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This article was downloaded by: [Montana State University Bozeman]On: 10 December 2012, At: 09:20Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK
Journal of Crop ImprovementPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/wcim20
The Ecology of Crop-WeedInteractionsBruce D. Maxwell a & Edward Luschei ba Department of Land Resources and EnvironmentalScience, Montana State University, Bozeman, MT,59717, USAb Department of Agronomy, University of Wisconsin,Madison, WI, 59706, USAVersion of record first published: 20 Oct 2008.
To cite this article: Bruce D. Maxwell & Edward Luschei (2004): The Ecology of Crop-Weed Interactions, Journal of Crop Improvement, 11:1-2, 137-151
To link to this article: http://dx.doi.org/10.1300/J411v11n01_07
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The Ecology of Crop-Weed Interactions:Towards a More Complete Model
of Weed Communities in Agroecosystems
Bruce D. MaxwellEdward Luschei
SUMMARY. Understanding the ecology of crop-weed interactions in-cludes a wide array of population and community ecology theory. Thefirst thorough application of theory in Weed Ecology was predictingweed impacts on crops. The generality of crop yield response to weedshas been well supported with a wealth of empirical studies, however, in-clusion of these models into weed management decision support sys-tems (DSS) has been less fruitful. Parameterization of the empiricalmodels has indicated extreme variability over space and time, even inexperimental plots. Thus, further development and implementation ofecologically based weed management must depend on understandingsources of crop response variation, understanding complexity of interac-tions among the many sources of variation, and the realization that fore-casting crop response will always have some level of uncertainty.Ecological theory may help us understand how to best construct concep-
Bruce D. Maxwell is Professor, Department of Land Resources and EnvironmentalScience, Montana State University, Bozeman, MT 59717 (E-mail: [email protected]).
Edward Luschei is Professor, Department of Agronomy, University of Wisconsin,Madison, WI 59706.
[Haworth co-indexing entry note]: “The Ecology of Crop-Weed Interactions: Towards a More CompleteModel of Weed Communities in Agroecosystems.” Maxwell, Bruce D., and Edward Luschei. Co-publishedsimultaneously in Journal of Crop Improvement (Food Products Press, an imprint of The Haworth Press, Inc.)Vol. 11, No. 1/2 (#21/22), 2004, pp. 137-151; and: New Dimensions in Agroecology (ed: David Clements, andAnil Shrestha) Food Products Press, an imprint of The Haworth Press, Inc., 2004, pp. 137-151. Single or mul-tiple copies of this article are available for a fee from The Haworth Document Delivery Service[1-800-HAWORTH, 9:00 a.m. - 5:00 p.m. (EST). E-mail address: [email protected]].
http://www.haworthpress.com/web/JCRIP© 2004 by The Haworth Press, Inc. All rights reserved.
Digital Object Identifer: 10.1300/J411v11n01_07 137
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tual and predictive models of crop-weed communities to accomplish ourapplied goals. [Article copies available for a fee from The Haworth DocumentDelivery Service: 1-800-HAWORTH. E-mail address: <[email protected]> Website: <http://www.HaworthPress.com> © 2004 by The Haworth Press,Inc. All rights reserved.]
KEYWORDS. Competitive interactions, weed ecology, niche assem-bly, neutral theory
INTRODUCTION
The interaction between crops and weeds has received extensivestudy. The primary purpose of these studies has been to quantify the im-pact of weeds on crops so that management of the weeds could be justi-fied (Cousens, 1987; Zimdahl, 1980). In this contribution, we examinethe ecological theories on interactions that may be important in structur-ing plant communities in agricultural systems. Our focus is applied inthat we are interested in interactions that have tangible results on ourability to achieve management objectives over short- and long-termtime horizons. We argue that while the historic focus on understandingof competitive relationships between weeds and crops has lead to amore accurate understanding of the short-term cost of weeds, the impor-tance of competitive interactions in structuring weed communities inagroecosystems remains unclear. We will briefly outline the logic be-hind the ecological models of plant interactions and dynamics from thescale of the individual through the community, highlighting areas weconsider to be particularly relevant to the design of ecologically soundmanagement practices.
