Weed resistance to different herbicide modes of action is driven by agricultural intensification

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Field Crops Research 292 (2023) 108819

Available online 7 January 2023

Weed resistance to different herbicide modes of action is driven by agricultural intensification

Philip E. Hulme

^*^,¹

Department of Pest Management and Conservation, Lincoln University, PO Box 85084, Christchurch, Canterbury, New Zealand

A R T I C L E I N F O Keywords:

Agricultural extension Developing economies FAO Glyphosate

Integrated weed management Sampling effects

Sustainable intensification

A B S T R A C T

Context or problem: The continued increase in numbers of herbicide-resistant weed species in field crops con- strains sustainable agricultural practices worldwide. Countries differ markedly in numbers of herbicide-resistant weed species in field crops yet the extent this reflects global variation in agricultural intensification is not known.

Objective or research question: To what extent does the global variation in the number of herbicide resistant weeds reflect differences in the magnitude of direct measures of agricultural intensification such as agrochemical inputs, indirect measures such as per capita GDP, or non-agronomic factors such as research intensity?

Methods: Best-subset regression analysis quantified whether national scale estimates of agricultural intensification such as per capita GDP, cropland area, as well as inputs of N-fertilizer and herbicide explained variation in numbers of herbicide-resistant weed species in field crops worldwide. The number of publications addressing herbicides was used as a proxy for sampling effort, while the time since the first record of resistance estimated the window of opportunity for weed species to become resistant. Analyses were undertaken across all herbicides as well as separately for four herbicide modes of action (ACCase, ALS, PSII and EPSPS inhibitors).

Results: Over 70% of the global variation in numbers of herbicide-resistant weed species was explained by national scale estimates of herbicide inputs, cropland area, per capita GDP as well as measures of research effort and the time since the first resistant weed was recorded. The explanatory ability of models for individual herbicide modes of action ranged from 40% (ACCase inhibitors) to 68% (PSII inhibitors), with per capita GDP and time since first record the most consistent explanatory variables.

Conclusions: Agricultural intensification, as captured by herbicide inputs and per capita GDP, was associated with increased numbers of herbicide-resistant weed species worldwide but the limited herbicide expertise in many countries means the scale of the problem is underestimated. The number of resistant weed species depended on how long resistance had been observed suggesting for many countries the problem will increase in the future, especially as different modes of action become more widely used.

Implications or significance: Many countries that have only recently recorded herbicide resistance in weeds are already on a trajectory for future increases but have limited capability to address this problem. Implementation of integrated weed management strategies to reduce the risk of herbicide-resistant weeds evolving should therefore be implemented proactively before the problem gets worse rather than reactively as has occurred in other countries.

1. Introduction

Since 1990 the quantity of herbicide applied globally to agricultural land has increased by more than 260% and now stands at over 3 million tonnes per annum (Fig. 1). Herbicides have played a central role in agricultural intensification that has resulted in progressive increases in crop yields worldwide (Gianessi, 2013) but in many cases this increase

in crop production has come at a cost of environmental degradation (Barbash et al., 2001; Davis et al., 2014). In addition, the intensification of agrochemical use has led to a progressive increase in herbicide-resistant weed species that now pose a significant constraint on sustainable agricultural practices around the world (Heap and Duke, 2018; Peterson et al., 2018). The vast majority of herbicide-resistant weed species occur in the major field crops grown worldwide (e.g.,

* Corresponding author.

E-mail address: [email protected].

1 0000-0001-5712-0474

Contents lists available at ScienceDirect

Field Crops Research

journal homepage: www.elsevier.com/locate/fcr

https://doi.org/10.1016/j.fcr.2023.108819

Received 8 July 2022; Received in revised form 23 December 2022; Accepted 4 January 2023

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canola, cotton, maize, rice, sorghum, soybeans, winter-, spring- and durum-wheat), as well as pulse crops and sown pastures (Heap, 2014).

In many cases, the problem of herbicide-resistant weeds in field crops has led to higher herbicide application rates (Renton et al., 2011) and the use of active ingredients that persist for longer in the environment (Devault et al., 2019). Furthermore, global patterns in the numbers of herbicide-resistant weed species are changing and although historically a problem for developed countries, the increasing intensification of agriculture in developing economies has resulted in a rapid increase in cases of herbicide-resistant weeds in these regions since 1990 (Heap,

2014; Peterson et al., 2018).

Although a link between herbicide usage and the subsequent likelihood of evolved herbicide resistance in weeds is generally expected (Liu et al., 2019; Peterson et al., 2018; Vencill et al., 2012), quantitative evidence from field surveys supporting such a relationship is largely absent. Epidemiological studies examining the relationship between historical herbicide use and resulting rates of herbicide resistance have rarely been explored due to limited availability of data on herbicide application rates (Baucom and Busi, 2019; Comont and Neve, 2021;

Squires et al., 2021). In the absence of data on herbicide application Fig. 1. Temporal trend in the amount of herbicide (1000 tonnes) applied to agricultural land worldwide between 1990 and 2018 presenting the trend for all herbicides. Data derived from information archived in the FAOSTAT database (Food and Agriculture Organisation of the United Nations, 2022) (fao.org/faostat) accessed on 22nd February 2022.

Fig. 2. Global patterns in the average annual quantity of herbicide applied per unit cropland (kg/ha) between 1990 and 2018. Data from derived from information archived in the FAOSTAT database (Food and Agriculture Organisation of the United Nations, 2022) (fao.org/faostat) accessed on 22nd February 2022.

