Structure and Function in Agroecosystem Design and Management - Chapter 4 pot

CHAPTER 4 Ecological Management of Crop-Weed Interactions Chris Doyle, Neil McRoberts, Ralph Kirkwood, and George Marshall CONTENTS Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Ecological Consequences of Modern Weed Control Systems . . . . . . . . . . . . 63 Weeds in the Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Weed Adaptation to Management Practices . . . . . . . . . . . . . . . . . . . . 64 In Search of New Approaches to Weed Management. . . . . . . . . . . . . 64 The Role of Mathematical Models in Predicting Weed Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Spatial and Temporal Dynamics of Weed Populations. . . . . . . . . . . . . . . . . . 66 The Dynamics of Weed Invasion and Spread. . . . . . . . . . . . . . . . . . . . 66 Predicting Weed Invasion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Seed Dispersal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 The Dynamics of Weed Population Density. . . . . . . . . . . . . . . . . . . . . 69 Optimum Weed Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Extrinsic Factors Affecting Weed Populations . . . . . . . . . . . . . . . . . . . 73 Weed Control Decision Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Timing of Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Optimal Weed Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Integrated Weed Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Required Advances in Modeling Weed-Crop Interactions . . . . 78 Biological Control of Weeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Weed Adaptation to Management Practices . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Adaptation to a Single Control Measure. . . . . . . . . . . . . . . . . . . . . . . . 81 Adaptation to Integrated Weed Management Systems . . . . . . . . . . . 83 61 0-8493-0904-2/01/$0.00+$.50 © 2001 by CRC Press LLC 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 61 62 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 INTRODUCTION In recent years, two very different approaches to controlling weeds have developed. On the one hand, there has been the introduction of herbicide- tolerant crops in North America with their specific reliance upon herbicides. Clearly, however, the widespread application of such techniques will alter the dynamic equilibrium which normally exists in vegetation. Thus, a key research issue must be the long-term ecological consequences of the regular use of nonselective herbicides on the community structure of seminatural vegetation (Willis, 1990). In direct contrast, in response to both public and industry concerns, there has been the development of sustainable systems of crop production, in which the emphasis has been on minimizing herbicide use. Instead, a mixture of biological, chemical, and mechanical methods are combined to control weeds, pests, and diseases to provide stable long-term protection to the crop (Lockhart et al., 1990; Swanton and Weise, 1991; Gressel, 1992; Wyse, 1994; Holt, 1994; Viaux and Rieu, 1995). Fundamental to this latter approach is a sound understanding of weed demography and of the efficacy and impact of different control methods. Although the two approaches represent very different strategies to weed control, both require an understanding of the population biology of weeds, including evolutionary aspects (Jordan and Jannink, 1997), and the dynamics of weed populations. Accordingly, this chapter summarizes current understanding on these matters, including the effects of crop rotation, tillage systems, and herbicide use on weed communities. However, one of the most striking developments in regard to research into improved management systems, with reduced dependency on herbicides, has been a move towards systems type investigations. Thus, Kropff et al. (1996) have stressed that the complexity of the population dynamics of weeds and of the crop-weed interactions necessitates the use of mathematical models. Certainly, models of weed infestation, population growth, and control have served as a valuable framework for organizing biological information on weeds and for developing weed control strategies (Mortimer et al., 1980; Doyle, 1991; Colbach and Debaeke, 1998). In particular, they have helped to identify information gaps, set research priorities, and suggest control strategies (Maxwell et al., 1988). Furthermore, their value has arguably extended beyond being simply useful research tools. Several key questions in weed control cannot be answered using conventional field trials because of the constraints of cost, time, or complexity (Doyle 1989; 1997). As such, models have come to serve as experimental test beds. Accordingly, this chapter will deliberately treat the ecological management of crop-weed interactions from a modeling and systems perspective. 