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The herbicide flufenacet, applied in pre-emergence, acts as an inhibitor of the synthesis of very long-chain fatty acids (HRAC group 15) and is an important component in the control of grasses in winter cereals, especially when resistance to other herbicide groups already exists. However, decreases in sensitivity due to increased flufenacet metabolism by glutathione transferase activity have already been described in some black-grass (Alopecurus myosuroides Huds.) populations. So far, little is known about the mechanisms of gene regulation in metabolically resistant weeds. Therefore, we aligned RNA-seq data from two sensitive black-grass populations and two black-grass populations with reduced sensitivity to flufenacet against the recently sequenced black-grass genome. In a differential gene expression analysis, an upregulation of genes involved in metabolic detoxification pathways, such as cytochrome P450 monooxygenases (CYPs), glycosyltransferases (GTs), glutathione transferases (GSTs) and ATP-binding cassette (ABC) transporters was observed. It was found that 7% of the GST genes in the two populations with reduced flufenacet sensitivity were significantly upregulated even without any herbicide application (constitutively) when compared to the two sensitive populations. Three of these genes are located next to each other on the third chromosome and represent a cluster. For each of the upregulated GST genes 3,5 kb of the upstream promoter region were investigated in silico for potential transcription factor binding sites (TFBSs). Some of them share common upstream motifs, such as an ocs element, previously described as enhancer element for GSTs. In addition, other motifs could act as potential binding sites for transcription factors (TFs) that were found upregulated. These belonged to various classes including WRKYs, basic helix-loop-helices (bHLH), basic leucine zippers (bZIP) or MADS-boxes. A better understanding of the regulation of resistanceassociated genes can contribute to improve diagnosis of herbicide resistance and help in predicting the evolution of cross-resistance, as well as contribute to the search for new active ingredients.

Herbicide Resistance is a major issue in weed control. Prediction tools can help to detect herbicide resistance development in early stages and enable farmers to take countermeasures. These tools can be simulation models combining population dynamics and genetics or AI methods like random forest. To evaluate and train prediction models the data base used is important. Models using population dynamics and genetics depend on a numerous number of plant physiological traits such as seed dormancy, germination probability or seed production, and knowledge about the genetical inheritance (how many genes are involved). Furthermore, the initial conditions like the distribution of the seedbank or proportion of resistant plants in the population are important. To train an AI based prediction tool a large data set of field history data together with the resistance status of the fields is needed. We show that a random forest model trained with an artificial data set generated by a mechanistic model (HERRMANN, 2000) is able to make predictions of the resistance status for real data sets with acceptable accuracies. Simulated model data are therefore equivalent to field data and can be used to investigate the effect of sampling schemes on the detection of resistance.

Herbicides are an important technology in the Integrated Weed Management (IWM) tool box aiming to control weeds in modern agriculture. Prediction tools to evaluate the risk of resistance evolution will greatly help to choose the best IWM strategy adapted to the local field situation. In a previous work (HERRMANN et al., 2016) a random forest risk assessment model based on a data set comprising field history, management, and resistance status of Alopecurus myosuroides populations in Southern Germany was created. In this study transferability of the model with respect to regions and comparable weeds was analysed based on a similar dataset from a region in Northern France. The data from France also contained information on Lolium spp. The data related to Germany and France were subjected to a cross-validation procedure by interchanging test and training data. Results showed that acceptable predictions can be obtained for training data from Germany applied to France and vice versa. Resistance status in LOLSS samples from France can be predicted with a good accuracy based on a combined training set of A. myosuroides samples from Germany and France.

Black-grass (Alopecurus myosuroides Huds.) and rye-grass (Lolium spp) are very competitive grass weeds. During the last decades, they have evolved resistance to post-emergence herbicides, in particular ACCase- and ALS- inhibitors. This requires more complex weed management strategies. To ensure good control of both weeds, pre-emergence treatments become increasingly important. Particularly flufenacet has become a key herbicide for the control of multiple-resistant black-grass and rye-grass. Yet, in some of those populations, reduced flufenacet efficacy start to be observed. In a screening with black-grass and rye-grass populations of worldwide origins, most populations could be controlled with the registered field rate of flufenacet, however differences in the level of flufenacet efficacy were observed and were in correlation with enhanced flufenacet metabolism. This was particularly the case for rye-grass populations. The use of flufenacet in mixtures with diflufenican, particularly in combination with flurtamone or metribuzin or aclonifen, improved the efficacy significantly. In decreased flufenacet sensitive populations, the efficacy of other pre-emergence herbicides like pendimethalin, prosulfocarb, S-metolachlor, dimethenamid-P and pethoxamid, was also significantly decreased whereas other herbicides like pyroxasulfone or diflufenican remained highly active. This decreased efficacy of flufenacet as well as S-metolachlor was associated with higher metabolism involving glutathione-s-transferases. Although differences between populations were sometimes relatively small, best weed management practices (e.g. application of full dose rates and mixtures) should be applied to reduce selection pressure and prevent the development of resistance. This is particularly important as flufenacet is one of the few still active herbicides suitable for the control of multiple-resistant grass weeds. Use of mixtures associated with agronomic solutions in an Integrated Weed Management approach has to be the main approach.