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rates, epidemiological studies have had to rely on other agronomic and environmental covariates that are potentially linked to the likelihood of herbicide resistance (Mascanzoni et al., 2018). The few quantitative assessments to date stem from studies that found a positive relationship between the amount of glyphosate applied to crops and the frequency of resistance in a single weed species. Reduced sensitivity to glyphosate in populations of blackgrass (Alopecurus myosuroides) in the UK was correlated with the frequency of historical herbicide application (Comont et al., 2019). Across 105 central Illinois grain farms, glyphosate resistant common water hemp (Amaranthus tuberculatus) was greatest in fields with frequent herbicide applications (Evans et al., 2016). The extent to which these relationships hold for other regions, different herbicide modes or action and/or target weed species is not known.

While the few epidemiological studies have largely focused on trends at a national or regional scale of a single weed species, the number of herbicide-resistant weed species differs markedly worldwide (Heap, 2014; Heap and Duke, 2018) and might also be expected to relate to the substantial variation in herbicide inputs seen across the globe (Fig. 2).

Average annual herbicide inputs per area of cropland are particularly high in China, Argentina and New Zealand yet these are not all among the top ranked countries in terms of numbers of herbicide-resistant weed species (Heap, 2014). Thus, a larger scale perspective might shed further light on a long-standing agronomic problem. Consistent with this view is recent evidence that the total amount of herbicide used in a country has been found to be a significant predictor in the number of herbicide-resistant weed species found in barley, maize and wheat crops worldwide (Hulme, 2022a). However, an unresolved issue is whether the drivers of herbicide resistance found for individual cereal crops also hold across all agronomic situations and different herbicide modes of action.

Variation across countries in the type of field crops grown and the extent farmers implement different agronomic options such as crop ro- tations, strategic tillage or precision weed management will likely lead to herbicide resistance evolving at different rates across the world (Beckie et al., 2019; Owen, 2016). In addition, weed species vary in their resistance to different herbicide modes of action (Hulme, 2022b) and thus may not respond to external drivers in a similar way. The use of different herbicide active ingredients has varied considerably since 1990 (Kniss, 2017). Today, glyphosate is the most widely used herbicide worldwide accounting for almost one third of all herbicide usage, an order of magnitude greater amount used than either 2,4-D or atrazine, the next two most popular active ingredients (Maggi et al., 2019).

Equally, different active ingredients are not used to a similar extent around the world and this not only reflects the availability of off-patent generic herbicide formulations but also regulatory barriers (Donley,

2019). Thus, it might be expected that the large-scale drivers of weed resistance to different herbicide modes of action might differ across the globe.

Based on a previous large-scale analysis of the drivers of herbicide resistance in cereal crops worldwide (Hulme, 2022a), six variables assessed at a national scale are expected to capture agronomic and economic factors known to influence the number of herbicide-resistant weed species recorded in different countries (Table 1). Among the agronomic variables, herbicide input is widely understood to be important. Average annual rates of herbicide inputs into field crop production vary markedly across the globe (Fig. 2, Duke, 2018) and the magnitude of herbicide input per unit area of cropland has previously be shown to be an important predictor of the number of herbicide-resistant weed species in barley, maize and wheat crops worldwide (Hulme, 2022a). In addition, the more extensive the area of cropland that might be subject to herbicide application, the greater the likelihood that some weeds will evolve herbicide resistance due to the higher diversity of crop weeds as well as higher numbers of individual plants (Mulugeta et al., 2001; Vidotto et al., 2016) likely to be exposed to herbicides. Although not intrinsically an agronomic variable, per capita GDP is strongly related to agricultural intensification and can be used as a proxy for overreliance on pesticide inputs (Schreinemachers and Tipraqsa, 2012;

Tilman et al., 2011). For example, at a national scale, low levels of per capita GDP are associated with pesticide under-use whereas at higher levels of income there is increasing over-use (Ghimire and Woodward, 2013). Thus, a positive relationship between per capita GDP and the number of herbicide-resistant weed species in a country might be expected. However, crop management may also act to reduce the selection pressure on herbicide resistance (Beckie et al., 2019). The application of fertilizer is known to increase the competitiveness of field crops and thus reduce weed performance that could result in lower likelihood of weeds evolving herbicide resistance (Little et al., 2021). However, some weeds will respond more strongly to fertilizer application than the crop and may as a result become more abundant {Blackshaw, 2008}. This likely explains why at a large-scale, the relationship between N-fertilizer inputs and the number of herbicide-resistant weed species in a country is mixed, being negative for maize but positive for wheat crops worldwide (Hulme, 2022a).

