, with two mills being used in Alberta. Wild oat (Avena fatua L.) (60%) and kochia (Bassia scoparia (L.) A.J. Scott) (50%) were the weeds most frequently mentioned as specific motivators of impact mill adoption. Average increased fuel cost from the mill was estimated at CAD$3.46 ha-1, with average annual maintenance costs of about $1500 per impact mill. Producers relied on information from mill companies and other early-adopting farmers primarily, followed by extension talks and social media. Research needs were also identified by producers that could inform the future direction of harvest weed seed control research in Canada. Future research should focus on confirming efficacy, optimizing combine settings, and looking at integrated systems with precision agriculture technologies.