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 62 ECOLOGICAL MANAGEMENT OF CROP-WEED INTERACTIONS 63 ECOLOGICAL CONSEQUENCES OF MODERN WEED CONTROL SYSTEMS Weeds in the Ecosystem Any ecosystem, made up as it is of an integrated community of the organisms present and their controlling environment, evolves over time into a relatively stable community. Interactions at the physical, chemical, and biological levels lead to the establishment of dynamic interrelationships among the species within the community and a degree of stability (Willis, 1990). However, most crop production systems directly aim to produce monocul- tures, as in arable crops, or simple mixtures of species, as in grass leys, in order to maximize crop yield or economic profitability. This means disturb- ing the “natural” vegetation of an area, either by introducing new species or selecting out specific species at the expense of others. Weed control strategies are concerned with controlling the unwanted species—a weed being defined as “a plant growing where it is not wanted” (Buchholtz, 1967; Roberts et al., 1982). Thus, the ingress of weeds into an area used for cropping is intrinsi- cally an adjustment towards a more natural plant community. Historically, weed control measures have been pursued to minimize the damage done by weeds to crop yields and quality. Weed control practices have typically involved a combination of periodic habitat disturbance through cul- tivation and crop rotation and more recently the widespread use of herbicides. On an ecological level, these practices have acted as a very powerful force in the interspecific selection of weed flora through the mechanisms of pre- adaptation, evolution, and alien immigration (Mortimer, 1990). Plant species may be pre-adapted in the sense that they are resident in a natural plant community within dispersal distance of a crop and come to predominate within the crop as a consequence of a change in management practices. The successful invasion of a crop by a species from the natural habitat, therefore, depends on a match of the life history characteristics of the weed to the habitat provided by the cropping system. As such, the combination of management practices and the pattern of crop development through time results in interspecific selection, leading to particular species becoming “weeds” (Cousens and Mortimer, 1995). However, management practices may give rise to interspecific selection as a result of evolutionary processes. Where agricultural practices are continued for a sufficient length of time and sufficient genetic variation occurs within a species, locally adapted races of weeds are likely to arise (Mortimer, 1990). Finally, where intensive agriculture is practiced, it is common for species not endemic to the area to be present as weeds. While additional species are con- tinually being introduced into agricultural environments, both inadvertently by industry and consciously by seed firms, few alien species succeed in estab- lishing themselves as damaging weeds. However, as with pre-adaptation, changes in land management practices are often a critical ingredient, as wit- nessed by the spread of Rhododendron ponticum in the U.K. (Mortimer, 1990). 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 63 64 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT Weed Adaptation to Management Practices The ability of weeds to adapt to changes in management practices is certainly one explanation for the persistent nature of crop yield losses to weeds, despite technological advances (Ghersa et al., 1994; Cousens and Mortimer, 1995). Thus, observations by Fryer and Chancellor (1970) suggested that the continued and widespread use of herbicides had markedly altered the composition of grassland weeds, but it was doubtful that it had led to the eradication of any weed species. In a specific experiment to examine the effects of several herbicides on species composition over a 5-year period, Mahn and Helmecke (1979) noted that, while the different herbicide treatments changed the density and dominance of individual weeds, there was no change in the species present in the community. Likewise, in a much longer trial involving herbicides on wheat, run over more than thirty years, Hume (1987) observed that no weeds were eliminated and no new species were able to invade the community. The only changes in community structure were changes in the relative abundance of species. Thus, fundamental to successful control of weeds is an ability to predict the evolutionary dynamics of weed populations, as shaped by human and natural factors (Jordan and Jannink, 1997). However, to make such predictions, a better understanding of the traits, and especially the variation of those traits, that confer adaptation to weed management practices is needed (Hartl and Clark, 1989). Focusing on the evolutionary dynamics and mechanisms will allow questions of practical sig- nificance in regard to ongoing weed adaptation to be addressed (Jordan and Jannink, 1997). These include the speed with which weed adaptation can erode the efficacy of non-chemical control methods. Insofar as weed adaptation proceeds at a pace that negates technological advances in control, then future research may need to concentrate on ways of impeding adaptation, raising the issue of whether it is possible to design management systems that inhibit weed evolution. In Search of New Approaches to Weed Management It is clear from the preceding discussion that, despite the high level of crop management and the array of options at the disposal of farmers, weeds continue to be a major problem. As Cousens and Mortimer (1995) noted, some grass weeds have become increasing problems in cereal crops, requir- ing new herbicides or major changes in cropping to ensure continued