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Cropping System
In subject area: Agricultural and Biological Sciences
Cropping systems are defined as interactive units comprising different types of input
management systems, tillage systems, and all the crops grown, along with their
management practices within a specified time scale. They typically include components
such as cropping patterns, tillage methods, soil fertility management, and strategies for
pest, disease, and weed management.
AI generated definition based on: Agricultural Systems, 2024

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Chapters and Articles
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Review article

Are (, ) good indicators of agricultural management practices?

2016, Soil Biology and BiochemistryC. Pelosi, J. Römbke

4.3 Cropping systems

The term cropping system refers to the crop rotation and the management
practices used on a particular field over a period of several years. Three
types of cropping systems are generally compared to the conventional
systems, which often involves short crop rotations as well as ploughing, the
use of pesticides and mineral fertilizers, but no organic inputs. These are:
(1) organic farming with ploughing, organic inputs but neither chemicals
nor fertilizers, (2) integrated systems with reduced ploughing and/or
chemical and fertilizer use, and (3) conservation agriculture, with no-tillage
but chemicals and mineral fertilizers, with or without a living mulch.
Alternative cropping systems (i.e., organic farming, integrated systems and
conservation systems) appear to be favorable for enchytraeid communities
(Topoliantz et al., 2000; Vavoulidou et al., 2006). Nakamura, who studied
the effects of cropping systems on soil invertebrates, found, in decreasing
order, higher enchytraeid abundance in direct drilling with organic mulch,
in direct drilling with synthetic fertilizers and in direct drilling compared to
conventional system (Nakamura, 1988b, 1989). They concluded that
enchytraeid diversity and abundance was positively influenced by organic
mulch and fertilizers and negatively affected by tillage (Nakamura, 1988a,
1988b; Nakamura and Fujita, 1988). Also, Zimmermann (1987) found
more enchytraeids in a direct-sown crop than at a site ploughed after
application of stable manure and growth of a winter rape intercrop. Thus,
no disturbance of the soil surface and application of mulch for soil animals
seem to promote enchytraeid communities.
Review article

Revisiting cropping systems research: An ecological framework

towards long-term weed management
2024, Agricultural SystemsDilshan I. Benaragama, ... Rob H. Gulden

1.2 Cropping systems research

Traditionally, a cropping system is known as cropping patterns on a farm
and their interactions with farm resources and management (Andrews and
Kassam, 1976). Since the definition of a cropping system can be wide
variable for this study, we consider a cropping system as an interactive unit
of different types of input management systems (organic vs. conventional),
tillage systems (tillage vs. no-till), and all the crops grown (temporally) and
their management practices, in a given time scale. Any cropping system
typically has four main components: i) cropping patterns and their
management, ii) tillage systems, iii) soil fertility management, iv) pest,
disease and weed management strategies. Even though livestock is an
important component of most farming systems and they have some weed
management implications, our framework focuses only on the crop
component. For this study, a cropping system is identified as an interactive
unit of input management systems, tillage systems, and all the crops grown
(temporally/spatially) and their management practices in a given time scale.
Cropping systems research has recently been given more attention in weed
research due to greater challenges in weed management. The global threat
of an increase in herbicide resistance (Heap, 2022) has become an issue in
weed management, and as a consequence, integrated and ecological weed
management research has gained more attention. On the other hand, with
the rise of organic production, alternative non-chemical weed control
approaches are in demand as well. Under these circumstances, integrating
ecological principles and strategies for weed management has been the
typical approach requiring agroecosystems or cropping systems thinking.
Early attempts to understand individual crop management practices on
weed dynamics, particularly the effect of tillage systems (Derksen et al.,
1993; Feldman et al., 1997) and crop rotations (Buhler et al., 2001; Cardina
et al., 2002), were found to be inadequate in understanding weed dynamics.
This was due mainly to the highly diverse nature of cropping systems with
interactions between crops, crop rotations, tillage and other input systems.
There exists a need then for more inclusive research that focuses on
understanding all the components found to be beneficial towards advancing
our understanding of weed dynamics, especially as it pertains to cropping
systems research that is long-term in nature. The need for and the
advantages of research on cropping systems are reviewed elsewhere
(Drinkwater, 2002; Drinkwater et al., 2016; Shrestha and Clements, 2003).
This study focuses on understanding some limitations in cropping systems
research focused on weed management and providing solutions for
cropping systems research to produce improved long-term weed
management.
Review article

Assessing the sustainability of cropping systems in single- and

multi-site studies. A review of methods
2016, European Journal of AgronomyViolaine Deytieux, ... Jacques Caneill
4.2 Description of cropping systems
According to Sebillotte (1978, 1990), a cropping system is defined by (i) the
nature of the crops and their sequence, and (ii) crop management, seen as a
logical and ordered sequence of agricultural techniques applied to each of
these crops, including the choice of cultivars. We analysed how this
definition was translated into descriptive variables and how these variables
were used. We structured the analysis according to the main technical
choices and the operations applied to crops (i.e. crop rotation, tillage, cover
crops or intercrops, fertilisation, pesticide use, sowing and harvest) (Table
4).
Table 4. Use of crop management data for assessing cropping systems, by study

design ((a) single-site vs. (b) multi-site), type of system assessed (ex-ante vs. ex-post

systems) and study objectives. Objectives identified: (A) description of the

variability of system performances; (B) identification of systems with a high level of

performance for one or several components of sustainability; (C) analysis of the

impact of a change due the introduction of a new policy or a new technique; (D)

identification of the drivers of system performances; (E) highlighting and analysis of

the trade-offs between sustainability components.

Study
design Objectives Use of crop
& of the Crop management management References
systems study data
assessed

Empty Crop Cover- Pesticide

Empty Cell Tillage Fertilisation Sowing Other Empty Cell Empty Cell
Cell rotation crops use

4a. Single-site

Ex-ante B X X – X X – – – Gonzalez-
Study
design Objectives Use of crop
& of the Crop management management References
systems study data
assessed

Empty Crop Cover- Pesticide

Empty Cell Tillage Fertilisation Sowing Other Empty Cell Empty Cell
Cell rotation crops use

Estrada et al.
(2008)

Kanellopoulos
B, C X – – X – – – –
et al. (2012)

Interpretation Kelly et al.

B, E X X X X – X X
of results (1996)

Ex-post Syswerda and

Interpretation
E X X X X X – – Robertson
of results
(2014)

Interpretation Bailey et al.

E X – – X X X –
of results (1999)

Interpretation Davis et al.

B, E X X – X X X –
of results (2012)

Interpretation Nemecek et a
B, E X X X X X – –
of results (2011a,b)

Pardo et al.
Interpretation
B, E X X X X X X – (2010), Deytie
of results
et al. (2012)

Interpretation Deike et al.

B, E X – – X X X –
of results (2008a)

Prato and
B X X – X X – – –
Herath (2007)

B X – X X X – – Interpretation Pimentel et al
Study
design Objectives Use of crop
& of the Crop management management References
systems study data
assessed

Empty Crop Cover- Pesticide

Empty Cell Tillage Fertilisation Sowing Other Empty Cell Empty Cell
Cell rotation crops use

of results (2005)

4b. Multi-site

Blazy et al.
C X X – X X X – –
(2010)

Moore et al.
Ex-ante C, D X – – X – X – –
(2011)

Vasileiadis et
A, B X X X X X X – –
(2013)

Ex-post Interpretation Bechini and

A X X – X X X –
of results Castoldi (2009

Interpretation Fumagalli et a
A X X – – – X X
of results (2011)

Interpretation Colomb et al.

