Assessing Ecosystem Services of Weeds in Organic Vs. Conventional Systems Within the Montepaldi Long-Term Experiment, Tuscany

Assessing ecosystem services of weeds in organic vs. conventional systems within the Montepaldi Long-Term Experiment, Tuscany

Emmelie A.H. Mohrmann

MSc. Thesis Report

December 2015

Farming Systems Ecology Group (FSE) Dipartimento di Scienze delle Produzioni Wageningen University, The Netherlands Agroalimentari e dell’Ambiente (DISPAA) Droevendaalsesteeg 1 – 6708 PB Wageningen University of Florence, Italy Piazzale delle Cascine, 18 - 50144 Firenze

Assessing ecosystem services of weeds in organic vs. conventional systems within the Montepaldi Long-Term Experiment, Tuscany

Emmelie A. H. Mohrmann

Reg. No. 920615576010

MSc Thesis Farming Systems Ecology (FSE80436)

Study Program: MSc. Organic Agriculture

Specialization: Agroecology

Supervisors: dr. ing. Johannes MS Scholberg and dr. Cesare Pacini

Examiner: Dr. Egbert Lantinga

Table of Contents Acronyms ...... v

Preface ...... vi

Executive summary: ...... vii

1. Introduction ...... 1

1.1 Agriculture and ecosystem services ...... 1

1.2 Weeds and ecosystem services ...... 2

1.3 Management system effect on crop-weed competition ...... 3

1.4 Trait based approach ...... 5

1.5 Knowledge gap and research questions ...... 5

1.6 Thesis structure ...... 6

2. Material and methods ...... 7

2.1 Framework ...... 7

2.2 Experimental site, design and cultural practices ...... 11

2.2.1 Experimental site ...... 11

2.2.2 Experimental design ...... 14

2.2.3 Cultural practices and rotation ...... 17

2.3 Sampling methodology, data analysis and statistical analysis ...... 20

2.3.1 Sampling methodology ...... 20

2.3.2 Data compilation and analysis ...... 20

2.3.3 Statistical analysis ...... 21

3. Results ...... 23

3.1 Yields ...... 23

3.2 Mass total weeds (MTW) ...... 25

3.3 Mass total weeds vs. crop yield ...... 33

3.3.1 Relative amount of mass total weeds compared to crop ...... 34

3.4 Number of weed species ...... 35

3.5 Functional trait differences amongst management systems for different crops...... 36

3.5.1 Provisioning services ...... 36

iii

3.5.2 Regulating services ...... 42

3.5.3 Supporting services ...... 48

4. Discussion ...... 54

4.1 Framework ...... 54

4.1.1 Limitations ...... 54

4.1.2 Strengths ...... 55

4.2 Dataset ...... 55

4.3 Mass total weeds and prevailing species ...... 56

4.4 Research questions ...... 57

4.4.1 Research question 1 ...... 57

4.4.2 Research question 2 ...... 58

4.4.3 Research question 3 ...... 60

4.5 Contextualization ...... 62

5. Conclusion ...... 62

6. Limitations and future research recommendations ...... 64

7. References ...... 66

8. Appendices ...... 75

Appendix 1 – Functional trait and service relations explained...... 75

Appendix 2 – Trait source information and selection methodology ...... 83

Appendix 3 – 95% confidence interval for mean LDM values of crops ...... 85

Appendix 4. Overview of crop-weed complementarity in terms of functional traits ...... 86

Appendix 5 – Mean values and significance of management system effect across crops ...... 89

Acronyms

MTW Mass Total Weeds (g) (may be referred to as total weed biomass) MTR Mass Tap Root species (g) MFR Mass Fibrous Root species (g) MNF Mass N-Fixating species (g) ELI Mean Ellenberg Nitrogen Index MLD Mass Nitrophilous (ELI > 7) species (g) AWH Average Weed Height (cm) MPC Mass Protruding (above Crop) species (g) ENI Mean Ellenberg Light Index MNP Mass Light Demanding (ENI > 7) species (g) MCT Mass Climbing Type species (g) MCR Mass Competitive Ruderal species (g) LDM Mean Leaf Dry Matter Content (g g-1) GDM Mass Greater LDM (than crop) species (g) MSS Mass Syrphid Supporting species (g) FPR Flowering Phenology Richness (No.) IPR Insect Flowering Phenology Richness (No.) MIP Mass Insect Pollinated species (g) DDM Mass Different LDM (than crop) species (g) MFT Mass Forb-Type species (g) NWS Number of weed species SAC Same as Crop DFC Different from Crop

Preface This research was conducted as part of my master studies in Organic Agriculture at Wageningen University. It was performed under the guidance of the Farming Systems Ecology (FSE) group at Wageningen University, and the Dipartimento di Scienze delle Produzioni Agroalimentari e dell’Ambiente (DISPAA) at the University of Florence, Italy.

One of the most interesting parts of this research is that it gave me the opportunity to develop my own framework towards a more holistic and alternative analysis of weed functions within a management system. Therefore, it gave me the opportunity to apply a great range of the knowledge that I had acquired during my studies, but also to gain fresh insights and develop new linkages. The concept of ecosystem services thereby brought me in contact with different knowledge levels from crop-weed interactions to wider ecosystem functioning. Moreover, it also gave me the opportunity to work with and apply a unique dataset from a long term experimental site. In doing so I had to learn about analysing complex datasets, and also utilizing and linking these to existing online datasets.

It also gave me the opportunity to work within an Italian agricultural and institutional settings, a country I have always felt a great warmth for, and hope to continue working with in the future. I am therefore very grateful to all the support I have received from both institutional sides. I am particularly grateful to Johannes Scholberg, Cesare Pacini and Concetta Vazanna for working with and guiding me through this sometimes challenging process. I would also like to thank the technical staff at UNIFI particularly; Giovanna Casella, Roberto Vivoli and Margherita Santoni for helping with the data handling, and the consultation staff at WUR; Bob Douma, Willemien Geertsema, Niels Anten and Bas Engel. I would further like to thank Marta Buondonno with whom I worked almost every day for sharing this experience with me. Lastly, but not least, I am grateful to my parents, housemates, colleagues and friends for always supporting and encouraging me throughout this process.

Executive summary: The intensification of agriculture has led to dramatic increases in productivity, however often at large environmental costs damaging the functioning of surrounding ecosystems. Ecosystems provide essential services for the benefit of humanity, most clearly the provisioning services of food, fiber and fuel. However, these provisioning services rely intrinsically on a series of supporting and regulating services such as soil fertility and pollination. The way in which agricultural systems are managed has a pivotal role on enhancing or degrading these ecosystem services.

One of these mechanisms is how management systems effect the composition of weeds within agricultural fields. Weeds are commonly known to have a negative impact on provisioning services, by competing with the crop for key resources such as nutrients, water and light. However, weeds can also contribute to a series of beneficial services such as enhanced biological control, increased pollinator provisioning and a series of supporting services such as soil formation, nutrient cycling and soil stability.

Management systems can have a strong influence on the composition of weed species. According to the resource pool diversity hypothesis, put forward by Smith et al., it appears conventionally managed systems may unintentionally enhance competition between weed species and crops, as herbicide usage, simple rotations and artificial fertilizers may lead to less diverse resource pools, lower weed species diversity and increased functional similarity to the crop.

Whereas in organically managed systems, diverse inputs and rotations tend to lead to greater weed species diversity and more diverse resource pools, therefore, potentially allowing for greater niche differentiation with the crop. Moreover, organic systems have often shown comparable yields to conventional systems, despite sustaining significantly higher weed biomass, suggesting that perhaps there is a greater potential for weed coexistence in organic systems.

Moreover, the enhanced diversity in organically managed systems, along with the shift towards more beneficial species such as insect pollinated weeds, may lead to organically managed systems having weed traits which are more beneficial in terms of supporting and regulating services.

Hence, the aim of this thesis was to uncover if the weed species present in organically managed systems have a greater potential for the delivery of ecosystem services, while mitigating potential disservices. This was done firstly through the creation of a framework which related the functional traits of weed species to the delivery of particular ecosystem services or

vii disservices. This framework was then applied through the existing historical database of the Montepaldi-Long Term Experiment (MOLTE), of the University of Florence in Tuscany, which has been ongoing since 1991.

Weed species biomass data of the three management systems present; conventional, newly- converted (2001) organic and old organic, were taken from a ten year period for the crops; maize, sunflower and wheat. This biomass data at species level was then combined with functional trait data collected from a series of online databases, namely through the functional trait database TRY. In turn, functional characteristics of the weed populations for the three management systems were statistically analysed for significant differences amongst the management systems for each crop. These differences were then analysed through the ecosystem service framework created, to uncover if weed populations differed in their ability to deliver ecosystem services and dis-services across management systems. This was supplemented with analysis on weed species diversity and yields looking also at potential year variability across crops and management systems.

The results of this thesis showed significant differences in the functional traits of weed species across management systems. Conventional maize had significantly higher total weed biomass, but also more fibrous rooted, nitrophilous and light demanding species, with species also having a higher average height and Ellenberg light index. Moreover, across all crops examined, the organic systems had a higher (insect pollinated) flower phenology richness.

Organically managed maize and wheat also had higher species diversity, with at least 1.5 times more weed species compared to conventionally managed systems, confirming previous research findings. Conventional maize and sunflower appeared to have a higher overall biomass, contrary to previous findings. This was attributed mainly to the high incidence of herbicide resistant problem species acquiring relatively large biomasses, most notably Xanthium Italicum (Italian Cocklebur) and Sorghum Halepense (Johnson grass). At population level it appeared that in maize and sunflower conventional weeds exhibited less niche differentiation in regards to the crop in terms of plant height and growing strategy, and root system in the case of sunflower, as compared to organic systems. This seems to indicate that these species can be more competitive and hence reduce the provisioning potential of the system.

Yields were found to be particularly low in general for maize and sunflower, most likely due to stressful climatic conditions. Moreover, yields appeared similar across management systems, contradictory to the usual yield gap experienced by organic systems. This was likely due to the stressful climatic conditions in which the organic systems may cope relatively better. Their appeared to be a correlation between total weed biomass and yield, suggesting that this

viii relatively lower conventional yield may also be due to the increased presence of more problematic weeds, however no direct causal relationship was developed.

In terms of ecosystem service delivery it appeared that in general the conventional systems indeed had more competitive weed functional traits in the case of maize and sunflower, which could lead to a lower yield and hence provisioning service potential. However, there appears to be a trade-off between the potential for weeds to deliver provisioning services vs. other supporting and regulating services. Organic maize and sunflower weeds appear to perform lower in many of these services, although this may be due to the inclusion of weed total biomass itself as a crude indicator of soil cover in many of these services. Moreover, it appears that this trade-off may not be the case in terms of pollination and biological control, where organic systems seem to have a potential for performing better, despite lower overall weed biomass. Taking management and local surroundings into consideration it may also be that these services have a higher potential for being delivered by other means within organic systems.

Interestingly enough, wheat seemed to follow a different trend all together, with better yields overall, and apparently much lower biomass compared to the other crops. In terms of service provisioning trends were less distinguishable between old organic and conventional, with new organic appearing to perform worst in provisioning but better in supporting and regulating services. These conflicting results suggest that winter cereals may follow a different pattern due to differences in the growing season.

In conclusion, it appears that, especially under more stressful circumstances, organic weeds may have a better potential for niche differentiation and reduced competition with the crop as compared to conventional systems. However, there may be a trade-off in terms of the provisioning of other supporting and regulating services, with the exception of pollination and biocontrol. Moreover, it is also suggested that winter cereals may follow different patterns, showing the importance of cropping seasonality in regards to weed composition.

Keywords: Organic agriculture, ecosystem services, weeds, functional traits, niche differentiation

1. Introduction

1.1 Agriculture and ecosystem services

The intensification of agricultural systems, characterized by increased inputs of agrochemicals, labour and capital (Sandhu et al., 2010), has dramatically increased productivity, leading to sufficient food production to supply global demands (Smil, 2000). However, this increased productivity is often associated with large negative externalities and environmental degradation (Kleijn and Sutherland 2003), damaging the functioning of surrounding ecosystems (Tilman et al. 2001, Sandhu et al., 2010). Ecosystem functioning is important as agricultural systems are also deeply dependent on the ecosystems which they are an integral part of (Mace et al., 2012). This is part of the ecosystem service concept in which agricultural systems are both providers and consumers of key ecosystem services. Ecosystem services can be defined as “the conditions and processes through which natural ecosystems, and the species that make them up, sustain and fulfil human life” (Daily, 1997). According to the Millennium Ecosystem Assessment (MA) (2005), ecosystem services may be divided in four main categories: supporting services, regulating services, provisioning services and cultural services.

Provisioning services include production of food, fiber, forage and fuel, preserving genetic resources and the supply of fresh water. Regulating services pertain to climate, air quality and water regulation, erosion prevention, pollination and pest management (biocontrol) amongst others. Supporting services include processes such as soil formation, nutrient cycling, primary production and habitat provisioning. While the category cultural services entails cultural diversity, ecotourism, aesthetic values etc. (MA, 2005). While agricultural systems primarily focus on the production of provisioning services, they also have the potential to produce other services, and are deeply dependent on supporting and regulating services such as soil structure and fertility, water provision, genetic biodiversity, pollination and biological control amongst many others (Fig. x.) (Power, 2010).

Fig. 1. Ecosystem Services and Agricultural Ecosystems (from Zhang et al., 2007)

However, as mentioned previously, agricultural systems also have the potential to create ecosystem dis-service, such as habitat loss, nutrient runoff and impacts of pesticides on non- target species (Zhang et al. 2007, Power 2010) (Fig. 1). Such dis-services also have a negative effect on agriculture itself, through negative feedback loops, resulting for example in increased pest pressure through a loss of natural predators or increased competition for water from other species (Zhang et al., 2007). Agricultural management systems can play a pivotal role in either enhancing or degrading many of these ecosystem services (Zhang et al. 2007, Sandhu et al. 2010, Power 2010). One way in which they can do this is through their effects on in field weed populations.

1.2 Weeds and ecosystem services Weeds are commonly perceived to have a negative impact on provisioning services, by competing with the crop for key resources such as nutrients, water and light (Zhang et al., 2007). However, weeds can also contribute to a series of beneficial services and reinforce positive feedback loops that may enhance ecosystem functioning. Weeds can furnish regulating services by acting as a food source for arthropods (Bàrberi et al. 2010), birds and other small mammals (Orlowski and Czarnecka 2007, Holland et al., 2006) thereby helping improve the potential for biological control, through a regulation of this biotic system (Fagundez, 2014). Other regulating services associated with weeds include water cycle regulation and water retention (Power, 2010). Presence of pollinators is also affected by the weed community composition, through the

2 provision of habitat and food resources (Gabriel and Tscharntke, 2007; Gibson et al., 2006). Weeds also contribute to a series of supporting services such as soil formation and nutrient cycling through litter decomposition, providing soil cover thereby enhancing biological activity, improving soil stability, erosion control, and nutrient retention (Fagundez, 2014, Wortman et al., 2010). Moreover, enhanced weed diversity can also help prevent the dominance of aggressive weed species which can be more problematic for the crop (Macák et al, 2005).

Hence, there appears to be a trade-off between the beneficial effects of weeds and the disservices they can provide. It seems that potentially organic management systems may be able to better strike this balance as numerous studies have shown that organic systems can have comparable yields despite sustaining much heavier weed biomass (Phelan 2009; Bedet 2000, Davis and Liebman 2001). Ryan et al. (2009, 2010) reported similar findings from long-term maize experiments, indicating that organic cropping systems may have a higher tolerance for greater weed abundance. They surmise that weed-crop competition relationships are different amongst organic and conventional systems, suggesting this could be due to differences in weed species composition, increased soil resource availability and a faster relative crop growth in organic systems compared to conventional. Phelan (2009) also states that the simple view of weeds being wholly incompatible with crops due to competition is too simplistic, and calls for a more ecological approach looking closer at niche dimensions.

1.3 Management system effect on crop-weed competition The resource pool diversity hypothesis put forward by Smith et al. (2009) offers a potential theory for these crop-weed relations. It explains how having more diverse resource pools, both in form and temporal availability, can allow for reduced competition between species by facilitating niche differentiation. It surmises that inter-specific competition between weeds and crops depends on the degree to which these species can occupy different niches and utilize different resources (Gause 1934; Chase and Leibold 2004; Silvertown 2004). These theories of niche differentiation and resource partitioning imply that resources vary along an environmental gradient, and that plant species differ in their requirements for, and methods in acquiring, these resources (McKane et al. 2002, Smith et al. 2009). These theories are also supplemented with the microbial-mediated resource hypothesis from Reynolds et al. (2003), which states that resource partitioning is in part mediated by microbial associations, which facilitates competition avoidance and species coexistence, by allowing plant species to utilize different resource pools.

Relating this to management systems, it appears that conventionally managed systems may unintentionally enhance competition between weed species and crops, compared to organic

3 systems. The usage of artificial fertilizers and simple short rotations leads to less diverse inputs and hence a less diverse resource pool both in form and temporal availability. Weeds and crops therefore would have to both acquire resources from the same restricted resource pool, thereby potentially enhancing competitive interactions (Smith et al., 2009). This draws upon the work of Tilman (1987) who found the domination of a few species at high N-levels suggesting the concentration of biomass within a few fast growing species. Therefore, conventional fertilization strategies may unintentionally lead to the domination of more aggressive weed species similar in resource needs to the crop (Phelan, 2009). Conventional practices can also have a deregulating effect on microbial communities, thereby further limiting access to more diverse resource pools (Altieri, 1999, Seymour, 2004). Moreover, selective pressures from herbicides and simple rotations will likely lead to weeds which are functionally more similar to the crop, thereby intensifying these competitive interactions (Barberi et al., 1997, Smith et al., 2009).

In organic systems diverse rotations and organic matter inputs will likely lead to more diverse inputs, and hence a more diverse resource pool both in form and temporal availability (Magdoff 1995, Smith et al. 2009). Therefore crops and weeds would be able to draw from more diverse resource pools, thereby potentially lowering competition. Organic fertilization strategies can also enhance microbial activity thereby, further facilitating access to these more diverse resource pools (Altieri, 1999). Moreover, the enhanced diversity of weed species in organic systems (Hyvönen et al., 2003, Roschewitz et al., 2005, Romero et al., 2008, Ulber et al., 2009), would further diversify inputs to the resource pool, and may also allow for a greater diversity of functional traits and resource acquisition traits (Smith et al., 2009), also reducing the amount of dominant weed species (Liebman & Dyck, 1993).