THEORIES OF PLANT COMMUNITY STRUCTURE
Relatively few studies have identified positive interactions betweencrops and weeds (Andow, 1988). In crop-weed competition studies, ifcrop yields were found to increase in the presence of weeds, there hasbeen a tendency to deem the data anomalous and provide an explanationfor why the data did not comply with accepted beliefs (i.e., weeds re-duce crop yield and/or quality). In our own work, the abilities to contin-uously measure yields with field monitoring devices on harvestingequipment allowed us to identify many regions, particularly where re-
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sources are limiting, that crop yields may be positively correlated withweed presence (Luschei et al., 2001). Perhaps these observations reflecta spatial variability in resources (water, fertility, etc.) to which bothcrops and weeds were similarly responding. Weeds may have a net pos-itive influence on crops by providing habitat for crop pest predators orpathogens or for crop pollinators (Andow, 1988). Thus, interactions be-tween crops and weeds may have many different results and can be cate-gorized (Table 1).
The acceptance of multiple crop-weed interaction outcomes has al-lowed for a more ecological approach to understanding the crop-weedmix as a plant community. Two general views have emerged that offerexplanation for how plant communities are structured. The most preva-lent view is that plant communities are groups of interacting specieswhose presence, absence, or relative abundance can be deduced from“assembly rules” that are based on the functional roles (inherently de-fined ecological niches) of each species in the community (Booth andSwanton, 2002; Diamond, 1975; Levin, 1970; MacArthur, 1970; Weiherand Keddy, 1999). According to this theory, species coexist in interac-tive equilibrium with the other species in the community. The stabilityof the community and its resistance to invasion is derived from theadaptive equilibrium of member species, each of which has evolved tobe the best competitor in its own ecological niche (Pontin, 1982). Eventhough this theory would place crop-weed interactions in a disequilib-rium state, because of the high frequency of disturbance in most annualagroecosystems, it would still maintain that the species that can co-oc-cur within a community are mostly determined by interspecific compe-tition for limited resources and other biotic interactions and abioticfactors (Booth and Swanton, 2002). This theory is particularly appeal-
Bruce D. Maxwell and Edward Luschei 139
TABLE 1. Types of interaction that can occur between two species that sharethe same environment (adapted from Burkholder, 1952).
Result of Interaction Between 2 Species (A & B)Types of Interference A B
competition mutualism + +commensalism + 0amensalism 0parasitism +
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ing to farmers and weed scientists, because it confirms the notion thatweeds by their very coexistence with the crop, compete for resourcesand thereby reduce crop yields. It is also appealing to agronomists thatpromote the use of inter-crops to fill the open niches left by a monocul-ture crop that otherwise would get filled by weeds. The niche assemblytheory would also suggest that disturbance that removes plants couldopen niches and thus be a major mechanism allowing invasion ofweeds. Thus, disturbed areas or areas accidentally not planted to thecrop would be expected to be good predictors of weed occurrence.
The other general theory that has emerged to explain communitystructure is based on the assumption that communities are open, non-equilibrium assemblages of species largely thrown together by chance,variability in demographic processes, and random dispersal (Hubbell,2001). Thus, this theory assumes that species come and go, and thattheir presence, absence or relative abundance is dictated primarily byrandom dispersal and stochastic local extinction. The intrinsic and ex-trinsic variability in ecological processes that would define an ecologi-cal niche preclude natural selection. Hubbell (2001) called this theUnified Neutral Theory and contrasted it with the more prevalent viewdescribed above, which he called the Niche-Assembly Theory. The Uni-fied Neutral Theory is appealing as a null hypothesis for explainingagroecosystem plant community structure because most cropping sys-tems are nonequilibrium as a result of high frequency disturbance fromtillage and planting. Thus, selection for traits that increase colonizationpotential (e.g., high fecundity and dispersal) over competitive abilitywould be expected, although the intensity of selection for any particulartraits will be low in more diverse cropping systems (Jordan and Jannink,1997). Still it is difficult to build a solid argument for either theory asdominant in explaining agroecosystem plant communities. For exam-ple, the Niche-Assembly Theory would seem to better account for theconsistent observation that weed species tend to aggregate, indicatingthat specific processes associated with habitat requirements, dispersal,response to management, and/or relative competitive ability, may inter-act to determine distribution and community structure.
