Integrated weed 
management for 
sustainable agriculture
Edited by Robert L. Zimdahl, Professor Emeritus 
Colorado State University, USA
BURLEIGH DODDS SERIES IN AGRICULTURAL SCIENCE
E-CHAPTER FROM THIS BOOK
http://dx.doi.org/10.19103/AS.2017.0025.02
© Burleigh Dodds Science Publishing Limited, 2018. All rights reserved.
Chapter taken from: Zimdahl, R. L. (ed.), Integrated weed management for sustainable agriculture, Burleigh Dodds Science 
Publishing, Cambridge, UK, 2018, (ISBN: 978 1 78676 164 4; www.bdspublishing.com)
Weed-plant interactions
Bruce Maxwell, Montana State University, USA
 1 Introduction
 2 Crop-weed interactions: an evolutionary perspective
 3 The nature of shared resource pools
 4 Direct competition for resources
 5 Indirect effects of competition
 6 Spatial and temporal dynamics
 7 Conclusion
 8 Where to look for further information
 9 References
1  Introduction
Crop or desired plant species co-occur with undesired species and thereby the co-occurring 
species become weeds. This human-imposed quality is based on the perception that there 
is an interaction that results in some negative effect of the weed on the crop or desired 
species. At odds with this perception is an evolutionary perspective that would expect 
co-occurring species to select traits to minimize the interaction in a shared and limited 
resource pool as well as selection for traits to outcompete co-occurring plants. So which 
traits are more likely to be selected remains a question: Those that minimize interaction or 
those that intensify the interaction through competition (Grime 2006). It is quite possible 
that some co-occurring species may not be interacting in competition for resources, 
or competition may be minimal or indirect so that control of the undesired species is 
not required. Clearly, it becomes important to gain an understanding of the potential 
interaction among desired and undesired species and use that knowledge to instruct a 
management decision. All too often the decision to manage is reduced to selecting the 
best tool (usually herbicide) to get rid of the weed driven by the precautionary principle. 
The precautionary principle applied here is that when in doubt about the interaction 
between the weed and the crop one should control the weed. Unfortunately, controlling 
weeds can be expensive and can have environmental side effects. So there are trade-
offs in the decision to control weeds and thus knowledge about the interaction between 
desired and undesired species should be gained to make the best decision about weed 
management (Swanton et al. 2015; Maxwell and Luschei 2004).
Weed-plant interactions 2
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2  Crop-weed interactions: an evolutionary perspective
If plants are under selection to minimize interaction through a resource pool, one would 
expect that species that had co-occurred for many generations would have differentiated 
their respective niche requirements (Zuppinger-Dingley et al. 2014). Narrowly defined a 
species niche is a multidimensional space from which a plant gathers resources for growth 
and reproduction to sustain a population. Plants contribute the most pollen and seed 
thereby contribute the most traits to the population following natural selection. Since 
reproductive output is proportional to resources available to a plant, the plants with the 
most resources make the greatest contribution of traits. So resource capture by individual 
plants is not just driven by selection for niche differentiation but also intraspecific 
competition between individuals of the same species which tends to select for traits 
that allow plants to optimize resource consumption and use efficiency (Kraft et al. 2015). 
Interspecific competition between plants (between crops and weeds) may select for more 
niche differentiation traits.
The complexity of understanding and ultimately predicting the outcome of competitive 
interactions among individual plants, much less among species, becomes difficult given 
the different selective constraints or histories (Keddy 1989; Lavorel and Garnier 2002). In 
addition, one does not need to spend much time observing a multispecies plant community 
to see that natural selection must vary significantly across space and time so the interplay 
among the selective processes for niche differentiation, resource consumption, resource 
use efficiency and other competition-related traits is inconsistent (Rees et al. 2001; Dudley 
1996; Kraft et al. 2015). What gives advantage to one set of traits in one place, or at one 
time, may not pay off for an individual at another point in space or time (e.g. Ellner 1985; 
Zuppinger-Dingley et al. 2014). Perennial plant species that must endure many conditions 
over time are challenged differently than annual species (Kunstler et al. 2016). Selection 
in one generation on one individual could be driven by one set of factors and another 
individual of the same species even in the same population could be driven by a different 
set of factors so if they interbreed what traits could we expect to emerge in a population? 