Hence, it appears that conventional management systems, compared to organic systems, may promote more competitive interactions between weeds and crops, encouraging the presence of more fast growing aggressive weed species which are more similar in resource needs and hence in behaviour to the crop. Hence, conventional systems may contain relatively more weed species which can have a negative effect on the provisioning services of a system.

In terms of supporting and regulating services, it seems that the enhanced diversity in organically managed systems, along with a shift towards more beneficial species such as insect pollinated weeds (Gabriel and Tscharntke, 2007; Romero et al., 2008), may lead to organically managed systems having weed traits which are more beneficial in terms of supporting and regulating services.

1.4 Trait based approach In order to further understand the beneficial and competitive interactions occurring between weeds and crops it can be useful to look at a functional trait level. The term “trait” can be defined as ‘any morphological, physiological or phenological feature measurable at the individual level’ (Violle et al., 2007). The final trait composition of a plant population determines the properties and functioning of the ecosystem. A trait-based approach hence uses plant functional traits to understand the functioning of organisms in relation to a certain ecosystem, the rules governing a community and the functionalities and services that are being provided (Garnier and Navas, 2012). Plant functional traits are associated with a large number of ecosystem services through different processes such as the rate of decomposition and mineralization, nutrient retention, sedimentation, evapotranspiration and net primary productivity (de Bello et al., 2010). Moreover, traits such as rooting depth, nutrient requirements, canopy characteristics and growth rates can help gage the degree of interspecific competition (Bastiaans, 2014) and complementarity resource use that can occur between the two species. Hence, functional trait divergence related to resource use, between the crop and weed communities, can be important to reduce the negative impact of weeds on crops (Garnier and Navas, 2012).

1.5 Knowledge gap and research questions Therefore, it seems organic management systems cause a shift in weed composition (Moreby et al., 1994, Albrecht and Mattheis 1998, Rydberg and Milberg, 2000), can potentially sustain higher levels of weed biomass (Phelan 2009; Bedet 2000, Davis and Liebman 2001, Ryan et al. 2009, 2010) and also deliver more ecosystem services in general (Sandhu et al., 2010). Moreover, it appears that organic management systems have a potential to decrease competitive relations interactions between crops and weeds (Smith et al., 2009). However, there appears to be a knowledge gap in terms of developing direct relations that can capture these changes in weed composition and link them to ecosystem service delivery. More specifically, to examine the potential trade-offs between increases in beneficial weeds and the potential disservices associated with resource competition between crop and weed components. This thesis thus aims to create an operational framework to examine how a shift in vegetation composition in an organic system, may potentially impact the deliverance of ecosystem services in arable cropping systems. Hence, this leads to the following research questions;

RQ1: Are there differences in weed species diversity in organic vs. conventionally managed systems?

RQ2: Are there differences in the functional traits of weed species populations in organic vs. conventionally managed systems, and hence in the potential for niche differentiation and complementarity with the crop?

RQ3: Do weed populations in these systems differ in their potential to deliver ecosystem services and disservices?

The corresponding hypothesis include:

H1: Organic systems, due to the use of more diverse rotation and soil amendments, along with the absence of herbicides, will enhance weed diversity as compared to conventional systems.

H2: There will be differences in the functional traits of weed species, with organic systems having weeds with a greater potential for niche differentiation and complementarity with the crop.

H3: Through niche differentiation and complementation weeds in organic systems will have the potential to deliver a broader suite of ecosystem services while potential disservices associated with weeds will be reduced compared to conventionally managed systems.

1.6 Thesis structure The first chapter of this thesis gives an overview of the current state of knowledge, and highlight knowledge gaps, hence leading to the aims and hypothesis of the thesis. The second chapter outlines the materials and methods used in this thesis, explaining the framework which was created, giving an overview of the data sources used for analysis and their context, finishing with an explanation of the statistical methodology. Chapter three gives an overview of the results found in this thesis in terms of yields, overall weed biomass, species composition and an overview of the functional trait differences found amongst agroecosystems per crop and ecosystem service. A discussion of the general findings and the research questions is presented in chapter 4 with chapters 5 and 6 containing conclusions and future recommendations respectively.

2. Material and methods

A conceptual framework was developed to link specific functional traits to ecosystem services and disservices, this framework was then used to address corresponding research questions. The framework was implemented using actual weed data from the Montepaldi-Long Term Experiment (MOLTE) in Florence, Tuscany. In section 2.1 the framework will be outlined, followed with a description of the experimental site, design and subsequent data analysis.

2.1 Framework The framework of this thesis was designed in a way to include three main ecosystem service categories that weeds may contribute to; provisioning, regulating and supporting services (Fig. 2). Cultural services were not included as this aspect is less straightforward in the case of weeds. These ecosystem services were divided into several subservices, which were related to several functional weed traits that can have an influence in the delivery of this subservice (Fig. 2.) Functional traits for each species were based on data obtained from databases, mainly through the online plant trait database TRY (Kattge et al., 2011). The weighting of the traits was based on the biomass of each species found at sampling frame level. Functional traits were chosen in such a manner that they best represented the ecosystem services deliverable by weed species, but also based upon availability of information, relevance and calculation of meaningful indices for different functional traits.

Fig. 2. Framework for analysing the potential delivery of ecosystem services through weed functional traits.

In terms of functional traits, for provisioning services, it was based on indirect delivery looking at potential competition weeds may pose to the crop, with crop yield being the main provisioning service within the overall management system. Hence, the focus was on complementation and niche differentiation. If weeds occupied resource niches that effectively complement those utilized by the crop, weeds may not compete to the extent that resource limitation will reduce yield. In terms of niche differentiation the main focus was on light, water and nutrients, and included Grime’s C-S-R (competitor - stress tolerator - ruderal) growing strategy (Grime, 1988) to account for several growth strategy related traits which could not be obtained directly.

Regulating services accounted for carbon sequestration, erosion control/ water regulation, biological control and pollination ,while supporting services pertain to soil formation/nutrient cycling and primary production/habitat provisioning. Therefore, each ecosystem service contains several subservices and corresponding functional traits which provide a measure about the weed species potential for provisioning such services. In order to quantify the presence of these functional traits, standard measurement units were used, these were expressed in g m-2, number m-2 and population averages depending on specific traits. An overview of the different ecosystem (sub) services and corresponding functional traits along with their units and expected effects on the relative services is provided in Table 1.

Ellenberg indicator values, Grime C-S-R strategy, total weed biomass, syrphid supporting species and flowering phenology richness are also included as functional traits. Although they are more indicative of functional behaviour, they provide proxies for indicators for which functional traits were not available. For the sake of simplicity they are also referred to as traits in the context of this thesis. For a more detailed explanation of the relations between the services, their functional traits and relative effects, see Appendix 1.

Table 1. Ecosystem services and subservice influenced by weed functional traits with consequent measurement units and their effects on the respective (sub) service Service/ functional trait Measurement units with abbreviations (m-2) Effect Provisioning services Water Root architecture type Mass tap root species (MTR) (g) SAC1 - Mass fibrous root species (MFR) (g) SAC - Nutrients N-fixation ability Mass N-fixating species (MNF) (g) + Root architecture type Mass tap root species (MTR) (g) SAC - Mass fibrous2 root species (MFR) (g) SAC - Ellenberg N-index Mean Ellenberg N-index (ENI) - Mass nitrophilous (ENI>7) species (MNP) (g) - Light Average height Average weed height (AWH) (cm) - Mass protruding (above crop) species (MPC) (g) - Ellenberg light index Mean Ellenberg light index (ELI) - Mass light demanding (ELI>7) species (MLD) (g) - Climbing ability Mass climbing type species (MCT) (g) - Growing strategy Grime C-S-R strategy Mass competitive ruderal (MCR) species (g) - Regulating services Carbon sequestration (climate regulation) Biomass Mass total weeds (MTW)4 (g) + Decomposition Mean leaf dry matter content (LDM) (g g-1) + Mass greater3 LDM (than crop) species (GDM) (g) + Erosion control/ water regulation Root architecture type Mass tap root species (MTR) (g) DFC5 ++ Mass fibrous root species (MFR) (g) DFC ++ Biomass Mass total weeds (MTW) (g) + Biological control Plant growth form Mass syrphid supporting species (MSS) (g) + Flowering phenology Flowering phenology richness (FPR) (No.) Insect flowering phenology richness (IPR) (No.) + Pollination syndrome Mass insect pollinated species (MIP) (g) + Biomass Mass total weeds (MTW) (g) + Pollination Flowering phenology Insect Flowering Phenology Richness (IPR) (No.) + Pollination syndrome Mass Insect Pollinated (MIP) species (g) + Supporting services Soil formation/ nutrient cycling Root architecture type Mass tap root species (MTR) (g) DFC ++ Mass fibrous root species (MFR) (g) DFC ++ Decomposition Mass different LDM (than crop) species (DDM) (g) +

Plant growth form Mass forb-type species (MFT) (g) + N-fixation ability Mass N-fixating species (MNF) (g) + Primary Production/ Habitat Provisioning Biomass Mass total weeds (MTW) (g) + Plant Growth Form Mass forb-type species (MFT) (g) + 1SAC – Same as crop, 2Fibrous also includes adventitious weeds 3In the case of GDM and MDD concerns significantly greater and different in respect to the crops values (based on a 95% CI of crop values obtained) 4 Conventionally this is referred to as total weed biomass, however to be consistent with other mass based traits this terminology is used 5DFC – Different from crop

Most of the functional traits are relatively straightforward, except for a few that may warrant further clarification. Ellenberg Indicator (EI) values range between 1 and 9 (Ecofact, 1999) and provide an indication about the sites in which these species are most commonly found. For EI- based light value this refers to whether plants are generally found in shadier or sunnier habitats, with 1 being deep shade vs 9 full sun. Therefore, it is assumed here that plants with higher values will have a higher preference for light and therefore may exhibit more competitive behaviour for light resources. The EI nitrogen value is generally used as an indicator of soil fertility, where higher values refer to species generally found in nitrogen-rich biotopes. Therefore, it may be used as a proxy for nitrophilous species, weeds that proliferate in high nitrogen habitats (Rydberg and Milberg 2012). As crops tend to be highly nitrogen demanding as well, species with high EI nitrogen values will potentially compete more with the crop for nitrogen, which in many cases is the nutrient most limiting to growth. Grime C-S-R strategy refers to a plant classification system, in which competitive-ruderal (CR) species are efficient in terms of extracting resource, while having relatively high growth and biomass accumulation rates, and short reproductive cycles. Most crops also fall under this category. Syrphid supporting species refers to species which according to Biolflor’s pollination syndrome (Kühn et al., 2004) are associated specifically with syrphids (hoverflies) or hymenopteres in general.

As mentioned previously, the traits themselves were not collected in field, but were based upon reported and published data from different databases. Most of the traits were accessed through TRY and/or complimented with other online databases and sources based on availability and reliability. In the case of flowering phenology and height, Italian values were used specifically (Pignatti et al., 2005 and 1982) as these traits may display greater geographical variation. For a detailed overview of the trait sources and selection methodology see Appendix 2.

2.2 Experimental site, design and cultural practices

2.2.1 Experimental site

Research center Field data was obtained from the Montepaldi Long-Term Experiment (MOLTE), an ongoing long- term experiment initiated in 1991 and managed by the University of Florence (UNIFI), Department of Agrifood Production and Environmental Sciences (DISPAA). MOLTE consists of three micro-management systems with different management systems, for the purpose of comparing organic and conventional management styles. For the purpose of this thesis field data was collected during the spring of 2015 and historical data was compiled from the period 2006 – 2013.

Experimental area The experimental site is located in San Casciano Val di Pesa near Florence, Tuscany, Central Italy (Fig. 3 and 4). The region consists of around 1,300,000 ha of agricultural land, compromising around 57% of the regional area (Pasquel, 2012). Farming is mainly arable farming (64%), woody perennial crop cultivation, including olive trees and vineyards (23%) and pastures (13%). Within agricultural systems the usage of irrigation is quite low compared to the rest of Italy, with only 4.3% of agricultural land (mainly arable crops) being irrigated. Furthermore, around 7% of the agricultural area and around 3.4% of the total farms in Tuscany are certified organic. As for the landscape, this region is characterized by soft sloping hills with reasonably high biodiversity, with around 33% of total farm area being dedicated to small woodlands (Pasquel, 2012).

As mentioned previously, the experimental site itself is located in San Casciano val di Pesa, in the Chianti Fiorentino area. This specific region has homogenous slopes with average heights between 250 and 350 m with the main forms of agriculture including vineyards for wine production and olive trees. Small patches of wooded areas are also commonly present within farmscapes. Furthermore, the region has a large number of agri-tourism operations accounting for around 34% of all tourism (Provincia di Firenze, 2013).

Experimental Site

Fig. 3. Map of Italy with experimental Fig. 4. Experimental fields of the MOLTE site. region Tuscany highlighted in red, and the experimental site in orange.

The MOLTE experimental area is around 15 ha consisting of 10 fields of roughly 1.3 ha’s each. The fields are located at 90 m asl. and are slightly sloping. The research fields are nestled between a small forested area to the northeast, with a provincial street and then the river Pesa and some forest patches on the south west side. Fig. 7 gives an overview of the experimental fields.

An overview of the soil characteristics of the different management systems is provided in Table 2. The soil texture ranges between silt loam and loam, with up to 24-26% clay, and presence of gravel material (around 6%). The soil is classified as Fluventic Xerochrepts, consisting of parent rock material which originates from Pliocene sediments and river Pesa fluvial deposits dating from the Holocene period (UNIFI – DISPAA). The soil organic matter content is generally rather low, with values between 1.5-2% with values seeming slightly higher in organic compared to conventional (1.9% compared to 1.7%, respectively).

Table 2. Average soil characteristics of cropping fields for different management systems (old organic, new organic and conventional) at MOLTE, measured October 2013 Soil characteristics Management system Old organic New organic Conventional

Clay (%) 24.2 24.6 26.0 Silt (%) 54.9 55.2 49.5 Sand (%) 20.9 20.2 24.6 Texture Silt-Loam Silt-Loam Loam1 pH 8.1 8.1 8.1 Organic Matter (%) 1.9 1.9 1.7 Nitrate (N ppm) 2.9 2.9 6.8 Total Nitrogen (N ‰) 1.4 1.3 1.2

Available Phosphorous (P22O6 ppm) 16.6 17.7 20.0

Exchangeable Potassium (K2O ppm) 219.0 212.7 209.4 C/N ratio 8.2 8.3 8.4 1One of the two conventional fields was classified as silt-loam and the other as loamy soil as it had slightly lower silt and higher sand content

Climate The climate is typical of the Mediterranean sub-Apennine zone. Annual rainfall amounts to around 770 mm, with most of the rain being concentrated in late autumn and early spring while the summers are relatively dry. Annual mean temperatures are around 14°C reaching maximums above 30°C in the summer and minimum temperatures of around -7°C in January. Average annual rainfall (mm) and rainfall days per year for the period 2005 through 2014 are provided in Fig. 5 while average and maximum temperatures (°C) per year are shown in Fig. 6.

Total Rainfall (mm) Total Rain Days 1600 120

1400 100 1200 80

1000

800 60

600 40

400 TotalRain days Total Rainfall TotalRainfall (mm) 20 200

0 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Year

Fig. 5. Total annual rainfall (mm) and rainfall days per year (data taken from the Montespertoli weather station (Regione Toscana, 2015).

Yearly Average Temperature Yearly Max Temperature 18 42

17 40

C) ° 16 38

15 36

14 34 Yearly Yearly Average Temperature( 13 32 Yearly MaxTermperature (

12 30 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Year

Fig. 6 Yearly average and maximum temperatures (°C), data were obtained from weather stations: S. Donato in Poggio (2005 – 2007) and Montespertoli (2008 – 2014) (Regione Toscana, 2015).

2.2.2 Experimental design The experimental design consists of three different management systems being 1) old organic; 2) new organic; and 3) conventional, these system were established in order to investigate

14 differences between organic and conventional management systems. The old organic and new organic currently consist of the same management system. However, historically the new organic system was managed using integrated management techniques and was only fully converted to organic in 2001. Both organic sections consist of 4 rectangular fields with a total of 5,2 ha each while the conventional section encompasses 2.6 ha divided over two fields. This is because the organic system operates on a 4 year rotation (roughly - Maize/Sunflower – Wheat/Barley – Legume – Wheat/Barley) whereas for the conventional system a 2 year rotation (roughly - Maize/ Sunflower – Wheat/ Barley) is used.

An overview of the experimental setup for each field measuring approximately 50 x 260 m (~1.3 ha) is provided in Fig. 7. On either side of the old organic section there is a diverse hedgerow including both small plants, bushes and trees, these hedgerows are several meters wide. Along all other sides of the experimental area, and in between the new organic and conventional sections, there are field margins of a few meters wide, containing pioneer species of natural vegetation ranging from small plants to occasional medium sized shrubs.

Old Organic New Organic Conventional

1 2 3 4 5 6 7 8 9 10 260 m

Hedgerows 50 m

Fig. 7 Outline for the three management systems (old organic, new organic and conventional) employed in MOLTE including fields of 1.3 ha along with key ecological infrastructure.

Crop selection There have been various arable crops and green manures cultivated at the MOLTE field site. In the context of this thesis, three crops were examined; Maize (Zea Mays), Wheat (Triticum Durum and Triticum Aesetivum) and Sunflower (Helianthus Annuus). These were selected as they were crops for which historically weed measurements were collected in a comparable manner across the three management systems. Furthermore, these three crops have quite diverse planting dates, canopy characteristics and growth requirements, thereby allowing for a possible differentiation of weed effects. Triticum Durum (durum wheat) and Triticum Aestivum (common

15 wheat) were combined and considered as ‘Wheat’ because these crops appeared to have similar cultural practices, growing behaviour, and yields.

Data Weed infestation data were collected in field during the spring of 2015, while historical records over the period 2005 – 2013 were retrieved from thesis and databases as well. Therefore, the data for this thesis were taken over 9 different years. This was done to increase the total sample number in order to ensure a more representative comparison of differences in herbaceous infestation between management systems, and not just possible year effects. The analysis thus was based upon a total of 33 sampling field units (Fig 3.). Within each field unit a certain number of frames (0.25 m2) were thrown across the whole field and all the weed species inside the frame were collected and analysed. The number of times a frame was thrown in each field unit was 12 for maize and 6 for sunflower and varied between 3, 4 and 6 for wheat (Table 3). However, in most all cases field units were chosen such that the number of repeated measurements per management system and crop combination was the same over time as much as possible. An overview of the different number of sampling frames for all the field units used in this thesis is shown in Table 3.