Empirical evidence for any single factor determining plant commu-nity structure is rare and often the variation in response is more indica-tive of a large random component in the processes, which would rejectall but a null hypothesis that may be stated as the Unified Neutral The-ory. Thus, we may conclude that ecological processes consistent withNiche Assembly Theory are acting on crop-weed communities, but alarge random component in these processes will often obscure them
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and/or the interaction among the processes may cloud the view of themechanisms. It is important to recognize the large random componentand adopt a philosophical approach to understanding the ecology ofcrop-weed interactions as probabilistic outcomes that have potentiallycomplex interactions as well as spatial and temporal variation. Ecologi-cal theories that describe plant community dynamics are useful in pro-viding a language within which we can frame hypotheses (Kareiva,1989). Even in the presence of large amounts of intrinsic stochasticity,there exists tremendous potential to manipulate agricultural systems tooptimize interactions at different time scales (Jordan and Jannink, 1997;Mohler, 2001). However, deriving agronomic recommendations fromecological theory requires careful consideration and explicit representa-tion of risk.
Diversity-Stability Hypothesis
Structural simplicity in agricultural production systems decreases thenumber of possible interactions between species. One consequence ofdesigning agricultural systems with low species diversity is that fewerinteractions are possible. Having fewer connections between elementsof an agricultural system is not intrinsically harmful or beneficial.However, it has been speculated that the productivity and stability ofagricultural systems may be adversely impacted by low diversity. Thediversity-stability hypothesis put forward by Elton (1958) stimulatedmuch discussion before being criticized (Peters, 1995) and systemati-cally dismantled (Goodman, 1975). Then Lehman and Tilman (2000)came full-circle to argue that both sides of the debate may be correct; di-versity can stabilize communities while simultaneously destabilizingcomponent populations. The design of improved agriculture systemsoften mirrors these issues (Swift and Anderson, 1993).
The natural connotation of stability being a desirable system-levelproperty has led to its incorporation into the lexical foundations of IPMapproaches to weed management (Norris, 1982; Clements et al., 1994).Maxwell (1999) observed that from the point of view of stability, con-ventional management trades the natural population regulating mecha-nisms that potentially exists in diverse communities for regulationbased on high-levels of chemical inputs. In addition, it was predictedthat the transition to lower input systems would probably be met withincreased complexity in overall system behavior, ruling out prescriptivemanagement.
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Ecological theory supports the intuitive notion that species diversityand temporal stability in total biomass production are linked. Lehman andTilman (2000) observed that three very different classes of models pre-dict a positive influence of plant species diversity on the stability of totalproduction. They also note that with respect to individual species, stabil-ity tends to decrease with increasing diversity. There are three effects thatcombine to produce the relationship between diversity and stability. Firstis the “over-yielding” effect, which results from the fact that species mix-tures are capable of more completely utilizing a resource spectrum thansingle species. Just as a disjoint resource use can enhance stability, re-source similarity or the “covariance effect” can also increase productivityby allowing one species to utilize resources foregone by a second. Lastly,to the extent that individual species environmental responses are un-correlated, the “portfolio effect” can hedge the total productivity againstany particular environment and thereby enhance stability.