A X X X X – – X
of results (2013)

Interpretation Deike et al.

D X – – X X – –
of results (2008b)

Interpretation Pacini et al.

D X – – – – – –
of results (2003)

Input in data Bürger et al.

A, D X X – X – X –
analysis (2012a,b)

A, D X X X – X – – Input in data Mézière et al.

analysis (2015a)
Study
design Objectives Use of crop
& of the Crop management management References
systems study data
assessed

Empty Crop Cover- Pesticide

Empty Cell Tillage Fertilisation Sowing Other Empty Cell Empty Cell
Cell rotation crops use

Interpretation Fumagalli et a
A, B X – X X – – –
of results (2012)

Interpretation Castoldi and

A, B X – – – – – –
of results Bechini (2010

Interpretation Mouron et al.

A, E, D X – – – X – –
of results (2006a)

Interpretation de Barros et a
A, E X – – X X – –
of results (2009)

Interpretation Lechenet et a
E X X – X X – –
of results (2014)

Input in data Mézière et al.

E X X X – X – –
analysis (2015b)

Interpretation Mouron et al.

E, D X – – – – X –
of results (2006b)

Interpretation Castoldi et al.

E, B – X – X X – X
of results (2010)

Interpretation Ponsioen et a
B X X – X X – –
of results (2006)

The character “–” is used when no information was available for one component of crop
management in the articles. As crop management data were used to describe and present
the system assessed in all articles, only the other types of use of these data are mentioned
in the table.
One way of describing cropping systems, found in only a few articles, was
simply to refer to a guideline, without providing any additional specific
information about each of the systems studied. For example, Mouron et al.
(2006a) studied farms managed according to the guidelines of the Swiss
integrated farm management programme. Pacini et al. (2003) distinguished
between organic, integrated and conventional farming systems according to
both EU regulations (organic and integrated systems) and the Italian code
of good agricultural practice (conventional systems). Cropping systems
were generally described by guidelines alone in studies in which a small
number of highly contrasting cropping systems (e.g. conventional vs.
organic) were assessed and compared, typically at a single site (Pacini et
al., 2003; Pimentel et al., 2005). However, when analysing a larger range of
cropping systems, distinguishing between them on the basis of guidelines
alone might conceal variability in crop management, limiting the analysis of
cropping system performances. Mouron et al. (2006a) showed that there
was considerable variability in the performance of apple systems managed
according to the same integrated farm management guidelines. These
findings highlighted the need for a better understanding of the effects of
specific aspects of system management on environmental performance.
The second way of describing cropping systems, found in most studies, was
to describe the combination of techniques comprising the cropping system,
with considerable differences between studies in terms of the number of
techniques described and the precision of the description. Crop rotation
was the cropping system component most frequently reported (97% of
articles; Table 4). It was described in terms of the sequence of crops in the
rotation (e.g. Deike et al., 2008b; Vasileiadis et al., 2013), the type of crop
rotation (e.g. Castoldi and Bechini, 2010), or the proportion and type of
crops in the rotation (e.g. Bürger et al. (2012b) used the percentage of
cereals and the presence/absence of spring crops in the rotation;
similarly, Mézière et al. (2015a) used the frequency of autumn-sown crops).
Tillage, fertilisation and pesticide use were other frequently mentioned
techniques (59%, 76% and 64% of articles, respectively), whereas other
agricultural techniques were rarely mentioned (32% of articles for cover
crops, 38% of articles for sowing techniques (date, density, cultivar choice),
4% of articles for other components, including irrigation), generally only in
studies focusing on these techniques or their effects. Pest management and
pesticide use were mostly described in studies investigating integrated pest
management or assessing performances linked to pesticide use (e.g. Deike
et al., 2008a; Vasileiadis et al., 2013). Tillage was usually characterised by
binary variables (e.g. with and without mouldboard ploughing in Bürger et
al. (2012a); conventional tillage vs. no tillage in Syswerda and Robertson
(2014)), by the type of tillage equipment (e.g. Fumagalli et al., 2011; Kelly
et al., 1996), or by the frequency of tillage and the different tillage
techniques used in the same cropping system (Blazy et al., 2010; Colomb et
al., 2013). Quantitative variables were used to describe the use of inputs,
such as fertiliser, and pest management techniques, and to indicate the
level of reliance on these inputs (e.g. quantity of nitrogen applied,
treatment frequency index for pesticides).
Thus, in our sample of articles, cropping systems were mostly described in
terms of a limited number of strategic features (crop rotation and soil
tillage strategies). Again, this might be sufficient to distinguish a small
number of contrasting systems. However, such a simple description of
cropping practices would probably rapidly become inadequate if the aim
was to analyse complex intrasystem consistency in a large range of
cropping systems involving different combinations of techniques, possibly
implemented at different sites.
Review article

Faba Beans in Sustainable Agriculture

2010, Field Crops ResearchErik S. Jensen, ... Henrik Hauggaard-Nielsen

A cropping system is characterized by three main factors: (1) the nature of

the crops and pastures in the system and how they respond to and affect the
biological, chemical and physical environment, (2) the succession of crops
and pastures in the system (from monoculture to species rich dynamic or
fixed rotations) and (3) the series of management techniques applied,
including varieties of crop and pasture species in the system. To develop
successful cropping systems it is necessary to understand how a crop such
as faba bean responds to biological, chemical, physical, and climatic
variables, and how this response can be influenced by management. It is
also important to determine how faba bean cultivation affects the
productivity of subsequent crops (Sebillotte, 1995; Crozat and Fustec, 2006;
Peoples et al., 2009a). A farmer's decision about which cropping system to
adopt will be based on: (1) externalities to the farm such as the climate
change, markets, regulations and the availability of new technologies, and
(2) the farmer's own goals about production requirements, economics,
and environmental stewardship, and attitude to factors such as risk (Tanaka
et al., 2002). The development of sustainable cropping systems is a complex
task, which involves many parameters, and it requires the necessary
knowledge to be able to respond to sudden changes in these parameters at
different scales, e.g. in the market or in parts of a field. The challenge is to
exploit synergism in time and space through crop sequencing to enhance
crop yields with improved resource use efficiency and a reduced risk of
negative impacts on the environment via integration of ecological
and agricultural sciences. A full evaluation of faba bean in a cropping
system requires knowledge on how faba bean reacts to the environment
created by the preceding crops and management before and during
cropping in addition to how the faba bean modifies the environment for the
subsequent crops in the system. The encouraging research findings on faba
bean's ability to fix N2 and to benefit following crops, and the emerging
problems with pea cultivation in some regions with a high proportion of
peas in the rotation, may also be factors which can stimulate a renewed
interest for the use of faba bean in future sustainable cropping systems.
Chapter

Crop management practices for carbon sequestration

2023, Agricultural Soil Sustainability and Carbon ManagementKalaiselvi B, ... Rajendra Hegde