Table 3. Number of repeated measurements using square frames (0.25 m2) for each field unit used in the thesis. Management system Crop Year Old Organic New Organic Conventional 6 6 6 Sunflower 2013 6 6 6 (Helianthus Annuus) 6 6 2015 6 6 6

2006 12 12 12 Maize 2007 12 12 12 (Zea Mays) 2008 12 12 12 2009 12 12 12

3 3 3 2010 Wheat 3 (Triticum Durum) 6 6 6 2011 6 6

Wheat 2009 3 3 3 (Triticum Aestivum) 2012 4 4 4

2.2.3 Cultural practices and rotation Due to the nature of using sampling data collected across cropping seasons there is a certain degree of variability in terms of cultural practices and preceding crops. An overview of the rotation that has been used since 2002 for the MOLTE experiment is shown in Table 4. Highlighted cells refer to the sampling units used for analysis in this thesis, with colour coding indicating the different cropping groups (maize, sunflower, wheat).

For the two organic systems, the rotation followed was identical and in general included Maize/Sunflower – Wheat/Barley – Legume – Wheat/Barley. However, during 2012/2013 and 2013/2014 the rotation was not maintained due to financial constraints. The overall rotation employed is based on a sequence in which soil and nutrient building legumes are alternated with crops with high nutrient requirements while minimum intervals of 3-5 years are used for crops that are prone to similar pests, diseases and/or weeds. In the conventional management system the rotation typically included a two year sequence such as Maize/ Sunflower – Wheat/ Barley.

An overview of the generalized cultural practices for the different crops and the different management systems is shown in Table 5. The main specific differences between organic and conventional practices included fertilization and weed management. In organic systems fertilization was based upon rotation every four years with two years of leguminous crop, in conventional systems this was based upon application of chemical fertilizers containing a combination of mainly nitrogen and phosphorous, generally applied prior to sowing. Weed management in organic systems was based upon rotation for all crops, weed hoeing and occasional false seed beds for maize, and just weed hoeing for sunflower. In conventional this was based upon herbicides and additional weed hoeing in the case of s

Table 4. MOLTE Rotation over the period 2002 – 2015, with coloured cells indicating units used for analysis in this thesis. Agro-ecosystem/ Field Old Organic New Organic Conventional Year 1 2 3 4 5 6 7 8 9 10

2002/2003 Barley +trif Trif II Barley s Maize Barley s Maize Barley + trif Faba bean Barley s Maize

2003/2004 Trif II Barley GM + Maize Barley a + trif GM + Maize Barley a +trif Trif II Barley Maize Barley

2004/2005 Barley a GM + Maize Barley a + trif Trif II Barley a + trif Trif II Barley a GM + Maize Barley a Maize

2005/2006 Maize1 Faba bean Trif I Trit Durum Faba bean Trit Durum Maize Trif I Maize Trit Durum

2006/2007 Trit D + trif Trit Aes Trit Durum Maize Trit Durum Maize Trit D + trif Trit Aes Trit Durum Maize

2007/2008 Trif II Trit Durum Maize Trit A + Trif Maize Trit A + Trif Trif II Trit Durum Maize Trit Aes

2008/2009 Trit Aes Maize Trit D + trif Trif II Trit D + trif Trif II Trit Aes Maize Trit Aes Maize Trit D + 2009/2010 Sunflower Trit D + medi Sunflower Trit Durum Sunflower Trit Durum Sunflower medi Trit Durum Trit Durum

2010/2011 Trit Durum Medicago Trit Durum FB GM + Sunfl Trit Durum FB GM + Sunfl Trit Durum Medicago Sunflower Trit Durum

2011/2012 Faba bean gr Medicago Faba bean gr Trit Aes Faba bean gr Trit Aes Faba bean gr Medicago Trit Aes Sunflower

2012/2013 Sunflower Medicago Sunflower Sunflower Sunflower Sunflower Sunflower Medicago Sunflower Sunflower

2013/2014 Barley Medicago Barley Barley Barley Barley Barley Medicago Barley Barley

2014/2015 Chickpea Barley Trif I Sunflower Chickpea Sunflower Trif I Barley Barley Sunflower ** trif - Trifolium, s - spring, a - autumn, I - first year, II - second year, GM - green manure, Trit - Triticum, Aes - aesetivum, D - durum, medi - medicago, gr - grain, FB - Faba bean, Sunfl - sunflower 1 In the cases of organic maize experiments were also done on the effect of using green manure as a cover crop, however for this thesis only control samples were used for which no GM was used.

Table 5. General cultural practices for different crops according to different management systems at MOLTE. Crop & Years Agro-ecosystem Tillage Sowing dates Fertilization Weed management Weed Harvest sampling Maize Organic -LCP (25 cm)1 April; 2006 – Green manure fallow in rotation Weed hoeing (May/June), July Sept 2006 -2009 (July - Oct) 2008 False seed beds (Oct'07 and '08) -DH (April) May; 20092 Conventional -RH for seedbed Chemical; Superphosphate or Herbicide - Primigran Gold (April) Diammonium phosphate, applied during sowing Urea (April)

Wheat Organic -CP (35 - 50 cm) Feb; 2009 Green manure fallow in rotation May July 2009- 2012 (Oct) Nov; 2010 Conventional -DH (Oct/ Jan for Jan; 2011 Chemical; Herbicides; Monocots – Diammonium phosphate (Nov - combinations of; GRASP, late sowing) Nov; 2012 Feb), Ataplus, ADIGOR, AXIAL Ammonium nitrate (Mar/ Apr), Dicots - LOGRAN or GRANSTAR Urea (Mar - May) ULTRA SX, applied in March - May Sunflower 2013, Organic -CP (Aug/Sep), May; 2013 Green manure fallow in rotation Weed hoeing (June/ May) June Oct/ Sep 2015 -DH (May/Jan), Apr; 2015 Conventional Urea (May/ June), 20:10:10 ('13 GOAL 480/500 applied during -RH (May/Apr) May) sowing9, Weed Hoeing (June/ May) LCP – Light Chisel Ploughing, DH – Disk Harrowing, RH – Rotary Harrowing, CP – Chisel Ploughing, v. – variety, TA – Triticum Aestivum, TD – Triticum 1Indicates tillage depth, 2In 2009 an early variety Sisred class 300 – 110 gg was planted instead in May 9Fusilade max, Syngenta applied in June 2013 in field 10 due to Sorghum infestation

2.3 Sampling methodology, data analysis and statistical analysis

2.3.1 Sampling methodology Weed measurements were based on sampling field sections of 0.25m2 following the throwing of a square metal sampling frame across the 50 x 260 m field. Depending on the target number of repeated measurements for each crop in that year, the field was partitioned into equal segments and then the frame was thrown randomly within that segment. All weeds found within the delineation of the frame were carefully removed, if possible with the root intact, and placed inside a plastic bag. Samples were then transported to the lab where weeds were grouped according to species, and the number of plants for each species was recorded. The samples were than dried (if fresh weight at species level > 0.5 g) and the dry weight per species was recorded.

2.3.2 Data compilation and analysis Functional trait data was compiled for each of the 80 species occurring in the dataset with a biomass of at least 0.1 g. This information was primarily obtained from TRY, a functional plant trait database, which includes linkages to other publically accessible online databases. The aim of this approach was to use data from a standard references framework thereby reducing potential variability when compiling information on specific traits for different species. If information was not available from TRY or when values across different databases showed high levels of variation, additional external sources were consulted and average values for the different databases were used. In the case of the more regionally specific traits, such as the Ellenberg Indicator values, flowering times and height, data from Italian sources were used instead. Appendix 2 gives an overview of the sources and source methodology used for each trait.

After collecting the functional traits, these were weighted by the biomass for each species found at the sampling unit level. This worked at three levels depending on the units in which the trait was measured. In the case when the unit was expressed in terms of mass equivalents, for each sample the biomass of the species that met the requirements for that specific trait were summed up. The second level, in the case of average values (e.g. Ellenberg indices or average weed height), traits were rated using integers, hence the biomass of each species was multiplied by its respective trait score and divided by the overall weed biomass, these values were than summed across all species. This takes into account the relative weight of each species according to its biomass. The third level was related to assessing the number of different flowering phenologies present within each sample.

2.3.3 Statistical analysis For the statistical analysis R was used (Version 3.2.2) in combination with R studio (Version 0.99.486). The packages used included; lme4 and pbkrtest. Mixed effect models were used to analyse the data to account for possible effects of random factors; year, field and plot (and variety in the case of wheat) thereby complying with the assumption of measurements being independent. Effects of crops were analysed separately, since not all crops occurred at each year and field. Since the main purpose of the research study was to compare management systems, these were treated in the statistical model as the “fixed effect”.

Most data were analysed using a linear mixed effect model (lmer) fitted by REML with the exception of count based data; NS, FPR and IPR for which generalized linear mixed effect models (glmer) fit by maximum likelihood with Laplace Approximation were used. Yields were also analysed using generalized linear mixed effect models.

Two different forms of data were used; average values (AR) and with replicate (WR) values. AR values implied using the average values of all the frames within a sampling plot (each year x crop x management system combination), and WR implied using all the frames as separate values, thereby including plot as an additional random effect. For the generalized linear models WR data was used, as the model cannot handle average values as they are no longer discrete. For all wheat data WR was also used as the number of sampling frames varied per year, thereby leading to possible biases if frames were averaged. For wheat variety was also included as an additional random effect to account for any possible variation by combining different varieties as a single factor. For all the linear mixed effect models maize and sunflower data AR was used as the number of sampling frames was the same for all sampling plots, thereby eliminating plot as a random effect.

Linear Mixed Effect Models For linear mixed effect models (LME), significant difference among management systems were tested using the Kenward-Roger approach. In short, significant difference between two mixed linear effect models was first calculated, one in which the fixed effect management system was included, and the other in which it was not. This is to evaluate whether the fixed effect has a significant effect on the outcome of the trait. P-values of smaller than 0.1 were considered to be significant. If crop-trait combinations contained only zero values no analysis was done.

The assumptions of the model were checked based on the p-values of the Shapiro-Wilkens (SW) test and plots of residuals were visually checked by looking at fitted plots, histogram and a QQ- plots. In the cases where data did not follow a normal distribution, a log10 transformation was

21 used prior to final statistical analysis. For mass-based indicators values below 0.1 grams were not included in subsequent analysis.

After determining for which crop-trait combinations there was a significant effect, pairwise comparison was used to test if traits were statistically significant across management systems. Mean comparison was based on the t-statistics provided by the linear mixed model, significance was determined if the t-statistic was greater than +/- 1.96.

Generalized linear mixed effect models For generalized linear mixed effect models (GLME) the glmer function of the lme4 package was used. This was done using a poisson distribution with log-link, and accounting for over dispersion.

Calculating relative difference with log transformations In order to calculate the multiplication factor between two significantly differing management system means under log transformation (the case for all GLME outcomes and log10 transformations for LME), the following formula was used to take into account the shift which occurs when doing a log transformation with random effects;

1 푀퐹 = exp⁡(푚 + ∑ 푣푎푟(푅퐸)) 푋,푌 푋,푌 2

Where, MF = multiplication factor between management systems X and Y, exp = exponent (e in the case of glme transformations, 10 in the case of log10 transformations), m = log difference between means of management systems X and Y, var(RE) = variance of random effects.

Standard Errors The calculation of standard error bars for graphical representation was based on the following formula;

푠 푆퐸 = ⁡ √푛

Where SE = standard error, s = standard deviation, n = sample size. In the case of averaged repetitions these calculations were based upon the averaged values. In the case a log transformation, original data will be displayed in the graphs including standard error values of the original data, with significance lettering based on log transformed data.

Trends and ‘best’ ecosystem service provider When speaking of trends this implies a difference of at least double in the case of gram based traits, 3% in the case of EI values, 5% in the case of AWH, and 30% in the case of flowering

22 phenologies. These values were chosen closely associated to the margin found with significant differences. Overview tables were created for the different ecosystem (sub) services in which both significant difference (S) and possible trends (T) based on numeric differences for weed traits amongst agroecosystems were shown. An agroecosystem was determined as performing ‘best’ for a sub service when it performed relatively better in terms of both significant differences and/or trends in any or all of the functional trait units of the subservice (unless exceptions indicated). If there was no significance or trend or if values were close to 0, the functional unit was not considered.

3. Results

3.1 Yields Management systems (e.g. organic vs conventional) did not have a significant yield effect for any of the crops with P-values being 0.757, 0.519, 0.275 for maize, sunflower and wheat, respectively. In general especially maize and sunflower yields appeared to be quite low (Fig. 8). In terms of average numeric values old organic appeared to perform best for maize, followed by conventional and new organic and for sunflower old organic also appeared to perform best, while for wheat conventional systems performed best (Fig. 8). However, the difference for maize and sunflower seemed minimal whereas difference for wheat between conventional and organic systems appeared to be most pronounced (0.8-0.9 t ha-1), although differences still were not significant. Across years and fields considerable variation was found in terms of yields, and overall ranking of systems performances was not always consistent, general patterns will be discussed in more detail below.

4.5 Conventional New Organic Old Organic 4

3.5

)

1 - 2.5

2 Yield Yield ha (t 1.5

0.5

0 Maize Sunflower Wheat

Fig. 8. Average crop yield (t ha-1) for maize, sunflower and wheat as affected by management system (conventional, new organic and old organic). Error bars indicate SE values amongst fields.

Maize For maize, there were noticeable differences, old organic performed much better in 2006, conventional in 2008, and in 2009 conventional yields appeared to be zero, because plots performed so poorly that it probably was not worthwhile to have harvest them (Fig. 9).

3 Conventional New Organic Old Organic

2.5

) 2

1 -

1.5

Yield Yield ha (t 1

0.5

0 2006 2007 2008 2009 Average Year

Fig. 9. Average maize yield (t ha-1) per year as affected by management system (conventional, new organic and old organic). Error bars indicate SE values amongst fields.

Sunflower As for sunflower there were also discrepancies between years, in 2013 old organic appeared to perform best, followed by new organic and then conventional, while in 2015 these trends appeared to be reversed (Fig. 10).

0.8 Conventional New Organic Old Organic 0.7

0.6

) 1 - 0.5 0.4

0.3 Yield Yield ha (t 0.2 0.1 0 2013 2015 Average Year

Fig. 10. Average sunflower yield (t ha-1) per year as affected by management system (conventional, new organic and old organic). Error bars indicate SE values amongst fields.

Wheat For wheat again there was no consistent or clear trend in terms of system performance (Fig. 11). In 2010 and 2012 conventional appeared to perform best, while in 2009 and 2011 it performed relatively poor. For the organic management systems there could potentially be a year effect with yields for Durum wheat (TD) appearing to be lower than those for common wheat (TA).

6 Conventional New Organic Old Organic

) 4

1 -

Yield Yield ha (t 2

0 2009 (TA) 2010 (TD) 2011 (TD) 2012 (TA) Average Year (Variety)

Fig. 11. Average wheat yield (t ha-1) per year as affected by management system (conventional, new organic and old organic). Error bars indicate SE values amongst fields. 3.2 Mass total weeds (MTW) As many of the functional traits are directly measured in terms of mass, and MTW is itself often used as a functional trait proxy, MTW trends will be first presented for different crop and management system combinations (Fig. 12).

450 Conventional New Organic Old Organic

400 a

) 350

2 - 300

250

200

150 b

Masstotal weeds m (g 100 b

0 Maize Sunflower Wheat

Fig. 12. Average mass total weeds (g m-2) in maize, sunflower and wheat crops for different management system (conventional, new organic, old organic), lettering indicates significant differences. Error bars indicate SE values amongst fields.

For maize the management system had a significant MTW effect, with a P-value of 0.055, with conventional having more than 3 times higher MTW compared to organic systems (Fig. 12). For sunflower and wheat MTW values where similar across management systems (P values were 0.333, and 0.436, respectively). However, for sunflower overall numeric MTW values were almost twice as high in conventional compared to old organic, and for wheat twice as high in new organic compared to old organic and conventional. Looking across crops, conventional and old organic wheat appeared to have lower overall MTW values compared to the other crops, whereas for conventional managed crops maize had much higher MTW values followed by sunflower while values were lowest for wheat. As can be seen from the error bars above, as with yield, high variability exists for MTW amongst years, warranting a closer look at the year level dynamics.

Maize When looking at a year level the picture becomes much clearer. In the case of maize conventional systems appeared to have consistently higher MTW values compared to organic systems (Fig. 13). Moreover, in 2009 there appears to be a great deal more weed biomass for the conventional management system.

700 Conventional New Organic Old Organic

600

)

2 - 500

400 a

300

200

Masstotal weeds m (g b 100 b

0 2006 2007 2008 2009 Average Year

Fig. 13. Average mass total weeds (g m-2) in maize crop per year, for different management system (conventional, new organic, old organic), lettering indicates significant differences. Error bars indicate SE values amongst fields.

In terms of species make up it appears this higher MTW in conventional is mainly due to a higher incidence of problem weeds with large amounts of biomass (Table 6) and competitive functional traits (Appendix 4). Most notably these include Sorghum Halepense (Johnson grass) with an average weed biomasses of 73 g m-2 reaching values as high as 1000 g m-2 in sampling frames, it is competitive with maize in terms of its root system, high ENI, ELI and AWH without beneficial IP and SS traits (Table x.) Convolvulus Arvensis (morning glory) was also prevalent in most fields albeit with a lower average biomass (20 g m-2), its competitive traits are mainly in terms of light by being a climbing type and having high ELI (Appendix 4). In 2009 Xanthium Italicum (Italian Cocklebur) was present in 67% of the samples with an average biomass of 326 g m-2 although values as high as 1500 g m-2 were observed as well, its competitive traits include high AWH, ELI and being a competitive ruderal without beneficial SS and IP traits. While these species are also found in organic systems, the absolute amounts and relative percentages appear much lower (Tables x and x), although Lolium Perenne (perennial ryegrass) is also consistently present in organic systems with values of around 19 g m-2, it is competitive in terms of its root system and high ENI and ELI values. It appears that the distribution of weed species in organic maize is more spread out as can be seen from the lower cumulative percentages (Table 6).