pro- ductivity. Herbicide resistance is also on the increase. As a result, it is widely accepted that programs in which weed control is almost exclusively achieved by herbicides can be very unstable (Swanton and Weise, 1991; Gressel, 1992; Zimdahl, 1993; Wyse, 1994; Shaw 1996). This acknowledgment, coupled with increasing public concern about the levels of chemicals being used and their potential environmental effects, has led to a renewed emphasis on long-term 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 64 ECOLOGICAL MANAGEMENT OF CROP-WEED INTERACTIONS 65 weed management and the integration of a range of environmentally safe and socially acceptable control tactics (Thill et al., 1991). Consequently, the focus of much recent weed research has become the study of how crop yields and weed interference are affected by changes in cropping management, including tillage methods, the timing and rates of herbicides, cover crops, and planting patterns (Swanton and Murphy, 1996). However, the efficacy of what has become termed integrated weed management (Thill et al., 1991; Elmore 1996) clearly depends on a thorough understanding of the population dynamics of weed communities and their constituent populations. In particular, it requires an understanding of • the factors that determine the rates at which weeds spread; • the rates at which they increase when they reach a given location; • the maximum extent to which they will increase; and • the ways in which the spatial spread and abundance of weeds can be minimized and reduced (Doyle, 1991; Cousens and Mortimer, 1995). For this reason, it has become fashionable to talk of the need to employ a systems approach to the study of weed control (Müller-Schärer and Frantzen, 1996; Swanton and Murphy, 1996). The problem, as a number of researchers (Cousens and Mortimer, 1995; Swanton and Murphy, 1996; Jordan and Jannink, 1997) have pointed out, is that research into integrated weed management (IWM) has not progressed beyond description. However, to be of practical use, IWM must move from a descriptive to a predictive phase. As Cousens and Mortimer (1995) have underlined, most studies of weed population dynamics are capable only of providing information on the outcomes of management changes, but not on the processes involved. Equally, few studies on integrated weed management have as their specific aim finding a solution to specific weed management problems. Finally, the emphasis of much work on natural communities is the prediction of long-term changes. However, Cousens and Mortimer (1995) argue that, not only is predicting long-term behavior of natural systems difficult, but it is also not what the farmers are interested in. They are concerned with the short- to medium-term consequences of their management actions and with plant communities that may be in a state of unstable equilibrium. Given this, it is interesting to ask the quality of our current ability to predict changes in weed populations. The Role of Mathematical Models in Predicting Weed Population Dynamics Linking management changes to models of crop-weed interactions, which include such issues as weed population dynamics and the ecophysical basis of competition, permits the prediction of future weed problems and 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 65 66 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT their solutions, together with the economic risks and benefits involved (Doyle, 1991; Doyle, 1997). Accordingly, the following discussion focuses on the ability of mathematical models to predict the changes in weed populations and the consequences of changes in weed management. The first part considers the contribution of quantitative models to the understanding of the spatial and temporal dynamics of weed populations. Central to this is an appreciation of the types of factors driving population change. At any given point in time, the state of a given weed population can be defined in terms of its spatial limits, its total size, its density, and its composition. From the moment that environmental and management changes occur, alterations in the state of the population will occur; it is the dynamics of these changes which are of interest. Nevertheless, comprehending the changes in the spatial distribution and abundance of weeds is only one element of weed management. It is necessary to understand how different management practices influence the size and spread of weed populations. Accordingly, the second part of the chapter looks at the various attempts to use biological and ecophysical models to explore the efficacy of integrated weed management systems. However, insofar as weeds adapt to management conditions, there is also a need to predict weed evolution (Jordan and Jannink, 1997). Thus, the third and final part of the chapter considers our ability to predict the speed with which weeds can adapt to control measures and whether management systems can be designed which impede weed evolution. SPATIAL AND TEMPORAL DYNAMICS OF WEED POPULATIONS The Dynamics of Weed Invasion and Spread As in medicine, prevention rather than cure is likely to be the most cost- effective strategy, so understanding how and why weeds invade a given area and being able to predict the pattern of spread is fundamental to control (Doyle, 1991). However, only very recently has any