5.6 Cropping systems

Cropping systems refer to the order in which crops are planted in a farming
system. Many researchers believe that increasing cropping intensity
increased SOC because of increased biomass and residue formation under
various crop coverings, which varies depending on component crops (Sainju
et al., 2002; Wang et al., 2010). Increased cropping intensity, including
intercropping, mixed cropping, and other methods of raising soil C through
biomass addition, can be achieved even in summer fallows (Sihag et al.,
2015). In addition to increasing biomass output, the intercropping method
promotes efficient space and light utilization, which reduces weed
competition for soil resources. Legume-based intercropping systems are
popular because of their benefits, which include a synergistic impact with
the countercrop due to atmospheric nitrogen fixation in root nodules and
high biomass production. In addition, legume-based intercropping has
indirect benefits such as reduced nitrogen fertilizer requirements, reduced
weed and insect–pest infestation, and improved nutritional balance (Hirst,
2009). Cropping systems that include crop stubble retention and organic
inputs have an impact on SOC dynamics and NPP on soil C inputs. Rice
cropping strategy enhances SOC buildup through assimilation of
atmospheric carbon (0.7 t C ha−1 year−1) (Bhattacharyya et al., 2013; Mathew
et al., 2020), and its root remnants preserves high SOC via stable soil
aggregates (Ma et al., 2021). Disruption of soil aggregation, rapid microbial
development and breakdown, enhanced soil aeration and biomass
production, and induced soil erosion are all effects of continuous or intense
cropping with high crop density (Datta et al., 2017). Long-term intensive
farming clearly depletes soil C stock at a rate of 0.1%–1.0% each year (Lal,
2013). Crop cultivation, microbiological characteristics, soil texture and
depth, mineral composition, soil temperature, moisture, and air composition
all influence soil carbon flow (Swift, 2001).
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Chapter

Integrated pest management in Africa: the necessary

foundation for insect resistance management
2023, Insect Resistance Management (Third Edition)Isaac O. Oyediran
3.1.1 Cropping systems in Africa
A cropping system is defined as the combination of crops grown in each
area within a defined period. The forms of agriculture and cropping systems
found throughout the world are the result of variation in local climate, soil,
economics, and social structure (Beek and Bennema, 1972; Harwood, 1975).
Some of the most common cropping systems in Africa (Gebregergis, 2016)
are described next.
Mixed cropping, sometimes called polyculture, is the practice of growing
more than one crop in a field at a given time. The system is characterized
by subsistence farming (Gebregergis, 2016).
Relay cropping involves growing one crop, and then planting another crop
(usually a cover crop) in the same field before harvesting the first. This
helps avoid competition between the main crop and the intercrop. It also
uses the field for a longer time, since the cover crop usually continues to
grow after the main crop is harvested. An example is planting maize, and
then sowing beans between the maize rows 4 weeks later (Gebregergis,
2016).
Sequential cropping involves growing two crops in the same field, one after
the other in the same year. In some places the rainy season is long enough
to grow two crops: either two main crops or one main crop followed by a
cover crop. Growing two crops may also be possible if there are two rainy
seasons, or if there is enough moisture left in the soil to grow a second
crop. An example of sequential cropping within a given year is planting
maize in the long rains, followed by beans during the short rains
(Gebregergis, 2016).
Crop rotation is defined by Leppan and Bosman (1923) as the successive
growing of different crops on the same fields to assist in maintaining soil
productivity. It has several benefits for soil and crop systems. Benefits
include lower incidence of weeds, insects, and plant diseases (Leppan and
Bosman, 1923). According to Helmers et al. (2001), the risk benefits of crop
diversification are generally well understood, but the additional effect of
rotational cropping on risk is less understood. In this regard, they
demonstrated that in the diversification from maize to a maize-soya bean
rotation system, 71% of the reduction in risk was due to the rotational effect
and 29% due to diversification.
Mono-cropping is defined as growing only one crop season after season. An
example is planting maize year after year in the same field (Gebregergis,
2016). This practice has several disadvantages: it is difficult to maintain
canopy on the soil; it encourages pests, diseases, and weeds; and it can
reduce the soil fertility. It is much better to rotate crops or use
intercropping or strip cropping.
Intercropping is the practice of growing more than one crop simultaneously
in alternating rows of the same field (Beets, 1990). Intercropping is
therefore a type of mixed cropping. Intercropping with maize in sub-arid
regions is a way to grow a staple crop while obtaining several benefits from
the additional crop. One of the main benefits of intercropping is an increase
in yield per area of land. For example, systems that intercrop maize with a
legume can reduce the amount of nutrients taken from the soil as compared
to a maize mono-crop (Mongi et al., 1976; Adu-Gyamfi et al., 2007).
Increased diversity of the physical structure of plants in an intercropping
system produces many benefits as well. Increased leaf canopy in
intercropping systems helps to reduce weed populations once the crops are
established (Beets, 1990). Furthermore, having a variety of root systems in
the soil reduces water loss, increases water uptake, and increases
transpiration. This increased transpiration may make
the microclimate cooler, which, along with increased leaf cover, helps to
cool the soil and reduce evaporation (Innis, 1997). In East Africa a benefit of
intercropping, maize (Zea mays L.) and sesame (Sesamum indicum L.), was
high found maize yields income in southeast Tanzania (Mkamilo, 2004.
Amede and Nigatu (2001) also showed that simultaneously planting maize
and sweet potato does not significantly decrease maize grain yields.
Intercropping of maize and cowpea (Vigna unguiculata) is beneficial to
nitrogen-poor soils (Vesterager et al., 2008). As cowpeas obtain most of
their nitrogen from the atmosphere, they do not compete with maize for
nitrogen in the soil. Maize yields were not significantly affected by
intercropping with cowpeas (Vesterager et al., 2008).
Increased crop production (overyielding) often observed in intercrops
compared to sole crops has been attributed to more efficient resource use
(Szumigalski and Van-Acker, 2008). Francis (1986), and Sivakumar
(1993) also reported that efficient and complete use of growth resources
such as solar energy, soil nutrients, and water is one of the advantages of
intercropping system over monoculture. Intercropping can also reduce pest
damage to crops. For example, insect and diseases are less when tomato
was intercropped with maize (Pino et al., 1994).
The benefits of intercropping are the efficient use of basic resources in the
cropping systems and complementary effects between the crops. One of the
main yield advantages in intercropping. The crops sown in intercrop can
make better overall use of resources than when grown separately (Willey
and Osiru, 1972). For example, intercropping tomato with cowpea
significantly reduced bacterial wilt compared with tomato alone (Michal et
al., 1997). Heavy infestation of Sesamia tabaci and Aphis gossypii was
recorded when tomato was intercropped with maize (Plana et al., 1995).
Significantly reduced incidence of diamondback moth, Plutella
xylostella was observed when cauliflowers Brassica oleracea var
botrytis were planted 30 days after the tomato Solanum
lycopersicum (Kandoria et al., 1999). Greatest infestation (5.6%)
of Helicoverpa armigera was recorded when intercropped with snap
beans Phaseolus vulgaris. It was, however, lowest (3.4%) when intercropped
with radishes Raphanus sativus. Total H. armigera infestation ranged from
17.0% in radishes as an intercrop to 28.2% where snap beans were
intercropped (Patil et al., 1997).
There are, however, some disadvantages in intercropping systems. These
can include yield reduction of the main crop, loss of productivity during
drought periods, and high labor inputs in regions where labor is scarce and
expensive (Gliessman, 1985). It is well documented that in most cases the
main crop in an intercropping system will not reach as high a yield as in a
monoculture, because there is competition among intercropped plants for
light, soil nutrients, and water (Willey, 1979). This yield reduction may be
economically significant if the main crop has a high market price than the
other intercropped plants. Another disadvantage that is likely to be
occurring is the higher cost of maintenance weeding, which may have to be
done by hand.
Strip cropping involves planting broad strips of several crops within a field.
These strips tend to be 3–9 m wide. On slopes, the strip is laid out along the
contour to prevent erosion. The following year, the farmer can rotate crops
by planting each strip with a different crop. On slopes, strip cropping has
many of the advantages of intercropping: it produces a variety of crops,
planting legumes improves soil fertility, and rotation helps reduce pest and
weed problems. At the same time, strip cropping avoids some of the
disadvantages of intercropping: managing single crops within a strip is
more convenient, and competition between the crops is reduced. An
example is planting alternating strips of maize, soybean, and
finger millet (Gebregergis, 2016).
The conclusion is that intercropping is an important cultural practice in pest
management and is based on the principle of reducing insect pests by
increasing the diversity of an ecosystem. It is also very efficient in managing
insect pests and triggering the natural enemies of insects species and can
be integrated into IPM. Intercropping can be another promising option for
delaying evolution of resistance by serving as refuges for the insect pests.
Therefore increasing the diversity of an ecosystem may be used as a
promising alternative refuge in Bacillus
thuringiensis (Bt) corn agroecosystems and be key component in managing
insect resistance in Bt crop ecosystems in Africa.
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Review article