Table 6. Mass total weeds (MTW) explained by most dominant weed species1 per management system and year for maize crop Management MTW Mass of weed species (g m-2) Cum % Explained system/ Crop (g m-2) By top Ant. Ave. Ama. Cir. Con. Cyn. Ech. Fal. Lol. Men. Pic. Pol. Set. Sin. Sor. Xan. three A. F. R. A. A. D. C. C. P. A. E. A. V. A. H. I. weeds Total Old Organic 2006 37.5 1.2 0.0 4.7 0.9 2.6 0.0 0.0 1.8 0.0 0.0 1.4 6.8 0.0 4.2 9.5 0.0 56 88 2007 69.5 2.3 0.0 1.6 3.5 2.9 0.0 0.0 0.6 8.2 6.6 0.2 0.7 18.0 4.7 3.8 4.5 47 83 2008 173.9 46.2 0.0 0.0 0.0 10.9 0.0 0.0 0.0 66.9 0.0 38.7 0.3 0.0 0.0 0.0 0.0 87 94 2009 83.4 0.0 0.0 0.0 0.0 20.5 0.0 0.0 0.2 0.3 0.0 0.0 0.0 0.4 6.3 41.2 1.1 81 84 Average 12.4 0.0 1.6 1.1 9.2 0.0 0.0 0.6 18.9 1.6 10.1 1.9 4.6 3.8 13.6 1.4 68 87 New Organic 2006 56.9 8.8 0.0 2.8 0.0 22.0 0.0 0.0 0.8 14.0 0.0 0.0 0.0 1.2 3.1 0.0 0.0 79 93 2007 58.1 0.3 0.0 0.2 0.0 4.3 0.0 0.0 0.4 2.9 0.0 0.5 0.0 24.6 12.9 0.6 0.0 72 81 2008 118.8 22.8 9.7 0.1 0.0 0.0 0.0 0.0 0.0 62.1 0.0 1.2 0.0 0.3 0.0 3.8 0.0 80 84 2009 69.2 0.2 0.0 0.0 13.7 39.8 0.0 0.0 0.1 0.0 0.0 0.0 0.0 1.2 7.5 0.0 0.0 88 90 Avearge 8.0 2.4 0.8 3.4 16.5 0.0 0.0 0.3 19.8 0.0 0.4 0.0 6.8 5.9 1.1 0.0 80 87 Conventional 2006 147.9 0.0 0.0 0.0 0.0 61.9 18.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 62.5 0.0 96 97 2007 173.3 0.0 0.0 0.0 22.0 23.2 0.0 0.0 0.1 1.2 0.0 0.0 0.0 0.1 0.0 104.5 19.7 86 99 2008 212.0 0.0 0.0 2.7 0.0 13.7 0.5 0.0 38.4 0.0 0.0 57.8 13.4 0.0 0.0 79.1 0.0 83 97 2009 599.9 0.0 0.0 28.9 39.0 17.3 0.0 111.8 0.0 0.6 0.0 0.0 0.0 7.8 0.0 45.2 326.6 81 96 Average 0.0 0.0 7.9 15.3 29.0 4.6 28.0 9.8 0.4 0.0 14.5 3.3 2.0 0.0 72.8 86.6 86 97 1 Dominant weed species (top three in terms of weed biomass) per management system and year combination highlighted in bold. Ant. A. - Anthemis Arvensis, Ave. F. - Avena Fatua, Ama. R. - Amaranthus Retroflexus, Cir. A. - Cirsium Arvense, Con. A. - Convolvulus Arvensis, Cyn. D. - Cynodon Dactylon, Ech. A. - Echinochloa Crus-galli, Fal. C. - Fallopia Convolvulus, Lol. P. - Lolium Perenne, Men. A. - Mentha Arvensis, Pic. E. - Picris Echioides, Pol. A. - Polygonum Aviculare, Set. V. - Setaria Virdis, Sin. A. - Sinapis Arvensis, Sor. H. - Sorghum Halepense, Xan. I. - Xanthium Italicum

Sunflower For sunflower trends were inconsistent across years (Fig 14). In 2013 MTW appeared to be much lower than in 2015. Furthermore, in 2013 conventional systems had relatively high MTW, followed by old organic and then new organic. Whereas in 2015 new organic had the highest MTW followed by conventional and then old organic.

350 Convetional New Organic Old Organic

300

)

2 - 250

200

150

100 Masstotal weeds m (g 50

0 2013 2015 Average Year

Fig. 14. Average mass total weeds (g m-2) for sunflower crop per year, for different management system (conventional, new organic, old organic), error bars indicate SE values amongst fields.

In terms of species make up in 2013 higher weed pressure in conventional systems was mainly attributed again to Sorghum Halepense (25 g m-2)1 and Xanthium Italicum (17 g m-2) infestations. What is particularly striking is the steep increase in weed biomass across agroecosystems in 2015 compared to 2013. For conventional this was mainly in terms of Xanthium Italicum (83% of samples) with an average mass of 167 g m-2, and occasional high incidences of Rumex spp and Sorghum Halepense. In the organic systems in 2015 there also appeared to be a higher presence of problem weeds with large biomasses, Sorghum Halepense (83% of samples) prevailed and had on average 85 g m-2 and 66 g m-2 for new and old organic, respectively. There was also sporadic high infestation (more than 150 g -2 per sampling frame) of Sinapis Arvensis in both organic systems, Lolium spp. in new organic and Melissa Officinialis (lemon balm) in old organic (Table 7).

1 In 2013 in Field 10 the application of Fusilade max, Syngenta for the Sorghum Halepense infestation after the weed sampling may have curbed some of the Sorghum Halepense infestation)

Table 7. Mass total weeds (MTW) and corresponding percentage distribution across dominant weed species1 per management system and year for sunflower crop Management MTW (g -2 system/ Year m ) Mass of weed species (g m-2) Cum % Explained By top three Cir. sp. Equ. A. Lol. M. Lol. P. Mel. O. Pic. E. Rum. C. Rum. sp. Sin. A. Sor. H. Xan. I. weeds Total Old Organic

2013 19.5 1.5 0.0 0.2 1.2 0.0 10.4 0.0 0.0 2.7 1.0 0.0 75 87 2015 179.6 0.0 0.0 0.0 0.0 38.9 3.2 0.0 0.0 27.5 66.1 0.0 74 76 Average 99.6 0.7 0.0 0.1 0.6 19.5 6.8 0.0 0.0 15.1 33.5 0.0 74 81 New Organic 2013 10.4 0.0 1.3 0.0 1.7 0.0 0.0 0.0 0.0 3.1 1.2 0.0 59 71 2015 311.9 0.0 0.0 104.7 67.3 0.0 4.7 0.0 0.0 26.5 84.8 0.0 82 92 Average 161.1 0.0 0.7 52.3 34.5 0.0 2.3 0.0 0.0 14.8 43.0 0.0 71 82 Conventional 2013 60.2 1.8 0.1 1.4 6.2 0.0 5.1 0.0 0.0 0.0 25.2 17.9 82 96 2015 272.1 0.0 0.0 0.0 0.0 0.0 0.0 10.7 62.3 8.9 4.7 167.4 88 93 Average 166.1 0.9 0.0 0.7 3.1 0.0 2.6 5.3 31.2 4.5 14.9 92.7 85 95 1 Dominant weed species (top three in terms of weed biomass) per management system and year combination highlighted in bold. Cir. sp. - Cirsium species, Equ. A. - Equisetum Arvense, Lol. M. - Lolium Multiflorum, Lol. P. - Lolium Perenne, Mel. O. - Melissa Officinialis, Pic. E. - Picris Echioides, Rum. C. - Rumex Crispus, Rum. sp. - Rumex species, Sin. A. - Sinapis Arvensis, Sor. H. - Sorghum Halepense, Xan. I. Xanthium Italicum

Wheat For wheat in most years (except 2012) new organic appeared to have a higher MTW, followed by old organic and conventional systems (Fig. 15). However, in 2012, conventional had relatively higher MTW values, although the overall MTW values were remarkably low. Another interesting observation is the high spike in MTW exhibited by new organic in 2010.

200 Conventional New Organic Old Organic

180

) 2

- 160 140 120 100 80 60

Masstotal weeds m (g 40 20 0 2009 (TA) 2010 (TD) 2011 (TD) 2012 (TA) Average Year

Fig. 15. Average mass total weeds (g m-2) in wheat crop per year, for different management system (conventional, new organic, old organic), error bars indicate SE values amongst fields.

When looking at species composition, the spike in weed biomass for new organic during 2010 was associated with Avena Sterilis (wild oat) and Lolium Multiflorum (Italian ryegrass). These species occurred in all samples and average weed biomass weights were 62 g m-2 and 76 g m-2, respectively. They are quite competitive with the crop having similar rooting behaviour and high ELI values, with high height and growing strategy in the case of Avena Sterilis, (Appendix 4) while having no beneficial traits in terms of being SS, IP or FT (Table 8).

Table 8. Mass total weeds (MTW) and corresponding percentage distribution across dominant weed species1 per management system and year for wheat crop Management MTW Cum % system/ Year (g m- Mass of weed species (g m-2) Explained 2) By top Ant. Ave. Ave. Cir. Con. Dac. Equ. Equ. Fal. Gal. Hel. Hel. Lol. Men. Pol. Sin. Son. Set. Ver. three A. F. S. A. A. G. A. T. C. A. A. T. M. sp. A. A. A. V. P. weeds Total Old Organic

2009 28.7 4.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.7 0.0 0.0 0.0 2.1 0.0 4.7 6.1 0.7 0.0 0.0 54 80 2010 68.5 0.0 0.0 28.4 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 1.3 35.3 0.0 0.0 0.0 0.0 0.0 0.0 95 95 2011 29.0 0.0 0.0 0.0 4.9 0.2 0.0 2.1 0.0 6.0 0.0 4.1 0.0 0.2 0.0 8.3 0.5 0.0 0.0 0.0 66 91 2012 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.1 0.0 0.0 0.4 0.0 96 46 Average 32.2 1.2 0.0 7.1 1.2 0.0 0.0 0.5 0.0 2.7 0.0 1.0 0.3 9.4 0.2 3.3 1.7 0.2 0.1 0.0 78 78 New Organic 2009 48.1 6.8 0.0 0.0 0.0 2.8 0.0 17.6 0.0 4.9 0.3 0.0 0.0 0.4 0.0 0.3 11.5 0.5 0.0 0.0 75 94 2010 176.4 0.0 0.0 61.6 0.0 5.2 1.7 0.0 0.0 1.6 0.1 0.0 0.0 75.7 0.0 0.3 0.0 25.2 0.0 0.0 92 97 2011 54.6 2.7 12.5 0.0 0.8 0.3 0.0 0.5 7.6 9.5 0.4 3.2 0.0 5.9 0.0 5.2 3.0 0.0 0.0 0.0 54 95 2012 1.9 0.0 0.0 1.6 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 100 Average 70.3 2.4 3.1 15.8 0.2 2.1 0.4 4.5 1.9 4.1 0.2 0.8 0.0 20.5 0.0 1.4 3.6 6.4 0.0 0.0 80 96 Conventional 2009 29.9 0.9 0.0 0.0 0.0 3.3 0.0 0.0 0.0 14.4 9.2 0.0 0.0 0.0 0.0 0.4 0.8 0.0 0.0 0.0 90 97 2010 30.5 0.0 0.0 0.0 8.1 0.0 0.0 0.0 0.0 0.0 4.7 0.0 0.1 16.6 0.0 0.1 0.0 0.0 0.0 0.0 96 97 2011 10.1 0.0 0.0 0.0 0.1 1.3 0.0 0.0 0.0 1.5 0.5 2.3 0.0 0.2 0.0 2.6 0.0 0.0 0.0 0.0 63 84 2012 9.1 0.0 0.0 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 6.6 0.0 0.0 0.0 0.0 0.0 0.5 96 99 Average 19.9 0.2 0.0 0.4 2.0 1.2 0.0 0.0 0.0 4.0 3.7 0.6 0.0 5.9 0.0 0.8 0.2 0.0 0.0 0.1 86 94 1 Dominant weed species (top three in terms of MTW) per management system and year combination highlighted in bold. Ant. A - Anthemis Arvensis, Ave. F. - Avena Fatua, Ave S. - Avena Sterilis, Cir. A. - Cirsium Arvense, Con. A. - Convolvulus Arvensis, Dac. G. - Dactylis Glomerata, Equ. A. - Equisetum Arvense, Equ T. - Equisetum Telemateia, Fal C. - Fallopia Convolvulus, Gal. A. - Gallium Aparine, Hel. A. - Helianthus Annuus, Hel. T. - Helianthus Tuberosus, Lol. M. - Lolium Multiflorum, Men. sp. - Mentha species, Pol. A. - Polygonum Aviculare, Sin. A. - Sinapis Arvensis, Son. A. - Sonchus Asper, Set. V. - Setaria Virdis, Ver. P. - Veronica Persica

3.3 Mass total weeds vs. crop yield Based on the regression analysis of yield vs. total weed biomass for the different crops, it appears that there is a negative correlation between mass total weeds and yield (Figs 16a-c). The equations and corresponding r-squared values for the different crops and management systems are presented in Table 6. For conventional maize and old and new organic sunflower these values were the highest (R2 0.80, 0.84 and 0.65, respectively) while for conventional sunflower and wheat there appeared to be no clear trend (R2 was 0.02 and 0.05 respectively), with the rest falling between 0.25 and 0.4 (Table 9).

3 A

2.5

) 2 Conventional

1 - New Organic 1.5 Old Organic

Yield Yield ha (t 1 Linear (Conventional) 0.5 Linear (New Organic) Linear (Old Organic) 0 0 200 400 600 800 Mass total weeds (g m-2)

0.9 6 0.8 B C 5

0.7

)

) 0.6 4

- - 0.5 3

0.4 Yield Yield ha (t Yield Yield ha (t 0.3 2 0.2 1 0.1 0 0 0 100 200 300 400 0 50 100 150 200 Mass total weeds (g m-2) Mass total weeds (g m-2)

Fig. 16. Effects of mass total weeds (g m-2) on yield (t ha-1) of maize (A), sunflower (B) and wheat (C) for the different management systems (conventional, new organic, old organic)

Table 9. Regression equations and corresponding R2 values for yield (y) in t ha-1) expressed as a function of total weed biomass (g m-2) for different crops and management systems.

Management Crop System Maize Sunflower Wheat

Conventional y=1.84 -0.0031x (R2=0.803) y=0.339 -0.0001x (R2=0.024) y=3.89 -0.0200x (R2=0.048) New organic y=1.82-0.0122x (R2=0.346) y=0.557-0.0012x (R2=0.645) y=3.12-0.0089x (R2=0.319) Old organic y=2.11-0.0109x (R2=0.395) y=0.695-0.0028x (R2=0.840) y=3.21-0.0190x (R2=0.278)

3.3.1 Relative amount of mass total weeds compared to crop Mass total weeds seem to account for a substantial amount of the total standing biomass especially for maize and potentially also for sunflower, although this is difficult to say as crop residue data was not available for this crop. In the case of wheat weeds not only appear to have a lower MTW but they also appear to make up a much smaller percentage of the total standing biomass due to the large crop presence in terms of yield and crop residue (Fig. 17).

500 Conventional New Organic Old Organic 450 400 350

300

2 -

250 g m g 200 150 100 50

Yield Yield Yield

MTW MTW MTW

CropResidue CropResidue CropResidue Maize Sunflower Wheat

Fig. 17. Overview of average yield, crop residue and MTW (Mass total weeds) per crop (g m-2) (crop residue information for sunflower was not available)

3.4 Number of weed species The average number of weed species (NWS) per m-2 was greatest for organic systems for maize and wheat (Fig. 18). For conventional maize this number was almost twice as low (1.7 and 1.97 times less for new organic and old organic respectively) while for conventional wheat the number of weed species was reduced by around 30% compared to organic systems (1.6 times less species). For sunflower, the number of weed species was similar across management systems (Fig. 18).

Conventional New Organic Old Organic 5.0 b b

4.5 b

) 2 - 4.0 b 3.5 a 3.0 2.5 a 2.0 1.5

1.0 Average Average numberofspecies (m 0.5 0.0 Maize Sunflower Wheat

Fig. 18. Average number of species (m-2) in maize, sunflower and wheat crops for different management system (conventional, new organic, old organic), lettering indicates significant differences. Error bars indicate SE values amongst fields.

3.5 Functional trait differences amongst management systems for different crops. In the following section, functional traits will be assessed using an ecosystem service framework in which each ecosystem service will be taken separately, examining for each cropping system the traits which have an effect on this service. First, general remarks will be made in terms of significant differences and clear trends observed across the management systems. Then the potential of the systems to provide the different ecosystem subservices will be compared and presented. This is done in order to contextualize the traits in terms of the overall framework and to facilitate the following discussion.

As a general observation significant differences in functional traits across management systems were quite low. Overall for maize differences were most pronounced while for sunflower and wheat the only significant results were from generalized linear mixed model analyses. Mean trait values in tabular form across management systems and crops are presented in Appendix 5.

3.5.1 Provisioning services In terms of provisioning services results in terms of specific traits are presented per crop starting with maize.

Maize For maize conventional management system had significantly higher values for MFR, MNP, MLD and also included weed species with a higher scores for AWH, and ELI compared to the organic management systems (Fig. 19).

Furthermore, in terms of numeric (non-significant) trends conventional systems also had higher MTR, MCT and MCR values than both organic management systems. Between the organic management systems differences were minimal, with the only significant difference being a significantly lower ELI for new organic compared to old organic (Fig. 19).

g m-2 cm m-2 Average a 70 8 a 250 Conventional a c b New Organic 60 7 Old Organic 200 6 50 b b a 5 150 40 a 4 30 100 b 3 b 20 b b 2 50 b b 10 1

0 0 0 MTR MFR MNF MNP MLD MPC MCT MCR AWH ENI ELI

Fig. 19. Functional weed traits related to potential provisioning services in maize across management systems (lettering indicates significant difference), MFR – Mass fibrous root species, MNF – Mass N-fixating species, MNP – Mass nitrophilous (ENI>7) species, MLD – Mass light demanding (ELI > 7) species, MPC – Mass protruducing (above crop) species, MCT – Mass climbing type species, MCR – Mass competitive ruderal species, AWH – Average weed height, ENI – Mean Ellenberg N-index, ELI – Ellenberg light index.

In the conventional systems there were more weeds species with traits which may make them more competitive for water (more MFR), nutrients (more MNP, MFR, MTR with the exception of ENI), light (more MLD, and higher AWH, ELI and MCT) and growing strategy (MCR). Differences between organic management systems suggest that perhaps old organic is better in terms of light (ELI). An overview of above mentioned results can be found in Table 10.