The repetitive use of herbicides as major tool to control troublesome weeds in agriculture caused an increase in resistant weeds lately, especially when Integrated Weed Management principles were ignored. In a case study approach we sampled blackgrass (Alopecurus myosuroides Huds.) in three distinctive locations for at least 3 years. Based on field infestation level, greenhouse biotests and laboratory analyses we grouped 23 fields as resistant (R), 28 fields as less sensitive (I) and 39 fields as sensitive (S) with regard to their ALS resistance status. Field history information was collected for 90 fields. Variables regarding the frequency of 1) summer crops, 2) winter cereals, 3) ploughing, 4) herbicide use, and 5) early versus late seeding were calculated. Fields with a higher frequency of summer crops, ploughing and later sowing dates in the crop rotation were less frequently grouped into R and I. No relationship was found between the number of modes of action used and the resistance status. Intensity of ALS-inhibitor use and use of grass herbicides played a role to distinguish resistant from sensitive fields. Our results suggest that cultural measures to bring the blackgrass population size to lower levels are more important than the selection by the herbicide.

Inhibitors of very long chain fatty acid synthesis (VLCFAs, HRAC group K3) including flufenacet-based products are key herbicides in weed management strategies in particular of Western European cereal growing areas. They offer an alternative mode of action to those of frequently applied post-emergence active ingredients e.g. ACCase and ALS chemistries. In comparison, herbicide resistance to VLCFAs inhibitors develops considerably slower. Yet, resistance to the ‘low-risk’ herbicide flufenacet was documented in multi-resistant ryegrass (Lolium spp.) in the northwestern United States. A 46-fold resistance shift in a population from Washington State was observed in a dose-response bioassay. The resistance levels described in black-grass (Alopecurus myosuroides Huds.) field populations are lower and generally within the range of environmentally caused inconsistency in efficacy. An artificial selection of two UK multi-resistant black-grass populations with flufenacet applied annually for eight successive years resulted in resistant progeny surviving the field rate. Besides characterizing the degree of resistance in black-grass and ryegrass populations, we demonstrated that flufenacet resistance in these populations was caused by enhanced metabolism. No cross-resistance between flufenacet and pyroxasulfone, the newest VLCFAs-inhibiting herbicide, occurred in the ryegrass population. A good understanding of the resistance mechanism and early diagnostics can help preserve the efficacy of flufenacet.

A population dynamic model was combined with a genetic model and embedded into a cellular automaton. The model was evaluated with data from two three year field trials which were conducted on commercial fields in Lower Saxony and Saxony where target-site resistance to acetolactate synthase (ALS) inhibitors was detected in Apera spica-venti. The cropping system consisted of continuous winter wheat in the trial period. On four plots different herbicide strategies were tested. These were continuous application of a soil herbicide, alternation between ALS inhibitor and soil herbicide, continuous use of an ALS inhibitor and two applications per growing season with different mode of actions (MoA). In the beginning of the trial soil samples were taken to estimate the number of viable seeds in the seed bank. This data was used to produce seed distribution maps by interpolating the estimated seed data over the field. These seed maps were then used as the initial seed bank in the model and simulations over three years were executed with the assumption of herbicide use as conducted in the field trial. A comparison of the model output with the field data showed very good analogies in the weed density. Also the development of resistance was reproduced well. The model can now be used to assess herbicide management strategies concerning the development of herbicide resistance for A. spica-venti.

The repetitive use of herbicides of a given mode of action as primary tools to control weeds in simplified cropping systems has led to the development of resistant populations. This study reports preliminary results of the infestation level and the herbicide resistance structure of blackgrass (Alopecurus myosuroides Huds.) in circa 40 neighboring fields in each of three locations in Southern Germany during 2010-2012. In each location one field with confirmed field resistance served as a starting point to survey the surrounding fields. Field infestation was assessed at the end of the season where seeds were harvested. Based on greenhouse biotests nearly all tested fields showed resistance to ACCase, but the pattern of ACCase mutations and metabolic resistance to fenoxaprop-ethyl showed differences according to space and time. High resistance to ALS was found in several fields in two locations where ALS-resistance was previously confirmed in a single field only leading to severe crop failure. Interestingly, either Pro197 or Trp574 seemed to dominate in these two locations, while almost no ALS target-site mutations were found in the third location. Target-site mutations appear to be the major mechanism for these early cases of ALS-resistance. A few fields also showed non-target site resistant plants to mesosulfuron-methyl. This unique data set provides new insights into the structure and development of 1) an established resistance (ACCase-inhibitors) and 2) a developing resistance (ALS-inhibitors). Multivariate analysis including greenhouse biotests data and target-site analyses corresponding to mutations conferring resistance showed that even spatially closely related fields develop different herbicide resistance patterns. This suggests that weed control measures have a major impact within each field and that resistance can evolve independently. In the current situation black-grass infestations can still be controlled in most of the fields. Integrated Weed Management tools can contribute to keep the selection pressure low and slow down resistance development.