In fact, there is some consistency of selective forces and thus part of understanding the 
interaction among plants of different or even the same species is understanding the 
consistency of the environment that they have been selected upon (Kraft et al. 2015). 
Rotating crop species is a well-known strategy for weed management because it narrows 
the spectrum of species that can tolerate the temporal changes in the environment shared 
with the crop.
The agroecosystem environment is made up of abiotic and biotic components and 
these are heavily influenced by disturbance and human land management (inputs, etc.). 
In most crop systems soil and plant management has the goal to make the environment 
consistent for the crop which makes selection on the co-occurring weed species in any 
given generation relatively consistent. However, in crop rotations where the crop species 
with different life cycles, morphologies, physiologies and phenologies can change, 
selection is heterogeneous and thus successful traits to allow coexistence with the crops 
would be expected to decrease with increased crop or community diversity (Milbau and 
Nijs 2004). Alternatively, diverse mixtures of grasses can be selected for greater resource 
use efficiency traits than the same species grown in monocultures over eight generations 
(Zuppinger-Dingley et al. 2014). These results suggest that selection in species mixtures 
may therefore increase species coexistence and allow increased mixture yields in 
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Weed-plant interactions 3
agriculture. These results suggest that crops should be selected under mixture conditions 
including weeds, cover crops and companion crops.
As atmospheric CO2 concentrations and subsequent climate changes have occurred, 
yet another temporal variation is introduced and the successful subset of available weed 
species to any given site may be even more narrowed and the interaction with the crop 
fundamentally changed (Blumenthal et al. 2014). Rees et al. (2001) evaluated multiple 
plant traits and their trade-offs in structuring annual plant communities based on empirical 
evidence from long-term studies. They identified that seed size, rapid exploitation and 
differential influence of seed herbivory often determine the importance of competition 
or colonization as the driving selective forces determining the relative success of different 
species in annual plant communities. Their observations lead to the competition-
colonization trade-off hypothesis where small-seeded species are predicted to be 
good colonists, but poor competitors and large seeded species are good competitors 
for resources but poor colonists. Although there are exceptions to this hypothesis, 
consideration of the potential for predicting competitive outcomes between weeds and 
crops must start with initial plant (seed) size as a critical trait. For example, for two plants 
with identical abilities to compete for resources that germinate and emerge at the same 
time, the one that started from a larger seed will get larger than the smaller seeded one. 
Kraft et al. (2015) quantified average fitness and stabilizing niche differences between 102 
plant species pairs and related the differences to 11 functional traits. Competitive fitness 
traits were correlated with exclusion of one of the species in a pair, but coexistence of 
the species pairs was not necessarily correlated with traits that they identified as niche 
differentiating. Coexistence of species pairs could only be associated with combinations 
of traits, representing differentiation in multiple dimensions. Thus, even in the relatively 
simple selective arena of agricultural systems there is yet to be a clear emergence of 
prevalence of competitive or niche differentiating traits in weeds. It is clear, however, 
that site-specific history could determine trait prevalence but may be not intensity of 
competition for resources.
3  The nature of shared resource pools
Plants begin growth as an individual genet by drawing on resources from the seed, 
producing a radicle followed by mainly haustorial cotyledons. True leaves are formed 
dedicated to photosynthesis and the plant begins drawing on resources from the 
surrounding environment. Resources shared among plants in the soil include water 
and nutrients and, above ground, light, physical space and CO2. If we turn our focus to 
competition for nutrients and water in the soil profile the multidimensional niche from 
which an individual plant draws resources is tangible and sharing the same soil volume 
of root systems from different plants, or among ramets of the same plant, suggests the 
potential for competition for resources. It turns out that plants may physically overlap, 
but they may be phenologically offset in time so there is minimal simultaneous growth 
and draw on the resource pool. For example, some weed species mostly grow after crop 
harvest and/or crop senescence (e.g. Kochia scoparia and Salsola kali in dry land small 
grain systems) which minimizes their competition with the crop (niche differentiation). 
It is also possible for plants to have mutualistic relationships between species that by 
close association in space may improve their ability to germinate, grow and ultimately 
Weed-plant interactions 4
© Burleigh Dodds Science Publishing Limited, 2018. All rights reserved.
reproduce. There is terminology for the different relationships that can occur between two 
species sharing the same niche (Table 1).