Revisiting cropping systems research: An ecological framework

towards long-term weed management
2024, Agricultural SystemsDilshan I. Benaragama, ... Rob H. Gulden

2.1 Quantifying cropping system functionality

The functionality of a cropping system refers to understanding the key
ecological mechanisms of a cropping system that contribute towards weed
dynamics. Agroecosystems are unique compared to most natural
ecosystems as they are characterized by frequent disturbances (tillage,
herbicides), high levels of resources (fertilizers), and low species
diversity (Gaba et al., 2014). Conventionally, cropping systems are
defined/identified by agronomic features such as differences in tillage,
herbicides used, crops and varieties and amount and type of nutrients
(tillage vs. no-till, organic vs conventional, low-input vs. high input,
monocropping vs. diversified rotations). Even though these agronomic
demarcations of cropping systems are sufficient to understand the effect on
crop yields, soil fertility, etc., it may not be adequate to understand the
dynamics of biotic components such as weeds, pests and diseases.
Moreover, it certainly does not advance our understanding of the ecological
functions that each crop management provides. Biological organisms
typically react to external cues associated with disturbance and resource
gradients. In an ecological context, typically, a cropping system results in
changes in soil disturbance (ex- tillage, mechanical weed control practices)
and aboveground or below-ground resources such as light, moisture and
nutrients (ex-crop type, fertilizer, planting arrangement). Thus, quantifying
these disturbances and resource gradients in cropping systems is crucial to
determining how weeds respond to ecological cues associated with cropping
systems.
Early attempts to numerically describe and extract the functionality of
cropping systems were made by computing metrics such as cropping
intensity index, crop diversity index, multiple cropping index and land
equivalent ratio (Strout, 1975). Later, other indices such as the planting
interval variation index (PIVI) Liebman and Nichols (2020) and the
rotational complexity index (RCI) (Bowles et al., 2020; Tiemann et al., 2015)
were developed. However, although easy to calculate, these indices lack
explanatory power. In contrast, Gaba et al. (2014) developed a broader
framework to characterize cropping systems based on the ecological impact
on plant performance. In their approach, cropping practices and
components can be classified along an environmental gradient of resources
and disturbances (Garnier et al., 2007; Grime, 1979). Accordingly, cropping
systems are characterized by i) climate and soil conditions, ii) resource
gradients, and iii) disturbance gradients. The ability to differentiate and
quantify cropping systems using different ecological gradients provides a
more mechanistic approach to cropping systems (see Gaba et al., 2014 for
details). Mahaut et al. (2019) have recently used this strategy to
characterize crop rotations into disturbance and resource gradients.
Further, they characterized cropping systems based on the mean ranks
given for each disturbance and resource gradients, thereby calculating
functional indices for each cropping system.
In our ecological framework, we suggest that defining and contrasting
cropping systems using resources and disturbance gradients suggested
by Gaba et al. (2014) could be an important primary requirement for
determining cropping system impacts on weed management (Table
1,Supplementary table 1). Thus, we considered it as our first pillar in the
proposed framework. However, we propose to expand the Gaba et al.
(2014) framework, including more criteria to cover the most prominent crop
production practices in different cropping systems. Even though the original
framework proposed by Gaba et al. (2014) covers disturbance gradients
more comprehensively, it does not provide a major focus on resource
gradients. In particular, their framework may not be sufficient to compare
systems with diversity in soil fertility inputs and other crop management
practices (ex-crop density, row spacing). For example, the light resource
gradient was quantified using the difference in plant height only. In the
current proposed framework, we emphasize the expansion of the light
resource gradient using multiple agronomic factors. Cropping systems
create resource gradients not only by the genetics of the crops
(species/cultivar traits) grown but also by agronomic practices such as crop
density and row spacing. All three of these components (crop height, row
spacing and crop density) collectively determine light gradients in a
cropping system and, therefore, need to be incorporated to determine light
resource gradient (Table 1). These three components are considered critical
factors in integrated weed management strategies in many cropping
systems (Kurtenbach et al., 2019; Shirtliffe and Benaragama, 2014).
Therefore, we proposed a light competition index considering both genetic
and agronomic crop production factors (Table 1).
Table 1. A scheme for quantifying (ranking) functional diversity among cropping

systems.

Cropping Gradient Empty Cell Crop sequence Mean SD CV

system

Empty Cell Empty Cell Monoculture wheat wheat whea wheat wheat wheat Empty Empty Empty
t Cell Cell Cell

No-till Disturbanc Tillage

conventional e (amount) 0 0 0 0 0 0 0 0 0

Herbicide 10 10 10 10 10 10 10 0 0

Crop
sequence NA 1 1 1 1 1 1 0 0

Competition
Resources index 6 6 6 6 6 6 6 0 0

Soil Fertility
(type of
inputs) 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0 0

Mean
diversity 3.42 0 0

Diversified
annual grain canola wheat pea barley flax wheat

Disturbanc Tillage
e (frequency) 0 0 0 0 0 0 0 0 0

Herbicide
(type) 20 10 20 10 20 10 15 5 0.333

Crop
sequence NA 3 3 3 3 3 3 0 0
 Cropping Gradient Empty Cell Crop sequence Mean SD CV
system

Empty Cell Empty Cell Monoculture wheat wheat whea wheat wheat wheat Empty Empty Empty
t Cell Cell Cell

Competition
Resources index 6 6 1 6 1 6 4 2.58 0.64

Soil fertility
(type of
inputs) 0.1 0.1 0.4 0.1 0.1 0.1 0.15 0 0

Mean
diversity 4.43 1.51 0.096

Monoculture wheat wheat wheat wheat wheat wheat

Tillage based Disturbanc Tillage

organic e (amount) 100 100 100 100 100 100 100 0 0

Herbicide 0 0 0 0 0 0 0 0 0

Crop
sequence NA 1 1 1 1 1 1 0 0

Competition
index 6 6 6 6 6 6 6 0 0

Soil Fertility
(type of
Resources inputs) 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0 0

Mean
diversity 21.46 0 0

Diversified
annual grain canola wheat pea barley GM wheat

Disturbanc Tillage
e (amount) 100 100 100 100 200 100 116.667 40.8248 0.35
 Cropping Gradient Empty Cell Crop sequence Mean SD CV
system

Empty Cell Empty Cell Monoculture wheat wheat whea wheat wheat wheat Empty Empty Empty
t Cell Cell Cell

Herbicide 0 0 0 0 0 0 0 0 0

Crop
sequence
(sowing date) NA 3 3 3 3 3 3 0 0

Competition
index 6 6 1 6 6 6 5.16667 2.04124 0.395

Soil Fertility
(type of
Resources inputs) 0.3 0.3 0.4 0.7 0.3 0.7 0.45 0.18028 0.401

Mean
diversity 36.644 12.8862 0.183

Modified from Gaba et al. (2014).