attention been paid to predicting the process of weed invasion. As late as the middle of the 1980s, Mack (1985) reported that there were no mathematical models simulating the spread of weeds. Part of the reason for this lack of models was that spatial processes were given very limited consideration in weed management models, which were almost exclusively concerned with the temporal dynamics of weeds. However, in the last decade there has been an increased interest in understanding the processes involved in the spread of weeds at both the national and regional level and within fields. The former has been driven by a concern to limit the geographic spread of unwanted plant species, while the latter gained impetus from the pressure to reduce herbicide usage and increase the efficacy of any chemical control. 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 66 ECOLOGICAL MANAGEMENT OF CROP-WEED INTERACTIONS 67 Predicting Weed Invasion The simplest model to simulate the geographic spread of weeds is obtained by assuming that a species spreads outwards along a front at a con- stant rate in all directions. If the distance advanced each year is r, and pre- suming that the spread starts from a single focus, the area A occupied after t years is given by A ϭ ␲ (rt) 2 (4.1) while the rate of annual increase in area is given by ᎏ d d A t ᎏ ϭ 2 ␲ r 2 t (4.2) and the instantaneous proportional rate of increase is then ΂ ᎏ d d A t ᎏ ΃ /A ϭ ᎏ 2 t ᎏ (4.3) Auld extended this simple model first by simulating the spread of weeds from several foci (Auld et al., 1979) and then by incorporating it within a model for predicting the population density of weeds (Auld and Coote, 1980). For any given site, the level of weed infestation in year t(P t ) was pre- sumed to increase according to the exponential model: P t ϭ P 0 (1 ϩ c) t (1 Ϫ s) t (4.4) where P 0 is the initial weed population at the site, c is the proportionate rate of growth, as given in Equation 4.3, and s is the proportion dispersed away from the site. The model was subsequently used (1) to simulate the possible spread of serrated tussock (Nasella trichotoma) in southeast Australia (Auld and Coote, 1981); (2) to gauge the potential costs of an effective regional control policy (Auld, Vere and Coote, 1982); and (3) to compare the costs of different strategies for controlling the spread of a localised weed population (Menz et al., 1980) Implicit in such a model is the assumption that weed seed is distributed equally in all directions, so the spread may be described by a series of con- centric circles. However, likening the spread of weeds to the ripples from a stone dropped in water involves considerable simplification of reality (Mack, 1985). In practice, environmental heterogeneity and spatial irregularity are likely to result in an uneven spread (Plumber and Keever, 1963; Rapoport, 1982). Random processes may also influence the observed pattern of weed diffusion, as Skellam (1951) noted in a seminal study, which modelled the areal spread of a plant population using random-walk techniques. As a consequence, more recent research has focussed on identifying areas potentially suitable for the growth of particular weed species. The earliest of these 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 67 68 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT studies, by Medd and Smith (1978), involved the development of a simple model to predict the growth, phenological development, and seed yield of musk thistle (Carduus nutans) from climatic data. Using the model, they were able to determine areas within Australia that were suitable for the growth and development of the weed, including uninfested regions. More recently, Panetta and Mitchell (1991) have used a computer program to analyze the climatic factors at locations where particular weed species occur in Australia in order to describe the climatic profiles of the species and to examine the possibility of the invasion of New Zealand by these species. Others, such as Patterson et al. (1979), Williams and Groves (1980), and Patterson (1990) have used experiments under controlled environment conditions to infer the limits to the spread of particular weed species. The problem with all these models that use climatic data to predict spread from present occurrences is that there is no guarantee that climate is the limiting factor (Cousens and Mortimer, 1995). However, the recent advent of geographic information systems (GIS) has allowed the spatial distribution of weeds to be mapped against a wider range of limiting factors, including soils, management techniques, competitor species, and climatic variables. As a consequence, it is possible to derive a more complex picture of the environmental and ecological determinants that favor the growth of a particular species. Such techniques have been used by Prather and Callihan (1993) to study the efficacy of eradication programs and by Wilson et al. (1993) to predict the environmental consequences of weed control. Nevertheless, even these models do not strictly predict whether a particular area will be invaded by a given weed species but rather if it is possible. Seed Dispersal Although the spatial diffusion models discussed may describe the spread of weeds, they are essentially descriptive models, in that they do not really explain the