The main reason to identify the alternatives to competition as a process among weeds 
and crops or other desired species is that we may identify positive relationships and thus 
management would not require weed removal. In addition, the process of natural selection 
acting on weeds that are inherently disadvantaged in most crop systems would be to select 
traits that allow avoidance of competition with the crop as much as those traits selected to 
be more competitive with the crop. Thus, there may be specific environmental conditions 
where the weed does not affect the crop or the threshold for significant economic effect 
is higher than that for the biological effect (Cousens 1987). The environment influencing 
the weed-crop interaction is likely to vary on small spatial scales (m2) and from growing 
season to growing season (Maxwell and Luschei 2004). Weed densities recorded across 
Table 1 Types of interactions between plants of two different species where the sign + or – indicates 
positive and negative interactions or 0 means no interaction (adapted from Burkholder 1952)
Types of interaction between species A and B Species A Species B
No interaction 0 0
Competition − −
Mutualism + +
Commensalism + 0
Amensalism 0 −
Parasitism + −
Figure 1 The relationship between dryland winter wheat grain yield and monocot weed density 
demonstrating variation indicative of variables other than weed abundance determining crop yield.
© Burleigh Dodds Science Publishing Limited, 2018. All rights reserved.
Weed-plant interactions 5
a wheat production field with site-specific yield information demonstrates the variation in 
weed impact across the field (Fig. 1). Clearly, factors other than weed density were playing 
a role in determining crop yield.
4  Direct competition for resources
Direct effects of interactions between plants through the resource pool are based 
on the study of resource uptake and allocation. Benjamin and Parks (2007) described 
an ecophysiological model for predicting monoculture and mixed species interactions 
to predict the effect of weeds on crops. Their ‘Conductance’ model provided a simple 
yet mechanistic method of simulating growth and competition for light based on space 
occupied by crown zones of plants in a range of competitive environments. Berger et 
al. (2013) similarly presented a spatially explicit, individual-based plant growth model 
(COMPETE) utilizing components of tested and validated mechanistic biophysical models 
(Maestra, PNM, LeachN and Gecros). The COMPETE model captures the major dynamics 
of the growth of a population of competing plants (i.e. formation of canopy hierarchies) 
and also simulates changes in plant transpiration as a function of plant size and competitive 
environment. Biophysical process models are gaining popularity particularly to predict 
plant species interaction changes that may occur with climate change in CO2-enriched 
atmosphere. The current utility of these models at management decision spatial scales or 
special scales that would capture crop–weed interactions is challenged by a large number 
of parameters requiring measurement on expensive instruments (Deen et al. 2003).
Alternatively, competition for resources among plants may be captured if one assumes 
that the rate of resource uptake by individual plants is proportional to plant size, resources 
are finite but renewable by some rate, and plant size is asymptotic (Tilman 1988). For 
example, the following equations can be used to simulate growth of individual plants in a 
population over time when they are competing for resource in a shared pool.
RS
RS
tt
= d
d
 (1)
RUN v Si
q d
q dt i
N
s
s i
s i
= + −
+















=
∑
1
1
1
 (2)
RAG RAG RS RUNt t t t= + −−1  (3)
GRi
GR RAG
RAG kt
max t
t s
=
+
 (4)
Si Si GRit t t= +−1  (5)
where RS is the resource supply rate to an individual target plant’s niche (neighbourhood); 
RUN is the sum of resources used by neighbouring plants over a specified time period 
(e.g. 1 week) and is a function of the sizes of N neighbour plants (Si) at the previous 
time (starting with seed size) and their distances (di) from the target plant as well as 
Weed-plant interactions 6
© Burleigh Dodds Science Publishing Limited, 2018. All rights reserved.
species-specific resource use efficiency (vs) and competition intensity (qs) parameters. RAG 
is the resources available for growth in the neighbourhood for the next time increment 
based on what resources remain after use in the previous time (RAGt–1) step, the resource 
supply rate (RS) and the RUN. GRi is the growth rate of the target plant and is a function of 
the maximum growth rate (GRmax), RAG and a species-specific growth efficiency parameter 
(ks). Then the size of each plant at the end of each time step is Sit calculated using 
Equations 1–5 in sequence. Assigning an x and y coordinate to each plant in a community 
allows calculation of each individual plant size over time and a mechanistic, yet empirically 
based, way to determine individual plant performance in a community where resources 
are shared (Milbau et al. 2007; Weiner 1982). The performance of plants in a multispecies 
community can also be explicitly considered with species-specific parameter values. In 
addition, by varying RS over time and space one can examine the range of environmental 
effects on competitive outcomes. This individual-based model can be parameterized by 
tracking plant sizes through a growing season and then estimating the resource dynamics 
without actually measuring the resource pool. More importantly it allows an understanding 
Figure 2 Plant maps showing time steps over growing season when at week 0 plants are seed (black 
dots) and crop (yellow dots). As plants grow circles enlarge depending on resources available. Weeds 
are green and crop plants are yellow. Red x indicates plants that died because they did not grow for 
three consecutive weeks due to lack of resources.