Cropping systems were adapted from Benaragama et al. (2016).
The details of the development of rankings can be obtained from the supplementary table
(S1).
NA- Not applicable.
Secondly, we propose to include soil resource gradients in understanding
the functionality of cropping systems. The amount and the type of resources
(organic vs. inorganic) can influence weed abundance, growth, and
competitive ability. Further, soil resource gradients may influence crop-
weed competition relationships in some cropping systems (Smith et al.,
2010), which can indirectly shape weed communities. Considering soil
resources as a functional gradient is crucial in understanding weed
dynamics, particularly when comparing organic and conventional cropping
systems. The key difference between the two systems (organic vs.
conventional) that could affect weed dynamics other than tillage and
herbicides would be the amount and type of soil resources (Smith et al.,
2010). Hence, we propose an extension of the framework of Gaba et al.
(2014) to include soil resource gradients as a functional component of
defining cropping systems (Fig. 2).

Sign in to download hi-res image

Fig. 2. Hierarchy of factors influencing weed dynamics in a cropping

system (modified from Smith and Mortensen, 2017).

Thirdly, we propose to modify Gaba et al. (2014) framework by integrating a

hierarchical ranking system for each crop production practice. In Gaba et
al. (2014), all components (tillage, rotation, resource capture) were given
similar weights in functional ranking. In contrast, we believe that each
resource and disturbance gradient in agroecosystems should have different
weights (different magnitude of ecological filters) in determining weed
dynamics. According to Smith and Mortensen (2017), the ecological filters
in agroecosystems act upon a hierarchical structure, where environmental
filters (climatic and edaphic) act on the broadest scale. Such filters exert
the most significant influence on weeds, followed by cropping system filters
(crops, crop rotations, tillage), management filters (weed control, fertilizer
application), and finally, biological filters (competition, herbivory and seed
predation). The cropping systems filter acts under the environmental filter,
while various biological filters operate within the cropping systems filters
(Fig. 2). Therefore, this hierarchical functionality should be maintained and
weighted accordingly when quantifying ecological gradients in a cropping
system. For instance, many studies have identified that the effect of crop
rotation on weed management was more significant under no-till systems
than tillage systems (Cordeau et al., 2017; Weisberger et al., 2019) thus,
tillage is a predominant factor in determining weed dynamics. In our
proposed framework, we emphasize that the differences in tillage and
herbicide applications among cropping systems should be given higher
weights compared to differences in crop rotations or other cultural
practices among cropping systems (Table 1). The weight-based ranking of
the cropping systems proposed (Table 1) is vital in comparing highly diverse
cropping systems. Such systems typically include comparisons between
tillage, fertilizer management, and crop rotation among organic and
conventional cropping systems. When the weight-based ranking system is
used to determine the variability of the whole cropping system diversity, we
further propose using standard deviation (SD) instead of coefficient of
variation (CV) as originally proposed by Gaba et al., 2014. This will allow
the real variation (diversity) in cropping systems to be captured since using
the CV will remove that magnitude of variability among crops in a cropping
system. An illustration of how to calculate cropping systems resource and
disturbance diversity is given in Table 1. Standard deviation may thus be a
better way to capture the magnitude of difference among cropping systems,
as proposed by our framework. Overall, we believe this modified framework
will better capture real gradients and their relative impacts on weed
dynamics and expand the functional approach originally proposed by Gaba
et al. (2014).
Review article
Revisiting cropping systems research: An ecological framework
towards long-term weed management
2024, Agricultural SystemsDilshan I. Benaragama, ... Rob H. Gulden

2 Framework for cropping systems research

2.1 Quantifying cropping system functionality
The functionality of a cropping system refers to understanding the key
ecological mechanisms of a cropping system that contribute towards weed
dynamics. Agroecosystems are unique compared to most natural
ecosystems as they are characterized by frequent disturbances (tillage,
herbicides), high levels of resources (fertilizers), and low species
diversity (Gaba et al., 2014). Conventionally, cropping systems are
defined/identified by agronomic features such as differences in tillage,
herbicides used, crops and varieties and amount and type of nutrients
(tillage vs. no-till, organic vs conventional, low-input vs. high input,
monocropping vs. diversified rotations). Even though these agronomic
demarcations of cropping systems are sufficient to understand the effect on
crop yields, soil fertility, etc., it may not be adequate to understand the
dynamics of biotic components such as weeds, pests and diseases.
Moreover, it certainly does not advance our understanding of the ecological
functions that each crop management provides. Biological organisms
typically react to external cues associated with disturbance and resource
gradients. In an ecological context, typically, a cropping system results in
changes in soil disturbance (ex- tillage, mechanical weed control practices)
and aboveground or below-ground resources such as light, moisture and
nutrients (ex-crop type, fertilizer, planting arrangement). Thus, quantifying
these disturbances and resource gradients in cropping systems is crucial to
determining how weeds respond to ecological cues associated with cropping
systems.
Early attempts to numerically describe and extract the functionality of
cropping systems were made by computing metrics such as cropping
intensity index, crop diversity index, multiple cropping index and land
equivalent ratio (Strout, 1975). Later, other indices such as the planting
interval variation index (PIVI) Liebman and Nichols (2020) and the
rotational complexity index (RCI) (Bowles et al., 2020; Tiemann et al., 2015)
were developed. However, although easy to calculate, these indices lack
explanatory power. In contrast, Gaba et al. (2014) developed a broader
framework to characterize cropping systems based on the ecological impact
on plant performance. In their approach, cropping practices and
components can be classified along an environmental gradient of resources
and disturbances (Garnier et al., 2007; Grime, 1979). Accordingly, cropping
systems are characterized by i) climate and soil conditions, ii) resource
gradients, and iii) disturbance gradients. The ability to differentiate and
quantify cropping systems using different ecological gradients provides a
more mechanistic approach to cropping systems (see Gaba et al., 2014 for
details). Mahaut et al. (2019) have recently used this strategy to
characterize crop rotations into disturbance and resource gradients.
Further, they characterized cropping systems based on the mean ranks
given for each disturbance and resource gradients, thereby calculating
functional indices for each cropping system.
In our ecological framework, we suggest that defining and contrasting
cropping systems using resources and disturbance gradients suggested
by Gaba et al. (2014) could be an important primary requirement for
determining cropping system impacts on weed management (Table
1,Supplementary table 1). Thus, we considered it as our first pillar in the
proposed framework. However, we propose to expand the Gaba et al.
(2014) framework, including more criteria to cover the most prominent crop
production practices in different cropping systems. Even though the original
framework proposed by Gaba et al. (2014) covers disturbance gradients
more comprehensively, it does not provide a major focus on resource
gradients. In particular, their framework may not be sufficient to compare
systems with diversity in soil fertility inputs and other crop management
practices (ex-crop density, row spacing). For example, the light resource
gradient was quantified using the difference in plant height only. In the
current proposed framework, we emphasize the expansion of the light
resource gradient using multiple agronomic factors. Cropping systems
create resource gradients not only by the genetics of the crops
(species/cultivar traits) grown but also by agronomic practices such as crop
density and row spacing. All three of these components (crop height, row
spacing and crop density) collectively determine light gradients in a
cropping system and, therefore, need to be incorporated to determine light
resource gradient (Table 1). These three components are considered critical
factors in integrated weed management strategies in many cropping
systems (Kurtenbach et al., 2019; Shirtliffe and Benaragama, 2014).
Therefore, we proposed a light competition index considering both genetic
and agronomic crop production factors (Table 1).
Table 1. A scheme for quantifying (ranking) functional diversity among cropping