mechanism through which dispersal occurs. As Cousens and Mortimer (1995) outline, the mechanisms are complex, including dispersal by wind, animals, water, and tillage operations, as well as vegetative spread. However, quantitative studies of weed dispersal have been few and most modeling work has focussed on wind dispersal. Thus, Smith and Kok (1984) studied the factors responsible for the direction and distance over which the seed of Carduus nutans was spread from a single point source. They found that local seed dispersal was a function of wind velocity and the degree of turbulence. Specifically, the observed seed dispersal could be described by a Gaussian plume model, in which the concentration of seeds (C) at a point (x,y,z) in three-dimensional space at a relative time (T) is given by C(x,y,z,T) ϭ ͵ T 0 Q(t)C 0 (x,y,z,t)dt (4.5) 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 68 ECOLOGICAL MANAGEMENT OF CROP-WEED INTERACTIONS 69 where C 0 denotes the rate at which seeds pass through the point x,y,z at time t, Q(t) is the rate at which seed is released and T is the cumulative time since the initial release of seed. However, the Gaussian dispersal model was originally constructed to describe movements of molecules in a gas cloud and so implicitly assumes that particles will continue to disperse indefinitely. With heavy particles, such as seeds, this is evidently not true. Thus, Johnson et al. (1981) used a different approach to predicting the distance (d) over which weed seeds would disperse, assuming a steady wind and no turbulence: d ϭ HU/V s (4.6) where H is the release height, U is the wind speed and V s is the terminal velocity of the propagule. However, while the model describes in some detail the mechanisms by which seed is spread, in the absence of a population com- ponent, it is difficult to see how it can be extended to study problems of weed invasion on a field or regional scale. A model that does combine mechanistic modeling of seed dispersal with the life-cycle dynamics of a weed population was developed by Ballaré et al. (1987). In their work, they simulated the population dynamics and spread of Datura ferox in a soybean crop. Apart from a series of simple mathematical expressions describing the life cycle of the weed, the model also included a specific weed-dispersal algorithm, in which the spatial dispersion of the weed over time was a function of both the dispersal characteristics of the species and the type and direction of the combine harvester. The result is a dispersion pattern in which the seed is principally spread in the direction of the combine moves. One weakness of these models of seed dispersal is that they describe the likelihood of weed invasion solely in terms of proximity to an existing area of infestation. While this may explain most of the observed spatial heterogeneity in weed incidence in arable crops, for perennial crops, such as forages, past management practices and weather conditions may be just as important in influencing the spatial configuration. In other words, the likelihood of invasion may be as much a function of the susceptibility of the area to invasion as it is to the proximity of the weed source. The Dynamics of Weed Population Density Given the presence of an infestation, using knowledge of the temporal dynamics of weed populations, it should be possible to predict how fast the weed population will grow in the absence of controls. Because of the complexity of the problem and the long-term character of weeds, as early as 1980 Mortimer et al. (1980) were advocating the use of simple mathematical models of the life cycle of weeds to predict population densities. The current state 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 69 70 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT Mature flowering plants Seed shed Seed bank Emergent seedlings Viable seedlings Predation Mortality Seed rate Germination rate Survival rate Flowering rate Figure 4.1 Diagrammatic representation of a typical weed life-cycle model. (Reprinted from Crop Protection, 10, Doyle, C.J., Mathematical Models in Weed Management, 432–446. Copyright 1991, with permission of Elsevier Science.) of the attempts to model life-cycle processes has been described in Doyle (1991), Cousens and Mortimer (1995), and Kropff et al. (1996). In general, comprehensive models based on physiological principles are only available for parts of the life-cycle, such as plant growth, competition (Kropff and Van Laar, 1993), germination, and emergence (Vleeshouwers and Bouwmeester, 1993). Instead, most models encompassing the whole life cycle have repre- sented it in terms of a series of growth stages, as diagrammatically repre- sented in Figure 4.1. The complex processes involved in the transition from one stage to the next are then “blended into a few lumped parameters like a germination rate, a reproduction rate and a mortality rate” (Kropff et al., 1996, p. 7). Good examples of such models are Cousens et al. (1986), Doyle et al. (1986), and Van der Weide and Van Groenendael (1990). However, the detail in which the life-cycle processes in weeds are studied is only one issue. More critically, there are various ways to extract the population dynamics from the life-cycle processes, and these different ways may lead to different results (Durrett and Levin, 1994; Kropff et al., 1996). In particular, three different approaches to modeling the integration of individual weed plants into a population have been adopted. Kropff et al. (1996) stylized these as (1) the density-based models, (2) the density-based models incorporating spatial processes, and (3) the individual-based models accounting for spatial processes. Of these, the most frequent modeling approach has been to assume that the key determinant of rates of population growth is the density of the weeds. 