© Burleigh Dodds Science Publishing Limited, 2018. All rights reserved.
Weed-plant interactions 7
of the complicated dynamics among individual plants drawing on a shared resource pool 
and thus a theoretical basis for the quantification of impacts of weeds on crops (McGlade 
1999). This model provides a visualization of the competition process (Fig. 2). What it does 
not account for is indirect competition where the same 3-dimensional space is shared by 
two species but they do not necessarily interact through the same resource pool.
The individual plant approach to characterizing population response avoids the 
assumptions of population-based theory (Watkinson 1996; Cousens and Mortimer 1995), 
averaging individual plant responses and thereby ignoring individual variation that could 
be critical to weed management decisions. For example, the standard for characterizing 
weed-crop competition outcomes was the use of average plant biomass in a population as 
the response variable to intra- and interspecific densities (Shinozaki and Kira 1956; Spitters 
1983; Firbank and Watkinson 1985; Radosevich 1987; Roush et al. 1989). Cousens (1991) 
suggested the more direct quantification of competition between crops and weeds with 
the hyperbolic equation where weed density (N) became a direct predictor of crop yield (Y):
Y Y
iN
iN awf
= −
+





1 1 /
where Ywf is the weed-free crop yield (assumed to be the maximum possible yield) and i 
and a are shape parameters. This equation was modified to include emergence time of the 
crop relative to the weed (Cousens et al. 1987) and incorporated into simulation models to 
demonstrate a range of alternative weed management practices (Maxwell and O’Donovan 
2007). The above models have been useful for understanding first principles of weed–crop 
interactions, but often require significant effort to estimate the parameters with small plot 
experiments and the parameter variation is high over crop growing regions and from year 
to year limiting the application of these responses (Jasieniuk et al. 1999).
The aforementioned approaches facilitated easy quantification of the effect of the weed 
on the crop and more importantly the effect of the crop on the weed to determine the future 
ramifications of leaving weeds if not managed. However, these approaches gloss over 
some aspects of the interaction between crop and weeds that the more individual-based 
models could pick up. Further, these glossed-over aspects could reveal opportunities for 
more integrated weed management. For example, the role of seed size of crop or weeds, 
emergence time of the crop relative to the weeds or crop plant spatial arrangement could 
all be manipulated in the model to explore ways to reduce the impact of weeds on a crop.
A number of studies have elucidated potentially important interactions among 
weeds, crops and cover crops that could represent effective ecologically based weed 
management. However, these empirical studies without a theoretical base often conclude 
with the requirement for more knowledge about what drives the crop-weed interaction 
in order to recommend general outcomes. A mounting number of studies have been 
conducted to evaluate how cover crops can influence weed population dynamics by 
maximizing competition with weeds (Mirsky et al. 2013; Den Hollander et al. 2007) 
and, when intercropped, minimize competition with the cash crop while maximizing 
competition with the weed (Benaragama et al. 2016; Hartwig and Amon 2002). In addition, 
some cover crops with legume species change the soil nitrogen dynamics favouring 
the crop or some of the weed species with little knowledge to base generalized weed 
management recommendations. An interesting study was conducted to evaluate how the 
relative proportions of cereal rye (Secale cereale L.) and hairy vetch (Vicia villosa Roth) 
sown in mixtures as a cover crop influenced winter annual weed suppression and vetch 
Weed-plant interactions 8
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nitrogen fixation (Hayden et al. 2014). Increasing the vetch in the cover mix increased 
nitrogen accumulation and improved weed suppression, but the mechanism for the 
successful result was not clear. Amini et al. (2014) provided a generalizable result drawing 
on competitive indices like crop leaf area index (LAI) (Zhao et al. 2006; Hansen et al. 2008) 
and growth rate to rank different crop varieties competitive potential with a single weed 
species (Steinmaus and Norris 2002; Kropff and Lotz 1992). If multiple weeds occur in the 
system the interactions become experimentally intractable. Thus, the understanding of 
the more complex weed community dynamics and effects on crops may require simulation 
models based on empirical parameterization of first principle relationships to build an 
understanding of the breadth of interactions and subsequent outcomes.