In addition to agronomic variables or their socioeconomic proxies, two variables that capture sampling effort have recently been found to be particularly important correlates of the number of herbicide-resistant weed species worldwide: time since first record of resistance and herbicide research intensity (Hulme, 2022a). The first measure of sampling bias is the time since the first record of resistance to that mode of action was found in a particular country. From a simple probabilistic view, the longer the time since resistance to a particular herbicide mode of action was first recorded in a country would indicate a greater opportunity for weed species to be exposed to the herbicide mode of action and become resistant. Thus, global variation in the number of weeds resistant to a particular herbicide mode of action might simply reflect the length of time that the mode of action has likely been used in a country. The second measure of sampling bias is variation across countries in the intensity of research (and associated infrastructure) addressing herbicides and weeds as determined by the number of publications on these topics generated by a country. Lower research intensity in this area could be associated with less awareness of the issue by national agron- omists and/or the absence of any testing or diagnostic services that could confirm the occurrence of herbicide resistance. For example, higher records of herbicide-resistant weed species have been found to reflect greater outputs of research publications on herbicides (excluding those specifically on herbicide resistance) in those countries investing more heavily in agronomic and agrochemical research and development (Hulme, 2022a). Given this background, the analyses presented here aimed to assess how effective national-scale agronomic and socioeconomic variables might be in predicting the variation in numbers of herbicide-resistant weed species across the globe, and whether such Table 1

Description of the six explanatory variables included in the analysis of country- level variation in the number of herbicide-resistant weed species worldwide. For each variable, the expected association with the number of herbicide-resistant weed species is presented and supported by a brief rationale.

Variable Association Rationale

Cropland area + Greater diversity of weeds and frequency of herbicide exposure where there is more cropland area

N-fertilizer input - Greater N-fertilizer input should boost crop competitiveness and reduce weed performance Herbicide input + The more herbicide used in a country the

stronger the selection pressure for resistance Per capita GDP

(pcGDP) + Higher per capita GDP related to agricultural intensification and overuse of pesticides Research articles + The greater the research intensity on herbicides

in a country the more likely resistance will be detected

Year of first resistance record

+ The longer the period since herbicide resistance was recorded in a country the greater the opportunity for further evolution to occur

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predictions are similar for different herbicide modes of action. Such information will help point to the role that agricultural intensification plays in the evolution of herbicide resistance in weeds globally and identify options to address this problem at an international scale.

2. Materials and methods 2.1. Data sources

Data on the total number of herbicide-resistant weed species recorded in different countries was retrieved from the International Herbicide-Resistant Weed Database (www.weedscience.org) on 22nd February 2022 (Heap, 2022). Selection of the target herbicide modes of action was based on each having a minimum of 30 countries where resistance to the mode of action had been recorded. This ensured that the case to independent variable ratio was no less than 5 (Austin and Steyerberg, 2015). As a result, analyses focused on four main herbicide modes of action: HRAC groups 1 (ACCase inhibitors), 2 (ALS inhibitors), 5 (PSII inhibitors) and 9 (EPSPS inhibitors).

Socioeconomic and agronomic explanatory variables were either extracted directly or derived from data archived in the Food and Agri- culture Organisation FAOSTAT (fao.org/faostat) database (Food and Agriculture Organisation of the United Nations, 2022). Four explanatory variables that exhibited relatively low collinearity were selected: per capita Gross Domestic Product (in 1000 US$), nitrogen-based fertilizer input per area of cropland (kg/ha), herbicide input per area of cropland (kg/ha), and the cropland area (ha) of the country. Data for each of these variables was averaged over the period 1990–2018 (coinciding with the latest data in FAOSTAT). The FAOSTAT database breaks down herbicide inputs into several classes of active ingredient: phenoxy hormone products, triazines, amides, carbamates, dinitroanilines, urea de- rivatives, sulfonyl urea, bipyridyls, uracil, and others not elsewhere classified (Food and Agriculture Organisation of the United Nations, 2021). However, together all these classes account for only around 10%

of the total inputs of herbicides used in any year and were not recorded in a consistent manner across countries implying an absence of international standards for reporting herbicide use. Furthermore, these classes do not map readily onto the HRAC classification and provide no data on the global use of EPSPS or ACCase inhibitors. Since the use of different herbicide modes of action is poorly quantified worldwide, these more detailed data in the FAOSTAT database could not be used in subsequent analyses.

For each country, the earliest record of any herbicide resistance as well as the earliest record of resistance to each of the four modes of action were extracted from the International Herbicide-Resistant Weed Database (Heap, 2022). These dates were used to calculate the number of years since 2022 that herbicide resistance first appeared in order to provide a measure of the length of time available for herbicide resistance to evolve to that mode of action in other weed species. Previously derived data (Hulme, 2022a) on the number of research articles on

herbicides produced between 1990 and 2018 by each country, and archived in the Clarivate Analytics Web of Science, was used to measure research effort. To capture research effort on herbicides in each country but avoid circularity, the search term used ((herbicide and weed) NOT

‘herbicide resistan*’) explicitly excluded articles that directly addressed herbicide resistance. The distributions of the dependent and all explanatory variables were log10-transformed to reduce any hetero- scedastic biases and improve the linearity of the relationships in the regression models. All explanatory variables had, at most, only moder- ate collinearity (r<0.7, Table A1) since correlation coefficients between independent variables of >0.7 are indicative of when collinearity begins to severely distort model estimation and subsequent prediction (Dormann et al., 2013).

2.2. Statistical analysis

An initial Welch’s ANOVA was undertaken across all four herbicide modes of action to identify any differences in the mean values of the six explanatory variables (log10-transformed) that might lead to variation in the trends encountered (Table 2). Subsequently, best subset regressions were used to examine the relative importance of the six explanatory variables on the number of herbicide-resistant weed species across multiple countries. The analysis first examined the trends in herbicide- resistant weed species across all countries irrespective of herbicide mode of action. Subsequently, analyses compared the drivers of resistance for each of the four target herbicide modes of action but only for those countries where there was at least one record of a weed resistant to that mode of action. The rationale for the exclusion from the analysis of countries for which there was no record of herbicide resistance to a particular herbicide mode of action was that without independent data on the country-level inputs of particular herbicide modes of action it was not possible to tell whether these absences in herbicide-resistant weeds were simply due to the herbicide not being used at all.

