Organic Plant Disease Management: Thinking Like a System

Organic Agriculture July 22, 2014 Print Friendly and PDF

eOrganic author:

Mark Boudreau, Hebert Green Agroecology


An organic grower must learn the art of systems thinking. He or she may not be entirely conscious of this, but the long-term planning required to set up a good rotation, the allocation of different crops and practices to different sites on the farm, the awareness and maintenance of biodiversity through mixed cropping or farmscaping—all these represent a systems approach to farming. Though some conventional farmers converting to organic may first think in terms of input substitutions—that is, replacing each synthetic fertilizer or pesticide with an organically-approved equivalent—they will quickly discover that, in most cases, multiple specific techniques and an overall generalized design must be synthesized into a whole that is more than the sum of its parts. This will act to maintain fertility, buffer against insect and disease outbreaks, and ultimately lead to a good and consistent harvest, sometimes in ways that aren't even clear to the grower.

And so, thinking like a system is not linear. A clear cause-and-effect relationship can't always be discerned or applied. As the crop, pests, and other organisms interact within the context of an ecosystem, any single management practice will have multiple impacts, which will themselves have impacts that will multiply and cascade through the system. For example, the relatively innocuous insecticidal soap that kills aphids may also hurt natural enemies of the aphids or other pests, or even insects that are feeding on certain weeds. So the population of these weeds increases, suppressing not only crops but other weeds. Perhaps the suppressed weeds are not able to flower, and so do not attract pollinators that are needed for some types of vegetables, which in turn lowers fruit set, and so on.

In this article I will describe some of the terms and concepts important in systems. In a separate page, each of these concepts is illustrated with an example scenario of a cabbage patch infested by aphids. You may wish to visit the page periodically as you read through this text (it has many images so this is best done with a high-speed internet connection).

Emergent Properties

How do we comprehend and manipulate overwhelming complexity and a myriad of elements and interactions? Maybe we do not need to consider every detail and minute process in a system to understand it and make predictions. In fact, a characteristic of complex systems is that we cannot predict its behavior from the individual actions of its constituent parts. The weather, the stock market, an ant colony, and human consciousness are all examples in which we are better off looking at the overall system rather than trying to extrapolate from a multitude of individual behaviors. We consider overall patterns in which properties emerge that only make sense in the context of the whole system. A systems approach emphasizes these emergent properties rather than each individual process from which it is constituted—the whole rather than its parts. And so it is down on the farm—especially an organic farm.

Systems are embedded in an environment

Everything in a complex system, such as an agroecosystem, is connected to what came before and what is around it. A system has a history that influences its behavior in the present. If you buy some land and start farming it, everything will be affected by the crops that came before and the way the soil was treated. A field with a past characterized by continuous corn and routine atrazine applications will require very different management decisions than a low-management pasture.

In addition to its historic environment, every farm is embedded in an ongoing physical and biological landscape. On the other side of the fence is another farmer who does things that can't be easily controlled. Pesticide sprays, pollen with engineered genes, weed seeds, and insect pests do not recognize land titles and will have an effect on neighboring fields. This is an example of how system boundaries are an important factor when we study systems and try to manipulate them. These boundaries must be considered not just by the farmer concerned with the practical aspects of, say, spray drift, but also by those studying agroecosystems. For instance, a study of energy efficiency must be very careful about where the system borders are placed. Efficiency may be very high when boundaries are set around an individual farm, but decrease as the region, state, nation, or entire world are considered. Irrigated tomatoes fertilized with synthetic nitrogen certainly consume more energy than rain-fed tomatoes fertilized by prior legume cover crops, but what if the former tomatoes are eaten locally and the latter are shipped 2000 miles and packaged? It all depends on where you set your boundaries.

Systems include feedback loops

The elements in a system communicate with each other—some directly, some indirectly. Often this communication results in self-regulation, such as when a product in a chain of chemical reactions inhibits the earlier reactions, so that an excessive build-up of the chemical is prevented. This is called a negative feedback loop. Regulation of glucose in blood, a population of caribou in the tundra with limited food, and the temperature in your centrally-heated house are examples of negative feedback loops. Less common are positive feedback loops, in which a process is reinforced by its outcome—oil use by a nation fosters more industrial growth, which encourages more oil use, for instance. Or adding organic matter to soil increases water-holding capacity and other factors that lead to more plant growth, which leads to additional organic matter. These systems usually reach a point at which some limit is reached and negative feedback kicks in, or the system breaks down. For example, the human bladder squeezes harder and harder as it becomes more and more empty, until nothing is left, and the muscle gives up.

Feedback loops play an important role when a system is suddenly altered or disturbed. A stable forest ecosystem may be able to recover after some of it is clear-cut or a species is eliminated by hunting, because populations that are normally suppressed by competition are released and will increase and fill in the gaps—a negative feedback loop in action. Indeed, disturbance can be the best measure of the integrity of a system's feedback loops.

Systems are "stochastic"

Some phenomena are highly predictable with great precision, like the expansion of mercury in a thermometer with a rise in temperature. These processes are deterministic. But there is a great deal of random variability in most systems, with many causes producing uncertain outcomes. This is especially true of biological systems. Such systems are not predictable in exact ways but best described in terms of probabilities, and are known as stochastic. The goal of much of modern industrial agriculture has been to eliminate uncertainty from production, and though under highly controlled conditions we can predict that x amount of nitrogen fertilizer produces y additional corn yield, nature is generally not so deterministic.