Table 10. Overview of the relative performance of different management systems for maize fields, in terms of provisioning services based on functional traits of prevailing weeds. Service/ Management system Effect Best system(s) Trait with greater value

CO-NO CO-OO OO-NO Water OO & NO MTR CO CO

MFR SAC - CO (S) CO (S)

Nutrients OO & NO* MNF + NO NO MTR CO CO

MFR SAC - CO (S) CO (S) ENI - NO OO * MNP - CO (S) CO (S) Light NO, OO AWH - CO (S) CO (S)

MPC - ELI - CO (S) CO (S) OO (S) MLD - CO (S) CO (S) MCT - CO CO Growing Strategy OO & NO MCR - CO CO (S) indicates significant difference, other differences are trends, * indicates exceptions, CO - conventional, NO - new organic, OO - old organic, SAC – same as crop

Sunflower There were no significant differences found among the management systems for sunflower (Fig. 20) However, based on numeric trends it appeared that conventional performed worse in terms of havinghigher values for MTR, MCR, AWH and ELI compared to both organic management systems while also having greater MLD and ENI values than old organic. In terms of the organic management systems, new organic tended to have greater MFR and ENI values than old organic, while old organic had slightly higher AWH values (Fig. 20).

g m-2 cm m-2 Average Conventional 70 8.00 140 New Organic 60 7.00 Old Organic 120 6.00 50 100 5.00 40 80 4.00 30 60 3.00

40 20 2.00

10 20 1.00

0 0 0.00 MTR MFR MNF MNP MLD MPC MCT MCR AWH ENI ELI Fig. 20. Functional weed traits related to potential provisioning services in sunflower across management systems, MFR – Mass fibrous root species, MNF – Mass N-fixating species, MNP – Mass nitrophilous (ENI>7) species, MLD – Mass light demanding (ELI > 7) species, MPC – Mass protruducing (above crop) species, MCT – Mass climbing type species, MCR – Mass competitive ruderal species, AWH – Average weed height, ENI – Mean Ellenberg N-index, ELI – Ellenberg light index.

In terms of specific provisioning subservices, it appeared that in conventional systems the type of weeds that prevailed may potentially be more competitive in terms of water (greater MTR values), nutrients (greater MTR and ENI values), light (higher AWH and ELI values for both organic systems and higher MLD vs MCT values for old organic and new organic, respectively). Moreover, weeds in conventional systems also tended to a larger extend to exhibit general competitive strategy (higher MCR values). An overview of above mentioned results can be found in Table 11.

Table 11. Overview of the relative performance of different management systems for sunflower fields, in terms of provisioning services based on functional traits of prevailing weeds. Service/ Management system Effect Best system(s) Trait with greater value

CO-NO CO-OO OO-NO Water OO & NO MTR SAC - CO CO MFR NO

Nutrients OO & NO MNF + NO NO MTR SAC - CO CO MFR NO

ENI - CO NO

MNP - Light NO, OO AWH - CO CO OO MPC - ELI - CO CO MLD - CO MCT - CO Growing Strategy OO & NO MCR - CO CO (S) indicates significant difference, other differences are trends, * indicates exceptions, CO - conventional, NO - new organic, OO - old organic, SAC – same as crop

Wheat There were no significant differences among management systems for wheat (Fig. 21). However, in terms of numeric trends it seems that new organic performed worst, having greater MFR and MCR values compared to the other management systems and also higher MNP values compared to old organic systems and greater MPC and MLD values compared to the conventional systems. However, this system also had greater MNF values which is positive (for the other crops this value was close to zero). But conventional did seem to have higher ENI values compared to both management systems and higher AWH values compared to the old organic system.

g m-2 cm m-2 Average 50 Conventional 70 8.00 45 New Organic Old Organic 60 7.00 40 6.00 35 50

30 5.00 40 25 4.00 20 30 3.00 15 20 2.00 10 10 5 1.00

0 0 0.00 MTR MFR MNF MNP MLD MPC MCT MCR AWH ENI ELI

Fig. 21. Functional weed traits related to potential provisioning services in wheat across management systems, MFR – Mass fibrous root species, MNF – Mass N-fixating species, MNP – Mass nitrophilous (ENI>7) species, MLD – Mass light demanding (ELI > 7) species, MPC – Mass protruducing (above crop) species, MCT – Mass climbing type species, MCR – Mass competitive ruderal species, AWH – Average weed height, ENI – Mean Ellenberg N-index, ELI – Ellenberg light index.

In terms of provisioning services, there are clear trends for specific traits although these were not always consistent across services. New organic seemed to perform worst for competition for water (greater MFR value) and growing strategy (greater MCR value). Old organic appeared to perform best in terms of nutrients (new organic had greater MFR and MNP values while conventional had greater ENI and lower MNF values). In terms of light conventional had higher AWH values while new organic had greater MPC and MLD values than conventional systems. An overview of above mentioned results can be found in Table 12.

Table 12. Overview of the relative performance of different management systems for wheat fields, in terms of provisioning services based on functional traits of prevailing weeds. Service/ Management system Effect Best System(s) Trait with greater value

CO-NO CO-OO OO-NO Water OO & CO MTR NO OO

MFR SAC - NO NO Nutrients OO MNF + NO OO NO * MTR NO OO

MFR SAC - NO NO ENI - CO CO

MNP - NO Light OO AWH - CO MPC - NO ELI - MLD - NO MCT - Growing Strategy OO & CO MCR - NO NO (S) indicates significant difference, other differences are trends, * indicates exceptions, CO - conventional, NO - new organic, OO - old organic, SAC – same as crop

3.5.2 Regulating services

Maize In terms of regulating services for maize conventional systems had significantly greater MTW and MFR values and a tendency for higher MTR values compared to organic management systems. In terms of FPR and IPR values, the organic systems had significantly higher values. For MSS and MIP overall numeric values were similar across systems (Fig. 22).

-2 -2 g m g-g number 300 0.2 4.0 a Convention b al New 0.18 3.5 b 250 Organic 0.16 3.0 0.14 200 2.5 b 0.12 a b a 150 0.1 2.0

0.08 1.5 b 100 a b 0.06 b 1.0 b 0.04 50 0.5 0.02

0 0 0.0 MTW GDM MTR MFR MSS MIP LDM FPR IPR

Fig.22. Functional weed traits related to potential regulating services in maize across management systems (lettering indicates significant difference), MTW – Mass total weeds, GDM – Mass greater LDM (than crop), MTR – Mass tap root species, MFR – Mass fibrous root species, MSS – Mass syrphid supporting species, MIP – Mass insect pollinated species, LDM – Mean leaf dry matter content, FPR – Flowering phenology richness, IPR – Insect flowering phenology richness.

For the relation to regulating services, weed traits in the conventional system showed a better potential for carbon sequestration (higher MTW values) and erosion control/ water regulation (greater MTW, MFR and MTR values). In terms of biological control, conventional systems had greater MTW values while organic systems had higher IPR and FPR values. In terms of pollination services the organic management systems seemed to perform better (greater IPR values). An overview of above mentioned results is presented in Table 13.

Table 10. Overview of the relative performance of different management systems for maize fields, in terms of regulating services based on functional traits of prevailing weeds. Service/ Management system with Effect Best System(s) Trait greater value

CO-NO CO-OO OO-NO Carbon Sequestration (Climate Regulation) CO MTW + CO (S) CO (S)

LDM + GDM + Erosion Control/ Water Regulation CO MTR DFC ++ CO CO MFR + CO (S) CO (S)

MTW + CO (S) CO (S) Biological Control Contradictions MSS +

FPR + NO (S) OO (S) IPR + NO (S) OO (S) MIP + MTW + CO (S) CO (S) Pollination OO & NO IPR + NO (S) OO (S) MIP +

(S) indicates significant difference, other differences are trends, * indicates exceptions, CO - conventional, NO - new organic, OO - old organic, DFC +

Sunflower The organic sunflower systems had significantly greater NFP and NIPF values compared to conventional systems (Fig. 23). Based on numeric values, there was also a tendency for old organic to have higher MIP values compared to both other systems. However, it appears that conventional had relatively high MTR, MTW and GDM values compared to old organic while MSS values were also higher than for new organic.

g m-2 g-g number-2

140 Conventional 0.25 4.0 New Organic 120 3.5 Old Organic b 0.2 3.0 100

2.5 a 0.15 80 b 2.0 b 60 0.1 1.5 40 1.0 a 0.05 20 0.5

0 0 0.0 MTW GDM MTR MFR MSS MIP LDM FPR IPR

Fig. 23. Functional weed traits related to potential regulating services in sunflower across management systems, MTW – Mass total weeds, GDM – Mass greater LDM (than crop), MTR – Mass tap root species, MFR – Mass fibrous root species, MSS – Mass syrphid supporting species, MIP – Mass insect pollinated species, LDM – Mean leaf dry matter content, FPR – Flowering phenology richness, IPR – Insect flowering phenology richness.

In terms of regulating services, conventional performed better in terms of carbon sequestration (higher MTW and GDM values compared to old organic) and erosion control/ water regulation (higher MTR values than organic management systems and higher MTW than old organic). However, organic management systems may be performing better in terms of pollination (higher IPR for both and MIP values for old organic). Regarding biological control, organic management systems in terms of flowering phenology richness and MIP performed better, although for other mass-based indices (MTW and MSS) values were lower. An overview of above mentioned results can be found in Table 14.

Table 14. Overview of the relative performance of different management systems for sunflower fields, in terms of regulating services based on functional traits of prevailing weeds Service/ Management system with Effect Best System(s) Trait greater value

CO-NO CO-OO OO-NO Carbon Sequestration (Climate Regulation) CO MTW + CO

LDM + GDM + CO Erosion Control/ Water Regulation CO MTR + CO CO MFR DFC ++ NO

MTW + CO Biological Control Contradictions MSS + CO OO

FPR + NO (S) OO (S) IPR + NO (S) OO (S) MIP + OO OO MTW + CO

Pollination OO, NO IPR + NO OO MIP + OO OO

(S) indicates significant difference, other differences are trends, CO - conventional, NO - new organic, OO - old organic, DFC – different from crop

Wheat For wheat the main significant difference was that new organic has significantly greater NFP values than conventional (Fig. 24) . Based on numeric trends it appeared that new organic performs better in terms of having relatively high MTW and MFR values compared to both other systems while having greater MTR and IPR values compared to the conventional system. Old organic appeared to perform better than conventional in terms of having greater MTR and FPR values. For MIP, LDM and MSS numeric values seemed to be similar across systems (Fig 24.).

g m-2 g-g number-2 4.5 70 Conventional 0.2 b

New Organic 0.18 4.0 60 Old Organic 0.16 3.5 50 0.14 a 3.0 0.12 40 2.5 0.1 30 2.0 0.08 1.5 20 0.06 1.0 0.04 10 0.02 0.5

0 0 0.0 MTW GDM MTR MFR MSS MIP LDM FPR IPR

Fig. 24. Functional weed traits related to potential regulating services in wheat across management systems, MTW – Mass total weeds, GDM – Mass greater LDM (than crop), MTR – Mass tap root species, MFR – Mass fibrous root species, MSS – Mass syrphid supporting species, MIP – Mass insect pollinated species, LDM – Mean leaf dry matter content, FPR – Flowering phenology richness, IPR – Insect flowering phenology richness.

Hence, new organic may performs better in terms of carbon sequestration (higher MTW value), erosion control/ water regulation (greater MFR and MTW values compared to both systems and MTR values compared to conventional), biological control (higher MTW values compared to both other systems and greater FPR and IPR values compared to conventional systems. Regarding pollination, old organic had higher IPR values than conventional while it scored average in terms of erosion control (having higher MTR values than conventional) and biological control (greater FPR values than conventional). An overview of above mentioned results is shown in Table 15.

Table 15. Overview of the relative performance of different management systems for wheat fields, in terms of regulating services based on functional traits of prevailing weeds. Service/ Management system with Best Effect Trait greater value System(s)

CO-NO CO-OO OO-NO Carbon Sequestration (Climate Regulation) NO MTW + NO NO

LDM + GDM + Erosion Control/ Water Regulation NO, OO MTR DFC ++ NO OO MFR + NO NO

MTW + NO NO

Biological Control NO, OO MSS +

FPR + NO (S) OO IPR NO

MIP + MTW + NO NO

Pollination NO IPR + NO

MIP +

(S) indicates significant difference, other differences are trends, * indicates exceptions, CO - conventional, NO - new organic, OO - old organic, DFC – different from crop

3.5.3 Supporting services

Maize In terms of supporting services, in conventional maize fields, weeds had greater MFR and MTW values compared to organic systems (Fig. 25). In terms of tentative trends, conventional systems appeared to also have greater MTR and MFT values compared to organic management systems (Fig. 25). New organic tended to have higher MNF while organic systems both also had slightly higher DDM values than conventional the system. However, overall values for both traits appeared to be extremely low.

g m-2 Conventional 300 a New Organic Old Organic 250

200

150 a

100 b b

b b 50

0 MTR MFR MFT MNF MTW DDM

Fig. 25. Functional weed traits related to potential supporting services in maize across management systems (lettering indicates significant difference), MTR – Mass tap root species, MFR – Mass fibrous root species, MFT – Mass ford-type species, MNF – Mass N-fixating species, MTW – Mass total weeds, DDM – Mass different LDM (than crop ) species

Based on significant difference in terms of MFR and MTW values, conventional systems have the potential to perform better in terms of weeds contributing more to both soil formation/ nutrient cycling and primary production/ habitat provisioning services (higher MFR, MTR and MFT values). An overview of these results is shown in Table 16.

Table 16. Overview of the relative performance of different management systems for maize fields, in terms supporting services based on functional traits of prevailing weeds. Service/ Management system with Effect Best System(s) Trait greater value

CO-NO CO-OO OO-NO Soil Formation/ Nutrient Cycling CO MTR DFC ++ CO CO

MFR + CO (S) CO (S) DDM + NO OO * MFT + CO CO MNF + NO NO * Primary Production/ Habitat Provisioning CO MTW + CO (S) CO (S) MFT + CO CO (S) indicates significant difference, other differences are trends, * indicates exceptions, CO - conventional, NO - new organic, OO - old organic, DFC – different from crop

Sunflower There were no significant differences across traits associated with supportive functions for sunflower (Fig. 26). In terms of tentative trends, it appears that conventional systems had higher MTR and MFT values compared to both organic systems while it also scored better in terms of DDM and MTW values compared to old organic. In terms of organics systems, old organic appears to perform better with respect to MFT values while new organic showed higher MFR values. Similar to maize systems, MNF values were close to zero (Figs. 26).

g m-2 Conventional 140 New Organic Old Organic 120

100

0 MTR MFR MFT MNF MTW DDM

Fig.26. Functional weed traits related to potential supporting services in sunflower across management systems, MTR – Mass tap root species, MFR – Mass fibrous root species, MFT – Mass ford-type species, MNF – Mass N-fixating species, MTW – Mass total weeds, DDM – Mass different LDM (than crop ) species

Overall it appears that conventional systems perform better in terms of both soil formation/ nutrient cycling (greater MTR and MFT values compared to both organic systems and higher DDM compared to old organic) and primary production/ habitat provisioning services (greater MFT values than both organic systems and higher MTW compared to old organic). An overview of above mentioned results can be found in Table 17.

Table 17. Overview of the relative performance of different management systems for sunflower fields, in terms supporting services based on functional traits of prevailing weeds. Service/ Management system with Best Effect Trait greater value System(s)

CO-NO CO-OO OO-NO Soil Formation/ Nutrient Cycling CO MTR + CO CO

MFR DFC ++ NO DDM + CO MFT + CO CO OO MNF + OO NO * Primary Production/ Habitat Provisioning CO MTW + CO MFT + CO CO OO * indicates exceptions, CO - conventional, NO - new organic, OO - old organic

Wheat For wheat there were no significant differences in terms of supporting service (Fig. 27). Based on numeric differences it appeared that new organic seemed to have perform better as it had relatively high values for MFR, DDM, MNF, MTW compared to both systems, and MTR and MFT compared to conventional. Furthermore, old organic appears to have higher MTR and MNF than the conventional system.

g m-2 Conventional 70 New Organic Old Organic 60

0 MTR MFR MFT MNF MTW DDM

Fig.27. Functional weed traits related to potential supporting services in wheat across management systems, MTR – Mass tap root species, MFR – Mass fibrous root species, MFT – Mass ford-type species, MNF – Mass N- fixating species, MTW – Mass total weeds, DDM – Mass different LDM (than crop ) species

Hence it appears that new organic performed best in terms of soil formation/ nutrient cycling (relatively high MFR, DDM, MNF, MTR and MFT scores compared to old organic and/or conventional systems) and primary production/habitat provisioning (greater MTW and MFT scores). Based on numeric ranking, old organic performing second best in terms of for soil formation/ nutrient cycling (greater MTR and MNF values compared to conventional). An overview of above mentioned results can be found in Table 18.

Table 18. Overview of the relative performance of different management systems for wheat fields, in terms supporting services based on functional traits of prevailing weeds. Service/ Management system with Effect Best System(s) Trait greater value

CO-NO CO-OO OO-NO Soil Formation/ Nutrient Cycling NO, OO MTR DFC ++ NO OO

MFR + NO NO DDM + NO NO MFT + NO MNF + NO OO NO Primary Production/ Habitat Provisioning NO MTW + NO NO MFT + NO

* indicates exceptions, CO - conventional, NO - new organic, OO - old organic, DFC – different from crop

4. Discussion The discussion starts with the framework itself, addressing its scope and limitations, than the used dataset will be critically assessed in terms of its structure and implementation. Following this the specific research questions will be addressed first at the crop level and then across crops. Finally the results will be contextualized in terms of crop and field aspects but also as related to the farming system and local environmental conditions as well.

4.1 Framework

4.1.1 Limitations The framework was somewhat limited in the sense that functional traits were based upon generic databases and not in situ field measurements hence, actual in field behaviour of these plants may be different. Moreover, the range of traits available from online sources, was also limited and complicated processes such as carbon sequestration had to be proxied with only a few functional traits. Furthermore, traits were scaled based on weighted total mass as a proxy of functionality, however this may not fully account for underlying processes and complexities such as the biomass allocation to certain functional traits. Moreover, there was the assumption that more is better without target threshold or minimum values.

4.1.2 Strengths Nonetheless, this thesis aimed for a holistic view including a broad range of ecosystem services, thereby avoiding the bias of certain services as all are essential for broader ecosystem functioning. Moreover, it allows for the flexibility to add or remove (sub) service components in potential future applications based on priorities and the specific context. The framework is easily reproducible by requiring only publically available databases, and data collected on mass per species, hence allowing for post-hoc analysis on existing weed databases, particularly those with long-term studies such as the Montepaldi research site. The use of quantitative traits based on the mass of weed species also allows for greater relevance and ease of implementation for action and on-farm type of research and may be more intuitive for farmers. Furthermore, it present a novel way of thinking about relationships between weeds and the surrounding management system. The idea of ‘ecosystem services’ delivered by weeds is one which is gaining recognition (Altieri, 1999; Nicholas & Altieri, 2013; Fagundez, 2014), however through integration in a quantitative framework this allows for an initial assessment and trade-off exploration between the potential benefits and disservices associated with weeds and how management styles influence this.