None of the above mechanisms aid the cash grain farmer interested inthe performance of a single crop species. The models examined byLehman and Tilman (2000) would predict a purely detrimental effect ofadditional species from the perspective of the farmer. We argue that thetheoretical support for this effect results from a fundamental inability ofdeterministic formalisms to capture a process which must be regardedas intrinsically stochastic at the farm-scale. Weather can greatly influ-ence the life-cycles of weeds, either by directly altering resource poolsor germination timing with respect to the crop. We have witnessedheavy wild oat (Avena fatua L.) infestations that can essentially die ofdrought where sparse populations survive. The inability of determinis-tic models to capture complex interactions does not make them intracta-ble, it simply places a constraint on their use as a predictive tool forprescriptive management. They can still be very useful for developingan understanding of the first principles of system behavior. In recentdiscussions about a shift from prediction to ecological forecasting,Clark et al. (2001) present a strong case for the need to use models with“fully specified” uncertainties to address system behavior with largeamounts of intrinsic uncertainty. They further note that landscape pro-cesses are often unpredictable from fine-grained (small plot experiment)studies (Carpenter et al., 1996; Clark et al., 2001). Interpolation of ex-perimental results from small plots must be weighed with appropriateskepticism. For example, small plot experiments that are conducted toquantify the affect of weed density on crop yield will almost always de-liver a negative hyperbolic response. One must be careful to interpretthis response as a first principle with appropriate levels of variation if
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the quantitative function is scaled up and thus logically will incur moreinteractions with other processes that can influence crop yield (Figure 1).
Theoretical Models of Ecological Interactions
Most theoretical models of agroecosystem interactions are simplyapplications of general ecology theory that was more focused on natural
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FIGURE 1. Spring wheat yield in response to wild oat density in small plot ex-periment (a) in Bozeman, MT and in a 150 ha production field (b) approxi-mately 300 km away near Sun River, MT in 1999.
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ecosystems. Agroecosystems are typically less complex than naturalsystems and thus one may conclude that the simplifications in the mod-els may not be as constrained when applied in agroecosystems. Gener-ally, however, the models are still gross simplifications. Simplificationsare most often introduced to enhance generality or make models analyt-ically tractable. Simplified models seldom improve predictive powerexcept at larger scales than the predictions were intended (i.e., Ockham’srazor). Thus simplified models further confirm first principles, but mayhave reduced utility for understanding the variability in responses andsubsequent probability of outcomes. Parsimony mandates that the bur-den of proof, for the need to adopt an increase in complexity, rests withthe advocates for constructing more complex models. It must be recog-nized that evidence provided by fine-grained studies is necessarilyweak due to the large intrinsic uncertainties in agricultural systems.This is not to say that the “higher order” interactions are not important,merely that the mathematical formalism so successful at describingphysical and chemical dynamics is limited in its suitability for use innatural or agroecosystems. We propose that the simplifications miss animportant set of affects arising from ecosystem services and interac-tions provided by more diverse communities. For example, weeds mayprovide habitat for crop pest predators or pathogens or for crop pol-linators (Andow, 1983). These kinds of effects are neglected, not be-cause they are not important, but because they are difficult to studybecause they depend so strongly on the particulars of a given situation.Any successful study of small size and short duration will avoid phe-nomenon requiring extensive replication in space or time. Indirect inter-actions that are extremely variable on the spatial or temporal scale atwhich we can study them will be interpreted as experimental noise.
The lack of an ability to observe or quantify interactions, which maywell stem from cultural, political and practical constraints on scientists,leads to conceptual inertia that predisposes us to discount complex in-teractions in favor of simple, direct and consistent phenomena. The im-mediate search for explanations involving direct mechanisms can leadus to not give due weight to alternative explanations. Sing (2002) foundwild oat could serve as a population sink for the wheat-stem sawfly(Cephus cinctus), a significant pest of wheat (Triticum aestivum L.) pro-duction in Montana. Van Wychen (2002) speculated that the role ofpopulation demographic processes and seed dispersal may be more im-portant in the formation of weed spatial pattern than direct competitiveeffects of wild oat in spring wheat crops.
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Investigating interactions in weed-crop systems necessitates choos-ing a spatial and temporal scale to study as well as specifying the obser-vations that will serve to define the state of a system. In the least complexmodels, individuals are represented as ensembles in a state-space andare described by a statistical property of the ensemble. More compli-cated individual-based models (McGlade, 1999) can be useful tools indiscovering how the entire ensemble behaves rather than simply track-ing the state of the population through a statistical property like themean individual response. The behavior of individuals, populations andcommunities is necessarily linked by their nested membership. Lotka(1925) described the dynamics of these aggregates as an extension of afield of statistical mechanics to “aggregates of living organisms.”