For best subset regression, an information theoretic approach to model selection using the second order Akaike Information Criterion (ΔAICc) was used to rank all possible combinations of the explanatory variables (for six explanatory variables this results in 63 models) to identify their relative importance of the different explanatory variables (Hulme, 2022a). The best subset models were identified as those whose ΔAICc was within 2 units of the minimum AICc score across all models since these models are generally viewed as having substantial empirical support (Burnham and Anderson, 2002). To assess the relative importance of each independent variable, its relative contribution to all models identified among the best subsets was estimated by calculating its mean standardised effect (t value of the regression coefficient).

Where there were multiple different models among the best subset, the selection of the most parsimonious model targeted the model that included the fewest explanatory variables and retained similar predictive power as more complex models as determined by the ten-fold cross-validated R²(Chatterjee and Simonoff, 2013; Ledolter, 2013;

Table 2

Means and standard errors for the number of weed species in a country resistant to one of four herbicide modes of action and each of the six explanatory variables used in the regression analyses for each of four modes of action (MoA): ACCase, ALS, PSII and EPSPS inhibition. Herbicide inputs are aggregated across all herbicide modes of action since data for each mode of action were not available. The P value of a one-way Welch’s ANOVA (assuming unequal variances) on the logi10-transformed data is presented and where statistically significant variation was found across the four modes of action, different superscripts indicate significant differences between means as assessed by the non-parametric Games-Howell post-hoc test.

ACCase ALS PSII EPSPS P

Number of countries 45 50 40 30

Mean number of resistant weed species 3.49±0.47^a 7.80±1.36^b 5.75±1.08^{a, b} 4.50±0.9^{a, b} 0.032

Per capita GDP (1000 US$) 19.83±2.73 21.94±2.61 21.79±3.07 19.59±3.17 0.734

N-fertilizer input (kg/ha) 99.60±13.1 97.20±11.90 92.76±9.31 82.04±9.96 0.531

Herbicide input all modes of action (kg/ha) 1.64±0.24 1.64±0.22 1.77±0.25 1.86±0.30 0.654

Cropland area (x 10⁶ha) 2.04±0.59^b 2.10±0.58 1.97±0.66 2.24±0.70 0.695

Research articles (1990–2018) 252.49±90.80 231.40±82.00 270.00±102.00 303.00±133.00 0.894

Years since first record 21.62±1.54^a 21.34±1.31^a 32.90±1.70^b 14.83±1.00^c <0.001

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Lindsey and Sheather, 2014). Low Variance-Inflation Factors (VIF <2) of all parameters included in the final model as well as the assumption of normality and homogeneity of residuals were checked and confirmed for each parsimonious model selected. All analyses were undertaken in Minitab 20.2 (Lesik, 2018).

3. Results

3.1. Global patterns in drivers of herbicide-resistant weed species richness On average, 10.63±1.72 (mean ±standard error) herbicide- resistant weed species have been recorded per country across the 70 countries in which herbicide resistance has been found. Considerable variation existed among countries with the United States (85 species) and Australia (52 species) being among the highest but over 25% of countries having only a single case. The most parsimonious model included five of the six explanatory variables and explained over 70% of the variation in numbers of herbicide-resistant weed species as well as having good predictive power (Table 3). N-fertilizer input was not

retained in the model. All other explanatory variables were positively associated with the numbers of herbicide-resistant weed species recorded in a country. There was also support for pcGDP acting as a proxy for agricultural intensification through its correlation with both herbicide (r=0.466, df 68, P<0.001, Table A1) and N-fertilizer input (r=0.475, df 68, P<0.001, Table A1). Although herbicide input was included in the final model, research intensity (as measured by the number of published research articles on herbicides) had the largest standardised effect. The number of research articles on herbicides published by a country increased with the length of time since the first record of herbicide resistance (r=0.454, df 68, P<0.001, Table A1).

3.2. Broadscale differences among the four herbicide modes of action As might be expected, the average number of weed species per country found to be resistant to each of the four modes of action was lower than the global value (Table 2). There was a significant twofold difference between the average number of herbicide-resistant weed species recorded in a country that were resistant to ACCase inhibitors Table 3

Summary of the most parsimonious regression model drawn from the best subsets describing the role different explanatory variables in the number of herbicide- resistant weed species found across all herbicide modes of action and separately for ACCase, ALS, PSII and EPSPS inhibitors. For each model, goodness of fit statistics are presented as well as the standardised effect (t value of regression coefficient) for each variable included in the model. For comparison, goodness of fit statistics are also presented for the full model that included all six explanatory variables.