5  Indirect effects of competition
A good example of indirect competition for resources that looks like competition would 
be where three plants share nearly the same space but one has root morphology that 
places most of its roots deep in the soil profile while the co-occurring species have mid-
profile or shallow root systems (Fig. 3). The shallow root system species captures small 
Figure 3 Three co-occurring species with different morphologies enabling them to occupy the same 
area but minimizing niche overlap.
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Weed-plant interactions 9
volume rain event moisture and has adapted this strategy for climates where growing 
season precipitation comes in small pulses. The other species commits to deeply stored 
soil moisture that may get stored outside of the growing seasons. Under dry conditions 
with pulse rain events the shallow-rooted species usurp all of the moisture and are able 
to increase biomass whereas the deep-rooted species slows its growth as it depletes the 
deep soil moisture reserve. If our measurement of competition is based on relative growth 
of these two species one may conclude that the shallow-rooted species outcompetes the 
deep-rooted species for resources even though the two species are not drawing on the 
same resource pool (direct competition) and have adapted morphologies to effectively 
share the same 2-dimensional space. In a dry growing season following a wet winter, 
the deep-rooted species may gain more biomass and appear as the superior competitor 
for resources. The weed ecologist may observe these contrasting results in the field in 
different years and thus design the definitive experiment to determine which species is 
the superior competitor for resources. He/she plants the two species together in a shallow 
pot and determines that one species has a higher relative growth rate than the other and 
definitively concludes that the deep-rooted species is more efficient at drawing on the 
resource pool. This is direct competition because each species is competing directly with 
the other for resources in the same space. The results of the experiment, however, may be 
irrelevant to the field condition where these two species co-occur but never draw on the 
same resource pool because their root morphologies use different niche space.
Another example of an indirect interaction that could easily be interpreted as direct 
competition for resources in a shared pool is where one species like a cover crop draws 
down the resource pool for weed species which in turn releases the crop from competition. 
Wells et al. (2013) showed that rye (Secale cereale) used as a cover crop in soybeans could 
pull down the soil nitrogen to a level that redroot pigweed (Amaranthus retroflexus) could 
not tolerate but the soybeans could thrive making it look like the competition was directly 
between the soybeans and pigweed.
In some cases the opposite of competition (mutualism or commensalism) has been shown 
where co-occurring crops and weeds resulted in enhanced resource availability, creating 
a direct facilitation (Brooker et al. 2016). Phosphorous availability can be enhanced on 
acidic soils by the secretion of organic acids and phosphatases by one species (Hinsinger 
et al. 2011; Li, Zhang and Zhang 2013) or N transfer from nitrogen-fixing legumes to 
co-occurring crops or weeds (Laberge et al. 2011; Bedousac et al. 2015). Increased 
availability of water in the upper layers of the soil profile through hydraulic lift is shown to 
increase soil water for co-occurring plants (Prieto et al. 2012). Niche complementarity can 
result as facilitation from herbivore protection or pollinator attraction (Brooker et al. 2015). 
Indirect facilitation through the soil microbial community has also been documented (Van 
der Heijden et al. 1998; Bennett, Daniell and White 2013). A meta-analysis indicated that 
arbuscular mycorrhizal fungi can directly suppress weeds that are weak hosts for the fungi, 
but also can indirectly suppress some strong host weeds in the presence of strong host 
crops that ultimately obtain the advantage (Li et al. 2016). These facilitative processes can 
differentially provide advantage to the weeds or crop in any given system at any particular 
time, so their interpretation must be made with caution and careful understanding of the 
drivers in variation before applying what appears to be a definitive outcome or assumption 
about the outcome of the interaction (e.g. Fig. 1 where detection of the negative effect of 
weeds is hard to distinguish). Direct competition for resources between weeds and crops 
can be occurring, but may be masked by niche differentiation, facilitative or mutualistic 
processes.