Weed control failures due to herbicide resistance are an increasing and worldwide problem significantly impacting crop yields. Herbicide resistance due to increased herbicide metabolism in weeds is not well characterized at the genetic level. An RNA-Seq transcriptome analysis was used to identify genes conferring metabolism-based herbicide resistance (MBHR) in a population (R) of a major global weed (Lolium rigidum), in which resistance to the herbicide diclofop-methyl was experimentally evolved through recurrent selection from a susceptible (S) progenitor population. A reference transcriptome of 19,623 contigs was assembled using 454 sequencing technology on a cDNA library and annotated using UniProt and Pfam databases. Transcriptomic-level gene expression was measured using Illumina 100 bp reads from untreated control, mock, and diclofop-methyl treatments of R and S. Due to the established importance of cytochrome P450 (CytP450), glutathione-S-transferase (GST), and glucosyltransferase (GT) genes in MBHR, 11 contigs with these annotations and higher constitutive expression in untreated R than in untreated S were selected as candidate genes for hypothesis testing, along with 17 additional differentially expressed contigs with annotations related to metabolism or signal transduction. In a forward genetics validation experiment, higher constitutive expression of nine contigs co-segregated with the resistance phenotype in an F2 population, including 3 CytP450, 3 GST, and 1 GT. At least nine genes with heritable increased constitutive expression are associated with MBHR trait. In a physiological validation experiment where 2, 4-D pre-treatment induced diclofop-methyl protection in S individuals due to increased metabolism, seven of the nine genetically-validated contigs were significantly induced. These data help explain accumulation of resistance-endowing genes and rapid evolution of MBHR, and provide the opportunity to improve diagnostics of MBHR using molecular tools such as transcriptional markers.

Due to herbicide selection pressure metabolic resistance has evolved in many weed species. In this paper we analyse the interaction between the branched chain amino acid (BBC) pathway and detoxifying pathways for herbicide breakdown. The four phase detoxification pathway of herbicides comprising the action of P450, GST, glycosyltransferase and ABC transporter is modelled by a system of coupled enzyme kinetic reactions represented by nonlinear differential equations. The herbicide under consideration inhibits the enzyme ALS, which is the key enzyme for the biosynthesis of branched amino acids. For the kinetics of ALS a Monod approach is employed with a binding site for the inhibitor. Synthetic and detoxification pathways are coupled. The model is used to study the production of branched amino acids under the action of ALS inhibitors for different structures and modes of action of the detoxification pathway. The model is capable of generating typical dose response curves and their shift in dependence of the activity pattern of the enzymes of the detoxification pathway of the inhibitor.

Cereals are major crops used for food and feed. By 2050, the world population is expected to be close to 10 billion requiring a doubling of the food production from a fixed area of arable land. The control of weeds in cropping systems is one key step to optimize yield. In the last several decades, herbicides have become the most effective management tool for adequate weed control. However, their repetitive use, as well as the limited number of modes of action (MoA) available, has led to the development of resistance in weeds. It has become imperative to change practices that lead to the development of weed resistance in order to protect those MoAs which are still effective in cereals. Several mechanisms of resistance have been developed by weeds to survive herbicide applications. Among them, gene mutations reducing or inhibiting herbicide binding by conferring amino-acid changes in a target enzyme (Target Site Resistance, TSR) and detoxification of the herbicide (Enhanced Metabolic Resistance, EMR) are the main mechanisms in key grass weeds found in cereal crops. These two mechanisms have been extensively studied during the last years and, thus, enabled the development of analytical tools for resistance diagnosis. Sustainable strategies for weed management using herbicides rely on accurate resistance diagnostics that permit optimization of treatment solutions that will lead to herbicide longevity. Greenhouse and laboratory tests used for resistance diagnosis will be reviewed with an emphasis on biochemical and molecular biology technologies. Cases of resistance to ACCase inhibitor herbicides will be presented as examples. Finally, the future development of these technologies will be discussed in the perspective of more practical uses.