4.2 Dataset The statistical analysis of the dataset resulted in relatively low significant results, especially in the case of sunflower and wheat. This was due to high intrinsic levels of variability in the data but also due to the statistical set-up. The total weed mass and differences across management systems showed considerable variation over time. This can be due to extreme climatic conditions (dry and hot) which were often reported, but also due to changes in management practices such as late sowing in maize in 2009, or alterations in the rotational scheme in sunflower in 2015. As for the statistical set-up this included many random factors (year, field, and in the case of wheat; plot and variety) reducing the degrees of freedom and hence the significance, for every additional factor. Moreover, the high variability amongst years combined with often low observations per factor may have made it difficult to fully capture these random effects. Furthermore, variety may not have been so random for organic wheat, as Triticum Durum yields appear relatively lower compared to Triticum Aestivum. For wheat the inclusion of plot as a random factor (due to inconsistent repeated measures over time) may have increased the overall variation within the statistical analysis due to high inherent variability among repeated measures. Overall the amount of repeated measures was also lowest for wheat (as low as 3 – 4 per field, and 63 overall) and sunflower (6 per field and 66 overall) and highest for maize (12 per field and 144 overall).

4.3 Mass total weeds and prevailing species

Maize Conventional maize consistently had a higher total weed biomass than the organic systems with on average three times higher mass total weeds, contradicting trends generally found in the literature (Phelan 2009; Bedet 2000; Davis and Liebman 2001; Ryan et al. 2009, 2010). This may be mainly due to the dominance of particular aggressive problem weeds that accumulate exceptionally large amounts of biomass, and were noted as having competitive functional traits. Most notably these include Sorghum Halepense (Johnson grass) a highly invasive problem weed (Cabi, 2015) with herbicide resistance (Powles, 2008), and Xanthium Italicum (Italian Cocklebur) a particularly problematic weed for maize potentially decreasing yields by 90% (Kazinczi et al., 2009). Both these species are resistant to the herbicide Primagram Gold used (Syngenta, 2015). These species show how in conventional systems problem weeds really have the potential to dominate and overwhelm a cropping system, especially when under acute growth stress (due to potential climatic factors or late planting as was especially the case in 2009).

Sunflower In terms of sunflower, high inter-year variability and inconsistent system effects prevailed. In 2013 higher weed pressure in conventional systems was also mainly attributed to Sorghum Halepense and Xanthium Italicum infestations. What is particularly striking is the steep increase in weed biomass across both agroecosystems in 2015 compared to 2013. For conventional this was mainly in terms of Xanthium Italicum and occasional high incidences of Rumex spp and Sorghum Halepense. When considering the herbicides used (GOAL 480 SC) it appears that these are also herbicide resistant species (DOW, 2015). For the organic systems there was also a large infedstation of Sorghum Halepense in 2015. There were some particularly bad droughts this year in June and July, leading to a lower competitive ability of the crop, and moreover both Xanthium Italicum and Sorghum Halepense both appear to have drought resistant characteristics (Zhang, 2013 and FAO, 2015), making them more competitive. The higher incidence of problem weeds for organic sunflower may also be allocated to the short rotation cycle, a forced deviation from the typical four year rotation which may explain the consequent increase in weed pressure.

Wheat In terms of wheat, compared to the other crops, it seemed to have lower weed pressure in general, although new organic did not completely follow this trend due to a high spike in weed biomass during 2010, attributed to two particularly problematic weeds Avena Sterilis (wild oat) and Lolium Multiflorum (Italian ryegrass). This spike may be because it appeared to be a particularly cold and wet year (Figs. 5 and 6). The lower weed pressure in general was also

56 mentioned by farmers in the area, and may be related to the higher sowing density of the crop and earlier (fall) planting dates, as this may have resulted in this crop to be more competitive and effective in suppressing weeds (Hald, 1999). It seems this lower weed pressure in general may be related to the relatively higher yields of wheat obtained compared to the maize and sunflower crops. Differences amongst old organic and conventional where minimal.

4.4 Research questions

4.4.1 Research question 1

RQ1: Are there differences in weed species diversity in organic vs. conventionally managed systems? H1: Organic systems, due to the use of more diverse rotation and soil amendments, along with the absence of herbicides, will enhance weed diversity as compared to conventional systems.

To answer this research question the number of species found within each sampling plot was looked at, as a measure of species richness. It was shown that for maize and wheat the organic systems had at least 1.5 times (P<0.1) more weed species than conventional systems, confirming previous research findings (Hyvönen et al., 2003; Roschewitz et al., 2005; Romero et al., 2008). This is most likely due to the diversified crop rotations, lack of herbicides and usage of organic amendments over readily available nutrients (Ulber et al., 2009, Hyvönen et al., 2003). For sunflower there was no significant difference found, this could be due to the altered rotation scheme and usage of a different herbicide, compared to the other crops, which did not seem to work as effectively, since a number of species were found with high presence which should have been controlled with the herbicide, notably; Lolium sp., Amaranthus sp. and Convolvulus Arvensis (Polygonum aviculare) (Dow, 2015).

In terms of weed distribution, in all the conventional cropping systems the three most prevalent weeds accounted for the highest cumulative biomass, accounting for on average 86% of MTW compared to 77% in new organic 73% in old organic. Moreover, at least in the case of maize and largely also for sunflower, conventional appeared to have a higher presence of problem weeds with large biomasses. This is likely because they are herbicide resistant (Dow, 2015 & Syngenta, 2015) giving them less competition from herbicide susceptible species, and because their more competitive growing strategies could take advantage of the highly available soluble nutrients from chemical fertilizers (Grime, 1973, Phelan 2009).

So it may be argued that organic management practices could potentially lead to increased weed diversity and a more even distribution across weed species. Conventional systems could

57 potentially face increased weed problems over time, especially in regards to certain herbicide resistant problem weeds, and under stressful climatic conditions.

4.4.2 Research question 2

RQ2: Are there differences in the functional traits of weed species in organic vs. conventionally managed systems, and hence in the potential for niche differentiation and complementarity with the crop? H1: There will be differences in the functional traits of weed species, with organic systems having weeds with a greater potential for niche differentiation and complementarity with the crop.

Based on the results, especially in maize, there appeared to be significant differences amongst the functional traits of weed species populations across management systems. In the case of conventional maize, there was a significantly higher amount of mass total weeds (MTW), fibrous rooted (MFR), nitrophilous (MNP) and light demanding (MLD) species, moreover species also had a higher average height (AWH) and scored higher in terms of the Ellenberg light index (ELI), and lower in terms of (insect pollinated) flowering phenology richness (FPR and IPR). As for sunflower and wheat, the only significant differences found were pertaining to (insect) flowering phenology richness, suggesting that at least in the case of flowering phenology there are significant differences in functional traits of weed species populations amongst management systems. There were also additional trend differences found, and some of these will be discussed below.

Root architecture type, average height and growing strategy, key indicators of below ground, above ground and general competitive behaviour, will be looked at in more detail to evaluate if organic systems exhibit a higher potential for niche differentiation. In the case of mass based traits, absolute amount but also percentile differences will be considered, an overview can be found in Table 19.

Root architecture type is an important indicator for below ground potential niche differentiation behaviour in terms of nutrient and water acquisition. For maize, which has a fibrous root system, conventional had a significantly higher amount, but lower percentage, of fibrous rooting weeds compared to the organic systems. Sunflower, which has a taproot system, appeared to have a higher amount and percentile distribution of taproot species in conventional systems compared to both organic systems, this is most likely due to the high presence of Xanthium Italicum in both these systems. For wheat, which has a fibrous root system, new organic appears to have the most and highest percentage of fibrous rooting species.

Average height, provides an indication for above-ground niche differentiation and competitive behaviour. Conventional maize had significantly taller weeds than those in the organic systems. Conventional sunflower also appears to have the tallest weeds. As both maize and sunflower are quite tall this seems to support the theory of herbicides may lead to increased weed-crop mimicry (Barrett, 1983). In wheat the only trend is old organic having the shortest weeds.

Growing strategy was also considered for a more detailed analysis as it is a good indicator of competitive behaviour of plants as whole, thereby internalizing several functional behaviours into a single indicator. As crops are competitive ruderal (CR) species, CR weed species may compete for similar niches with the crop. Conventional maize and sunflower both appeared to have a higher absolute amount and percentage of CR species, supporting the theory that selection pressures in conventional systems may lead to weeds exhibiting more similar growing strategies compared to the crop (Martinez-Ghersa et al., 2000 & Smith et al., 2009). As for wheat, new organic appears to have more CR species and a higher percentage.

Therefore, coming back to the research question, taking into account both absolute amounts and percentile distributions of functional traits, it appears that in maize and sunflower organic systems in general performed better with regards to niche differentiation, seeming to support the idea that successional herbicide pressures and simple rotations in conventional systems may lead to functionally similar traits to the crop and hence more competitive weed species (Smith et al., 2009). The somewhat contradictory and uncertain results found for wheat, confirm previous research results found for winter cereals (Hald, 1999).

Table 19. Overview of potential niche differentiation based on functional traits of weed species in regards to crop Crop Management system Functional Trait MTW (g) MTR (g) % MTR MFR (g) % MFR MCR (g) % MCR AWH (cm) Maize Conventional 283.3 a 140.4 50% 142.9 a 50% 163.8 58% 64.5 a New Organic 75.8 b 25.7 34% 49.8 b 66% 40.3 53% 42.9 b Old Organic 91.1 b 36.5 40% 54.5 b 60% 33.8 37% 47.5 b Sunflower Conventional 130.81 94.5 72% 36.3 28% 77.7 59% 67.8 New Organic 85.8 14.7 17% 71 83% 14.8 17% 59.8 Old Organic 59.5 26.7 45% 32.8 55% 13.2 22% 64.6 Wheat Conventional 19.5 7.1 36% 12.4 64% 8.4 43% 63.5 New Organic 60.7 18.5 30% 42.3 70% 37.6 62% 62.6 Old Organic 29.6 14.6 49% 15 51% 14.4 49% 59.9 Red colour indicates the worst performing management system, green the best, lettering indicates significant differences, other differences are based upon trends (double the amount of grams, 5% higher height) 1New organic is less than half conventional but still included as a trend, MTW – Mass total weeds, MTR – Mass tap root species, MFR – Mass fibrous root species, MCR – Mass competitive ruderal species, AWH – Average weed height

4.4.3 Research question 3

RQ3: Do weed populations in these systems differ in their potential to deliver ecosystem services and disservices? H3: Through niche differentiation and complementation weeds in organic systems will have the potential to deliver a broader suite of ecosystem services while potential disservices associated with weeds will be reduced compared to conventionally managed systems.

Weeds can impede crop growth, and hence the provisioning services of an agricultural system by competing for key resources such as nutrients, water and light (Zhang et al., 2007). Yields for maize and sunflower in general seemed quite low compared to national averages, around 1.0 t ha-1 compared to 8.1 t ha-1 for maize and 0.5 t ha-1 compared to 2.1 t ha-1 for sunflower, while wheat was more comparable 2.9 t ha-1 compared to 3.7 t ha-1 (FAO, 2013). This was generally explained due to unfavourable climatic conditions, as the region is characterized by very warm and dry summers with droughts occurring quite commonly during the cropping season. Moreover there was no on site irrigation to supplement rainfall. This can have a depressing effect on the crop yield and reduce its weed suppressive ability. Especially during crop establishment and the grain-filling stage these crops are particularly vulnerable to water stress (Campos et al, 2004; Ahmad et al., 2009), leading to irreversible reductions in growth and/or yield. The better performance of wheat may also be due to its vegetative development during the relatively wet part of the year. Some management effects, such as late sowing of maize in 2009, and rotational deviations for sunflower in 2015 may also account for some yield reductions.

There was no significant effect of management system on yield, with organic even seeming to perform slightly better overall for maize and sunflower, which is in contrast with findings in the literature where organic cereals tend to have lower yields (Seufert et al., 2012). This may be because the organic systems can perform relatively better under limited and variable moisture availability due the enhanced water holding capacity and infiltration capacities of their soils (Letter et al., 2003; Colla et al., 2000). Moreover, the relatively high weed biomass sustained by the conventional systems, including the presence of particular problem weeds in maize and sunflower, could also partially account for this yield reduction. Based on the linear regression equations for yield as a function of total weed biomass, across all systems there was an inverse relationship, although r-squared values at times were rather low most likely due to the low number of observations and the influence of other management and external factors. Nonetheless, all considering it is evident that weeds appear to have a negative impact on yield, hence warranting a further analysis on a more trait based level to uncover differences in the

60 relative competing ability of weeds across agroecosystems, and their complementarity in other ecosystem service provisioning.

Ecosystem service delivery of weeds Based on the thesis framework, management systems seem to differ in the potential of their weeds to deliver ecosystem services (Table 20).

Table 20. Overview of the ‘best’ performing management system(s) in terms of the potential of its weeds to deliver specific ecosystem services and subservices based on observed significant differences (S) and trends (T) in functional traits. Ecosystem Services/ Maize Sunflower Wheat Subservices Provisioning Services

Water OO and NO (S) OO and NO (T) OO and CO (T)

OO and NO (T, OO and NO (S/T, T– OO (T, MNF Nutrients ENI exception ENI exception) exception) NO)

Light NO, OO (S/T) OO and NO (T) OO (T)

Growing Strategy OO and NO (T) OO and NO (T) OO and CO (T) Regulating Services

Carbon Sequestration (Climate CO (S) CO (T) NO (T) Regulation) Erosion Control/ Water CO (S) CO (T) NO, OO (T) Regulation Biological Control Contradictions Contradictions NO, OO (T) OO and NO Pollination OO and NO (S/T) NO (T) (S/T) Supporting Services Soil Formation/ Nutrient CO (S/T, T – MNF CO (T, MNF NO, OO (T) Cycling exception) exception) Primary Production/ Habitat CO (S) CO (T) NO (T) Provisioning *S – Significant difference, T – Trend difference. OO – Old organic, NO – New organic, CO – Conventional

For maize and sunflower the organic management systems appear to have less competitive weeds, thereby performing potentially better in terms of provisioning services. This seems related to the relatively better yield performance of these systems compared to their usual yield gap (Seufert et al., 2012). However, there appears to be a trade-off with relatively lower performance in certain regulating, and all the supporting services. Although, the usage of mass total weeds (MTW) as a crude indicator for ground cover in many of these subservices may

61 distort results in favour of management systems with higher MTW in general, especially when other functional indicators such as leaf dry matter based traits and N-fixation ability had very low values. Moreover, pollination services seem to be higher, confirming previous research findings (Power et al., 2012; Gabriel and Tscharntke, 2007; Romero et al., 2008), and biological control services may also be greater if MTW is not considered as a proxy of soil cover. Pollination services are particularly important as removal of weeds is a major factor in the reduction of pollinators in agroecosystems (Nicholls and Altieri, 2013),

In the case of wheat results are not so clear, moreover total weed biomass was lower compared to other crops, and hence is likely to have less of an impact in general. It appears new organic performs worst for provisioning and best for supporting and regulating, with old organic seeming to perform slightly better than conventional in certain services. These more uncertain differences are in line with previous research which showed the uncertain and often contradictory impact of winter cereals on weeds as compared to spring crops (Hald, 1999).

4.5 Contextualization Moreover, if we further contextualize these results, weeds in the wheat crop not only appear to have a lower biomass compared to the other crops, they also seem to make up a much smaller proportion of the total standing biomass. Hence, they are likely to have less of an effect on the delivery of ecosystem services, as compared to the crop. For maize, and potentially sunflower, weeds seem to make up a bigger proportion of the total standing biomass and hence are likely to have a bigger relative influence on ecosystem (dis) services.

When considering the farming system level, organic systems may contribute better to regulating and supporting services as a whole, due to their diverse rotations and organic matter inputs, fallow periods and structural biodiversity elements such as hedgerows and fallow periods (Sandhu et al., 2010). Therefore, although these services are also important in-field, they may be less important in organic systems compared to conventional systems. Hence, perhaps in-field weeds in organic systems can afford to perform a little poorer in these services. Moreover, the high biodiversity elements of the area may also help to supplement services in the case of biological control and pollination (Tscharntke et al., 2005).

5. Conclusion Conventional maize had significantly higher total weed biomass, and more fibrous rooted, nitrophilous and light demanding species, with species also having a higher average height and Ellenberg light index. Moreover, across all crops examined; maize, sunflower and wheat, the organic systems had a higher (insect pollinated) flowering phenology richness.

In terms of species diversity organic maize and wheat had a higher species richness, with at least 1.5 times more weed species compared to conventional systems, confirming previous research findings (Hyvönen et al., 2003; Roschewitz et al., 2005; Romero et al., 2008). Moreover, across all cropping systems conventional systems also appear to have a lower distribution of weed species, despite maize and sunflower having apparently higher total weed biomass, with this higher biomass being contrary to previous findings (Phelan 2009; Bedet 2000l Davis and Liebman 2001; Ryan et al. 2009, 2010).

This may be explained due to the high incidence of herbicide resistant (Dow, 2015 and Syngenta, 2015) problem species acquiring relatively large biomasses, most notably Xanthium Italicum (Italian Cocklebur) and Sorghum Halepense (Johnson grass). These species seem to be more competitive, occupying similar niches to the crop as they are likely capable of utilizing more aggressively the easily available N supply in these systems (Tilman, 1987). When looking at the weed species population level, weeds present in conventional sunflower and maize as a whole also seem to exhibit less niche differentiation in terms of plant height and growing strategy, including root system in the case of sunflower. Hence, this appears to confirm the theory that selection pressure and less diverse resource pools may lead to more functionally similar weeds compared to the crop (Martinez-Ghersa et al., 2000 & Smith et al., 2009), thereby possibly reducing the provisioning potential of these systems through enhanced competition.

Yields were found to be particularly low and variable for these two crops, most likely due to stressful climatic factors, however, they were similar across management systems, despite conventional yields traditionally being known to be higher (Seufert et al., 2012). This suggests that perhaps under more stressful circumstances organic systems can perform relatively better than conventional (Letter et al., 2003; Colla et. al., 2000). Their appeared to be a correlation between total weed biomass and yield, suggesting that this relatively lower conventional yield may also be due to the increased presence of more problematic weeds, however no direct causal relationship was developed.