Goodness of fit Explanatory variables

Model R² R²adjusted R²crossvalidated N-fertilizer input Herbicide input Cropland area pcGDP Research articles Time

All MoA 74.7 72.7 67.8 3.41 2.45 2.91 3.82 2.31

Full 74.8 72.3 65.9

ACCase 42.3 39.5 36.2 4.39 4.47

Full 43.3 34.1 9.8

ALS 58.8 57.1 55.6 4.87 4.05

Full 64.1 58.9 54.0

PSII 70.0 67.5 66.1 2.97 3.59 3.10

Full 71.3 66.1 57.7

EPSPS 61.4 57.0 47.3 4.13 3.21 3.20

Full 69.2 61.2 28.0

Fig. 3. Relative importance of each explanatory variable in explaining the number of weeds found in countries worldwide that are resistant to the following herbicide modes of action:

ACCase (HRAC group 1), ALS (HRAC group 2), PSII (HRAC group 5) or EPSPS (HRAC group 9) inhibitors. The relative importance of each variable was measured by its standardised effect (t-value of its regression coefficient) averaged across all regression models included in the best subset for a particular herbicide mode of action. Explanatory variables include measures of agricultural intensification (cropland area, herbicide and N-fertilizer input, per capita GDP) as well as sources of sampling biases (research effort as measured as the number of research articles published on herbicides in a country, and the time since the first record of resistance). The dotted line represent the threshold t-value for statistical significance P=0.05.

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(HRAC group 1) and ALS inhibitors (HRAC group 2). This contrast did not appear to reflect significant differences between these modes of action in any of the six explanatory variables (Table 2). Although the number of years since herbicide resistance was first recorded also exhibited a twofold difference between the earliest (PSII inhibitors HRAC group 5) and most recently recorded (EPSPS inhibitors HRAC group 9) mode of action, this was not associated with the variation found in the average number of resistant weeds found to date (Table 2). Un- derstandably, each subset of the data on individual herbicide modes of action contained fewer countries than when all modes of action were combined (Table A2). While more than 27% of the countries had cases of resistance to all four modes of action, almost one third had only records of resistance to a single mode of action. However, there appeared to be no systematic biases in country representation within these two groups and both included developed and developing economies across multiple different continents and climate regions. The country composition was most similar between PSII and EPSPS inhibitors (Jaccard similarity 57.14%) and least between ACCase and ALS inhibitors (Jaccard similarity 33.33%).

3.3. Relative importance of explanatory variables for herbicide mode of action

Although all six explanatory variables were included in at least one of the best subset regression models, the average standardised effect for herbicide and N-fertilizer inputs were always below the critical t-value for statistical significance at P=0.05 (Fig. 3). Similarly, these two variables were never included in the most parsimonious regression model for any of the four herbicide modes of action (Table 3). All other explanatory variables were positively associated with the numbers of herbicide-resistant weed species recorded in a country. Nevertheless, the relative importance of the remaining four explanatory variables in the best subset models appeared to be particular to each of the four herbicide modes of action. For example, cropland area had particularly large average standardised effect for ACCase and EPSPS inhibitors but explained little of the variation found for ALS and PSII inhibitors (Fig. 3). No explanatory variable had consistently large, standardised effects across all four herbicide modes of action but both pcGDP and time since the first record of herbicide resistance exhibited average standardised effects above the threshold for statistical significance (P=0.05) in all but one mode of action.

The most parsimonious regression models accounted for a statistically significant (P<0.001) proportion of the variation in numbers of herbicide-resistant weed species for each of the four herbicide modes of action (Table 3). However, the models differed in both the amount of variation explained and their robustness at prediction. Except for ACCase inhibitors, models explained more than 50% of the variation in the number of weeds resistant to a particular herbicide mode of action.

The relatively poor performance of the ACCase model may reflect that compared to other herbicide modes of action comparatively few weed species have become resistant in each country and variation in numbers across countries was generally lower (Table 2). Nevertheless, all models exhibited greater predictive robustness than the corresponding full model and this was particularly marked in the models for ACCase and EPSPS inhibitors (Table 3). The explanatory variables retained in the most parsimonious models were different for each herbicide mode of action but did reflect those found to have large average standardised effects in the best subset analysis. Comparison of the adjusted and crossvalidated R²emphasises that explaining and predicting variation in the numbers of resistant weeds across all herbicide modes of action was more successful than attempts to do so for individual herbicide modes of action.

3.4. Testing the expectations of the explanatory variables

There was general support for the expectations outlined for each of

the explanatory variables (Table 1). Trends consistent with the expec- tation of a positive relationship with the number of herbicide-resistant weed species in a country were always found for herbicide input, pcGDP, cropland area, research articles and time since first record.

There was no indication that N-fertilizer input was an important predictor of herbicide-resistant weed species numbers in any of the models.

Although herbicide input was retained in the most parsimonious model across all herbicide modes of action and had one of the larger standardised effects, it was never included in the individual models for the four separate modes of action. Relationships among the explanatory variables differed for each mode of action (Table A1). Only the positive relationships between pcGDP and both N-fertilizer inputs and with the number of research articles was found for each of the four herbicide modes of action. It was evident that a model of the numbers of herbicide- resistant weed species worldwide derived across all herbicide modes of action could not be used to predict trends in resistance to any individual mode of action.

4. Discussion

The number of herbicide-resistant weed species recorded across multiple countries worldwide could be largely explained by only five national-scale variables: cropland area, herbicide input, per capita GDP, number of research articles, and the year of first resistance. There was support for the role of higher herbicide inputs being strongly associated with a greater number of herbicide-resistant weeds. The global trend of increasing amounts of herbicide being applied to agricultural land since 1990 is also apparent at a country level where herbicide inputs have increased over time (Benbrook, 2016; Kniss, 2017; Sharma et al., 2019) and is indicative that many countries are likely to experience an increase in herbicide-resistant weeds in the future. In addition, other features of agricultural intensification, as captured by growth in per capita GDP, such as larger farm sizes, increased mechanisation, tillage, and irrigation (Jackson et al., 2013) can also influence the development of herbicide resistance. For example, larger farms make mechanisation more finan- cially viable and can lead to a shift away from hand-weeding and other labour-intensive approaches to weed management resulting in a greater reliance on herbicides (Peterson et al., 2018). Irrigation can improve herbicide performance under dry conditions but can also facilitate gene flow by moving seeds as well as provide refuges for herbicide-resistant weeds along irrigation ditches (Vencill et al., 2012).