Neighboring fields in three local areas of southern Germany have been investigated for the infestation level and herbicide resistance structure of black-grass (Alopecurus myosuroides Huds.). One field within a local area, each with confirmed resistance, served as starting point to survey the surrounding fields. Eighty percent of the fields had very few black-grass ears prior to harvest, with mainly ears from single plants spread over the field. Infestation in the other fields was in large patches or widespread, yet in most situations it did not significantly impact on yield level. Resistance to ACCase-inhibiting herbicides was found on all tested fields in each region. Plants with a target-site mutation to ACCase inhibitors were found in all samples; in addition, most plants also exhibited non-target-site resistance. All five mutations conferring ACCase resistance were found. The diversity of the mutations between areas suggests that resistance evolved independently in most fields. At two locations, each one with confirmed ALS resistance, additional fields with reduced ALS efficacy were detected. At one location only the mutation P197T was found, at the second the W574L mutation was also found. Target-site resistance appears to be the major mechanism for these early cases of ALS inhibitor resistance. Understanding the resistance development in individual fields and the spatial dynamics requires investigation over several years. The example of ACCase resistance to black-grass demonstrates how a specific mode of action can be rendered ineffective over a whole region. It provides a possible model for ALS-inhibiting herbicides. In the current situation, farmers are able to manage the black-grass infestation quite well. Resistance in a field seems to develop first in patches with high population densities. Resistance management should therefore focus on the management of the seed bank of each field, using all measures to keep the population pressure low.

In the last years in practice, soil acting herbicides have become the backbone for the control of heavy infestations of grass weed populations. The known unsteady efficacy of HRAC group K1, K3 and N herbicides is controversially discussed and became linked with the development of herbicide resistance. In general, the testing of soil-acting compounds even under controlled glasshouse conditions as well as the confirmation of possible resistance is difficult and needs specific methods and a careful result interpretation. The reliability of test methods is therefore of extreme importance. The reference testing procedure such as plant density, irrigation and temperature conditions have been shown to influence the activity of the soil applied herbicides and to result in extreme differences in the efficacy levels in general, but also in differences between resistant and susceptible biotypes. In our trials, the seed density could be ranked as the most sensitive factor. Of the tested products, Cadou SC was the most consistent herbicide followed by Boxer and Stomp Aqua. Possible reduced efficacy effects in glasshouse trials could not be verified by field trial results. The evaluation of field trials from Northern Germany did not show a significant decrease in sensitivity of black-grass (Alopecurus myosuroides Huds.) to flufenacet or flufenact plus diflufenican. It could be shown that seasonal variation in soil moisture, amount of rainfall, temperature conditions and application timing all influenced the efficacy level in the field the most.

Introduction: Herbicides are an important technology in the Integrated Weed Management (IWM) tool box aiming to control weeds in modern agriculture. Prediction tools to evaluate the risk of resistance evolution will greatly help to choose the best IWM strategy adapted to the local field situation. These comprise classical simulation models, mechanistic models (MMs), combining population dynamics and genetics, and recently artificial intelligence (AI) methods such as random forest. In this paper, both approaches are compared. Materials and methods: Artificial data were generated by an MM and used as training dataset for a random forest classifier. Field history information was taken from two previous studies. The data include the field histories and resistance status of Alopecurus myosuroides of 98 fields from the Hohenlohe area in Germany and 131 from the Champagne area in France. Results and discussion: With accuracies of approximately 80%, the results obtained by the random forest method applied to model-generated data and real field data, respectively, are well comparable. This concerns the ranking of prediction variables and the prediction of the resistance status of a real field and a "model field". Predictions with model outcomes as training sets and, vice versa, predictions of a "model field" with real data as training sets and predictions by splitting of field data could be made with nearly the same accuracies. Conclusion: Complementarity is shown between both approaches with the advantages of AI such as random forest to avoid approximations inherent to complex MMs. [ABSTRACT FROM AUTHOR]

Herbicide resistance has become a major issue in recent decades. Because diagnostics is still expensive, prediction models are helping to assess risks of resistance evolution. In this paper the influence of weed management on the evolution of resistance of the grass Alopecurus myosuroides Huds to ALSinhibitors is investigated based on field history data from two regions, Hohenlohe in Germany and Champagne in France respectively. Champagne data also comprise information on Lolium spp. Using a random forest method variable importance and performance measures were obtained for a large number of single analyses allowing for a statistical analysis of the four performance measures, type I error, type II error, AUC and accuracy. It could be shown that acceptable predictions can be obtained for training data from Hohenlohe applied to Champagne and vice versa. It turned out that in nearly all analyses false negative classifications are more frequent than false positive classifications. Based on a combined training set of A.myosuroides samples from Hohenlohe and Champagne resistance status of Lolium spp. from the Champagne dataset can be predicted with a good accuracy. This suggest that resistance evolution to ALS-inhibitors of the two grasses are closely related. This work is a first step to set a simple herbicide resistance prediction tool to the users based on field history weed management data. [ABSTRACT FROM AUTHOR]