When looking again at a population level it appears in general the conventional systems indeed had more competitive weed functional traits in the case of maize and sunflower, which could lead to a lower yield and hence provisioning service potential. However, there appears to be a trade-off between the potential for weeds to deliver provisioning services vs. other supporting and regulating services (Fagundez, et al., 2014). Organic maize and sunflower weeds appear to perform lower in many of these services, although this may be due to the inclusion of weed total biomass itself as a crude indicator of soil cover in many of these services. Moreover, it appears that this trade-off may not be the case in terms of pollination and biological control, where

63 organic systems seem to have a potential for performing better, despite lower overall weed biomass. More beneficial pollination and biocontrol species were also found in other research (Power et al., 2012; Gabriel and Tscharntke, 2007; Romero et al., 2008). Taking management and local surroundings into consideration it may also be that these services have a higher potential for being delivered by other means within organic systems.

Interestingly enough, wheat seemed to follow a different trend all together, suggesting that winter cereals may follow a different pattern due to differences in the growing season (Hald, 1999).

In conclusion, it appears that, especially under more stressful circumstances, organic weeds may have a better potential for niche differentiation and reduced competition with the crop as compared to conventional systems, potentially confirming the resource-pool diversity hypothesis (Smith et al, 2009). However, there may be a trade-off in terms of the provisioning of other supporting and regulating services, with the exception of pollination and biocontrol, where organic weed species may have a better potential. Moreover, it is also suggested that winter cereals may follow different patterns, showing the importance of cropping seasonality in regards to weed composition (Hald, 1999).

6. Limitations and future research recommendations Recommendations for the framework would be the inclusion of traits better defining processes proxied by weed total biomass such as soil cover, although maintaining public trait availability would be desirable. Moreover, the integration of threshold values could allow for more meaningful comparison across management systems. In terms of statistical analysis, it would be recommended to limit the number of random variables, and increase the number of observations ensuring standardized sampling methodologies with sufficient samples.

In terms of further research suggestions it is recommended that more work is done looking at interactions at functional trait level between weeds and crops, to confirm if the potential greater complementary of functional traits in organic management systems really does allow for greater coexistence and less yield reduction. Moreover it would be best if, in terms of supporting and regulating services, other contextual factors such as direct management factors (tillage, fertilization, and agrochemical usage), surrounding natural elements and climate could be included to more accurately determine the potential influence of these factors and the needs of the particular area.

As a whole this framework and analysis has been an attempt to change the perception of weed presence within agroecosystems. Thinking about weeds and their interactions on a functional trait basis, this framework has sought to help better quantify and understanding the interactions between weeds and crops. From this thesis it also appears that organic, and in particular old organic management systems may be better at striking this balance allowing for lower weeds, but still providing additional services such as biological control and pollination, particularly when taking into consideration the management system and the local surroundings. It would be interesting to apply this framework into other contexts, especially ones in which organic weeds have a higher weed biomass, as is traditionally suggested, to see how these trait-ecosystem service relations play out.

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8. Appendices

Appendix 1 – Functional trait and service relations explained In the following appendix an overview is given of the functional traits and their respective units used in the framework, and how these relate to the different ecosystem subservices. Measurement units are also listed followed by the effect of the trait on the subservice, + indicates a positive effect, - a negative effect, with SAC referring to same as crop.

Provisioning Services – Defined as products which are obtained from the ecosystem. This is based on the degree of niche differentiation weeds display in regards to the crop.  Water: o Root architecture type - . Mass Tap Root (MTR) species (g) (SAC -); For fibrous rooted crops (wheat and maize) in theory taproot species can allow for niche differentiation, and hence less competition as they can seek their water deeper in the soil profile. For taproot crops (sunflower) there could be a potential for increased competition as both seek water resources in deeper soil depths. . Mass Fibrous Root (MFR) species (g) (SAC -); For fibrous rooted crops (wheat and maize), these types of weeds would be expected to exhibit increased competition as they seek water resources within the same soil profile. For taproot crops (sunflower) fibrous rooted weed species can exhibit a certain degree of niche differentiation, due to the sunflower’s deeper rooting, however, the sunflower will still have lateral roots spreading in the upper soil layer therefore this competition reduction effect is not as straightforward.  Nutrients: o N-fixation ability . Mass N-Fixating (MNF) species (g) (+) - As the crops in question do not fix nitrogen, the fixation of nitrogen by weed species could provide an alternative means of acquiring nutrient resources, and therefore niche differentiation and competition avoidance. Furthermore, through N- fixation additional nutrients can be made free for the crop during weed plant senescence, thereby having a positive impact. Of course, it needs to be taken into account that in an agricultural system the actual amount of fixation may be reduced due to a high availability of readily available nitrogen, more so in conventional than organic systems.

o Root Architecture Type . Mass Tap Root (MTR) species (g) (SAC +/-) – same mechanism as water acquisition. . Mass Fibrous Root (MFR) species (g) (SAC +/-) – same mechanism as water acquisition. o Ellenberg N-index . Mean Ellenberg N-index (ENI) (1-9) (-) – The theory here is that species generally found on more nitrogen rich soils (higher ENI number) are likely to be more nitrophilous species, thereby consuming more readily available nitrogen at a faster rate, thereby generating more competition with the crop. Hence the higher the ENI value of the weed population the more likely it is to contain species which are more nitrogen demanding and competing with the crop. . Mass Nitrophilous (ENI>7) (MNP) species (g) (-) – Plants with a ENI value of 7 are defined as those that tend to be found in richly fertile places (Ecofact, 1999). Therefore, plants with a value of 7 or greater are likely to be more nitrogen demanding and hence compete more aggressively with the crop for this resource.  Light: o Average Plant Height . Average Weed Height (AWH) (cm) (-) - The higher the weeds tend to grow, the more competition they would be likely to generate with the crop in regards to light. . Mass Protruding (above Crop) (MPC) species (g) (-) – Mass of species which have an average height greater than the average height of the crop. Gives an indication about the presence of weed species which are likely to pursue growing strategies to surpass the height of the crop, thereby generating more light competition with the crop. o Ellenberg light index . Mean Ellenberg Light Index (ELI) (-) - This value gives an indication about in which sort of light conditions these species are generally found, and therefore also gives an indicator about their light preference. It would be assumed then that lower indicator values would indicate weed species with a lower light preference and hence would be less likely to pursue aggressive strategies for light capture.

. Mass Light Demanding (ELI>7) MLD species (g)- Ellenberg index value 7 for light refers to plants that are generally found in well-lit places but can also be found in partial shade (Ecofact, 1999). Therefore, 7 and higher will indicate plants which are likely to pursue more aggressive light growth strategies, therefore these plants could be said to have the potential for greater light competition. o Climbing Plants . Mass Climbing Type (MCT) species (g) (-) - Climbing plants pursue growing strategies in which they can directly attach themselves to the crop in order to reach the light, therefore these types of species can be identified as particularly competitive for the crops light resources.  Growing Strategies o Grime C-S-R Strategy . Mass Competitive Ruderal (MCR) species (g) (-) - Competitive ruderal species are species which are competitive; therefore highly efficient at obtaining resources through high growth and productivity rates, and ruderal; hence fast growing and quick to complete lifecycles. Crops are generally considered CR for this reason. Hence, weeds which are also classified as CR species will generally be more competitive in regards to the crop as they are likely to utilize similar growing strategies to the crop. Regulating Services - “Benefits obtained from the regulation of ecosystem processes”  Carbon Sequestration (Climate Regulation) - Generally seen as inputs - outputs of carbon, in this case biomass is used as a proxy for inputs, and LDMC as one for outputs (decomposition). o Biomass . Mass Total Weeds (MTW) (g) (+) - Carbon sequestration is mainly driven by two factors, high C inputs through primary production, and low C outputs through slow decomposition. Hence for weeds total biomass can serve as an indicator of the inputs of total carbon to the system. o Decomposition . Mean Leaf Dry Matter Content (LDM) (g g-1) (+) - Generally higher LDM implies slower decomposition (Kazakou et al., 2009), therefore if the average LDM is higher this will imply slower decomposition and hence potentially higher carbon sequestration . Mass Greater (than crop) LDM (GDM) species (g) (+) – Mass of species with a LDM significantly greater than the crop. Significantly greater here

is based upon a LDM value greater than the 95% confidence interval of the crop (based upon crop values obtained). (See Appendix 3 for details.) The idea here is that if the weed have a LDM greater than the crop it will have a slower decomposability than the crop, and therefore help to a greater extent slow down carbon losses and hence have a positive impact on carbon sequestration.  Erosion Control/ Water Regulation - In general weeds can be quite helpful in preventing erosion, as they may appear in between the rows of the crops, thereby protecting the soil in this vulnerable section, this is particularly important during early establishment. o Root Architecture Type . Mass Tap Root (MTR) species (g) (DFC ++) - A combination of taproot and fibrous species are ideal for erosion control and water regulation. Taproots are good at anchoring the soil, furthermore, they are also capable of breaking soil pans and accessing deeper water sources, hence enhancing water cycling. Therefore, taking into consideration the crop, if the crop is fibrous, taproot species would be preferential to compliment the dominating traits of the crop. If the crop is a taproot species than fibrous species would likely be preferred. . Mass Fibrous Root (MFR) species (g) (DFC ++) - Fibrous roots hold the soil together and reinforce it while simultaneously creating many pores through which water infiltration can be enhanced. Based on what was said previously, with fibrous crops fibrous weed species would not compliment the services of the crop as well. With taproot weed species, fibrous species would likely complement the services of the crop better. o Biomass . Mass Total Weeds (MTW) (g) (+) Having soil cover is an important deterrent for soil erosion. This is because the vegetation provides a protective layer on top of the soil which stops the gravitational impact of rainfall, thereby deterring splash erosion. In this case total biomass is used as an indicator of above ground soil vegetation cover.  Biological Control - Weeds can help to provide a shelter and food source for biologically beneficial insects, in the otherwise monotonous environment created by the crop. o Syrphid Supporting Plant species . Mass Syrphid Supporting (MSS) species (g) (+) - Many species of Syrphids are useful as biocontrol agents as their larvae can prey on pest insects such as aphids and leafhoppers. Therefore, the amount of species

supporting syrphids can serve as an indicator of this particular biocontrol element. o Flowering Phenology - Diverse flowering phenologies provide invertebrates and beneficial insects a diversity of resources to consume throughout the growing season (Marshall et al., 2001). . Flowering Phenology Richness (FPR) (No.) (+) – Biocontrol species require habitat and additional energy resources (pollen and nectar) to survive. It is essential that these are provided throughout the growing season, hence having a diversity of flowering phenologies suggests the potential presence of weeds at more diverse stages in their growing form. Hence, having a higher diversity of these phenologies can suggest having a wider range of these weed services available throughout the growing season. Moreover it also gives an indication of the diversity of flowering resources available in general, where higher diversity of flowering resources implies the attraction of a higher diversity of biocontrol species, this is especially important for specialist biocontrol species. Flowering phenology richness in general gives an indication more about the diversity of habitat provisioning of weeds throughout the growing season. . Insect Flowering Phenology Richness (IPR) (No.) (+) – Insect flowering phenology richness works in the same way as flowering phenology richness however targets more the ability of weeds to provide a diversity of alternative energy sources to biocontrol species throughout the cropping season. Having a diversity of insect pollinated flowering phenologies suggests that these resources may become available to the insects with a greater spread over the cropping season, hence sustaining them with sufficient resources throughout the growing season when pests may be scarce. Moreover, as mentioned above diversity of flowering phenology also gives an indication of the diversity of biocontrol species which can be attracted to the field. o Pollination syndrome . Mass Insect Pollinated (MIP) species (g) (+) – As mentioned above weeds can serve an alternative source of energy and habitat provisioning to biocontrol species. Particularly the nectar and pollen from insect pollinated weed species can help to sustain biocontrol species with pests

are scarce, or to attract adult species of biocontrol species to lay biocontrol larva near the crop. o Biomass . Mass Total Weeds (MTW) (g) (+) - Gives an indication of ground cover which is generally good for ground beetles and other beneficial species for biological control.  Pollination - Pollination is important within a cropping system as some plants in the rotation (such as sunflower) rely directly on pollinating services from insects. Even if the crop in question does not require pollination, perhaps neighbouring fields or those following the rotation will, thereby providing a bridging source of pollen and nectar resources for pollinators.. Weeds provide food and shelter for pollinators, and their removal is a major factor in the reduction of pollinators in agroecosystems (Nicholls and Altieri, 2013), hence these pollinator provisioning weeds need to be maintained to help in the survival of pollinator populations. o Flowering Phenology - Diverse flowering phenologies provide pollinators a diversity of resources to consume throughout the growing season (Marshall et al., 2001) . Insect Flowering Phenology Richness (IPR) (No.) (+) – as mentioned above with biocontrol species, IPR gives an indication of the temporal availability of pollinator resources. Moreover it also gives an indication of the diversity of flowering resources available in general, where higher diversity of flowering resources implies the attraction of a higher diversity of pollinators to the field (Blüthgen and Klein, 2011). o Pollination Syndrome . Mass Insect Pollinated (MIP) species (g) (+) - Insect pollinated plants attract pollinators to the field, providing diversity to the otherwise monotonous offer of the crops. Supporting Services - “Services necessary for the production of all other ecosystem services”  Soil Formation/ Nutrient Cycling - o Root Architecture Type - The crop root architecture type is an important consideration here to be complemented. . Mass Tap Root (MTR) species (g) (DFC ++) - Taproot species can be important for drawing nutrients from deeper in the soil profile, thereby avoiding leaching and enhancing nutrient cycling. They are also good at breaking up soil pans and thereby help with the structural integrity of the soil.

. Mass Fibrous Root (MFR) species (g) (DFC ++) – Fibrous species are important for acquiring nutrients from the topmost soil layer and storing these in thin fibrous roots to avoid these losses. In turn, these thin roots decompose readily, thereby helping with nutrient turnover and providing a stable source of humus to the soil. o Decomposition . Mass Different (than crop) LDM (DDM) species (g) (+) Diversity of litter traits has a significant influence on litter decomposition and in turn on nutrient mineralization (Hattenschwiler et al. 2005). Furthermore, LDM is a good indicator of the decomposability of plant species (Kazakou et al., 2009). Hence, a plant population with a diversity of LDM implies a diversity in decomposability and hence can enhance nutrient cycling within a system. As crops provide a highly uniform litter to the soil, weeds can play a key role in complementing the litter traits of the crop in order to enhance nutrient cycling. A diversity in decomposition will imply a diversity of food for the soil organisms and will therefore be better in maintaining their essential populations. o Plant Growth Form . Mass Forb-type (MFT) species (g) (+) - Forb species tend to have more invertebrates associated with them due to their increased structural complexity (Marshall et al., 2001). These invertebrates are essential for nutrient cycling and soil formation. o N- Fixation ability . Mass N-Fixating (MNF) species (g) (+) N-fixing species can add additional nitrogen into the soil from the air, thereby enhancing the soil’s supply of nitrogen after these plants decompose. Again here management needs to be taken into consideration as there may already be a great deal of nitrogen available, and hence these species may not fix as much nitrogen, moreover it may not be as necessary.  Primary Production/ Habitat Provisioning o Biomass . Mass Total Weeds (MTW) (g) (+) – Besides the input of crop residues weeds can also compliment this supply of primary production, and provide habitat to smaller species and invertebrates. o Plant Growth Form

. Mass Forb-type (MFT) species (g) (+) -Again here forb species can have more invertebrates associated with them, thereby providing a better habitat for them. Structural complexity of plant canopies is important for attracting invertebrates, hence forb weed species are better at providing such complexity than graminoids (Marshall et al., 2001).

Appendix 2 – Trait source information and selection methodology Table A1. Trait source information and selection methodology Trait Accessed Database Reference Selection Methodology through TRY

Root Architecture x Ecological Flora of the British Isles Fitter & Peat, 1994 TRY data used, complemented with other Type x CLO-PLA : a Database of Clonal Growth in Klimesova & de Bello databases in the case of conflicts or low Plants observations (<2) CABI CABI, 2015 eFloras Flora of North America Editorial Committee, eds. 1993+ Illinois Wildflowers Hilty, 2015 Weedinfo.ca Cowbrough, 2015 FEIS USDA Forest Service, 2015

N-Fixation Ability x Categorical Plant Traits Database unpub. TRY data used, assumption of N-fixation x PLANTSdata USDA Green, 2009 for Fabaceae family, Vasco, Moran and x Leaf Physiology Database Kattge et al., 2009 Ambrose, 2013 consulted for Equisetum NA Vasco, Moran and Ambrose, 2013 spp.

Ellenberg Nitrogen TR8 Pignatti, Menegoni & Pietrosanti, Pignatti, Menegoni & Pietrosanti, 2005 & Light Indices 2005 through Bocci, 2015 data used, accessed through TR8 (Bocci, 2015), compared to average TRY and Flora x PLANTATT - Attributes of British and Irish Hill, Preston & Roy, 2004 von Bayern data, with large differences Plants (>2), average of all used x Ecological Flora of the British Isles Fitter & Peat, 1994 Flora von Bayern Flora von Bayern, 2015

Average Plant Flora d'Italia Pignatti, 1982 Average of max and min height used from Height x The LEDA Traitbase Kleyer et al., 2008 Pignatti, 1982, when not available mean

height taken from LEDA traitbase

Climbing Plant Type x The LEDA Traitbase Kleyer et al., 2008 LEDA Traitbase used

Grime C-S-R x BiolFlor Database Kühn, Durka and BiolFlor Database used, if not available Strategy Klotz, 2004 average of genus, or otherwise family used

Leaf Dry Matter x The LEDA Traitbase Kleyer et al., 2008 LEDA traitbase used, average of Content mentioned LDMC used, values 2x higher or lower than SD removed. If no values available, average of genus, family or of class used based on availability

Syrphid Supporting x BiolFlor Database Kühn, Durka and Klotz, 2004 Categorized as yes if syrphids or hymenopteres mentioned under pollinators

Flowering Flora d'Italia Pignatti, 1982 Flora d’Italia used Phenology

Pollination x BiolFlor Database Kühn, Durka and Klotz, 2004 BiolFlor Database used, yes if insects are Syndrome mentioned under pollen vector or pollinators.