Conservation-tillage places greater reliance on herbicide use and has tended to increase problems of herbicide resistance (Norsworthy et al., 2012) and is more easily adopted in developed economies (Lal, 2007).

Increasing numbers of herbicide-resistant weed species are most likely to be seen in developing economies as increasing per capita GDP leads to higher labour costs making herbicides an increasingly cost-effective option for farmers (Beltran et al., 2012).

As might be expected from a consideration of species-area relationships, more herbicide-resistant weed species have been recorded in countries with extensive cropland areas. Unfortunately, there is limited evidence that areas of cropland are declining as a result of agricultural intensification (Potapov et al., 2022; Ramankutty et al., 2018; Rudel et al., 2009). Instead, the area of cropland worldwide will likely increase in the future to meet growing global food demand (Cao et al., 2021;

Fischer et al., 2014; Zabel et al., 2019) and the number of herbicide-resistant weed species is expected to follow suit. These agronomic trends all point to a greater number of herbicide-resistant weed species being recorded in a country in the future. It is probable that increases in the total number of resistant weeds in a country are only a matter of time since the greater the time since the first reported resistance case, the greater the likelihood that other species will also evolve resistance to a particular herbicide mode of action. This is likely since herbicide use will select for resistance across multiple species and where it has evolved in one species it is likely to evolve in another given enough time.

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The global burden of herbicide-resistant weeds is likely to currently be underestimated particularly in regions where limited research capability has constrained surveys of herbicide-resistant weed species and resulted in few national records of resistance. Several countries have only recorded their first case of herbicide-resistant weed species within the last decade (e.g., Kazakhstan, Uruguay, Syria). Given that there may be several years delay between increases in agricultural intensification and the subsequent detection of herbicide-resistant weed species these regions may be expected to see the greatest increase in resistant weeds over the next decade. For example, Russia and India, countries that are among those with the largest crop areas in the world, have few herbicide-resistant weeds but both are predicted by the overall regression model to have at least three times the number of resistant weeds than currently observed. However, it is not possible to use the regression model to separate whether these herbicide-resistant weed species are already present but under sampled, or yet to evolve resistance in the future.

Broadly similar patterns were found when individual herbicide modes of action were examined separately. However, a key difference was the absence of any indication that total herbicide inputs influence the number of weeds resistant to specific herbicide modes of action. This is perhaps unsurprising since individual herbicide modes of action are unlikely to track the global trends in total herbicide inputs worldwide since they will vary in their market availability, agronomic suitability and application rates around the world (Kraehmer et al., 2014). For example, glyphosate is the most heavily applied (by volume) active ingredient worldwide (Benbrook, 2016) but is disproportionally used in countries where genetically-engineered herbicide-tolerant crops are cultivated (e.g., Argentina, Brazil, Canada, United States etc.). Similarly, herbicides that inhibit acetolactate synthase (ALS) are applied at such low rates (e.g., <0.01 kg/ha) that any global variation in use would be lost in national estimates aggregated across all herbicides and recorded in kg/ha (Whitcomb, 1999). Finally, farmers may switch to different modes of action when resistance first appears in a crop to counter this emerging threat and thus total herbicide use might increase but not select for resistance to that particular herbicide mode of action. Unfor- tunately, the absence of data on the quantity of herbicide applied worldwide broken down by mode of action prevents a detailed analysis.

This limitation parallels the problems found in regional-scale epidemiological studies where gathering data on herbicide application rates from farm records is often challenging (Comont et al., 2019; Mascanzoni et al., 2018), Organisations such as the UN Food and Agriculture Organisation might alleviate this problem by requesting countries provide more detailed data on herbicide application rates that is better aligned with the global standard HRAC classification. Such an approach would not only shed greater light on the drivers of herbicide resistance in weeds but also establish a firmer foundation upon which to assess the transition of global agriculture towards a more sustainable footprint (Zhang et al., 2021).

Cropland area was the only agronomic variable retained in the individual models and only for ACCase and EPSPS inhibitors. For these modes of action, the greater the cropland area in a country, the greater the number of herbicide-resistant weed species. This is consistent with larger cropland areas containing a greater range of weed species, as well as individual plants, likely to be exposed to herbicide application (Vidotto et al., 2016). It might be expected that the strongest association between total cropland area and the number of weeds resistant to a particular mode of action would be where herbicides are applied not only across a wide area but also across a range of crops that would increase the diversity of weeds exposed to that herbicide mode of action.