Weed-plant interactions 10
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6  Spatial and temporal dynamics
Most crops have been selected under conditions of plentiful resources, whereas weeds 
were more likely required to survive and produce offspring in a wide range of growing 
conditions. Thus one might expect that under a typical crop production scenario the crop 
might be better physiologically equipped to rapidly use resources than the co-occurring 
weed species. It has been widely shown that crops can offer significant competition to 
weeds in many cases (Andrew et al. 2015; Rasmussen 2004). Planting strategies such 
as narrow row spacing and early planting that maximize competition often provide 
advantage to the crop over co-occurring weeds and those advantages can translate 
across generations (Evers and Bastiaans 2016; Fahad et al. 2015; Kolb and Gallandt 2013; 
Maxwell and O’Donovan 2007). Planting depth and/or planting time can also increase 
crop competition with weeds by maximizing the likelihood of the crops capturing soil 
resources ahead of the weeds.
Increasing crop density by decreasing the distance between rows and/or decreasing 
within-row plant spacing is an optimization problem that maximizes the effect of the crop 
on the weed without affecting the marketable crop product. Intraspecific competition can 
be just as effective on the crop as interspecific competition. Generally, rectangularity near 
1.0, the ratio between inter-row and intra-row distance between plants (Willey and Heath 
1969), has been identified as optimum. However, research that specifically addresses the 
light interception by the crop canopy resulting in weed shading is also important and 
may reveal different ideal crop plant arrangements to maximize competition for light 
(Maddonni et al. 2001).
Competition for light and the influence of light quality may be interpreted as competition. 
The mere presence of another species can influence the reflected light quality which can 
have negative effects on the growth of a neighbour plant (Gundel et al. 2014; Balare an 
Casal 2000). It has been shown that light quality can change plant morphologies to avoid 
(niche differentiate) competition for light. Liu et al. (2009) proposed that changes in the red 
to far red ratio of light acts as an early signal of pending competition by a physiologically 
triggered shade avoidance response that additionally had a physiological cost-limiting 
plant growth. Traits that provide advantages in a competitive environment can have trade-
offs, and sorting among the different mechanisms that determine weed effects on crops is 
no easy task and experimentally requires special methods (Swanton et al. 2015).
7  Conclusion
There is great need for understanding the potential for weed impact on crops. 
Plant competition for resources in a shared environment is an important aspect of 
understanding effects. Thousands of dollars are often spent on a single production field 
to control weeds and sometimes with significant potential for non-target environmental 
impact. Unfortunately most weed management is conducted under the precautionary 
principle, because there is great uncertainty about the extent of weed effects on current 
or future crops if not managed. Even with weeds that have been extensively studied and 
their crop effects quantified, translation of study results to field scale has rarely been 
accomplished. Annual variability and site-to-site variability have challenged the ability 
to make prescriptive recommendations. It has become clear that fundamental processes 
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Weed-plant interactions 11
including competition for resources among plants and interpreting ecological processes 
for management recommendations requires an understanding of how to scale those 
processes appropriately. This remains a great challenge for Weed Scientists.
8  Where to look for further information
Future research into the interaction between weeds and desired plant species would best 
be focused at the individual spatial scale where each plant neighbourhood is characterized 
to specifically capture the potential resource interactions (Damgaard and Weiner 2017). 
Stratified random selection of target individual plants in a plant community followed by 
neighbour plant quantification by height, canopy cover, distance from target and angular 
dispersion in the neighbourhood are effective ways to discern the spectrum of plant 
interactions in a community (Bussler et al. 1995). Neighbourhood approaches to study 
competition for resources are often considered too detailed to be useful for management. 
However, a frequency distribution of properly selected neighbourhood conditions in a 
plant community can provide a mechanism to scale up inference space meaningful to 
management. Useful publications and papers cited within the chapter are listed at: 
http://plen.ku.dk/english/employees/?pure=en%2Fpersons%2Fjacob-weiner(dd3c08ff-
333b-4591-9968-3957845a6fa1)%2Fpublications.html.
9 References
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vulgaris L.) cultivars and redroot pigweed (Amaranthus retroflexus L.). European Journal of 
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Andrew, I. K. S., Storkey, J. and Sparkes, D. L. (2015). A review of the potential for competitive cereal 
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© Burleigh Dodds Science Publishing Limited, 2018. All rights reserved.
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