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Evolution meets agriculture: what drives the evolution of xenobiotic resistance?

In agriculture and healthcare we us use xenobiotic chemicals (herbicides, fungicides, insecticides, antibiotics) to manage pests and diseases. Resistance has evolved to all these types of xenobiotics, rendering them ineffective with important consequences for crop production and health.

Go to the profile of Robert Freckleton
Feb 13, 2018
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The paper in Nature Ecology & Evolution is here: http://go.nature.com/2F4iUQF

In an example of convergent evolution, one ecotype of the weed Barnyardgrass (Echinochloa crus-galli var oryzicola L.) appears morphologically indistinguishable from domesticated rice (Oryza sativa L.). Barnyardgrass is a weed because it reduces the yields of rice, and on many smallholder farms hand weeding remains the main weed control method. This behaviour has selected for weeds that mimic the crop as weeds that look similar to rice avoiding being removed. This is an evolved resistance: when we manage systems containing wild species in a selective manner, evolution is inevitable.

In agriculture and healthcare we us use xenobiotic chemicals (herbicides, fungicides, insecticides, antibiotics) to manage pests and diseases. Resistance has evolved to all these types of xenobiotics, rendering them ineffective with important consequences for crop production and health.

Our study attempted to answer the important question, how can we learn from past management of agricultural systems to reduce the likelihood of resistance evolving in the future?

Current strategies for managing resistance revolve around diversifying management and the range of chemicals used. Similar techniques have been proposed in medicine and agriculture, but in there is not yet a consensus on what is the best approach.

We examined the evolution of herbicide resistance in Black-grass (Alopecurus myosuroides) in the UK. This has become a widespread weed: we found the weed present in 88% of 24,824 quadrats that we monitored. It has spread northward in recent years and we found the weed in areas where it had not been found in previous decades. 

The driver for this spread is evolved herbicide resistance: we found that weeds in fields with higher densities are more resistant to herbicides. Once resistance has evolved it does not seem to go away: two years later, fields with high densities still had high densities, despite farmers employing a suite of different management techniques. We estimate that the economic costs of this are very high: the costs of weed management have doubled as a consequence of evolved resistance. 

We conducted a retrospective analysis to try to answer the question of how management could reduce resistance: the idea was to compare current levels of evolved resistance with historical management data in order to find which management practices were associated with lower resistance.

The results were simple: farms that used a greater volume of herbicide had more resistance! Beyond this we found little evidence for a role of any other management techniques: neither the diversity of chemicals used (i.e. whether farmers used a variety of herbicides or just one) or diversity of cropping mattered, despite both being advocated as methods to reduce the evolution of resistance.

These results offer two important insights. First, diversifying management, as many suggest, will work as a technique for reducing the evolution of resistance only if this enables farmers to reduce their inputs of herbicides. If inputs continue, or even increase, then this technique will not work. For instance in our dataset, the volume and diversity of herbicide products are positively related to each other. Looking forward, current techniques such as ‘stale seed beds’ are built around a strategy of maximising opportunities to use chemicals on emerging seedlings. Our results predict that resistance will evolve as a simple function of accumulated inputs, and such management could accelerate this.

Second, the example in the introductory paragraph was chosen carefully: even in the absence of chemicals, directional selection from the repeated use of the same management will lead to evolution of resistance. We need to design management systems in which evolution is anticipated. Apart from focussing on densities and yields, there needs to be an appreciation of resistance. 

New techniques such as precision agriculture (PA) offers the possibility of targeted applications of chemicals: for example robots could give doses of herbicide at the level of individual plants. But this might simply select for resistance evolution – for example mimics that defeat the ID skills of robots, paralleling the evolution of mimicry in Barnyardgrass. Or PA could be used to apply optimum doses depending on the resistance status of individuals and hence minimise the evolution of resistance: something not yet thought about, but becoming possible with advances in molecular diagnostic methods. However in developing these methods we have to bear in mind that evolution will seek to defeat our technology.

In the meantime the results that we have obtained suggests a simple rule of thumb: just using more herbicide will select for more resistance! 

 

 

 

 

 

Go to the profile of Robert Freckleton

Robert Freckleton

Professor, University of Sheffield

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