The strong standardised effect of cropland area found in models for the EPSPS inhibitor glyphosate likely reflects that it is used across a wide agricultural area, with an estimate of 477 million ha treated in 2014 (Busi et al., 2018) but also its broad-spectrum activity that encompasses a wide range of crops as well (Duke, 2018). Similarly, while ACCase inhibitors are highly selective, killing only grasses at the recommended

application rate, they were applied on over 120 million ha in 2014 (Busi et al., 2018) and are widely used across a wide range of broadleaved field crops particularly to manage glyphosate resistant grass weeds (Takano et al., 2021). Consistent with this trend is that the absence of any association with cropland area for PSII inhibitors reflects that this herbicide mode of action is less widely applied globally (only 17 million ha treated in 2014 (Busi et al., 2018)) than the other modes of action examined here. In contrast, while ALS inhibitors are one of the most widely applied herbicide modes of action worldwide (over 500 million ha in 2014 (Busi et al., 2018)) there was no relationship with cropland area. This may reflect that where a single mode of action such as ALS inhibitors includes both selective (e.g., imazapic) and non-selective (e.

g., imazapyr) herbicides a crude estimate of cropland area may not adequately reflect the specific crop situations that are targeted. The harvested area of the specific crops targeted by particular modes of action may be a better predictor of the number of herbicide-resistant weed species, as has previously been found for cereal crops (Hulme, 2022a).

The absence of national-scale data summarising the specific inputs of different herbicide modes of action undoubtedly limit the extent to which the variation in the numbers of herbicide-resistant weed species worldwide could be explained. As a result, the models for individual herbicide modes of action tended to have a lower goodness of fit and predictive power than analyses that encompassed all modes of action.

The lack of input data for specific herbicide modes of action may also explain the increased importance of per capita GDP in all individual models except for ALS inhibitors. Per capita GDP is known to capture the broader aspects of agricultural intensification including herbicide input (Ghimire and Woodward, 2013; Hafner, 2003; Schreinemachers and Tipraqsa, 2012). However, per capita GDP is also correlated with the gross expenditure on research, number of research publications and patents produced per population (Al and Taskin, 2015). Thus, higher records of herbicide-resistant weed species could reflect greater research intensity on herbicides and resistant weeds in those countries investing more heavily in agronomic and agrochemical research and development. This was found to be supported when trends were examined across all herbicide modes of action and in the separate models for PSII and ALS inhibitors. Across herbicide modes of action as well as indi- vidually for ALS, PSII and EPSPS, the number of herbicide-resistant weeds in a country increased over time since the first record of herbicide resistance. The exception was for ACCase inhibitors, which may simply reflect the relatively few weeds that have become resistant to this herbicide mode of action in any one country. The evidence that to date most countries in the world have only experienced cases of weed resistance to one or two herbicide modes of action indicates considerable potential for further cases as farmers increasingly apply different active ingredients to combat existing problems of resistance.

Rarely do recommendations for more sustainable agriculture consider the unintended consequence of revised management strategies on the likelihood of increased selection for herbicide resistance in weeds (Riemens et al., 2022; Yvoz et al., 2020). A diverse crop rotation is generally thought to lead to a greater diversity of herbicide modes of action, and subsequently reduced selection pressure for the evolution of herbicide resistance (Riemens et al., 2022). However, the rotation of herbicide modes of action has the potential to exacerbate resistance problems by selecting for non-target site resistance mechanisms (Neve, 2007). Similarly a reduction in herbicide application rates is often promoted based on evidence that this can occur without resulting in a yield loss if other agronomic practices are incorporated into crop management (Colbach and Cordeau, 2018). However, such analyses ignore the fact that repeated use of a particular herbicide mode of action, especially at lower than recommended application rates, can lead to the evolution of resistant weeds (Baucom, 2019). More competitive crop cultivars are also seen as a key component of integrated weed management but while weed scientists have argued for the breeding of more competitive crop cultivars as a tool to combat herbicide-resistant weed

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species, plant breeders have instead largely focused on traits such as yield, seed quality and disease resistance (Andrew et al., 2015). Thus, future frameworks for integrated weed management need to be based on a more formal understanding of how different weed management strategies can inadvertently select for herbicide resistance.

Despite almost half a century of research on the occurrence and evolution of herbicide resistance in agronomic weeds, the number of herbicide-resistant weed species continues to increase around the world (Heap, 2014; Heap and Duke, 2018). Three major agronomic drivers of herbicide resistance: herbicide inputs, the area of cropland and agricultural intensification (as measured by per capita GDP) are all projected to increase in the future. The consequences in terms of increased numbers of herbicide-resistant weed species will fall most heavily on developing countries, many of which have only relatively recently recorded the first instances of herbicide resistance. Indeed, the scale of the problem is probably under reported in many developing economies where research infrastructure and extension support are limited. Thus, at a global scale at least, there is evidence that current weed management practices are not preventing the evolution of new cases of herbicide resistance.

Such an understanding can only be based upon knowledge of the specific herbicide modes of action being applied as part of an integrated weed management strategy. National scale data on herbicide inputs is difficult to obtain but the few studies that have admirably attempted to quantify this aspect of weed management at regional scales have generally failed to distinguish between applications using different herbicide modes of action and focused on application frequency rather than the application rate of specific active ingredients (Herzog et al., 2006; Lechenet et al., 2016; Yvoz et al., 2020). Furthermore, such surveys do not subsequently assess the level of herbicide resistance in the farms surveyed (Buddenhagen et al., 2021). This absence of detailed information on historical herbicide application and levels of herbicide resistance at the farm scale is a major impediment to the development of optimum weed management strategies. Such data would go a long way to scaling up knowledge on herbicide resistance at national scales and provide a firmer basis for forecasting risks in the future.