920103_CRC20_0904_CH04 1/13/01 10:46 AM Page 70 [...]... Farnham, 48 7 49 4 Swanton, C.J and Weise, S.F., 1991 Integrated weed management: the rationale and approach Weed Technol., 5:657 –663 Swanton, C.J and Murphy, S.D., 1996 Weed science beyond the weeds: the role of integrated weed management (IWM) in agroecosystem health Weed Sci., 44 :43 7 44 5 Swinton, S.M and King, R.P., 19 94 A bioeconomic model for weed management in corn and soybean Agric Syst., 44 :313... adapted to management practices Thus, Thomas et al (1996) reported that in Canada there had been a change in the composition of weed communities in cereals with the shift 920103_CRC20_09 04_ CH 04 84 1/13/01 10 :46 AM Page 84 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT from fallowing to continuous cropping In an earlier study, Buhler and Daniel (1988) observed that under continuous corn... 920103_CRC20_09 04_ CH 04 72 1/13/01 10 :46 AM Page 72 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT can be made between the individual-based models (e.g., Pacala and Silander, 1985) and cellular automaton models (e.g., Silvertown et al., 1992), the underlying principles can be understood by examining the work of Pacala and Silander (1985, 1987) The basic idea is that the performance of an individual... management Bioscience, 44 :85– 94 Goldberg, D.E and Werner, P.A., 1983 Equivalence of competitors in plant communities: a null hypothesis and a field experimental approach Am J Bot., 70:1098 –11 04 González-Andujar, J.L and Fernandez-Quintanilla, C., 1991 Modelling the population dynamics of Avena sterilis under dry-land cropping systems J Appl Ecol., 28:16 –27 González-Andujar, J.L and Perry, J.N., 1995... Modelling the effects of weeds on crop production Weed Res., 28 :46 5 47 1 920103_CRC20_09 04_ CH 04 90 1/13/01 10 :46 AM Page 90 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT Kropff, M.J and Spitters, C.J.T., 1992 An eco-physiological model of interspecific competition, applied to the influence of Chenopodium album L on sugar beet I Model description and parameterisation Weed Res., 32 :43 7 45 0... population models must be improved in three key areas if they are to make a tangible contribution to the evaluation and management of cropping systems: (1) incorporation of 920103_CRC20_09 04_ CH 04 78 1/13/01 10 :46 AM Page 78 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT weed-crop interactions, (2) degree of detail in the description of crop management, and (3) the explicit recognition... Lybecker, D.W., and Swinton, S.M., 1996 GWM: general weed management model Agric Syst., 50:355–376 920103_CRC20_09 04_ CH 04 94 1/13/01 10 :46 AM Page 94 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT Wilkerson, G.G., Jones, J.W., Coble, H.D., and Gunsolus, J.L., 1990 SOYWEED: a simulation model of soybean and common cocklebur growth and competition Agron J., 82:1003–1010 Williams, J.D and Groves,... importance of being discrete (and spatial) Theoret Popul Biol., 46 :363 –3 94 Dyer, W.E., 1995 Exploiting weed seed dormancy and germination requirements through agronomic practices Weed Sci., 43 :49 8–503 Elmore, C.L., 1996 A reintroduction to integrated weed management Weed Sci., 44 :40 9 41 3 Forcella, F., 1993 Seedling emergence model for velvetleaf Agron J., 85:929–933 Frank, J.R., Schwartz, P.H and Potts, W.E.,... and duration will interact to produce seasonal variation in the selection process, which in turn 920103_CRC20_09 04_ CH 04 82 1/13/01 10 :46 AM Page 82 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT will depend on the phenology and growth of the weed species For instance, with pre-emergent herbicide control of weeds that show germination over a protracted period, the intensity of selection... 1989 Principles of Population Genetics, Sinauer Associates, Sunderland, MA Hassell, M.P., 1975 Density dependence in single species populations J Appl Ecol., 40 :47 3 48 6 Holt, J.S., 19 94 Impact of weed control on weeds: new problems and research needs Weed Technol., 8 :40 0 40 2 Holt, J.S., 1995 Plant responses to light: a potential tool for weed management Weed Sci., 43 :47 4 48 2 Hume, L., 1987 Long-term . the evaluation and management of cropping systems: (1) incorporation of 920103_CRC20_09 04_ CH 04 1/13/01 10 :46 AM Page 77 78 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT weed-crop interactions,. ϩ ␣ (N 0,t ϩ ␥ N 1,t ϩ ␦ N 2,t )] ␤ 920103_CRC20_09 04_ CH 04 1/13/01 10 :46 AM Page 71 72 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT can be made between the individual-based models (e.g., Pacala and Silander, 1985) and cellular. to attack. 920103_CRC20_09 04_ CH 04 1/13/01 10 :46 AM Page 75 76 STRUCTURE AND FUNCTION IN AGROECOSYSTEMS DESIGN AND MANAGEMENT Timing of Control More recently, interest in the threshold level at

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