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Exploitation of heterogeneity in crops: Mixtures

A large plot of wheat mixtures experiment at Balruddery Farm
Variety or cultivar mixtures enable not only diversification of resistance genes but also the combination of other mutually complementary characteristics.

So what are mixtures?

In a natural ecosystem epidemics are the exception. In an intensive modern agricultural crop they are often the rule. This is largely because there exists a degree of natural balance between hosts and their pathogens in natural ecosystems ensuring long-term survival of both. This balance is not a static one. Its dynamics are essentially the rate-limiting parameters of epidemics and can often be manipulated to tilt the balance more in favour of either host or pathogen. Spatial deployment of mixed host genotypes is one such manipulable parameter in epidemic control.

There are two key components which can be manipulated spatially:

  • the density of susceptible hosts
  • the barrier effect of resistant hosts.

A third component which may be important in some host–pathogen interactions is induced resistance. As levels of inducible resistance varies between genotypes, this may be optimised by careful choice of variety.

Crop monocultures are successful in obtaining maximum yield in high-input agriculture under near-optimal environmental conditions. They are suitable for risk-averse, highly profitable, non-sustainable agricultural systems where environmental considerations are not high priority.

Under more sustainable conditions, sub-optimal for maximum yield, crop mixtures are superior to monocultures, providing greater yield and quality stability, and better exploiting all the resources available through enhanced crop plasticity. They also offer the opportunity to reduce inputs, which can increase profitability and lower risk in a well-designed programme.

Alternatively their plasticity can be exploited to extend crop production into more marginal, higher risk environments. However, they are suited to all environments, including high input near-optimal ones, and also for growing specialist genotypes, for example, low glycosidic nitrile (GN) barley varieties along with other genotypes which compensate for their agronomic deficiencies.

Crop mixtures clearly have many advantages, but they are also perceived, by some end users, to have disadvantages, mainly lack of homogeneity. For example, barley maltsters assume, if they stick to a single variety and a restricted range of grain nitrogen values, that environmental variation can be disregarded.

However, the variation within a single ear, within a field and, certainly, between different fields indicates that end users are in effect dealing with an acceptable level of heterogeneity. In practice it has been shown that homogeneity is likely to be no greater in carefully chosen mixtures than in monocultures, and that some heterogeneity may be beneficial, resulting in, for example, enhanced spirit yield in barley (Newton et al., 1998).

The other adverse perception relates to the difficulty of identifying and quantifying the components of a mixture. However, modern molecular techniques, especially genetic fingerprinting, permit genotypes to be unambiguously identified and quantified in a mixture.

In practice crop mixtures range from agro-forestry through to multilines, that is, from diversity at the plant order level down to the single plant resistance genes which determine resistance to a single genotype of a pathogen species. Within this range are some mixtures which are used very effectively even in high input western agriculture to control pathogens and thereby reduce pesticide inputs.

Variety or cultivar mixtures enable not only diversification of resistance genes but also the combination of other mutually complementary characteristics not present together in single host genotypes. In cereals these are particularly effective.

Mixtures of agronomically compatible fodder crop species are also grown such as different cereal species or legume and cereal combinations. Pathogen control is seldom complete, but it can be cost effective compared with fungicide, notwithstanding environmental considerations.

Modelling to optimise mixtures

In models we have investigated effects on selection for virulent strains of the pathogen of major resistance genes, patchiness, connectivity and scale. In this approach, the main biological interactions between host and host, host and pathogen, or pathogen and pathogen are represented in the form of mathematical functions specified by parameters which quantify the relative strengths of interactions or the rates at which processes evolve. Currently we are considering what pathogen features determine the interaction with the host and the evolution towards virulence (mutation rates, spatial continuity or isolation, mechanisms of dispersal, genetic diversity).

Figure 1: Mathematical concepts of mixtures model

Figure 1: Mathematical concepts of mixtures model

Examples of processes and interactions which are typically represented in models of plant epidemics include gene-for-gene interactions between host and pathogen whereby a host only suffers attack by pathogens with a specific genotype, the development of pathogen populations on hosts, for example, sporulation and lesion growth, and the spatial movement of pathogens from one host to another.

While deterministic models have been extremely successful in some applications, there is a growing appreciation that in other cases random, or stochastic, processes must be modelled in order to reproduce the behaviour encountered in the real world. These are incorporated into spatial models, an approach which is particularly relevant in plant epidemiology.

Figure 2: Output of a mixtures model

Figure 2: Output of a mixtures model

Who do mixtures benefit?

Mixtures have benefits for many groups.

  • Industry / End users / Retailers
  • Farmers
  • Organic producers
  • Politicians / Government
  • Ecologists / Environmentalists
  • Molecular biologists
  • Modellers / Theoreticians
  • Plant pathologists / Entomologists

Industry / End users / Retailers

  • More stable quality (acceptable level of heterogeneity)
  • Reduced environmental effect on quality
  • Lower cost (from higher yield and reduced inputs)
  • Extended life of niche varieties by blending with others which compensate for their deficiencies
  • Reduced agrochemical residues from reduced pesticide inputs (ecologically sound, greener image, price premium potential)
  • Novel product potential - for example, organic beer


  • Increased yield from same inputs, or reduced inputs leading to increased profitability
  • New markets demanding lower inputs / reduced residues
  • Price premium potential from organic or extenso reduced residue markets
  • Stable yield and quality across fields and between years

Organic producers

  • Disease control without agrochemicals potential
  • Better resource exploitation from improved yields
  • Opportunities to grow old varieties with better abilities to exploit soil nutrients with modern varieties with improved traits for end users
  • Exploit canopy architecture to achieve competitiveness against weeds

Politicians / Government

  • Immediately available technology to reduce pesticide inputs
  • Improved nutrient exploitation potential therefore less nitrogen run-off to water sources
  • Greater supply quality and quantity stability
  • Improved prospects for organic producers

Ecologists / Environmentalists

  • Increased crop biodiversity
  • Reduced pesticides usage
  • More insects in more diverse habitats

Molecular biologists

  • Opportunities to test genes not yet in commercially acceptable backgrounds in an agronomically adapted environment
  • Rapid exploitation of major / specific (easily manipulated) resistance genes in a sustainable environment

Modellers / Theoreticians

  • Experimental systems to manipulate spatial and genetic epidemiological parameters to test models and improve understanding.

Plant pathologists / Entomologists

  • Manipulation of spatial, morphological and genetic factors to slow epidemics
  • Study of habitats to manipulate the balance between predators and insect pathogens
  • Mission statement: To exploit knowledge of heterogeneous trophic interactions in vegetation systems to achieve a sustainable agricultural ecosystem?

Contact: Adrian Newton or Stuart Swanston, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK


Areas of Interest

Printed from /research/groups/cell-and-molecular-sciences/rhynchosporium-barley/epidemiology/mixtures on 25/04/18 07:48:36 AM

The James Hutton Research Institute is the result of the merger in April 2011 of MLURI and SCRI. This merger formed a new powerhouse for research into food, land use, and climate change.