5. Conclusions

Results supported prior expectations that inputs of herbicide, cropland area, per capita GDP, research intensity, and time since first record of herbicide resistance would have a positive association with the number of herbicide-resistant weeds worldwide. The finding that only five explanatory variables were able to explain more than 70% of the variation in numbers of herbicide-resistant weeds found across 70 countries highlights that macroscale analyses of major agronomic issues

can provide novel insight into a topic often only examined at a field- scale. When models were run separately for each of four different herbicide modes of action, they accounted for less of the variance but even in these analyses, between two and three independent variables were still often able to explain upwards of 50% of the variation in numbers of herbicide-resistant weeds worldwide. The lower explanatory power of these models was likely due to the absence of global data on the country- level application of different herbicide modes of action and was reflected in a lack of an herbicide effect in any of the models. Nevertheless, across all analyses the general trends were similar and indicated that agricultural intensification, as captured by either by the level of herbicide inputs and/or per capita GDP, was associated with increased numbers of herbicide-resistant weed species worldwide. In addition, the number of resistant weed species found in a country depended on how long resistance had been observed suggesting that many countries with low numbers of herbicide resistant weeds are already on a trajectory towards a worsening problem in the future. The current lack of expertise in the study of the use of herbicides to control agricultural weeds in these countries not only indicates that the current problem of herbicide- resistant weeds is underestimated but also that there is limited capability for proactively managing this problem in the future.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability

Data are all in the public domain and the sources are provided in the manuscript.

Acknowledgements

The author thanks Ian Heap for access to a subset of the International Herbicide-Resistant Weed Database and Trevor James for constructive comments on a previous draft. The research was funded by the New Zealand Ministry of Business, Innovation and Employment under the project “Managing Herbicide Resistance” (grant number C10X1806).

Appendix

See. Table A1. and Table A2.

Table A1

Pairwise Pearson correlation coefficients (and their P values) for all six explanatory variables for data from all countries with at least one case of herbicide resistance and for those countries with at least one cases of resistance to one of four specific herbicide modes of action (ACCase, ALS, PSII and EPSPS inhibitors). Correlations greater than 0.5 are highlighted in bold font.

Variable 1 Variable 2 All MoA P ACCase P ALS P PSII P EPSPS P

N-fertilizer input Time 0.349 0.003 -0.100 0.513 0.185 0.197 0.483 0.002 -0.193 0.306

Herbicide input Time 0.331 0.005 0.035 0.821 0.282 0.049 0.232 0.149 0.268 0.152

Per capita GDP Time 0.403 0.001 0.104 0.499 0.274 0.054 0.546 <0.001 0.046 0.809

Cropland area Time 0.060 0.625 0.315 0.035 0.153 0.289 0.001 0.996 0.192 0.309

Research articles Time 0.454 <0001 0.417 0.004 0.406 0.003 0.386 0.014 0.117 0.538

Herbicide input N-fertilizer input 0.348 0.003 0.343 0.023 0.355 0.012 0.397 0.011 0.189 0.318

Per capita GDP N-fertilizer input 0.475 <0001 0.502 <0.001 0.408 0.003 0.397 0.011 0.414 0.023 Cropland area N-fertilizer input -0.278 0.020 -0.261 0.083 -0.383 0.006 -0.275 0.086 -0.227 0.228 Research articles N-fertilizer input 0.414 <0001 0.398 0.007 0.387 0.005 0.228 0.158 0.407 0.025 Per capita GDP Herbicide input 0.466 <0001 0.522 <0.001 0.312 0.029 0.259 0.107 0.195 0.301

Cropland area Herbicide input -0.306 0.011 -0.288 0.058 -0.342 0.016 -0.307 0.054 -0.264 0.159

Research articles Herbicide input 0.054 0.662 0.109 0.48 -0.005 0.975 -0.150 0.356 0.099 0.602

Cropland area Per capita GDP -0.220 0.068 -0.276 0.066 -0.363 0.010 -0.200 0.216 -0.166 0.381

Research articles Per capita GDP 0.452 <0001 0.447 0.002 0.331 0.019 0.425 0.006 0.619 <0.001 Research articles Cropland area 0.513 <0001 0.548 <0.001 0.492 <0.001 0.663 <0.001 0.499 0.005

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Representation of countries in the different analyses for each herbicide mode of action.

Herbicide mode of action

Country ACCase ALS PSII EPSPS

Argentina + + +

Australia + + + +

Austria + +

Belgium + + +

Bolivia + + +

Brazil + + + +

Bulgaria +

Canada + + + +

Chile + + +

China + + + +

Colombia + + + +

Costa Rica + + + +

Cyprus + +

Czech Republic + + +

Denmark + +

Ecuador +

Egypt + +

El Salvador +

Ethiopia +

Fiji

Finland +

France + + + +

Germany + + +

Greece + + + +

Guatemala +

Honduras +

Hungary + +

India + + +

Indonesia +

Iran + + +

Ireland + +

Israel + + + +

Italy + + + +

Japan + + + +

Kazakhstan +

Kenya

Latvia +

Lithuania +

Malaysia + + + +

Mexico + + +

Netherlands + + +

New Zealand + + + +

Nicaragua +

Norway + +

Pakistan +

Panama +

Paraguay + + +

Philippines +

Poland + + + +

Portugal + + +

Russia +

Saudi Arabia +

Serbia + +

Slovenia +

South Africa + + + +

South Korea + + +

Spain + + + +

Sri Lanka +

Sweden + + +

Switzerland + + + +

Syria +

Thailand + +

Tunisia +

Turkey + + +

Ukraine +

United Kingdom + + +

United States + + + +

Uruguay +

Venezuela + + + +