Growth Form x BROT Plant Trait Database Paula et al., 2009 Various TRY databses used x Categorical Plant Traits Database unpub. x BiolFlor Database Kühn, Durka and Klotz, 2004 x PLANTSdata USDA

Appendix 3 – 95% confidence interval for mean LDM values of crops In this appendix calculations are given for the construction of a 95% confidence interval for the mean lead dry matter (LDM) values of the different crops, in order to determine significantly different and greater LDM values.

Zea Mays

Values: 0.163, 0.167, 0.227, 0.227, Mean: 0.196, SD: 0.035, N = 4, t* = 3.182

훿 0.035 Standard Error = = ⁡ =⁡0.025 √푛 √4

Confidence interval = 0.196 +/- 0.080

95% confident mean falls between 0.116 < X < 0.276

Helianthus Annuus

Values: 0.131, 0.135, Mean: 0.133, SD: 0.003, N = 2, t* = 12.71

훿 0.003 Standard Error = = ⁡ =⁡0.002 √푛 √2

Confidence interval = 0.133 +/- 0.029

95% confident mean falls between 0.104 < X < 0.162

Triticum sp.

Values: 0.234, 0.224, Mean: 0.229, SD: 0.006, N = 2, t* = 12.71

훿 0.006 Standard Error = = ⁡ =⁡0.004 √푛 √2

Confidence interval = 0.229 +/- 0.061

95% confident mean falls between 0.167 < X < 0.291

Appendix 4. Overview of crop-weed complementarity in terms of functional traits Table A2. Overview of the functional traits of maize and the respective trait complementarity of the most dominant weed species1 Functional traits Crop Overview TR FR NF ENI NP AWH PC ELI LD CT GS LDM GDM SS FP IP DDM FT Maize x 8 x 2.25 8 x CR 0.196 - 7-9. - - Species Anthemis arvensis + - - 6 + 0.3 + 7 - + - (CR) 0.142 - + 4-6. + - + Avena fatua - - - 7 - 0.6 + 6 + + - (CR) 0.242 - - 4-6. - - - Amaranthus retroflexus + + - 8 - 0.6 + 9 - + - (CR) 0.220 - - 6-10. - - + Cirsium arvense + - - 7 - 1 + 8 - + + (C) 0.143 - + 5-9. + - + Convolvulus arvensis + - - 5 + 0.5 + 7 - - - (CR) 0.173 - + 4-10. + - + Cynodon dactylon - - - 4 + 0.35 + 8 - + CS 0.225 - - 6-9. - - - Echinochloa crus-galli - - - 8 - 0.9 + 6 + + - (CR) 0.190 - - 6-10. - - - Fallopia convolvulus + - - 4 + 0.75 + 8 - - - (CR) 0.200 - + 5-8. + - + Lolium perenne - - - 7 - 0.3 + 8 - + + (C) 0.215 - - 3-10. - - - Mentha arvensis - - - 6 + 0.4 + 6 + + + (C) 0.176 - - 6-9. + - + Picris echioides + + - 4 + 0.5 + 7 - + + (CSR) 0.129 - + 6-8. + - + Polygonum aviculare + + - 4 + 0.35 + 7 - + + (R) 0.208 - + 6-10. + - + Setaria viridis - - - 7 - 0.35 + 7 - + + (R) 0.274 - - 6-10. - - - Sinapis arvensis + + - 6 + 0.75 + 7 - + - (CR) 0.110 - + 3-10. + + + Sorghum halepense - - - 8 - 1.05 + 8 - + + (C) 0.223 - - 7-10. - - - Xanthium italicum + + - 6 + 0.75 + 8 - + - (CR) 0.200 - - 7-10. - - + x - indicates presence of trait, ' ' - indicates no presence of the trait, + indicates presence of weed trait complementarity to the crop, - indicates presence of traits not complementary to the crop TR - Tap root, FR - Fibrous root, NF - N-fixating, ENI -Ellenberg N-index, NP - Nitrophilous (ENI > 7), AWH - Average weed height, PC - protruding (above crop), ELI - Ellenberg light index, LD - Light demanding (ELI > 7), CT - Climbing type, GS - Grime Strategy, C - Competitive, R - Ruderal, S - Stress-tolerator, LDM - Leaf dry matter content (g g-1), GDM - Greater* LDM (than crop), SS - Syrphid supporting, FP - Flowering phenology, IP - Insect pollinated, DDM - Different* LDM (than crop)* Significantly based on 95% confidence interval of crop values

Table A3. Overview of the functional traits of sunflower and the respective trait complementarity of the most dominant weed species1 Functional traits Crop Overview TR FR NF ENI NP AWH PC ELI LD CT GS LDM GDM SS FP IP DDM FT x 7 x 1.5 8 x CR 0.133 x 7-10. x x Species Cirsium sp. - + - 7.5 - 0.75 + 8 - + + (C) 0.142 - + 6-10. + - + Equisetum arvense + + + 5 + 0.35 + 6 + + - (CR) 0.239 + - 3-5. - + - Lolium multiflorum + + - 6 + 0.3 + 7 - + + (C) 0.265 + - 5-7. - + - Lolium perenne + + - 7 - 0.3 + 8 - + + (C) 0.215 + - 3-10. - + - Melissa officinalis + + - 5 + 0.65 + 6 + + + (C) 0.176 + + 5-8. + + + Picris echioides - - - 4 + 0.5 + 7 - + + (CSR) 0.129 - + 6-8. + - + Rumex crispus - + - 5 + 0.9 + 7 - + + (C) 0.105 - + 5-7. - - + Rumex sp. - + - 5 + 0.875 + 7.5 - + + (C) 0.104 - + 5-8. - - + Sinapis arvensis - - - 6 + 0.75 + 7 - + - (CR) 0.110 - + 3-10. + - + Sorghum halepense + + - 8 - 1.05 + 8 - + + (C) 0.223 + - 7-10. - + - Xanthium italicum - + - 6 + 0.75 + 8 - + - (CR) 0.200 + - 7-10. - + + x - indicates presence of trait, ' ' - indicates no presence of the trait, + indicates presence of weed trait complementarity to the crop, - indicates presence of traits not complementary to the crop

TR - Tap root, FR - Fibrous root, NF - N-fixating, ENI -Ellenberg N-index, NP - Nitrophilous (ENI > 7), AWH - Average weed height, PC - protruding (above crop), ELI - Ellenberg light index, LD - Light demanding (ELI > 7), CT - Climbing type, GS - Grime Strategy, C - Competitive, R - Ruderal, S - Stress-tolerator, LDM - Leaf dry matter content (g g-1), GDM - Greater* LDM (than crop), SS - Syrphid supporting, FP - Flowering phenology, IP - Insect pollinated, DDM - Different* LDM (than crop)* Significantly based on 95% confidence interval of crop values

Table A4. Overview of the functional traits of wheat and the respective trait complementarity of the most dominant weed species1 Functional traits Crop Overview TR FR NF ENI NP AWH PC ELI LD CT GS LDM GDM SS FP IP DDM FT x 6 0.85 8 x CR 0.230 5-6. Species Anthemis Arvensis + - - 6 + 0.3 + 7 - + - (CR) 0.142 - + 4-6. + + + Avena Fatua - - - 7 - 0.6 + 6 + + - (CR) 0.242 - - 4-6. - - - Avena Sterilis - - - 6 + 0.85 - 8 - + - (CR) 0.193 - - 4-6. - - - Cirsium Arvense + - - 7 - 1 - 8 - + + (C) 0.143 - + 5-9. + + + Convolvulus Arvensis + - - 5 + 0.5 + 7 - - - (CR) 0.173 - + 4-10. + - + Dactylis Glomerata - - - 6 + 0.9 - 7 - + + (C) 0.263 - - 5-7. - - - Equisetum Arvense - - + 5 + 0.35 + 6 + + - (CR) 0.239 - - 3-5. - - - Equisetum Telmateia - - + 5 + 1.25 - 5 + + + (CS) 0.120 - - 3-5. - + - Fallopia Convolvulus + - - 4 + 0.75 + 8 - - - (CR) 0.200 - + 5-8. + - + Galium Aparine + - - 4 + 0.85 - 8 - - - (CR) 0.140 - + 3-9. + + + Helianthus Annuus + + - 7 - 1.5 - 8 - + - (CR) 0.133 - + 7-10. + + + Helianthus Tuberosus - - - 7 - 1.5 - 8 - + + (C) 0.127 - + 8-10. + + + Lolium Multiflorum - - - 6 + 0.3 + 7 - + + (C) 0.265 - - 5-7. - - - Mentha Sp. - - - 6 + 0.4 + 6 + + + (C) 0.176 - - 6-9. + - + Polygonum Aviculare + + - 4 + 0.35 + 7 - + + (R) 0.208 - + 6-10. + - + Sinapis Arvensis + + - 6 + 0.75 + 7 - + - (CR) 0.110 - + 3-10. + + + Sonchus Asper + + - 7 - 0.65 + 7 - + - (CR) 0.124 - + 1-12. + + + Setaria Viridis - - - 7 - 0.35 + 7 - + + (R) 0.274 - - 6-10. - - - Veronica Persica + - - 6 + 0.275 + 8 - + + (R) 0.120 - + 1-12. + + + x - indicates presence of trait, ' ' - indicates no presence of the trait, + indicates presence of weed trait complementarity to the crop, - indicates presence of traits not complementary to the crop TR - Tap root, FR - Fibrous root, NF - N-fixating, ENI -Ellenberg N-index, NP - Nitrophilous (ENI > 7), AWH - Average weed height, PC - protruding (above crop), ELI - Ellenberg light index, LD - Light demanding (ELI > 7), CT - Climbing type, GS - Grime Strategy, C - Competitive, R - Ruderal, S - Stress-tolerator, LDM - Leaf dry matter content (g g-1), GDM - Greater* LDM (than crop), SS - Syrphid supporting, FP - Flowering phenology, IP - Insect pollinated, DDM - Different* LDM (than crop)* Significantly based on 95% confidence interval of crop values

Appendix 5 – Mean values and significance of management system effect across crops In this appendix mean values and the significance of management effects across crops are given for different functional weed traits.

MTW

Table A5.1. MTW (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 283.3 a 130.8 19.5 192 .0 New Organic 75.8 b 85.8 60.7 74.8 Old Organic 91.1 b 59.5 29.6 68.6 Average 150.1 88.5 37.4

For maize the management system effect is significant (P = 0.055) after log10 transformation, different letters indicate management system means that differ significantly according to the t-test (t = +/- 1.96). For sunflower and wheat there are no significant management system effects (P = 0.333 and 0.436 respectively).

MTR

Table A5.2. MTR (g m-2) per management system and crop Maize Sunflower Wheat Average

Convention al 140.4 94.5 7.1 100.9 New Organic 25.7 14.7 18.5 21.2 Old Organic 36.5 26.7 14.6 28.9 Average 67.5 40.8 13.7 Management system effect was not significant (P = 0.136, 0.140, 0.704 for maize, sunflower and wheat respectively).

MFR

Table A5.3. MFR (g m -2 ) per management system and crop Maize Sunflower Wheat Average

Conventional 142.9 a 36.3 12.4 91.2 New Organic 49.8 b 71.0 42.3 53.4 Old Organic 54.5 b 32.8 15.0 39.7 Average 82.4 47.7 23.7 For maize the management system effect was significant (P = 0.056), different letters indicate management system means that differ significantly according to the t test (t = +/- 1.96). For sunflower and wheat there were no significant management system effects (P = 0.616 and 0.475 respectively).

MNF

Table A5.4. MNF (g m -2 ) per management system and crop Maize Sunflower Wheat Average

Conventional 1 .24 0.04 0.06 0.72 New Organic 2.85 1.00 8.47 3.69 Old Organic 0.62 0.00 2.05 0.80 Average 1.57 0.38 3.70 Management system effect was not significant (P = 0.178, 0.566, 0.411 for maize, sunflower and wheat respectively).

MCT

Table A5.5. MCT (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 38.8 2.8 6.9 24.1 New Organic 17.4 1.3 7.7 11.0 Old Organic 9.9 1.8 4.1 6.5 Average 22.1 1.9 6.2 Management system effect was not significant (P = 0.197, 0.639, 0.682 for maize, sunflower and wheat respectively).

FPR

Table A5.6. FPR (number m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 2.2 am 2.4 as 3.0 aw 2.4

New Organic 3.4 bm (0.051) 3.2 bs (0.058) 4.2 bw (0.065) 3.5

Old Organic 3.7 bm (0.019) 2.9 4.0 3.6 Average 3.1 2.9 3.8 Management system effect was significant among crops (across crops was not tested) different letters indicate management system means that differ significantly (subscript denotes crop). Parenthetical values indicate p-values of differences between management systems.

IPR

Table A5.7. IPR (number m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 1.13 am 0.83 as 2.47 1.37

New Organic 2.02 bm (0.002) 2.00 bs (0.003) 3.36 2.33

Old Organic 2.35 bm (<0.001) 1.79 bs (0.009) 3.00 2.36 Average 1.83 1.61 2.97 Management system effect was significant among crops maize and sunflower (across crops was not tested) different letters indicate management system means that differ significantly (subscript denotes crop). Parenthetical values indicate p-values of differences between management systems.

NS Table A5.8. NS (number m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 2.3 am 3.3 3.1 aw 2.7

New Organic 3.6 bm (0.067) 3.4 4.5 bw (0.047) 3.8

Old Organic 4.3 bm (0.016) 3.3 4.4 bw (0.055) 4.0 Average 3.4 3.3 4.0 Management system effect was significant among crops among crops maize and w (across crops was not tested) different letters indicate management system means that differ significantly (subscript denotes crop). Parenthetical values indicate p-values of differences between management systems.

AWH

Table A5.9. AWH (m) per management system and crop Maize Sunflower Wheat Average

Conventional 0.645 a 0.678 0.635 0.65 New Organic 0.429 b 0.598 0.626 0.518 Old Organic 0.475 b 0.646 0.599 0.547 Average 0.516 0.639 0.620 Management system effect was significant for maize (P = 0.0245), different letters indicate management system means that differ significantly according to the t test (t = +/- 1.96). For sunflower and wheat there are no significant management system effects (P = 0.794 and 0.867 respectively).

MPC Table A5.10. MPC (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 0 0 7.3 1.6 New Organic 0 0 15.9 3.7 Old Organic 0 0 9.9 2.3 Average 0 0 11.2 Management system effect was not significant (P = 0.705 for wheat)

ELI Table A5.11. ELI per management system and crop Maize Sunflower Wheat Average

Conventional 7.61 a 7.75 7.25 7.5 6 New Organic 7.22 c 7.19 7.29 7.23 Old Organic 7.38 b 7.22 7.39 7.34 Average 7.40 7.36 7.31 Management system effect was significant for maize (P = 0.030), different letters indicate management system means that differ significantly according to the t test (t = +/- 1.96). For sunflower and wheat there are no significant management system effects (P = 0.154 and 0.814 respectively).

MLD Table A5.12. MLD (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 250. 0 a 130.8 16.1 172.5 New Organic 70.5 b 84.7 46.8 68.6 Old Organic 89.4 b 49.8 28.0 64.9 Average 136.6 84.6 31.0 Management system effect was significant for maize (P = 0.042) after log transformation, different letters indicate management system means that differ significantly according to the t test (t = +/- 1.96). For sunflower and wheat there are no significant management system effects (P = 0.165 and 0.813 respectively).

ENI Table A5.13. ENI per management system and crop Maize Sunflower Wheat Average

Conventional 5.87 6.48 5.8 1 5.99 New Organic 6.07 6.60 5.36 6.03 Old Organic 6.10 6.18 5.37 5.96 Average 6.01 6.40 5.51 Management system effect was not significant for maize (P = 0.747, 0.642 and 0.423 for maize, sunflower and wheat respectively).

MNP Table A5.14. MNP (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 126.8 (2.047) a 24.8 6.8 78.3 New Organic 35.9 (1.472) b 46.6 13.0 33.2 Old Organic 45.4 (1.613) b 26.2 6.0 31.3 Average 69.4 (5.132) 33.2 8.7 Management system effect was significant for maize (P = 0.093) after log transformation, different letters indicate management system means that differ significantly according to the t test (t = +/- 1.96). For sunflower and wheat there are no significant management system effects (P = 0.939 and 0.611 respectively).

MCR Table A5.15. MCR (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 163.8 77.7 8.4 110.8 New Organic 40.3 14.8 37.6 33.2 Old Organic 33.8 13.2 14.4 24.0 Average 79.3 31.4 20.7 Management system effect was not significant (P = 0.158, 0.853 and 0.204 for maize, sunflower and wheat respectively).

LDM Table A5.16. LDM (g-g ) per management system and crop Maize Sunflower Wheat Average

Conventional 0.193 0.202 0.188 0.194 New Organic 0.191 0.192 0.193 0.192 Old Organic 0.182 0.174 0.197 0.183 Average 0.189 0.188 0.193 Management system effect was not significant (P = 0.697, 0.553 and 0.798 for maize, sunflower and wheat respectively).

DDM Table A5.17. DDM (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 0.0 119.3 7.1 26.9 New Organic 6.3 72.3 15.9 15.9 Old Organic 5.2 39.5 7.7 14.5 Average 3.8 73.2 10.4 Management system effect was not significant (P = 0.697, 0.210 and 0.609 for maize, sunflower and wheat respectively).

GDM Table A5.18. GDM (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 0.0 98.6 0 .0 20.9 New Organic 0.0 72.3 0.0 18.5 Old Organic 0.5 39.5 0.0 18.5 Average 0.2 67.6 0.0 Management system effect was not significant (P = 0.704, 0.553 and 0.798 for maize, sunflower and wheat respectively).

MFT Table A5.19. MFT (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 172.8 107.1 12 .0 122.9 New Organic 42.9 16.5 30.6 33.3 Old Organic 50.6 35.2 19.8 39.5 Average 88.8 48.0 21.2 Management system effect was not significant (P = 0.102, 0.141 and 0.462 for maize, sunflower and wheat respectively).

MIP Table A5.20. MIP (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 76.0 1 1.0 11.9 47.9 New Organic 38.7 15.1 23.6 29.2 Old Organic 45.5 33.2 17.8 35.9 Average 53.4 20.6 18.1 Management system effect was not significant (P = 0.224, 0.252 and 0.614 for maize, sunflower and wheat respectively).

MSS Table A5.21. MSS (g m-2) per management system and crop Maize Sunflower Wheat Average

Conventional 72.0 35.4 12.0 50.8 New Organic 38.8 15.4 23.6 29.3 Old Organic 44.9 35.1 18.5 36.2 Average 51.9 28.0 18.3 Management system effect was not significant (P = 0.318, 0.253 and 0.760 for maize, sunflower and wheat respectively).