It so happens that many of the components that play an importantrole in nature, both organic and inorganic, are built up of largenumbers of individuals, themselves very small as compared withthe aggregations they form. Accordingly, the study of systems ofthis kind can be taken up in two separate aspects, namely, first withattention centered upon the phenomena displayed by the compo-nent aggregates in bulk . . . and secondly conducted with the atten-tion centered primarily upon the phenomena displayed by theindividuals of which the aggregates are composed . . . It is evidentthat between these two branches or aspects of the general disci-pline there is an inherent relation, arising from the fact that thebulk effects observed are the nature of a statistical manifestation orthe resultant of the detail working of the micro-individuals. A.J.Lotka (1925)
Describing the behavior of a system state defined on one level by lev-els directly below is referred to as hierarchical modeling (Allen andStar, 1982). Maurer (1999) goes so far as to refer to the modeling ofcommunity dynamics as “modeling kinetics in hierarchical systems.” Ifwe are to carry Lotka’s comparison between the dynamics of ensemblesof organisms and inert matter further, then the dynamics are overwhelm-ingly driven by two-species interactions (the so-called “pair approxima-tion” in analogy with the relative rarity of three particle collisions in agas). By making assumptions about the system being near “dynamicalequilibrium,” one can derive (Maurer, 1999) the form of the well-knownset of differential equations called the Lotka-Volterra equations that de-scribe the statistical properties of the ensemble of organisms. There hasbeen some discussion in the ecological literature on the significance of
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“higher order” interactions (Kareiva, 1994), but models to capture anygenerality about these interactions are rare.
Lotka-Volterra Model
While the Lotka-Volterra model worked remarkably well for de-scribing animal populations, it was less successful for plants for a num-ber of reasons (Harper, 1977). These reasons include: stationarity, lackof “mixing” for plants, ambiguity about what constitutes an individual(genets versus ramets) and census difficulties, primarily because of theseed phase in the life history of plants. Driven by local interaction and ageneral lack of mixing, some noted that plant interactions were inher-ently spatial. From a modeling point of view local interconnectednesscan be represented explicitly using agent based models (Berger et al.2002), cellular automata (Wissel, 2000; Wolfram, 2002) or “neighbor-hood” models (Pacala and Silander, 1990; Stoll and Weiner, 2000).
Tilman (1982) took a different tact and extended the differential-equations of Lotka and Volterra to explicitly describe the changing re-source levels. He demonstrated that the dynamics of (n) species utiliz-ing (m) resources could be described by (m + n) differential equationsand that the interaction was solely through shared resource pools (sothat the species could be studied independently with respect to their re-source utilization). With a host of assumptions including the existenceof “equilibrium” dynamics, he described the conditions necessary forcoexistence or competitive exclusion.
Competitive Exclusion Principle
The domination of ecological theory and experimentation centeredon competitive interactions began with Gause (1934) and the develop-ment of his Competitive Exclusion Principle. The origins of the Competi-tive Exclusion Principle are said to be partly based upon the mathematicalmodel created by Lotka and Volterra in the 1920s (Begon, Harper, andTownsend, 1996). The theoretical underpinnings of this principle arepervasive in plant and weed ecology. Gause (1934), a Russian microbi-ologist, grew different yeast species together in petri dishes on uniformmedia and measured the population growth rate of each species. Ini-tially, growth rates (dN/dt) were depressed for both species compared torates when grown in isolation, indicating competition for limited re-sources. However, over time one species became dominant in the mixtureand the other species became extinct. Thus, the Competitive Exclusion
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Principle suggests that two species cannot coexist when they have iden-tical needs of a limited resource and species that coexist in nature mustevolve ecological differences in order to share an environment. Stablecoexistence of two species was only possible where intraspecific com-petition was greater than interspecific competition for the species in acommunity (Harper, 1977; May, 1981; Ricklefs, 1979).