In an agroecosystem, there is genetic variability among populations, especially the "wild" weeds and diseases and insects. But there is also variation due to the environment's interaction with the genes present, and variation in habitat, microclimate, soil structure, and so on.

Systems are adaptive

Feedback loops can make systems self-regulating, as we have seen. If conditions change, a complex system may adjust to the new situation through other mechanisms as well, and thus adapt to the environment. These adaptations can be short- or long-term. Plants use hormonal signals to germinate when soil is wet and warm, grow towards light, and flower when the day is a particular length. These short-term adaptations to changing environments manifest physiological processes which are written in the organism's genes. Changes in the genetic code over generations are themselves long-term adaptations, driven by natural selection. This ability to adapt makes it much more likely that the system will survive over time.

Systems are full of surprises

Many elements in a system are tightly coupled to other elements, as has been studied extensively with predator–prey relationships. A negative feedback loop exists in these relationships so that an increase in prey leads to an increase in predators, which in turn leads to a decrease in prey as more are consumed, and ultimately a decrease in predators as the prey runs out. The cycle starts over, keeping the numbers of both species within a certain range. In many ways this may be considered a stable system. However, even relatively simple systems with only a few interacting elements can generate unpredictable situations that are counter-intuitive. A disturbance often provokes a surprise, because it may move the system to an entirely different state. Many systems have multiple stable states.

Systems can exhibit different types of stability

We are guilty of playing fast-and-loose with the term stability, resulting in the downfall of many ecological discussions. We need to be very precise in our use of the term.

One form of stability in a system is resistance to change. In this case, a small perturbation will not have an effect on the system; only a major disturbance can alter it.

Another form of stability in a system is resilience. In this case, though a disturbance may alter a system, it has a strong tendency to return to its earlier state.

System stability may be affected by complexity

You may have heard it said that "more complex biological systems are more stable", and in fact this has often been used to advocate for organic agriculture. This generalization, first proposed by ecologist Charles Elton in the 1950s, has been the source of much debate since, supported by some studies but refuted by others. Part of the problem is that the claim depends on how you define stability, as well as complexity. It is also affected by the boundaries of the system, and how pristine or managed the system is.

That said, there is good evidence that agricultural systems, which are relatively simple and highly disturbed by definition, may indeed exhibit greater resistance and resilience when some types of complexity are increased. Complexity does come in many forms. For instance, heterogeneity in the crop canopy may be beneficial, created through species mixtures, cultivar mixtures, different-age plants, patchy distribution, or even the presence of weeds.


We have discussed many aspects of a system in this article and applied them to agroecosystems. These include:

  • Emergent properties
  • System history and boundaries
  • Feedback loops, both negative and positive
  • Disturbance
  • Random variability and probability
  • Ability to adapt
  • Coupling of system elements
  • Unpredictable and counter-intuitive behavior
  • Multiple stable states
  • Resistance and resilience
  • Complexity, its forms, and its influence on stability

Working in a highly simplified, industrial paradigm for agriculture, which views processes as linear and attempts to control each one in isolation, may backfire and lead to problems like exaggerated pest outbreaks and dependence on high chemical use. Moreover, it fails to take advantage of allowing—even encouraging—the evolution of a complex system on the farm. Thinking like a system has long-term payoffs which are not necessarily attributable to one particular action or technique, but nonetheless provide good, stable production in the face of malevolent outside forces, and for the long haul.

Additional Resources

  • Brookhaven Symposium in Biology. 1969. Diversity and stability in ecological systems. No. 22, Brookhaven National Laboratory, Upton, N.Y.
  • Creative Learning Exchange [Online]. Available at: (verified 16 Dec 2010).
  • May, R. M. 1974. Stability and complexity in model ecosystems, 2nd ed. Princeton University Press, Princeton, N. J.
  • North, K., and K. Henderson. 2004. Whole-farm planning. NOFA-New York, Cobleskill, NY. NOFA Organic Principles and Practices Handbook Series. Publication available for purchase at (verified 16 Dec 2010).
  • Rice, P., E. Sawin, and D. Meadows. 2000. The corn system project: Defining an economically and ecologically sustainable commodity corn system [Online]. Sustainability Institute, Four Corners, VT.
  • Sterman, J. D. 2002. System dynamics modeling: tools for learning in a complex world. IEEE Engineering Management Review 30(1): 42–52.
  • van der Werf, E. 1991. Transition is a matter of watching and observing. ETC Foundation, L, Leusden, The Netherlands. Available online at: (verified 16 Dec 2010).

Software for modeling and diagramming complex systems:

This is an eOrganic article and was reviewed for compliance with National Organic Program regulations by members of the eOrganic community. Always check with your organic certification agency before adopting new practices or using new materials. For more information, refer to eOrganic's articles on organic certification.

eOrganic 2976

Connect with us

  • Twitter
  • Facebook
  • YouTube


This is where you can find research-based information from America's land-grant universities enabled by



This work is supported by the USDA National Institute of Food and Agriculture, New Technologies for Ag Extension project.