systems.

Cropping Gradient Empty Cell Crop sequence Mean SD CV

system

Empty Cell Empty Cell Monoculture wheat wheat whea wheat wheat wheat Empty Empty Empty
t Cell Cell Cell

No-till Disturbanc Tillage

conventional e (amount) 0 0 0 0 0 0 0 0 0

Herbicide 10 10 10 10 10 10 10 0 0

Crop
sequence NA 1 1 1 1 1 1 0 0

Competition
Resources index 6 6 6 6 6 6 6 0 0

Soil Fertility
(type of
inputs) 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0 0

Mean
diversity 3.42 0 0
 Cropping Gradient Empty Cell Crop sequence Mean SD CV
system

Empty Cell Empty Cell Monoculture wheat wheat whea wheat wheat wheat Empty Empty Empty
t Cell Cell Cell

Diversified
annual grain canola wheat pea barley flax wheat

Disturbanc Tillage
e (frequency) 0 0 0 0 0 0 0 0 0

Herbicide
(type) 20 10 20 10 20 10 15 5 0.333

Crop
sequence NA 3 3 3 3 3 3 0 0

Competition
Resources index 6 6 1 6 1 6 4 2.58 0.64

Soil fertility
(type of
inputs) 0.1 0.1 0.4 0.1 0.1 0.1 0.15 0 0

Mean
diversity 4.43 1.51 0.096

Monoculture wheat wheat wheat wheat wheat wheat

Tillage based Disturbanc Tillage

organic e (amount) 100 100 100 100 100 100 100 0 0

Herbicide 0 0 0 0 0 0 0 0 0

Crop
sequence NA 1 1 1 1 1 1 0 0

Competition
index 6 6 6 6 6 6 6 0 0

Resources Soil Fertility 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0 0
(type of
 Cropping Gradient Empty Cell Crop sequence Mean SD CV
system

Empty Cell Empty Cell Monoculture wheat wheat whea wheat wheat wheat Empty Empty Empty
t Cell Cell Cell

inputs)

Mean
diversity 21.46 0 0

Diversified
annual grain canola wheat pea barley GM wheat

Disturbanc Tillage
e (amount) 100 100 100 100 200 100 116.667 40.8248 0.35

Herbicide 0 0 0 0 0 0 0 0 0

Crop
sequence
(sowing date) NA 3 3 3 3 3 3 0 0

Competition
index 6 6 1 6 6 6 5.16667 2.04124 0.395

Soil Fertility
(type of
Resources inputs) 0.3 0.3 0.4 0.7 0.3 0.7 0.45 0.18028 0.401

Mean
diversity 36.644 12.8862 0.183

Modified from Gaba et al. (2014).

Cropping systems were adapted from Benaragama et al. (2016).
The details of the development of rankings can be obtained from the supplementary table
(S1).
NA- Not applicable.
Secondly, we propose to include soil resource gradients in understanding
the functionality of cropping systems. The amount and the type of resources
(organic vs. inorganic) can influence weed abundance, growth, and
competitive ability. Further, soil resource gradients may influence crop-
weed competition relationships in some cropping systems (Smith et al.,
2010), which can indirectly shape weed communities. Considering soil
resources as a functional gradient is crucial in understanding weed
dynamics, particularly when comparing organic and conventional cropping
systems. The key difference between the two systems (organic vs.
conventional) that could affect weed dynamics other than tillage and
herbicides would be the amount and type of soil resources (Smith et al.,
2010). Hence, we propose an extension of the framework of Gaba et al.
(2014) to include soil resource gradients as a functional component of
defining cropping systems (Fig. 2).

Sign in to download hi-res image

Fig. 2. Hierarchy of factors influencing weed dynamics in a cropping

system (modified from Smith and Mortensen, 2017).