Other concepts that grew from the Competitive Exclusion Principleinclude ideas about how to define the multidimensional space (habitat)needs of a species based on resource needs and environmental toler-ances including response to predation, herbivory and pathogens. NicheAssembly Theory holds that natural selection drives species within acommunity to partition the environment (use different parts of the envi-ronment), with the result that competition is minimized, thereby defin-ing a species’ niche.
The Competitive Exclusion Principle has not met with total accep-tance, and it is perhaps the blurred relationship with competition and theother biotic and abiotic influences that prevents its approval (Crawley,1997; Begon, Harper, and Townsend, 1996). Nevertheless, in many re-spects the theory and models used to characterize the interactionsamong plants developed following Gause (1934) are simply variants onthe Competitive Exclusion Principle. They have sought to include theeffects of variable environments and species adaptations to the environ-ment to account for species coexistence (Chesson, 1986; Grime, 1984;Huston, 1979; Hutchinson, 1959; Levin, 1979; Ricklefs and Schluter,1993). Clearly, one should not be surprised by the prevalence of the as-sumption of competition for resources as the driving first principlemechanism determining the structure and function of weed communi-ties in crops. The various strengths and weaknesses of the variants onthe Competitive Exclusion Principle can be argued, but all representsome potential for explaining weed-crop community structure. Theycan be generally grouped into equilibrium versus non-equilibrium as-sumptions about the plant community. Depending on the time scale,equilibrium (long-time scale) or non-equilibrium (short time scale) the-ories may be most suitable for understanding interactions among weedsand crops.
CONCLUSIONS
We have reviewed the hypotheses developed to explain the structureof plant communities and by association weed communities in annual
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crops. It has generally become accepted that no single putative mecha-nism gives rise to communities but we can treat hypotheses as Commu-nity Assembly Rules that we can draw upon to both explain and manageplant community structure (Clements et al. 1994; Booth and Swanton,2002). In general the above hypotheses can be summarized as follows:If communities are equilibrium assemblages then the maintenance ofspecies richness depends on rare species having some advantage overcommon ones by the common ones suffering density-dependent reduc-tions in survival or fecundity, the rare species obtaining some fre-quency-dependent advantage in recruitment, or frequency independentspatial stochasticity providing an ephemeral, but reliable refuge fromsuperior competitors. Thereby, we encourage the use of Assembly RuleTheory as first principles, but maintain that one must be acutely awareof complex interactions and stochastic variability that can shroud mech-anism and lead to abandonment of predictive models based on the firstprinciples. Therefore, we suggest continuing to employ first principlemodels to understand and even forecast outcomes for management, butplacing greater focus on study of variation across time and space so thatmanagement alternatives can be placed in the context of probabilities ofimprovement over accepted or conventional approaches to manage-ment. First principle models purposely ignore or assume constant manyinteracting factors in order to expose the first principles. Thus, a goodway to incorporate first principles into models (DSS) that will be ap-plied in management without explicitly knowing how the interactionsinfluence responses, is to empirically characterize the variation in thefirst principle responses under typical management conditions and re-port outcomes as probabilities. Eventually, more complex models thatcapture specific interactions can be added to improve forecasting ofmanagement outcomes.
We have attempted to describe the importance of both theory andsimplicity to the conceptualization and implementation of agroecologicalprinciples. Redesigning agricultural systems may necessitate encourag-ing interactions that are inherently more complicated, yet the frame-work we employ to describe and understand dynamics should not beforced to match this complexity.
Thus, agroecology is the science of integrating the biological, agro-nomic and socioeconomic disciplines to understand and predict the be-havior of agroecosystems (Gliesman, 1998). The ecological approachto understanding agronomic systems begins with embracing the com-plexity of interactions across time and space scales and hierarchical sys-
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tems of organization (Clements and Shrestha, 2004). This does notmean that we cannot forecast outcomes until we fully understand thecomplex interactions, it only means that we must provide managementrelated forecasts of system response in the form of probabilities.
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