Thirdly, we propose to modify Gaba et al. (2014) framework by integrating a
hierarchical ranking system for each crop production practice. In Gaba et
al. (2014), all components (tillage, rotation, resource capture) were given
similar weights in functional ranking. In contrast, we believe that each
resource and disturbance gradient in agroecosystems should have different
weights (different magnitude of ecological filters) in determining weed
dynamics. According to Smith and Mortensen (2017), the ecological filters
in agroecosystems act upon a hierarchical structure, where environmental
filters (climatic and edaphic) act on the broadest scale. Such filters exert
the most significant influence on weeds, followed by cropping system filters
(crops, crop rotations, tillage), management filters (weed control, fertilizer
application), and finally, biological filters (competition, herbivory and seed
predation). The cropping systems filter acts under the environmental filter,
while various biological filters operate within the cropping systems filters
(Fig. 2). Therefore, this hierarchical functionality should be maintained and
weighted accordingly when quantifying ecological gradients in a cropping
system. For instance, many studies have identified that the effect of crop
rotation on weed management was more significant under no-till systems
than tillage systems (Cordeau et al., 2017; Weisberger et al., 2019) thus,
tillage is a predominant factor in determining weed dynamics. In our
proposed framework, we emphasize that the differences in tillage and
herbicide applications among cropping systems should be given higher
weights compared to differences in crop rotations or other cultural
practices among cropping systems (Table 1). The weight-based ranking of
the cropping systems proposed (Table 1) is vital in comparing highly diverse
cropping systems. Such systems typically include comparisons between
tillage, fertilizer management, and crop rotation among organic and
conventional cropping systems. When the weight-based ranking system is
used to determine the variability of the whole cropping system diversity, we
further propose using standard deviation (SD) instead of coefficient of
variation (CV) as originally proposed by Gaba et al., 2014. This will allow
the real variation (diversity) in cropping systems to be captured since using
the CV will remove that magnitude of variability among crops in a cropping
system. An illustration of how to calculate cropping systems resource and
disturbance diversity is given in Table 1. Standard deviation may thus be a
better way to capture the magnitude of difference among cropping systems,
as proposed by our framework. Overall, we believe this modified framework
will better capture real gradients and their relative impacts on weed
dynamics and expand the functional approach originally proposed by Gaba
et al. (2014).
2.2 Use of functional traits to determine weed response to cropping systems
Plant classification based on functional traits attempts to describe species
by their morphological, physiological, and phenological features by
measuring individual functional traits and their impact on fitness (Violle et
al., 2007). Plant fitness can be categorized into three components: growth,
survival, and reproduction. Each can be measured using the three
performance traits: vegetative biomass, reproductive output and plant
survival (Violle et al., 2007). Since so many functional traits are associated
with the above performance traits (Violle et al., 2007), there is a need to
identify the most important common traits to distinguish species in their
response to environmental conditions. The leaf-height-seed (LHS) scheme,
for example, has identified major dimensions of variation in plant responses
to the environment by representing the three fitness dimensions: resource
use (L), competition (H), and regeneration (S) (Westoby, 1998). Indeed, the
importance of using plant functional traits to approach the interactions
between plants and the environment in natural ecosystems has been
recently acknowledged (Funk et al., 2017).
Most cropping systems research that is focused on understanding weed
community composition among different cropping systems has dealt with
understanding weed taxonomic differences among cropping systems (see
for example, Benaragama et al., 2019) and thus had limited generalizability
to other cropping systems. According to community assembly theory,
frequent, predictable disturbances in an agroecosystem act as ecological
filters and select functional traits, providing more opportunities to predict
weed communities (Perronne et al., 2014). Booth and Swanton
(2002) and Garnier and Navas (2012) have made several founding attempts
to establish the functional trait approach in weed science. Numerous other
weed scientists (Fried et al., 2008; Gaba et al., 2014; Mahaut et al.,
2019; Ryan et al., 2010a) have since followed a more applied approach to
understanding weed community dynamics using functional traits among
diverse cropping systems. The LHS strategy can be used in weed research
to study weed responses to diverse cropping systems (see (Gaba et al.,
2014). Weed functional traits (seed weight, canopy height and phenological
traits) are related to sowing season and crop architecture, emphasizing the
role of crop phenology in weed community composition (Gunton et al.,
2001). A good example of this comes from a comprehensive study by Fried
et al. (2012) who using over one thousand winter wheat fields in France,
demonstrated that tillage intensity filters weeds according to height, seed
weight, life forms and dispersal.
Approaching plant community dynamics through a functional lens is well
established in natural ecosystems research, and is gaining interest in
agroecosystems. Yet, we argue that the common approach of using the LHS
strategy to understand weed dynamics may not be sufficient in
agroecosystems since weed communities are less diverse and are
dominated by a few weed species. Therefore, more fine-scale functional
traits, such as phenological and seed traits, could be indispensable to better
understanding weed community dynamics. For instance, Perronne et al.
(2014) identified that phenological traits (emergence time, time to
flowering) were more affected than the LHS strategy among
weeds. Ashworth et al. (2016) demonstrated that the flowering time
of Raphanus raphanistrum L. has been shortened by consistent selection for
an early flowering phenotype, which has been selected for by early
harvesting strategies to manage herbicide resistance. In North
America, Gulden et al. (2011) identified that perennials and annuals with
late germination and a short duration between recruitment and anthesis
have been selected in herbicide-resistant cropping systems. These studies
confirm the importance of the functional trait-oriented approach rather than
a taxonomic-oriented approach in predicting weed communities in arable
lands. Yet, our knowledge of cropping systems' impact on these functional
traits is still limited. Therefore, further identification of weed functional
traits associated with cropping systems is needed.
Most functional approaches in determining species communities mainly
focus on interspecific level differences in a community. Understanding
interspecific functional traits may be sufficient for studies in natural
ecosystems given the high species diversity and potentially even
agroecosystems on a large spatial scale. However, such an approach may
not be sufficient in many agroecosystems with low weed diversity. Trait
values vary among individuals in a population (intraspecific) and thus
influence how they respond to the environment (Fridley et al., 2007),
competition (Fridley and Grime, 2010) and community assembly (Jung et al.,
2010; Siefert, 2012). Intraspecific functional traits can differ even among
habitats within a field (within a crop, crop edge, field margin) (Perronne et
al., 2014). Yet, the intraspecific variation of weed functional traits and their
implications in weed management have received minimal attention.
Understanding the variation in intraspecific traits is important in weed
research, focusing on herbicide resistance, where a weed population, rather
than the community, is of interest. Therefore, we propose expanding the
functional trait-based approach from interspecific towards an intraspecific
trait approach in such instances. However, the level of traits needed to be
measured depends on the cropping systems and associated problems.
Overall, our ecological framework emphasizes the importance of moving
towards an approach based on functional traits that allow quantifying
interspecific and intraspecific variation. The trait-based approach will be
useful in understanding how the cropping system's functional differences
influence long-term weed dynamics. Further, this approach will help to
generalize weed community response to diverse cropping systems and
predict the abundance of functional groups under future cropping systems.
2.3 Understanding weed seed persistence
Predicting weed abundance in agroecosystems is a global challenge that
requires understanding the mechanisms of weed ecology and trait evolution
in response to climate change and changing cropping systems (Nakabayashi
and Leubner-Metzger, 2021). Even though many mechanistic modeling
approaches have been attempted, all have had limitations in predicting
weeds, likely due to their inability to integrate the functionality of weeds
and cropping systems. The main factor hindering the prediction of weed
abundance with simulation models tends to be the lack of integration of
weed persistence data into the models (Fernández Farnocchia et al., 2021).
Weed persistence is the ability to continually evolve, survive, thrive, and
reproduce under a variety of natural and anthropogenic selection pressures
(Shrestha et al., 2022). In order to increase the predictability of cropping
systems effect on weed dynamics, it is essential to understand weed seed
dynamics since the seed is the main component that carries the past legacy
of crop management to future generations.
Traditionally, most cropping systems research focused on weed seed
dynamics, and their persistence focused on understanding weed soil seed
banks. It is well recognized that understanding the seed in the soil seed
bank has many weed management implications. The soil seed bank is the
memory of the aboveground plant community (Cavers and Benoit, 1989).
Understanding weed seed banks is essential to understanding weed seed
persistence, succession, and evolutionary dynamics (Grime, 1981).
Therefore, much attention has been given to studying weed soil seed banks.
However, most cropping systems studies that studied weed seed banks
were focused on understanding and managing the quantity and composition
of the weed soil seed banks (Barberi and Lo Cascio, 2001; Buhler et al.,
2001; Swanton and Vazan, 2022). Thus, most cropping systems studies
could not relate below-ground dynamics with the above-ground (Harbuck et
al., 2009; Ryan et al., 2010a). Our proposed framework attempts to expand
weed seed persistence beyond quantifying weed soil seed banks with a
holistic approach to determine cropping systems effect on weed seed
persistence, enabling us to predict weed associations.
Within this proposed framework, we emphasize that cropping systems
research should focus on understanding weed seed dynamics via the weed
seed ecological spectrum. The ecological spectrum of weed seeds refers to
weed seed response (phenotypical, structural, physiological and
biochemical) to environmental cues on the mother plant and in the soil seed
bank. According to Saatkamp et al. (2019), it is the coordinated ecological
trait-based response of seed to disperse, germinate and establish.
Weed seed germination, dormancy, seed decay, and weed seed
predation are the main processes determining weed seed persistence in the
weed seed bank. These factors are governed by genetic traits, the maternal
environment in which the seed develops, and the environment the seed
encounters in the soil (Schutte et al., 2008; Wolf et al., 1998;). Seed
physical traits (size, mass, seed coat thickness, color), physiological
(dormancy, vigor and viability), and biochemical traits (lipid content) are
known to influence seed persistence. Seed morpho-physiological traits are
among the essential traits for adaptive evolution in plants. Therefore,
understanding these traits and their relation to diverse cropping systems
and to seed persistence is indispensable in devising sustainable weed
management strategies.
The basis of our framework is built upon the hypothesis that the weed
maternal environment is altered due to diverse cropping systems, and these
maternal environmental cues alter the weed seed ecological spectrum that
is adaptive. Maternal environment effects on seeds refer to the effect of the
external ecological environment of the maternal generation on the quality of
offspring (Mousseau and Fox, 1998; Roach and Wulff, 1987). Environmental
factors such as photoperiod, moisture, light, temperature and fertilizer are
some of the primary factors influencing seed quality (Baskin and Baskin,
1998; Fenner, 1991). These maternal environmental cues can act as
predictors of the future environment of offspring and, thus, are essential to
adaptive fitness (Donohue and Schmitt, 1999). Furthermore, these maternal
environmental effects can determine the immediate phenological
development of progeny by its influence on characteristics such as
dormancy level, germination percentage, time of emergence, and seedling
vigor (Galloway, 2001; Miao et al., 1991). Further, these maternally induced
ecological imprints can determine soil seed bank persistence and offspring
dynamics (emergence, growth, and phenology) other than environmental
factors in the soil seed bank itself. Yet, these are yet to be explored. Most
studies examining the impact of the maternal environment on seed
persistence were carried out under laboratory conditions with the alteration
of one particular environmental factor. The direct or indirect effect of
cropping systems mediated maternal effects on seed persistence is mostly
unknown. Besides genotype and edaphic factors, cropping systems can
influence these seed functional traits (Galloway, 2001; Platenkamp and
Shaw, 1993). Therefore, understanding how cropping systems mediate
maternal environmental effects on the seed has predictive and management
implications. Cropping systems with diverse crop management practices
such as tillage, crop rotation, crop type, seeding time, and soil fertility
management can influence the maternal environment of weeds, thus
altering weed seed traits relevant to persistence. Yet, the evidence of such
cropping systems mediated maternal environmental effects on weed
persistence is minimal. In our proposed framework, we highlight the
importance of understanding the impact of cropping systems on these seed-
related functional traits that are important for seed persistence. Thus,
cropping systems research needs to focus on determining the effect of crop
and weed management on weed seed traits and their adaptive capacity both
due to short-term transgenerational maternal effects and long-term
evolutionary effects. A better understanding of these functional traits can
help predict weed dynamics better.
2.4 Determining weed temporal dynamics
Weed response to cropping systems can be two-fold. Either they respond to
short-term changes in crop production practices and random weather
conditions altering their density and composition or may show consistent
adaptations to more predictable frequent long-term cropping systems and
climatic changes. Differentiating the fluctuational changes particularly due
to random changes in weather conditions and long-term directional changes
in weed composition, can be indispensable in predicting the outcome of
cropping systems on weed dynamics. Most comprehensive cropping systems
research is long-term in nature (particularly where there are crop
rotations). Further, these experimental treatments are repeatedly applied to
the same experimental units over time, providing opportunities to better
understand weed temporal dynamics. However, most cropping systems
studies carried out in the past have either not leaned into understanding
real temporal dynamics or have not utilized proper statistical tools to
understand real temporal dynamics. Appropriate data analysis procedures
are indispensable to understanding the weed dynamics in long-term
cropping systems research. Functional differences among cropping systems
and their impact on weed functional dynamics can only be understood when
appropriate statistical tools are utilized. When weed density and biomass
are of interest, univariate methods such as analysis of variance using linear
models are commonly used. When weed community composition (both
above ground and below ground) is of interest, multivariate techniques such
as CCA (Canonical correspondence analysis), RDA (redundancy analysis)
and NMDS (Non-metric multidimensional scaling) are the most common
approaches are typically used (Moonen and Barberi, 2004; Fried et al.,
2008; Ryan et al., 2010b; Sosnoskie et al., 2006). We found that some of
these approaches are insufficient to understand long-term weed dynamics,
which is essential to enhance our ability to predict weeds associated with
cropping systems.
Common multivariate tools utilized in cropping systems research, such as
CCA and RDA, are used to determine weed community composition but do
not have a mechanism to understand the treatment by time interaction.
Further, these models do not consider the repeated nature of the data
collected and so the legacy effects. A more advanced multivariate ordination
method, known as the principal response curve (PRC) method (Van den
Brink and Braak, 1999), can be used to overcome such limitations. In
contrast to traditional multivariate ordination techniques (CCA, RDA), this
method allows to examine community dynamics over time through a simple
graphical representation. Even though this methodology has been used in
ecotoxicology and restoration ecology studies (Palik and Kastendick,
2010; Poulin et al., 2013), it has not been utilized in weed science or in
agronomy research. Benaragama et al. (2019) used the PRC approach to
model weed composition data in a long-term cropping systems study with a
complicated treatment and experimental design. In that study, the weed
community in three diverse input systems and three crop rotations was
studied over 18 years (three six-year crop rotation cycles). This approach
revealed the year-to-year variability of weed community composition,
directional changes in weed composition and, importantly, the influence of
weather conditions on temporal dynamics. According to the proposed
framework, in addition to the weed taxonomic composition dynamics, long-
term changes in weed species (plant and seed) functional traits are crucial
in understanding the evolutionary adaptations of weeds to cropping
systems. This can only be achieved using appropriate statistical analysis
beyond measuring the functional traits in a given time. Most previous
research on weed functional traits has focused on comparing cropping
system/crop management effects at a given time point. Statistical
approaches, therefore, should extend to understanding the relationship
between treatments (cropping systems), species, and their functional traits
along a time scale. The PRC method can also be extended to understand the
temporal dynamics of species' functional traits. In that sense, principal
response curves could be developed separately for species functional
categories and taxonomic categories. Using this approach, one could fully
understand how functional traits (plant and seed) change over time for each
cropping system, providing insights into the adaptive evolution of weeds
due to diverse cropping systems.
Review article

Modelling cropping system effects on crop pest dynamics: How

to compromise between process analysis and decision aid
2010, Plant ScienceNathalie Colbach

Pesticides cannot simply be replaced by alternative curative control

techniques such as mechanical weeding as the latter are usually
considerably less efficient than synthetic pesticides [9]. It is therefore
necessary to improve our knowledge on the processes in the agro-
ecosystem and, particularly, the effects of cropping systems on these
processes. A cropping system was initially defined as the combination of a
crop succession and the cultivation strategies used to manage each crop,
and applied to a field [10–12]. Many pests though rarely stay inside a given
field but disperse via seeds, spores, insect flights, etc. It is therefore
necessary to extend the temporal concept of cropping system to integrate
spatial components, i.e. the location of cropping systems in a landscape and
the uncultivated landscape elements such as hedges, riverbeds, roads, etc.
that interfere with the life-cycle and dispersal of pests.

Related terms:

 Agricultural Science
 Herbicide

 Cultivar

 Conservation Agriculture

 Maize

 Irrigation

 Climate Change

 Greenhouse Gas

 Annuals

 Soil Organic Carbon

View all Topics

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